diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 59935c25..955fc7d9 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -16,35 +16,33 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: ["3.10", "3.11", "3.12"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
       - name: Check out repository
         uses: actions/checkout@v4
 
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+      - name: Install uv and set the python version
+        uses: astral-sh/setup-uv@v5
         with:
           python-version: ${{ matrix.python-version }}
-          cache: "pip"
 
-      - name: Update pip and install dependencies
+      - name: Install the project
+        run: uv sync --all-extras
+
+      - name: Run linters (3.13-ubuntu only)
+        if: |
+          matrix.python-version == '3.13' &&
+          matrix.os == 'ubuntu-latest'
         run: |
-          python -m pip install --upgrade pip
-          python -m pip install ".[docs,test]"
+          uv run ruff format --check .
+          uv run ruff check .
 
-      - name: Run tests
+      - name: Run documentation tests
         run: |
-          python -m doctest README.md
-          python -m pytest docs --nbval --nbval-current-env -p no:randomly
-          python -m pytest tests \
-            --cov=matching --cov-fail-under=100 --hypothesis-profile=ci
+          uv run python -m doctest README.md
+          uv run pytest docs --nbval --nbval-current-env -p no:randomly
 
-      - name: Install and run linters (3.12-ubuntu only)
-        if: |
-          matrix.python-version == '3.12' &&
-          matrix.os == 'ubuntu-latest'
+      - name: Run unit tests
         run: |
-          python -m pip install ".[lint]"
-          python -m ruff check .
-          python -m ruff format --check .
+          uv run pytest tests --cov --cov-fail-under=100 --hypothesis-profile=ci
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 69039680..8d19341a 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -12,17 +12,23 @@ jobs:
             contents: write
         steps:
             - name: Check out repository
-              uses: actions/checkout@v4
+              uses: actions/checkout@v3
+
             - name: Set up Quarto
               uses: quarto-dev/quarto-actions/setup@v2
-            - name: Install Python and dependencies
-              uses: actions/setup-python@v5
-              with:
-                python-version: "3.12"
-                cache: "pip"
-            - run: |
-                python -m pip install ".[dev]"
-                quartodoc build
+
+            - name: Install uv
+              uses: astral-sh/setup-uv@v5
+
+            - name: Set up Python
+              run: |
+                uv python install
+
+            - name: Install package and build docs
+              run: |
+                uv sync --all-extras
+                uv run quartodoc build
+
             - name: Render and publish
               uses: quarto-dev/quarto-actions/publish@v2
               with:
diff --git a/README.md b/README.md
index 6cb4767a..b9e8b6c9 100644
--- a/README.md
+++ b/README.md
@@ -21,22 +21,22 @@ In `matching`, we deal with four types of matching game:
 
 ## Installation
 
-Matching requires Python 3.5 or above, and relies only on
-[NumPy](http://www.numpy.org/) for general use.
+Matching requires Python 3.10 or above, and relies only on the scientific stack ([NumPy](http://www.numpy.org/) and
+[SciPy](https://scipy.org/)) for general use.
 
 The library is most easily installed using `pip`:
 
 ```bash
-    $ python -m pip install matching
+$ python -m pip install matching
 ```
 
 However, if you would like to install it from source then go ahead and
 clone the GitHub repository:
 
 ```bash
-    $ git clone https://github.com/daffidwilde/matching.git
-    $ cd matching
-    $ python -m pip install .
+$ git clone https://github.com/daffidwilde/matching.git
+$ cd matching
+$ python -m pip install .
 ```
 
 ## Documentation
@@ -95,11 +95,11 @@ largely look and behave like one. It is in fact an instance of the
 
 ```python
 >>> type(matching)
-<class 'matching.matchings.single.SingleMatching'>
+<class 'matching.matchings.SMMatching'>
 >>> isinstance(matching, dict)
 True
 >>> matching
-SingleMatching({'F': 'C', 'D': 'B', 'E': 'A'}, keys="reviewers", values="suitors")
+SMMatching({'F': 'C', 'D': 'B', 'E': 'A'}, keys="reviewers", values="suitors")
 
 ```
 
@@ -118,7 +118,7 @@ One of the limitations of this library is the time complexities of the
 algorithm implementations. In practical terms, the running time of any
 of the algorithms in Matching is negligible but the theoretic complexity
 of each has not yet been attained. For example, an instance of HR with
-400 applicants and 20 hospitals is solved in less than one tenth of a
+400 applicants and 20 hospitals is solved in around one tenth of a
 second:
 
 ```python
@@ -127,18 +127,18 @@ second:
 >>> prng = np.random.default_rng(0)
 >>> num_residents, num_hospitals = 400, 20
 >>> resident_prefs = {
-...     r: np.argsort(prng.random(size=num_hospitals))
+...     r: list(np.argsort(prng.random(size=num_hospitals)))
 ...     for r in range(num_residents)
 ... }
 >>> hospital_prefs = {
-...     h: np.argsort(prng.random(size=num_residents))
+...     h: list(np.argsort(prng.random(size=num_residents)))
 ...     for h in range(num_hospitals)
 ... }
 >>> capacities = {h: num_hospitals for h in hospital_prefs}
->>> game = HospitalResident.create_from_dictionaries(
+>>> game = HospitalResident.from_preferences(
 ...     resident_prefs, hospital_prefs, capacities
 ... )
->>> _ = game.solve() # 48.6 ms ± 963 µs per loop
+>>> _ = game.solve() # 118 ms ± 847 µs per loop
 
 ```
 
diff --git a/docs/how-to/check_matching_status.ipynb b/docs/how-to/check_matching_status.ipynb
index 682182b3..779a7b8f 100644
--- a/docs/how-to/check_matching_status.ipynb
+++ b/docs/how-to/check_matching_status.ipynb
@@ -39,9 +39,7 @@
     "\n",
     "project_supervisors = {\"X1\": \"X\", \"X2\": \"X\", \"Y1\": \"Y\", \"Y2\": \"Y\"}\n",
     "project_capacities = {project: 1 for project in project_supervisors}\n",
-    "supervisor_capacities = {\n",
-    "    supervisor: 2 for supervisor in supervisor_preferences\n",
-    "}\n",
+    "supervisor_capacities = {supervisor: 2 for supervisor in supervisor_preferences}\n",
     "\n",
     "\n",
     "game = StudentAllocation.create_from_dictionaries(\n",
@@ -85,9 +83,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "matching-docs",
+   "display_name": ".venv",
    "language": "python",
-   "name": "matching-docs"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -99,7 +97,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.13.1"
   }
  },
  "nbformat": 4,
diff --git a/docs/how-to/choose_optimality.ipynb b/docs/how-to/choose_optimality.ipynb
index 9d78354a..5747d0bc 100644
--- a/docs/how-to/choose_optimality.ipynb
+++ b/docs/how-to/choose_optimality.ipynb
@@ -47,7 +47,7 @@
     {
      "data": {
       "text/plain": [
-       "SingleMatching({'Y': 'C', 'Z': 'B', 'X': 'A'}, keys=\"reviewers\", values=\"suitors\")"
+       "SMMatching({'Y': 'C', 'Z': 'B', 'X': 'A'}, keys=\"reviewers\", values=\"suitors\")"
       ]
      },
      "execution_count": 2,
@@ -56,9 +56,7 @@
     }
    ],
    "source": [
-    "game = StableMarriage.from_preferences(\n",
-    "    suitor_preferences, reviewer_preferences\n",
-    ")\n",
+    "game = StableMarriage.from_preferences(suitor_preferences, reviewer_preferences)\n",
     "\n",
     "game.solve(optimal=\"suitor\")"
    ]
@@ -71,7 +69,7 @@
     {
      "data": {
       "text/plain": [
-       "SingleMatching({'Y': 'A', 'Z': 'B', 'X': 'C'}, keys=\"reviewers\", values=\"suitors\")"
+       "SMMatching({'Y': 'A', 'Z': 'B', 'X': 'C'}, keys=\"reviewers\", values=\"suitors\")"
       ]
      },
      "execution_count": 3,
@@ -80,9 +78,7 @@
     }
    ],
    "source": [
-    "game = StableMarriage.from_preferences(\n",
-    "    suitor_preferences, reviewer_preferences\n",
-    ")\n",
+    "game = StableMarriage.from_preferences(suitor_preferences, reviewer_preferences)\n",
     "\n",
     "game.solve(optimal=\"reviewer\")"
    ]
@@ -90,7 +86,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "matching",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -104,7 +100,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.13.1"
   }
  },
  "nbformat": 4,
diff --git a/docs/how-to/create_from_dictionaries.ipynb b/docs/how-to/create_from_dictionaries.ipynb
index 98dc6eb6..35132ba8 100644
--- a/docs/how-to/create_from_dictionaries.ipynb
+++ b/docs/how-to/create_from_dictionaries.ipynb
@@ -28,9 +28,7 @@
     "\n",
     "project_supervisors = {\"X1\": \"X\", \"X2\": \"X\", \"Y1\": \"Y\", \"Y2\": \"Y\"}\n",
     "project_capacities = {project: 1 for project in project_supervisors}\n",
-    "supervisor_capacities = {\n",
-    "    supervisor: 2 for supervisor in supervisor_preferences\n",
-    "}"
+    "supervisor_capacities = {supervisor: 2 for supervisor in supervisor_preferences}"
    ]
   },
   {
diff --git a/docs/tutorials/hospital_resident.ipynb b/docs/tutorials/hospital_resident.ipynb
index d136dbaf..25af594e 100644
--- a/docs/tutorials/hospital_resident.ipynb
+++ b/docs/tutorials/hospital_resident.ipynb
@@ -46,6 +46,7 @@
    "outputs": [],
    "source": [
     "import urllib\n",
+    "import warnings\n",
     "\n",
     "import yaml\n",
     "\n",
@@ -126,9 +127,11 @@
    "source": [
     "from matching.games import HospitalResident\n",
     "\n",
-    "game = HospitalResident.create_from_dictionaries(\n",
-    "    resident_preferences, hospital_preferences, hospital_capacities\n",
-    ")"
+    "with warnings.catch_warnings():\n",
+    "    warnings.simplefilter(\"ignore\")\n",
+    "    game = HospitalResident.from_preferences(\n",
+    "        resident_preferences, hospital_preferences, hospital_capacities\n",
+    "    )"
    ]
   },
   {
@@ -171,19 +174,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dewi Sant (30 / 30): [067, 022, 023, 158, 139, 065, 160, 131, 011, 137, 039, 045, 013, 046, 072, 037, 086, 152, 144, 154, 130, 040, 010, 159, 083, 019, 169, 193, 168, 079]\n",
-      "Prince Charles (29 / 30): [027, 133, 106, 081, 051, 044, 069, 157, 110, 119, 129, 107, 135, 034, 007, 194, 198, 061, 087, 041, 183, 136, 059, 178, 009, 008, 031, 070, 026]\n",
-      "Prince of Wales (30 / 30): [143, 128, 048, 175, 078, 132, 151, 030, 124, 138, 088, 004, 199, 173, 017, 097, 064, 025, 112, 181, 171, 196, 111, 035, 185, 156, 140, 001, 197, 177]\n",
-      "Royal Glamorgan (30 / 30): [073, 118, 096, 089, 014, 126, 142, 053, 021, 018, 104, 015, 147, 153, 033, 113, 146, 076, 123, 042, 117, 024, 029, 000, 016, 134, 058, 166, 075, 174]\n",
-      "Royal Gwent (27 / 30): [028, 105, 115, 095, 054, 006, 120, 161, 187, 164, 091, 141, 036, 184, 071, 155, 066, 182, 189, 002, 191, 068, 090, 145, 163, 121, 180]\n",
-      "St. David (30 / 30): [149, 101, 150, 172, 165, 020, 049, 094, 060, 116, 056, 005, 093, 188, 043, 108, 192, 092, 167, 114, 012, 063, 077, 162, 085, 195, 032, 099, 084, 127]\n",
-      "University (24 / 30): [109, 003, 057, 170, 176, 100, 122, 080, 038, 082, 102, 052, 062, 055, 047, 074, 050, 179, 125, 186, 148, 103, 098, 190]\n"
+      "Dewi Sant (30 / 30): ['010', '011', '013', '019', '022', '023', '037', '039', '040', '045', '046', '065', '067', '072', '079', '083', '086', '130', '131', '137', '139', '144', '152', '154', '158', '159', '160', '168', '169', '193']\n",
+      "Prince Charles (29 / 30): ['007', '008', '009', '026', '027', '031', '034', '041', '044', '051', '059', '061', '069', '070', '087', '107', '110', '119', '129', '133', '135', '136', '157', '178', '183', '194', '198', '106', '081']\n",
+      "Prince of Wales (30 / 30): ['001', '004', '017', '030', '035', '048', '064', '078', '088', '097', '111', '112', '124', '128', '132', '138', '140', '143', '151', '156', '171', '173', '175', '177', '181', '185', '196', '197', '199', '025']\n",
+      "Royal Glamorgan (30 / 30): ['000', '014', '015', '016', '018', '021', '024', '029', '033', '042', '053', '058', '073', '075', '076', '089', '096', '104', '113', '117', '118', '123', '126', '134', '142', '146', '147', '153', '166', '174']\n",
+      "Royal Gwent (27 / 30): ['002', '006', '028', '036', '054', '068', '071', '090', '091', '105', '120', '121', '141', '145', '155', '161', '163', '164', '180', '182', '184', '187', '189', '191', '066', '115', '095']\n",
+      "St. David (30 / 30): ['005', '012', '020', '032', '043', '049', '056', '060', '063', '077', '084', '085', '092', '093', '094', '099', '101', '108', '114', '116', '127', '149', '150', '162', '165', '167', '172', '188', '192', '195']\n",
+      "University (24 / 30): ['038', '047', '050', '052', '055', '057', '062', '074', '080', '082', '098', '100', '102', '103', '109', '122', '148', '170', '176', '179', '186', '190', '125', '003']\n"
      ]
     }
    ],
    "source": [
     "for hospital, residents in solution.items():\n",
-    "    print(f\"{hospital} ({len(residents)} / {hospital.capacity}): {residents}\")"
+    "    capacity = hospital_capacities[hospital]\n",
+    "    print(f\"{hospital} ({len(residents)} / {capacity}): {residents}\")"
    ]
   },
   {
@@ -217,11 +221,9 @@
     "matched_residents = []\n",
     "for _, residents in solution.items():\n",
     "    for resident in residents:\n",
-    "        matched_residents.append(resident.name)\n",
+    "        matched_residents.append(resident)\n",
     "\n",
-    "unmatched_residents = set(resident_preferences.keys()).difference(\n",
-    "    matched_residents\n",
-    ")\n",
+    "unmatched_residents = set(resident_preferences.keys()).difference(matched_residents)\n",
     "unmatched_residents"
    ]
   },
@@ -238,7 +240,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "matching",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -252,7 +254,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.13.1"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/stable_marriage.ipynb b/docs/tutorials/stable_marriage.ipynb
index b4aa2ef7..94d73366 100644
--- a/docs/tutorials/stable_marriage.ipynb
+++ b/docs/tutorials/stable_marriage.ipynb
@@ -99,7 +99,7 @@
     {
      "data": {
       "text/plain": [
-       "SingleMatching({'Lydia': 'Wickham', 'Elizabeth': 'Darcy', 'Jane': 'Bingley', 'Charlotte': 'Collins'}, keys=\"reviewers\", values=\"suitors\")"
+       "SMMatching({'Lydia': 'Wickham', 'Elizabeth': 'Darcy', 'Jane': 'Bingley', 'Charlotte': 'Collins'}, keys=\"reviewers\", values=\"suitors\")"
       ]
      },
      "execution_count": 3,
@@ -123,7 +123,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "matching",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -137,7 +137,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.13.1"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/stable_roommates.ipynb b/docs/tutorials/stable_roommates.ipynb
index e1f3154d..e4704038 100644
--- a/docs/tutorials/stable_roommates.ipynb
+++ b/docs/tutorials/stable_roommates.ipynb
@@ -130,7 +130,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "matching",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -144,7 +144,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.13.1"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/student_allocation.ipynb b/docs/tutorials/student_allocation.ipynb
index 303114aa..25c0c5a5 100644
--- a/docs/tutorials/student_allocation.ipynb
+++ b/docs/tutorials/student_allocation.ipynb
@@ -53,15 +53,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "raw_students = pd.read_csv(\n",
-    "    \"https://zenodo.org/record/3514287/files/students.csv\"\n",
-    ")\n",
-    "raw_projects = pd.read_csv(\n",
-    "    \"https://zenodo.org/record/3514287/files/projects.csv\"\n",
-    ")\n",
-    "raw_supervisors = pd.read_csv(\n",
-    "    \"https://zenodo.org/record/3514287/files/supervisors.csv\"\n",
-    ")"
+    "raw_students = pd.read_csv(\"https://zenodo.org/record/3514287/files/students.csv\")\n",
+    "raw_projects = pd.read_csv(\"https://zenodo.org/record/3514287/files/projects.csv\")\n",
+    "raw_supervisors = pd.read_csv(\"https://zenodo.org/record/3514287/files/supervisors.csv\")"
    ]
   },
   {
@@ -360,11 +354,7 @@
     }
    ],
    "source": [
-    "students = (\n",
-    "    raw_students.copy()\n",
-    "    .dropna(subset=choices, how=\"all\")\n",
-    "    .reset_index(drop=True)\n",
-    ")\n",
+    "students = raw_students.copy().dropna(subset=choices, how=\"all\").reset_index(drop=True)\n",
     "\n",
     "students.head()"
    ]
@@ -778,9 +768,7 @@
     "supervisor_to_preferences = {}\n",
     "for supervisor in supervisor_names:\n",
     "    supervisor_preferences = []\n",
-    "    supervisor_projects = [\n",
-    "        p for p, s in project_to_supervisor.items() if s == supervisor\n",
-    "    ]\n",
+    "    supervisor_projects = [p for p, s in project_to_supervisor.items() if s == supervisor]\n",
     "\n",
     "    for student in sorted_students:\n",
     "        student_preferences = student_to_preferences[student]\n",
@@ -823,9 +811,7 @@
     }
    ],
    "source": [
-    "unranked_supervisors = set(supervisor_names).difference(\n",
-    "    supervisor_to_preferences.keys()\n",
-    ")\n",
+    "unranked_supervisors = set(supervisor_names).difference(supervisor_to_preferences.keys())\n",
     "\n",
     "\n",
     "unranked_projects = set(project_codes).difference(\n",
@@ -934,9 +920,7 @@
    ],
    "source": [
     "for supervisor, supervisor_capacity in supervisor_to_capacity.items():\n",
-    "    supervisor_projects = [\n",
-    "        p for p, s in project_to_supervisor.items() if s == supervisor\n",
-    "    ]\n",
+    "    supervisor_projects = [p for p, s in project_to_supervisor.items() if s == supervisor]\n",
     "    supervisor_project_capacities = [\n",
     "        project_to_capacity[project] for project in supervisor_projects\n",
     "    ]\n",
@@ -997,7 +981,7 @@
     {
      "data": {
       "text/plain": [
-       "{A0: [], A1: [190019, 190034], A2: [190017], B0: [], B1: [190091], B2: [], C0: [190003, 190068], C1: [190070], C2: [190079, 190062], D0: [190015, 190039, 190009], D1: [190008, 190056], D2: [], E0: [], E1: [190063, 190022], E2: [], F0: [190011], F1: [190094], G0: [190096], G1: [], G2: [190000, 190045, 190077], H0: [], H1: [190078], H2: [190023, 190098], I0: [190004], I1: [], I2: [], J0: [190021], J1: [190097, 190089, 190061, 190014], J2: [190085, 190090], K0: [190074, 190095], K1: [190024, 190013], K2: [190052, 190006], L0: [190072], M0: [190086, 190053], M1: [190054, 190081, 190075], N0: [190049, 190032], N1: [190046, 190044, 190037, 190057], N2: [190031, 190060], O0: [190016], O1: [], P0: [190047], P1: [190076, 190027], P2: [190093], P3: [190071], Q0: [190001], R0: [190048], R1: [], R2: [], S0: [190069], S1: [190092, 190042, 190084, 190066, 190067], T0: [], T1: [], T2: [190036], U0: [190010, 190002, 190059, 190030], U1: [], U2: [190055], V0: [190073], V1: [], V2: [190018], W0: [190026], W1: [190058], X0: [190088, 190080, 190012], X1: [190028], X2: [190043, 190064, 190020], Y0: [190083, 190007, 190099], Y1: [190065, 190050], Y2: [190033], Z0: [190051], Z1: [190005, 190038]}"
+       "_MultipleMatching({A0: [], A1: [190019, 190034], A2: [190017], B0: [], B1: [190091], B2: [], C0: [190003, 190068], C1: [190070], C2: [190079, 190062], D0: [190015, 190039, 190009], D1: [190008, 190056], D2: [], E0: [], E1: [190063, 190022], E2: [], F0: [190011], F1: [190094], G0: [190096], G1: [], G2: [190000, 190045, 190077], H0: [], H1: [190078], H2: [190023, 190098], I0: [190004], I1: [], I2: [], J0: [190021], J1: [190097, 190089, 190061, 190014], J2: [190085, 190090], K0: [190074, 190095], K1: [190024, 190013], K2: [190052, 190006], L0: [190072], M0: [190086, 190053], M1: [190054, 190081, 190075], N0: [190049, 190032], N1: [190046, 190044, 190037, 190057], N2: [190031, 190060], O0: [190016], O1: [], P0: [190047], P1: [190076, 190027], P2: [190093], P3: [190071], Q0: [190001], R0: [190048], R1: [], R2: [], S0: [190069], S1: [190092, 190042, 190084, 190066, 190067], T0: [], T1: [], T2: [190036], U0: [190010, 190002, 190059, 190030], U1: [], U2: [190055], V0: [190073], V1: [], V2: [190018], W0: [190026], W1: [190058], X0: [190088, 190080, 190012], X1: [190028], X2: [190043, 190064, 190020], Y0: [190083, 190007, 190099], Y1: [190065, 190050], Y2: [190033], Z0: [190051], Z1: [190005, 190038]}, keys=\"keys\", values=\"values\")"
       ]
      },
      "execution_count": 23,
@@ -1052,13 +1036,11 @@
    "outputs": [],
    "source": [
     "supervisor_free_spaces = {\n",
-    "    supervisor: supervisor.capacity - len(supervisor.matching)\n",
-    "    for supervisor in game.supervisors\n",
+    "    supervisor: supervisor.capacity - len(supervisor.matching) for supervisor in game.supervisors\n",
     "}\n",
     "\n",
     "supervisor_utilisation = {\n",
-    "    supervisor: len(supervisor.matching) / supervisor.capacity\n",
-    "    for supervisor in game.supervisors\n",
+    "    supervisor: len(supervisor.matching) / supervisor.capacity for supervisor in game.supervisors\n",
     "}"
    ]
   },
@@ -1083,7 +1065,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 2400x1200 with 1 Axes>"
       ]
@@ -1117,7 +1099,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1fc38aa4b90>"
+       "<matplotlib.legend.Legend at 0x7f43827a8830>"
       ]
      },
      "execution_count": 27,
@@ -1126,7 +1108,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 2400x1200 with 1 Axes>"
       ]
@@ -1142,9 +1124,7 @@
     "ax.hist(values)\n",
     "\n",
     "ylims = ax.get_ylim()\n",
-    "ax.vlines(\n",
-    "    np.mean(list(values)), *ylims, \"tab:orange\", \"dashed\", label=\"Mean\", lw=3\n",
-    ")\n",
+    "ax.vlines(np.mean(list(values)), *ylims, \"tab:orange\", \"dashed\", label=\"Mean\", lw=3)\n",
     "ax.set_ylim(*ylims)\n",
     "\n",
     "ax.set_xlabel(\"Utilisation\")\n",
@@ -1171,13 +1151,11 @@
    "outputs": [],
    "source": [
     "project_free_spaces = {\n",
-    "    project.name: project.capacity - len(project.matching)\n",
-    "    for project in game.projects\n",
+    "    project.name: project.capacity - len(project.matching) for project in game.projects\n",
     "}\n",
     "\n",
     "project_utilisation = {\n",
-    "    project.name: len(project.matching) / project.capacity\n",
-    "    for project in game.projects\n",
+    "    project.name: len(project.matching) / project.capacity for project in game.projects\n",
     "}"
    ]
   },
@@ -1202,7 +1180,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 2400x1200 with 1 Axes>"
       ]
@@ -1235,7 +1213,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1fc3ab53e10>"
+       "<matplotlib.legend.Legend at 0x7f437fdf6c10>"
       ]
      },
      "execution_count": 30,
@@ -1244,7 +1222,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 2400x1200 with 1 Axes>"
       ]
@@ -1260,9 +1238,7 @@
     "ax.hist(values)\n",
     "\n",
     "ylims = ax.get_ylim()\n",
-    "ax.vlines(\n",
-    "    np.mean(list(values)), *ylims, \"tab:orange\", \"dashed\", label=\"Mean\", lw=3\n",
-    ")\n",
+    "ax.vlines(np.mean(list(values)), *ylims, \"tab:orange\", \"dashed\", label=\"Mean\", lw=3)\n",
     "ax.set_ylim(*ylims)\n",
     "\n",
     "ax.set_xlabel(\"Utilisation\")\n",
@@ -1298,9 +1274,7 @@
     "for project, project_students in matching.items():\n",
     "    for student in project_students:\n",
     "        inverted_matching[student.name] = project.name\n",
-    "        student_preference_of_matching.append(\n",
-    "            student._pref_names.index(project.name)\n",
-    "        )"
+    "        student_preference_of_matching.append(student._pref_names.index(project.name))"
    ]
   },
   {
@@ -1572,7 +1546,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 2400x1200 with 1 Axes>"
       ]
@@ -1618,7 +1592,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1fc3ca06810>"
+       "<matplotlib.legend.Legend at 0x7f437fd02490>"
       ]
      },
      "execution_count": 36,
@@ -1627,7 +1601,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 2400x1200 with 1 Axes>"
       ]
@@ -1692,9 +1666,7 @@
    "outputs": [],
    "source": [
     "project_with_space_names = [\n",
-    "    project.name\n",
-    "    for project in game.projects\n",
-    "    if len(project.matching) < project.capacity\n",
+    "    project.name for project in game.projects if len(project.matching) < project.capacity\n",
     "] + list(unranked_projects)\n",
     "\n",
     "supervisor_with_space_names = [\n",
@@ -1745,17 +1717,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "projects_with_space[\"supervisor_capacity\"] = projects_with_space[\n",
-    "    \"supervisor\"\n",
-    "].apply(lambda x: get_capacity(supervisors, \"supervisor\", x))\n",
+    "projects_with_space[\"supervisor_capacity\"] = projects_with_space[\"supervisor\"].apply(\n",
+    "    lambda x: get_capacity(supervisors, \"supervisor\", x)\n",
+    ")\n",
     "\n",
     "projects_with_space[\"project_matches\"] = projects_with_space[\"code\"].apply(\n",
     "    lambda x: get_number_of_matches(x, \"projects\", game)\n",
     ")\n",
     "\n",
-    "projects_with_space[\"supervisor_matches\"] = projects_with_space[\n",
-    "    \"supervisor\"\n",
-    "].apply(lambda x: get_number_of_matches(x, \"supervisors\", game))\n",
+    "projects_with_space[\"supervisor_matches\"] = projects_with_space[\"supervisor\"].apply(\n",
+    "    lambda x: get_number_of_matches(x, \"supervisors\", game)\n",
+    ")\n",
     "\n",
     "projects_with_space = projects_with_space[\n",
     "    [\n",
@@ -1873,9 +1845,7 @@
     }
    ],
    "source": [
-    "projects_with_space = projects_with_space.set_index(\n",
-    "    [\"supervisor\", \"code\"]\n",
-    ").sort_index()\n",
+    "projects_with_space = projects_with_space.set_index([\"supervisor\", \"code\"]).sort_index()\n",
     "\n",
     "projects_with_space"
    ]
@@ -1891,7 +1861,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "matching",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -1905,7 +1875,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.13.1"
   }
  },
  "nbformat": 4,
diff --git a/pyproject.toml b/pyproject.toml
index 3033b7c1..46bfcd06 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,3 @@
-[build-system]
-requires = ["setuptools>=62"]
-build-backend = "setuptools.build_meta"
-
 [project]
 name = "matching"
 authors = [
@@ -11,7 +7,7 @@ authors = [
 description = "A package for solving matching games"
 readme = "README.md"
 requires-python = ">=3.10"
-license = {text = "MIT License"}
+license = {text = "MIT Licence"}
 keywords = [
     "game-theory",
     "gale-shapley",
@@ -29,29 +25,36 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Information Analysis",
 ]
 dependencies = [
-    "numpy>=1.21.6",
+    "numpy>=2.2.4",
     "scipy>=1.11.3",
 ]
 dynamic = ["version"]
 
+[build-system]
+requires = ["setuptools>=62"]
+build-backend = "setuptools.build_meta"
+
 [project.optional-dependencies]
 test = [
-    "hypothesis>=6.31.6",
-    "nbval>=0.10.0",
-    "pytest>=6.2.5",
-    "pytest-cov>=3.0.0",
-    "pytest-randomly>=3.10",
+    "hypothesis>=6.129.4",
+    "nbval>=0.11.0",
+    "pytest>=7.4.4",
+    "pytest-cov>=6.0.0",
+    "pytest-randomly>=3.16.0",
 ]
 docs = [
-    "ipython>=7.10",
-    "matplotlib>=3",
-    "nbsphinx>=0.8.8",
-    "pandas>=1.1.5",
-    "PyYAML>=6",
-    "quartodoc>=0.5.0; python_version>'3.8'",
+    "ipython>=7.34.0",
+    "matplotlib>=3.10.1",
+    "nbsphinx>=0.9.7",
+    "pandas>=2.2.3",
+    "pyyaml>=6.0.1",
+    "quartodoc>=0.9.1",
+]
+dev = [
+    "matching[docs,test]",
+    "pytest-sugar>=1.0.0",
+    "ruff>=0.11.1",
 ]
-lint = ["ruff<1.0"]
-dev = ["matching[docs,lint,test]", "pre-commit"]
 
 [project.urls]
 homepage = "https://github.com/daffidwilde/matching"
@@ -61,12 +64,9 @@ changelog = "https://github.com/daffidwilde/matching/blob/main/CHANGES.md"
 [tool.setuptools.dynamic]
 version = {attr = "matching.__version__"}
 
-[tool.coverage.report]
-omit = ["src/**/__init__.py"]
-
 [tool.ruff]
+line-length = 99
 extend-include = ["*.ipynb"]
-line-length = 79
 
 [tool.ruff.lint]
 extend-select = ["D", "I", "W"]
@@ -83,3 +83,6 @@ known-first-party = ["matching"]
 
 [tool.ruff.lint.pydocstyle]
 convention = "numpy"
+
+[tool.coverage.run]
+branch = true
diff --git a/src/matching/__init__.py b/src/matching/__init__.py
index 538e3ee7..efb21ee2 100644
--- a/src/matching/__init__.py
+++ b/src/matching/__init__.py
@@ -7,8 +7,10 @@
 
     warnings.simplefilter("always")
 
+from . import matchings
 from .base import BaseGame, BaseMatching, BasePlayer
-from .matchings import MultipleMatching, SingleMatching
+from .matchings import _MultipleMatching as MultipleMatching
+from .matchings import _SingleMatching as SingleMatching
 from .players import Hospital, Player, Project, Supervisor
 
 __version__ = "2.0.0"
@@ -18,11 +20,11 @@
     "BaseMatching",
     "BasePlayer",
     "Hospital",
-    "Matching",
     "MultipleMatching",
     "Player",
     "Project",
     "SingleMatching",
     "Supervisor",
     "__version__",
+    "matchings",
 ]
diff --git a/src/matching/algorithms/hospital_resident.py b/src/matching/algorithms/hospital_resident.py
index 61cf491c..52d6c1a3 100644
--- a/src/matching/algorithms/hospital_resident.py
+++ b/src/matching/algorithms/hospital_resident.py
@@ -13,9 +13,9 @@ def _unmatch_pair(resident, hospital):
 def _check_available(hospital):
     """Check if a hospital is willing and able to take an applicant."""
 
-    return len(hospital.matching) < hospital.capacity and set(
-        hospital.prefs
-    ).difference(hospital.matching)
+    return len(hospital.matching) < hospital.capacity and set(hospital.prefs).difference(
+        hospital.matching
+    )
 
 
 def hospital_resident(residents, hospitals, optimal="resident"):
diff --git a/src/matching/algorithms/student_allocation.py b/src/matching/algorithms/student_allocation.py
index 81db0d76..383af816 100644
--- a/src/matching/algorithms/student_allocation.py
+++ b/src/matching/algorithms/student_allocation.py
@@ -111,9 +111,7 @@ def student_optimal(students, projects):
             successors = supervisor.get_successors()
             for successor in successors:
                 supervisor_projects = [
-                    project
-                    for project in supervisor.projects
-                    if project in successor.prefs
+                    project for project in supervisor.projects if project in successor.prefs
                 ]
 
                 for project in supervisor_projects:
@@ -167,9 +165,7 @@ def supervisor_optimal(projects, supervisors):
             _delete_pair(student, successor)
 
         free_supervisors = [
-            supervisor
-            for supervisor in supervisors
-            if supervisor.get_favourite() is not None
+            supervisor for supervisor in supervisors if supervisor.get_favourite() is not None
         ]
 
     return {p: p.matching for p in projects}
diff --git a/src/matching/base.py b/src/matching/base.py
index 2255a0f0..e6e6fac7 100644
--- a/src/matching/base.py
+++ b/src/matching/base.py
@@ -151,9 +151,7 @@ def _check_inputs_player_prefs_unique(self, party):
                     unique_prefs.append(other)
                 else:
                     warnings.warn(
-                        PreferencesChangedWarning(
-                            f"{player} has ranked {other} multiple times."
-                        )
+                        PreferencesChangedWarning(f"{player} has ranked {other} multiple times.")
                     )
 
             if self.clean:
@@ -172,8 +170,7 @@ def _check_inputs_player_prefs_all_in_party(self, party, other_party):
                 if other not in others:
                     warnings.warn(
                         PreferencesChangedWarning(
-                            f"{player} has ranked a non-{other_party[:-1]}: "
-                            f"{other}."
+                            f"{player} has ranked a non-{other_party[:-1]}: {other}."
                         )
                     )
                     if self.clean:
@@ -187,11 +184,7 @@ def _check_inputs_player_prefs_nonempty(self, party, other_party):
 
         for player in vars(self)[party]:
             if not player.prefs:
-                warnings.warn(
-                    PlayerExcludedWarning(
-                        f"{player} has an empty preference list."
-                    )
-                )
+                warnings.warn(PlayerExcludedWarning(f"{player} has an empty preference list."))
                 if self.clean:
                     self._remove_player(player, party, other_party)
 
diff --git a/src/matching/games/_old_hospital_resident.py b/src/matching/games/_old_hospital_resident.py
new file mode 100644
index 00000000..c65054ef
--- /dev/null
+++ b/src/matching/games/_old_hospital_resident.py
@@ -0,0 +1,268 @@
+"""An old module for the hospital-resident assignment problem."""
+
+import copy
+import warnings
+
+from matching import BaseGame, MultipleMatching
+from matching import Player as Resident
+from matching.algorithms import hospital_resident
+from matching.exceptions import (
+    MatchingError,
+    PlayerExcludedWarning,
+    PreferencesChangedWarning,
+)
+from matching.players import Hospital
+
+
+class HospitalResident(BaseGame):
+    """Solver for the hospital-resident assignment problem (HR).
+
+    In this case, a blocking pair is any resident-hospital pair that
+    satisfies **all** of the following:
+
+        - They are present in each other's preference lists;
+        - either the resident is unmatched, or they prefer the hospital
+          to their current match;
+        - either the hospital is under-subscribed, or they prefer the
+          resident to at least one of their current matches.
+
+    Parameters
+    ----------
+    residents : list of Player
+        The residents in the matching game. Each resident must rank a
+        subset of those in ``hospitals``.
+    hospitals : list of Hospital
+        The hospitals in the matching game. Each hospital must rank all
+        of (and only) the residents which rank it.
+    clean : bool
+        Indicator for whether the players of the game should be cleaned.
+        Cleaning is reductive in nature, removing players from the game
+        and/or other player's preferences if they do not meet the
+        requirements of the game.
+
+    Attributes
+    ----------
+    matching : MultipleMatching or None
+        Once the game is solved, a matching is available as a
+        ``MultipleMatching`` object with the hospitals as keys and their
+        resident matches as values. Initialises as ``None``.
+    blocking_pairs : list of (Player, Hospital) or None
+        Initialises as ``None``. Otherwise, a list of the
+        resident-hospital blocking pairs.
+    """
+
+    def __init__(self, residents, hospitals, clean=False):
+        residents, hospitals = copy.deepcopy([residents, hospitals])
+        self.residents = residents
+        self.hospitals = hospitals
+        self.clean = clean
+
+        self._all_residents = residents
+        self._all_hospitals = hospitals
+
+        super().__init__(clean)
+        self.check_inputs()
+
+    @classmethod
+    def create_from_dictionaries(cls, resident_prefs, hospital_prefs, capacities, clean=False):
+        """Create an instance from a set of dictionaries.
+
+        A preference dictionary for residents and hospitals is required,
+        along with a dictionary detailing the hospital capacities. If
+        ``clean``, then remove players from the game and/or player
+        preferences if they do not satisfy the conditions of the game.
+        """
+
+        residents, hospitals = _make_players(resident_prefs, hospital_prefs, capacities)
+        game = cls(residents, hospitals, clean)
+
+        return game
+
+    def solve(self, optimal="resident"):
+        """Solve the instance of HR. Return the matching.
+
+        The party optimality can be controlled using the ``optimal``
+        parameter.
+        """
+
+        self.matching = MultipleMatching(
+            hospital_resident(self.residents, self.hospitals, optimal)
+        )
+        return self.matching
+
+    def check_validity(self):
+        """Check whether the current matching is valid."""
+
+        unacceptable_issues = self._check_for_unacceptable_matches(
+            "residents"
+        ) + self._check_for_unacceptable_matches("hospitals")
+
+        oversubscribed_issues = self._check_for_oversubscribed_players("hospitals")
+
+        if unacceptable_issues or oversubscribed_issues:
+            raise MatchingError(
+                unacceptable_matches=unacceptable_issues,
+                oversubscribed_hospitals=oversubscribed_issues,
+            )
+
+        return True
+
+    def _check_for_unacceptable_matches(self, party):
+        """Check that no one in ``party`` has an unacceptable match."""
+
+        issues = []
+        for player in vars(self)[party]:
+            issue = player.check_if_match_is_unacceptable(unmatched_okay=True)
+            if isinstance(issue, list):
+                issues.extend(issue)
+            elif isinstance(issue, str):
+                issues.append(issue)
+
+        return issues
+
+    def _check_for_oversubscribed_players(self, party):
+        """Check that no player in `party` is over-subscribed."""
+
+        issues = []
+        for player in vars(self)[party]:
+            issue = player.check_if_oversubscribed()
+            if issue:
+                issues.append(issue)
+
+        return issues
+
+    def check_stability(self):
+        """Check for the existence of any blocking pairs."""
+
+        blocking_pairs = []
+        for resident in self.residents:
+            for hospital in self.hospitals:
+                if (
+                    _check_mutual_preference(resident, hospital)
+                    and _check_resident_unhappy(resident, hospital)
+                    and _check_hospital_unhappy(resident, hospital)
+                ):
+                    blocking_pairs.append((resident, hospital))
+
+        self.blocking_pairs = blocking_pairs
+        return not any(blocking_pairs)
+
+    def check_inputs(self):
+        """Check if any rules of the game have been broken.
+
+        Any violations will be flagged as warnings. If the ``clean``
+        attribute is in use, then any violations will be removed.
+        """
+
+        self._check_inputs_player_prefs_unique("residents")
+        self._check_inputs_player_prefs_unique("hospitals")
+
+        self._check_inputs_player_prefs_all_in_party("residents", "hospitals")
+        self._check_inputs_player_prefs_all_in_party("hospitals", "residents")
+
+        self._check_inputs_player_prefs_all_reciprocated("hospitals")
+        self._check_inputs_player_reciprocated_all_prefs("hospitals", "residents")
+
+        self._check_inputs_player_prefs_nonempty("residents", "hospitals")
+        self._check_inputs_player_prefs_nonempty("hospitals", "residents")
+
+        self._check_inputs_player_capacity("hospitals", "residents")
+
+    def _check_inputs_player_prefs_all_reciprocated(self, party):
+        """Check everyone has only ranked players who ranked them."""
+
+        for player in vars(self)[party]:
+            for other in player.prefs:
+                if player not in other.prefs:
+                    warnings.warn(
+                        PreferencesChangedWarning(f"{player} ranked {other} but they did not.")
+                    )
+                    if self.clean:
+                        player._forget(other)
+
+    def _check_inputs_player_reciprocated_all_prefs(self, party, other_party):
+        """Check everyone has ranked all the players who ranked them."""
+
+        players = vars(self)[party]
+        others = vars(self)[other_party]
+        for player in players:
+            others_that_ranked = [other for other in others if player in other.prefs]
+            for other in others_that_ranked:
+                if other not in player.prefs:
+                    warnings.warn(
+                        PreferencesChangedWarning(f"{other} ranked {player} but they did not.")
+                    )
+                    if self.clean:
+                        other._forget(player)
+
+    def _check_inputs_player_capacity(self, party, other_party):
+        """Check everyone has a capacity of at least one."""
+
+        for player in vars(self)[party]:
+            if player.capacity < 1:
+                warnings.warn(PlayerExcludedWarning(player))
+
+                if self.clean:
+                    self._remove_player(player, party, other_party)
+
+
+def _check_mutual_preference(resident, hospital):
+    """Check whether two players have a preference of each other."""
+
+    return resident in hospital.prefs and hospital in resident.prefs
+
+
+def _check_resident_unhappy(resident, hospital):
+    """Check whether a resident is unhappy given a hospital.
+
+    An unhappy resident is either unmatched or they prefer the hospital
+    to their current match.
+    """
+
+    return resident.matching is None or resident.prefers(hospital, resident.matching)
+
+
+def _check_hospital_unhappy(resident, hospital):
+    """Check whether a hospital is unhappy given a resident.
+
+    An unhappy hospital is either under-subscribed or they prefer the
+    resident to at least one of their current matches.
+    """
+
+    return len(hospital.matching) < hospital.capacity or any(
+        [hospital.prefers(resident, match) for match in hospital.matching]
+    )
+
+
+def _make_players(resident_prefs, hospital_prefs, capacities):
+    """Make a set of residents and hospitals from the dictionaries."""
+
+    resident_dict, hospital_dict = _make_instances(resident_prefs, hospital_prefs, capacities)
+
+    for resident_name, resident in resident_dict.items():
+        prefs = [hospital_dict[name] for name in resident_prefs[resident_name]]
+        resident.set_prefs(prefs)
+
+    for hospital_name, hospital in hospital_dict.items():
+        prefs = [resident_dict[name] for name in hospital_prefs[hospital_name]]
+        hospital.set_prefs(prefs)
+
+    residents = list(resident_dict.values())
+    hospitals = list(hospital_dict.values())
+
+    return residents, hospitals
+
+
+def _make_instances(resident_prefs, hospital_prefs, capacities):
+    """Create ``Player`` (resident) and ``Hospital`` instances."""
+
+    resident_dict, hospital_dict = {}, {}
+    for resident_name in resident_prefs:
+        resident = Resident(name=resident_name)
+        resident_dict[resident_name] = resident
+    for hospital_name in hospital_prefs:
+        capacity = capacities[hospital_name]
+        hospital = Hospital(name=hospital_name, capacity=capacity)
+        hospital_dict[hospital_name] = hospital
+
+    return resident_dict, hospital_dict
diff --git a/src/matching/games/hospital_resident.py b/src/matching/games/hospital_resident.py
index 13593fd5..d15831da 100644
--- a/src/matching/games/hospital_resident.py
+++ b/src/matching/games/hospital_resident.py
@@ -1,286 +1,358 @@
 """The HR game class and supporting functions."""
 
-import copy
 import warnings
 
-from matching import BaseGame, MultipleMatching
-from matching import Player as Resident
-from matching.algorithms import hospital_resident
-from matching.exceptions import (
-    MatchingError,
-    PlayerExcludedWarning,
-    PreferencesChangedWarning,
-)
-from matching.players import Hospital
+import numpy as np
 
+from matching import convert, matchings
 
-class HospitalResident(BaseGame):
-    """Solver for the hospital-resident assignment problem (HR).
 
-    In this case, a blocking pair is any resident-hospital pair that
-    satisfies **all** of the following:
-
-        - They are present in each other's preference lists;
-        - either the resident is unmatched, or they prefer the hospital
-          to their current match;
-        - either the hospital is under-subscribed, or they prefer the
-          resident to at least one of their current matches.
+class HospitalResident:
+    """
+    Solver for the hospital-resident assignment problem (HR).
 
     Parameters
     ----------
-    residents : list of Player
-        The residents in the matching game. Each resident must rank a
-        subset of those in ``hospitals``.
-    hospitals : list of Hospital
-        The hospitals in the matching game. Each hospital must rank all
-        of (and only) the residents which rank it.
-    clean : bool
-        Indicator for whether the players of the game should be cleaned.
-        Cleaning is reductive in nature, removing players from the game
-        and/or other player's preferences if they do not meet the
-        requirements of the game.
+    resident_ranks : np.ndarray
+        The rank matrix of all hospitals by the residents.
+    hospital_ranks : np.ndarray
+        The rank matrix of all residents by the hospitals.
+    capacities : np.ndarray
+        Capacity matrix for the hospitals.
 
     Attributes
     ----------
+    num_residents : int
+        Number of residents.
+    num_hospitals : int
+        Number of hospitals.
     matching : MultipleMatching or None
-        Once the game is solved, a matching is available as a
-        ``MultipleMatching`` object with the hospitals as keys and their
-        resident matches as values. Initialises as ``None``.
-    blocking_pairs : list of (Player, Hospital) or None
-        Initialises as ``None``. Otherwise, a list of the
-        resident-hospital blocking pairs.
+        Once the game is solved, a matching is available. This uses the
+        indices of the hospital and resident rank matrices as keys and
+        values, respectively, in a `MultipleMatching` object.
+        Initialises as `None`.
     """
 
-    def __init__(self, residents, hospitals, clean=False):
-        residents, hospitals = copy.deepcopy([residents, hospitals])
-        self.residents = residents
-        self.hospitals = hospitals
-        self.clean = clean
+    def __init__(self, resident_ranks, hospital_ranks, capacities):
+        self.resident_ranks = resident_ranks.copy()
+        self.hospital_ranks = hospital_ranks.copy()
+        self.capacities = capacities.copy()
 
-        self._all_residents = residents
-        self._all_hospitals = hospitals
+        self.num_residents = len(resident_ranks)
+        self.num_hospitals = len(hospital_ranks)
+        self.matching = None
+        self._preference_lookup = None
 
-        super().__init__(clean)
-        self.check_inputs()
+        self.check_input_validity()
 
     @classmethod
-    def create_from_dictionaries(
-        cls, resident_prefs, hospital_prefs, capacities, clean=False
-    ):
-        """Create an instance from a set of dictionaries.
-
-        A preference dictionary for residents and hospitals is required,
-        along with a dictionary detailing the hospital capacities. If
-        ``clean``, then remove players from the game and/or player
-        preferences if they do not satisfy the conditions of the game.
+    def from_utilities(cls, resident_utils, hospital_utils, capacities):
         """
-
-        residents, hospitals = _make_players(
-            resident_prefs, hospital_prefs, capacities
-        )
-        game = cls(residents, hospitals, clean)
-
-        return game
-
-    def solve(self, optimal="resident"):
-        """Solve the instance of HR. Return the matching.
-
-        The party optimality can be controlled using the ``optimal``
-        parameter.
+        Create an instance of HR from utility matrices.
+
+        Higher utilities indicate higher preferences. If there are any
+        ties, they are broken in order of appearance.
+
+        Parameters
+        ----------
+        resident_utils : np.ndarray
+            Resident utility matrix.
+        hospital_utils : np.ndarray
+            Hospital utility matrix.
+        capacities : np.ndarray
+            Hospital capacity vector.
+
+        Returns
+        -------
+        HospitalResident
+            An instance of HR with utilities resolved as rank matrices.
         """
+        resident_ranks = convert.utility_to_rank(resident_utils)
+        hospital_ranks = convert.utility_to_rank(hospital_utils)
 
-        self.matching = MultipleMatching(
-            hospital_resident(self.residents, self.hospitals, optimal)
-        )
-        return self.matching
-
-    def check_validity(self):
-        """Check whether the current matching is valid."""
-
-        unacceptable_issues = self._check_for_unacceptable_matches(
-            "residents"
-        ) + self._check_for_unacceptable_matches("hospitals")
-
-        oversubscribed_issues = self._check_for_oversubscribed_players(
-            "hospitals"
-        )
+        return cls(resident_ranks, hospital_ranks, capacities)
 
-        if unacceptable_issues or oversubscribed_issues:
-            raise MatchingError(
-                unacceptable_matches=unacceptable_issues,
-                oversubscribed_hospitals=oversubscribed_issues,
-            )
-
-        return True
-
-    def _check_for_unacceptable_matches(self, party):
-        """Check that no one in ``party`` has an unacceptable match."""
-
-        issues = []
-        for player in vars(self)[party]:
-            issue = player.check_if_match_is_unacceptable(unmatched_okay=True)
-            if isinstance(issue, list):
-                issues.extend(issue)
-            elif isinstance(issue, str):
-                issues.append(issue)
-
-        return issues
-
-    def _check_for_oversubscribed_players(self, party):
-        """Check that no player in `party` is over-subscribed."""
-
-        issues = []
-        for player in vars(self)[party]:
-            issue = player.check_if_oversubscribed()
-            if issue:
-                issues.append(issue)
-
-        return issues
-
-    def check_stability(self):
-        """Check for the existence of any blocking pairs."""
+    @classmethod
+    def from_preferences(cls, resident_prefs, hospital_prefs, capacities):
+        """
+        Create an instance of HR from preference list dictionaries.
+
+        Each dictionary should contain a strict ordering of the other
+        side by each player. The ranking is taken by the order of the
+        preference list.
+
+        Parameters
+        ----------
+        resident_prefs : dict
+            Resident preference lists.
+        hospital_prefs : dict
+            Hospital preference lists.
+        capacities : dict
+            Hospital capacities.
+
+        Returns
+        -------
+        HospitalResident
+            An instance of HR with preference lists resolved as rank
+            matrices.
+        """
+        residents, hospitals = sorted(resident_prefs), sorted(hospital_prefs)
 
-        blocking_pairs = []
-        for resident in self.residents:
-            for hospital in self.hospitals:
-                if (
-                    _check_mutual_preference(resident, hospital)
-                    and _check_resident_unhappy(resident, hospital)
-                    and _check_hospital_unhappy(resident, hospital)
-                ):
-                    blocking_pairs.append((resident, hospital))
+        resident_ranks = convert.preference_to_rank(resident_prefs, hospitals)
+        hospital_ranks = convert.preference_to_rank(hospital_prefs, residents)
+        capacity_array = np.array([capacities.get(h, 0) for h in hospitals])
 
-        self.blocking_pairs = blocking_pairs
-        return not any(blocking_pairs)
+        game = cls(resident_ranks, hospital_ranks, capacity_array)
+        game._preference_lookup = {"residents": residents, "hospitals": hospitals}
 
-    def check_inputs(self):
-        """Check if any rules of the game have been broken.
+        return game
 
-        Any violations will be flagged as warnings. If the ``clean``
-        attribute is in use, then any violations will be removed.
+    def check_input_validity(self):
         """
+        Determine whether this game instance is valid or not.
 
-        self._check_inputs_player_prefs_unique("residents")
-        self._check_inputs_player_prefs_unique("hospitals")
-
-        self._check_inputs_player_prefs_all_in_party("residents", "hospitals")
-        self._check_inputs_player_prefs_all_in_party("hospitals", "residents")
-
-        self._check_inputs_player_prefs_all_reciprocated("hospitals")
-        self._check_inputs_player_reciprocated_all_prefs(
-            "hospitals", "residents"
-        )
+        Invalid games can still be solved, but the matching will not be
+        truly stable in the absence of blocking pairs.
 
-        self._check_inputs_player_prefs_nonempty("residents", "hospitals")
-        self._check_inputs_player_prefs_nonempty("hospitals", "residents")
+        Warns
+        -----
+        UserWarning
+            If (a) any player has not made a strict, exhaustive ranking
+            of the players who ranked them, or (b) any hospital has an
+            invalid capacity.
+        """
+        for hospital, ranks in enumerate(self.hospital_ranks):
+            self._check_player_ranks(hospital, ranks, self.resident_ranks, "hospital")
 
-        self._check_inputs_player_capacity("hospitals", "residents")
+        for resident, ranks in enumerate(self.resident_ranks):
+            self._check_player_ranks(resident, ranks, self.hospital_ranks, "resident")
 
-    def _check_inputs_player_prefs_all_reciprocated(self, party):
-        """Check everyone has only ranked players who ranked them."""
+        self._check_capacities()
 
-        for player in vars(self)[party]:
-            for other in player.prefs:
-                if player not in other.prefs:
-                    warnings.warn(
-                        PreferencesChangedWarning(
-                            f"{player} ranked {other} but they did not."
-                        )
-                    )
-                    if self.clean:
-                        player._forget(other)
+    @staticmethod
+    def _check_player_ranks(player, player_ranks, other_ranks, side):
+        """
+        Check whether a player has made a valid ranking.
+
+        Parameters
+        ----------
+        player : int
+            Player for whom to check the ranks.
+        player_ranks : np.ndarray
+            Ranking by the player.
+        other_ranks : np.ndarray
+            Rankings of the players on the other side.
+        side : {"resident", "hospital"}
+            Side of the matching for the player.
+
+        Warns
+        -----
+        UserWarning
+            If the player has not made a strict, exhaustive ranking of
+            the all the players who ranked them.
+        """
+        ranked_player = [other for other, ranks in enumerate(other_ranks) if player in ranks]
+        same_size = len(ranked_player) == len(player_ranks)
+        same_elements = set(ranked_player) == set(player_ranks)
+        if not same_size or not same_elements:
+            warnings.warn(
+                f"{side.title()} {player} has not made a strict, exhaustive ranking of "
+                f"the players who ranked them.",
+                UserWarning,
+            )
 
-    def _check_inputs_player_reciprocated_all_prefs(self, party, other_party):
-        """Check everyone has ranked all the players who ranked them."""
+    def _check_capacities(self):
+        """
+        Check whether the hospital capacities are valid.
 
-        players = vars(self)[party]
-        others = vars(self)[other_party]
-        for player in players:
-            others_that_ranked = [
-                other for other in others if player in other.prefs
-            ]
-            for other in others_that_ranked:
-                if other not in player.prefs:
-                    warnings.warn(
-                        PreferencesChangedWarning(
-                            f"{other} ranked {player} but they did not."
-                        )
-                    )
-                    if self.clean:
-                        other._forget(player)
+        Warns
+        -----
+        UserWarning
+            If any hospital has an invalid capacity.
+        """
+        for hospital, capacity in enumerate(self.capacities):
+            if capacity <= 0:
+                warnings.warn(
+                    f"Hospital {hospital} has a capacity of {capacity}.",
+                    UserWarning,
+                )
 
-    def _check_inputs_player_capacity(self, party, other_party):
-        """Check everyone has a capacity of at least one."""
+    def solve(self, optimal="resident"):
+        """
+        Solve the instance of HR.
+
+        This method uses the adapted Gale-Shapley algorithms introduced
+        by Alvin Roth in 1984 (https://doi.org/10.1086/261272). The
+        algorithms find a unique, stable and party-optimal matching for
+        any valid set of residents and hospitals.
+
+        The optimality of the matching is with respect to one party and
+        is subsequently the worst stable matching for the other party.
+
+        Parameters
+        ----------
+        optimal : {"resident", "hospital"}, default="resident"
+            Party for whom to optimise the matching.
+
+        Raises
+        ------
+        ValueError
+            If `optimal` is anything other than the permitted values.
+
+        Returns
+        -------
+        HRMatching
+            A dictionary-like object containing the matching. The keys
+            correspond to the hospitals in the instance, while the
+            values are lists of the residents matched to them.
+        """
+        if optimal == "resident":
+            matching = self._resident_optimal()
+        elif optimal == "hospital":
+            matching = self._hospital_optimal()
+        else:
+            raise ValueError(
+                f'Invalid choice for `optimal`. Must be "resident" or "hospital", not "{optimal}".'
+            )
 
-        for player in vars(self)[party]:
-            if player.capacity < 1:
-                warnings.warn(PlayerExcludedWarning(player))
+        self.matching = matchings.HRMatching(matching, keys="hospitals", values="residents")
+        if self._preference_lookup:
+            self._convert_matching_to_preferences()
 
-                if self.clean:
-                    self._remove_player(player, party, other_party)
+        return self.matching
 
+    def _resident_optimal(self):
+        """
+        Execute the resident-optimal algorithm given some rankings.
 
-def _check_mutual_preference(resident, hospital):
-    """Check whether two players have a preference of each other."""
+        Returns
+        -------
+        dict
+            Solution mapping hospitals to their matched residents.
+        """
+        resident_ranks = self.resident_ranks
+        hospital_ranks = self.hospital_ranks
+        capacities = self.capacities
 
-    return resident in hospital.prefs and hospital in resident.prefs
+        matching = {h: [] for h in range(self.num_hospitals)}
+        free_residents = set(range(self.num_residents))
 
+        while free_residents:
+            resident = free_residents.pop()
+            resident_rank = resident_ranks[resident]
+            if np.min(resident_rank) == self.num_hospitals:
+                continue
 
-def _check_resident_unhappy(resident, hospital):
-    """Check whether a resident is unhappy given a hospital.
+            hospital = resident_rank.argmin()
+            hospital_rank = hospital_ranks[hospital]
+            hospital_matches = matching[hospital]
+            capacity = capacities[hospital]
 
-    An unhappy resident is either unmatched or they prefer the hospital
-    to their current match.
-    """
+            if len(hospital_matches) == capacity:
+                idx = hospital_rank[hospital_matches].argmax()
+                worst = hospital_matches[idx]
+                del hospital_matches[idx]
+                free_residents.add(worst)
 
-    return resident.matching is None or resident.prefers(
-        hospital, resident.matching
-    )
+            hospital_matches.append(resident)
 
+            if len(hospital_matches) == capacity:
+                worst = hospital_matches[hospital_rank[hospital_matches].argmax()]
+                successors = np.where(hospital_rank > hospital_rank[worst])
+                resident_ranks[successors, hospital] = self.num_hospitals
+                hospital_rank[successors] = self.num_residents
 
-def _check_hospital_unhappy(resident, hospital):
-    """Check whether a hospital is unhappy given a resident.
+        return matching
 
-    An unhappy hospital is either under-subscribed or they prefer the
-    resident to at least one of their current matches.
-    """
+    def _hospital_optimal(self):
+        """
+        Execute the hospital-optimal algorithm given some rankings.
 
-    return len(hospital.matching) < hospital.capacity or any(
-        [hospital.prefers(resident, match) for match in hospital.matching]
-    )
+        Returns
+        -------
+        dict
+            Solution mapping hospitals to their matched residents.
+        """
 
+        def _get_current_match(resident, matching):
+            """
+            Get the current match for a resident (and its index) if any.
+
+            Parameters
+            ----------
+            resident : int
+                Resident for whom to search.
+            matching : dict
+                Mapping of hospitals to their matched residents.
+
+            Returns
+            -------
+            tuple[int, int] | None
+                Currently matched hospital and its position in the
+                resident's ranking or `None` if the resident is free.
+            """
+            for hospital, residents in matching.items():
+                for idx, res in enumerate(residents):
+                    if res == resident:
+                        return hospital, idx
+
+            return None, None
+
+        resident_ranks = self.resident_ranks.copy()
+        hospital_ranks = self.hospital_ranks.copy()
+        capacities = self.capacities
+
+        matching = {h: [] for h in range(self.num_hospitals)}
+        free_hospitals = set(range(self.num_hospitals))
+
+        while free_hospitals:
+            hospital = free_hospitals.pop()
+            hospital_rank = hospital_ranks[hospital]
+            hospital_matches = matching.get(hospital)
+            options = [
+                res if i not in hospital_matches else self.num_residents
+                for i, res in enumerate(hospital_rank)
+            ]
 
-def _make_players(resident_prefs, hospital_prefs, capacities):
-    """Make a set of residents and hospitals from the dictionaries."""
+            is_at_capacity = len(hospital_matches) == capacities[hospital]
+            has_no_options = np.min(options) == self.num_residents
+            has_no_ranking = np.min(hospital_rank) == self.num_residents
+            if is_at_capacity or has_no_options or has_no_ranking:
+                continue
 
-    resident_dict, hospital_dict = _make_instances(
-        resident_prefs, hospital_prefs, capacities
-    )
+            resident = np.argmin(options)
+            resident_rank = resident_ranks[resident]
 
-    for resident_name, resident in resident_dict.items():
-        prefs = [hospital_dict[name] for name in resident_prefs[resident_name]]
-        resident.set_prefs(prefs)
+            current_match, idx = _get_current_match(resident, matching)
+            if current_match is not None:
+                current_match_matches = matching[current_match]
+                del current_match_matches[idx]
+                free_hospitals.add(current_match)
 
-    for hospital_name, hospital in hospital_dict.items():
-        prefs = [resident_dict[name] for name in hospital_prefs[hospital_name]]
-        hospital.set_prefs(prefs)
+            hospital_matches.append(resident)
+            free_hospitals.add(hospital)
 
-    residents = list(resident_dict.values())
-    hospitals = list(hospital_dict.values())
+            successors = np.where(resident_rank > resident_rank[hospital])
+            hospital_ranks[successors, resident] = self.num_residents
+            resident_rank[successors] = self.num_hospitals
 
-    return residents, hospitals
+        return matching
 
+    def _convert_matching_to_preferences(self):
+        """
+        Replace the rank indices with preference terms in a matching.
 
-def _make_instances(resident_prefs, hospital_prefs, capacities):
-    """Create ``Player`` (resident) and ``Hospital`` instances."""
+        This internal function is included for users who wish to create
+        a matching from a set of preference list dictionaries.
 
-    resident_dict, hospital_dict = {}, {}
-    for resident_name in resident_prefs:
-        resident = Resident(name=resident_name)
-        resident_dict[resident_name] = resident
-    for hospital_name in hospital_prefs:
-        capacity = capacities[hospital_name]
-        hospital = Hospital(name=hospital_name, capacity=capacity)
-        hospital_dict[hospital_name] = hospital
+        Attributes
+        ----------
+        HRMatching
+            The converted matching instance.
+        """
+        converted = {}
+        residents, hospitals = self._preference_lookup.values()
+        for hospital, resident_matches in self.matching.items():
+            converted[hospitals[hospital]] = [residents[resident] for resident in resident_matches]
 
-    return resident_dict, hospital_dict
+        self.matching = matchings.HRMatching(converted, keys="hospitals", values="residents")
diff --git a/src/matching/games/stable_marriage.py b/src/matching/games/stable_marriage.py
index b0ff8d54..2bfcc86a 100644
--- a/src/matching/games/stable_marriage.py
+++ b/src/matching/games/stable_marriage.py
@@ -4,8 +4,7 @@
 
 import numpy as np
 
-from matching import convert
-from matching.matchings import SingleMatching
+from matching import convert, matchings
 
 
 class StableMarriage:
@@ -60,7 +59,7 @@ def from_utilities(cls, suitor_utils, reviewer_utils):
 
         Returns
         -------
-        game : StableMarriage
+        StableMarriage
             An instance of SM with utilities resolved as rank matrices.
         """
 
@@ -87,7 +86,7 @@ def from_preferences(cls, suitor_prefs, reviewer_prefs):
 
         Returns
         -------
-        game : StableMarriage
+        StableMarriage
             An instance of SM with preference lists resolved as rank
             matrices.
         """
@@ -102,6 +101,29 @@ def from_preferences(cls, suitor_prefs, reviewer_prefs):
 
         return game
 
+    def check_input_validity(self):
+        """
+        Determine whether this game instance is valid or not.
+
+        Invalid games can still be solved, but the matching will not be
+        truly stable in the absence of blocking pairs.
+
+        Warns
+        -----
+        UserWarning
+            If (a) the player sets are not the same size; or (b) any
+            player has not made a strict, exhaustive, and unique ranking
+            of the players on the other side of the matching.
+        """
+
+        self._check_number_of_players()
+
+        for suitor, ranks in enumerate(self.suitor_ranks):
+            self._check_player_ranks(suitor, ranks, "suitor")
+
+        for reviewer, ranks in enumerate(self.reviewer_ranks):
+            self._check_player_ranks(reviewer, ranks, "reviewer")
+
     def _check_number_of_players(self):
         """
         Check whether the player sets are the same size.
@@ -150,28 +172,58 @@ def _check_player_ranks(self, player, ranks, side):
                 UserWarning,
             )
 
-    def check_input_validity(self):
+    def solve(self, optimal="suitor"):
         """
-        Determine whether this game instance is valid or not.
+        Solve the instance of SM.
 
-        Invalid games can still be solved, but the matching will not be
-        truly stable in the absence of blocking pairs.
+        This method uses an extended version of the Gale-Shapley
+        algorithm that makes use of the inherent structures of SM
+        instances. The algorithm finds a unique, stable and optimal
+        matching for any valid set of suitors and reviewers.
 
-        Warns
-        -----
-        UserWarning
-            If (a) the player sets are not the same size; or (b) any
-            player has not made a strict, exhaustive, and unique ranking
-            of the players on the other side of the matching.
+        The optimality of the matching is with respect to one party and
+        is subsequently the worst stable matching for the other party.
+
+        Parameters
+        ----------
+        optimal : {"suitor", "reviewer"}, default="suitor"
+            Party for whom to optimise the matching.
+
+        Raises
+        ------
+        ValueError
+            If `optimal` is anything other than the permitted values.
+
+        Returns
+        -------
+        SMMatching
+            A dictionary-like object containing the matching. The keys
+            correspond to the reviewers in the instance, while the
+            values are the suitors.
         """
 
-        self._check_number_of_players()
+        if optimal not in ("suitor", "reviewer"):
+            raise ValueError(
+                f'Invalid choice for `optimal`. Must be "suitor" or "reviewer", not "{optimal}".'
+            )
 
-        for suitor, ranks in enumerate(self.suitor_ranks):
-            self._check_player_ranks(suitor, ranks, "suitor")
+        keys, values = "reviewers", "suitors"
 
-        for reviewer, ranks in enumerate(self.reviewer_ranks):
-            self._check_player_ranks(reviewer, ranks, "reviewer")
+        if optimal == "reviewer":
+            self._invert_player_sets()
+            keys, values = values, keys
+
+        matching = matchings.SMMatching(self._stable_marriage(), keys=keys, values=values)
+
+        if optimal == "reviewer":
+            matching = matching.invert()
+            self._invert_player_sets()
+
+        self.matching = matching
+        if self._preference_lookup:
+            self._convert_matching_to_preferences()
+
+        return self.matching
 
     def _invert_player_sets(self):
         """
@@ -196,7 +248,7 @@ def _stable_marriage(self):
 
         Returns
         -------
-        matching : dict
+        dict
             Solution to the game instance.
         """
 
@@ -210,10 +262,7 @@ def _stable_marriage(self):
             reviewer_rank = reviewer_ranks[reviewer]
 
             current = matching.get(reviewer)
-            if (
-                current is not None
-                and (suitor_ranks[current] < self.num_reviewers).any()
-            ):
+            if current is not None and (suitor_ranks[current] < self.num_reviewers).any():
                 free_suitors.append(current)
 
             matching[reviewer] = suitor
@@ -233,7 +282,7 @@ def _convert_matching_to_preferences(self):
 
         Attributes
         ----------
-        matching : SingleMatching
+        SMMatching
             The converted matching instance.
         """
 
@@ -242,63 +291,4 @@ def _convert_matching_to_preferences(self):
         for reviewer, suitor in self.matching.items():
             converted[reviewers[reviewer]] = suitors[suitor]
 
-        self.matching = SingleMatching(
-            converted, valid=self.matching.valid, stable=self.matching.stable
-        )
-
-    def solve(self, optimal="suitor"):
-        """
-        Solve the instance of SM.
-
-        This method uses an extended version of the Gale-Shapley
-        algorithm that makes use of the inherent structures of SM
-        instances. The algorithm finds a unique, stable and optimal
-        matching for any valid set of suitors and reviewers.
-
-        The optimality of the matching is with respect to one party and
-        is subsequently the worst stable matching for the other party.
-
-        Parameters
-        ----------
-        optimal : {"suitor", "reviewer"}, default "suitor"
-            Party for whom to optimise the matching. Must be one of
-            `"suitor"` or `"reviewer"`. Default is `"suitor"`.
-
-        Raises
-        ------
-        ValueError
-            If `optimal` is anything other than the permitted values.
-
-        Returns
-        -------
-        matching : SingleMatching
-            A dictionary-like object containing the matching. The keys
-            correspond to the reviewers in the instance, while the
-            values are the suitors.
-        """
-
-        if optimal not in ("suitor", "reviewer"):
-            raise ValueError(
-                "Invalid choice for `optimal`. "
-                f'Must be "suitor" or "reviewer", not "{optimal}".'
-            )
-
-        keys, values = "reviewers", "suitors"
-
-        if optimal == "reviewer":
-            self._invert_player_sets()
-            keys, values = values, keys
-
-        matching = SingleMatching(
-            self._stable_marriage(), keys=keys, values=values
-        )
-
-        if optimal == "reviewer":
-            matching = matching.invert()
-            self._invert_player_sets()
-
-        self.matching = matching
-        if self._preference_lookup:
-            self._convert_matching_to_preferences()
-
-        return self.matching
+        self.matching = matchings.SMMatching(converted)
diff --git a/src/matching/games/stable_roommates.py b/src/matching/games/stable_roommates.py
index 678fac53..77701ba4 100644
--- a/src/matching/games/stable_roommates.py
+++ b/src/matching/games/stable_roommates.py
@@ -75,9 +75,9 @@ def check_stability(self):
             for other in others:
                 if (other, player) not in blocking_pairs:
                     both_matched = player.matching and other.matching
-                    prefer_each_other = player.prefers(
-                        other, player.matching
-                    ) and other.prefers(player, other.matching)
+                    prefer_each_other = player.prefers(other, player.matching) and other.prefers(
+                        player, other.matching
+                    )
                     if both_matched and prefer_each_other:
                         blocking_pairs.append((player, other))
 
diff --git a/src/matching/games/student_allocation.py b/src/matching/games/student_allocation.py
index 0f576d05..3c8756e2 100644
--- a/src/matching/games/student_allocation.py
+++ b/src/matching/games/student_allocation.py
@@ -11,9 +11,10 @@
     MatchingError,
     PreferencesChangedWarning,
 )
-from matching.games import HospitalResident
 from matching.players import Project, Supervisor
 
+from ._old_hospital_resident import HospitalResident
+
 
 class StudentAllocation(HospitalResident):
     """Solver for the student-allocation problem (SA).
@@ -63,9 +64,7 @@ class StudentAllocation(HospitalResident):
     """
 
     def __init__(self, students, projects, supervisors, clean=False):
-        students, projects, supervisors = copy.deepcopy(
-            [students, projects, supervisors]
-        )
+        students, projects, supervisors = copy.deepcopy([students, projects, supervisors])
         self.students = students
         self.projects = projects
         self.supervisors = supervisors
@@ -131,9 +130,7 @@ def solve(self, optimal="student"):
         """
 
         self.matching = MultipleMatching(
-            student_allocation(
-                self.students, self.projects, self.supervisors, optimal
-            )
+            student_allocation(self.students, self.projects, self.supervisors, optimal)
         )
         return self.matching
 
@@ -192,15 +189,11 @@ def check_inputs(self):
         self._check_inputs_player_prefs_nonempty("supervisors", "students")
 
         self._check_inputs_player_prefs_all_reciprocated("projects")
-        self._check_inputs_player_reciprocated_all_prefs(
-            "projects", "students"
-        )
+        self._check_inputs_player_reciprocated_all_prefs("projects", "students")
         self._check_inputs_player_prefs_nonempty("projects", "students")
 
         self._check_inputs_player_prefs_all_reciprocated("supervisors")
-        self._check_inputs_player_reciprocated_all_prefs(
-            "supervisors", "students"
-        )
+        self._check_inputs_player_reciprocated_all_prefs("supervisors", "students")
         self._check_inputs_player_prefs_nonempty("supervisors", "students")
 
         self._check_inputs_player_capacity("projects", "students")
@@ -218,9 +211,7 @@ def _check_inputs_player_prefs_all_reciprocated(self, party):
         if party == "supervisors":
             for supervisor in self.supervisors:
                 for student in supervisor.prefs:
-                    student_prefs_supervisors = {
-                        p.supervisor for p in student.prefs
-                    }
+                    student_prefs_supervisors = {p.supervisor for p in student.prefs}
                     if supervisor not in student_prefs_supervisors:
                         warnings.warn(
                             PreferencesChangedWarning(
@@ -248,10 +239,7 @@ def _check_inputs_player_reciprocated_all_prefs(self, party, other_party):
                 students_that_ranked = [
                     student
                     for student in self.students
-                    if any(
-                        project in student.prefs
-                        for project in supervisor.projects
-                    )
+                    if any(project in student.prefs for project in supervisor.projects)
                 ]
 
                 for student in students_that_ranked:
@@ -264,15 +252,11 @@ def _check_inputs_player_reciprocated_all_prefs(self, party, other_party):
                         )
 
                         if self.clean:
-                            for project in set(supervisor.projects) & set(
-                                student.prefs
-                            ):
+                            for project in set(supervisor.projects) & set(student.prefs):
                                 student._forget(project)
 
         else:
-            super()._check_inputs_player_reciprocated_all_prefs(
-                party, other_party
-            )
+            super()._check_inputs_player_reciprocated_all_prefs(party, other_party)
 
     def _check_inputs_supervisor_capacities_sufficient(self):
         """Check each supervisor has space for its largest project."""
@@ -295,9 +279,7 @@ def _check_inputs_supervisor_capacities_necessary(self):
         """Check each supervisor has no surplus given their projects."""
 
         for supervisor in self.supervisors:
-            total_project_capacity = sum(
-                p.capacity for p in supervisor.projects
-            )
+            total_project_capacity = sum(p.capacity for p in supervisor.projects)
 
             if supervisor.capacity > total_project_capacity:
                 warnings.warn(
@@ -319,9 +301,7 @@ def _check_student_unhappy(student, project):
     their current match.
     """
 
-    return student.matching is None or student.prefers(
-        project, student.matching
-    )
+    return student.matching is None or student.prefers(project, student.matching)
 
 
 def _check_project_unhappy(project, student):
@@ -341,8 +321,7 @@ def _check_project_unhappy(project, student):
 
     project_undersubscribed = len(project.matching) < project.capacity
     both_undersubscribed = (
-        project_undersubscribed
-        and len(supervisor.matching) < supervisor.capacity
+        project_undersubscribed and len(supervisor.matching) < supervisor.capacity
     )
 
     supervisor_full = len(supervisor.matching) == supervisor.capacity
@@ -353,9 +332,7 @@ def _check_project_unhappy(project, student):
 
     project_upsetting_supervisor = len(
         project.matching
-    ) == project.capacity and supervisor.prefers(
-        student, project.get_worst_match()
-    )
+    ) == project.capacity and supervisor.prefers(student, project.get_worst_match())
 
     return (
         both_undersubscribed
diff --git a/src/matching/matchings.py b/src/matching/matchings.py
new file mode 100644
index 00000000..dcaf4b5d
--- /dev/null
+++ b/src/matching/matchings.py
@@ -0,0 +1,110 @@
+"""Module for the matching object classes."""
+
+import collections
+
+
+class _BaseMatching(dict):
+    """
+    Dictionary-like object for holding game solutions. For inheritance.
+
+    Parameters
+    ----------
+    dictionary : dict
+        Dictionary of matched up pairs.
+    keys : str
+        Name of the key-side party. Renamed `keys_`.
+    values : str
+        Name of the value-side party. Renamed `values_`.
+    """
+
+    KEYS = "keys"
+    VALUES = "values"
+
+    def __init__(self, dictionary=None, *, keys=None, values=None):
+        super().__init__(dictionary or {})
+
+        self.keys_ = keys or self.KEYS
+        self.values_ = values or self.VALUES
+
+    def __repr__(self):
+        name = self.__class__.__name__
+        return f'{name}({super().__repr__()}, keys="{self.keys_}", values="{self.values_}")'
+
+    def __eq__(self, other):
+        if isinstance(other, _BaseMatching):
+            return super().__eq__(other) and vars(self) == vars(other)
+        if isinstance(other, dict):
+            return super().__eq__(other)
+
+
+class _SingleMatching(_BaseMatching):
+    """
+    Dictionary-like object for solutions to games with singular matches.
+
+    Parameters
+    ----------
+    dictionary : dict
+        Dictionary of matched up pairs.
+    keys : str
+        Name of the key-side party. Renamed `keys_`.
+    values : str
+        Name of the value-side party. Renamed `values_`.
+    """
+
+    def invert(self):
+        """
+        Invert the keys and values in the dictionary.
+
+        Returns
+        -------
+        inverted : SingleMatching
+            An inverted matching.
+        """
+        inverted = {val: key for key, val in self.items()}
+
+        return self.__class__(inverted, keys=self.values_, values=self.keys_)
+
+
+class _MultipleMatching(_BaseMatching):
+    """
+    Dictionary-like object for solutions to games with multiple matches.
+
+    Parameters
+    ----------
+    dictionary : dict
+        Dictionary mapping players to their matches.
+    keys : str
+        Name of the key-side party. Renamed `keys_`.
+    values : str
+        Name of the value-side party. Renamed `values_`.
+    """
+
+    def invert(self):
+        """
+        Invert the keys and list values in the dictionary.
+
+        Returns
+        -------
+        MultipleMatching
+            An inverted matching.
+        """
+        inverted = collections.defaultdict(list)
+        for key, values in self.items():
+            for value in values:
+                inverted[value].append(key)
+
+        return self.__class__(dict(inverted), keys=self.values_, values=self.keys_)
+
+
+class SMMatching(_SingleMatching):
+    """Dictionary-like object for solutions to SM game instances."""
+
+    KEYS = "reviewers"
+    VALUES = "suitors"
+
+
+class HRMatching(_MultipleMatching):
+    """Dictionary-like object for solutions to HR game instances."""
+
+    KEYS = "hospitals"
+    VALUES = "residents"
diff --git a/src/matching/matchings/__init__.py b/src/matching/matchings/__init__.py
deleted file mode 100644
index c7b049ed..00000000
--- a/src/matching/matchings/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""Classes for different types of matchings."""
-
-from .multiple import MultipleMatching
-from .single import SingleMatching
-
-__all__ = ["MultipleMatching", "SingleMatching"]
diff --git a/src/matching/matchings/multiple.py b/src/matching/matchings/multiple.py
deleted file mode 100644
index da253c3e..00000000
--- a/src/matching/matchings/multiple.py
+++ /dev/null
@@ -1,35 +0,0 @@
-"""Classes for handling multiple match matchings."""
-
-from matching.base import BaseMatching
-from matching.players import Player
-
-
-class MultipleMatching(BaseMatching):
-    """Matching class for games with multiple matches like HR or SA.
-
-    Parameters
-    ----------
-    dictionary
-        A dictionary comprised of ``Hospital, List[Player]`` pairs.
-    """
-
-    def __init__(self, dictionary):
-        super().__init__(dictionary)
-
-    def __setitem__(self, player, new):
-        """Set a player's match and match each of them to the player.
-
-        First check if the player is present, and that the new match is
-        a valid collection of players.
-        """
-
-        self._check_player_in_keys(player)
-        self._check_new_valid_type(new, (list, tuple))
-        for other in new:
-            self._check_new_valid_type(other, Player)
-
-        player.matching = new
-        for other in new:
-            other.matching = player
-
-        self._data[player] = new
diff --git a/src/matching/matchings/single.py b/src/matching/matchings/single.py
deleted file mode 100644
index 73f56486..00000000
--- a/src/matching/matchings/single.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""Classes for handling single match matchings."""
-
-
-class SingleMatching(dict):
-    """
-    Dictionary-like object for solutions to games with singular matches.
-
-    Parameters
-    ----------
-    dictionary : dict
-        Dictionary of matched up pairs.
-    keys : str
-        Name of the key-side party. Renamed `keys_`.
-    values : str
-        Name of the value-side party. Renamed `values_`.
-    valid : bool or None
-        Validity of the matching. Initialises as `None`.
-    stable : bool or None
-        Stability of the matching. Initialises as `None`.
-    """
-
-    def __init__(
-        self,
-        dictionary=None,
-        *,
-        keys="reviewers",
-        values="suitors",
-        valid=None,
-        stable=None,
-    ):
-        super().__init__(dictionary or {})
-
-        self.keys_ = keys
-        self.values_ = values
-        self.valid = valid
-        self.stable = stable
-
-    def __repr__(self):
-        return (
-            f"SingleMatching({super().__repr__()}, "
-            f'keys="{self.keys_}", values="{self.values_}")'
-        )
-
-    def __eq__(self, other):
-        return super().__eq__(other) and vars(self) == vars(other)
-
-    def invert(self):
-        """
-        Invert the keys and values in the dictionary.
-
-        Creates a new matching instance with keys and values reversed,
-        and passes over the validity and stability attributes.
-
-        Returns
-        -------
-        inverted : SingleMatching
-            An inverted matching.
-        """
-        return SingleMatching(
-            {val: key for key, val in self.items()},
-            keys=self.values_,
-            values=self.keys_,
-            valid=self.valid,
-            stable=self.stable,
-        )
diff --git a/src/matching/players/supervisor.py b/src/matching/players/supervisor.py
index 421cc260..16d593e8 100644
--- a/src/matching/players/supervisor.py
+++ b/src/matching/players/supervisor.py
@@ -60,9 +60,7 @@ def set_prefs(self, students):
         self._original_prefs = students[:]
 
         for project in self.projects:
-            acceptable = [
-                student for student in students if project in student.prefs
-            ]
+            acceptable = [student for student in students if project in student.prefs]
             project.set_prefs(acceptable)
 
     def get_favourite(self):
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 00000000..64bfcf1b
--- /dev/null
+++ b/tests/__init__.py
@@ -0,0 +1 @@
+"""Tests for the `matching` package."""
diff --git a/tests/base/test_matching.py b/tests/base/test_matching.py
index 153e4e14..51325151 100644
--- a/tests/base/test_matching.py
+++ b/tests/base/test_matching.py
@@ -65,9 +65,7 @@ def test_getitem(dictionary):
     """Check that you can access items in a matching correctly."""
 
     matching = BaseMatching(dictionary)
-    for (mkey, mval), (dkey, dval) in zip(
-        matching.items(), dictionary.items()
-    ):
+    for (mkey, mval), (dkey, dval) in zip(matching.items(), dictionary.items()):
         assert matching[mkey] == mval
         assert (mkey, mval) == (dkey, dval)
 
diff --git a/tests/common.py b/tests/common.py
new file mode 100644
index 00000000..b0c83c34
--- /dev/null
+++ b/tests/common.py
@@ -0,0 +1,58 @@
+"""Objects used among many tests."""
+
+from unittest import mock
+
+import numpy as np
+from hypothesis import strategies as st
+from hypothesis.extra import numpy as st_numpy
+
+
+def mocked_game(game, *args):
+    """Create an instance of a game that mocks its input validator."""
+
+    with mock.patch(f"matching.games.{game.__name__}.check_input_validity") as validator:
+        instance = game(*args)
+
+    validator.assert_called_once_with()
+
+    return instance
+
+
+@st.composite
+def st_single_ranks(draw, nrows: int, ncols: int | None = None):
+    """Create a single rank matrix."""
+    if ncols is None:
+        ncols = nrows
+
+    rank = draw(
+        st.lists(
+            st.permutations(range(ncols)),
+            min_size=nrows,
+            max_size=nrows,
+        )
+    )
+
+    return np.array(rank)
+
+
+@st.composite
+def st_single_utilities(draw, nrows: int, ncols: int | None = None):
+    """Create a single utility matrix."""
+    if ncols is None:
+        ncols = nrows
+
+    utility = draw(
+        st_numpy.arrays(
+            dtype=float,
+            elements=st.floats(0, 1, allow_nan=False),
+            shape=(nrows, ncols),
+        )
+    )
+
+    return utility
+
+
+@st.composite
+def st_sizes(draw, nmin=1, nmax=5):
+    """Create a size for a side in a game."""
+    return draw(st.integers(nmin, nmax))
diff --git a/tests/hospital_resident/strategies.py b/tests/hospital_resident/strategies.py
new file mode 100644
index 00000000..9a99d997
--- /dev/null
+++ b/tests/hospital_resident/strategies.py
@@ -0,0 +1,103 @@
+"""Composite strategies for HR unit tests."""
+
+import numpy as np
+from hypothesis import strategies as st
+
+from ..common import st_single_ranks, st_single_utilities, st_sizes
+
+
+@st.composite
+def st_ranks(draw, hmin=1, hmax=3, rmin=1, rmax=5):
+    """Create a set of rankings for a test."""
+
+    hsize = draw(st_sizes(hmin, hmax))
+    rsize = draw(st_sizes(rmin, rmax))
+
+    resident_ranks = draw(st_single_ranks(rsize, hsize))
+    hospital_ranks = draw(st_single_ranks(hsize, rsize))
+
+    return resident_ranks, hospital_ranks
+
+
+@st.composite
+def st_capacities(draw, hmin=1, hmax=3):
+    """Create a capacity vector."""
+
+    size = draw(st_sizes(hmin, hmax))
+    capacities = draw(st.lists(st.integers(1, 2), min_size=size, max_size=size))
+
+    return np.array(capacities)
+
+
+@st.composite
+def st_ranks_capacities(draw, hmin=1, hmax=3, rmin=1, rmax=5):
+    """Create a set of rankings and capacities for a test."""
+
+    hsize = draw(st_sizes(hmin, hmax))
+    rsize = draw(st_sizes(rmin, rmax))
+
+    resident_ranks, hospital_ranks = draw(st_ranks(hsize, hsize, rsize, rsize))
+    capacities = draw(st_capacities(hsize, hsize))
+
+    return resident_ranks, hospital_ranks, capacities
+
+
+@st.composite
+def st_utilities_capacities(draw, hmin=1, hmax=3, rmin=1, rmax=5):
+    """Create a set of utilities and capacities for a test."""
+
+    hsize = draw(st_sizes(hmin, hmax))
+    rsize = draw(st_sizes(rmin, rmax))
+
+    resident_utility = draw(st_single_utilities(rsize, hsize))
+    hospital_utility = draw(st_single_utilities(hsize, rsize))
+    capacities = draw(st_capacities(hsize, hsize))
+
+    return resident_utility, hospital_utility, capacities
+
+
+@st.composite
+def st_preferences_capacities(draw, hmin=1, hmax=3, rmin=1, rmax=5):
+    """Create a set of preferences and capacities for a test."""
+
+    hsize = draw(st_sizes(hmin, hmax))
+    rsize = draw(st_sizes(rmin, rmax))
+
+    residents = draw(st.lists(st.integers(), min_size=rsize, max_size=rsize, unique=True))
+    hospitals = draw(st.lists(st.text(), min_size=hsize, max_size=hsize, unique=True))
+
+    resident_preferences = {r: draw(st.permutations(hospitals)) for r in residents}
+    hospital_preferences = {h: draw(st.permutations(residents)) for h in hospitals}
+    capacities = dict(zip(hospital_preferences, draw(st_capacities(hsize, hsize))))
+
+    return resident_preferences, hospital_preferences, capacities
+
+
+@st.composite
+def st_preference_matchings(draw, hmin=1, hmax=3, rmin=1, rmax=5):
+    """Create a set of preferences and a matching to go with them."""
+
+    resident_preferences, hospital_preferences, capacities = draw(
+        st_preferences_capacities(hmin, hmax, rmin, rmax)
+    )
+
+    matched_residents = draw(
+        st.lists(st.sampled_from(list(resident_preferences.keys())), unique=True)
+    )
+    resident_matching = {}
+    spaces = capacities.copy()
+    for resident in matched_residents:
+        resident_idx = list(resident_preferences.keys()).index(resident)
+        hospital = draw(st.sampled_from(resident_preferences[resident]))
+        hospital_idx = list(hospital_preferences.keys()).index(hospital)
+        if spaces[hospital]:
+            resident_matching[resident_idx] = hospital_idx
+            spaces[hospital] -= 1
+
+    matching = {
+        hospital: [r for r in resident_matching if resident_matching[r] == hospital]
+        for hospital, _ in enumerate(hospital_preferences)
+        if hospital in resident_matching.values()
+    }
+
+    return resident_preferences, hospital_preferences, capacities, matching
diff --git a/tests/hospital_resident/test_algorithm.py b/tests/hospital_resident/test_algorithm.py
index 204ef6c6..4a07c6bf 100644
--- a/tests/hospital_resident/test_algorithm.py
+++ b/tests/hospital_resident/test_algorithm.py
@@ -1,69 +1,146 @@
-"""Tests for the Hospital-Resident algorithm."""
+"""Unit tests for the Hospital-Resident algorithms."""
 
 import numpy as np
 from hypothesis import given
 
-from matching.algorithms.hospital_resident import (
-    hospital_optimal,
-    hospital_resident,
-    resident_optimal,
-)
+from matching.games import HospitalResident
 
-from .util import players
+from ..common import mocked_game
+from .strategies import st_ranks_capacities
 
 
-@given(players_=players())
-def test_hospital_resident(players_):
-    """Test the algorithm produces a valid solution."""
+def _assert_matching_is_valid_shape(matching, hospital_ranks, resident_ranks, capacities):
+    """Assert that the matching has the right shape and elements."""
 
-    residents, hospitals = players_
+    assert isinstance(matching, dict)
 
-    matching = hospital_resident(residents, hospitals)
-    assert set(hospitals) == set(matching.keys())
+    matched_hospitals = set(matching.keys())
+    matched_residents = set([r for rs in matching.values() for r in rs])
 
-    matched_residents = {r for rs in matching.values() for r in rs}
-    for resident in residents:
-        if resident.matching:
-            assert resident in matched_residents
-        else:
-            assert resident not in matched_residents
+    assert matched_hospitals <= set(np.unique(resident_ranks))
+    assert matched_residents <= set(np.unique(hospital_ranks))
 
+    for hospital, residents in matching.items():
+        assert len(residents) <= capacities[hospital]
 
-@given(players_=players())
-def test_resident_optimal(players_):
-    """Test that the resident-optimal algorithm is resident-optimal."""
 
-    residents, hospitals = players_
+@given(st_ranks_capacities())
+def test_resident_optimal_resident_optimal(ranks_capacities):
+    """
+    Check the resident-optimal algorithm is optimal for residents.
 
-    matching = resident_optimal(residents, hospitals)
-    assert set(hospitals) == set(matching.keys())
+    We affirm this by going through the residents and checking that
+    either they are unmatched or they prefer their match to any other
+    hospital.
+    """
+    resident_ranks, hospital_ranks, capacities = ranks_capacities
+    game = mocked_game(HospitalResident, *ranks_capacities)
 
-    assert all(
-        [
-            r in set(residents)
-            for r in {
-                r_match for matches in matching.values() for r_match in matches
-            }
-        ]
-    )
+    matching = game._resident_optimal()
 
-    for resident in residents:
-        if resident.matching:
-            assert resident.prefs.index(resident.matching) == 0
+    _assert_matching_is_valid_shape(matching, hospital_ranks, resident_ranks, capacities)
 
+    for resident, resident_rank in enumerate(game.resident_ranks):
+        hospital = next((h for h, rs in matching.items() if resident in rs), None)
+        if hospital is None:
+            continue
 
-@given(players_=players())
-def test_hospital_optimal(players_):
-    """Test that the hospital-optimal algorithm is hospital-optimal."""
+        preferred_hospitals, *_ = np.where(resident_rank < resident_rank[hospital])
+        assert not preferred_hospitals.any()
 
-    _, hospitals = players_
 
-    matching = hospital_optimal(hospitals)
-    assert set(hospitals) == set(matching.keys())
+@given(st_ranks_capacities())
+def test_resident_optimal_hospital_pessimal(ranks_capacities):
+    """
+    Check the resident-optimal algorithm is pessimal for hospitals.
 
-    for hospital, matches in matching.items():
-        old_idx = -np.inf
-        for resident in matches:
-            idx = hospital.prefs.index(resident)
-            assert idx >= old_idx
-            old_idx = idx
+    We affirm this by going through the hospitals and checking that if
+    they prefer a resident to their worst current match, then the
+    resident matched to them already or prefers their match to the
+    hospital.
+    """
+    resident_ranks, hospital_ranks, capacities = ranks_capacities
+    game = mocked_game(HospitalResident, *ranks_capacities)
+
+    matching = game._resident_optimal()
+
+    _assert_matching_is_valid_shape(matching, hospital_ranks, resident_ranks, capacities)
+
+    for hospital, residents in matching.items():
+        if not residents:
+            continue
+
+        hospital_rank = game.hospital_ranks[hospital]
+        worst_match = hospital_rank[residents].max()
+        preferred_residents, *_ = np.where(hospital_rank < worst_match)
+        for preferred in preferred_residents:
+            if preferred in residents:
+                continue
+
+            preferred_rank = game.resident_ranks[preferred]
+            partner = next((h for h, rs in matching.items() if preferred in rs), None)
+
+            assert partner is not None
+            assert preferred_rank[partner] < preferred_rank[hospital]
+
+
+@given(st_ranks_capacities())
+def test_hospital_optimal_hospital_optimal(ranks_capacities):
+    """
+    Check the hospital-optimal algorithm is optimal for hospitals.
+
+    We affirm this by going through the matching and checking that for
+    each hospital, if they prefer a resident to their best current
+    match, then the resident prefers their match to the hospital.
+    """
+    resident_ranks, hospital_ranks, capacities = ranks_capacities
+    game = mocked_game(HospitalResident, *ranks_capacities)
+
+    matching = game._hospital_optimal()
+
+    _assert_matching_is_valid_shape(matching, hospital_ranks, resident_ranks, capacities)
+
+    for hospital, residents in matching.items():
+        if not residents:
+            continue
+
+        hospital_rank = game.hospital_ranks[hospital]
+        best_match = hospital_rank[residents].min()
+        preferred_residents, *_ = np.where(hospital_rank < best_match)
+        for preferred in preferred_residents:
+            preferred_rank = game.resident_ranks[preferred]
+            partner = next((h for h, rs in matching.items() if preferred in rs), None)
+
+            assert partner is not None
+            assert preferred_rank[partner] < preferred_rank[hospital]
+
+
+@given(st_ranks_capacities())
+def test_hospital_optimal_resident_pessimal(ranks_capacities):
+    """
+    Check the hospital-optimal algorithm is pessimal for residents.
+
+    We affirm this by going through the residents and checking that if
+    they prefer a hospital to their current match, then the hospital
+    prefers their worst match to the resident.
+    """
+    resident_ranks, hospital_ranks, capacities = ranks_capacities
+    game = mocked_game(HospitalResident, *ranks_capacities)
+
+    matching = game._hospital_optimal()
+
+    _assert_matching_is_valid_shape(matching, hospital_ranks, resident_ranks, capacities)
+
+    for resident, resident_rank in enumerate(game.resident_ranks):
+        hospital = next((h for h, rs in matching.items() if resident in rs), None)
+        if hospital is None:
+            continue
+
+        preferred_hospitals, *_ = np.where(resident_rank < resident_rank[hospital])
+        for preferred in preferred_hospitals:
+            preferred_matches = matching.get(preferred)
+            assert preferred_matches is not None
+
+            preferred_rank = game.hospital_ranks[preferred]
+            worst_match = preferred_rank[preferred_matches].max()
+            assert preferred_rank[resident] > worst_match
diff --git a/tests/hospital_resident/test_examples.py b/tests/hospital_resident/test_examples.py
index 4f1c5e4e..373f9ecb 100644
--- a/tests/hospital_resident/test_examples.py
+++ b/tests/hospital_resident/test_examples.py
@@ -2,13 +2,13 @@
 
 import json
 import os
+import warnings
 
 from matching.games import HospitalResident
 
 
 def test_readme_example():
-    """Test the example used in the repo README."""
-
+    """Test the example formerly used in the README."""
     resident_prefs = {
         "A": ["C"],
         "S": ["C", "M"],
@@ -16,90 +16,86 @@ def test_readme_example():
         "J": ["C", "G", "M"],
         "L": ["M", "C", "G"],
     }
-
     hospital_prefs = {
         "M": ["D", "L", "S", "J"],
         "C": ["D", "A", "S", "L", "J"],
         "G": ["D", "J", "L"],
     }
-
     capacities = {hosp: 2 for hosp in hospital_prefs}
 
-    game = HospitalResident.create_from_dictionaries(
-        resident_prefs, hospital_prefs, capacities
-    )
-    (A, S, D, J, L), (M, C, G) = game.residents, game.hospitals
-
+    game = HospitalResident.from_preferences(resident_prefs, hospital_prefs, capacities)
     matching = game.solve()
-    assert matching == {M: [L, S], C: [D, A], G: [J]}
+
+    assert matching == {"M": ["L", "S"], "C": ["A", "D"], "G": ["J"]}
 
 
 def test_example_in_issue_67():
     """Test the example given in #67."""
-
     group_prefs = {
         "Group 1": ["Intellectual property", "Privacy"],
         "Group 2": ["Privacy", "Fairness in AI"],
         "Group 3": ["Privacy", "Social media"],
     }
-
     topic_hospital_prefs = {
         "Fairness in AI": ["Group 2"],
         "Intellectual property": ["Group 1"],
         "Privacy": ["Group 3", "Group 2", "Group 1"],
         "Social media": ["Group 3"],
     }
-
     capacities = {t: 2 for t in topic_hospital_prefs}
 
-    game = HospitalResident.create_from_dictionaries(
-        group_prefs, topic_hospital_prefs, capacities
-    )
-    (G1, G2, G3), (F, I, P, S) = game.residents, game.hospitals
-
+    game = HospitalResident.from_preferences(group_prefs, topic_hospital_prefs, capacities)
     matching = game.solve()
-    assert matching == {I: [G1], P: [G3, G2], F: [], S: []}
+
+    assert matching == {
+        "Fairness in AI": [],
+        "Intellectual property": ["Group 1"],
+        "Privacy": ["Group 2", "Group 3"],
+        "Social media": [],
+    }
 
 
 def test_resident_loses_all_preferences():
     """Test example that forces a resident to be removed."""
-
     resident_prefs = {"A": ["X"], "B": ["X", "Y"]}
     hospital_prefs = {"X": ["B", "A"], "Y": ["B"]}
     capacities = {"X": 1, "Y": 1}
 
-    game = HospitalResident.create_from_dictionaries(
-        resident_prefs, hospital_prefs, capacities
-    )
-    (_, B), (X, Y) = game.residents, game.hospitals
-
+    game = HospitalResident.from_preferences(resident_prefs, hospital_prefs, capacities)
     matching = game.solve()
-    assert matching == {X: [B], Y: []}
+
+    assert matching == {"X": ["B"], "Y": []}
 
 
 def test_example_in_issue_159():
-    """Test the example given in #159.
+    """
+    Test the example given in #159.
 
     The example in this is particularly large, so we've moved the
     dictionaries to a test data directory.
     """
-
     here = os.path.join(os.path.dirname(__file__))
     with open(os.path.join(here, "data", "issue_159.json"), "r") as f:
         preferences = json.load(f)
 
-    resident_prefs = {
-        int(res): prefs for res, prefs in preferences["residents"].items()
-    }
-    hospital_prefs = {
-        int(hos): prefs for hos, prefs in preferences["hospitals"].items()
-    }
-
+    resident_prefs = {int(res): prefs for res, prefs in preferences["residents"].items()}
+    hospital_prefs = {int(hos): prefs for hos, prefs in preferences["hospitals"].items()}
     capacities = {hospital: 1 for hospital in hospital_prefs}
 
-    game = HospitalResident.create_from_dictionaries(
-        resident_prefs, hospital_prefs, capacities, clean=True
-    )
-
-    for player in game.residents + game.hospitals:
-        assert player.prefs
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        game = HospitalResident.from_preferences(resident_prefs, hospital_prefs, capacities)
+
+    for ranking, preferences in zip(game.resident_ranks, resident_prefs.values()):
+        minimum, limit = min(ranking), game.num_hospitals
+        if not preferences:
+            minimum == limit
+        else:
+            assert minimum < limit
+
+    for ranking, preferences in zip(game.hospital_ranks, hospital_prefs.values()):
+        minimum, limit = min(ranking), game.num_residents
+        if not preferences:
+            assert minimum == limit
+        else:
+            assert minimum < limit
diff --git a/tests/hospital_resident/test_game.py b/tests/hospital_resident/test_game.py
new file mode 100644
index 00000000..0d4baa0e
--- /dev/null
+++ b/tests/hospital_resident/test_game.py
@@ -0,0 +1,277 @@
+"""Unit tests for the HospitalResident class."""
+
+import warnings
+from unittest import mock
+
+import numpy as np
+import pytest
+from hypothesis import given
+from hypothesis import strategies as st
+
+from matching import matchings
+from matching.games import HospitalResident, hospital_resident
+
+from ..common import mocked_game
+from .strategies import (
+    st_preference_matchings,
+    st_preferences_capacities,
+    st_ranks_capacities,
+    st_utilities_capacities,
+)
+
+
+@given(st_ranks_capacities())
+def test_init(ranks_capacities):
+    """Check instantiation given some rankings and capacities."""
+
+    resident_ranks, hospital_ranks, capacities = ranks_capacities
+    game = mocked_game(HospitalResident, *ranks_capacities)
+
+    assert isinstance(game, HospitalResident)
+    assert (game.resident_ranks == resident_ranks).all()
+    assert (game.hospital_ranks == hospital_ranks).all()
+    assert (game.capacities == capacities).all()
+
+    assert game.num_residents == len(resident_ranks)
+    assert game.num_hospitals == len(hospital_ranks)
+    assert game.matching is None
+    assert game._preference_lookup is None
+
+
+@given(st_utilities_capacities())
+def test_from_utilities(utilities_capacities):
+    """Check instantiation from utility matrices."""
+
+    *utilities, capacities = utilities_capacities
+    resident_utility, hospital_utility = utilities
+
+    with (
+        mock.patch("matching.games.HospitalResident.check_input_validity") as validator,
+        mock.patch("matching.convert.utility_to_rank") as ranker,
+    ):
+        effects = (resident_utility.argsort(), hospital_utility.argsort())
+        ranker.side_effect = list(effects)
+        game = HospitalResident.from_utilities(resident_utility, hospital_utility, capacities)
+
+    assert isinstance(game, HospitalResident)
+    assert (game.resident_ranks == effects[0]).all()
+    assert (game.hospital_ranks == effects[1]).all()
+    assert (game.capacities == capacities).all()
+
+    assert game.num_residents == len(resident_utility)
+    assert game.num_hospitals == len(hospital_utility)
+    assert game.matching is None
+    assert game._preference_lookup is None
+
+    assert ranker.call_count == 2
+    for call, utility in zip(ranker.call_args_list, utilities):
+        arg, *_ = call.args
+        assert np.array_equal(arg, utility)
+
+    validator.assert_called_once_with()
+
+
+@given(st_preferences_capacities())
+def test_from_preferences(preferences_capacities):
+    """Check instantiation from preference list dictionaries."""
+
+    *preferences, capacities = preferences_capacities
+    resident_preferences, hospital_preferences = preferences
+
+    with (
+        mock.patch("matching.games.HospitalResident.check_input_validity") as validator,
+        mock.patch("matching.convert.preference_to_rank") as ranker,
+    ):
+        effects = (
+            np.array(list(resident_preferences.values())),
+            np.array(list(hospital_preferences.values())),
+        )
+        ranker.side_effect = list(effects)
+        game = HospitalResident.from_preferences(
+            resident_preferences, hospital_preferences, capacities
+        )
+
+    assert isinstance(game, HospitalResident)
+    assert (game.resident_ranks == effects[0]).all()
+    assert (game.hospital_ranks == effects[1]).all()
+
+    assert game.num_residents == len(resident_preferences)
+    assert game.num_hospitals == len(hospital_preferences)
+    assert game.matching is None
+
+    assert isinstance(game._preference_lookup, dict)
+    assert game._preference_lookup == {
+        "residents": sorted(resident_preferences),
+        "hospitals": sorted(hospital_preferences),
+    }
+
+    assert isinstance(game.capacities, np.ndarray)
+    assert game.capacities.shape == (len(hospital_preferences),)
+    for cap, hospital in zip(game.capacities, game._preference_lookup["hospitals"]):
+        assert cap == capacities.get(hospital)
+
+    assert ranker.call_count == 2
+    for call, preference, others in zip(
+        ranker.call_args_list,
+        preferences,
+        (hospital_preferences, resident_preferences),
+    ):
+        assert call.args == (preference, sorted(others))
+
+    validator.assert_called_once_with()
+
+
+@given(st_ranks_capacities())
+def test_check_input_validity(ranks_capacities):
+    """
+    Check that the input validator works as expected.
+
+    This method is not called in the constructor, so we need to call
+    it manually. We mock the stage functions this method wraps to
+    check that they are called correctly.
+    """
+    resident_ranks, hospital_ranks, capacities = ranks_capacities
+    game = mocked_game(HospitalResident, resident_ranks, hospital_ranks, capacities)
+
+    with (
+        mock.patch.object(game, "_check_player_ranks") as mock_check_ranks,
+        mock.patch.object(game, "_check_capacities") as mock_check_capacities,
+    ):
+        game.check_input_validity()
+
+    assert mock_check_ranks.call_count == len(resident_ranks) + len(hospital_ranks)
+
+    call_args = mock_check_ranks.call_args_list
+    for hospital, hospital_rank in enumerate(hospital_ranks):
+        call = call_args.pop(0)
+        assert call.kwargs == {}
+        for arg, item in zip(call.args, (hospital, hospital_rank, resident_ranks, "hospital")):
+            assert np.equal(arg, item).all()
+
+    for resident, resident_rank in enumerate(resident_ranks):
+        call = call_args.pop(0)
+        assert call.kwargs == {}
+        for arg, item in zip(call.args, (resident, resident_rank, hospital_ranks, "resident")):
+            assert np.equal(arg, item).all()
+
+    mock_check_capacities.assert_called_once_with()
+
+
+@given(st_ranks_capacities())
+def test_check_player_ranks_no_warning(ranks_capacities):
+    """Check that the player ranks check can not raise a warning."""
+    resident_ranks, hospital_ranks, capacities = ranks_capacities
+    game = mocked_game(HospitalResident, resident_ranks, hospital_ranks, capacities)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        game._check_player_ranks(0, resident_ranks[0], hospital_ranks, "resident")
+
+
+@given(st_ranks_capacities())
+def test_check_player_ranks_warns(ranks_capacities):
+    """Check that the player ranks check can raise a warning."""
+    resident_ranks, hospital_ranks, capacities = ranks_capacities
+    game = mocked_game(HospitalResident, resident_ranks, hospital_ranks, capacities)
+    hospital_ranks[0][-1] = 1000
+
+    with pytest.warns(UserWarning, match="Hospital 0 has not made a strict"):
+        game._check_player_ranks(0, hospital_ranks[0], resident_ranks, "hospital")
+
+
+@given(st_ranks_capacities())
+def test_check_capacities_no_warning(ranks_capacities):
+    """Check that the capacity check can not raise a warning."""
+    resident_ranks, hospital_ranks, capacities = ranks_capacities
+    game = mocked_game(HospitalResident, resident_ranks, hospital_ranks, capacities)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        game._check_capacities()
+
+
+@given(st_ranks_capacities())
+def test_check_capacities_warns(ranks_capacities):
+    """Check that the capacity check can raise a warning."""
+    resident_ranks, hospital_ranks, capacities = ranks_capacities
+    game = mocked_game(HospitalResident, resident_ranks, hospital_ranks, capacities)
+    game.capacities[0] = 0
+
+    with pytest.warns(UserWarning, match="Hospital 0 has a capacity of 0"):
+        game._check_capacities()
+
+
+@given(st_ranks_capacities(), st.sampled_from(["resident", "hospital"]), st.booleans())
+def test_solve(ranks_capacities, optimal, preference_lookup):
+    """
+    Check the solver works as it should.
+
+    This method wraps the algorithm functions for the HR game, as well
+    as the matching-preference conversion function. So, we mock all of
+    these and check they are called correctly.
+    """
+    resident_ranks, hospital_ranks, capacities = ranks_capacities
+    game = mocked_game(HospitalResident, *ranks_capacities)
+    game._preference_lookup = preference_lookup
+
+    with (
+        mock.patch.object(game, "_resident_optimal") as mock_resident_optimal,
+        mock.patch.object(game, "_hospital_optimal") as mock_hospital_optimal,
+        mock.patch.object(game, "_convert_matching_to_preferences") as mock_convert,
+        mock.patch.object(hospital_resident.matchings, "HRMatching") as mock_matching,
+    ):
+        matching = game.solve(optimal=optimal)
+
+    assert matching == game.matching == mock_matching.return_value
+
+    if optimal == "resident":
+        mock_resident_optimal.assert_called_once_with()
+        mock_hospital_optimal.assert_not_called()
+        mock_matching.assert_called_once_with(
+            mock_resident_optimal.return_value, keys="hospitals", values="residents"
+        )
+    if optimal == "hospital":
+        mock_hospital_optimal.assert_called_once_with()
+        mock_resident_optimal.assert_not_called()
+        mock_matching.assert_called_once_with(
+            mock_hospital_optimal.return_value, keys="hospitals", values="residents"
+        )
+
+    if preference_lookup:
+        mock_convert.assert_called_once_with()
+    else:
+        mock_convert.assert_not_called()
+
+
+@given(st_ranks_capacities(), st.text(min_size=1))
+def test_solve_raises_with_bad_optimal(ranks_capacities, optimal):
+    """Check that the solver raises an error with a bad optimal value."""
+    resident_ranks, hospital_ranks, capacities = ranks_capacities
+    game = mocked_game(HospitalResident, *ranks_capacities)
+
+    with pytest.raises(ValueError, match="Invalid choice for `optimal`"):
+        game.solve(optimal=optimal)
+
+
+@given(st_preference_matchings())
+def test_convert_matching_to_preferences(preference_matchings):
+    """Test that a matching can use the terms from some preferences."""
+
+    resident_prefs, hospital_prefs, capacities, matching = preference_matchings
+
+    with mock.patch.object(HospitalResident, "check_input_validity") as validator:
+        game = HospitalResident.from_preferences(resident_prefs, hospital_prefs, capacities)
+
+    game.matching = matchings.HRMatching(matching)
+
+    game._convert_matching_to_preferences()
+    converted = game.matching
+
+    assert isinstance(converted, matchings.HRMatching)
+    assert converted.keys_ == "hospitals"
+    assert converted.values_ == "residents"
+
+    assert set(converted.keys()) <= set(hospital_prefs)
+    assert set([r for rs in converted.values() for r in rs]) <= set(resident_prefs)
+
+    validator.assert_called_once_with()
diff --git a/tests/hospital_resident/test_solver.py b/tests/hospital_resident/test_solver.py
deleted file mode 100644
index dfac0b0d..00000000
--- a/tests/hospital_resident/test_solver.py
+++ /dev/null
@@ -1,371 +0,0 @@
-"""Unit tests for the HR solver."""
-
-import warnings
-
-import pytest
-from hypothesis import given
-from hypothesis.strategies import booleans, sampled_from
-
-from matching import MultipleMatching
-from matching import Player as Resident
-from matching.exceptions import (
-    MatchingError,
-    PlayerExcludedWarning,
-    PreferencesChangedWarning,
-)
-from matching.games import HospitalResident
-from matching.players import Hospital
-
-from .util import connections, games, players
-
-
-@given(players=players(), clean=booleans())
-def test_init(players, clean):
-    """Test for correct instantiation given a set of players."""
-
-    residents, hospitals = players
-
-    game = HospitalResident(residents, hospitals, clean)
-
-    for resident, game_resident in zip(residents, game.residents):
-        assert resident.name == game_resident.name
-        assert resident._pref_names == game_resident._pref_names
-
-    for hospital, game_hospital in zip(hospitals, game.hospitals):
-        assert hospital.name == game_hospital.name
-        assert hospital._pref_names == game_hospital._pref_names
-        assert hospital.capacity == game_hospital.capacity
-
-    assert all([resident.matching is None for resident in game.residents])
-    assert all([hospital.matching == [] for hospital in game.hospitals])
-    assert game.matching is None
-
-
-@given(connections=connections(), clean=booleans())
-def test_create_from_dictionaries(connections, clean):
-    """Test for correct instantiation given a set of dictionaries."""
-
-    resident_prefs, hospital_prefs, capacities = connections
-
-    game = HospitalResident.create_from_dictionaries(
-        resident_prefs, hospital_prefs, capacities, clean
-    )
-
-    for resident in game.residents:
-        assert resident._pref_names == resident_prefs[resident.name]
-        assert resident.matching is None
-
-    for hospital in game.hospitals:
-        assert hospital._pref_names == hospital_prefs[hospital.name]
-        assert hospital.capacity == capacities[hospital.name]
-        assert hospital.matching == []
-
-    assert game.matching is None
-    assert game.clean is clean
-
-
-@given(game=games())
-def test_check_inputs(game):
-    """Test that inputs to an instance of HR can be verified."""
-
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")
-        game.check_inputs()
-
-    assert game.residents == game._all_residents
-    assert game.hospitals == game._all_hospitals
-
-
-@given(game=games())
-def test_check_inputs_resident_prefs_all_hospitals(game):
-    """Test each resident has only hospitals in its preference list.
-
-    If not, check that a warning is caught and the resident's
-    preferences are changed.
-    """
-
-    resident = game.residents[0]
-    resident.prefs = [Resident("foo")]
-
-    with pytest.warns(PreferencesChangedWarning) as record:
-        game._check_inputs_player_prefs_all_in_party("residents", "hospitals")
-
-    assert len(record) == 1
-
-    message = str(record[0].message)
-
-    assert resident.name in message
-    assert "foo" in message
-
-    if game.clean:
-        assert resident.prefs == []
-
-
-@given(game=games())
-def test_check_inputs_hospital_prefs_all_residents(game):
-    """Test each hospital has only residents in its preference list.
-
-    If not, check that a warning is caught and the hospitals's
-    preferences are changed.
-    """
-
-    hospital = game.hospitals[0]
-    hospital.prefs = [Resident("foo")]
-
-    with pytest.warns(PreferencesChangedWarning) as record:
-        game._check_inputs_player_prefs_all_in_party("hospitals", "residents")
-
-    assert len(record) == 1
-
-    message = str(record[0].message)
-
-    assert hospital.name in message
-    assert "foo" in message
-
-    if game.clean:
-        assert hospital.prefs == []
-
-
-@given(game=games())
-def test_check_inputs_hospital_prefs_all_reciprocated(game):
-    """Test each hospital has ranked only residents that have ranked it.
-
-    If not, check that a warning is caught and the hospital has
-    forgotten any such residents.
-    """
-
-    hospital = game.hospitals[0]
-    resident = hospital.prefs[0]
-    resident._forget(hospital)
-
-    with pytest.warns(PreferencesChangedWarning) as record:
-        game._check_inputs_player_prefs_all_reciprocated("hospitals")
-
-    assert len(record) == 1
-
-    message = str(record[0].message)
-
-    assert hospital.name in message
-    assert resident.name in message
-
-    if game.clean:
-        assert resident not in hospital.prefs
-
-
-@given(game=games())
-def test_check_inputs_hospital_reciprocated_all_prefs(game):
-    """Test each hospital has ranked all residents that have ranked it.
-
-    If not, check that a warning is caught and any such resident has
-    forgotten the hospital.
-    """
-
-    hospital = game.hospitals[0]
-    resident = hospital.prefs[0]
-    hospital._forget(resident)
-
-    with pytest.warns(PreferencesChangedWarning) as record:
-        game._check_inputs_player_reciprocated_all_prefs(
-            "hospitals", "residents"
-        )
-
-    assert len(record) == 1
-
-    message = str(record[0].message)
-
-    assert hospital.name in message
-    assert resident.name in message
-
-    if game.clean:
-        assert hospital not in resident.prefs
-
-
-@given(game=games())
-def test_check_inputs_resident_prefs_all_nonempty(game):
-    """Test that each resident has a non-empty preference list.
-
-    If not, check that a warning is caught and the resident has been
-    removed from the game.
-    """
-
-    resident = game.residents[0]
-    resident.prefs = []
-
-    with pytest.warns(PlayerExcludedWarning) as record:
-        game._check_inputs_player_prefs_nonempty("residents", "hospitals")
-
-    assert len(record) == 1
-    assert resident.name in str(record[0].message)
-
-    if game.clean:
-        assert resident not in game.residents
-
-
-@given(game=games())
-def test_check_inputs_hospital_prefs_all_nonempty(game):
-    """Test that each hospital has a non-empty preference list.
-
-    If not, check that a warning is caught and the player has been
-    removed from the game.
-    """
-
-    hospital = game.hospitals[0]
-    hospital.prefs = []
-
-    with pytest.warns(PlayerExcludedWarning) as record:
-        game._check_inputs_player_prefs_nonempty("hospitals", "residents")
-
-    assert len(record) == 1
-    assert hospital.name in str(record[0].message)
-
-    if game.clean:
-        assert hospital not in game.hospitals
-
-
-@given(game=games())
-def test_check_inputs_hospital_capacity(game):
-    """Test each hospital has a positive integer capacity.
-
-    If not, raise an Exception detailing the hospital.
-    """
-
-    hospital = game.hospitals[0]
-    capacity = hospital.capacity
-    hospital.capacity = 0
-
-    assert hospital._original_capacity == capacity
-
-    with pytest.warns(PlayerExcludedWarning) as record:
-        game._check_inputs_player_capacity("hospitals", "residents")
-
-    assert len(record) == 1
-    assert hospital.name in str(record[0].message)
-
-    if game.clean:
-        assert hospital not in game.hospitals
-
-
-@given(game=games(), optimal=sampled_from(["resident", "hospital"]))
-def test_solve(game, optimal):
-    """Test for the correct solving of games."""
-
-    matching = game.solve(optimal)
-    assert isinstance(matching, MultipleMatching)
-
-    hospitals = sorted(game.hospitals, key=lambda h: h.name)
-    matching_keys = sorted(matching.keys(), key=lambda k: k.name)
-    for game_hospital, hospital in zip(matching_keys, hospitals):
-        assert game_hospital.name == hospital.name
-        assert game_hospital._pref_names == hospital._pref_names
-        assert game_hospital.capacity == hospital.capacity
-
-    matched_residents = [
-        resident for match in matching.values() for resident in match
-    ]
-
-    assert matched_residents != [] and set(matched_residents).issubset(
-        set(game.residents)
-    )
-
-    for resident in set(game.residents) - set(matched_residents):
-        assert resident.matching is None
-
-
-@given(game=games())
-def test_check_validity(game):
-    """Test for a valid matching when the game is solved."""
-
-    game.solve()
-    assert game.check_validity()
-
-
-@given(game=games())
-def test_check_for_unacceptable_matches_residents(game):
-    """Test that matched residents have a preference of their match."""
-
-    resident = game.residents[0]
-    hospital = Hospital(name="foo", capacity=1)
-    resident.matching = hospital
-
-    issues = game._check_for_unacceptable_matches("residents")
-    assert len(issues) == 1
-
-    issue = issues[0]
-    assert issue.startswith(resident.name)
-    assert issue.endswith(f"{resident.prefs}.")
-    assert hospital.name in issue
-
-    with pytest.raises(MatchingError) as e:
-        game.check_validity()
-        error = e.unacceptable_matches[0]
-        assert issue == error
-
-
-@given(game=games())
-def test_check_for_unacceptable_matches_hospitals(game):
-    """Test that each hospital has a preference of all its matches."""
-
-    hospital = game.hospitals[0]
-    resident = Resident(name="foo")
-    hospital.matching.append(resident)
-
-    issues = game._check_for_unacceptable_matches("hospitals")
-    assert len(issues) == 1
-
-    issue = issues[0]
-    assert issue.startswith(hospital.name)
-    assert issue.endswith(f"{hospital.prefs}.")
-    assert resident.name in issue
-
-    with pytest.raises(MatchingError) as e:
-        game.check_validity()
-        error = e.unacceptable_matches[0]
-        assert issue == error
-
-
-@given(game=games())
-def test_check_for_oversubscribed_hospitals(game):
-    """Test that no hospitals can be oversubscribed."""
-
-    hospital = game.hospitals[0]
-    hospital.matching = range(hospital.capacity + 1)
-
-    issues = game._check_for_oversubscribed_players("hospitals")
-    assert len(issues) == 1
-
-    issue = issues[0]
-    assert issue.startswith(hospital.name)
-    assert issue.endswith(f"{hospital.capacity}.")
-    assert str(hospital.matching) in issue
-
-    with pytest.raises(MatchingError) as e:
-        game.check_validity()
-        error = e.oversubscribed_hospitals[0]
-        assert issue == error
-
-
-def test_check_stability():
-    """Test checker for whether matching is stable or not."""
-
-    residents = [Resident("A"), Resident("B"), Resident("C")]
-    hospitals = [Hospital("X", 2), Hospital("Y", 2)]
-
-    (a, b, c), (x, y) = residents, hospitals
-
-    a.set_prefs([x, y])
-    b.set_prefs([y])
-    c.set_prefs([y, x])
-
-    x.set_prefs([c, a])
-    y.set_prefs([a, b, c])
-
-    game = HospitalResident(residents, hospitals)
-
-    matching = game.solve()
-    assert game.check_stability()
-
-    (a, b, c), (x, y) = game.residents, game.hospitals
-    matching[x] = [c]
-    matching[y] = [a, b]
-
-    assert not game.check_stability()
diff --git a/tests/hospital_resident/util.py b/tests/hospital_resident/util.py
index fb130df9..eb6f4ec5 100644
--- a/tests/hospital_resident/util.py
+++ b/tests/hospital_resident/util.py
@@ -43,9 +43,7 @@ def connections(
     resident_prefs = {}
     hospital_prefs = {h: [] for h in hospital_names}
     for resident in resident_names:
-        hospitals = draw(
-            lists(sampled_from(hospital_names), min_size=1, unique=True)
-        )
+        hospitals = draw(lists(sampled_from(hospital_names), min_size=1, unique=True))
         resident_prefs[resident] = hospitals
         for hospital in hospitals:
             hospital_prefs[hospital].append(resident)
diff --git a/tests/matchings/__init__.py b/tests/matchings/__init__.py
new file mode 100644
index 00000000..ad36acb8
--- /dev/null
+++ b/tests/matchings/__init__.py
@@ -0,0 +1 @@
+"""Tests for the `matching.matchings` module."""
diff --git a/tests/matchings/test_hospital_resident.py b/tests/matchings/test_hospital_resident.py
new file mode 100644
index 00000000..7792f905
--- /dev/null
+++ b/tests/matchings/test_hospital_resident.py
@@ -0,0 +1,109 @@
+"""Tests for the `HRMatching` class."""
+
+from hypothesis import given
+from hypothesis import strategies as st
+
+from matching import matchings
+
+from ..common import st_sizes
+
+
+@st.composite
+def st_params(draw, hmin=1, hmax=3, rmin=1, rmax=5):
+    """Create a parameter set for a HRMatching instance."""
+
+    hsize = draw(st_sizes(hmin, hmax))
+    rsize = draw(st_sizes(rmin, rmax))
+    capacities = draw(st.lists(st.integers(1, 2), min_size=hsize, max_size=hsize))
+
+    matched_residents = draw(st.lists(st.integers(0, rsize - 1), unique=True))
+    if not matched_residents:
+        matching = None
+    else:
+        resident_matching = {}
+        for resident in matched_residents:
+            hospital = draw(st.integers(0, hsize - 1))
+            if capacities[hospital]:
+                resident_matching[resident] = hospital
+                capacities[hospital] -= 1
+
+        matching = {
+            hospital: [r for r in resident_matching if resident_matching[r] == hospital]
+            for hospital in range(hsize)
+            if hospital in resident_matching.values()
+        }
+
+    params = dict(
+        dictionary=matching, keys=draw(st.text(min_size=1)), values=draw(st.text(min_size=1))
+    )
+
+    return params
+
+
+@st.composite
+def st_matchings(draw, hmin=1, hmax=3, rmin=1, rmax=5):
+    """Create a HRMatching instance."""
+
+    params = draw(st_params(hmin, hmax, rmin, rmax))
+
+    return matchings.HRMatching(**params)
+
+
+@given(st_params())
+def test_init(params):
+    """Check that a HRMatching can be created correctly."""
+
+    matching = matchings.HRMatching(**params)
+
+    assert isinstance(matching, matchings.HRMatching)
+    assert isinstance(matching, dict)
+
+    dictionary = params["dictionary"] or {}
+    assert matching.items() == dictionary.items()
+    assert vars(matching) == {"keys_": params["keys"], "values_": params["values"]}
+
+
+@given(st_matchings())
+def test_repr(matching):
+    """Check that the string representation of a matching is correct."""
+
+    repr_ = repr(matching)
+
+    assert isinstance(repr_, str)
+    assert repr_.startswith("HRMatching")
+    assert str(dict(matching)) in repr_
+    assert matching.keys_ in repr_
+    assert matching.values_ in repr_
+
+
+@given(st_params())
+def test_eq(params):
+    """Check that two HRMatchings are equal."""
+
+    matching1 = matchings.HRMatching(**params)
+    matching2 = matchings.HRMatching(**params)
+
+    assert matching1 == matching2
+    assert matching1 is not matching2
+
+    if params["dictionary"] is not None:
+        key = next(iter(params["dictionary"].keys()))
+        matching2[key] = None
+
+        assert matching1 != matching2
+        assert vars(matching1) == vars(matching2)
+
+
+@given(st_matchings())
+def test_invert(matching):
+    """Check that the invert method works correctly."""
+
+    inverted = matching.invert()
+
+    assert isinstance(inverted, matchings.HRMatching)
+    assert inverted.keys_ == matching.values_
+    assert inverted.values_ == matching.keys_
+
+    for key, values in matching.items():
+        for value in values:
+            assert inverted[value] == [key]
diff --git a/tests/matchings/test_single.py b/tests/matchings/test_single.py
deleted file mode 100644
index a9085e2c..00000000
--- a/tests/matchings/test_single.py
+++ /dev/null
@@ -1,106 +0,0 @@
-"""Unit tests for the SingleMatching class."""
-
-from hypothesis import given
-from hypothesis import strategies as st
-
-from matching.matchings import SingleMatching
-
-
-@st.composite
-def st_single_params(draw, min_size=2, max_size=5):
-    """Create a parameter set for a SingleMatching instance."""
-
-    size = draw(st.integers(min_size, max_size))
-    midpoint = size // 2
-    players = list(range(size))
-    keys, values = players[:midpoint], players[midpoint:]
-    dictionary = draw(st.sampled_from((None, dict(zip(keys, values)))))
-
-    keys = draw(st.text())
-    values = draw(st.text())
-    valid = draw(st.sampled_from((None, False, True)))
-    stable = draw(st.sampled_from((None, False, True)))
-
-    params = dict(
-        dictionary=dictionary,
-        keys=keys,
-        values=values,
-        valid=valid,
-        stable=stable,
-    )
-
-    return params
-
-
-@st.composite
-def st_singles(draw, min_size=2, max_size=5):
-    """Create a SingleMatching instance."""
-
-    params = draw(st_single_params(min_size, max_size))
-
-    return SingleMatching(**params)
-
-
-@given(st_single_params())
-def test_init(params):
-    """Check that a SingleMatching can be created correctly."""
-
-    matching = SingleMatching(**params)
-
-    assert isinstance(matching, SingleMatching)
-    assert isinstance(matching, dict)
-
-    dictionary = params["dictionary"] or {}
-    assert matching.items() == dictionary.items()
-    assert vars(matching) == {
-        "keys_": params["keys"],
-        "values_": params["values"],
-        "valid": params["valid"],
-        "stable": params["stable"],
-    }
-
-
-@given(st_singles())
-def test_repr(matching):
-    """Check that the string representation of a matching is correct."""
-
-    repr_ = repr(matching)
-
-    assert isinstance(repr_, str)
-    assert repr_.startswith("SingleMatching")
-    assert str(dict(matching)) in repr_
-    assert matching.keys_ in repr_
-    assert matching.values_ in repr_
-
-
-@given(st_single_params())
-def test_eq(params):
-    """Check the equivalence dunder works as expected."""
-
-    matching1 = SingleMatching(**params)
-    matching2 = SingleMatching(**params)
-
-    assert matching1 == matching2
-
-    if params["dictionary"] is not None:
-        key = next(iter(params["dictionary"].keys()))
-        matching2[key] = None
-
-        assert matching1 != matching2
-        assert vars(matching1) == vars(matching2)
-
-
-@given(st_singles())
-def test_invert(matching):
-    """Check the matching inverter works as it should."""
-
-    inverted = matching.invert()
-
-    assert isinstance(inverted, SingleMatching)
-    assert set(inverted.items()) == set(
-        (val, key) for key, val in matching.items()
-    )
-    assert inverted.keys_ == matching.values_
-    assert inverted.values_ == matching.keys_
-    assert inverted.valid == matching.valid
-    assert inverted.stable == matching.stable
diff --git a/tests/matchings/test_stable_marriage.py b/tests/matchings/test_stable_marriage.py
new file mode 100644
index 00000000..da02ef0c
--- /dev/null
+++ b/tests/matchings/test_stable_marriage.py
@@ -0,0 +1,92 @@
+"""Tests for the `SMMatching` class."""
+
+from hypothesis import given
+from hypothesis import strategies as st
+
+from matching import matchings
+
+from ..common import st_sizes
+
+
+@st.composite
+def st_params(draw, min_size=2, max_size=5):
+    """Create a parameter set for a SMMatching instance."""
+
+    size = draw(st_sizes(min_size, max_size))
+    midpoint = size // 2
+    players = list(range(size))
+    keys, values = players[:midpoint], players[midpoint:]
+    dictionary = draw(st.sampled_from((None, dict(zip(keys, values)))))
+
+    keys = draw(st.text(min_size=1))
+    values = draw(st.text(min_size=1))
+
+    params = dict(dictionary=dictionary, keys=keys, values=values)
+
+    return params
+
+
+@st.composite
+def st_matchings(draw, min_size=2, max_size=5):
+    """Create a SMMatching instance."""
+
+    params = draw(st_params(min_size, max_size))
+
+    return matchings.SMMatching(**params)
+
+
+@given(st_params())
+def test_init(params):
+    """Check that a SMMatching can be created correctly."""
+
+    matching = matchings.SMMatching(**params)
+
+    assert isinstance(matching, matchings.SMMatching)
+    assert isinstance(matching, dict)
+
+    dictionary = params["dictionary"] or {}
+    assert matching.items() == dictionary.items()
+    assert vars(matching) == {"keys_": params["keys"], "values_": params["values"]}
+
+
+@given(st_matchings())
+def test_repr(matching):
+    """Check that the string representation of a matching is correct."""
+
+    repr_ = repr(matching)
+
+    assert isinstance(repr_, str)
+    assert repr_.startswith("SMMatching")
+    assert str(dict(matching)) in repr_
+    assert matching.keys_ in repr_
+    assert matching.values_ in repr_
+
+
+@given(st_params())
+def test_eq(params):
+    """Check the equivalence dunder works as expected."""
+
+    matching1 = matchings.SMMatching(**params)
+    matching2 = matchings.SMMatching(**params)
+
+    assert matching1 == matching2
+    assert matching1 is not matching2
+
+    if params["dictionary"] is not None:
+        key = next(iter(params["dictionary"].keys()))
+        matching2[key] = None
+
+        assert matching1 != matching2
+        assert vars(matching1) == vars(matching2)
+
+
+@given(st_matchings())
+def test_invert(matching):
+    """Check the matching inverter works as it should."""
+
+    inverted = matching.invert()
+
+    assert isinstance(inverted, matchings.SMMatching)
+    assert set(inverted.items()) == set((val, key) for key, val in matching.items())
+    assert inverted.keys_ == matching.values_
+    assert inverted.values_ == matching.keys_
diff --git a/tests/stable_marriage/strategies.py b/tests/stable_marriage/strategies.py
index 97616e04..a8013bbc 100644
--- a/tests/stable_marriage/strategies.py
+++ b/tests/stable_marriage/strategies.py
@@ -1,36 +1,8 @@
 """Composite strategies for SM unit tests."""
 
-from unittest import mock
-
-import numpy as np
 from hypothesis import strategies as st
-from hypothesis.extra import numpy as st_numpy
-
-from matching.games import StableMarriage
-
-
-def mocked_game(suitor_ranks, reviewer_ranks):
-    """Create an instance of SM that mocks the input validator."""
-
-    with mock.patch(
-        "matching.games.StableMarriage.check_input_validity"
-    ) as validator:
-        game = StableMarriage(suitor_ranks, reviewer_ranks)
-
-    validator.assert_called_once_with()
-
-    return game
-
-
-@st.composite
-def st_single_ranks(draw, size):
-    """Create a single rank matrix."""
-
-    rank = draw(
-        st.lists(st.permutations(range(size)), min_size=size, max_size=size)
-    )
 
-    return np.array(rank)
+from ..common import st_single_ranks, st_single_utilities
 
 
 @st.composite
@@ -56,21 +28,6 @@ def st_player_ranks(draw, pmin=1, pmax=5):
     return suitor_ranks, reviewer_ranks, player, ranks, side
 
 
-@st.composite
-def st_single_utilities(draw, size):
-    """Create a single utility matrix."""
-
-    utility = draw(
-        st_numpy.arrays(
-            dtype=float,
-            elements=st.floats(0, 1, allow_nan=False),
-            shape=(size, size),
-        )
-    )
-
-    return utility
-
-
 @st.composite
 def st_utilities(draw, pmin=1, pmax=5):
     """Create a set of utility matrices."""
diff --git a/tests/stable_marriage/test_algorithm.py b/tests/stable_marriage/test_algorithm.py
index 8c4a4782..6581f03e 100644
--- a/tests/stable_marriage/test_algorithm.py
+++ b/tests/stable_marriage/test_algorithm.py
@@ -1,9 +1,12 @@
-"""Integration tests for the Stable Marriage Problem algorithm."""
+"""Unit tests for the Stable Marriage algorithm."""
 
 import numpy as np
 from hypothesis import given
 
-from .strategies import mocked_game, st_ranks
+from matching.games import StableMarriage
+
+from ..common import mocked_game
+from .strategies import st_ranks
 
 
 def _assert_matching_is_valid_shape(matching, suitor_ranks, reviewer_ranks):
@@ -12,9 +15,7 @@ def _assert_matching_is_valid_shape(matching, suitor_ranks, reviewer_ranks):
     assert isinstance(matching, dict)
 
     assert (np.sort(list(matching.keys())) == np.unique(suitor_ranks)).all()
-    assert (
-        np.sort(list(matching.values())) == np.unique(reviewer_ranks)
-    ).all()
+    assert (np.sort(list(matching.values())) == np.unique(reviewer_ranks)).all()
 
 
 @given(st_ranks())
@@ -22,7 +23,7 @@ def test_stable_marriage_suitor_optimal(ranks):
     """Test that the SM algorithm is valid and suitor-optimal."""
 
     suitor_ranks, reviewer_ranks = ranks
-    game = mocked_game(*ranks)
+    game = mocked_game(StableMarriage, *ranks)
 
     matching = game._stable_marriage()
 
@@ -31,10 +32,7 @@ def test_stable_marriage_suitor_optimal(ranks):
     for reviewer, suitor in matching.items():
         suitor_rank = game.suitor_ranks[suitor]
         preferred_reviewers, *_ = np.where(suitor_rank < suitor_rank[reviewer])
-        for preferred in preferred_reviewers:
-            preferred_rank = game.reviewer_ranks[preferred]
-            partner = matching[preferred]
-            assert preferred_rank[suitor] > preferred_rank[partner]
+        assert not preferred_reviewers.any()
 
 
 @given(st_ranks())
@@ -42,7 +40,7 @@ def test_stable_marriage_reviewer_pessimal(ranks):
     """Test that the SM algorithm is valid and reviewer-pessimal."""
 
     suitor_ranks, reviewer_ranks = ranks
-    game = mocked_game(*ranks)
+    game = mocked_game(StableMarriage, *ranks)
 
     matching = game._stable_marriage()
 
@@ -54,7 +52,5 @@ def test_stable_marriage_reviewer_pessimal(ranks):
 
         for lesser in lesser_suitors:
             lesser_rank = game.suitor_ranks[lesser]
-            partner = next(
-                rev for rev, sui in matching.items() if sui == lesser
-            )
+            partner = next(rev for rev, sui in matching.items() if sui == lesser)
             assert lesser_rank[partner] < lesser_rank[reviewer]
diff --git a/tests/stable_marriage/test_examples.py b/tests/stable_marriage/test_examples.py
index 18c1cd2d..a9401047 100644
--- a/tests/stable_marriage/test_examples.py
+++ b/tests/stable_marriage/test_examples.py
@@ -25,12 +25,10 @@ def test_pride_and_prejudice():
         "L": ("B", "W", "D", "C"),
     }
 
-    game = StableMarriage.from_preferences(
-        suitor_preferences, reviewer_preferences
-    )
+    game = StableMarriage.from_preferences(suitor_preferences, reviewer_preferences)
     matching = game.solve()
 
-    assert dict(matching) == {"J": "B", "C": "C", "E": "D", "L": "W"}
+    assert matching == {"J": "B", "C": "C", "E": "D", "L": "W"}
 
 
 def test_readme_example():
@@ -47,12 +45,10 @@ def test_readme_example():
         "F": ["C", "B", "A"],
     }
 
-    game = StableMarriage.from_preferences(
-        suitor_preferences, reviewer_preferences
-    )
+    game = StableMarriage.from_preferences(suitor_preferences, reviewer_preferences)
     matching = game.solve()
 
-    assert dict(matching) == {"E": "A", "D": "B", "F": "C"}
+    assert matching == {"E": "A", "D": "B", "F": "C"}
 
 
 def test_matchingr_example():
@@ -66,4 +62,4 @@ def test_matchingr_example():
 
     matching = game.solve()
 
-    assert dict(matching) == {0: 1, 1: 0}
+    assert matching == {0: 1, 1: 0}
diff --git a/tests/stable_marriage/test_game.py b/tests/stable_marriage/test_game.py
index 7881353b..a6e4f8df 100644
--- a/tests/stable_marriage/test_game.py
+++ b/tests/stable_marriage/test_game.py
@@ -9,10 +9,10 @@
 from hypothesis import strategies as st
 
 from matching.games import StableMarriage
-from matching.matchings import SingleMatching
+from matching.matchings import SMMatching
 
+from ..common import mocked_game
 from .strategies import (
-    mocked_game,
     st_player_ranks,
     st_preference_matchings,
     st_preferences,
@@ -26,8 +26,9 @@ def test_init(ranks):
     """Test for correct instantiation given some rankings."""
 
     suitor_ranks, reviewer_ranks = ranks
-    game = mocked_game(suitor_ranks, reviewer_ranks)
+    game = mocked_game(StableMarriage, suitor_ranks, reviewer_ranks)
 
+    assert isinstance(game, StableMarriage)
     assert (game.suitor_ranks == suitor_ranks).all()
     assert (game.reviewer_ranks == reviewer_ranks).all()
 
@@ -44,9 +45,7 @@ def test_from_utilities(utilities):
     suitor_utility, reviewer_utility = utilities
 
     with (
-        mock.patch(
-            "matching.games.StableMarriage.check_input_validity"
-        ) as validator,
+        mock.patch("matching.games.StableMarriage.check_input_validity") as validator,
         mock.patch("matching.convert.utility_to_rank") as ranker,
     ):
         effects = (suitor_utility.argsort(), reviewer_utility.argsort())
@@ -77,9 +76,7 @@ def test_from_preferences(preferences):
     suitor_prefs, reviewer_prefs = preferences
 
     with (
-        mock.patch(
-            "matching.games.StableMarriage.check_input_validity"
-        ) as validator,
+        mock.patch("matching.games.StableMarriage.check_input_validity") as validator,
         mock.patch("matching.convert.preference_to_rank") as ranker,
     ):
         effects = (
@@ -118,7 +115,7 @@ def test_from_preferences(preferences):
 def test_check_number_of_players_no_warning(ranks):
     """Test the number of players can be checked without warning."""
 
-    game = mocked_game(*ranks)
+    game = mocked_game(StableMarriage, *ranks)
 
     with warnings.catch_warnings():
         warnings.simplefilter("error")
@@ -132,7 +129,7 @@ def test_check_number_of_players_warning(ranks):
     suitor_ranks, reviewer_ranks = ranks
     suitor_ranks = np.vstack((suitor_ranks, suitor_ranks[-1][::-1]))
 
-    game = mocked_game(suitor_ranks, reviewer_ranks)
+    game = mocked_game(StableMarriage, suitor_ranks, reviewer_ranks)
 
     match = (
         r"^Number of suitors \(\d{1,2}\) "
@@ -147,7 +144,7 @@ def test_check_player_ranks_no_warning(player_ranks):
     """Test the rank checker runs without warning for a valid set."""
 
     suitor_ranks, reviewer_ranks, player, ranks, side = player_ranks
-    game = mocked_game(suitor_ranks, reviewer_ranks)
+    game = mocked_game(StableMarriage, suitor_ranks, reviewer_ranks)
 
     with warnings.catch_warnings():
         warnings.simplefilter("error")
@@ -161,7 +158,7 @@ def test_check_player_ranks_warning(player_ranks):
     suitor_ranks, reviewer_ranks, player, ranks, side = player_ranks
     ranks[-1] = 1000
 
-    game = mocked_game(suitor_ranks, reviewer_ranks)
+    game = mocked_game(StableMarriage, suitor_ranks, reviewer_ranks)
 
     match = f"{side.title()} {player} has not strictly ranked"
     with pytest.warns(UserWarning, match=match):
@@ -179,23 +176,17 @@ def test_check_input_validity(ranks):
     """Test the logic of the input validator."""
 
     suitor_ranks, reviewer_ranks = ranks
-    game = mocked_game(*ranks)
+    game = mocked_game(StableMarriage, *ranks)
 
     with (
-        mock.patch(
-            "matching.games.StableMarriage._check_number_of_players"
-        ) as check_num_players,
-        mock.patch(
-            "matching.games.StableMarriage._check_player_ranks"
-        ) as check_player_ranks,
+        mock.patch("matching.games.StableMarriage._check_number_of_players") as check_num_players,
+        mock.patch("matching.games.StableMarriage._check_player_ranks") as check_player_ranks,
     ):
         game.check_input_validity()
 
     check_num_players.assert_called_once_with()
 
-    assert check_player_ranks.call_count == (
-        len(suitor_ranks) + len(reviewer_ranks)
-    )
+    assert check_player_ranks.call_count == (len(suitor_ranks) + len(reviewer_ranks))
 
     suitor_args = _zip_enumerated_ranks_with_side(suitor_ranks, "suitor")
     reviewer_args = _zip_enumerated_ranks_with_side(reviewer_ranks, "reviewer")
@@ -214,7 +205,7 @@ def test_invert_player_sets(ranks):
     """Test that the player set attributes can be swapped."""
 
     suitor_ranks, reviewer_ranks = ranks
-    game = mocked_game(*ranks)
+    game = mocked_game(StableMarriage, *ranks)
 
     game._invert_player_sets()
 
@@ -232,18 +223,12 @@ def test_invert_player_sets(ranks):
 def test_solve_valid_optimal(ranks, optimal, solution):
     """Test the solver runs as it should with valid inputs."""
 
-    game = mocked_game(*ranks)
+    game = mocked_game(StableMarriage, *ranks)
 
     with (
-        mock.patch(
-            "matching.games.StableMarriage._invert_player_sets"
-        ) as player_set_inverter,
-        mock.patch(
-            "matching.games.StableMarriage._stable_marriage"
-        ) as stable_marriage,
-        mock.patch(
-            "matching.matchings.SingleMatching.invert"
-        ) as matching_inverter,
+        mock.patch("matching.games.StableMarriage._invert_player_sets") as player_set_inverter,
+        mock.patch("matching.games.StableMarriage._stable_marriage") as stable_marriage,
+        mock.patch("matching.matchings.SMMatching.invert") as matching_inverter,
     ):
         stable_marriage.return_value = solution
         matching_inverter.return_value = "inverted_matching"
@@ -260,7 +245,7 @@ def test_solve_valid_optimal(ranks, optimal, solution):
         ]
         assert matching == "inverted_matching"
     else:
-        assert isinstance(matching, SingleMatching)
+        assert isinstance(matching, SMMatching)
         assert dict(matching) == solution
 
 
@@ -268,19 +253,13 @@ def test_solve_valid_optimal(ranks, optimal, solution):
 def test_solve_invalid_optimal_raises(ranks, optimal):
     """Test the solver raises an error with invalid optimal."""
 
-    game = mocked_game(*ranks)
+    game = mocked_game(StableMarriage, *ranks)
 
     match = "^Invalid choice for `optimal`."
     with (
-        mock.patch(
-            "matching.games.StableMarriage._invert_player_sets"
-        ) as player_set_inverter,
-        mock.patch(
-            "matching.games.StableMarriage._stable_marriage"
-        ) as stable_marriage,
-        mock.patch(
-            "matching.matchings.SingleMatching.invert"
-        ) as matching_inverter,
+        mock.patch("matching.games.StableMarriage._invert_player_sets") as player_set_inverter,
+        mock.patch("matching.games.StableMarriage._stable_marriage") as stable_marriage,
+        mock.patch("matching.matchings.SMMatching.invert") as matching_inverter,
         pytest.raises(ValueError, match=match),
     ):
         game.solve(optimal)
@@ -296,21 +275,19 @@ def test_convert_matching_to_preferences(preference_matchings):
 
     suitor_prefs, reviewer_prefs, matching = preference_matchings
 
-    # the arrays here aren't used anywhere internally, just placeholders
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
+    with mock.patch.object(StableMarriage, "check_input_validity") as validator:
         game = StableMarriage.from_preferences(suitor_prefs, reviewer_prefs)
 
-    game.matching = SingleMatching(matching)
+    game.matching = SMMatching(matching)
 
     game._convert_matching_to_preferences()
     converted = game.matching
 
-    assert isinstance(converted, SingleMatching)
+    assert isinstance(converted, SMMatching)
     assert converted.keys_ == "reviewers"
     assert converted.values_ == "suitors"
-    assert converted.valid is None
-    assert converted.stable is None
 
     assert set(converted.keys()) == set(reviewer_prefs)
     assert set(converted.values()) == set(suitor_prefs)
+
+    validator.assert_called_once_with()
diff --git a/tests/stable_marriage/util.py b/tests/stable_marriage/util.py
index 566538e6..718adbee 100644
--- a/tests/stable_marriage/util.py
+++ b/tests/stable_marriage/util.py
@@ -53,21 +53,15 @@ def make_prefs(player_names, seed):
 
     np.random.seed(seed)
     suitor_names, reviewer_names = player_names
-    suitor_prefs = {
-        name: np.random.permutation(reviewer_names).tolist()
-        for name in suitor_names
-    }
+    suitor_prefs = {name: np.random.permutation(reviewer_names).tolist() for name in suitor_names}
     reviewer_prefs = {
-        name: np.random.permutation(suitor_names).tolist()
-        for name in reviewer_names
+        name: np.random.permutation(suitor_names).tolist() for name in reviewer_names
     }
 
     return suitor_prefs, reviewer_prefs
 
 
 STABLE_MARRIAGE = given(
-    player_names=get_player_names(
-        suitor_pool=["A", "B", "C"], reviewer_pool=["X", "Y", "Z"]
-    ),
+    player_names=get_player_names(suitor_pool=["A", "B", "C"], reviewer_pool=["X", "Y", "Z"]),
     seed=integers(min_value=0, max_value=2**32 - 1),
 )
diff --git a/tests/stable_roommates/test_algorithm.py b/tests/stable_roommates/test_algorithm.py
index bef68f9e..a15b4d3c 100644
--- a/tests/stable_roommates/test_algorithm.py
+++ b/tests/stable_roommates/test_algorithm.py
@@ -22,9 +22,7 @@ def test_first_phase(players):
 
     players = first_phase(players)
 
-    player_matched = {
-        player: player.matching is not None for player in players
-    }
+    player_matched = {player: player.matching is not None for player in players}
     assert sum(player_matched.values()) >= len(players) - 1
 
     for player in players:
diff --git a/tests/stable_roommates/test_examples.py b/tests/stable_roommates/test_examples.py
index 2450acb1..ecc12be1 100644
--- a/tests/stable_roommates/test_examples.py
+++ b/tests/stable_roommates/test_examples.py
@@ -84,10 +84,7 @@ def test_large_example_from_book():
 def test_example_in_issue_64():
     """Test the example provided in #64."""
 
-    players = [
-        Player(name)
-        for name in ("charlie", "peter", "elise", "paul", "kelly", "sam")
-    ]
+    players = [Player(name) for name in ("charlie", "peter", "elise", "paul", "kelly", "sam")]
     charlie, peter, elise, paul, kelly, sam = players
 
     charlie.set_prefs([peter, paul, sam, kelly, elise])
diff --git a/tests/student_allocation/test_algorithm.py b/tests/student_allocation/test_algorithm.py
index f2ef7f0b..32a3e14b 100644
--- a/tests/student_allocation/test_algorithm.py
+++ b/tests/student_allocation/test_algorithm.py
@@ -34,9 +34,7 @@ def test_student_allocation(
 
 
 @STUDENT_ALLOCATION
-def test_student_optimal(
-    student_names, project_names, supervisor_names, capacities, seed, clean
-):
+def test_student_optimal(student_names, project_names, supervisor_names, capacities, seed, clean):
     """Test that the student-optimal algorithm is student-optimal."""
 
     np.random.seed(seed)
@@ -47,12 +45,7 @@ def test_student_optimal(
 
     assert set(projects) == set(matching.keys())
     assert all(
-        [
-            s in set(students)
-            for s in {
-                match for matches in matching.values() for match in matches
-            }
-        ]
+        [s in set(students) for s in {match for matches in matching.values() for match in matches}]
     )
 
     for student in students:
@@ -74,12 +67,7 @@ def test_supervisor_optimal(
 
     assert set(projects) == set(matching.keys())
     assert all(
-        [
-            s in set(students)
-            for s in {
-                match for matches in matching.values() for match in matches
-            }
-        ]
+        [s in set(students) for s in {match for matches in matching.values() for match in matches}]
     )
 
     for supervisor in supervisors:
diff --git a/tests/student_allocation/test_examples.py b/tests/student_allocation/test_examples.py
index bafb86f1..f1dcbac3 100644
--- a/tests/student_allocation/test_examples.py
+++ b/tests/student_allocation/test_examples.py
@@ -34,7 +34,5 @@ def test_example_in_docs():
     a, b, c, d, e = game.students
     x1, x2, y1, y2 = game.projects
 
-    matching = student_allocation(
-        game.students, game.projects, game.supervisors
-    )
+    matching = student_allocation(game.students, game.projects, game.supervisors)
     assert matching == {x1: [c, a], x2: [], y1: [d], y2: [b, e]}
diff --git a/tests/student_allocation/test_solver.py b/tests/student_allocation/test_solver.py
index f94297a3..db02a9f5 100644
--- a/tests/student_allocation/test_solver.py
+++ b/tests/student_allocation/test_solver.py
@@ -18,9 +18,7 @@
 
 
 @STUDENT_ALLOCATION
-def test_init(
-    student_names, project_names, supervisor_names, capacities, seed, clean
-):
+def test_init(student_names, project_names, supervisor_names, capacities, seed, clean):
     """Test for correct instantiation given some players."""
 
     students, projects, supervisors, game = make_game(
@@ -90,9 +88,7 @@ def test_remove_supervisor_and_projects(
 ):
     """Test that a supervisor and its projects can be removed."""
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
 
     supervisor = game.supervisors[0]
     projects = supervisor.projects
@@ -103,14 +99,10 @@ def test_remove_supervisor_and_projects(
 
 
 @STUDENT_ALLOCATION
-def test_remove_student(
-    student_names, project_names, supervisor_names, capacities, seed, clean
-):
+def test_remove_student(student_names, project_names, supervisor_names, capacities, seed, clean):
     """Test that a student can be removed."""
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
 
     student = game.students[0]
 
@@ -119,14 +111,10 @@ def test_remove_student(
 
 
 @STUDENT_ALLOCATION
-def test_check_inputs(
-    student_names, project_names, supervisor_names, capacities, seed, clean
-):
+def test_check_inputs(student_names, project_names, supervisor_names, capacities, seed, clean):
     """Test that inputs to an instance of SA can be verified."""
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
 
     with warnings.catch_warnings(record=True):
         warnings.simplefilter("error")
@@ -147,9 +135,7 @@ def test_check_inputs_project_prefs_all_reciprocated(
     any such students.
     """
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
 
     project = game.projects[0]
     student = project.prefs[0]
@@ -179,9 +165,7 @@ def test_check_inputs_supervisor_prefs_all_reciprocated(
     projects have forgotten any such students.
     """
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
 
     supervisor = game.supervisors[0]
     student = supervisor.prefs[0]
@@ -202,9 +186,7 @@ def test_check_inputs_supervisor_prefs_all_reciprocated(
 
     if clean:
         assert student not in supervisor.prefs
-        assert all(
-            student not in project.prefs for project in supervisor.projects
-        )
+        assert all(student not in project.prefs for project in supervisor.projects)
 
 
 @STUDENT_ALLOCATION
@@ -217,18 +199,14 @@ def test_check_inputs_project_reciprocated_all_prefs(
     forgotten the project.
     """
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
 
     project = game.projects[0]
     student = project.prefs[0]
     project._forget(student)
 
     with pytest.warns(PreferencesChangedWarning) as record:
-        game._check_inputs_player_reciprocated_all_prefs(
-            "projects", "students"
-        )
+        game._check_inputs_player_reciprocated_all_prefs("projects", "students")
 
     assert len(record) == 1
 
@@ -252,18 +230,14 @@ def test_check_inputs_supervisor_reciprocated_all_prefs(
     forgotten all projects belonging to that supervisor.
     """
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
 
     supervisor = game.supervisors[0]
     student = supervisor.prefs[0]
     supervisor.prefs.remove(student)
 
     with pytest.warns(PreferencesChangedWarning) as record:
-        game._check_inputs_player_reciprocated_all_prefs(
-            "supervisors", "students"
-        )
+        game._check_inputs_player_reciprocated_all_prefs("supervisors", "students")
 
     assert len(record) == 1
 
@@ -274,9 +248,7 @@ def test_check_inputs_supervisor_reciprocated_all_prefs(
 
     if clean:
         assert supervisor not in student.prefs
-        assert all(
-            project not in student.prefs for project in supervisor.projects
-        )
+        assert all(project not in student.prefs for project in supervisor.projects)
 
 
 @STUDENT_ALLOCATION
@@ -289,9 +261,7 @@ def test_check_inputs_supervisor_capacities_sufficient(
     updated to their supervisor's.
     """
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
 
     project = game.projects[0]
     supervisor_capacity = project.supervisor.capacity
@@ -322,9 +292,7 @@ def test_check_inputs_supervisor_capacities_necessary(
     updated to be the sum of its projects.
     """
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
 
     supervisor = game.supervisors[0]
     total_project_capacity = sum(p.capacity for p in supervisor.projects)
@@ -346,9 +314,7 @@ def test_check_inputs_supervisor_capacities_necessary(
 
 
 @STUDENT_ALLOCATION
-def test_solve(
-    student_names, project_names, supervisor_names, capacities, seed, clean
-):
+def test_solve(student_names, project_names, supervisor_names, capacities, seed, clean):
     """Test that the class can solve games correctly."""
 
     for optimal in ["student", "supervisor"]:
@@ -372,12 +338,8 @@ def test_solve(
             assert game_project.capacity == project.capacity
             assert game_project.supervisor.name == project.supervisor.name
 
-        matched_students = [
-            stud for match in matching.values() for stud in match
-        ]
-        assert matched_students != [] and set(matched_students).issubset(
-            set(game.students)
-        )
+        matched_students = [stud for match in matching.values() for stud in match]
+        assert matched_students != [] and set(matched_students).issubset(set(game.students))
 
         for student in matched_students:
             supervisor = student.matching.supervisor
@@ -388,14 +350,10 @@ def test_solve(
 
 
 @STUDENT_ALLOCATION
-def test_check_validity(
-    student_names, project_names, supervisor_names, capacities, seed, clean
-):
+def test_check_validity(student_names, project_names, supervisor_names, capacities, seed, clean):
     """Test for a valid matching when the game is solved."""
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
 
     game.solve()
     assert game.check_validity()
@@ -407,9 +365,7 @@ def test_check_for_unacceptable_matches_students(
 ):
     """Test that each matched student must have ranked their match."""
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
     game.solve()
 
     student = game.students[0]
@@ -430,9 +386,7 @@ def test_check_for_unacceptable_matches_projects(
 ):
     """Test that each project must rank all their matches."""
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
     game.solve()
 
     project = game.projects[0]
@@ -453,9 +407,7 @@ def test_check_for_unacceptable_matches_supervisors(
 ):
     """Test that each supervisor must rank all their matches."""
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
     game.solve()
 
     supervisor = game.supervisors[0]
@@ -476,9 +428,7 @@ def test_check_for_oversubscribed_projects(
 ):
     """Test that all projects must not be over-subscribed."""
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
     game.solve()
 
     project = game.projects[0]
@@ -498,9 +448,7 @@ def test_check_for_oversubscribed_supervisors(
 ):
     """Test that all supervisors must not be over-subscribed."""
 
-    *_, game = make_game(
-        student_names, project_names, supervisor_names, capacities, seed, clean
-    )
+    *_, game = make_game(student_names, project_names, supervisor_names, capacities, seed, clean)
     game.solve()
 
     supervisor = game.supervisors[0]
diff --git a/tests/student_allocation/util.py b/tests/student_allocation/util.py
index 7545ccd8..46fe5ee4 100644
--- a/tests/student_allocation/util.py
+++ b/tests/student_allocation/util.py
@@ -15,15 +15,9 @@
 def get_possible_prefs(players):
     """Get list of possible non-empty preferences from some players."""
 
-    all_ordered_subsets = {
-        tuple(set(sub)) for sub in it.product(players, repeat=len(players))
-    }
+    all_ordered_subsets = {tuple(set(sub)) for sub in it.product(players, repeat=len(players))}
 
-    possible_prefs = [
-        list(perm)
-        for sub in all_ordered_subsets
-        for perm in it.permutations(sub)
-    ]
+    possible_prefs = [list(perm) for sub in all_ordered_subsets for perm in it.permutations(sub)]
 
     return possible_prefs
 
@@ -32,12 +26,8 @@ def make_players(student_names, project_names, supervisor_names, capacities):
     """Given some names and capacities, make a set of players for SA."""
 
     students = [Student(name) for name in student_names]
-    projects = [
-        Project(name, cap) for name, cap in zip(project_names, capacities)
-    ]
-    supervisors = [
-        Supervisor(name, capacity=None) for name in supervisor_names
-    ]
+    projects = [Project(name, cap) for name, cap in zip(project_names, capacities)]
+    supervisors = [Supervisor(name, capacity=None) for name in supervisor_names]
 
     if len(students) > len(projects):
         students = students[: len(projects)]
@@ -45,9 +35,7 @@ def make_players(student_names, project_names, supervisor_names, capacities):
     for project in projects:
         project.set_supervisor(np.random.choice(supervisors))
 
-    supervisors = [
-        supervisor for supervisor in supervisors if supervisor.projects
-    ]
+    supervisors = [supervisor for supervisor in supervisors if supervisor.projects]
     for supervisor in supervisors:
         capacities = sorted([proj.capacity for proj in supervisor.projects])
         min_cap, max_cap = max(capacities), sum(capacities)
@@ -72,9 +60,7 @@ def make_players(student_names, project_names, supervisor_names, capacities):
     return students, projects, supervisors
 
 
-def make_game(
-    student_names, project_names, supervisor_names, capacities, seed, clean
-):
+def make_game(student_names, project_names, supervisor_names, capacities, seed, clean):
     """Make all of the players and the game itself."""
 
     np.random.seed(seed)
@@ -86,9 +72,7 @@ def make_game(
     return students, projects, supervisors, game
 
 
-def make_connections(
-    student_names, project_names, supervisor_names, capacities, seed
-):
+def make_connections(student_names, project_names, supervisor_names, capacities, seed):
     """Make a set of preferences and affiliations from some names."""
 
     np.random.seed(seed)
@@ -114,9 +98,7 @@ def make_connections(
             if student not in sup_prefs:
                 sup_prefs.append(student)
 
-    chosen_projects = {
-        p for projects in student_prefs.values() for p in projects
-    }
+    chosen_projects = {p for projects in student_prefs.values() for p in projects}
     for project in set(project_names) - chosen_projects:
         del project_supervisors[project]
         del project_capacities[project]
@@ -161,9 +143,7 @@ def make_connections(
         max_size=3,
         unique=True,
     ),
-    capacities=lists(
-        integers(min_value=1, max_value=2), min_size=5, max_size=5
-    ),
+    capacities=lists(integers(min_value=1, max_value=2), min_size=5, max_size=5),
     seed=integers(min_value=0, max_value=2**32 - 1),
     clean=booleans(),
 )
diff --git a/tests/test_convert.py b/tests/test_convert.py
index caa9be02..eb3c5528 100644
--- a/tests/test_convert.py
+++ b/tests/test_convert.py
@@ -13,9 +13,7 @@ def utilities(draw, smin=1, smax=10, unique=True):
     """Create a utility matrix for a test."""
 
     shape = draw(st.tuples(st.integers(smin, smax), st.integers(smin, smax)))
-    utilities = draw(
-        arrays(float, shape, elements=st.floats(0, 1), unique=unique)
-    )
+    utilities = draw(arrays(float, shape, elements=st.floats(0, 1), unique=unique))
 
     return utilities
 
@@ -24,12 +22,8 @@ def utilities(draw, smin=1, smax=10, unique=True):
 def preferences(draw, pmin=1, pmax=10):
     """Create a preference dictionary to test."""
 
-    players = draw(
-        st.lists(st.integers(), min_size=pmin, max_size=pmax, unique=True)
-    )
-    others = draw(
-        st.lists(st.text(), min_size=pmin, max_size=pmax, unique=True)
-    )
+    players = draw(st.lists(st.integers(), min_size=pmin, max_size=pmax, unique=True))
+    others = draw(st.lists(st.text(), min_size=pmin, max_size=pmax, unique=True))
 
     preference = {player: draw(st.permutations(others)) for player in players}
 
diff --git a/tests/test_matchings.py b/tests/test_matchings.py
deleted file mode 100644
index 85bb654d..00000000
--- a/tests/test_matchings.py
+++ /dev/null
@@ -1,58 +0,0 @@
-"""Tests for the matching classes."""
-
-from hypothesis import given
-from hypothesis.strategies import (
-    composite,
-    integers,
-    lists,
-    sampled_from,
-    text,
-)
-
-from matching import MultipleMatching
-from matching.players import Hospital, Player
-
-
-@composite
-def multiples(
-    draw,
-    host_names_from=text(),
-    player_names_from=text(),
-    min_hosts=2,
-    max_hosts=5,
-    min_players=10,
-    max_players=20,
-):
-    """A strategy for creating multiple-match matchings from players."""
-
-    num_hosts = draw(integers(min_value=min_hosts, max_value=max_hosts))
-    num_players = draw(integers(min_value=min_players, max_value=max_players))
-
-    hosts = [
-        Hospital(draw(host_names_from), max_players) for _ in range(num_hosts)
-    ]
-    players = [Player(draw(player_names_from)) for _ in range(num_players)]
-
-    dictionary = {}
-    for host in hosts:
-        matches = draw(lists(sampled_from(players), min_size=0, unique=True))
-        dictionary[host] = matches
-
-    return dictionary
-
-
-@given(dictionary=multiples())
-def test_multiple_setitem(dictionary):
-    """Test that a host player in a matching can have a list match."""
-
-    matching = MultipleMatching(dictionary)
-    host = list(dictionary.keys())[0]
-    players = list(
-        {player for players in dictionary.values() for player in players}
-    )[:-1]
-
-    matching[host] = players
-    assert matching[host] == players
-    assert host.matching == players
-    for player in players:
-        assert player.matching == host
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 00000000..b0574075
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,2408 @@
+version = 1
+revision = 1
+requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "alabaster"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a6/f8/d9c74d0daf3f742840fd818d69cfae176fa332022fd44e3469487d5a9420/alabaster-1.0.0.tar.gz", hash = "sha256:c00dca57bca26fa62a6d7d0a9fcce65f3e026e9bfe33e9c538fd3fbb2144fd9e", size = 24210 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl", hash = "sha256:fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b", size = 13929 },
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
+]
+
+[[package]]
+name = "appnope"
+version = "0.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321 },
+]
+
+[[package]]
+name = "asttokens"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4a/e7/82da0a03e7ba5141f05cce0d302e6eed121ae055e0456ca228bf693984bc/asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7", size = 61978 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/25/8a/c46dcc25341b5bce5472c718902eb3d38600a903b14fa6aeecef3f21a46f/asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2", size = 26918 },
+]
+
+[[package]]
+name = "attrs"
+version = "25.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5a/b0/1367933a8532ee6ff8d63537de4f1177af4bff9f3e829baf7331f595bb24/attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b", size = 812032 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3", size = 63815 },
+]
+
+[[package]]
+name = "babel"
+version = "2.17.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/6b/d52e42361e1aa00709585ecc30b3f9684b3ab62530771402248b1b1d6240/babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d", size = 9951852 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537 },
+]
+
+[[package]]
+name = "beartype"
+version = "0.20.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/48/fe1177f4272a1bc344f3371414aa5b76e19c30d7280d711ce90c5335a6f5/beartype-0.20.0.tar.gz", hash = "sha256:599ecc86b88549bcb6d1af626f44d85ffbb9151ace5d7f9f3b493dce2ffee529", size = 1390635 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/61/ce/9cf60b72e3fbd36a6dd16e31ecbe9ae137a8ead77cb24a5f75302adfc9a9/beartype-0.20.0-py3-none-any.whl", hash = "sha256:090d10e3540b3fca209a0ab5f1c15f9652a075da0a7249c2e6713011e9e5f6ef", size = 1139097 },
+]
+
+[[package]]
+name = "beautifulsoup4"
+version = "4.13.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "soupsieve" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f0/3c/adaf39ce1fb4afdd21b611e3d530b183bb7759c9b673d60db0e347fd4439/beautifulsoup4-4.13.3.tar.gz", hash = "sha256:1bd32405dacc920b42b83ba01644747ed77456a65760e285fbc47633ceddaf8b", size = 619516 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f9/49/6abb616eb3cbab6a7cca303dc02fdf3836de2e0b834bf966a7f5271a34d8/beautifulsoup4-4.13.3-py3-none-any.whl", hash = "sha256:99045d7d3f08f91f0d656bc9b7efbae189426cd913d830294a15eefa0ea4df16", size = 186015 },
+]
+
+[[package]]
+name = "black"
+version = "25.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "mypy-extensions" },
+    { name = "packaging" },
+    { name = "pathspec" },
+    { name = "platformdirs" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/94/49/26a7b0f3f35da4b5a65f081943b7bcd22d7002f5f0fb8098ec1ff21cb6ef/black-25.1.0.tar.gz", hash = "sha256:33496d5cd1222ad73391352b4ae8da15253c5de89b93a80b3e2c8d9a19ec2666", size = 649449 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4d/3b/4ba3f93ac8d90410423fdd31d7541ada9bcee1df32fb90d26de41ed40e1d/black-25.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:759e7ec1e050a15f89b770cefbf91ebee8917aac5c20483bc2d80a6c3a04df32", size = 1629419 },
+    { url = "https://files.pythonhosted.org/packages/b4/02/0bde0485146a8a5e694daed47561785e8b77a0466ccc1f3e485d5ef2925e/black-25.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0e519ecf93120f34243e6b0054db49c00a35f84f195d5bce7e9f5cfc578fc2da", size = 1461080 },
+    { url = "https://files.pythonhosted.org/packages/52/0e/abdf75183c830eaca7589144ff96d49bce73d7ec6ad12ef62185cc0f79a2/black-25.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:055e59b198df7ac0b7efca5ad7ff2516bca343276c466be72eb04a3bcc1f82d7", size = 1766886 },
+    { url = "https://files.pythonhosted.org/packages/dc/a6/97d8bb65b1d8a41f8a6736222ba0a334db7b7b77b8023ab4568288f23973/black-25.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:db8ea9917d6f8fc62abd90d944920d95e73c83a5ee3383493e35d271aca872e9", size = 1419404 },
+    { url = "https://files.pythonhosted.org/packages/7e/4f/87f596aca05c3ce5b94b8663dbfe242a12843caaa82dd3f85f1ffdc3f177/black-25.1.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a39337598244de4bae26475f77dda852ea00a93bd4c728e09eacd827ec929df0", size = 1614372 },
+    { url = "https://files.pythonhosted.org/packages/e7/d0/2c34c36190b741c59c901e56ab7f6e54dad8df05a6272a9747ecef7c6036/black-25.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:96c1c7cd856bba8e20094e36e0f948718dc688dba4a9d78c3adde52b9e6c2299", size = 1442865 },
+    { url = "https://files.pythonhosted.org/packages/21/d4/7518c72262468430ead45cf22bd86c883a6448b9eb43672765d69a8f1248/black-25.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bce2e264d59c91e52d8000d507eb20a9aca4a778731a08cfff7e5ac4a4bb7096", size = 1749699 },
+    { url = "https://files.pythonhosted.org/packages/58/db/4f5beb989b547f79096e035c4981ceb36ac2b552d0ac5f2620e941501c99/black-25.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:172b1dbff09f86ce6f4eb8edf9dede08b1fce58ba194c87d7a4f1a5aa2f5b3c2", size = 1428028 },
+    { url = "https://files.pythonhosted.org/packages/83/71/3fe4741df7adf015ad8dfa082dd36c94ca86bb21f25608eb247b4afb15b2/black-25.1.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:4b60580e829091e6f9238c848ea6750efed72140b91b048770b64e74fe04908b", size = 1650988 },
+    { url = "https://files.pythonhosted.org/packages/13/f3/89aac8a83d73937ccd39bbe8fc6ac8860c11cfa0af5b1c96d081facac844/black-25.1.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1e2978f6df243b155ef5fa7e558a43037c3079093ed5d10fd84c43900f2d8ecc", size = 1453985 },
+    { url = "https://files.pythonhosted.org/packages/6f/22/b99efca33f1f3a1d2552c714b1e1b5ae92efac6c43e790ad539a163d1754/black-25.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3b48735872ec535027d979e8dcb20bf4f70b5ac75a8ea99f127c106a7d7aba9f", size = 1783816 },
+    { url = "https://files.pythonhosted.org/packages/18/7e/a27c3ad3822b6f2e0e00d63d58ff6299a99a5b3aee69fa77cd4b0076b261/black-25.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:ea0213189960bda9cf99be5b8c8ce66bb054af5e9e861249cd23471bd7b0b3ba", size = 1440860 },
+    { url = "https://files.pythonhosted.org/packages/98/87/0edf98916640efa5d0696e1abb0a8357b52e69e82322628f25bf14d263d1/black-25.1.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8f0b18a02996a836cc9c9c78e5babec10930862827b1b724ddfe98ccf2f2fe4f", size = 1650673 },
+    { url = "https://files.pythonhosted.org/packages/52/e5/f7bf17207cf87fa6e9b676576749c6b6ed0d70f179a3d812c997870291c3/black-25.1.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:afebb7098bfbc70037a053b91ae8437c3857482d3a690fefc03e9ff7aa9a5fd3", size = 1453190 },
+    { url = "https://files.pythonhosted.org/packages/e3/ee/adda3d46d4a9120772fae6de454c8495603c37c4c3b9c60f25b1ab6401fe/black-25.1.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:030b9759066a4ee5e5aca28c3c77f9c64789cdd4de8ac1df642c40b708be6171", size = 1782926 },
+    { url = "https://files.pythonhosted.org/packages/cc/64/94eb5f45dcb997d2082f097a3944cfc7fe87e071907f677e80788a2d7b7a/black-25.1.0-cp313-cp313-win_amd64.whl", hash = "sha256:a22f402b410566e2d1c950708c77ebf5ebd5d0d88a6a2e87c86d9fb48afa0d18", size = 1442613 },
+    { url = "https://files.pythonhosted.org/packages/09/71/54e999902aed72baf26bca0d50781b01838251a462612966e9fc4891eadd/black-25.1.0-py3-none-any.whl", hash = "sha256:95e8176dae143ba9097f351d174fdaf0ccd29efb414b362ae3fd72bf0f710717", size = 207646 },
+]
+
+[[package]]
+name = "bleach"
+version = "6.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "webencodings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/76/9a/0e33f5054c54d349ea62c277191c020c2d6ef1d65ab2cb1993f91ec846d1/bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f", size = 203083 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fc/55/96142937f66150805c25c4d0f31ee4132fd33497753400734f9dfdcbdc66/bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e", size = 163406 },
+]
+
+[package.optional-dependencies]
+css = [
+    { name = "tinycss2" },
+]
+
+[[package]]
+name = "certifi"
+version = "2025.1.31"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1c/ab/c9f1e32b7b1bf505bf26f0ef697775960db7932abeb7b516de930ba2705f/certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651", size = 167577 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/38/fc/bce832fd4fd99766c04d1ee0eead6b0ec6486fb100ae5e74c1d91292b982/certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe", size = 166393 },
+]
+
+[[package]]
+name = "cffi"
+version = "1.17.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pycparser" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fc/97/c783634659c2920c3fc70419e3af40972dbaf758daa229a7d6ea6135c90d/cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824", size = 516621 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/90/07/f44ca684db4e4f08a3fdc6eeb9a0d15dc6883efc7b8c90357fdbf74e186c/cffi-1.17.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:df8b1c11f177bc2313ec4b2d46baec87a5f3e71fc8b45dab2ee7cae86d9aba14", size = 182191 },
+    { url = "https://files.pythonhosted.org/packages/08/fd/cc2fedbd887223f9f5d170c96e57cbf655df9831a6546c1727ae13fa977a/cffi-1.17.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8f2cdc858323644ab277e9bb925ad72ae0e67f69e804f4898c070998d50b1a67", size = 178592 },
+    { url = "https://files.pythonhosted.org/packages/de/cc/4635c320081c78d6ffc2cab0a76025b691a91204f4aa317d568ff9280a2d/cffi-1.17.1-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:edae79245293e15384b51f88b00613ba9f7198016a5948b5dddf4917d4d26382", size = 426024 },
+    { url = "https://files.pythonhosted.org/packages/b6/7b/3b2b250f3aab91abe5f8a51ada1b717935fdaec53f790ad4100fe2ec64d1/cffi-1.17.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:45398b671ac6d70e67da8e4224a065cec6a93541bb7aebe1b198a61b58c7b702", size = 448188 },
+    { url = "https://files.pythonhosted.org/packages/d3/48/1b9283ebbf0ec065148d8de05d647a986c5f22586b18120020452fff8f5d/cffi-1.17.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ad9413ccdeda48c5afdae7e4fa2192157e991ff761e7ab8fdd8926f40b160cc3", size = 455571 },
+    { url = "https://files.pythonhosted.org/packages/40/87/3b8452525437b40f39ca7ff70276679772ee7e8b394934ff60e63b7b090c/cffi-1.17.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5da5719280082ac6bd9aa7becb3938dc9f9cbd57fac7d2871717b1feb0902ab6", size = 436687 },
+    { url = "https://files.pythonhosted.org/packages/8d/fb/4da72871d177d63649ac449aec2e8a29efe0274035880c7af59101ca2232/cffi-1.17.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2bb1a08b8008b281856e5971307cc386a8e9c5b625ac297e853d36da6efe9c17", size = 446211 },
+    { url = "https://files.pythonhosted.org/packages/ab/a0/62f00bcb411332106c02b663b26f3545a9ef136f80d5df746c05878f8c4b/cffi-1.17.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:045d61c734659cc045141be4bae381a41d89b741f795af1dd018bfb532fd0df8", size = 461325 },
+    { url = "https://files.pythonhosted.org/packages/36/83/76127035ed2e7e27b0787604d99da630ac3123bfb02d8e80c633f218a11d/cffi-1.17.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:6883e737d7d9e4899a8a695e00ec36bd4e5e4f18fabe0aca0efe0a4b44cdb13e", size = 438784 },
+    { url = "https://files.pythonhosted.org/packages/21/81/a6cd025db2f08ac88b901b745c163d884641909641f9b826e8cb87645942/cffi-1.17.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:6b8b4a92e1c65048ff98cfe1f735ef8f1ceb72e3d5f0c25fdb12087a23da22be", size = 461564 },
+    { url = "https://files.pythonhosted.org/packages/f8/fe/4d41c2f200c4a457933dbd98d3cf4e911870877bd94d9656cc0fcb390681/cffi-1.17.1-cp310-cp310-win32.whl", hash = "sha256:c9c3d058ebabb74db66e431095118094d06abf53284d9c81f27300d0e0d8bc7c", size = 171804 },
+    { url = "https://files.pythonhosted.org/packages/d1/b6/0b0f5ab93b0df4acc49cae758c81fe4e5ef26c3ae2e10cc69249dfd8b3ab/cffi-1.17.1-cp310-cp310-win_amd64.whl", hash = "sha256:0f048dcf80db46f0098ccac01132761580d28e28bc0f78ae0d58048063317e15", size = 181299 },
+    { url = "https://files.pythonhosted.org/packages/6b/f4/927e3a8899e52a27fa57a48607ff7dc91a9ebe97399b357b85a0c7892e00/cffi-1.17.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a45e3c6913c5b87b3ff120dcdc03f6131fa0065027d0ed7ee6190736a74cd401", size = 182264 },
+    { url = "https://files.pythonhosted.org/packages/6c/f5/6c3a8efe5f503175aaddcbea6ad0d2c96dad6f5abb205750d1b3df44ef29/cffi-1.17.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:30c5e0cb5ae493c04c8b42916e52ca38079f1b235c2f8ae5f4527b963c401caf", size = 178651 },
+    { url = "https://files.pythonhosted.org/packages/94/dd/a3f0118e688d1b1a57553da23b16bdade96d2f9bcda4d32e7d2838047ff7/cffi-1.17.1-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f75c7ab1f9e4aca5414ed4d8e5c0e303a34f4421f8a0d47a4d019ceff0ab6af4", size = 445259 },
+    { url = "https://files.pythonhosted.org/packages/2e/ea/70ce63780f096e16ce8588efe039d3c4f91deb1dc01e9c73a287939c79a6/cffi-1.17.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a1ed2dd2972641495a3ec98445e09766f077aee98a1c896dcb4ad0d303628e41", size = 469200 },
+    { url = "https://files.pythonhosted.org/packages/1c/a0/a4fa9f4f781bda074c3ddd57a572b060fa0df7655d2a4247bbe277200146/cffi-1.17.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:46bf43160c1a35f7ec506d254e5c890f3c03648a4dbac12d624e4490a7046cd1", size = 477235 },
+    { url = "https://files.pythonhosted.org/packages/62/12/ce8710b5b8affbcdd5c6e367217c242524ad17a02fe5beec3ee339f69f85/cffi-1.17.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a24ed04c8ffd54b0729c07cee15a81d964e6fee0e3d4d342a27b020d22959dc6", size = 459721 },
+    { url = "https://files.pythonhosted.org/packages/ff/6b/d45873c5e0242196f042d555526f92aa9e0c32355a1be1ff8c27f077fd37/cffi-1.17.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:610faea79c43e44c71e1ec53a554553fa22321b65fae24889706c0a84d4ad86d", size = 467242 },
+    { url = "https://files.pythonhosted.org/packages/1a/52/d9a0e523a572fbccf2955f5abe883cfa8bcc570d7faeee06336fbd50c9fc/cffi-1.17.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:a9b15d491f3ad5d692e11f6b71f7857e7835eb677955c00cc0aefcd0669adaf6", size = 477999 },
+    { url = "https://files.pythonhosted.org/packages/44/74/f2a2460684a1a2d00ca799ad880d54652841a780c4c97b87754f660c7603/cffi-1.17.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:de2ea4b5833625383e464549fec1bc395c1bdeeb5f25c4a3a82b5a8c756ec22f", size = 454242 },
+    { url = "https://files.pythonhosted.org/packages/f8/4a/34599cac7dfcd888ff54e801afe06a19c17787dfd94495ab0c8d35fe99fb/cffi-1.17.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:fc48c783f9c87e60831201f2cce7f3b2e4846bf4d8728eabe54d60700b318a0b", size = 478604 },
+    { url = "https://files.pythonhosted.org/packages/34/33/e1b8a1ba29025adbdcda5fb3a36f94c03d771c1b7b12f726ff7fef2ebe36/cffi-1.17.1-cp311-cp311-win32.whl", hash = "sha256:85a950a4ac9c359340d5963966e3e0a94a676bd6245a4b55bc43949eee26a655", size = 171727 },
+    { url = "https://files.pythonhosted.org/packages/3d/97/50228be003bb2802627d28ec0627837ac0bf35c90cf769812056f235b2d1/cffi-1.17.1-cp311-cp311-win_amd64.whl", hash = "sha256:caaf0640ef5f5517f49bc275eca1406b0ffa6aa184892812030f04c2abf589a0", size = 181400 },
+    { url = "https://files.pythonhosted.org/packages/5a/84/e94227139ee5fb4d600a7a4927f322e1d4aea6fdc50bd3fca8493caba23f/cffi-1.17.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:805b4371bf7197c329fcb3ead37e710d1bca9da5d583f5073b799d5c5bd1eee4", size = 183178 },
+    { url = "https://files.pythonhosted.org/packages/da/ee/fb72c2b48656111c4ef27f0f91da355e130a923473bf5ee75c5643d00cca/cffi-1.17.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:733e99bc2df47476e3848417c5a4540522f234dfd4ef3ab7fafdf555b082ec0c", size = 178840 },
+    { url = "https://files.pythonhosted.org/packages/cc/b6/db007700f67d151abadf508cbfd6a1884f57eab90b1bb985c4c8c02b0f28/cffi-1.17.1-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1257bdabf294dceb59f5e70c64a3e2f462c30c7ad68092d01bbbfb1c16b1ba36", size = 454803 },
+    { url = "https://files.pythonhosted.org/packages/1a/df/f8d151540d8c200eb1c6fba8cd0dfd40904f1b0682ea705c36e6c2e97ab3/cffi-1.17.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:da95af8214998d77a98cc14e3a3bd00aa191526343078b530ceb0bd710fb48a5", size = 478850 },
+    { url = "https://files.pythonhosted.org/packages/28/c0/b31116332a547fd2677ae5b78a2ef662dfc8023d67f41b2a83f7c2aa78b1/cffi-1.17.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d63afe322132c194cf832bfec0dc69a99fb9bb6bbd550f161a49e9e855cc78ff", size = 485729 },
+    { url = "https://files.pythonhosted.org/packages/91/2b/9a1ddfa5c7f13cab007a2c9cc295b70fbbda7cb10a286aa6810338e60ea1/cffi-1.17.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f79fc4fc25f1c8698ff97788206bb3c2598949bfe0fef03d299eb1b5356ada99", size = 471256 },
+    { url = "https://files.pythonhosted.org/packages/b2/d5/da47df7004cb17e4955df6a43d14b3b4ae77737dff8bf7f8f333196717bf/cffi-1.17.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b62ce867176a75d03a665bad002af8e6d54644fad99a3c70905c543130e39d93", size = 479424 },
+    { url = "https://files.pythonhosted.org/packages/0b/ac/2a28bcf513e93a219c8a4e8e125534f4f6db03e3179ba1c45e949b76212c/cffi-1.17.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:386c8bf53c502fff58903061338ce4f4950cbdcb23e2902d86c0f722b786bbe3", size = 484568 },
+    { url = "https://files.pythonhosted.org/packages/d4/38/ca8a4f639065f14ae0f1d9751e70447a261f1a30fa7547a828ae08142465/cffi-1.17.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:4ceb10419a9adf4460ea14cfd6bc43d08701f0835e979bf821052f1805850fe8", size = 488736 },
+    { url = "https://files.pythonhosted.org/packages/86/c5/28b2d6f799ec0bdecf44dced2ec5ed43e0eb63097b0f58c293583b406582/cffi-1.17.1-cp312-cp312-win32.whl", hash = "sha256:a08d7e755f8ed21095a310a693525137cfe756ce62d066e53f502a83dc550f65", size = 172448 },
+    { url = "https://files.pythonhosted.org/packages/50/b9/db34c4755a7bd1cb2d1603ac3863f22bcecbd1ba29e5ee841a4bc510b294/cffi-1.17.1-cp312-cp312-win_amd64.whl", hash = "sha256:51392eae71afec0d0c8fb1a53b204dbb3bcabcb3c9b807eedf3e1e6ccf2de903", size = 181976 },
+    { url = "https://files.pythonhosted.org/packages/8d/f8/dd6c246b148639254dad4d6803eb6a54e8c85c6e11ec9df2cffa87571dbe/cffi-1.17.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f3a2b4222ce6b60e2e8b337bb9596923045681d71e5a082783484d845390938e", size = 182989 },
+    { url = "https://files.pythonhosted.org/packages/8b/f1/672d303ddf17c24fc83afd712316fda78dc6fce1cd53011b839483e1ecc8/cffi-1.17.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0984a4925a435b1da406122d4d7968dd861c1385afe3b45ba82b750f229811e2", size = 178802 },
+    { url = "https://files.pythonhosted.org/packages/0e/2d/eab2e858a91fdff70533cab61dcff4a1f55ec60425832ddfdc9cd36bc8af/cffi-1.17.1-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d01b12eeeb4427d3110de311e1774046ad344f5b1a7403101878976ecd7a10f3", size = 454792 },
+    { url = "https://files.pythonhosted.org/packages/75/b2/fbaec7c4455c604e29388d55599b99ebcc250a60050610fadde58932b7ee/cffi-1.17.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:706510fe141c86a69c8ddc029c7910003a17353970cff3b904ff0686a5927683", size = 478893 },
+    { url = "https://files.pythonhosted.org/packages/4f/b7/6e4a2162178bf1935c336d4da8a9352cccab4d3a5d7914065490f08c0690/cffi-1.17.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:de55b766c7aa2e2a3092c51e0483d700341182f08e67c63630d5b6f200bb28e5", size = 485810 },
+    { url = "https://files.pythonhosted.org/packages/c7/8a/1d0e4a9c26e54746dc08c2c6c037889124d4f59dffd853a659fa545f1b40/cffi-1.17.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c59d6e989d07460165cc5ad3c61f9fd8f1b4796eacbd81cee78957842b834af4", size = 471200 },
+    { url = "https://files.pythonhosted.org/packages/26/9f/1aab65a6c0db35f43c4d1b4f580e8df53914310afc10ae0397d29d697af4/cffi-1.17.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd398dbc6773384a17fe0d3e7eeb8d1a21c2200473ee6806bb5e6a8e62bb73dd", size = 479447 },
+    { url = "https://files.pythonhosted.org/packages/5f/e4/fb8b3dd8dc0e98edf1135ff067ae070bb32ef9d509d6cb0f538cd6f7483f/cffi-1.17.1-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:3edc8d958eb099c634dace3c7e16560ae474aa3803a5df240542b305d14e14ed", size = 484358 },
+    { url = "https://files.pythonhosted.org/packages/f1/47/d7145bf2dc04684935d57d67dff9d6d795b2ba2796806bb109864be3a151/cffi-1.17.1-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:72e72408cad3d5419375fc87d289076ee319835bdfa2caad331e377589aebba9", size = 488469 },
+    { url = "https://files.pythonhosted.org/packages/bf/ee/f94057fa6426481d663b88637a9a10e859e492c73d0384514a17d78ee205/cffi-1.17.1-cp313-cp313-win32.whl", hash = "sha256:e03eab0a8677fa80d646b5ddece1cbeaf556c313dcfac435ba11f107ba117b5d", size = 172475 },
+    { url = "https://files.pythonhosted.org/packages/7c/fc/6a8cb64e5f0324877d503c854da15d76c1e50eb722e320b15345c4d0c6de/cffi-1.17.1-cp313-cp313-win_amd64.whl", hash = "sha256:f6a16c31041f09ead72d69f583767292f750d24913dadacf5756b966aacb3f1a", size = 182009 },
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.4.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/16/b0/572805e227f01586461c80e0fd25d65a2115599cc9dad142fee4b747c357/charset_normalizer-3.4.1.tar.gz", hash = "sha256:44251f18cd68a75b56585dd00dae26183e102cd5e0f9f1466e6df5da2ed64ea3", size = 123188 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0d/58/5580c1716040bc89206c77d8f74418caf82ce519aae06450393ca73475d1/charset_normalizer-3.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:91b36a978b5ae0ee86c394f5a54d6ef44db1de0815eb43de826d41d21e4af3de", size = 198013 },
+    { url = "https://files.pythonhosted.org/packages/d0/11/00341177ae71c6f5159a08168bcb98c6e6d196d372c94511f9f6c9afe0c6/charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7461baadb4dc00fd9e0acbe254e3d7d2112e7f92ced2adc96e54ef6501c5f176", size = 141285 },
+    { url = "https://files.pythonhosted.org/packages/01/09/11d684ea5819e5a8f5100fb0b38cf8d02b514746607934134d31233e02c8/charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e218488cd232553829be0664c2292d3af2eeeb94b32bea483cf79ac6a694e037", size = 151449 },
+    { url = "https://files.pythonhosted.org/packages/08/06/9f5a12939db324d905dc1f70591ae7d7898d030d7662f0d426e2286f68c9/charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:80ed5e856eb7f30115aaf94e4a08114ccc8813e6ed1b5efa74f9f82e8509858f", size = 143892 },
+    { url = "https://files.pythonhosted.org/packages/93/62/5e89cdfe04584cb7f4d36003ffa2936681b03ecc0754f8e969c2becb7e24/charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b010a7a4fd316c3c484d482922d13044979e78d1861f0e0650423144c616a46a", size = 146123 },
+    { url = "https://files.pythonhosted.org/packages/a9/ac/ab729a15c516da2ab70a05f8722ecfccc3f04ed7a18e45c75bbbaa347d61/charset_normalizer-3.4.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4532bff1b8421fd0a320463030c7520f56a79c9024a4e88f01c537316019005a", size = 147943 },
+    { url = "https://files.pythonhosted.org/packages/03/d2/3f392f23f042615689456e9a274640c1d2e5dd1d52de36ab8f7955f8f050/charset_normalizer-3.4.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:d973f03c0cb71c5ed99037b870f2be986c3c05e63622c017ea9816881d2dd247", size = 142063 },
+    { url = "https://files.pythonhosted.org/packages/f2/e3/e20aae5e1039a2cd9b08d9205f52142329f887f8cf70da3650326670bddf/charset_normalizer-3.4.1-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:3a3bd0dcd373514dcec91c411ddb9632c0d7d92aed7093b8c3bbb6d69ca74408", size = 150578 },
+    { url = "https://files.pythonhosted.org/packages/8d/af/779ad72a4da0aed925e1139d458adc486e61076d7ecdcc09e610ea8678db/charset_normalizer-3.4.1-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:d9c3cdf5390dcd29aa8056d13e8e99526cda0305acc038b96b30352aff5ff2bb", size = 153629 },
+    { url = "https://files.pythonhosted.org/packages/c2/b6/7aa450b278e7aa92cf7732140bfd8be21f5f29d5bf334ae987c945276639/charset_normalizer-3.4.1-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:2bdfe3ac2e1bbe5b59a1a63721eb3b95fc9b6817ae4a46debbb4e11f6232428d", size = 150778 },
+    { url = "https://files.pythonhosted.org/packages/39/f4/d9f4f712d0951dcbfd42920d3db81b00dd23b6ab520419626f4023334056/charset_normalizer-3.4.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:eab677309cdb30d047996b36d34caeda1dc91149e4fdca0b1a039b3f79d9a807", size = 146453 },
+    { url = "https://files.pythonhosted.org/packages/49/2b/999d0314e4ee0cff3cb83e6bc9aeddd397eeed693edb4facb901eb8fbb69/charset_normalizer-3.4.1-cp310-cp310-win32.whl", hash = "sha256:c0429126cf75e16c4f0ad00ee0eae4242dc652290f940152ca8c75c3a4b6ee8f", size = 95479 },
+    { url = "https://files.pythonhosted.org/packages/2d/ce/3cbed41cff67e455a386fb5e5dd8906cdda2ed92fbc6297921f2e4419309/charset_normalizer-3.4.1-cp310-cp310-win_amd64.whl", hash = "sha256:9f0b8b1c6d84c8034a44893aba5e767bf9c7a211e313a9605d9c617d7083829f", size = 102790 },
+    { url = "https://files.pythonhosted.org/packages/72/80/41ef5d5a7935d2d3a773e3eaebf0a9350542f2cab4eac59a7a4741fbbbbe/charset_normalizer-3.4.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:8bfa33f4f2672964266e940dd22a195989ba31669bd84629f05fab3ef4e2d125", size = 194995 },
+    { url = "https://files.pythonhosted.org/packages/7a/28/0b9fefa7b8b080ec492110af6d88aa3dea91c464b17d53474b6e9ba5d2c5/charset_normalizer-3.4.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:28bf57629c75e810b6ae989f03c0828d64d6b26a5e205535585f96093e405ed1", size = 139471 },
+    { url = "https://files.pythonhosted.org/packages/71/64/d24ab1a997efb06402e3fc07317e94da358e2585165930d9d59ad45fcae2/charset_normalizer-3.4.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f08ff5e948271dc7e18a35641d2f11a4cd8dfd5634f55228b691e62b37125eb3", size = 149831 },
+    { url = "https://files.pythonhosted.org/packages/37/ed/be39e5258e198655240db5e19e0b11379163ad7070962d6b0c87ed2c4d39/charset_normalizer-3.4.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:234ac59ea147c59ee4da87a0c0f098e9c8d169f4dc2a159ef720f1a61bbe27cd", size = 142335 },
+    { url = "https://files.pythonhosted.org/packages/88/83/489e9504711fa05d8dde1574996408026bdbdbd938f23be67deebb5eca92/charset_normalizer-3.4.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd4ec41f914fa74ad1b8304bbc634b3de73d2a0889bd32076342a573e0779e00", size = 143862 },
+    { url = "https://files.pythonhosted.org/packages/c6/c7/32da20821cf387b759ad24627a9aca289d2822de929b8a41b6241767b461/charset_normalizer-3.4.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:eea6ee1db730b3483adf394ea72f808b6e18cf3cb6454b4d86e04fa8c4327a12", size = 145673 },
+    { url = "https://files.pythonhosted.org/packages/68/85/f4288e96039abdd5aeb5c546fa20a37b50da71b5cf01e75e87f16cd43304/charset_normalizer-3.4.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c96836c97b1238e9c9e3fe90844c947d5afbf4f4c92762679acfe19927d81d77", size = 140211 },
+    { url = "https://files.pythonhosted.org/packages/28/a3/a42e70d03cbdabc18997baf4f0227c73591a08041c149e710045c281f97b/charset_normalizer-3.4.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:4d86f7aff21ee58f26dcf5ae81a9addbd914115cdebcbb2217e4f0ed8982e146", size = 148039 },
+    { url = "https://files.pythonhosted.org/packages/85/e4/65699e8ab3014ecbe6f5c71d1a55d810fb716bbfd74f6283d5c2aa87febf/charset_normalizer-3.4.1-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:09b5e6733cbd160dcc09589227187e242a30a49ca5cefa5a7edd3f9d19ed53fd", size = 151939 },
+    { url = "https://files.pythonhosted.org/packages/b1/82/8e9fe624cc5374193de6860aba3ea8070f584c8565ee77c168ec13274bd2/charset_normalizer-3.4.1-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:5777ee0881f9499ed0f71cc82cf873d9a0ca8af166dfa0af8ec4e675b7df48e6", size = 149075 },
+    { url = "https://files.pythonhosted.org/packages/3d/7b/82865ba54c765560c8433f65e8acb9217cb839a9e32b42af4aa8e945870f/charset_normalizer-3.4.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:237bdbe6159cff53b4f24f397d43c6336c6b0b42affbe857970cefbb620911c8", size = 144340 },
+    { url = "https://files.pythonhosted.org/packages/b5/b6/9674a4b7d4d99a0d2df9b215da766ee682718f88055751e1e5e753c82db0/charset_normalizer-3.4.1-cp311-cp311-win32.whl", hash = "sha256:8417cb1f36cc0bc7eaba8ccb0e04d55f0ee52df06df3ad55259b9a323555fc8b", size = 95205 },
+    { url = "https://files.pythonhosted.org/packages/1e/ab/45b180e175de4402dcf7547e4fb617283bae54ce35c27930a6f35b6bef15/charset_normalizer-3.4.1-cp311-cp311-win_amd64.whl", hash = "sha256:d7f50a1f8c450f3925cb367d011448c39239bb3eb4117c36a6d354794de4ce76", size = 102441 },
+    { url = "https://files.pythonhosted.org/packages/0a/9a/dd1e1cdceb841925b7798369a09279bd1cf183cef0f9ddf15a3a6502ee45/charset_normalizer-3.4.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:73d94b58ec7fecbc7366247d3b0b10a21681004153238750bb67bd9012414545", size = 196105 },
+    { url = "https://files.pythonhosted.org/packages/d3/8c/90bfabf8c4809ecb648f39794cf2a84ff2e7d2a6cf159fe68d9a26160467/charset_normalizer-3.4.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dad3e487649f498dd991eeb901125411559b22e8d7ab25d3aeb1af367df5efd7", size = 140404 },
+    { url = "https://files.pythonhosted.org/packages/ad/8f/e410d57c721945ea3b4f1a04b74f70ce8fa800d393d72899f0a40526401f/charset_normalizer-3.4.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c30197aa96e8eed02200a83fba2657b4c3acd0f0aa4bdc9f6c1af8e8962e0757", size = 150423 },
+    { url = "https://files.pythonhosted.org/packages/f0/b8/e6825e25deb691ff98cf5c9072ee0605dc2acfca98af70c2d1b1bc75190d/charset_normalizer-3.4.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2369eea1ee4a7610a860d88f268eb39b95cb588acd7235e02fd5a5601773d4fa", size = 143184 },
+    { url = "https://files.pythonhosted.org/packages/3e/a2/513f6cbe752421f16d969e32f3583762bfd583848b763913ddab8d9bfd4f/charset_normalizer-3.4.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc2722592d8998c870fa4e290c2eec2c1569b87fe58618e67d38b4665dfa680d", size = 145268 },
+    { url = "https://files.pythonhosted.org/packages/74/94/8a5277664f27c3c438546f3eb53b33f5b19568eb7424736bdc440a88a31f/charset_normalizer-3.4.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ffc9202a29ab3920fa812879e95a9e78b2465fd10be7fcbd042899695d75e616", size = 147601 },
+    { url = "https://files.pythonhosted.org/packages/7c/5f/6d352c51ee763623a98e31194823518e09bfa48be2a7e8383cf691bbb3d0/charset_normalizer-3.4.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:804a4d582ba6e5b747c625bf1255e6b1507465494a40a2130978bda7b932c90b", size = 141098 },
+    { url = "https://files.pythonhosted.org/packages/78/d4/f5704cb629ba5ab16d1d3d741396aec6dc3ca2b67757c45b0599bb010478/charset_normalizer-3.4.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:0f55e69f030f7163dffe9fd0752b32f070566451afe180f99dbeeb81f511ad8d", size = 149520 },
+    { url = "https://files.pythonhosted.org/packages/c5/96/64120b1d02b81785f222b976c0fb79a35875457fa9bb40827678e54d1bc8/charset_normalizer-3.4.1-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:c4c3e6da02df6fa1410a7680bd3f63d4f710232d3139089536310d027950696a", size = 152852 },
+    { url = "https://files.pythonhosted.org/packages/84/c9/98e3732278a99f47d487fd3468bc60b882920cef29d1fa6ca460a1fdf4e6/charset_normalizer-3.4.1-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:5df196eb874dae23dcfb968c83d4f8fdccb333330fe1fc278ac5ceeb101003a9", size = 150488 },
+    { url = "https://files.pythonhosted.org/packages/13/0e/9c8d4cb99c98c1007cc11eda969ebfe837bbbd0acdb4736d228ccaabcd22/charset_normalizer-3.4.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e358e64305fe12299a08e08978f51fc21fac060dcfcddd95453eabe5b93ed0e1", size = 146192 },
+    { url = "https://files.pythonhosted.org/packages/b2/21/2b6b5b860781a0b49427309cb8670785aa543fb2178de875b87b9cc97746/charset_normalizer-3.4.1-cp312-cp312-win32.whl", hash = "sha256:9b23ca7ef998bc739bf6ffc077c2116917eabcc901f88da1b9856b210ef63f35", size = 95550 },
+    { url = "https://files.pythonhosted.org/packages/21/5b/1b390b03b1d16c7e382b561c5329f83cc06623916aab983e8ab9239c7d5c/charset_normalizer-3.4.1-cp312-cp312-win_amd64.whl", hash = "sha256:6ff8a4a60c227ad87030d76e99cd1698345d4491638dfa6673027c48b3cd395f", size = 102785 },
+    { url = "https://files.pythonhosted.org/packages/38/94/ce8e6f63d18049672c76d07d119304e1e2d7c6098f0841b51c666e9f44a0/charset_normalizer-3.4.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:aabfa34badd18f1da5ec1bc2715cadc8dca465868a4e73a0173466b688f29dda", size = 195698 },
+    { url = "https://files.pythonhosted.org/packages/24/2e/dfdd9770664aae179a96561cc6952ff08f9a8cd09a908f259a9dfa063568/charset_normalizer-3.4.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:22e14b5d70560b8dd51ec22863f370d1e595ac3d024cb8ad7d308b4cd95f8313", size = 140162 },
+    { url = "https://files.pythonhosted.org/packages/24/4e/f646b9093cff8fc86f2d60af2de4dc17c759de9d554f130b140ea4738ca6/charset_normalizer-3.4.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8436c508b408b82d87dc5f62496973a1805cd46727c34440b0d29d8a2f50a6c9", size = 150263 },
+    { url = "https://files.pythonhosted.org/packages/5e/67/2937f8d548c3ef6e2f9aab0f6e21001056f692d43282b165e7c56023e6dd/charset_normalizer-3.4.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2d074908e1aecee37a7635990b2c6d504cd4766c7bc9fc86d63f9c09af3fa11b", size = 142966 },
+    { url = "https://files.pythonhosted.org/packages/52/ed/b7f4f07de100bdb95c1756d3a4d17b90c1a3c53715c1a476f8738058e0fa/charset_normalizer-3.4.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:955f8851919303c92343d2f66165294848d57e9bba6cf6e3625485a70a038d11", size = 144992 },
+    { url = "https://files.pythonhosted.org/packages/96/2c/d49710a6dbcd3776265f4c923bb73ebe83933dfbaa841c5da850fe0fd20b/charset_normalizer-3.4.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:44ecbf16649486d4aebafeaa7ec4c9fed8b88101f4dd612dcaf65d5e815f837f", size = 147162 },
+    { url = "https://files.pythonhosted.org/packages/b4/41/35ff1f9a6bd380303dea55e44c4933b4cc3c4850988927d4082ada230273/charset_normalizer-3.4.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:0924e81d3d5e70f8126529951dac65c1010cdf117bb75eb02dd12339b57749dd", size = 140972 },
+    { url = "https://files.pythonhosted.org/packages/fb/43/c6a0b685fe6910d08ba971f62cd9c3e862a85770395ba5d9cad4fede33ab/charset_normalizer-3.4.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:2967f74ad52c3b98de4c3b32e1a44e32975e008a9cd2a8cc8966d6a5218c5cb2", size = 149095 },
+    { url = "https://files.pythonhosted.org/packages/4c/ff/a9a504662452e2d2878512115638966e75633519ec11f25fca3d2049a94a/charset_normalizer-3.4.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:c75cb2a3e389853835e84a2d8fb2b81a10645b503eca9bcb98df6b5a43eb8886", size = 152668 },
+    { url = "https://files.pythonhosted.org/packages/6c/71/189996b6d9a4b932564701628af5cee6716733e9165af1d5e1b285c530ed/charset_normalizer-3.4.1-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:09b26ae6b1abf0d27570633b2b078a2a20419c99d66fb2823173d73f188ce601", size = 150073 },
+    { url = "https://files.pythonhosted.org/packages/e4/93/946a86ce20790e11312c87c75ba68d5f6ad2208cfb52b2d6a2c32840d922/charset_normalizer-3.4.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:fa88b843d6e211393a37219e6a1c1df99d35e8fd90446f1118f4216e307e48cd", size = 145732 },
+    { url = "https://files.pythonhosted.org/packages/cd/e5/131d2fb1b0dddafc37be4f3a2fa79aa4c037368be9423061dccadfd90091/charset_normalizer-3.4.1-cp313-cp313-win32.whl", hash = "sha256:eb8178fe3dba6450a3e024e95ac49ed3400e506fd4e9e5c32d30adda88cbd407", size = 95391 },
+    { url = "https://files.pythonhosted.org/packages/27/f2/4f9a69cc7712b9b5ad8fdb87039fd89abba997ad5cbe690d1835d40405b0/charset_normalizer-3.4.1-cp313-cp313-win_amd64.whl", hash = "sha256:b1ac5992a838106edb89654e0aebfc24f5848ae2547d22c2c3f66454daa11971", size = 102702 },
+    { url = "https://files.pythonhosted.org/packages/0e/f6/65ecc6878a89bb1c23a086ea335ad4bf21a588990c3f535a227b9eea9108/charset_normalizer-3.4.1-py3-none-any.whl", hash = "sha256:d98b1668f06378c6dbefec3b92299716b931cd4e6061f3c875a71ced1780ab85", size = 49767 },
+]
+
+[[package]]
+name = "click"
+version = "8.1.8"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/d4/7ebdbd03970677812aac39c869717059dbb71a4cfc033ca6e5221787892c/click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2", size = 98188 },
+]
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
+]
+
+[[package]]
+name = "comm"
+version = "0.2.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e9/a8/fb783cb0abe2b5fded9f55e5703015cdf1c9c85b3669087c538dd15a6a86/comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e", size = 6210 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e6/75/49e5bfe642f71f272236b5b2d2691cf915a7283cc0ceda56357b61daa538/comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3", size = 7180 },
+]
+
+[[package]]
+name = "contourpy"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/25/c2/fc7193cc5383637ff390a712e88e4ded0452c9fbcf84abe3de5ea3df1866/contourpy-1.3.1.tar.gz", hash = "sha256:dfd97abd83335045a913e3bcc4a09c0ceadbe66580cf573fe961f4a825efa699", size = 13465753 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b2/a3/80937fe3efe0edacf67c9a20b955139a1a622730042c1ea991956f2704ad/contourpy-1.3.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a045f341a77b77e1c5de31e74e966537bba9f3c4099b35bf4c2e3939dd54cdab", size = 268466 },
+    { url = "https://files.pythonhosted.org/packages/82/1d/e3eaebb4aa2d7311528c048350ca8e99cdacfafd99da87bc0a5f8d81f2c2/contourpy-1.3.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:500360b77259914f7805af7462e41f9cb7ca92ad38e9f94d6c8641b089338124", size = 253314 },
+    { url = "https://files.pythonhosted.org/packages/de/f3/d796b22d1a2b587acc8100ba8c07fb7b5e17fde265a7bb05ab967f4c935a/contourpy-1.3.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b2f926efda994cdf3c8d3fdb40b9962f86edbc4457e739277b961eced3d0b4c1", size = 312003 },
+    { url = "https://files.pythonhosted.org/packages/bf/f5/0e67902bc4394daee8daa39c81d4f00b50e063ee1a46cb3938cc65585d36/contourpy-1.3.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:adce39d67c0edf383647a3a007de0a45fd1b08dedaa5318404f1a73059c2512b", size = 351896 },
+    { url = "https://files.pythonhosted.org/packages/1f/d6/e766395723f6256d45d6e67c13bb638dd1fa9dc10ef912dc7dd3dcfc19de/contourpy-1.3.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:abbb49fb7dac584e5abc6636b7b2a7227111c4f771005853e7d25176daaf8453", size = 320814 },
+    { url = "https://files.pythonhosted.org/packages/a9/57/86c500d63b3e26e5b73a28b8291a67c5608d4aa87ebd17bd15bb33c178bc/contourpy-1.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a0cffcbede75c059f535725c1680dfb17b6ba8753f0c74b14e6a9c68c29d7ea3", size = 324969 },
+    { url = "https://files.pythonhosted.org/packages/b8/62/bb146d1289d6b3450bccc4642e7f4413b92ebffd9bf2e91b0404323704a7/contourpy-1.3.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:ab29962927945d89d9b293eabd0d59aea28d887d4f3be6c22deaefbb938a7277", size = 1265162 },
+    { url = "https://files.pythonhosted.org/packages/18/04/9f7d132ce49a212c8e767042cc80ae390f728060d2eea47058f55b9eff1c/contourpy-1.3.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:974d8145f8ca354498005b5b981165b74a195abfae9a8129df3e56771961d595", size = 1324328 },
+    { url = "https://files.pythonhosted.org/packages/46/23/196813901be3f97c83ababdab1382e13e0edc0bb4e7b49a7bff15fcf754e/contourpy-1.3.1-cp310-cp310-win32.whl", hash = "sha256:ac4578ac281983f63b400f7fe6c101bedc10651650eef012be1ccffcbacf3697", size = 173861 },
+    { url = "https://files.pythonhosted.org/packages/e0/82/c372be3fc000a3b2005061ca623a0d1ecd2eaafb10d9e883a2fc8566e951/contourpy-1.3.1-cp310-cp310-win_amd64.whl", hash = "sha256:174e758c66bbc1c8576992cec9599ce8b6672b741b5d336b5c74e35ac382b18e", size = 218566 },
+    { url = "https://files.pythonhosted.org/packages/12/bb/11250d2906ee2e8b466b5f93e6b19d525f3e0254ac8b445b56e618527718/contourpy-1.3.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3e8b974d8db2c5610fb4e76307e265de0edb655ae8169e8b21f41807ccbeec4b", size = 269555 },
+    { url = "https://files.pythonhosted.org/packages/67/71/1e6e95aee21a500415f5d2dbf037bf4567529b6a4e986594d7026ec5ae90/contourpy-1.3.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:20914c8c973f41456337652a6eeca26d2148aa96dd7ac323b74516988bea89fc", size = 254549 },
+    { url = "https://files.pythonhosted.org/packages/31/2c/b88986e8d79ac45efe9d8801ae341525f38e087449b6c2f2e6050468a42c/contourpy-1.3.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:19d40d37c1c3a4961b4619dd9d77b12124a453cc3d02bb31a07d58ef684d3d86", size = 313000 },
+    { url = "https://files.pythonhosted.org/packages/c4/18/65280989b151fcf33a8352f992eff71e61b968bef7432fbfde3a364f0730/contourpy-1.3.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:113231fe3825ebf6f15eaa8bc1f5b0ddc19d42b733345eae0934cb291beb88b6", size = 352925 },
+    { url = "https://files.pythonhosted.org/packages/f5/c7/5fd0146c93220dbfe1a2e0f98969293b86ca9bc041d6c90c0e065f4619ad/contourpy-1.3.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4dbbc03a40f916a8420e420d63e96a1258d3d1b58cbdfd8d1f07b49fcbd38e85", size = 323693 },
+    { url = "https://files.pythonhosted.org/packages/85/fc/7fa5d17daf77306840a4e84668a48ddff09e6bc09ba4e37e85ffc8e4faa3/contourpy-1.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a04ecd68acbd77fa2d39723ceca4c3197cb2969633836ced1bea14e219d077c", size = 326184 },
+    { url = "https://files.pythonhosted.org/packages/ef/e7/104065c8270c7397c9571620d3ab880558957216f2b5ebb7e040f85eeb22/contourpy-1.3.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c414fc1ed8ee1dbd5da626cf3710c6013d3d27456651d156711fa24f24bd1291", size = 1268031 },
+    { url = "https://files.pythonhosted.org/packages/e2/4a/c788d0bdbf32c8113c2354493ed291f924d4793c4a2e85b69e737a21a658/contourpy-1.3.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:31c1b55c1f34f80557d3830d3dd93ba722ce7e33a0b472cba0ec3b6535684d8f", size = 1325995 },
+    { url = "https://files.pythonhosted.org/packages/a6/e6/a2f351a90d955f8b0564caf1ebe4b1451a3f01f83e5e3a414055a5b8bccb/contourpy-1.3.1-cp311-cp311-win32.whl", hash = "sha256:f611e628ef06670df83fce17805c344710ca5cde01edfdc72751311da8585375", size = 174396 },
+    { url = "https://files.pythonhosted.org/packages/a8/7e/cd93cab453720a5d6cb75588cc17dcdc08fc3484b9de98b885924ff61900/contourpy-1.3.1-cp311-cp311-win_amd64.whl", hash = "sha256:b2bdca22a27e35f16794cf585832e542123296b4687f9fd96822db6bae17bfc9", size = 219787 },
+    { url = "https://files.pythonhosted.org/packages/37/6b/175f60227d3e7f5f1549fcb374592be311293132207e451c3d7c654c25fb/contourpy-1.3.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:0ffa84be8e0bd33410b17189f7164c3589c229ce5db85798076a3fa136d0e509", size = 271494 },
+    { url = "https://files.pythonhosted.org/packages/6b/6a/7833cfae2c1e63d1d8875a50fd23371394f540ce809d7383550681a1fa64/contourpy-1.3.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:805617228ba7e2cbbfb6c503858e626ab528ac2a32a04a2fe88ffaf6b02c32bc", size = 255444 },
+    { url = "https://files.pythonhosted.org/packages/7f/b3/7859efce66eaca5c14ba7619791b084ed02d868d76b928ff56890d2d059d/contourpy-1.3.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ade08d343436a94e633db932e7e8407fe7de8083967962b46bdfc1b0ced39454", size = 307628 },
+    { url = "https://files.pythonhosted.org/packages/48/b2/011415f5e3f0a50b1e285a0bf78eb5d92a4df000553570f0851b6e309076/contourpy-1.3.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:47734d7073fb4590b4a40122b35917cd77be5722d80683b249dac1de266aac80", size = 347271 },
+    { url = "https://files.pythonhosted.org/packages/84/7d/ef19b1db0f45b151ac78c65127235239a8cf21a59d1ce8507ce03e89a30b/contourpy-1.3.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2ba94a401342fc0f8b948e57d977557fbf4d515f03c67682dd5c6191cb2d16ec", size = 318906 },
+    { url = "https://files.pythonhosted.org/packages/ba/99/6794142b90b853a9155316c8f470d2e4821fe6f086b03e372aca848227dd/contourpy-1.3.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:efa874e87e4a647fd2e4f514d5e91c7d493697127beb95e77d2f7561f6905bd9", size = 323622 },
+    { url = "https://files.pythonhosted.org/packages/3c/0f/37d2c84a900cd8eb54e105f4fa9aebd275e14e266736778bb5dccbf3bbbb/contourpy-1.3.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1bf98051f1045b15c87868dbaea84f92408337d4f81d0e449ee41920ea121d3b", size = 1266699 },
+    { url = "https://files.pythonhosted.org/packages/3a/8a/deb5e11dc7d9cc8f0f9c8b29d4f062203f3af230ba83c30a6b161a6effc9/contourpy-1.3.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:61332c87493b00091423e747ea78200659dc09bdf7fd69edd5e98cef5d3e9a8d", size = 1326395 },
+    { url = "https://files.pythonhosted.org/packages/1a/35/7e267ae7c13aaf12322ccc493531f1e7f2eb8fba2927b9d7a05ff615df7a/contourpy-1.3.1-cp312-cp312-win32.whl", hash = "sha256:e914a8cb05ce5c809dd0fe350cfbb4e881bde5e2a38dc04e3afe1b3e58bd158e", size = 175354 },
+    { url = "https://files.pythonhosted.org/packages/a1/35/c2de8823211d07e8a79ab018ef03960716c5dff6f4d5bff5af87fd682992/contourpy-1.3.1-cp312-cp312-win_amd64.whl", hash = "sha256:08d9d449a61cf53033612cb368f3a1b26cd7835d9b8cd326647efe43bca7568d", size = 220971 },
+    { url = "https://files.pythonhosted.org/packages/9a/e7/de62050dce687c5e96f946a93546910bc67e483fe05324439e329ff36105/contourpy-1.3.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:a761d9ccfc5e2ecd1bf05534eda382aa14c3e4f9205ba5b1684ecfe400716ef2", size = 271548 },
+    { url = "https://files.pythonhosted.org/packages/78/4d/c2a09ae014ae984c6bdd29c11e74d3121b25eaa117eca0bb76340efd7e1c/contourpy-1.3.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:523a8ee12edfa36f6d2a49407f705a6ef4c5098de4f498619787e272de93f2d5", size = 255576 },
+    { url = "https://files.pythonhosted.org/packages/ab/8a/915380ee96a5638bda80cd061ccb8e666bfdccea38d5741cb69e6dbd61fc/contourpy-1.3.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ece6df05e2c41bd46776fbc712e0996f7c94e0d0543af1656956d150c4ca7c81", size = 306635 },
+    { url = "https://files.pythonhosted.org/packages/29/5c/c83ce09375428298acd4e6582aeb68b1e0d1447f877fa993d9bf6cd3b0a0/contourpy-1.3.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:573abb30e0e05bf31ed067d2f82500ecfdaec15627a59d63ea2d95714790f5c2", size = 345925 },
+    { url = "https://files.pythonhosted.org/packages/29/63/5b52f4a15e80c66c8078a641a3bfacd6e07106835682454647aca1afc852/contourpy-1.3.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a9fa36448e6a3a1a9a2ba23c02012c43ed88905ec80163f2ffe2421c7192a5d7", size = 318000 },
+    { url = "https://files.pythonhosted.org/packages/9a/e2/30ca086c692691129849198659bf0556d72a757fe2769eb9620a27169296/contourpy-1.3.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3ea9924d28fc5586bf0b42d15f590b10c224117e74409dd7a0be3b62b74a501c", size = 322689 },
+    { url = "https://files.pythonhosted.org/packages/6b/77/f37812ef700f1f185d348394debf33f22d531e714cf6a35d13d68a7003c7/contourpy-1.3.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5b75aa69cb4d6f137b36f7eb2ace9280cfb60c55dc5f61c731fdf6f037f958a3", size = 1268413 },
+    { url = "https://files.pythonhosted.org/packages/3f/6d/ce84e79cdd128542ebeb268f84abb4b093af78e7f8ec504676673d2675bc/contourpy-1.3.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:041b640d4ec01922083645a94bb3b2e777e6b626788f4095cf21abbe266413c1", size = 1326530 },
+    { url = "https://files.pythonhosted.org/packages/72/22/8282f4eae20c73c89bee7a82a19c4e27af9b57bb602ecaa00713d5bdb54d/contourpy-1.3.1-cp313-cp313-win32.whl", hash = "sha256:36987a15e8ace5f58d4d5da9dca82d498c2bbb28dff6e5d04fbfcc35a9cb3a82", size = 175315 },
+    { url = "https://files.pythonhosted.org/packages/e3/d5/28bca491f65312b438fbf076589dcde7f6f966b196d900777f5811b9c4e2/contourpy-1.3.1-cp313-cp313-win_amd64.whl", hash = "sha256:a7895f46d47671fa7ceec40f31fae721da51ad34bdca0bee83e38870b1f47ffd", size = 220987 },
+    { url = "https://files.pythonhosted.org/packages/2f/24/a4b285d6adaaf9746e4700932f579f1a7b6f9681109f694cfa233ae75c4e/contourpy-1.3.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:9ddeb796389dadcd884c7eb07bd14ef12408aaae358f0e2ae24114d797eede30", size = 285001 },
+    { url = "https://files.pythonhosted.org/packages/48/1d/fb49a401b5ca4f06ccf467cd6c4f1fd65767e63c21322b29b04ec40b40b9/contourpy-1.3.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:19c1555a6801c2f084c7ddc1c6e11f02eb6a6016ca1318dd5452ba3f613a1751", size = 268553 },
+    { url = "https://files.pythonhosted.org/packages/79/1e/4aef9470d13fd029087388fae750dccb49a50c012a6c8d1d634295caa644/contourpy-1.3.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:841ad858cff65c2c04bf93875e384ccb82b654574a6d7f30453a04f04af71342", size = 310386 },
+    { url = "https://files.pythonhosted.org/packages/b0/34/910dc706ed70153b60392b5305c708c9810d425bde12499c9184a1100888/contourpy-1.3.1-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4318af1c925fb9a4fb190559ef3eec206845f63e80fb603d47f2d6d67683901c", size = 349806 },
+    { url = "https://files.pythonhosted.org/packages/31/3c/faee6a40d66d7f2a87f7102236bf4780c57990dd7f98e5ff29881b1b1344/contourpy-1.3.1-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:14c102b0eab282427b662cb590f2e9340a9d91a1c297f48729431f2dcd16e14f", size = 321108 },
+    { url = "https://files.pythonhosted.org/packages/17/69/390dc9b20dd4bb20585651d7316cc3054b7d4a7b4f8b710b2b698e08968d/contourpy-1.3.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:05e806338bfeaa006acbdeba0ad681a10be63b26e1b17317bfac3c5d98f36cda", size = 327291 },
+    { url = "https://files.pythonhosted.org/packages/ef/74/7030b67c4e941fe1e5424a3d988080e83568030ce0355f7c9fc556455b01/contourpy-1.3.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4d76d5993a34ef3df5181ba3c92fabb93f1eaa5729504fb03423fcd9f3177242", size = 1263752 },
+    { url = "https://files.pythonhosted.org/packages/f0/ed/92d86f183a8615f13f6b9cbfc5d4298a509d6ce433432e21da838b4b63f4/contourpy-1.3.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:89785bb2a1980c1bd87f0cb1517a71cde374776a5f150936b82580ae6ead44a1", size = 1318403 },
+    { url = "https://files.pythonhosted.org/packages/b3/0e/c8e4950c77dcfc897c71d61e56690a0a9df39543d2164040301b5df8e67b/contourpy-1.3.1-cp313-cp313t-win32.whl", hash = "sha256:8eb96e79b9f3dcadbad2a3891672f81cdcab7f95b27f28f1c67d75f045b6b4f1", size = 185117 },
+    { url = "https://files.pythonhosted.org/packages/c1/31/1ae946f11dfbd229222e6d6ad8e7bd1891d3d48bde5fbf7a0beb9491f8e3/contourpy-1.3.1-cp313-cp313t-win_amd64.whl", hash = "sha256:287ccc248c9e0d0566934e7d606201abd74761b5703d804ff3df8935f523d546", size = 236668 },
+    { url = "https://files.pythonhosted.org/packages/3e/4f/e56862e64b52b55b5ddcff4090085521fc228ceb09a88390a2b103dccd1b/contourpy-1.3.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:b457d6430833cee8e4b8e9b6f07aa1c161e5e0d52e118dc102c8f9bd7dd060d6", size = 265605 },
+    { url = "https://files.pythonhosted.org/packages/b0/2e/52bfeeaa4541889f23d8eadc6386b442ee2470bd3cff9baa67deb2dd5c57/contourpy-1.3.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb76c1a154b83991a3cbbf0dfeb26ec2833ad56f95540b442c73950af2013750", size = 315040 },
+    { url = "https://files.pythonhosted.org/packages/52/94/86bfae441707205634d80392e873295652fc313dfd93c233c52c4dc07874/contourpy-1.3.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:44a29502ca9c7b5ba389e620d44f2fbe792b1fb5734e8b931ad307071ec58c53", size = 218221 },
+]
+
+[[package]]
+name = "coverage"
+version = "7.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/02/36/465f5492443265e1278f9a82ffe6aeed3f1db779da0d6e7d4611a5cfb6af/coverage-7.7.0.tar.gz", hash = "sha256:cd879d4646055a573775a1cec863d00c9ff8c55860f8b17f6d8eee9140c06166", size = 809969 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/10/f5/2b801fe88f199707cf9ec66dcee036e7073b5a208a4a161b64371b3f1e35/coverage-7.7.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a538a23119d1e2e2ce077e902d02ea3d8e0641786ef6e0faf11ce82324743944", size = 210608 },
+    { url = "https://files.pythonhosted.org/packages/07/44/bcc030cf977d1069a28742c0a67284c6e831ef172f914055b3d45da83f89/coverage-7.7.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1586ad158523f4133499a4f322b230e2cfef9cc724820dbd58595a5a236186f4", size = 211042 },
+    { url = "https://files.pythonhosted.org/packages/2c/3f/b427f17e1bcf3e1f5ac42fc0b6cb623616f2aedcfc7fde17a058afb62568/coverage-7.7.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7b6c96d69928a3a6767fab8dc1ce8a02cf0156836ccb1e820c7f45a423570d98", size = 240168 },
+    { url = "https://files.pythonhosted.org/packages/58/92/6e8d71c5e651f152ffc518ec4cd7add87035533e88af29e233635c0f0dfb/coverage-7.7.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7f18d47641282664276977c604b5a261e51fefc2980f5271d547d706b06a837f", size = 238079 },
+    { url = "https://files.pythonhosted.org/packages/40/33/1c25ae35c16972dc379c24cd7dde20359d076dee00687825c92a53e43b02/coverage-7.7.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f2a1e18a85bd066c7c556d85277a7adf4651f259b2579113844835ba1a74aafd", size = 239216 },
+    { url = "https://files.pythonhosted.org/packages/4d/3d/adf40bdd07a49e1880632c1bc6b31f42d32cf0bfe6b4d294a8f706d70078/coverage-7.7.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:70f0925c4e2bfc965369f417e7cc72538fd1ba91639cf1e4ef4b1a6b50439b3b", size = 239126 },
+    { url = "https://files.pythonhosted.org/packages/72/a5/51e39811cd0ec0569a25fe8e6bac0a00efa222a3e49d51d64f5ba0dce24a/coverage-7.7.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:b0fac2088ec4aaeb5468b814bd3ff5e5978364bfbce5e567c44c9e2854469f6c", size = 237842 },
+    { url = "https://files.pythonhosted.org/packages/ab/b7/c5796906cd9eed6d258138f1fddc8d6af01b6d07b3c183bac4a9731ac383/coverage-7.7.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:b3e212a894d8ae07fde2ca8b43d666a6d49bbbddb10da0f6a74ca7bd31f20054", size = 238136 },
+    { url = "https://files.pythonhosted.org/packages/d7/8a/bd34ea3c602b3ef323a001d375f9b1d663e901079bb26b5f9b8f96fae32b/coverage-7.7.0-cp310-cp310-win32.whl", hash = "sha256:f32b165bf6dfea0846a9c9c38b7e1d68f313956d60a15cde5d1709fddcaf3bee", size = 213320 },
+    { url = "https://files.pythonhosted.org/packages/94/60/6e7efe849e305a233623a80aaeba7ebb02809fa63ab8a1e49c4323b8083b/coverage-7.7.0-cp310-cp310-win_amd64.whl", hash = "sha256:a2454b12a3f12cc4698f3508912e6225ec63682e2ca5a96f80a2b93cef9e63f3", size = 214219 },
+    { url = "https://files.pythonhosted.org/packages/e8/ec/9e0c9358a3bd56b1ddbf266b889ea9d51ee29e58fb72712d5600663fa806/coverage-7.7.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a0a207c87a9f743c8072d059b4711f8d13c456eb42dac778a7d2e5d4f3c253a7", size = 210722 },
+    { url = "https://files.pythonhosted.org/packages/be/bd/7b47a4302423a13960ee30682900d7ca20cee15c978b1d9ea9594d59d352/coverage-7.7.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2d673e3add00048215c2cc507f1228a7523fd8bf34f279ac98334c9b07bd2656", size = 211154 },
+    { url = "https://files.pythonhosted.org/packages/c6/7c/ae54d9022440196bf9f3fad535388678a3db186980ff58a4956ddeb849a2/coverage-7.7.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f81fe93dc1b8e5673f33443c0786c14b77e36f1025973b85e07c70353e46882b", size = 243787 },
+    { url = "https://files.pythonhosted.org/packages/2d/21/913a2a2d89a2221f4410fbea4ff84e64ddf4367a4b9eb2c328bd01a1a401/coverage-7.7.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d8c7524779003d59948c51b4fcbf1ca4e27c26a7d75984f63488f3625c328b9b", size = 241473 },
+    { url = "https://files.pythonhosted.org/packages/40/f1/5ae36fffd542fb86ab3b2d5e012af0840265f3dd001ad0ffabe9e4dbdcf6/coverage-7.7.0-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4c124025430249118d018dcedc8b7426f39373527c845093132196f2a483b6dd", size = 243259 },
+    { url = "https://files.pythonhosted.org/packages/47/1b/abc87bad7f606a4df321bd8300413fe13700099a163e7d63453c7c70c1b2/coverage-7.7.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:e7f559c36d5cdc448ee13e7e56ed7b6b5d44a40a511d584d388a0f5d940977ba", size = 242904 },
+    { url = "https://files.pythonhosted.org/packages/e0/b3/ff0cf15f5709996727dda2fa00af6f4da92ea3e16168400346f2f742341a/coverage-7.7.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:37cbc7b0d93dfd133e33c7ec01123fbb90401dce174c3b6661d8d36fb1e30608", size = 241079 },
+    { url = "https://files.pythonhosted.org/packages/05/c9/fcad82aad05b1eb8040e6c25ae7a1303716cc05718d4dd326e0fab31aa14/coverage-7.7.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:7d2a65876274acf544703e943c010b60bd79404e3623a1e5d52b64a6e2728de5", size = 241617 },
+    { url = "https://files.pythonhosted.org/packages/59/9f/d1efe149afa5c3a459c08bf04f7e6917ef4ee8e3440df5c3e87d6b972870/coverage-7.7.0-cp311-cp311-win32.whl", hash = "sha256:f5a2f71d6a91238e7628f23538c26aa464d390cbdedf12ee2a7a0fb92a24482a", size = 213372 },
+    { url = "https://files.pythonhosted.org/packages/88/d2/4b58f03e399185b01fb3168d4b870882de9c7a10e273f99c8f25ec690302/coverage-7.7.0-cp311-cp311-win_amd64.whl", hash = "sha256:ae8006772c6b0fa53c33747913473e064985dac4d65f77fd2fdc6474e7cd54e4", size = 214285 },
+    { url = "https://files.pythonhosted.org/packages/b7/47/f7b870caa26082ff8033be074ac61dc175a6b0c965adf7b910f92a6d7cfe/coverage-7.7.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:056d3017ed67e7ddf266e6f57378ece543755a4c9231e997789ab3bd11392c94", size = 210907 },
+    { url = "https://files.pythonhosted.org/packages/ea/eb/40b39bdc6c1da403257f0fcb2c1b2fd81ff9f66c13abbe3862f42780e1c1/coverage-7.7.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:33c1394d8407e2771547583b66a85d07ed441ff8fae5a4adb4237ad39ece60db", size = 211162 },
+    { url = "https://files.pythonhosted.org/packages/53/08/42a2db41b4646d6261122773e222dd7105e2306526f2d7846de6fee808ec/coverage-7.7.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4fbb7a0c3c21908520149d7751cf5b74eb9b38b54d62997b1e9b3ac19a8ee2fe", size = 245223 },
+    { url = "https://files.pythonhosted.org/packages/78/2a/0ceb328a7e67e8639d5c7800b8161d4b5f489073ac8d5ac33b11eadee218/coverage-7.7.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bb356e7ae7c2da13f404bf8f75be90f743c6df8d4607022e759f5d7d89fe83f8", size = 242114 },
+    { url = "https://files.pythonhosted.org/packages/ba/68/42b13b849d40af1581830ff06c60f4ec84649764f4a58d5c6e20ae11cbd4/coverage-7.7.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bce730d484038e97f27ea2dbe5d392ec5c2261f28c319a3bb266f6b213650135", size = 244371 },
+    { url = "https://files.pythonhosted.org/packages/68/66/ab7c3b9fdbeb8bdd322f5b67b1886463834dba2014a534caba60fb0075ea/coverage-7.7.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:aa4dff57fc21a575672176d5ab0ef15a927199e775c5e8a3d75162ab2b0c7705", size = 244134 },
+    { url = "https://files.pythonhosted.org/packages/01/74/b833d299a479681957d6b238e16a0725586e1d56ec1e43658f3184550bb0/coverage-7.7.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:b667b91f4f714b17af2a18e220015c941d1cf8b07c17f2160033dbe1e64149f0", size = 242353 },
+    { url = "https://files.pythonhosted.org/packages/f9/c5/0ed656d65da39bbab8e8fc367dc3d465a7501fea0f2b1caccfb4f6361c9f/coverage-7.7.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:693d921621a0c8043bfdc61f7d4df5ea6d22165fe8b807cac21eb80dd94e4bbd", size = 243543 },
+    { url = "https://files.pythonhosted.org/packages/87/b5/142bcff3828e4cce5d4c9ddc9222de1664464263acca09638e4eb0dbda7c/coverage-7.7.0-cp312-cp312-win32.whl", hash = "sha256:52fc89602cde411a4196c8c6894afb384f2125f34c031774f82a4f2608c59d7d", size = 213543 },
+    { url = "https://files.pythonhosted.org/packages/29/74/99d226985def03284bad6a9aff27a1079a8881ec7523b5980b00a5260527/coverage-7.7.0-cp312-cp312-win_amd64.whl", hash = "sha256:0ce8cf59e09d31a4915ff4c3b94c6514af4c84b22c4cc8ad7c3c546a86150a92", size = 214344 },
+    { url = "https://files.pythonhosted.org/packages/45/2f/df6235ec963b9eb6b6b2f3c24f70448f1ffa13b9a481c155a6caff176395/coverage-7.7.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:4545485fef7a8a2d8f30e6f79ce719eb154aab7e44217eb444c1d38239af2072", size = 210934 },
+    { url = "https://files.pythonhosted.org/packages/f3/85/ff19510bf642e334845318ddb73a550d2b17082831fa9ae053ce72288be7/coverage-7.7.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1393e5aa9441dafb0162c36c8506c648b89aea9565b31f6bfa351e66c11bcd82", size = 211212 },
+    { url = "https://files.pythonhosted.org/packages/2d/6a/af6582a419550d35eacc3e1bf9f4a936dda0ae559632a0bc4e3aef694ac8/coverage-7.7.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:316f29cc3392fa3912493ee4c83afa4a0e2db04ff69600711f8c03997c39baaa", size = 244727 },
+    { url = "https://files.pythonhosted.org/packages/55/62/7c49526111c91f3d7d27e111c22c8d08722f5b661c3f031b625b4d7bc4d9/coverage-7.7.0-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e1ffde1d6bc2a92f9c9207d1ad808550873748ac2d4d923c815b866baa343b3f", size = 241768 },
+    { url = "https://files.pythonhosted.org/packages/62/4b/2dc27700782be9795cbbbe98394dd19ef74815d78d5027ed894972cd1b4a/coverage-7.7.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:416e2a8845eaff288f97eaf76ab40367deafb9073ffc47bf2a583f26b05e5265", size = 243790 },
+    { url = "https://files.pythonhosted.org/packages/d3/11/9cc1ae56d3015edca69437f3121c2b44de309f6828980b29e4cc9b13246d/coverage-7.7.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5efdeff5f353ed3352c04e6b318ab05c6ce9249c25ed3c2090c6e9cadda1e3b2", size = 243861 },
+    { url = "https://files.pythonhosted.org/packages/db/e4/2398ed93edcf42ff43002d91c37be11514d825cec382606654fd44f4b8fa/coverage-7.7.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:57f3bd0d29bf2bd9325c0ff9cc532a175110c4bf8f412c05b2405fd35745266d", size = 241942 },
+    { url = "https://files.pythonhosted.org/packages/ec/fe/b6bd35b17a2b8d26bdb21d5ea4351a837ec01edf552655e833629af05b90/coverage-7.7.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:3ab7090f04b12dc6469882ce81244572779d3a4b67eea1c96fb9ecc8c607ef39", size = 243228 },
+    { url = "https://files.pythonhosted.org/packages/6d/06/d8701bae1e5d865edeb00a6c2a71bd7659ca6af349789271c6fd16a57909/coverage-7.7.0-cp313-cp313-win32.whl", hash = "sha256:180e3fc68ee4dc5af8b33b6ca4e3bb8aa1abe25eedcb958ba5cff7123071af68", size = 213572 },
+    { url = "https://files.pythonhosted.org/packages/d7/c1/7e67780bfcaed6bed20100c9e1b2645e3414577b4bdad169578325249045/coverage-7.7.0-cp313-cp313-win_amd64.whl", hash = "sha256:55143aa13c49491f5606f05b49ed88663446dce3a4d3c5d77baa4e36a16d3573", size = 214372 },
+    { url = "https://files.pythonhosted.org/packages/ed/25/50b0447442a415ad3da33093c589d9ef945dd6933225f1ce0ac97476397e/coverage-7.7.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:cc41374d2f27d81d6558f8a24e5c114580ffefc197fd43eabd7058182f743322", size = 211774 },
+    { url = "https://files.pythonhosted.org/packages/13/cc/3daddc707e934d3c0aafaa4a9b217f53fcf4133d4e40cc6ae63aa51243b8/coverage-7.7.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:89078312f06237417adda7c021c33f80f7a6d2db8572a5f6c330d89b080061ce", size = 211995 },
+    { url = "https://files.pythonhosted.org/packages/98/99/c92f43355d3d67f6bf8c946a350f2174e18f9ea7c8a1e36c9eb84ab7d20b/coverage-7.7.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5b2f144444879363ea8834cd7b6869d79ac796cb8f864b0cfdde50296cd95816", size = 256226 },
+    { url = "https://files.pythonhosted.org/packages/25/62/65f0f33c08e0a1632f1e487b9c2d252e8bad6a77a942836043972b0ba6d2/coverage-7.7.0-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:60e6347d1ed882b1159ffea172cb8466ee46c665af4ca397edbf10ff53e9ffaf", size = 251937 },
+    { url = "https://files.pythonhosted.org/packages/b2/10/99a9565aaeb159aade178c6509c8324a9c9e825b01f02242a37c2a8869f8/coverage-7.7.0-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb203c0afffaf1a8f5b9659a013f8f16a1b2cad3a80a8733ceedc968c0cf4c57", size = 254276 },
+    { url = "https://files.pythonhosted.org/packages/a7/12/206196edbf0b82250b11bf5c252fe25ebaa2b7c8d66edb0c194e7b3403fe/coverage-7.7.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:ad0edaa97cb983d9f2ff48cadddc3e1fb09f24aa558abeb4dc9a0dbacd12cbb4", size = 255366 },
+    { url = "https://files.pythonhosted.org/packages/a5/82/a2abb8d4cdd99c6a443ab6682c0eee5797490a2113a45ffaa8b6b31c5dcc/coverage-7.7.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:c5f8a5364fc37b2f172c26a038bc7ec4885f429de4a05fc10fdcb53fb5834c5c", size = 253536 },
+    { url = "https://files.pythonhosted.org/packages/4d/7d/3747e000e60ad5dd8157bd978f99979967d56cb35c55235980c85305db86/coverage-7.7.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:c4e09534037933bf6eb31d804e72c52ec23219b32c1730f9152feabbd7499463", size = 254344 },
+    { url = "https://files.pythonhosted.org/packages/45/56/7c33f8a6de1b3b079374d2ae490ccf76fb7c094a23f72d10f071989fc3ef/coverage-7.7.0-cp313-cp313t-win32.whl", hash = "sha256:1b336d06af14f8da5b1f391e8dec03634daf54dfcb4d1c4fb6d04c09d83cef90", size = 214284 },
+    { url = "https://files.pythonhosted.org/packages/95/ab/657bfa6171800a67bd1c005402f06d6b78610820ef1364ea4f85b04bbb5b/coverage-7.7.0-cp313-cp313t-win_amd64.whl", hash = "sha256:b54a1ee4c6f1905a436cbaa04b26626d27925a41cbc3a337e2d3ff7038187f07", size = 215445 },
+    { url = "https://files.pythonhosted.org/packages/cb/69/6a5eac32d2e8721274ef75df1b9fd6a8f7e8231e41ff7bc5501f19835f25/coverage-7.7.0-pp39.pp310.pp311-none-any.whl", hash = "sha256:3b0e6e54591ae0d7427def8a4d40fca99df6b899d10354bab73cd5609807261c", size = 202813 },
+    { url = "https://files.pythonhosted.org/packages/2a/ac/60f409a448e5b0e9b8539716f683568aa5848c1be903cdbbc805a552cdf8/coverage-7.7.0-py3-none-any.whl", hash = "sha256:708f0a1105ef2b11c79ed54ed31f17e6325ac936501fc373f24be3e6a578146a", size = 202803 },
+]
+
+[package.optional-dependencies]
+toml = [
+    { name = "tomli", marker = "python_full_version <= '3.11'" },
+]
+
+[[package]]
+name = "cycler"
+version = "0.12.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321 },
+]
+
+[[package]]
+name = "debugpy"
+version = "1.8.13"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/51/d4/f35f539e11c9344652f362c22413ec5078f677ac71229dc9b4f6f85ccaa3/debugpy-1.8.13.tar.gz", hash = "sha256:837e7bef95bdefba426ae38b9a94821ebdc5bea55627879cd48165c90b9e50ce", size = 1641193 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3f/32/901c7204cceb3262fdf38f4c25c9a46372c11661e8490e9ea702bc4ff448/debugpy-1.8.13-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:06859f68e817966723ffe046b896b1bd75c665996a77313370336ee9e1de3e90", size = 2076250 },
+    { url = "https://files.pythonhosted.org/packages/95/10/77fe746851c8d84838a807da60c7bd0ac8627a6107d6917dd3293bf8628c/debugpy-1.8.13-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb56c2db69fb8df3168bc857d7b7d2494fed295dfdbde9a45f27b4b152f37520", size = 3560883 },
+    { url = "https://files.pythonhosted.org/packages/a1/ef/28f8db2070e453dda0e49b356e339d0b4e1d38058d4c4ea9e88cdc8ee8e7/debugpy-1.8.13-cp310-cp310-win32.whl", hash = "sha256:46abe0b821cad751fc1fb9f860fb2e68d75e2c5d360986d0136cd1db8cad4428", size = 5180149 },
+    { url = "https://files.pythonhosted.org/packages/89/16/1d53a80caf5862627d3eaffb217d4079d7e4a1df6729a2d5153733661efd/debugpy-1.8.13-cp310-cp310-win_amd64.whl", hash = "sha256:dc7b77f5d32674686a5f06955e4b18c0e41fb5a605f5b33cf225790f114cfeec", size = 5212540 },
+    { url = "https://files.pythonhosted.org/packages/31/90/dd2fcad8364f0964f476537481985198ce6e879760281ad1cec289f1aa71/debugpy-1.8.13-cp311-cp311-macosx_14_0_universal2.whl", hash = "sha256:eee02b2ed52a563126c97bf04194af48f2fe1f68bb522a312b05935798e922ff", size = 2174802 },
+    { url = "https://files.pythonhosted.org/packages/5c/c9/06ff65f15eb30dbdafd45d1575770b842ce3869ad5580a77f4e5590f1be7/debugpy-1.8.13-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4caca674206e97c85c034c1efab4483f33971d4e02e73081265ecb612af65377", size = 3133620 },
+    { url = "https://files.pythonhosted.org/packages/3b/49/798a4092bde16a4650f17ac5f2301d4d37e1972d65462fb25c80a83b4790/debugpy-1.8.13-cp311-cp311-win32.whl", hash = "sha256:7d9a05efc6973b5aaf076d779cf3a6bbb1199e059a17738a2aa9d27a53bcc888", size = 5104764 },
+    { url = "https://files.pythonhosted.org/packages/cd/d5/3684d7561c8ba2797305cf8259619acccb8d6ebe2117bb33a6897c235eee/debugpy-1.8.13-cp311-cp311-win_amd64.whl", hash = "sha256:62f9b4a861c256f37e163ada8cf5a81f4c8d5148fc17ee31fb46813bd658cdcc", size = 5129670 },
+    { url = "https://files.pythonhosted.org/packages/79/ad/dff929b6b5403feaab0af0e5bb460fd723f9c62538b718a9af819b8fff20/debugpy-1.8.13-cp312-cp312-macosx_14_0_universal2.whl", hash = "sha256:2b8de94c5c78aa0d0ed79023eb27c7c56a64c68217d881bee2ffbcb13951d0c1", size = 2501004 },
+    { url = "https://files.pythonhosted.org/packages/d6/4f/b7d42e6679f0bb525888c278b0c0d2b6dff26ed42795230bb46eaae4f9b3/debugpy-1.8.13-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:887d54276cefbe7290a754424b077e41efa405a3e07122d8897de54709dbe522", size = 4222346 },
+    { url = "https://files.pythonhosted.org/packages/ec/18/d9b3e88e85d41f68f77235112adc31012a784e45a3fcdbb039777d570a0f/debugpy-1.8.13-cp312-cp312-win32.whl", hash = "sha256:3872ce5453b17837ef47fb9f3edc25085ff998ce63543f45ba7af41e7f7d370f", size = 5226639 },
+    { url = "https://files.pythonhosted.org/packages/c9/f7/0df18a4f530ed3cc06f0060f548efe9e3316102101e311739d906f5650be/debugpy-1.8.13-cp312-cp312-win_amd64.whl", hash = "sha256:63ca7670563c320503fea26ac688988d9d6b9c6a12abc8a8cf2e7dd8e5f6b6ea", size = 5268735 },
+    { url = "https://files.pythonhosted.org/packages/b1/db/ae7cd645c1826aae557cebccbc448f0cc9a818d364efb88f8d80e7a03f41/debugpy-1.8.13-cp313-cp313-macosx_14_0_universal2.whl", hash = "sha256:31abc9618be4edad0b3e3a85277bc9ab51a2d9f708ead0d99ffb5bb750e18503", size = 2485416 },
+    { url = "https://files.pythonhosted.org/packages/ec/ed/db4b10ff3b5bb30fe41d9e86444a08bb6448e4d8265e7768450b8408dd36/debugpy-1.8.13-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a0bd87557f97bced5513a74088af0b84982b6ccb2e254b9312e29e8a5c4270eb", size = 4218784 },
+    { url = "https://files.pythonhosted.org/packages/82/82/ed81852a8d94086f51664d032d83c7f87cd2b087c6ea70dabec7c1ba813d/debugpy-1.8.13-cp313-cp313-win32.whl", hash = "sha256:5268ae7fdca75f526d04465931cb0bd24577477ff50e8bb03dab90983f4ebd02", size = 5226270 },
+    { url = "https://files.pythonhosted.org/packages/15/63/aa92fb341a78ec40f1c414ec7a7885c2ee17032eee00d12cee0cdc502af4/debugpy-1.8.13-cp313-cp313-win_amd64.whl", hash = "sha256:79ce4ed40966c4c1631d0131606b055a5a2f8e430e3f7bf8fd3744b09943e8e8", size = 5268621 },
+    { url = "https://files.pythonhosted.org/packages/37/4f/0b65410a08b6452bfd3f7ed6f3610f1a31fb127f46836e82d31797065dcb/debugpy-1.8.13-py2.py3-none-any.whl", hash = "sha256:d4ba115cdd0e3a70942bd562adba9ec8c651fe69ddde2298a1be296fc331906f", size = 5229306 },
+]
+
+[[package]]
+name = "decorator"
+version = "5.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360", size = 56711 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4e/8c/f3147f5c4b73e7550fe5f9352eaa956ae838d5c51eb58e7a25b9f3e2643b/decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a", size = 9190 },
+]
+
+[[package]]
+name = "defusedxml"
+version = "0.7.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0f/d5/c66da9b79e5bdb124974bfe172b4daf3c984ebd9c2a06e2b8a4dc7331c72/defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69", size = 75520 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604 },
+]
+
+[[package]]
+name = "docutils"
+version = "0.21.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ae/ed/aefcc8cd0ba62a0560c3c18c33925362d46c6075480bfa4df87b28e169a9/docutils-0.21.2.tar.gz", hash = "sha256:3a6b18732edf182daa3cd12775bbb338cf5691468f91eeeb109deff6ebfa986f", size = 2204444 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl", hash = "sha256:dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2", size = 587408 },
+]
+
+[[package]]
+name = "exceptiongroup"
+version = "1.2.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/09/35/2495c4ac46b980e4ca1f6ad6db102322ef3ad2410b79fdde159a4b0f3b92/exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc", size = 28883 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/02/cc/b7e31358aac6ed1ef2bb790a9746ac2c69bcb3c8588b41616914eb106eaf/exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b", size = 16453 },
+]
+
+[[package]]
+name = "executing"
+version = "2.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/91/50/a9d80c47ff289c611ff12e63f7c5d13942c65d68125160cefd768c73e6e4/executing-2.2.0.tar.gz", hash = "sha256:5d108c028108fe2551d1a7b2e8b713341e2cb4fc0aa7dcf966fa4327a5226755", size = 978693 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7b/8f/c4d9bafc34ad7ad5d8dc16dd1347ee0e507a52c3adb6bfa8887e1c6a26ba/executing-2.2.0-py2.py3-none-any.whl", hash = "sha256:11387150cad388d62750327a53d3339fad4888b39a6fe233c3afbb54ecffd3aa", size = 26702 },
+]
+
+[[package]]
+name = "fastjsonschema"
+version = "2.21.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8b/50/4b769ce1ac4071a1ef6d86b1a3fb56cdc3a37615e8c5519e1af96cdac366/fastjsonschema-2.21.1.tar.gz", hash = "sha256:794d4f0a58f848961ba16af7b9c85a3e88cd360df008c59aac6fc5ae9323b5d4", size = 373939 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/90/2b/0817a2b257fe88725c25589d89aec060581aabf668707a8d03b2e9e0cb2a/fastjsonschema-2.21.1-py3-none-any.whl", hash = "sha256:c9e5b7e908310918cf494a434eeb31384dd84a98b57a30bcb1f535015b554667", size = 23924 },
+]
+
+[[package]]
+name = "fonttools"
+version = "4.56.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1c/8c/9ffa2a555af0e5e5d0e2ed7fdd8c9bef474ed676995bb4c57c9cd0014248/fonttools-4.56.0.tar.gz", hash = "sha256:a114d1567e1a1586b7e9e7fc2ff686ca542a82769a296cef131e4c4af51e58f4", size = 3462892 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/5e/6ac30c2cc6a29454260f13c9c6422fc509b7982c13cd4597041260d8f482/fonttools-4.56.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:331954d002dbf5e704c7f3756028e21db07097c19722569983ba4d74df014000", size = 2752190 },
+    { url = "https://files.pythonhosted.org/packages/92/3a/ac382a8396d1b420ee45eeb0f65b614a9ca7abbb23a1b17524054f0f2200/fonttools-4.56.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8d1613abd5af2f93c05867b3a3759a56e8bf97eb79b1da76b2bc10892f96ff16", size = 2280624 },
+    { url = "https://files.pythonhosted.org/packages/8a/ae/00b58bfe20e9ff7fbc3dda38f5d127913942b5e252288ea9583099a31bf5/fonttools-4.56.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:705837eae384fe21cee5e5746fd4f4b2f06f87544fa60f60740007e0aa600311", size = 4562074 },
+    { url = "https://files.pythonhosted.org/packages/46/d0/0004ca8f6a200252e5bd6982ed99b5fe58c4c59efaf5f516621c4cd8f703/fonttools-4.56.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc871904a53a9d4d908673c6faa15689874af1c7c5ac403a8e12d967ebd0c0dc", size = 4604747 },
+    { url = "https://files.pythonhosted.org/packages/45/ea/c8862bd3e09d143ef8ed8268ec8a7d477828f960954889e65288ac050b08/fonttools-4.56.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:38b947de71748bab150259ee05a775e8a0635891568e9fdb3cdd7d0e0004e62f", size = 4559025 },
+    { url = "https://files.pythonhosted.org/packages/8f/75/bb88a9552ec1de31a414066257bfd9f40f4ada00074f7a3799ea39b5741f/fonttools-4.56.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:86b2a1013ef7a64d2e94606632683f07712045ed86d937c11ef4dde97319c086", size = 4728482 },
+    { url = "https://files.pythonhosted.org/packages/2a/5f/80a2b640df1e1bb7d459d62c8b3f37fe83fd413897e549106d4ebe6371f5/fonttools-4.56.0-cp310-cp310-win32.whl", hash = "sha256:133bedb9a5c6376ad43e6518b7e2cd2f866a05b1998f14842631d5feb36b5786", size = 2155557 },
+    { url = "https://files.pythonhosted.org/packages/8f/85/0904f9dbe51ac70d878d3242a8583b9453a09105c3ed19c6301247fd0d3a/fonttools-4.56.0-cp310-cp310-win_amd64.whl", hash = "sha256:17f39313b649037f6c800209984a11fc256a6137cbe5487091c6c7187cae4685", size = 2200017 },
+    { url = "https://files.pythonhosted.org/packages/35/56/a2f3e777d48fcae7ecd29de4d96352d84e5ea9871e5f3fc88241521572cf/fonttools-4.56.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:7ef04bc7827adb7532be3d14462390dd71287644516af3f1e67f1e6ff9c6d6df", size = 2753325 },
+    { url = "https://files.pythonhosted.org/packages/71/85/d483e9c4e5ed586b183bf037a353e8d766366b54fd15519b30e6178a6a6e/fonttools-4.56.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:ffda9b8cd9cb8b301cae2602ec62375b59e2e2108a117746f12215145e3f786c", size = 2281554 },
+    { url = "https://files.pythonhosted.org/packages/09/67/060473b832b2fade03c127019794df6dc02d9bc66fa4210b8e0d8a99d1e5/fonttools-4.56.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2e993e8db36306cc3f1734edc8ea67906c55f98683d6fd34c3fc5593fdbba4c", size = 4869260 },
+    { url = "https://files.pythonhosted.org/packages/28/e9/47c02d5a7027e8ed841ab6a10ca00c93dadd5f16742f1af1fa3f9978adf4/fonttools-4.56.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:003548eadd674175510773f73fb2060bb46adb77c94854af3e0cc5bc70260049", size = 4898508 },
+    { url = "https://files.pythonhosted.org/packages/bf/8a/221d456d1afb8ca043cfd078f59f187ee5d0a580f4b49351b9ce95121f57/fonttools-4.56.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:bd9825822e7bb243f285013e653f6741954d8147427aaa0324a862cdbf4cbf62", size = 4877700 },
+    { url = "https://files.pythonhosted.org/packages/a4/8c/e503863adf7a6aeff7b960e2f66fa44dd0c29a7a8b79765b2821950d7b05/fonttools-4.56.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:b23d30a2c0b992fb1c4f8ac9bfde44b5586d23457759b6cf9a787f1a35179ee0", size = 5045817 },
+    { url = "https://files.pythonhosted.org/packages/2b/50/79ba3b7e42f4eaa70b82b9e79155f0f6797858dc8a97862428b6852c6aee/fonttools-4.56.0-cp311-cp311-win32.whl", hash = "sha256:47b5e4680002ae1756d3ae3b6114e20aaee6cc5c69d1e5911f5ffffd3ee46c6b", size = 2154426 },
+    { url = "https://files.pythonhosted.org/packages/3b/90/4926e653041c4116ecd43e50e3c79f5daae6dcafc58ceb64bc4f71dd4924/fonttools-4.56.0-cp311-cp311-win_amd64.whl", hash = "sha256:14a3e3e6b211660db54ca1ef7006401e4a694e53ffd4553ab9bc87ead01d0f05", size = 2200937 },
+    { url = "https://files.pythonhosted.org/packages/39/32/71cfd6877999576a11824a7fe7bc0bb57c5c72b1f4536fa56a3e39552643/fonttools-4.56.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:d6f195c14c01bd057bc9b4f70756b510e009c83c5ea67b25ced3e2c38e6ee6e9", size = 2747757 },
+    { url = "https://files.pythonhosted.org/packages/15/52/d9f716b072c5061a0b915dd4c387f74bef44c68c069e2195c753905bd9b7/fonttools-4.56.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:fa760e5fe8b50cbc2d71884a1eff2ed2b95a005f02dda2fa431560db0ddd927f", size = 2279007 },
+    { url = "https://files.pythonhosted.org/packages/d1/97/f1b3a8afa9a0d814a092a25cd42f59ccb98a0bb7a295e6e02fc9ba744214/fonttools-4.56.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d54a45d30251f1d729e69e5b675f9a08b7da413391a1227781e2a297fa37f6d2", size = 4783991 },
+    { url = "https://files.pythonhosted.org/packages/95/70/2a781bedc1c45a0c61d29c56425609b22ed7f971da5d7e5df2679488741b/fonttools-4.56.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:661a8995d11e6e4914a44ca7d52d1286e2d9b154f685a4d1f69add8418961563", size = 4855109 },
+    { url = "https://files.pythonhosted.org/packages/0c/02/a2597858e61a5e3fb6a14d5f6be9e6eb4eaf090da56ad70cedcbdd201685/fonttools-4.56.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:9d94449ad0a5f2a8bf5d2f8d71d65088aee48adbe45f3c5f8e00e3ad861ed81a", size = 4762496 },
+    { url = "https://files.pythonhosted.org/packages/f2/00/aaf00100d6078fdc73f7352b44589804af9dc12b182a2540b16002152ba4/fonttools-4.56.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f59746f7953f69cc3290ce2f971ab01056e55ddd0fb8b792c31a8acd7fee2d28", size = 4990094 },
+    { url = "https://files.pythonhosted.org/packages/bf/dc/3ff1db522460db60cf3adaf1b64e0c72b43406717d139786d3fa1eb20709/fonttools-4.56.0-cp312-cp312-win32.whl", hash = "sha256:bce60f9a977c9d3d51de475af3f3581d9b36952e1f8fc19a1f2254f1dda7ce9c", size = 2142888 },
+    { url = "https://files.pythonhosted.org/packages/6f/e3/5a181a85777f7809076e51f7422e0dc77eb04676c40ec8bf6a49d390d1ff/fonttools-4.56.0-cp312-cp312-win_amd64.whl", hash = "sha256:300c310bb725b2bdb4f5fc7e148e190bd69f01925c7ab437b9c0ca3e1c7cd9ba", size = 2189734 },
+    { url = "https://files.pythonhosted.org/packages/a5/55/f06b48d48e0b4ec3a3489efafe9bd4d81b6e0802ac51026e3ee4634e89ba/fonttools-4.56.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:f20e2c0dfab82983a90f3d00703ac0960412036153e5023eed2b4641d7d5e692", size = 2735127 },
+    { url = "https://files.pythonhosted.org/packages/59/db/d2c7c9b6dd5cbd46f183e650a47403ffb88fca17484eb7c4b1cd88f9e513/fonttools-4.56.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f36a0868f47b7566237640c026c65a86d09a3d9ca5df1cd039e30a1da73098a0", size = 2272519 },
+    { url = "https://files.pythonhosted.org/packages/4d/a2/da62d779c34a0e0c06415f02eab7fa3466de5d46df459c0275a255cefc65/fonttools-4.56.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:62b4c6802fa28e14dba010e75190e0e6228513573f1eeae57b11aa1a39b7e5b1", size = 4762423 },
+    { url = "https://files.pythonhosted.org/packages/be/6a/fd4018e0448c8a5e12138906411282c5eab51a598493f080a9f0960e658f/fonttools-4.56.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a05d1f07eb0a7d755fbe01fee1fd255c3a4d3730130cf1bfefb682d18fd2fcea", size = 4834442 },
+    { url = "https://files.pythonhosted.org/packages/6d/63/fa1dec8efb35bc11ef9c39b2d74754b45d48a3ccb2cf78c0109c0af639e8/fonttools-4.56.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:0073b62c3438cf0058488c002ea90489e8801d3a7af5ce5f7c05c105bee815c3", size = 4742800 },
+    { url = "https://files.pythonhosted.org/packages/dd/f4/963247ae8c73ccc4cf2929e7162f595c81dbe17997d1d0ea77da24a217c9/fonttools-4.56.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e2cad98c94833465bcf28f51c248aaf07ca022efc6a3eba750ad9c1e0256d278", size = 4963746 },
+    { url = "https://files.pythonhosted.org/packages/ea/e0/46f9600c39c644b54e4420f941f75fa200d9288c9ae171e5d80918b8cbb9/fonttools-4.56.0-cp313-cp313-win32.whl", hash = "sha256:d0cb73ccf7f6d7ca8d0bc7ea8ac0a5b84969a41c56ac3ac3422a24df2680546f", size = 2140927 },
+    { url = "https://files.pythonhosted.org/packages/27/6d/3edda54f98a550a0473f032d8050315fbc8f1b76a0d9f3879b72ebb2cdd6/fonttools-4.56.0-cp313-cp313-win_amd64.whl", hash = "sha256:62cc1253827d1e500fde9dbe981219fea4eb000fd63402283472d38e7d8aa1c6", size = 2186709 },
+    { url = "https://files.pythonhosted.org/packages/bf/ff/44934a031ce5a39125415eb405b9efb76fe7f9586b75291d66ae5cbfc4e6/fonttools-4.56.0-py3-none-any.whl", hash = "sha256:1088182f68c303b50ca4dc0c82d42083d176cba37af1937e1a976a31149d4d14", size = 1089800 },
+]
+
+[[package]]
+name = "griffe"
+version = "1.6.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/2f/f2/b00eb72b853ecb5bf31dd47857cdf6767e380ca24ec2910d43b3fa7cc500/griffe-1.6.2.tar.gz", hash = "sha256:3a46fa7bd83280909b63c12b9a975732a927dd97809efe5b7972290b606c5d91", size = 392836 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4e/bc/bd8b7de5e748e078b6be648e76b47189a9182b1ac1eb7791ff7969f39f27/griffe-1.6.2-py3-none-any.whl", hash = "sha256:6399f7e663150e4278a312a8e8a14d2f3d7bd86e2ef2f8056a1058e38579c2ee", size = 128638 },
+]
+
+[[package]]
+name = "hypothesis"
+version = "6.129.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "attrs" },
+    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
+    { name = "sortedcontainers" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/bb/98/0051e770d36f7e0a55bcfa3590790448d57ed2f355da9adbb957b1f545d9/hypothesis-6.129.4.tar.gz", hash = "sha256:e9fd66c25b8f0aa6395ce6728360892c3af22529cc16cae7512a4672776d4781", size = 425235 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8d/7c/7266143385cbd19c839f9b61cc660d74c5ce2626fea41d8b215ccc5cfba3/hypothesis-6.129.4-py3-none-any.whl", hash = "sha256:45a31fe2b936688b2954f375c7f87e9dfefa4f2cddfa31cdeba15d77600e1286", size = 489542 },
+]
+
+[[package]]
+name = "idna"
+version = "3.10"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f1/70/7703c29685631f5a7590aa73f1f1d3fa9a380e654b86af429e0934a32f7d/idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9", size = 190490 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442 },
+]
+
+[[package]]
+name = "imagesize"
+version = "1.4.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a7/84/62473fb57d61e31fef6e36d64a179c8781605429fd927b5dd608c997be31/imagesize-1.4.1.tar.gz", hash = "sha256:69150444affb9cb0d5cc5a92b3676f0b2fb7cd9ae39e947a5e11a36b4497cd4a", size = 1280026 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl", hash = "sha256:0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b", size = 8769 },
+]
+
+[[package]]
+name = "importlib-metadata"
+version = "8.6.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "zipp" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/33/08/c1395a292bb23fd03bdf572a1357c5a733d3eecbab877641ceacab23db6e/importlib_metadata-8.6.1.tar.gz", hash = "sha256:310b41d755445d74569f993ccfc22838295d9fe005425094fad953d7f15c8580", size = 55767 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/79/9d/0fb148dc4d6fa4a7dd1d8378168d9b4cd8d4560a6fbf6f0121c5fc34eb68/importlib_metadata-8.6.1-py3-none-any.whl", hash = "sha256:02a89390c1e15fdfdc0d7c6b25cb3e62650d0494005c97d6f148bf5b9787525e", size = 26971 },
+]
+
+[[package]]
+name = "importlib-resources"
+version = "6.5.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/cf/8c/f834fbf984f691b4f7ff60f50b514cc3de5cc08abfc3295564dd89c5e2e7/importlib_resources-6.5.2.tar.gz", hash = "sha256:185f87adef5bcc288449d98fb4fba07cea78bc036455dd44c5fc4a2fe78fed2c", size = 44693 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a4/ed/1f1afb2e9e7f38a545d628f864d562a5ae64fe6f7a10e28ffb9b185b4e89/importlib_resources-6.5.2-py3-none-any.whl", hash = "sha256:789cfdc3ed28c78b67a06acb8126751ced69a3d5f79c095a98298cd8a760ccec", size = 37461 },
+]
+
+[[package]]
+name = "iniconfig"
+version = "2.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f2/97/ebf4da567aa6827c909642694d71c9fcf53e5b504f2d96afea02718862f3/iniconfig-2.1.0.tar.gz", hash = "sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7", size = 4793 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760", size = 6050 },
+]
+
+[[package]]
+name = "ipykernel"
+version = "6.29.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "appnope", marker = "sys_platform == 'darwin'" },
+    { name = "comm" },
+    { name = "debugpy" },
+    { name = "ipython", version = "8.34.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "ipython", version = "9.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "jupyter-client" },
+    { name = "jupyter-core" },
+    { name = "matplotlib-inline" },
+    { name = "nest-asyncio" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyzmq" },
+    { name = "tornado" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e9/5c/67594cb0c7055dc50814b21731c22a601101ea3b1b50a9a1b090e11f5d0f/ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215", size = 163367 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/94/5c/368ae6c01c7628438358e6d337c19b05425727fbb221d2a3c4303c372f42/ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5", size = 117173 },
+]
+
+[[package]]
+name = "ipython"
+version = "8.34.0"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.11'",
+]
+dependencies = [
+    { name = "colorama", marker = "python_full_version < '3.11' and sys_platform == 'win32'" },
+    { name = "decorator", marker = "python_full_version < '3.11'" },
+    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
+    { name = "jedi", marker = "python_full_version < '3.11'" },
+    { name = "matplotlib-inline", marker = "python_full_version < '3.11'" },
+    { name = "pexpect", marker = "python_full_version < '3.11' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "prompt-toolkit", marker = "python_full_version < '3.11'" },
+    { name = "pygments", marker = "python_full_version < '3.11'" },
+    { name = "stack-data", marker = "python_full_version < '3.11'" },
+    { name = "traitlets", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/13/18/1a60aa62e9d272fcd7e658a89e1c148da10e1a5d38edcbcd834b52ca7492/ipython-8.34.0.tar.gz", hash = "sha256:c31d658e754673ecc6514583e7dda8069e47136eb62458816b7d1e6625948b5a", size = 5508477 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/04/78/45615356bb973904856808183ae2a5fba1f360e9d682314d79766f4b88f2/ipython-8.34.0-py3-none-any.whl", hash = "sha256:0419883fa46e0baa182c5d50ebb8d6b49df1889fdb70750ad6d8cfe678eda6e3", size = 826731 },
+]
+
+[[package]]
+name = "ipython"
+version = "9.0.2"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+]
+dependencies = [
+    { name = "colorama", marker = "python_full_version >= '3.11' and sys_platform == 'win32'" },
+    { name = "decorator", marker = "python_full_version >= '3.11'" },
+    { name = "ipython-pygments-lexers", marker = "python_full_version >= '3.11'" },
+    { name = "jedi", marker = "python_full_version >= '3.11'" },
+    { name = "matplotlib-inline", marker = "python_full_version >= '3.11'" },
+    { name = "pexpect", marker = "python_full_version >= '3.11' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "prompt-toolkit", marker = "python_full_version >= '3.11'" },
+    { name = "pygments", marker = "python_full_version >= '3.11'" },
+    { name = "stack-data", marker = "python_full_version >= '3.11'" },
+    { name = "traitlets", marker = "python_full_version >= '3.11'" },
+    { name = "typing-extensions", marker = "python_full_version == '3.11.*'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7d/ce/012a0f40ca58a966f87a6e894d6828e2817657cbdf522b02a5d3a87d92ce/ipython-9.0.2.tar.gz", hash = "sha256:ec7b479e3e5656bf4f58c652c120494df1820f4f28f522fb7ca09e213c2aab52", size = 4366102 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/20/3a/917cb9e72f4e1a4ea13c862533205ae1319bd664119189ee5cc9e4e95ebf/ipython-9.0.2-py3-none-any.whl", hash = "sha256:143ef3ea6fb1e1bffb4c74b114051de653ffb7737a3f7ab1670e657ca6ae8c44", size = 600524 },
+]
+
+[[package]]
+name = "ipython-pygments-lexers"
+version = "1.1.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pygments", marker = "python_full_version >= '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ef/4c/5dd1d8af08107f88c7f741ead7a40854b8ac24ddf9ae850afbcf698aa552/ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81", size = 8393 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d9/33/1f075bf72b0b747cb3288d011319aaf64083cf2efef8354174e3ed4540e2/ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c", size = 8074 },
+]
+
+[[package]]
+name = "jedi"
+version = "0.19.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "parso" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/72/3a/79a912fbd4d8dd6fbb02bf69afd3bb72cf0c729bb3063c6f4498603db17a/jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0", size = 1231287 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9", size = 1572278 },
+]
+
+[[package]]
+name = "jinja2"
+version = "3.1.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markupsafe" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899 },
+]
+
+[[package]]
+name = "jsonschema"
+version = "4.23.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "attrs" },
+    { name = "jsonschema-specifications" },
+    { name = "referencing" },
+    { name = "rpds-py" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/38/2e/03362ee4034a4c917f697890ccd4aec0800ccf9ded7f511971c75451deec/jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4", size = 325778 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/69/4a/4f9dbeb84e8850557c02365a0eee0649abe5eb1d84af92a25731c6c0f922/jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566", size = 88462 },
+]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2024.10.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "referencing" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/10/db/58f950c996c793472e336ff3655b13fbcf1e3b359dcf52dcf3ed3b52c352/jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272", size = 15561 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d1/0f/8910b19ac0670a0f80ce1008e5e751c4a57e14d2c4c13a482aa6079fa9d6/jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf", size = 18459 },
+]
+
+[[package]]
+name = "jupyter-client"
+version = "8.6.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jupyter-core" },
+    { name = "python-dateutil" },
+    { name = "pyzmq" },
+    { name = "tornado" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/71/22/bf9f12fdaeae18019a468b68952a60fe6dbab5d67cd2a103cac7659b41ca/jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419", size = 342019 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/11/85/b0394e0b6fcccd2c1eeefc230978a6f8cb0c5df1e4cd3e7625735a0d7d1e/jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f", size = 106105 },
+]
+
+[[package]]
+name = "jupyter-core"
+version = "5.7.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "platformdirs" },
+    { name = "pywin32", marker = "platform_python_implementation != 'PyPy' and sys_platform == 'win32'" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/00/11/b56381fa6c3f4cc5d2cf54a7dbf98ad9aa0b339ef7a601d6053538b079a7/jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9", size = 87629 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c9/fb/108ecd1fe961941959ad0ee4e12ee7b8b1477247f30b1fdfd83ceaf017f0/jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409", size = 28965 },
+]
+
+[[package]]
+name = "jupyterlab-pygments"
+version = "0.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/90/51/9187be60d989df97f5f0aba133fa54e7300f17616e065d1ada7d7646b6d6/jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d", size = 512900 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b1/dd/ead9d8ea85bf202d90cc513b533f9c363121c7792674f78e0d8a854b63b4/jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780", size = 15884 },
+]
+
+[[package]]
+name = "kiwisolver"
+version = "1.4.8"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/82/59/7c91426a8ac292e1cdd53a63b6d9439abd573c875c3f92c146767dd33faf/kiwisolver-1.4.8.tar.gz", hash = "sha256:23d5f023bdc8c7e54eb65f03ca5d5bb25b601eac4d7f1a042888a1f45237987e", size = 97538 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/47/5f/4d8e9e852d98ecd26cdf8eaf7ed8bc33174033bba5e07001b289f07308fd/kiwisolver-1.4.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:88c6f252f6816a73b1f8c904f7bbe02fd67c09a69f7cb8a0eecdbf5ce78e63db", size = 124623 },
+    { url = "https://files.pythonhosted.org/packages/1d/70/7f5af2a18a76fe92ea14675f8bd88ce53ee79e37900fa5f1a1d8e0b42998/kiwisolver-1.4.8-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c72941acb7b67138f35b879bbe85be0f6c6a70cab78fe3ef6db9c024d9223e5b", size = 66720 },
+    { url = "https://files.pythonhosted.org/packages/c6/13/e15f804a142353aefd089fadc8f1d985561a15358c97aca27b0979cb0785/kiwisolver-1.4.8-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ce2cf1e5688edcb727fdf7cd1bbd0b6416758996826a8be1d958f91880d0809d", size = 65413 },
+    { url = "https://files.pythonhosted.org/packages/ce/6d/67d36c4d2054e83fb875c6b59d0809d5c530de8148846b1370475eeeece9/kiwisolver-1.4.8-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:c8bf637892dc6e6aad2bc6d4d69d08764166e5e3f69d469e55427b6ac001b19d", size = 1650826 },
+    { url = "https://files.pythonhosted.org/packages/de/c6/7b9bb8044e150d4d1558423a1568e4f227193662a02231064e3824f37e0a/kiwisolver-1.4.8-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:034d2c891f76bd3edbdb3ea11140d8510dca675443da7304205a2eaa45d8334c", size = 1628231 },
+    { url = "https://files.pythonhosted.org/packages/b6/38/ad10d437563063eaaedbe2c3540a71101fc7fb07a7e71f855e93ea4de605/kiwisolver-1.4.8-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d47b28d1dfe0793d5e96bce90835e17edf9a499b53969b03c6c47ea5985844c3", size = 1408938 },
+    { url = "https://files.pythonhosted.org/packages/52/ce/c0106b3bd7f9e665c5f5bc1e07cc95b5dabd4e08e3dad42dbe2faad467e7/kiwisolver-1.4.8-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:eb158fe28ca0c29f2260cca8c43005329ad58452c36f0edf298204de32a9a3ed", size = 1422799 },
+    { url = "https://files.pythonhosted.org/packages/d0/87/efb704b1d75dc9758087ba374c0f23d3254505edaedd09cf9d247f7878b9/kiwisolver-1.4.8-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d5536185fce131780ebd809f8e623bf4030ce1b161353166c49a3c74c287897f", size = 1354362 },
+    { url = "https://files.pythonhosted.org/packages/eb/b3/fd760dc214ec9a8f208b99e42e8f0130ff4b384eca8b29dd0efc62052176/kiwisolver-1.4.8-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:369b75d40abedc1da2c1f4de13f3482cb99e3237b38726710f4a793432b1c5ff", size = 2222695 },
+    { url = "https://files.pythonhosted.org/packages/a2/09/a27fb36cca3fc01700687cc45dae7a6a5f8eeb5f657b9f710f788748e10d/kiwisolver-1.4.8-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:641f2ddf9358c80faa22e22eb4c9f54bd3f0e442e038728f500e3b978d00aa7d", size = 2370802 },
+    { url = "https://files.pythonhosted.org/packages/3d/c3/ba0a0346db35fe4dc1f2f2cf8b99362fbb922d7562e5f911f7ce7a7b60fa/kiwisolver-1.4.8-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:d561d2d8883e0819445cfe58d7ddd673e4015c3c57261d7bdcd3710d0d14005c", size = 2334646 },
+    { url = "https://files.pythonhosted.org/packages/41/52/942cf69e562f5ed253ac67d5c92a693745f0bed3c81f49fc0cbebe4d6b00/kiwisolver-1.4.8-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:1732e065704b47c9afca7ffa272f845300a4eb959276bf6970dc07265e73b605", size = 2467260 },
+    { url = "https://files.pythonhosted.org/packages/32/26/2d9668f30d8a494b0411d4d7d4ea1345ba12deb6a75274d58dd6ea01e951/kiwisolver-1.4.8-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:bcb1ebc3547619c3b58a39e2448af089ea2ef44b37988caf432447374941574e", size = 2288633 },
+    { url = "https://files.pythonhosted.org/packages/98/99/0dd05071654aa44fe5d5e350729961e7bb535372935a45ac89a8924316e6/kiwisolver-1.4.8-cp310-cp310-win_amd64.whl", hash = "sha256:89c107041f7b27844179ea9c85d6da275aa55ecf28413e87624d033cf1f6b751", size = 71885 },
+    { url = "https://files.pythonhosted.org/packages/6c/fc/822e532262a97442989335394d441cd1d0448c2e46d26d3e04efca84df22/kiwisolver-1.4.8-cp310-cp310-win_arm64.whl", hash = "sha256:b5773efa2be9eb9fcf5415ea3ab70fc785d598729fd6057bea38d539ead28271", size = 65175 },
+    { url = "https://files.pythonhosted.org/packages/da/ed/c913ee28936c371418cb167b128066ffb20bbf37771eecc2c97edf8a6e4c/kiwisolver-1.4.8-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:a4d3601908c560bdf880f07d94f31d734afd1bb71e96585cace0e38ef44c6d84", size = 124635 },
+    { url = "https://files.pythonhosted.org/packages/4c/45/4a7f896f7467aaf5f56ef093d1f329346f3b594e77c6a3c327b2d415f521/kiwisolver-1.4.8-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:856b269c4d28a5c0d5e6c1955ec36ebfd1651ac00e1ce0afa3e28da95293b561", size = 66717 },
+    { url = "https://files.pythonhosted.org/packages/5f/b4/c12b3ac0852a3a68f94598d4c8d569f55361beef6159dce4e7b624160da2/kiwisolver-1.4.8-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c2b9a96e0f326205af81a15718a9073328df1173a2619a68553decb7097fd5d7", size = 65413 },
+    { url = "https://files.pythonhosted.org/packages/a9/98/1df4089b1ed23d83d410adfdc5947245c753bddfbe06541c4aae330e9e70/kiwisolver-1.4.8-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c5020c83e8553f770cb3b5fc13faac40f17e0b205bd237aebd21d53d733adb03", size = 1343994 },
+    { url = "https://files.pythonhosted.org/packages/8d/bf/b4b169b050c8421a7c53ea1ea74e4ef9c335ee9013216c558a047f162d20/kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dace81d28c787956bfbfbbfd72fdcef014f37d9b48830829e488fdb32b49d954", size = 1434804 },
+    { url = "https://files.pythonhosted.org/packages/66/5a/e13bd341fbcf73325ea60fdc8af752addf75c5079867af2e04cc41f34434/kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:11e1022b524bd48ae56c9b4f9296bce77e15a2e42a502cceba602f804b32bb79", size = 1450690 },
+    { url = "https://files.pythonhosted.org/packages/9b/4f/5955dcb376ba4a830384cc6fab7d7547bd6759fe75a09564910e9e3bb8ea/kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3b9b4d2892fefc886f30301cdd80debd8bb01ecdf165a449eb6e78f79f0fabd6", size = 1376839 },
+    { url = "https://files.pythonhosted.org/packages/3a/97/5edbed69a9d0caa2e4aa616ae7df8127e10f6586940aa683a496c2c280b9/kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a96c0e790ee875d65e340ab383700e2b4891677b7fcd30a699146f9384a2bb0", size = 1435109 },
+    { url = "https://files.pythonhosted.org/packages/13/fc/e756382cb64e556af6c1809a1bbb22c141bbc2445049f2da06b420fe52bf/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:23454ff084b07ac54ca8be535f4174170c1094a4cff78fbae4f73a4bcc0d4dab", size = 2245269 },
+    { url = "https://files.pythonhosted.org/packages/76/15/e59e45829d7f41c776d138245cabae6515cb4eb44b418f6d4109c478b481/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:87b287251ad6488e95b4f0b4a79a6d04d3ea35fde6340eb38fbd1ca9cd35bbbc", size = 2393468 },
+    { url = "https://files.pythonhosted.org/packages/e9/39/483558c2a913ab8384d6e4b66a932406f87c95a6080112433da5ed668559/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:b21dbe165081142b1232a240fc6383fd32cdd877ca6cc89eab93e5f5883e1c25", size = 2355394 },
+    { url = "https://files.pythonhosted.org/packages/01/aa/efad1fbca6570a161d29224f14b082960c7e08268a133fe5dc0f6906820e/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:768cade2c2df13db52475bd28d3a3fac8c9eff04b0e9e2fda0f3760f20b3f7fc", size = 2490901 },
+    { url = "https://files.pythonhosted.org/packages/c9/4f/15988966ba46bcd5ab9d0c8296914436720dd67fca689ae1a75b4ec1c72f/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:d47cfb2650f0e103d4bf68b0b5804c68da97272c84bb12850d877a95c056bd67", size = 2312306 },
+    { url = "https://files.pythonhosted.org/packages/2d/27/bdf1c769c83f74d98cbc34483a972f221440703054894a37d174fba8aa68/kiwisolver-1.4.8-cp311-cp311-win_amd64.whl", hash = "sha256:ed33ca2002a779a2e20eeb06aea7721b6e47f2d4b8a8ece979d8ba9e2a167e34", size = 71966 },
+    { url = "https://files.pythonhosted.org/packages/4a/c9/9642ea855604aeb2968a8e145fc662edf61db7632ad2e4fb92424be6b6c0/kiwisolver-1.4.8-cp311-cp311-win_arm64.whl", hash = "sha256:16523b40aab60426ffdebe33ac374457cf62863e330a90a0383639ce14bf44b2", size = 65311 },
+    { url = "https://files.pythonhosted.org/packages/fc/aa/cea685c4ab647f349c3bc92d2daf7ae34c8e8cf405a6dcd3a497f58a2ac3/kiwisolver-1.4.8-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:d6af5e8815fd02997cb6ad9bbed0ee1e60014438ee1a5c2444c96f87b8843502", size = 124152 },
+    { url = "https://files.pythonhosted.org/packages/c5/0b/8db6d2e2452d60d5ebc4ce4b204feeb16176a851fd42462f66ade6808084/kiwisolver-1.4.8-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:bade438f86e21d91e0cf5dd7c0ed00cda0f77c8c1616bd83f9fc157fa6760d31", size = 66555 },
+    { url = "https://files.pythonhosted.org/packages/60/26/d6a0db6785dd35d3ba5bf2b2df0aedc5af089962c6eb2cbf67a15b81369e/kiwisolver-1.4.8-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b83dc6769ddbc57613280118fb4ce3cd08899cc3369f7d0e0fab518a7cf37fdb", size = 65067 },
+    { url = "https://files.pythonhosted.org/packages/c9/ed/1d97f7e3561e09757a196231edccc1bcf59d55ddccefa2afc9c615abd8e0/kiwisolver-1.4.8-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:111793b232842991be367ed828076b03d96202c19221b5ebab421ce8bcad016f", size = 1378443 },
+    { url = "https://files.pythonhosted.org/packages/29/61/39d30b99954e6b46f760e6289c12fede2ab96a254c443639052d1b573fbc/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:257af1622860e51b1a9d0ce387bf5c2c4f36a90594cb9514f55b074bcc787cfc", size = 1472728 },
+    { url = "https://files.pythonhosted.org/packages/0c/3e/804163b932f7603ef256e4a715e5843a9600802bb23a68b4e08c8c0ff61d/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:69b5637c3f316cab1ec1c9a12b8c5f4750a4c4b71af9157645bf32830e39c03a", size = 1478388 },
+    { url = "https://files.pythonhosted.org/packages/8a/9e/60eaa75169a154700be74f875a4d9961b11ba048bef315fbe89cb6999056/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:782bb86f245ec18009890e7cb8d13a5ef54dcf2ebe18ed65f795e635a96a1c6a", size = 1413849 },
+    { url = "https://files.pythonhosted.org/packages/bc/b3/9458adb9472e61a998c8c4d95cfdfec91c73c53a375b30b1428310f923e4/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc978a80a0db3a66d25767b03688f1147a69e6237175c0f4ffffaaedf744055a", size = 1475533 },
+    { url = "https://files.pythonhosted.org/packages/e4/7a/0a42d9571e35798de80aef4bb43a9b672aa7f8e58643d7bd1950398ffb0a/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:36dbbfd34838500a31f52c9786990d00150860e46cd5041386f217101350f0d3", size = 2268898 },
+    { url = "https://files.pythonhosted.org/packages/d9/07/1255dc8d80271400126ed8db35a1795b1a2c098ac3a72645075d06fe5c5d/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:eaa973f1e05131de5ff3569bbba7f5fd07ea0595d3870ed4a526d486fe57fa1b", size = 2425605 },
+    { url = "https://files.pythonhosted.org/packages/84/df/5a3b4cf13780ef6f6942df67b138b03b7e79e9f1f08f57c49957d5867f6e/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:a66f60f8d0c87ab7f59b6fb80e642ebb29fec354a4dfad687ca4092ae69d04f4", size = 2375801 },
+    { url = "https://files.pythonhosted.org/packages/8f/10/2348d068e8b0f635c8c86892788dac7a6b5c0cb12356620ab575775aad89/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:858416b7fb777a53f0c59ca08190ce24e9abbd3cffa18886a5781b8e3e26f65d", size = 2520077 },
+    { url = "https://files.pythonhosted.org/packages/32/d8/014b89fee5d4dce157d814303b0fce4d31385a2af4c41fed194b173b81ac/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:085940635c62697391baafaaeabdf3dd7a6c3643577dde337f4d66eba021b2b8", size = 2338410 },
+    { url = "https://files.pythonhosted.org/packages/bd/72/dfff0cc97f2a0776e1c9eb5bef1ddfd45f46246c6533b0191887a427bca5/kiwisolver-1.4.8-cp312-cp312-win_amd64.whl", hash = "sha256:01c3d31902c7db5fb6182832713d3b4122ad9317c2c5877d0539227d96bb2e50", size = 71853 },
+    { url = "https://files.pythonhosted.org/packages/dc/85/220d13d914485c0948a00f0b9eb419efaf6da81b7d72e88ce2391f7aed8d/kiwisolver-1.4.8-cp312-cp312-win_arm64.whl", hash = "sha256:a3c44cb68861de93f0c4a8175fbaa691f0aa22550c331fefef02b618a9dcb476", size = 65424 },
+    { url = "https://files.pythonhosted.org/packages/79/b3/e62464a652f4f8cd9006e13d07abad844a47df1e6537f73ddfbf1bc997ec/kiwisolver-1.4.8-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:1c8ceb754339793c24aee1c9fb2485b5b1f5bb1c2c214ff13368431e51fc9a09", size = 124156 },
+    { url = "https://files.pythonhosted.org/packages/8d/2d/f13d06998b546a2ad4f48607a146e045bbe48030774de29f90bdc573df15/kiwisolver-1.4.8-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:54a62808ac74b5e55a04a408cda6156f986cefbcf0ada13572696b507cc92fa1", size = 66555 },
+    { url = "https://files.pythonhosted.org/packages/59/e3/b8bd14b0a54998a9fd1e8da591c60998dc003618cb19a3f94cb233ec1511/kiwisolver-1.4.8-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:68269e60ee4929893aad82666821aaacbd455284124817af45c11e50a4b42e3c", size = 65071 },
+    { url = "https://files.pythonhosted.org/packages/f0/1c/6c86f6d85ffe4d0ce04228d976f00674f1df5dc893bf2dd4f1928748f187/kiwisolver-1.4.8-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:34d142fba9c464bc3bbfeff15c96eab0e7310343d6aefb62a79d51421fcc5f1b", size = 1378053 },
+    { url = "https://files.pythonhosted.org/packages/4e/b9/1c6e9f6dcb103ac5cf87cb695845f5fa71379021500153566d8a8a9fc291/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3ddc373e0eef45b59197de815b1b28ef89ae3955e7722cc9710fb91cd77b7f47", size = 1472278 },
+    { url = "https://files.pythonhosted.org/packages/ee/81/aca1eb176de671f8bda479b11acdc42c132b61a2ac861c883907dde6debb/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:77e6f57a20b9bd4e1e2cedda4d0b986ebd0216236f0106e55c28aea3d3d69b16", size = 1478139 },
+    { url = "https://files.pythonhosted.org/packages/49/f4/e081522473671c97b2687d380e9e4c26f748a86363ce5af48b4a28e48d06/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:08e77738ed7538f036cd1170cbed942ef749137b1311fa2bbe2a7fda2f6bf3cc", size = 1413517 },
+    { url = "https://files.pythonhosted.org/packages/8f/e9/6a7d025d8da8c4931522922cd706105aa32b3291d1add8c5427cdcd66e63/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a5ce1e481a74b44dd5e92ff03ea0cb371ae7a0268318e202be06c8f04f4f1246", size = 1474952 },
+    { url = "https://files.pythonhosted.org/packages/82/13/13fa685ae167bee5d94b415991c4fc7bb0a1b6ebea6e753a87044b209678/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:fc2ace710ba7c1dfd1a3b42530b62b9ceed115f19a1656adefce7b1782a37794", size = 2269132 },
+    { url = "https://files.pythonhosted.org/packages/ef/92/bb7c9395489b99a6cb41d502d3686bac692586db2045adc19e45ee64ed23/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:3452046c37c7692bd52b0e752b87954ef86ee2224e624ef7ce6cb21e8c41cc1b", size = 2425997 },
+    { url = "https://files.pythonhosted.org/packages/ed/12/87f0e9271e2b63d35d0d8524954145837dd1a6c15b62a2d8c1ebe0f182b4/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:7e9a60b50fe8b2ec6f448fe8d81b07e40141bfced7f896309df271a0b92f80f3", size = 2376060 },
+    { url = "https://files.pythonhosted.org/packages/02/6e/c8af39288edbce8bf0fa35dee427b082758a4b71e9c91ef18fa667782138/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:918139571133f366e8362fa4a297aeba86c7816b7ecf0bc79168080e2bd79957", size = 2520471 },
+    { url = "https://files.pythonhosted.org/packages/13/78/df381bc7b26e535c91469f77f16adcd073beb3e2dd25042efd064af82323/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e063ef9f89885a1d68dd8b2e18f5ead48653176d10a0e324e3b0030e3a69adeb", size = 2338793 },
+    { url = "https://files.pythonhosted.org/packages/d0/dc/c1abe38c37c071d0fc71c9a474fd0b9ede05d42f5a458d584619cfd2371a/kiwisolver-1.4.8-cp313-cp313-win_amd64.whl", hash = "sha256:a17b7c4f5b2c51bb68ed379defd608a03954a1845dfed7cc0117f1cc8a9b7fd2", size = 71855 },
+    { url = "https://files.pythonhosted.org/packages/a0/b6/21529d595b126ac298fdd90b705d87d4c5693de60023e0efcb4f387ed99e/kiwisolver-1.4.8-cp313-cp313-win_arm64.whl", hash = "sha256:3cd3bc628b25f74aedc6d374d5babf0166a92ff1317f46267f12d2ed54bc1d30", size = 65430 },
+    { url = "https://files.pythonhosted.org/packages/34/bd/b89380b7298e3af9b39f49334e3e2a4af0e04819789f04b43d560516c0c8/kiwisolver-1.4.8-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:370fd2df41660ed4e26b8c9d6bbcad668fbe2560462cba151a721d49e5b6628c", size = 126294 },
+    { url = "https://files.pythonhosted.org/packages/83/41/5857dc72e5e4148eaac5aa76e0703e594e4465f8ab7ec0fc60e3a9bb8fea/kiwisolver-1.4.8-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:84a2f830d42707de1d191b9490ac186bf7997a9495d4e9072210a1296345f7dc", size = 67736 },
+    { url = "https://files.pythonhosted.org/packages/e1/d1/be059b8db56ac270489fb0b3297fd1e53d195ba76e9bbb30e5401fa6b759/kiwisolver-1.4.8-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:7a3ad337add5148cf51ce0b55642dc551c0b9d6248458a757f98796ca7348712", size = 66194 },
+    { url = "https://files.pythonhosted.org/packages/e1/83/4b73975f149819eb7dcf9299ed467eba068ecb16439a98990dcb12e63fdd/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7506488470f41169b86d8c9aeff587293f530a23a23a49d6bc64dab66bedc71e", size = 1465942 },
+    { url = "https://files.pythonhosted.org/packages/c7/2c/30a5cdde5102958e602c07466bce058b9d7cb48734aa7a4327261ac8e002/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2f0121b07b356a22fb0414cec4666bbe36fd6d0d759db3d37228f496ed67c880", size = 1595341 },
+    { url = "https://files.pythonhosted.org/packages/ff/9b/1e71db1c000385aa069704f5990574b8244cce854ecd83119c19e83c9586/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d6d6bd87df62c27d4185de7c511c6248040afae67028a8a22012b010bc7ad062", size = 1598455 },
+    { url = "https://files.pythonhosted.org/packages/85/92/c8fec52ddf06231b31cbb779af77e99b8253cd96bd135250b9498144c78b/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:291331973c64bb9cce50bbe871fb2e675c4331dab4f31abe89f175ad7679a4d7", size = 1522138 },
+    { url = "https://files.pythonhosted.org/packages/0b/51/9eb7e2cd07a15d8bdd976f6190c0164f92ce1904e5c0c79198c4972926b7/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:893f5525bb92d3d735878ec00f781b2de998333659507d29ea4466208df37bed", size = 1582857 },
+    { url = "https://files.pythonhosted.org/packages/0f/95/c5a00387a5405e68ba32cc64af65ce881a39b98d73cc394b24143bebc5b8/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:b47a465040146981dc9db8647981b8cb96366fbc8d452b031e4f8fdffec3f26d", size = 2293129 },
+    { url = "https://files.pythonhosted.org/packages/44/83/eeb7af7d706b8347548313fa3a3a15931f404533cc54fe01f39e830dd231/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:99cea8b9dd34ff80c521aef46a1dddb0dcc0283cf18bde6d756f1e6f31772165", size = 2421538 },
+    { url = "https://files.pythonhosted.org/packages/05/f9/27e94c1b3eb29e6933b6986ffc5fa1177d2cd1f0c8efc5f02c91c9ac61de/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:151dffc4865e5fe6dafce5480fab84f950d14566c480c08a53c663a0020504b6", size = 2390661 },
+    { url = "https://files.pythonhosted.org/packages/d9/d4/3c9735faa36ac591a4afcc2980d2691000506050b7a7e80bcfe44048daa7/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:577facaa411c10421314598b50413aa1ebcf5126f704f1e5d72d7e4e9f020d90", size = 2546710 },
+    { url = "https://files.pythonhosted.org/packages/4c/fa/be89a49c640930180657482a74970cdcf6f7072c8d2471e1babe17a222dc/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:be4816dc51c8a471749d664161b434912eee82f2ea66bd7628bd14583a833e85", size = 2349213 },
+    { url = "https://files.pythonhosted.org/packages/1f/f9/ae81c47a43e33b93b0a9819cac6723257f5da2a5a60daf46aa5c7226ea85/kiwisolver-1.4.8-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:e7a019419b7b510f0f7c9dceff8c5eae2392037eae483a7f9162625233802b0a", size = 60403 },
+    { url = "https://files.pythonhosted.org/packages/58/ca/f92b5cb6f4ce0c1ebfcfe3e2e42b96917e16f7090e45b21102941924f18f/kiwisolver-1.4.8-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:286b18e86682fd2217a48fc6be6b0f20c1d0ed10958d8dc53453ad58d7be0bf8", size = 58657 },
+    { url = "https://files.pythonhosted.org/packages/80/28/ae0240f732f0484d3a4dc885d055653c47144bdf59b670aae0ec3c65a7c8/kiwisolver-1.4.8-pp310-pypy310_pp73-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4191ee8dfd0be1c3666ccbac178c5a05d5f8d689bbe3fc92f3c4abec817f8fe0", size = 84948 },
+    { url = "https://files.pythonhosted.org/packages/5d/eb/78d50346c51db22c7203c1611f9b513075f35c4e0e4877c5dde378d66043/kiwisolver-1.4.8-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7cd2785b9391f2873ad46088ed7599a6a71e762e1ea33e87514b1a441ed1da1c", size = 81186 },
+    { url = "https://files.pythonhosted.org/packages/43/f8/7259f18c77adca88d5f64f9a522792e178b2691f3748817a8750c2d216ef/kiwisolver-1.4.8-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c07b29089b7ba090b6f1a669f1411f27221c3662b3a1b7010e67b59bb5a6f10b", size = 80279 },
+    { url = "https://files.pythonhosted.org/packages/3a/1d/50ad811d1c5dae091e4cf046beba925bcae0a610e79ae4c538f996f63ed5/kiwisolver-1.4.8-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:65ea09a5a3faadd59c2ce96dc7bf0f364986a315949dc6374f04396b0d60e09b", size = 71762 },
+]
+
+[[package]]
+name = "markdown-it-py"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mdurl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/38/71/3b932df36c1a044d397a1f92d1cf91ee0a503d91e470cbd670aa66b07ed0/markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb", size = 74596 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1", size = 87528 },
+]
+
+[[package]]
+name = "markupsafe"
+version = "3.0.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b2/97/5d42485e71dfc078108a86d6de8fa46db44a1a9295e89c5d6d4a06e23a62/markupsafe-3.0.2.tar.gz", hash = "sha256:ee55d3edf80167e48ea11a923c7386f4669df67d7994554387f84e7d8b0a2bf0", size = 20537 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/04/90/d08277ce111dd22f77149fd1a5d4653eeb3b3eaacbdfcbae5afb2600eebd/MarkupSafe-3.0.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7e94c425039cde14257288fd61dcfb01963e658efbc0ff54f5306b06054700f8", size = 14357 },
+    { url = "https://files.pythonhosted.org/packages/04/e1/6e2194baeae0bca1fae6629dc0cbbb968d4d941469cbab11a3872edff374/MarkupSafe-3.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9e2d922824181480953426608b81967de705c3cef4d1af983af849d7bd619158", size = 12393 },
+    { url = "https://files.pythonhosted.org/packages/1d/69/35fa85a8ece0a437493dc61ce0bb6d459dcba482c34197e3efc829aa357f/MarkupSafe-3.0.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:38a9ef736c01fccdd6600705b09dc574584b89bea478200c5fbf112a6b0d5579", size = 21732 },
+    { url = "https://files.pythonhosted.org/packages/22/35/137da042dfb4720b638d2937c38a9c2df83fe32d20e8c8f3185dbfef05f7/MarkupSafe-3.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bbcb445fa71794da8f178f0f6d66789a28d7319071af7a496d4d507ed566270d", size = 20866 },
+    { url = "https://files.pythonhosted.org/packages/29/28/6d029a903727a1b62edb51863232152fd335d602def598dade38996887f0/MarkupSafe-3.0.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:57cb5a3cf367aeb1d316576250f65edec5bb3be939e9247ae594b4bcbc317dfb", size = 20964 },
+    { url = "https://files.pythonhosted.org/packages/cc/cd/07438f95f83e8bc028279909d9c9bd39e24149b0d60053a97b2bc4f8aa51/MarkupSafe-3.0.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:3809ede931876f5b2ec92eef964286840ed3540dadf803dd570c3b7e13141a3b", size = 21977 },
+    { url = "https://files.pythonhosted.org/packages/29/01/84b57395b4cc062f9c4c55ce0df7d3108ca32397299d9df00fedd9117d3d/MarkupSafe-3.0.2-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:e07c3764494e3776c602c1e78e298937c3315ccc9043ead7e685b7f2b8d47b3c", size = 21366 },
+    { url = "https://files.pythonhosted.org/packages/bd/6e/61ebf08d8940553afff20d1fb1ba7294b6f8d279df9fd0c0db911b4bbcfd/MarkupSafe-3.0.2-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:b424c77b206d63d500bcb69fa55ed8d0e6a3774056bdc4839fc9298a7edca171", size = 21091 },
+    { url = "https://files.pythonhosted.org/packages/11/23/ffbf53694e8c94ebd1e7e491de185124277964344733c45481f32ede2499/MarkupSafe-3.0.2-cp310-cp310-win32.whl", hash = "sha256:fcabf5ff6eea076f859677f5f0b6b5c1a51e70a376b0579e0eadef8db48c6b50", size = 15065 },
+    { url = "https://files.pythonhosted.org/packages/44/06/e7175d06dd6e9172d4a69a72592cb3f7a996a9c396eee29082826449bbc3/MarkupSafe-3.0.2-cp310-cp310-win_amd64.whl", hash = "sha256:6af100e168aa82a50e186c82875a5893c5597a0c1ccdb0d8b40240b1f28b969a", size = 15514 },
+    { url = "https://files.pythonhosted.org/packages/6b/28/bbf83e3f76936960b850435576dd5e67034e200469571be53f69174a2dfd/MarkupSafe-3.0.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:9025b4018f3a1314059769c7bf15441064b2207cb3f065e6ea1e7359cb46db9d", size = 14353 },
+    { url = "https://files.pythonhosted.org/packages/6c/30/316d194b093cde57d448a4c3209f22e3046c5bb2fb0820b118292b334be7/MarkupSafe-3.0.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:93335ca3812df2f366e80509ae119189886b0f3c2b81325d39efdb84a1e2ae93", size = 12392 },
+    { url = "https://files.pythonhosted.org/packages/f2/96/9cdafba8445d3a53cae530aaf83c38ec64c4d5427d975c974084af5bc5d2/MarkupSafe-3.0.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2cb8438c3cbb25e220c2ab33bb226559e7afb3baec11c4f218ffa7308603c832", size = 23984 },
+    { url = "https://files.pythonhosted.org/packages/f1/a4/aefb044a2cd8d7334c8a47d3fb2c9f328ac48cb349468cc31c20b539305f/MarkupSafe-3.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a123e330ef0853c6e822384873bef7507557d8e4a082961e1defa947aa59ba84", size = 23120 },
+    { url = "https://files.pythonhosted.org/packages/8d/21/5e4851379f88f3fad1de30361db501300d4f07bcad047d3cb0449fc51f8c/MarkupSafe-3.0.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1e084f686b92e5b83186b07e8a17fc09e38fff551f3602b249881fec658d3eca", size = 23032 },
+    { url = "https://files.pythonhosted.org/packages/00/7b/e92c64e079b2d0d7ddf69899c98842f3f9a60a1ae72657c89ce2655c999d/MarkupSafe-3.0.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d8213e09c917a951de9d09ecee036d5c7d36cb6cb7dbaece4c71a60d79fb9798", size = 24057 },
+    { url = "https://files.pythonhosted.org/packages/f9/ac/46f960ca323037caa0a10662ef97d0a4728e890334fc156b9f9e52bcc4ca/MarkupSafe-3.0.2-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:5b02fb34468b6aaa40dfc198d813a641e3a63b98c2b05a16b9f80b7ec314185e", size = 23359 },
+    { url = "https://files.pythonhosted.org/packages/69/84/83439e16197337b8b14b6a5b9c2105fff81d42c2a7c5b58ac7b62ee2c3b1/MarkupSafe-3.0.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:0bff5e0ae4ef2e1ae4fdf2dfd5b76c75e5c2fa4132d05fc1b0dabcd20c7e28c4", size = 23306 },
+    { url = "https://files.pythonhosted.org/packages/9a/34/a15aa69f01e2181ed8d2b685c0d2f6655d5cca2c4db0ddea775e631918cd/MarkupSafe-3.0.2-cp311-cp311-win32.whl", hash = "sha256:6c89876f41da747c8d3677a2b540fb32ef5715f97b66eeb0c6b66f5e3ef6f59d", size = 15094 },
+    { url = "https://files.pythonhosted.org/packages/da/b8/3a3bd761922d416f3dc5d00bfbed11f66b1ab89a0c2b6e887240a30b0f6b/MarkupSafe-3.0.2-cp311-cp311-win_amd64.whl", hash = "sha256:70a87b411535ccad5ef2f1df5136506a10775d267e197e4cf531ced10537bd6b", size = 15521 },
+    { url = "https://files.pythonhosted.org/packages/22/09/d1f21434c97fc42f09d290cbb6350d44eb12f09cc62c9476effdb33a18aa/MarkupSafe-3.0.2-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:9778bd8ab0a994ebf6f84c2b949e65736d5575320a17ae8984a77fab08db94cf", size = 14274 },
+    { url = "https://files.pythonhosted.org/packages/6b/b0/18f76bba336fa5aecf79d45dcd6c806c280ec44538b3c13671d49099fdd0/MarkupSafe-3.0.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:846ade7b71e3536c4e56b386c2a47adf5741d2d8b94ec9dc3e92e5e1ee1e2225", size = 12348 },
+    { url = "https://files.pythonhosted.org/packages/e0/25/dd5c0f6ac1311e9b40f4af06c78efde0f3b5cbf02502f8ef9501294c425b/MarkupSafe-3.0.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1c99d261bd2d5f6b59325c92c73df481e05e57f19837bdca8413b9eac4bd8028", size = 24149 },
+    { url = "https://files.pythonhosted.org/packages/f3/f0/89e7aadfb3749d0f52234a0c8c7867877876e0a20b60e2188e9850794c17/MarkupSafe-3.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e17c96c14e19278594aa4841ec148115f9c7615a47382ecb6b82bd8fea3ab0c8", size = 23118 },
+    { url = "https://files.pythonhosted.org/packages/d5/da/f2eeb64c723f5e3777bc081da884b414671982008c47dcc1873d81f625b6/MarkupSafe-3.0.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:88416bd1e65dcea10bc7569faacb2c20ce071dd1f87539ca2ab364bf6231393c", size = 22993 },
+    { url = "https://files.pythonhosted.org/packages/da/0e/1f32af846df486dce7c227fe0f2398dc7e2e51d4a370508281f3c1c5cddc/MarkupSafe-3.0.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:2181e67807fc2fa785d0592dc2d6206c019b9502410671cc905d132a92866557", size = 24178 },
+    { url = "https://files.pythonhosted.org/packages/c4/f6/bb3ca0532de8086cbff5f06d137064c8410d10779c4c127e0e47d17c0b71/MarkupSafe-3.0.2-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:52305740fe773d09cffb16f8ed0427942901f00adedac82ec8b67752f58a1b22", size = 23319 },
+    { url = "https://files.pythonhosted.org/packages/a2/82/8be4c96ffee03c5b4a034e60a31294daf481e12c7c43ab8e34a1453ee48b/MarkupSafe-3.0.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:ad10d3ded218f1039f11a75f8091880239651b52e9bb592ca27de44eed242a48", size = 23352 },
+    { url = "https://files.pythonhosted.org/packages/51/ae/97827349d3fcffee7e184bdf7f41cd6b88d9919c80f0263ba7acd1bbcb18/MarkupSafe-3.0.2-cp312-cp312-win32.whl", hash = "sha256:0f4ca02bea9a23221c0182836703cbf8930c5e9454bacce27e767509fa286a30", size = 15097 },
+    { url = "https://files.pythonhosted.org/packages/c1/80/a61f99dc3a936413c3ee4e1eecac96c0da5ed07ad56fd975f1a9da5bc630/MarkupSafe-3.0.2-cp312-cp312-win_amd64.whl", hash = "sha256:8e06879fc22a25ca47312fbe7c8264eb0b662f6db27cb2d3bbbc74b1df4b9b87", size = 15601 },
+    { url = "https://files.pythonhosted.org/packages/83/0e/67eb10a7ecc77a0c2bbe2b0235765b98d164d81600746914bebada795e97/MarkupSafe-3.0.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:ba9527cdd4c926ed0760bc301f6728ef34d841f405abf9d4f959c478421e4efd", size = 14274 },
+    { url = "https://files.pythonhosted.org/packages/2b/6d/9409f3684d3335375d04e5f05744dfe7e9f120062c9857df4ab490a1031a/MarkupSafe-3.0.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:f8b3d067f2e40fe93e1ccdd6b2e1d16c43140e76f02fb1319a05cf2b79d99430", size = 12352 },
+    { url = "https://files.pythonhosted.org/packages/d2/f5/6eadfcd3885ea85fe2a7c128315cc1bb7241e1987443d78c8fe712d03091/MarkupSafe-3.0.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:569511d3b58c8791ab4c2e1285575265991e6d8f8700c7be0e88f86cb0672094", size = 24122 },
+    { url = "https://files.pythonhosted.org/packages/0c/91/96cf928db8236f1bfab6ce15ad070dfdd02ed88261c2afafd4b43575e9e9/MarkupSafe-3.0.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:15ab75ef81add55874e7ab7055e9c397312385bd9ced94920f2802310c930396", size = 23085 },
+    { url = "https://files.pythonhosted.org/packages/c2/cf/c9d56af24d56ea04daae7ac0940232d31d5a8354f2b457c6d856b2057d69/MarkupSafe-3.0.2-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f3818cb119498c0678015754eba762e0d61e5b52d34c8b13d770f0719f7b1d79", size = 22978 },
+    { url = "https://files.pythonhosted.org/packages/2a/9f/8619835cd6a711d6272d62abb78c033bda638fdc54c4e7f4272cf1c0962b/MarkupSafe-3.0.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:cdb82a876c47801bb54a690c5ae105a46b392ac6099881cdfb9f6e95e4014c6a", size = 24208 },
+    { url = "https://files.pythonhosted.org/packages/f9/bf/176950a1792b2cd2102b8ffeb5133e1ed984547b75db47c25a67d3359f77/MarkupSafe-3.0.2-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:cabc348d87e913db6ab4aa100f01b08f481097838bdddf7c7a84b7575b7309ca", size = 23357 },
+    { url = "https://files.pythonhosted.org/packages/ce/4f/9a02c1d335caabe5c4efb90e1b6e8ee944aa245c1aaaab8e8a618987d816/MarkupSafe-3.0.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:444dcda765c8a838eaae23112db52f1efaf750daddb2d9ca300bcae1039adc5c", size = 23344 },
+    { url = "https://files.pythonhosted.org/packages/ee/55/c271b57db36f748f0e04a759ace9f8f759ccf22b4960c270c78a394f58be/MarkupSafe-3.0.2-cp313-cp313-win32.whl", hash = "sha256:bcf3e58998965654fdaff38e58584d8937aa3096ab5354d493c77d1fdd66d7a1", size = 15101 },
+    { url = "https://files.pythonhosted.org/packages/29/88/07df22d2dd4df40aba9f3e402e6dc1b8ee86297dddbad4872bd5e7b0094f/MarkupSafe-3.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:e6a2a455bd412959b57a172ce6328d2dd1f01cb2135efda2e4576e8a23fa3b0f", size = 15603 },
+    { url = "https://files.pythonhosted.org/packages/62/6a/8b89d24db2d32d433dffcd6a8779159da109842434f1dd2f6e71f32f738c/MarkupSafe-3.0.2-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:b5a6b3ada725cea8a5e634536b1b01c30bcdcd7f9c6fff4151548d5bf6b3a36c", size = 14510 },
+    { url = "https://files.pythonhosted.org/packages/7a/06/a10f955f70a2e5a9bf78d11a161029d278eeacbd35ef806c3fd17b13060d/MarkupSafe-3.0.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:a904af0a6162c73e3edcb969eeeb53a63ceeb5d8cf642fade7d39e7963a22ddb", size = 12486 },
+    { url = "https://files.pythonhosted.org/packages/34/cf/65d4a571869a1a9078198ca28f39fba5fbb910f952f9dbc5220afff9f5e6/MarkupSafe-3.0.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4aa4e5faecf353ed117801a068ebab7b7e09ffb6e1d5e412dc852e0da018126c", size = 25480 },
+    { url = "https://files.pythonhosted.org/packages/0c/e3/90e9651924c430b885468b56b3d597cabf6d72be4b24a0acd1fa0e12af67/MarkupSafe-3.0.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c0ef13eaeee5b615fb07c9a7dadb38eac06a0608b41570d8ade51c56539e509d", size = 23914 },
+    { url = "https://files.pythonhosted.org/packages/66/8c/6c7cf61f95d63bb866db39085150df1f2a5bd3335298f14a66b48e92659c/MarkupSafe-3.0.2-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d16a81a06776313e817c951135cf7340a3e91e8c1ff2fac444cfd75fffa04afe", size = 23796 },
+    { url = "https://files.pythonhosted.org/packages/bb/35/cbe9238ec3f47ac9a7c8b3df7a808e7cb50fe149dc7039f5f454b3fba218/MarkupSafe-3.0.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:6381026f158fdb7c72a168278597a5e3a5222e83ea18f543112b2662a9b699c5", size = 25473 },
+    { url = "https://files.pythonhosted.org/packages/e6/32/7621a4382488aa283cc05e8984a9c219abad3bca087be9ec77e89939ded9/MarkupSafe-3.0.2-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:3d79d162e7be8f996986c064d1c7c817f6df3a77fe3d6859f6f9e7be4b8c213a", size = 24114 },
+    { url = "https://files.pythonhosted.org/packages/0d/80/0985960e4b89922cb5a0bac0ed39c5b96cbc1a536a99f30e8c220a996ed9/MarkupSafe-3.0.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:131a3c7689c85f5ad20f9f6fb1b866f402c445b220c19fe4308c0b147ccd2ad9", size = 24098 },
+    { url = "https://files.pythonhosted.org/packages/82/78/fedb03c7d5380df2427038ec8d973587e90561b2d90cd472ce9254cf348b/MarkupSafe-3.0.2-cp313-cp313t-win32.whl", hash = "sha256:ba8062ed2cf21c07a9e295d5b8a2a5ce678b913b45fdf68c32d95d6c1291e0b6", size = 15208 },
+    { url = "https://files.pythonhosted.org/packages/4f/65/6079a46068dfceaeabb5dcad6d674f5f5c61a6fa5673746f42a9f4c233b3/MarkupSafe-3.0.2-cp313-cp313t-win_amd64.whl", hash = "sha256:e444a31f8db13eb18ada366ab3cf45fd4b31e4db1236a4448f68778c1d1a5a2f", size = 15739 },
+]
+
+[[package]]
+name = "matching"
+source = { editable = "." }
+dependencies = [
+    { name = "numpy" },
+    { name = "scipy" },
+]
+
+[package.optional-dependencies]
+dev = [
+    { name = "hypothesis" },
+    { name = "ipython", version = "8.34.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "ipython", version = "9.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "matplotlib" },
+    { name = "nbsphinx" },
+    { name = "nbval" },
+    { name = "pandas" },
+    { name = "pytest" },
+    { name = "pytest-cov" },
+    { name = "pytest-randomly" },
+    { name = "pytest-sugar" },
+    { name = "pyyaml" },
+    { name = "quartodoc" },
+    { name = "ruff" },
+]
+docs = [
+    { name = "ipython", version = "8.34.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "ipython", version = "9.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "matplotlib" },
+    { name = "nbsphinx" },
+    { name = "pandas" },
+    { name = "pyyaml" },
+    { name = "quartodoc" },
+]
+test = [
+    { name = "hypothesis" },
+    { name = "nbval" },
+    { name = "pytest" },
+    { name = "pytest-cov" },
+    { name = "pytest-randomly" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "hypothesis", marker = "extra == 'test'", specifier = ">=6.129.4" },
+    { name = "ipython", marker = "extra == 'docs'", specifier = ">=7.34.0" },
+    { name = "matching", extras = ["docs", "test"], marker = "extra == 'dev'" },
+    { name = "matplotlib", marker = "extra == 'docs'", specifier = ">=3.10.1" },
+    { name = "nbsphinx", marker = "extra == 'docs'", specifier = ">=0.9.7" },
+    { name = "nbval", marker = "extra == 'test'", specifier = ">=0.11.0" },
+    { name = "numpy", specifier = ">=2.2.4" },
+    { name = "pandas", marker = "extra == 'docs'", specifier = ">=2.2.3" },
+    { name = "pytest", marker = "extra == 'test'", specifier = ">=7.4.4" },
+    { name = "pytest-cov", marker = "extra == 'test'", specifier = ">=6.0.0" },
+    { name = "pytest-randomly", marker = "extra == 'test'", specifier = ">=3.16.0" },
+    { name = "pytest-sugar", marker = "extra == 'dev'", specifier = ">=1.0.0" },
+    { name = "pyyaml", marker = "extra == 'docs'", specifier = ">=6.0.1" },
+    { name = "quartodoc", marker = "extra == 'docs'", specifier = ">=0.9.1" },
+    { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.11.1" },
+    { name = "scipy", specifier = ">=1.11.3" },
+]
+provides-extras = ["test", "docs", "dev"]
+
+[[package]]
+name = "matplotlib"
+version = "3.10.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "contourpy" },
+    { name = "cycler" },
+    { name = "fonttools" },
+    { name = "kiwisolver" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pillow" },
+    { name = "pyparsing" },
+    { name = "python-dateutil" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/2f/08/b89867ecea2e305f408fbb417139a8dd941ecf7b23a2e02157c36da546f0/matplotlib-3.10.1.tar.gz", hash = "sha256:e8d2d0e3881b129268585bf4765ad3ee73a4591d77b9a18c214ac7e3a79fb2ba", size = 36743335 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ee/b1/f70e27cf1cd76ce2a5e1aa5579d05afe3236052c6d9b9a96325bc823a17e/matplotlib-3.10.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:ff2ae14910be903f4a24afdbb6d7d3a6c44da210fc7d42790b87aeac92238a16", size = 8163654 },
+    { url = "https://files.pythonhosted.org/packages/26/af/5ec3d4636106718bb62503a03297125d4514f98fe818461bd9e6b9d116e4/matplotlib-3.10.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0721a3fd3d5756ed593220a8b86808a36c5031fce489adb5b31ee6dbb47dd5b2", size = 8037943 },
+    { url = "https://files.pythonhosted.org/packages/a1/3d/07f9003a71b698b848c9925d05979ffa94a75cd25d1a587202f0bb58aa81/matplotlib-3.10.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d0673b4b8f131890eb3a1ad058d6e065fb3c6e71f160089b65f8515373394698", size = 8449510 },
+    { url = "https://files.pythonhosted.org/packages/12/87/9472d4513ff83b7cd864311821793ab72234fa201ab77310ec1b585d27e2/matplotlib-3.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8e875b95ac59a7908978fe307ecdbdd9a26af7fa0f33f474a27fcf8c99f64a19", size = 8586585 },
+    { url = "https://files.pythonhosted.org/packages/31/9e/fe74d237d2963adae8608faeb21f778cf246dbbf4746cef87cffbc82c4b6/matplotlib-3.10.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:2589659ea30726284c6c91037216f64a506a9822f8e50592d48ac16a2f29e044", size = 9397911 },
+    { url = "https://files.pythonhosted.org/packages/b6/1b/025d3e59e8a4281ab463162ad7d072575354a1916aba81b6a11507dfc524/matplotlib-3.10.1-cp310-cp310-win_amd64.whl", hash = "sha256:a97ff127f295817bc34517255c9db6e71de8eddaab7f837b7d341dee9f2f587f", size = 8052998 },
+    { url = "https://files.pythonhosted.org/packages/a5/14/a1b840075be247bb1834b22c1e1d558740b0f618fe3a823740181ca557a1/matplotlib-3.10.1-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:057206ff2d6ab82ff3e94ebd94463d084760ca682ed5f150817b859372ec4401", size = 8174669 },
+    { url = "https://files.pythonhosted.org/packages/0a/e4/300b08e3e08f9c98b0d5635f42edabf2f7a1d634e64cb0318a71a44ff720/matplotlib-3.10.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a144867dd6bf8ba8cb5fc81a158b645037e11b3e5cf8a50bd5f9917cb863adfe", size = 8047996 },
+    { url = "https://files.pythonhosted.org/packages/75/f9/8d99ff5a2498a5f1ccf919fb46fb945109623c6108216f10f96428f388bc/matplotlib-3.10.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:56c5d9fcd9879aa8040f196a235e2dcbdf7dd03ab5b07c0696f80bc6cf04bedd", size = 8461612 },
+    { url = "https://files.pythonhosted.org/packages/40/b8/53fa08a5eaf78d3a7213fd6da1feec4bae14a81d9805e567013811ff0e85/matplotlib-3.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0f69dc9713e4ad2fb21a1c30e37bd445d496524257dfda40ff4a8efb3604ab5c", size = 8602258 },
+    { url = "https://files.pythonhosted.org/packages/40/87/4397d2ce808467af86684a622dd112664553e81752ea8bf61bdd89d24a41/matplotlib-3.10.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:4c59af3e8aca75d7744b68e8e78a669e91ccbcf1ac35d0102a7b1b46883f1dd7", size = 9408896 },
+    { url = "https://files.pythonhosted.org/packages/d7/68/0d03098b3feb786cbd494df0aac15b571effda7f7cbdec267e8a8d398c16/matplotlib-3.10.1-cp311-cp311-win_amd64.whl", hash = "sha256:11b65088c6f3dae784bc72e8d039a2580186285f87448babb9ddb2ad0082993a", size = 8061281 },
+    { url = "https://files.pythonhosted.org/packages/7c/1d/5e0dc3b59c034e43de16f94deb68f4ad8a96b3ea00f4b37c160b7474928e/matplotlib-3.10.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:66e907a06e68cb6cfd652c193311d61a12b54f56809cafbed9736ce5ad92f107", size = 8175488 },
+    { url = "https://files.pythonhosted.org/packages/7a/81/dae7e14042e74da658c3336ab9799128e09a1ee03964f2d89630b5d12106/matplotlib-3.10.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:e9b4bb156abb8fa5e5b2b460196f7db7264fc6d62678c03457979e7d5254b7be", size = 8046264 },
+    { url = "https://files.pythonhosted.org/packages/21/c4/22516775dcde10fc9c9571d155f90710761b028fc44f660508106c363c97/matplotlib-3.10.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1985ad3d97f51307a2cbfc801a930f120def19ba22864182dacef55277102ba6", size = 8452048 },
+    { url = "https://files.pythonhosted.org/packages/63/23/c0615001f67ce7c96b3051d856baedc0c818a2ed84570b9bf9bde200f85d/matplotlib-3.10.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c96f2c2f825d1257e437a1482c5a2cf4fee15db4261bd6fc0750f81ba2b4ba3d", size = 8597111 },
+    { url = "https://files.pythonhosted.org/packages/ca/c0/a07939a82aed77770514348f4568177d7dadab9787ebc618a616fe3d665e/matplotlib-3.10.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:35e87384ee9e488d8dd5a2dd7baf471178d38b90618d8ea147aced4ab59c9bea", size = 9402771 },
+    { url = "https://files.pythonhosted.org/packages/a6/b6/a9405484fb40746fdc6ae4502b16a9d6e53282ba5baaf9ebe2da579f68c4/matplotlib-3.10.1-cp312-cp312-win_amd64.whl", hash = "sha256:cfd414bce89cc78a7e1d25202e979b3f1af799e416010a20ab2b5ebb3a02425c", size = 8063742 },
+    { url = "https://files.pythonhosted.org/packages/60/73/6770ff5e5523d00f3bc584acb6031e29ee5c8adc2336b16cd1d003675fe0/matplotlib-3.10.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c42eee41e1b60fd83ee3292ed83a97a5f2a8239b10c26715d8a6172226988d7b", size = 8176112 },
+    { url = "https://files.pythonhosted.org/packages/08/97/b0ca5da0ed54a3f6599c3ab568bdda65269bc27c21a2c97868c1625e4554/matplotlib-3.10.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:4f0647b17b667ae745c13721602b540f7aadb2a32c5b96e924cd4fea5dcb90f1", size = 8046931 },
+    { url = "https://files.pythonhosted.org/packages/df/9a/1acbdc3b165d4ce2dcd2b1a6d4ffb46a7220ceee960c922c3d50d8514067/matplotlib-3.10.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aa3854b5f9473564ef40a41bc922be978fab217776e9ae1545c9b3a5cf2092a3", size = 8453422 },
+    { url = "https://files.pythonhosted.org/packages/51/d0/2bc4368abf766203e548dc7ab57cf7e9c621f1a3c72b516cc7715347b179/matplotlib-3.10.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e496c01441be4c7d5f96d4e40f7fca06e20dcb40e44c8daa2e740e1757ad9e6", size = 8596819 },
+    { url = "https://files.pythonhosted.org/packages/ab/1b/8b350f8a1746c37ab69dda7d7528d1fc696efb06db6ade9727b7887be16d/matplotlib-3.10.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5d45d3f5245be5b469843450617dcad9af75ca50568acf59997bed9311131a0b", size = 9402782 },
+    { url = "https://files.pythonhosted.org/packages/89/06/f570373d24d93503988ba8d04f213a372fa1ce48381c5eb15da985728498/matplotlib-3.10.1-cp313-cp313-win_amd64.whl", hash = "sha256:8e8e25b1209161d20dfe93037c8a7f7ca796ec9aa326e6e4588d8c4a5dd1e473", size = 8063812 },
+    { url = "https://files.pythonhosted.org/packages/fc/e0/8c811a925b5a7ad75135f0e5af46408b78af88bbb02a1df775100ef9bfef/matplotlib-3.10.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:19b06241ad89c3ae9469e07d77efa87041eac65d78df4fcf9cac318028009b01", size = 8214021 },
+    { url = "https://files.pythonhosted.org/packages/4a/34/319ec2139f68ba26da9d00fce2ff9f27679fb799a6c8e7358539801fd629/matplotlib-3.10.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:01e63101ebb3014e6e9f80d9cf9ee361a8599ddca2c3e166c563628b39305dbb", size = 8090782 },
+    { url = "https://files.pythonhosted.org/packages/77/ea/9812124ab9a99df5b2eec1110e9b2edc0b8f77039abf4c56e0a376e84a29/matplotlib-3.10.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3f06bad951eea6422ac4e8bdebcf3a70c59ea0a03338c5d2b109f57b64eb3972", size = 8478901 },
+    { url = "https://files.pythonhosted.org/packages/c9/db/b05bf463689134789b06dea85828f8ebe506fa1e37593f723b65b86c9582/matplotlib-3.10.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a3dfb036f34873b46978f55e240cff7a239f6c4409eac62d8145bad3fc6ba5a3", size = 8613864 },
+    { url = "https://files.pythonhosted.org/packages/c2/04/41ccec4409f3023a7576df3b5c025f1a8c8b81fbfe922ecfd837ac36e081/matplotlib-3.10.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dc6ab14a7ab3b4d813b88ba957fc05c79493a037f54e246162033591e770de6f", size = 9409487 },
+    { url = "https://files.pythonhosted.org/packages/ac/c2/0d5aae823bdcc42cc99327ecdd4d28585e15ccd5218c453b7bcd827f3421/matplotlib-3.10.1-cp313-cp313t-win_amd64.whl", hash = "sha256:bc411ebd5889a78dabbc457b3fa153203e22248bfa6eedc6797be5df0164dbf9", size = 8134832 },
+    { url = "https://files.pythonhosted.org/packages/c8/f6/10adb696d8cbeed2ab4c2e26ecf1c80dd3847bbf3891f4a0c362e0e08a5a/matplotlib-3.10.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:648406f1899f9a818cef8c0231b44dcfc4ff36f167101c3fd1c9151f24220fdc", size = 8158685 },
+    { url = "https://files.pythonhosted.org/packages/3f/84/0603d917406072763e7f9bb37747d3d74d7ecd4b943a8c947cc3ae1cf7af/matplotlib-3.10.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:02582304e352f40520727984a5a18f37e8187861f954fea9be7ef06569cf85b4", size = 8035491 },
+    { url = "https://files.pythonhosted.org/packages/fd/7d/6a8b31dd07ed856b3eae001c9129670ef75c4698fa1c2a6ac9f00a4a7054/matplotlib-3.10.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d3809916157ba871bcdd33d3493acd7fe3037db5daa917ca6e77975a94cef779", size = 8590087 },
+]
+
+[[package]]
+name = "matplotlib-inline"
+version = "0.1.7"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/99/5b/a36a337438a14116b16480db471ad061c36c3694df7c2084a0da7ba538b7/matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90", size = 8159 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8f/8e/9ad090d3553c280a8060fbf6e24dc1c0c29704ee7d1c372f0c174aa59285/matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca", size = 9899 },
+]
+
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d6/54/cfe61301667036ec958cb99bd3efefba235e65cdeb9c84d24a8293ba1d90/mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba", size = 8729 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979 },
+]
+
+[[package]]
+name = "mistune"
+version = "3.1.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c4/79/bda47f7dd7c3c55770478d6d02c9960c430b0cf1773b72366ff89126ea31/mistune-3.1.3.tar.gz", hash = "sha256:a7035c21782b2becb6be62f8f25d3df81ccb4d6fa477a6525b15af06539f02a0", size = 94347 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/01/4d/23c4e4f09da849e127e9f123241946c23c1e30f45a88366879e064211815/mistune-3.1.3-py3-none-any.whl", hash = "sha256:1a32314113cff28aa6432e99e522677c8587fd83e3d51c29b82a52409c842bd9", size = 53410 },
+]
+
+[[package]]
+name = "mypy-extensions"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/98/a4/1ab47638b92648243faf97a5aeb6ea83059cc3624972ab6b8d2316078d3f/mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782", size = 4433 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2a/e2/5d3f6ada4297caebe1a2add3b126fe800c96f56dbe5d1988a2cbe0b267aa/mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d", size = 4695 },
+]
+
+[[package]]
+name = "nbclient"
+version = "0.10.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jupyter-client" },
+    { name = "jupyter-core" },
+    { name = "nbformat" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/87/66/7ffd18d58eae90d5721f9f39212327695b749e23ad44b3881744eaf4d9e8/nbclient-0.10.2.tar.gz", hash = "sha256:90b7fc6b810630db87a6d0c2250b1f0ab4cf4d3c27a299b0cde78a4ed3fd9193", size = 62424 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/34/6d/e7fa07f03a4a7b221d94b4d586edb754a9b0dc3c9e2c93353e9fa4e0d117/nbclient-0.10.2-py3-none-any.whl", hash = "sha256:4ffee11e788b4a27fabeb7955547e4318a5298f34342a4bfd01f2e1faaeadc3d", size = 25434 },
+]
+
+[[package]]
+name = "nbconvert"
+version = "7.16.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "beautifulsoup4" },
+    { name = "bleach", extra = ["css"] },
+    { name = "defusedxml" },
+    { name = "jinja2" },
+    { name = "jupyter-core" },
+    { name = "jupyterlab-pygments" },
+    { name = "markupsafe" },
+    { name = "mistune" },
+    { name = "nbclient" },
+    { name = "nbformat" },
+    { name = "packaging" },
+    { name = "pandocfilters" },
+    { name = "pygments" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a3/59/f28e15fc47ffb73af68a8d9b47367a8630d76e97ae85ad18271b9db96fdf/nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582", size = 857715 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/cc/9a/cd673b2f773a12c992f41309ef81b99da1690426bd2f96957a7ade0d3ed7/nbconvert-7.16.6-py3-none-any.whl", hash = "sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b", size = 258525 },
+]
+
+[[package]]
+name = "nbformat"
+version = "5.10.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "fastjsonschema" },
+    { name = "jsonschema" },
+    { name = "jupyter-core" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/6d/fd/91545e604bc3dad7dca9ed03284086039b294c6b3d75c0d2fa45f9e9caf3/nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a", size = 142749 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b", size = 78454 },
+]
+
+[[package]]
+name = "nbsphinx"
+version = "0.9.7"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "docutils" },
+    { name = "jinja2" },
+    { name = "nbconvert" },
+    { name = "nbformat" },
+    { name = "sphinx" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/1e/84/b1856b7651ac34e965aa567a158714c7f3bd42a1b1ce76bf423ffb99872c/nbsphinx-0.9.7.tar.gz", hash = "sha256:abd298a686d55fa894ef697c51d44f24e53aa312dadae38e82920f250a5456fe", size = 180479 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/49/2d/8c8e635bcc6757573d311bb3c5445426382f280da32b8cd6d82d501ef4a4/nbsphinx-0.9.7-py3-none-any.whl", hash = "sha256:7292c3767fea29e405c60743eee5393682a83982ab202ff98f5eb2db02629da8", size = 31660 },
+]
+
+[[package]]
+name = "nbval"
+version = "0.11.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "coverage" },
+    { name = "ipykernel" },
+    { name = "jupyter-client" },
+    { name = "nbformat" },
+    { name = "pytest" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/28/be/22bd64d09e0cb53258f83b6fc455f05f18a78e3e5c109ccb6af42f1f49a2/nbval-0.11.0.tar.gz", hash = "sha256:77c95797607b0a968babd2597ee3494102d25c3ad37435debbdac0e46e379094", size = 62718 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2c/5c/eb1e3ce54c4e94c7734b3831756c63f21badb3de91a98d77b9e23c0ca76a/nbval-0.11.0-py2.py3-none-any.whl", hash = "sha256:307aecc866c9a1e8a13bb5bbb008a702bacfda2394dff6fe504a3108a58042a0", size = 24013 },
+]
+
+[[package]]
+name = "nest-asyncio"
+version = "1.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195 },
+]
+
+[[package]]
+name = "numpy"
+version = "2.2.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e1/78/31103410a57bc2c2b93a3597340a8119588571f6a4539067546cb9a0bfac/numpy-2.2.4.tar.gz", hash = "sha256:9ba03692a45d3eef66559efe1d1096c4b9b75c0986b5dff5530c378fb8331d4f", size = 20270701 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/04/89/a79e86e5c1433926ed7d60cb267fb64aa578b6101ab645800fd43b4801de/numpy-2.2.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8146f3550d627252269ac42ae660281d673eb6f8b32f113538e0cc2a9aed42b9", size = 21250661 },
+    { url = "https://files.pythonhosted.org/packages/79/c2/f50921beb8afd60ed9589ad880332cfefdb805422210d327fb48f12b7a81/numpy-2.2.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e642d86b8f956098b564a45e6f6ce68a22c2c97a04f5acd3f221f57b8cb850ae", size = 14389926 },
+    { url = "https://files.pythonhosted.org/packages/c7/b9/2c4e96130b0b0f97b0ef4a06d6dae3b39d058b21a5e2fa2decd7fd6b1c8f/numpy-2.2.4-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:a84eda42bd12edc36eb5b53bbcc9b406820d3353f1994b6cfe453a33ff101775", size = 5428329 },
+    { url = "https://files.pythonhosted.org/packages/7f/a5/3d7094aa898f4fc5c84cdfb26beeae780352d43f5d8bdec966c4393d644c/numpy-2.2.4-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:4ba5054787e89c59c593a4169830ab362ac2bee8a969249dc56e5d7d20ff8df9", size = 6963559 },
+    { url = "https://files.pythonhosted.org/packages/4c/22/fb1be710a14434c09080dd4a0acc08939f612ec02efcb04b9e210474782d/numpy-2.2.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7716e4a9b7af82c06a2543c53ca476fa0b57e4d760481273e09da04b74ee6ee2", size = 14368066 },
+    { url = "https://files.pythonhosted.org/packages/c2/07/2e5cc71193e3ef3a219ffcf6ca4858e46ea2be09c026ddd480d596b32867/numpy-2.2.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:adf8c1d66f432ce577d0197dceaac2ac00c0759f573f28516246351c58a85020", size = 16417040 },
+    { url = "https://files.pythonhosted.org/packages/1a/97/3b1537776ad9a6d1a41813818343745e8dd928a2916d4c9edcd9a8af1dac/numpy-2.2.4-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:218f061d2faa73621fa23d6359442b0fc658d5b9a70801373625d958259eaca3", size = 15879862 },
+    { url = "https://files.pythonhosted.org/packages/b0/b7/4472f603dd45ef36ff3d8e84e84fe02d9467c78f92cc121633dce6da307b/numpy-2.2.4-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:df2f57871a96bbc1b69733cd4c51dc33bea66146b8c63cacbfed73eec0883017", size = 18206032 },
+    { url = "https://files.pythonhosted.org/packages/0d/bd/6a092963fb82e6c5aa0d0440635827bbb2910da229545473bbb58c537ed3/numpy-2.2.4-cp310-cp310-win32.whl", hash = "sha256:a0258ad1f44f138b791327961caedffbf9612bfa504ab9597157806faa95194a", size = 6608517 },
+    { url = "https://files.pythonhosted.org/packages/01/e3/cb04627bc2a1638948bc13e818df26495aa18e20d5be1ed95ab2b10b6847/numpy-2.2.4-cp310-cp310-win_amd64.whl", hash = "sha256:0d54974f9cf14acf49c60f0f7f4084b6579d24d439453d5fc5805d46a165b542", size = 12943498 },
+    { url = "https://files.pythonhosted.org/packages/16/fb/09e778ee3a8ea0d4dc8329cca0a9c9e65fed847d08e37eba74cb7ed4b252/numpy-2.2.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:e9e0a277bb2eb5d8a7407e14688b85fd8ad628ee4e0c7930415687b6564207a4", size = 21254989 },
+    { url = "https://files.pythonhosted.org/packages/a2/0a/1212befdbecab5d80eca3cde47d304cad986ad4eec7d85a42e0b6d2cc2ef/numpy-2.2.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9eeea959168ea555e556b8188da5fa7831e21d91ce031e95ce23747b7609f8a4", size = 14425910 },
+    { url = "https://files.pythonhosted.org/packages/2b/3e/e7247c1d4f15086bb106c8d43c925b0b2ea20270224f5186fa48d4fb5cbd/numpy-2.2.4-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:bd3ad3b0a40e713fc68f99ecfd07124195333f1e689387c180813f0e94309d6f", size = 5426490 },
+    { url = "https://files.pythonhosted.org/packages/5d/fa/aa7cd6be51419b894c5787a8a93c3302a1ed4f82d35beb0613ec15bdd0e2/numpy-2.2.4-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:cf28633d64294969c019c6df4ff37f5698e8326db68cc2b66576a51fad634880", size = 6967754 },
+    { url = "https://files.pythonhosted.org/packages/d5/ee/96457c943265de9fadeb3d2ffdbab003f7fba13d971084a9876affcda095/numpy-2.2.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2fa8fa7697ad1646b5c93de1719965844e004fcad23c91228aca1cf0800044a1", size = 14373079 },
+    { url = "https://files.pythonhosted.org/packages/c5/5c/ceefca458559f0ccc7a982319f37ed07b0d7b526964ae6cc61f8ad1b6119/numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f4162988a360a29af158aeb4a2f4f09ffed6a969c9776f8f3bdee9b06a8ab7e5", size = 16428819 },
+    { url = "https://files.pythonhosted.org/packages/22/31/9b2ac8eee99e001eb6add9fa27514ef5e9faf176169057a12860af52704c/numpy-2.2.4-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:892c10d6a73e0f14935c31229e03325a7b3093fafd6ce0af704be7f894d95687", size = 15881470 },
+    { url = "https://files.pythonhosted.org/packages/f0/dc/8569b5f25ff30484b555ad8a3f537e0225d091abec386c9420cf5f7a2976/numpy-2.2.4-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:db1f1c22173ac1c58db249ae48aa7ead29f534b9a948bc56828337aa84a32ed6", size = 18218144 },
+    { url = "https://files.pythonhosted.org/packages/5e/05/463c023a39bdeb9bb43a99e7dee2c664cb68d5bb87d14f92482b9f6011cc/numpy-2.2.4-cp311-cp311-win32.whl", hash = "sha256:ea2bb7e2ae9e37d96835b3576a4fa4b3a97592fbea8ef7c3587078b0068b8f09", size = 6606368 },
+    { url = "https://files.pythonhosted.org/packages/8b/72/10c1d2d82101c468a28adc35de6c77b308f288cfd0b88e1070f15b98e00c/numpy-2.2.4-cp311-cp311-win_amd64.whl", hash = "sha256:f7de08cbe5551911886d1ab60de58448c6df0f67d9feb7d1fb21e9875ef95e91", size = 12947526 },
+    { url = "https://files.pythonhosted.org/packages/a2/30/182db21d4f2a95904cec1a6f779479ea1ac07c0647f064dea454ec650c42/numpy-2.2.4-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:a7b9084668aa0f64e64bd00d27ba5146ef1c3a8835f3bd912e7a9e01326804c4", size = 20947156 },
+    { url = "https://files.pythonhosted.org/packages/24/6d/9483566acfbda6c62c6bc74b6e981c777229d2af93c8eb2469b26ac1b7bc/numpy-2.2.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:dbe512c511956b893d2dacd007d955a3f03d555ae05cfa3ff1c1ff6df8851854", size = 14133092 },
+    { url = "https://files.pythonhosted.org/packages/27/f6/dba8a258acbf9d2bed2525cdcbb9493ef9bae5199d7a9cb92ee7e9b2aea6/numpy-2.2.4-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:bb649f8b207ab07caebba230d851b579a3c8711a851d29efe15008e31bb4de24", size = 5163515 },
+    { url = "https://files.pythonhosted.org/packages/62/30/82116199d1c249446723c68f2c9da40d7f062551036f50b8c4caa42ae252/numpy-2.2.4-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:f34dc300df798742b3d06515aa2a0aee20941c13579d7a2f2e10af01ae4901ee", size = 6696558 },
+    { url = "https://files.pythonhosted.org/packages/0e/b2/54122b3c6df5df3e87582b2e9430f1bdb63af4023c739ba300164c9ae503/numpy-2.2.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c3f7ac96b16955634e223b579a3e5798df59007ca43e8d451a0e6a50f6bfdfba", size = 14084742 },
+    { url = "https://files.pythonhosted.org/packages/02/e2/e2cbb8d634151aab9528ef7b8bab52ee4ab10e076509285602c2a3a686e0/numpy-2.2.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4f92084defa704deadd4e0a5ab1dc52d8ac9e8a8ef617f3fbb853e79b0ea3592", size = 16134051 },
+    { url = "https://files.pythonhosted.org/packages/8e/21/efd47800e4affc993e8be50c1b768de038363dd88865920439ef7b422c60/numpy-2.2.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:7a4e84a6283b36632e2a5b56e121961f6542ab886bc9e12f8f9818b3c266bfbb", size = 15578972 },
+    { url = "https://files.pythonhosted.org/packages/04/1e/f8bb88f6157045dd5d9b27ccf433d016981032690969aa5c19e332b138c0/numpy-2.2.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:11c43995255eb4127115956495f43e9343736edb7fcdb0d973defd9de14cd84f", size = 17898106 },
+    { url = "https://files.pythonhosted.org/packages/2b/93/df59a5a3897c1f036ae8ff845e45f4081bb06943039ae28a3c1c7c780f22/numpy-2.2.4-cp312-cp312-win32.whl", hash = "sha256:65ef3468b53269eb5fdb3a5c09508c032b793da03251d5f8722b1194f1790c00", size = 6311190 },
+    { url = "https://files.pythonhosted.org/packages/46/69/8c4f928741c2a8efa255fdc7e9097527c6dc4e4df147e3cadc5d9357ce85/numpy-2.2.4-cp312-cp312-win_amd64.whl", hash = "sha256:2aad3c17ed2ff455b8eaafe06bcdae0062a1db77cb99f4b9cbb5f4ecb13c5146", size = 12644305 },
+    { url = "https://files.pythonhosted.org/packages/2a/d0/bd5ad792e78017f5decfb2ecc947422a3669a34f775679a76317af671ffc/numpy-2.2.4-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:1cf4e5c6a278d620dee9ddeb487dc6a860f9b199eadeecc567f777daace1e9e7", size = 20933623 },
+    { url = "https://files.pythonhosted.org/packages/c3/bc/2b3545766337b95409868f8e62053135bdc7fa2ce630aba983a2aa60b559/numpy-2.2.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1974afec0b479e50438fc3648974268f972e2d908ddb6d7fb634598cdb8260a0", size = 14148681 },
+    { url = "https://files.pythonhosted.org/packages/6a/70/67b24d68a56551d43a6ec9fe8c5f91b526d4c1a46a6387b956bf2d64744e/numpy-2.2.4-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:79bd5f0a02aa16808fcbc79a9a376a147cc1045f7dfe44c6e7d53fa8b8a79392", size = 5148759 },
+    { url = "https://files.pythonhosted.org/packages/1c/8b/e2fc8a75fcb7be12d90b31477c9356c0cbb44abce7ffb36be39a0017afad/numpy-2.2.4-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:3387dd7232804b341165cedcb90694565a6015433ee076c6754775e85d86f1fc", size = 6683092 },
+    { url = "https://files.pythonhosted.org/packages/13/73/41b7b27f169ecf368b52533edb72e56a133f9e86256e809e169362553b49/numpy-2.2.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6f527d8fdb0286fd2fd97a2a96c6be17ba4232da346931d967a0630050dfd298", size = 14081422 },
+    { url = "https://files.pythonhosted.org/packages/4b/04/e208ff3ae3ddfbafc05910f89546382f15a3f10186b1f56bd99f159689c2/numpy-2.2.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bce43e386c16898b91e162e5baaad90c4b06f9dcbe36282490032cec98dc8ae7", size = 16132202 },
+    { url = "https://files.pythonhosted.org/packages/fe/bc/2218160574d862d5e55f803d88ddcad88beff94791f9c5f86d67bd8fbf1c/numpy-2.2.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:31504f970f563d99f71a3512d0c01a645b692b12a63630d6aafa0939e52361e6", size = 15573131 },
+    { url = "https://files.pythonhosted.org/packages/a5/78/97c775bc4f05abc8a8426436b7cb1be806a02a2994b195945600855e3a25/numpy-2.2.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:81413336ef121a6ba746892fad881a83351ee3e1e4011f52e97fba79233611fd", size = 17894270 },
+    { url = "https://files.pythonhosted.org/packages/b9/eb/38c06217a5f6de27dcb41524ca95a44e395e6a1decdc0c99fec0832ce6ae/numpy-2.2.4-cp313-cp313-win32.whl", hash = "sha256:f486038e44caa08dbd97275a9a35a283a8f1d2f0ee60ac260a1790e76660833c", size = 6308141 },
+    { url = "https://files.pythonhosted.org/packages/52/17/d0dd10ab6d125c6d11ffb6dfa3423c3571befab8358d4f85cd4471964fcd/numpy-2.2.4-cp313-cp313-win_amd64.whl", hash = "sha256:207a2b8441cc8b6a2a78c9ddc64d00d20c303d79fba08c577752f080c4007ee3", size = 12636885 },
+    { url = "https://files.pythonhosted.org/packages/fa/e2/793288ede17a0fdc921172916efb40f3cbc2aa97e76c5c84aba6dc7e8747/numpy-2.2.4-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:8120575cb4882318c791f839a4fd66161a6fa46f3f0a5e613071aae35b5dd8f8", size = 20961829 },
+    { url = "https://files.pythonhosted.org/packages/3a/75/bb4573f6c462afd1ea5cbedcc362fe3e9bdbcc57aefd37c681be1155fbaa/numpy-2.2.4-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:a761ba0fa886a7bb33c6c8f6f20213735cb19642c580a931c625ee377ee8bd39", size = 14161419 },
+    { url = "https://files.pythonhosted.org/packages/03/68/07b4cd01090ca46c7a336958b413cdbe75002286295f2addea767b7f16c9/numpy-2.2.4-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:ac0280f1ba4a4bfff363a99a6aceed4f8e123f8a9b234c89140f5e894e452ecd", size = 5196414 },
+    { url = "https://files.pythonhosted.org/packages/a5/fd/d4a29478d622fedff5c4b4b4cedfc37a00691079623c0575978d2446db9e/numpy-2.2.4-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:879cf3a9a2b53a4672a168c21375166171bc3932b7e21f622201811c43cdd3b0", size = 6709379 },
+    { url = "https://files.pythonhosted.org/packages/41/78/96dddb75bb9be730b87c72f30ffdd62611aba234e4e460576a068c98eff6/numpy-2.2.4-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f05d4198c1bacc9124018109c5fba2f3201dbe7ab6e92ff100494f236209c960", size = 14051725 },
+    { url = "https://files.pythonhosted.org/packages/00/06/5306b8199bffac2a29d9119c11f457f6c7d41115a335b78d3f86fad4dbe8/numpy-2.2.4-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e2f085ce2e813a50dfd0e01fbfc0c12bbe5d2063d99f8b29da30e544fb6483b8", size = 16101638 },
+    { url = "https://files.pythonhosted.org/packages/fa/03/74c5b631ee1ded596945c12027649e6344614144369fd3ec1aaced782882/numpy-2.2.4-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:92bda934a791c01d6d9d8e038363c50918ef7c40601552a58ac84c9613a665bc", size = 15571717 },
+    { url = "https://files.pythonhosted.org/packages/cb/dc/4fc7c0283abe0981e3b89f9b332a134e237dd476b0c018e1e21083310c31/numpy-2.2.4-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ee4d528022f4c5ff67332469e10efe06a267e32f4067dc76bb7e2cddf3cd25ff", size = 17879998 },
+    { url = "https://files.pythonhosted.org/packages/e5/2b/878576190c5cfa29ed896b518cc516aecc7c98a919e20706c12480465f43/numpy-2.2.4-cp313-cp313t-win32.whl", hash = "sha256:05c076d531e9998e7e694c36e8b349969c56eadd2cdcd07242958489d79a7286", size = 6366896 },
+    { url = "https://files.pythonhosted.org/packages/3e/05/eb7eec66b95cf697f08c754ef26c3549d03ebd682819f794cb039574a0a6/numpy-2.2.4-cp313-cp313t-win_amd64.whl", hash = "sha256:188dcbca89834cc2e14eb2f106c96d6d46f200fe0200310fc29089657379c58d", size = 12739119 },
+    { url = "https://files.pythonhosted.org/packages/b2/5c/f09c33a511aff41a098e6ef3498465d95f6360621034a3d95f47edbc9119/numpy-2.2.4-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:7051ee569db5fbac144335e0f3b9c2337e0c8d5c9fee015f259a5bd70772b7e8", size = 21081956 },
+    { url = "https://files.pythonhosted.org/packages/ba/30/74c48b3b6494c4b820b7fa1781d441e94d87a08daa5b35d222f06ba41a6f/numpy-2.2.4-pp310-pypy310_pp73-macosx_14_0_x86_64.whl", hash = "sha256:ab2939cd5bec30a7430cbdb2287b63151b77cf9624de0532d629c9a1c59b1d5c", size = 6827143 },
+    { url = "https://files.pythonhosted.org/packages/54/f5/ab0d2f48b490535c7a80e05da4a98902b632369efc04f0e47bb31ca97d8f/numpy-2.2.4-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d0f35b19894a9e08639fd60a1ec1978cb7f5f7f1eace62f38dd36be8aecdef4d", size = 16233350 },
+    { url = "https://files.pythonhosted.org/packages/3b/3a/2f6d8c1f8e45d496bca6baaec93208035faeb40d5735c25afac092ec9a12/numpy-2.2.4-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:b4adfbbc64014976d2f91084915ca4e626fbf2057fb81af209c1a6d776d23e3d", size = 12857565 },
+]
+
+[[package]]
+name = "packaging"
+version = "24.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d0/63/68dbb6eb2de9cb10ee4c9c14a0148804425e13c4fb20d61cce69f53106da/packaging-24.2.tar.gz", hash = "sha256:c228a6dc5e932d346bc5739379109d49e8853dd8223571c7c5b55260edc0b97f", size = 163950 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/88/ef/eb23f262cca3c0c4eb7ab1933c3b1f03d021f2c48f54763065b6f0e321be/packaging-24.2-py3-none-any.whl", hash = "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759", size = 65451 },
+]
+
+[[package]]
+name = "pandas"
+version = "2.2.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "python-dateutil" },
+    { name = "pytz" },
+    { name = "tzdata" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9c/d6/9f8431bacc2e19dca897724cd097b1bb224a6ad5433784a44b587c7c13af/pandas-2.2.3.tar.gz", hash = "sha256:4f18ba62b61d7e192368b84517265a99b4d7ee8912f8708660fb4a366cc82667", size = 4399213 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/aa/70/c853aec59839bceed032d52010ff5f1b8d87dc3114b762e4ba2727661a3b/pandas-2.2.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1948ddde24197a0f7add2bdc4ca83bf2b1ef84a1bc8ccffd95eda17fd836ecb5", size = 12580827 },
+    { url = "https://files.pythonhosted.org/packages/99/f2/c4527768739ffa4469b2b4fff05aa3768a478aed89a2f271a79a40eee984/pandas-2.2.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:381175499d3802cde0eabbaf6324cce0c4f5d52ca6f8c377c29ad442f50f6348", size = 11303897 },
+    { url = "https://files.pythonhosted.org/packages/ed/12/86c1747ea27989d7a4064f806ce2bae2c6d575b950be087837bdfcabacc9/pandas-2.2.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d9c45366def9a3dd85a6454c0e7908f2b3b8e9c138f5dc38fed7ce720d8453ed", size = 66480908 },
+    { url = "https://files.pythonhosted.org/packages/44/50/7db2cd5e6373ae796f0ddad3675268c8d59fb6076e66f0c339d61cea886b/pandas-2.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:86976a1c5b25ae3f8ccae3a5306e443569ee3c3faf444dfd0f41cda24667ad57", size = 13064210 },
+    { url = "https://files.pythonhosted.org/packages/61/61/a89015a6d5536cb0d6c3ba02cebed51a95538cf83472975275e28ebf7d0c/pandas-2.2.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b8661b0238a69d7aafe156b7fa86c44b881387509653fdf857bebc5e4008ad42", size = 16754292 },
+    { url = "https://files.pythonhosted.org/packages/ce/0d/4cc7b69ce37fac07645a94e1d4b0880b15999494372c1523508511b09e40/pandas-2.2.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:37e0aced3e8f539eccf2e099f65cdb9c8aa85109b0be6e93e2baff94264bdc6f", size = 14416379 },
+    { url = "https://files.pythonhosted.org/packages/31/9e/6ebb433de864a6cd45716af52a4d7a8c3c9aaf3a98368e61db9e69e69a9c/pandas-2.2.3-cp310-cp310-win_amd64.whl", hash = "sha256:56534ce0746a58afaf7942ba4863e0ef81c9c50d3f0ae93e9497d6a41a057645", size = 11598471 },
+    { url = "https://files.pythonhosted.org/packages/a8/44/d9502bf0ed197ba9bf1103c9867d5904ddcaf869e52329787fc54ed70cc8/pandas-2.2.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:66108071e1b935240e74525006034333f98bcdb87ea116de573a6a0dccb6c039", size = 12602222 },
+    { url = "https://files.pythonhosted.org/packages/52/11/9eac327a38834f162b8250aab32a6781339c69afe7574368fffe46387edf/pandas-2.2.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7c2875855b0ff77b2a64a0365e24455d9990730d6431b9e0ee18ad8acee13dbd", size = 11321274 },
+    { url = "https://files.pythonhosted.org/packages/45/fb/c4beeb084718598ba19aa9f5abbc8aed8b42f90930da861fcb1acdb54c3a/pandas-2.2.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cd8d0c3be0515c12fed0bdbae072551c8b54b7192c7b1fda0ba56059a0179698", size = 15579836 },
+    { url = "https://files.pythonhosted.org/packages/cd/5f/4dba1d39bb9c38d574a9a22548c540177f78ea47b32f99c0ff2ec499fac5/pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c124333816c3a9b03fbeef3a9f230ba9a737e9e5bb4060aa2107a86cc0a497fc", size = 13058505 },
+    { url = "https://files.pythonhosted.org/packages/b9/57/708135b90391995361636634df1f1130d03ba456e95bcf576fada459115a/pandas-2.2.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:63cc132e40a2e084cf01adf0775b15ac515ba905d7dcca47e9a251819c575ef3", size = 16744420 },
+    { url = "https://files.pythonhosted.org/packages/86/4a/03ed6b7ee323cf30404265c284cee9c65c56a212e0a08d9ee06984ba2240/pandas-2.2.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:29401dbfa9ad77319367d36940cd8a0b3a11aba16063e39632d98b0e931ddf32", size = 14440457 },
+    { url = "https://files.pythonhosted.org/packages/ed/8c/87ddf1fcb55d11f9f847e3c69bb1c6f8e46e2f40ab1a2d2abadb2401b007/pandas-2.2.3-cp311-cp311-win_amd64.whl", hash = "sha256:3fc6873a41186404dad67245896a6e440baacc92f5b716ccd1bc9ed2995ab2c5", size = 11617166 },
+    { url = "https://files.pythonhosted.org/packages/17/a3/fb2734118db0af37ea7433f57f722c0a56687e14b14690edff0cdb4b7e58/pandas-2.2.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b1d432e8d08679a40e2a6d8b2f9770a5c21793a6f9f47fdd52c5ce1948a5a8a9", size = 12529893 },
+    { url = "https://files.pythonhosted.org/packages/e1/0c/ad295fd74bfac85358fd579e271cded3ac969de81f62dd0142c426b9da91/pandas-2.2.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a5a1595fe639f5988ba6a8e5bc9649af3baf26df3998a0abe56c02609392e0a4", size = 11363475 },
+    { url = "https://files.pythonhosted.org/packages/c6/2a/4bba3f03f7d07207481fed47f5b35f556c7441acddc368ec43d6643c5777/pandas-2.2.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5de54125a92bb4d1c051c0659e6fcb75256bf799a732a87184e5ea503965bce3", size = 15188645 },
+    { url = "https://files.pythonhosted.org/packages/38/f8/d8fddee9ed0d0c0f4a2132c1dfcf0e3e53265055da8df952a53e7eaf178c/pandas-2.2.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fffb8ae78d8af97f849404f21411c95062db1496aeb3e56f146f0355c9989319", size = 12739445 },
+    { url = "https://files.pythonhosted.org/packages/20/e8/45a05d9c39d2cea61ab175dbe6a2de1d05b679e8de2011da4ee190d7e748/pandas-2.2.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:6dfcb5ee8d4d50c06a51c2fffa6cff6272098ad6540aed1a76d15fb9318194d8", size = 16359235 },
+    { url = "https://files.pythonhosted.org/packages/1d/99/617d07a6a5e429ff90c90da64d428516605a1ec7d7bea494235e1c3882de/pandas-2.2.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:062309c1b9ea12a50e8ce661145c6aab431b1e99530d3cd60640e255778bd43a", size = 14056756 },
+    { url = "https://files.pythonhosted.org/packages/29/d4/1244ab8edf173a10fd601f7e13b9566c1b525c4f365d6bee918e68381889/pandas-2.2.3-cp312-cp312-win_amd64.whl", hash = "sha256:59ef3764d0fe818125a5097d2ae867ca3fa64df032331b7e0917cf5d7bf66b13", size = 11504248 },
+    { url = "https://files.pythonhosted.org/packages/64/22/3b8f4e0ed70644e85cfdcd57454686b9057c6c38d2f74fe4b8bc2527214a/pandas-2.2.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f00d1345d84d8c86a63e476bb4955e46458b304b9575dcf71102b5c705320015", size = 12477643 },
+    { url = "https://files.pythonhosted.org/packages/e4/93/b3f5d1838500e22c8d793625da672f3eec046b1a99257666c94446969282/pandas-2.2.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3508d914817e153ad359d7e069d752cdd736a247c322d932eb89e6bc84217f28", size = 11281573 },
+    { url = "https://files.pythonhosted.org/packages/f5/94/6c79b07f0e5aab1dcfa35a75f4817f5c4f677931d4234afcd75f0e6a66ca/pandas-2.2.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:22a9d949bfc9a502d320aa04e5d02feab689d61da4e7764b62c30b991c42c5f0", size = 15196085 },
+    { url = "https://files.pythonhosted.org/packages/e8/31/aa8da88ca0eadbabd0a639788a6da13bb2ff6edbbb9f29aa786450a30a91/pandas-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3a255b2c19987fbbe62a9dfd6cff7ff2aa9ccab3fc75218fd4b7530f01efa24", size = 12711809 },
+    { url = "https://files.pythonhosted.org/packages/ee/7c/c6dbdb0cb2a4344cacfb8de1c5808ca885b2e4dcfde8008266608f9372af/pandas-2.2.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:800250ecdadb6d9c78eae4990da62743b857b470883fa27f652db8bdde7f6659", size = 16356316 },
+    { url = "https://files.pythonhosted.org/packages/57/b7/8b757e7d92023b832869fa8881a992696a0bfe2e26f72c9ae9f255988d42/pandas-2.2.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6374c452ff3ec675a8f46fd9ab25c4ad0ba590b71cf0656f8b6daa5202bca3fb", size = 14022055 },
+    { url = "https://files.pythonhosted.org/packages/3b/bc/4b18e2b8c002572c5a441a64826252ce5da2aa738855747247a971988043/pandas-2.2.3-cp313-cp313-win_amd64.whl", hash = "sha256:61c5ad4043f791b61dd4752191d9f07f0ae412515d59ba8f005832a532f8736d", size = 11481175 },
+    { url = "https://files.pythonhosted.org/packages/76/a3/a5d88146815e972d40d19247b2c162e88213ef51c7c25993942c39dbf41d/pandas-2.2.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:3b71f27954685ee685317063bf13c7709a7ba74fc996b84fc6821c59b0f06468", size = 12615650 },
+    { url = "https://files.pythonhosted.org/packages/9c/8c/f0fd18f6140ddafc0c24122c8a964e48294acc579d47def376fef12bcb4a/pandas-2.2.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:38cf8125c40dae9d5acc10fa66af8ea6fdf760b2714ee482ca691fc66e6fcb18", size = 11290177 },
+    { url = "https://files.pythonhosted.org/packages/ed/f9/e995754eab9c0f14c6777401f7eece0943840b7a9fc932221c19d1abee9f/pandas-2.2.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ba96630bc17c875161df3818780af30e43be9b166ce51c9a18c1feae342906c2", size = 14651526 },
+    { url = "https://files.pythonhosted.org/packages/25/b0/98d6ae2e1abac4f35230aa756005e8654649d305df9a28b16b9ae4353bff/pandas-2.2.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1db71525a1538b30142094edb9adc10be3f3e176748cd7acc2240c2f2e5aa3a4", size = 11871013 },
+    { url = "https://files.pythonhosted.org/packages/cc/57/0f72a10f9db6a4628744c8e8f0df4e6e21de01212c7c981d31e50ffc8328/pandas-2.2.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:15c0e1e02e93116177d29ff83e8b1619c93ddc9c49083f237d4312337a61165d", size = 15711620 },
+    { url = "https://files.pythonhosted.org/packages/ab/5f/b38085618b950b79d2d9164a711c52b10aefc0ae6833b96f626b7021b2ed/pandas-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ad5b65698ab28ed8d7f18790a0dc58005c7629f227be9ecc1072aa74c0c1d43a", size = 13098436 },
+]
+
+[[package]]
+name = "pandocfilters"
+version = "1.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/70/6f/3dd4940bbe001c06a65f88e36bad298bc7a0de5036115639926b0c5c0458/pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e", size = 8454 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ef/af/4fbc8cab944db5d21b7e2a5b8e9211a03a79852b1157e2c102fcc61ac440/pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc", size = 8663 },
+]
+
+[[package]]
+name = "parso"
+version = "0.8.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/66/94/68e2e17afaa9169cf6412ab0f28623903be73d1b32e208d9e8e541bb086d/parso-0.8.4.tar.gz", hash = "sha256:eb3a7b58240fb99099a345571deecc0f9540ea5f4dd2fe14c2a99d6b281ab92d", size = 400609 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c6/ac/dac4a63f978e4dcb3c6d3a78c4d8e0192a113d288502a1216950c41b1027/parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18", size = 103650 },
+]
+
+[[package]]
+name = "pathspec"
+version = "0.12.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ca/bc/f35b8446f4531a7cb215605d100cd88b7ac6f44ab3fc94870c120ab3adbf/pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712", size = 51043 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08", size = 31191 },
+]
+
+[[package]]
+name = "pexpect"
+version = "4.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "ptyprocess" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9e/c3/059298687310d527a58bb01f3b1965787ee3b40dce76752eda8b44e9a2c5/pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523", size = 63772 },
+]
+
+[[package]]
+name = "pillow"
+version = "11.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f3/af/c097e544e7bd278333db77933e535098c259609c4eb3b85381109602fb5b/pillow-11.1.0.tar.gz", hash = "sha256:368da70808b36d73b4b390a8ffac11069f8a5c85f29eff1f1b01bcf3ef5b2a20", size = 46742715 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/50/1c/2dcea34ac3d7bc96a1fd1bd0a6e06a57c67167fec2cff8d95d88229a8817/pillow-11.1.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:e1abe69aca89514737465752b4bcaf8016de61b3be1397a8fc260ba33321b3a8", size = 3229983 },
+    { url = "https://files.pythonhosted.org/packages/14/ca/6bec3df25e4c88432681de94a3531cc738bd85dea6c7aa6ab6f81ad8bd11/pillow-11.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c640e5a06869c75994624551f45e5506e4256562ead981cce820d5ab39ae2192", size = 3101831 },
+    { url = "https://files.pythonhosted.org/packages/d4/2c/668e18e5521e46eb9667b09e501d8e07049eb5bfe39d56be0724a43117e6/pillow-11.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a07dba04c5e22824816b2615ad7a7484432d7f540e6fa86af60d2de57b0fcee2", size = 4314074 },
+    { url = "https://files.pythonhosted.org/packages/02/80/79f99b714f0fc25f6a8499ecfd1f810df12aec170ea1e32a4f75746051ce/pillow-11.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e267b0ed063341f3e60acd25c05200df4193e15a4a5807075cd71225a2386e26", size = 4394933 },
+    { url = "https://files.pythonhosted.org/packages/81/aa/8d4ad25dc11fd10a2001d5b8a80fdc0e564ac33b293bdfe04ed387e0fd95/pillow-11.1.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:bd165131fd51697e22421d0e467997ad31621b74bfc0b75956608cb2906dda07", size = 4353349 },
+    { url = "https://files.pythonhosted.org/packages/84/7a/cd0c3eaf4a28cb2a74bdd19129f7726277a7f30c4f8424cd27a62987d864/pillow-11.1.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:abc56501c3fd148d60659aae0af6ddc149660469082859fa7b066a298bde9482", size = 4476532 },
+    { url = "https://files.pythonhosted.org/packages/8f/8b/a907fdd3ae8f01c7670dfb1499c53c28e217c338b47a813af8d815e7ce97/pillow-11.1.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:54ce1c9a16a9561b6d6d8cb30089ab1e5eb66918cb47d457bd996ef34182922e", size = 4279789 },
+    { url = "https://files.pythonhosted.org/packages/6f/9a/9f139d9e8cccd661c3efbf6898967a9a337eb2e9be2b454ba0a09533100d/pillow-11.1.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:73ddde795ee9b06257dac5ad42fcb07f3b9b813f8c1f7f870f402f4dc54b5269", size = 4413131 },
+    { url = "https://files.pythonhosted.org/packages/a8/68/0d8d461f42a3f37432203c8e6df94da10ac8081b6d35af1c203bf3111088/pillow-11.1.0-cp310-cp310-win32.whl", hash = "sha256:3a5fe20a7b66e8135d7fd617b13272626a28278d0e578c98720d9ba4b2439d49", size = 2291213 },
+    { url = "https://files.pythonhosted.org/packages/14/81/d0dff759a74ba87715509af9f6cb21fa21d93b02b3316ed43bda83664db9/pillow-11.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:b6123aa4a59d75f06e9dd3dac5bf8bc9aa383121bb3dd9a7a612e05eabc9961a", size = 2625725 },
+    { url = "https://files.pythonhosted.org/packages/ce/1f/8d50c096a1d58ef0584ddc37e6f602828515219e9d2428e14ce50f5ecad1/pillow-11.1.0-cp310-cp310-win_arm64.whl", hash = "sha256:a76da0a31da6fcae4210aa94fd779c65c75786bc9af06289cd1c184451ef7a65", size = 2375213 },
+    { url = "https://files.pythonhosted.org/packages/dd/d6/2000bfd8d5414fb70cbbe52c8332f2283ff30ed66a9cde42716c8ecbe22c/pillow-11.1.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:e06695e0326d05b06833b40b7ef477e475d0b1ba3a6d27da1bb48c23209bf457", size = 3229968 },
+    { url = "https://files.pythonhosted.org/packages/d9/45/3fe487010dd9ce0a06adf9b8ff4f273cc0a44536e234b0fad3532a42c15b/pillow-11.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:96f82000e12f23e4f29346e42702b6ed9a2f2fea34a740dd5ffffcc8c539eb35", size = 3101806 },
+    { url = "https://files.pythonhosted.org/packages/e3/72/776b3629c47d9d5f1c160113158a7a7ad177688d3a1159cd3b62ded5a33a/pillow-11.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a3cd561ded2cf2bbae44d4605837221b987c216cff94f49dfeed63488bb228d2", size = 4322283 },
+    { url = "https://files.pythonhosted.org/packages/e4/c2/e25199e7e4e71d64eeb869f5b72c7ddec70e0a87926398785ab944d92375/pillow-11.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f189805c8be5ca5add39e6f899e6ce2ed824e65fb45f3c28cb2841911da19070", size = 4402945 },
+    { url = "https://files.pythonhosted.org/packages/c1/ed/51d6136c9d5911f78632b1b86c45241c712c5a80ed7fa7f9120a5dff1eba/pillow-11.1.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:dd0052e9db3474df30433f83a71b9b23bd9e4ef1de13d92df21a52c0303b8ab6", size = 4361228 },
+    { url = "https://files.pythonhosted.org/packages/48/a4/fbfe9d5581d7b111b28f1d8c2762dee92e9821bb209af9fa83c940e507a0/pillow-11.1.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:837060a8599b8f5d402e97197d4924f05a2e0d68756998345c829c33186217b1", size = 4484021 },
+    { url = "https://files.pythonhosted.org/packages/39/db/0b3c1a5018117f3c1d4df671fb8e47d08937f27519e8614bbe86153b65a5/pillow-11.1.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:aa8dd43daa836b9a8128dbe7d923423e5ad86f50a7a14dc688194b7be5c0dea2", size = 4287449 },
+    { url = "https://files.pythonhosted.org/packages/d9/58/bc128da7fea8c89fc85e09f773c4901e95b5936000e6f303222490c052f3/pillow-11.1.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:0a2f91f8a8b367e7a57c6e91cd25af510168091fb89ec5146003e424e1558a96", size = 4419972 },
+    { url = "https://files.pythonhosted.org/packages/5f/bb/58f34379bde9fe197f51841c5bbe8830c28bbb6d3801f16a83b8f2ad37df/pillow-11.1.0-cp311-cp311-win32.whl", hash = "sha256:c12fc111ef090845de2bb15009372175d76ac99969bdf31e2ce9b42e4b8cd88f", size = 2291201 },
+    { url = "https://files.pythonhosted.org/packages/3a/c6/fce9255272bcf0c39e15abd2f8fd8429a954cf344469eaceb9d0d1366913/pillow-11.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:fbd43429d0d7ed6533b25fc993861b8fd512c42d04514a0dd6337fb3ccf22761", size = 2625686 },
+    { url = "https://files.pythonhosted.org/packages/c8/52/8ba066d569d932365509054859f74f2a9abee273edcef5cd75e4bc3e831e/pillow-11.1.0-cp311-cp311-win_arm64.whl", hash = "sha256:f7955ecf5609dee9442cbface754f2c6e541d9e6eda87fad7f7a989b0bdb9d71", size = 2375194 },
+    { url = "https://files.pythonhosted.org/packages/95/20/9ce6ed62c91c073fcaa23d216e68289e19d95fb8188b9fb7a63d36771db8/pillow-11.1.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:2062ffb1d36544d42fcaa277b069c88b01bb7298f4efa06731a7fd6cc290b81a", size = 3226818 },
+    { url = "https://files.pythonhosted.org/packages/b9/d8/f6004d98579a2596c098d1e30d10b248798cceff82d2b77aa914875bfea1/pillow-11.1.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a85b653980faad27e88b141348707ceeef8a1186f75ecc600c395dcac19f385b", size = 3101662 },
+    { url = "https://files.pythonhosted.org/packages/08/d9/892e705f90051c7a2574d9f24579c9e100c828700d78a63239676f960b74/pillow-11.1.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9409c080586d1f683df3f184f20e36fb647f2e0bc3988094d4fd8c9f4eb1b3b3", size = 4329317 },
+    { url = "https://files.pythonhosted.org/packages/8c/aa/7f29711f26680eab0bcd3ecdd6d23ed6bce180d82e3f6380fb7ae35fcf3b/pillow-11.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7fdadc077553621911f27ce206ffcbec7d3f8d7b50e0da39f10997e8e2bb7f6a", size = 4412999 },
+    { url = "https://files.pythonhosted.org/packages/c8/c4/8f0fe3b9e0f7196f6d0bbb151f9fba323d72a41da068610c4c960b16632a/pillow-11.1.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:93a18841d09bcdd774dcdc308e4537e1f867b3dec059c131fde0327899734aa1", size = 4368819 },
+    { url = "https://files.pythonhosted.org/packages/38/0d/84200ed6a871ce386ddc82904bfadc0c6b28b0c0ec78176871a4679e40b3/pillow-11.1.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:9aa9aeddeed452b2f616ff5507459e7bab436916ccb10961c4a382cd3e03f47f", size = 4496081 },
+    { url = "https://files.pythonhosted.org/packages/84/9c/9bcd66f714d7e25b64118e3952d52841a4babc6d97b6d28e2261c52045d4/pillow-11.1.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3cdcdb0b896e981678eee140d882b70092dac83ac1cdf6b3a60e2216a73f2b91", size = 4296513 },
+    { url = "https://files.pythonhosted.org/packages/db/61/ada2a226e22da011b45f7104c95ebda1b63dcbb0c378ad0f7c2a710f8fd2/pillow-11.1.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:36ba10b9cb413e7c7dfa3e189aba252deee0602c86c309799da5a74009ac7a1c", size = 4431298 },
+    { url = "https://files.pythonhosted.org/packages/e7/c4/fc6e86750523f367923522014b821c11ebc5ad402e659d8c9d09b3c9d70c/pillow-11.1.0-cp312-cp312-win32.whl", hash = "sha256:cfd5cd998c2e36a862d0e27b2df63237e67273f2fc78f47445b14e73a810e7e6", size = 2291630 },
+    { url = "https://files.pythonhosted.org/packages/08/5c/2104299949b9d504baf3f4d35f73dbd14ef31bbd1ddc2c1b66a5b7dfda44/pillow-11.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:a697cd8ba0383bba3d2d3ada02b34ed268cb548b369943cd349007730c92bddf", size = 2626369 },
+    { url = "https://files.pythonhosted.org/packages/37/f3/9b18362206b244167c958984b57c7f70a0289bfb59a530dd8af5f699b910/pillow-11.1.0-cp312-cp312-win_arm64.whl", hash = "sha256:4dd43a78897793f60766563969442020e90eb7847463eca901e41ba186a7d4a5", size = 2375240 },
+    { url = "https://files.pythonhosted.org/packages/b3/31/9ca79cafdce364fd5c980cd3416c20ce1bebd235b470d262f9d24d810184/pillow-11.1.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:ae98e14432d458fc3de11a77ccb3ae65ddce70f730e7c76140653048c71bfcbc", size = 3226640 },
+    { url = "https://files.pythonhosted.org/packages/ac/0f/ff07ad45a1f172a497aa393b13a9d81a32e1477ef0e869d030e3c1532521/pillow-11.1.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:cc1331b6d5a6e144aeb5e626f4375f5b7ae9934ba620c0ac6b3e43d5e683a0f0", size = 3101437 },
+    { url = "https://files.pythonhosted.org/packages/08/2f/9906fca87a68d29ec4530be1f893149e0cb64a86d1f9f70a7cfcdfe8ae44/pillow-11.1.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:758e9d4ef15d3560214cddbc97b8ef3ef86ce04d62ddac17ad39ba87e89bd3b1", size = 4326605 },
+    { url = "https://files.pythonhosted.org/packages/b0/0f/f3547ee15b145bc5c8b336401b2d4c9d9da67da9dcb572d7c0d4103d2c69/pillow-11.1.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b523466b1a31d0dcef7c5be1f20b942919b62fd6e9a9be199d035509cbefc0ec", size = 4411173 },
+    { url = "https://files.pythonhosted.org/packages/b1/df/bf8176aa5db515c5de584c5e00df9bab0713548fd780c82a86cba2c2fedb/pillow-11.1.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:9044b5e4f7083f209c4e35aa5dd54b1dd5b112b108648f5c902ad586d4f945c5", size = 4369145 },
+    { url = "https://files.pythonhosted.org/packages/de/7c/7433122d1cfadc740f577cb55526fdc39129a648ac65ce64db2eb7209277/pillow-11.1.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:3764d53e09cdedd91bee65c2527815d315c6b90d7b8b79759cc48d7bf5d4f114", size = 4496340 },
+    { url = "https://files.pythonhosted.org/packages/25/46/dd94b93ca6bd555588835f2504bd90c00d5438fe131cf01cfa0c5131a19d/pillow-11.1.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:31eba6bbdd27dde97b0174ddf0297d7a9c3a507a8a1480e1e60ef914fe23d352", size = 4296906 },
+    { url = "https://files.pythonhosted.org/packages/a8/28/2f9d32014dfc7753e586db9add35b8a41b7a3b46540e965cb6d6bc607bd2/pillow-11.1.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b5d658fbd9f0d6eea113aea286b21d3cd4d3fd978157cbf2447a6035916506d3", size = 4431759 },
+    { url = "https://files.pythonhosted.org/packages/33/48/19c2cbe7403870fbe8b7737d19eb013f46299cdfe4501573367f6396c775/pillow-11.1.0-cp313-cp313-win32.whl", hash = "sha256:f86d3a7a9af5d826744fabf4afd15b9dfef44fe69a98541f666f66fbb8d3fef9", size = 2291657 },
+    { url = "https://files.pythonhosted.org/packages/3b/ad/285c556747d34c399f332ba7c1a595ba245796ef3e22eae190f5364bb62b/pillow-11.1.0-cp313-cp313-win_amd64.whl", hash = "sha256:593c5fd6be85da83656b93ffcccc2312d2d149d251e98588b14fbc288fd8909c", size = 2626304 },
+    { url = "https://files.pythonhosted.org/packages/e5/7b/ef35a71163bf36db06e9c8729608f78dedf032fc8313d19bd4be5c2588f3/pillow-11.1.0-cp313-cp313-win_arm64.whl", hash = "sha256:11633d58b6ee5733bde153a8dafd25e505ea3d32e261accd388827ee987baf65", size = 2375117 },
+    { url = "https://files.pythonhosted.org/packages/79/30/77f54228401e84d6791354888549b45824ab0ffde659bafa67956303a09f/pillow-11.1.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:70ca5ef3b3b1c4a0812b5c63c57c23b63e53bc38e758b37a951e5bc466449861", size = 3230060 },
+    { url = "https://files.pythonhosted.org/packages/ce/b1/56723b74b07dd64c1010fee011951ea9c35a43d8020acd03111f14298225/pillow-11.1.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:8000376f139d4d38d6851eb149b321a52bb8893a88dae8ee7d95840431977081", size = 3106192 },
+    { url = "https://files.pythonhosted.org/packages/e1/cd/7bf7180e08f80a4dcc6b4c3a0aa9e0b0ae57168562726a05dc8aa8fa66b0/pillow-11.1.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ee85f0696a17dd28fbcfceb59f9510aa71934b483d1f5601d1030c3c8304f3c", size = 4446805 },
+    { url = "https://files.pythonhosted.org/packages/97/42/87c856ea30c8ed97e8efbe672b58c8304dee0573f8c7cab62ae9e31db6ae/pillow-11.1.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:dd0e081319328928531df7a0e63621caf67652c8464303fd102141b785ef9547", size = 4530623 },
+    { url = "https://files.pythonhosted.org/packages/ff/41/026879e90c84a88e33fb00cc6bd915ac2743c67e87a18f80270dfe3c2041/pillow-11.1.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e63e4e5081de46517099dc30abe418122f54531a6ae2ebc8680bcd7096860eab", size = 4465191 },
+    { url = "https://files.pythonhosted.org/packages/e5/fb/a7960e838bc5df57a2ce23183bfd2290d97c33028b96bde332a9057834d3/pillow-11.1.0-cp313-cp313t-win32.whl", hash = "sha256:dda60aa465b861324e65a78c9f5cf0f4bc713e4309f83bc387be158b077963d9", size = 2295494 },
+    { url = "https://files.pythonhosted.org/packages/d7/6c/6ec83ee2f6f0fda8d4cf89045c6be4b0373ebfc363ba8538f8c999f63fcd/pillow-11.1.0-cp313-cp313t-win_amd64.whl", hash = "sha256:ad5db5781c774ab9a9b2c4302bbf0c1014960a0a7be63278d13ae6fdf88126fe", size = 2631595 },
+    { url = "https://files.pythonhosted.org/packages/cf/6c/41c21c6c8af92b9fea313aa47c75de49e2f9a467964ee33eb0135d47eb64/pillow-11.1.0-cp313-cp313t-win_arm64.whl", hash = "sha256:67cd427c68926108778a9005f2a04adbd5e67c442ed21d95389fe1d595458756", size = 2377651 },
+    { url = "https://files.pythonhosted.org/packages/fa/c5/389961578fb677b8b3244fcd934f720ed25a148b9a5cc81c91bdf59d8588/pillow-11.1.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:8c730dc3a83e5ac137fbc92dfcfe1511ce3b2b5d7578315b63dbbb76f7f51d90", size = 3198345 },
+    { url = "https://files.pythonhosted.org/packages/c4/fa/803c0e50ffee74d4b965229e816af55276eac1d5806712de86f9371858fd/pillow-11.1.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:7d33d2fae0e8b170b6a6c57400e077412240f6f5bb2a342cf1ee512a787942bb", size = 3072938 },
+    { url = "https://files.pythonhosted.org/packages/dc/67/2a3a5f8012b5d8c63fe53958ba906c1b1d0482ebed5618057ef4d22f8076/pillow-11.1.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a8d65b38173085f24bc07f8b6c505cbb7418009fa1a1fcb111b1f4961814a442", size = 3400049 },
+    { url = "https://files.pythonhosted.org/packages/e5/a0/514f0d317446c98c478d1872497eb92e7cde67003fed74f696441e647446/pillow-11.1.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:015c6e863faa4779251436db398ae75051469f7c903b043a48f078e437656f83", size = 3422431 },
+    { url = "https://files.pythonhosted.org/packages/cd/00/20f40a935514037b7d3f87adfc87d2c538430ea625b63b3af8c3f5578e72/pillow-11.1.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:d44ff19eea13ae4acdaaab0179fa68c0c6f2f45d66a4d8ec1eda7d6cecbcc15f", size = 3446208 },
+    { url = "https://files.pythonhosted.org/packages/28/3c/7de681727963043e093c72e6c3348411b0185eab3263100d4490234ba2f6/pillow-11.1.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d3d8da4a631471dfaf94c10c85f5277b1f8e42ac42bade1ac67da4b4a7359b73", size = 3509746 },
+    { url = "https://files.pythonhosted.org/packages/41/67/936f9814bdd74b2dfd4822f1f7725ab5d8ff4103919a1664eb4874c58b2f/pillow-11.1.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:4637b88343166249fe8aa94e7c4a62a180c4b3898283bb5d3d2fd5fe10d8e4e0", size = 2626353 },
+]
+
+[[package]]
+name = "platformdirs"
+version = "4.3.7"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b6/2d/7d512a3913d60623e7eb945c6d1b4f0bddf1d0b7ada5225274c87e5b53d1/platformdirs-4.3.7.tar.gz", hash = "sha256:eb437d586b6a0986388f0d6f74aa0cde27b48d0e3d66843640bfb6bdcdb6e351", size = 21291 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6d/45/59578566b3275b8fd9157885918fcd0c4d74162928a5310926887b856a51/platformdirs-4.3.7-py3-none-any.whl", hash = "sha256:a03875334331946f13c549dbd8f4bac7a13a50a895a0eb1e8c6a8ace80d40a94", size = 18499 },
+]
+
+[[package]]
+name = "pluggy"
+version = "1.5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/96/2d/02d4312c973c6050a18b314a5ad0b3210edb65a906f868e31c111dede4a6/pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1", size = 67955 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669", size = 20556 },
+]
+
+[[package]]
+name = "plum-dispatch"
+version = "2.5.7"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "beartype" },
+    { name = "rich" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f4/46/ab3928e864b0a88a8ae6987b3da3b7ae32fe0a610264f33272139275dab5/plum_dispatch-2.5.7.tar.gz", hash = "sha256:a7908ad5563b93f387e3817eb0412ad40cfbad04bc61d869cf7a76cd58a3895d", size = 35452 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2b/31/21609a9be48e877bc33b089a7f495c853215def5aeb9564a31c210d9d769/plum_dispatch-2.5.7-py3-none-any.whl", hash = "sha256:06471782eea0b3798c1e79dca2af2165bafcfa5eb595540b514ddd81053b1ede", size = 42612 },
+]
+
+[[package]]
+name = "prompt-toolkit"
+version = "3.0.50"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "wcwidth" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a1/e1/bd15cb8ffdcfeeb2bdc215de3c3cffca11408d829e4b8416dcfe71ba8854/prompt_toolkit-3.0.50.tar.gz", hash = "sha256:544748f3860a2623ca5cd6d2795e7a14f3d0e1c3c9728359013f79877fc89bab", size = 429087 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e4/ea/d836f008d33151c7a1f62caf3d8dd782e4d15f6a43897f64480c2b8de2ad/prompt_toolkit-3.0.50-py3-none-any.whl", hash = "sha256:9b6427eb19e479d98acff65196a307c555eb567989e6d88ebbb1b509d9779198", size = 387816 },
+]
+
+[[package]]
+name = "psutil"
+version = "7.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2a/80/336820c1ad9286a4ded7e845b2eccfcb27851ab8ac6abece774a6ff4d3de/psutil-7.0.0.tar.gz", hash = "sha256:7be9c3eba38beccb6495ea33afd982a44074b78f28c434a1f51cc07fd315c456", size = 497003 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ed/e6/2d26234410f8b8abdbf891c9da62bee396583f713fb9f3325a4760875d22/psutil-7.0.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:101d71dc322e3cffd7cea0650b09b3d08b8e7c4109dd6809fe452dfd00e58b25", size = 238051 },
+    { url = "https://files.pythonhosted.org/packages/04/8b/30f930733afe425e3cbfc0e1468a30a18942350c1a8816acfade80c005c4/psutil-7.0.0-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:39db632f6bb862eeccf56660871433e111b6ea58f2caea825571951d4b6aa3da", size = 239535 },
+    { url = "https://files.pythonhosted.org/packages/2a/ed/d362e84620dd22876b55389248e522338ed1bf134a5edd3b8231d7207f6d/psutil-7.0.0-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1fcee592b4c6f146991ca55919ea3d1f8926497a713ed7faaf8225e174581e91", size = 275004 },
+    { url = "https://files.pythonhosted.org/packages/bf/b9/b0eb3f3cbcb734d930fdf839431606844a825b23eaf9a6ab371edac8162c/psutil-7.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b1388a4f6875d7e2aff5c4ca1cc16c545ed41dd8bb596cefea80111db353a34", size = 277986 },
+    { url = "https://files.pythonhosted.org/packages/eb/a2/709e0fe2f093556c17fbafda93ac032257242cabcc7ff3369e2cb76a97aa/psutil-7.0.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5f098451abc2828f7dc6b58d44b532b22f2088f4999a937557b603ce72b1993", size = 279544 },
+    { url = "https://files.pythonhosted.org/packages/50/e6/eecf58810b9d12e6427369784efe814a1eec0f492084ce8eb8f4d89d6d61/psutil-7.0.0-cp37-abi3-win32.whl", hash = "sha256:ba3fcef7523064a6c9da440fc4d6bd07da93ac726b5733c29027d7dc95b39d99", size = 241053 },
+    { url = "https://files.pythonhosted.org/packages/50/1b/6921afe68c74868b4c9fa424dad3be35b095e16687989ebbb50ce4fceb7c/psutil-7.0.0-cp37-abi3-win_amd64.whl", hash = "sha256:4cf3d4eb1aa9b348dec30105c55cd9b7d4629285735a102beb4441e38db90553", size = 244885 },
+]
+
+[[package]]
+name = "ptyprocess"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/20/e5/16ff212c1e452235a90aeb09066144d0c5a6a8c0834397e03f5224495c4e/ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220", size = 70762 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35", size = 13993 },
+]
+
+[[package]]
+name = "pure-eval"
+version = "0.2.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/cd/05/0a34433a064256a578f1783a10da6df098ceaa4a57bbeaa96a6c0352786b/pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42", size = 19752 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842 },
+]
+
+[[package]]
+name = "pycparser"
+version = "2.22"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1d/b2/31537cf4b1ca988837256c910a668b553fceb8f069bedc4b1c826024b52c/pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6", size = 172736 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/13/a3/a812df4e2dd5696d1f351d58b8fe16a405b234ad2886a0dab9183fb78109/pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc", size = 117552 },
+]
+
+[[package]]
+name = "pydantic"
+version = "2.10.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "annotated-types" },
+    { name = "pydantic-core" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b7/ae/d5220c5c52b158b1de7ca89fc5edb72f304a70a4c540c84c8844bf4008de/pydantic-2.10.6.tar.gz", hash = "sha256:ca5daa827cce33de7a42be142548b0096bf05a7e7b365aebfa5f8eeec7128236", size = 761681 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f4/3c/8cc1cc84deffa6e25d2d0c688ebb80635dfdbf1dbea3e30c541c8cf4d860/pydantic-2.10.6-py3-none-any.whl", hash = "sha256:427d664bf0b8a2b34ff5dd0f5a18df00591adcee7198fbd71981054cef37b584", size = 431696 },
+]
+
+[[package]]
+name = "pydantic-core"
+version = "2.27.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fc/01/f3e5ac5e7c25833db5eb555f7b7ab24cd6f8c322d3a3ad2d67a952dc0abc/pydantic_core-2.27.2.tar.gz", hash = "sha256:eb026e5a4c1fee05726072337ff51d1efb6f59090b7da90d30ea58625b1ffb39", size = 413443 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3a/bc/fed5f74b5d802cf9a03e83f60f18864e90e3aed7223adaca5ffb7a8d8d64/pydantic_core-2.27.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:2d367ca20b2f14095a8f4fa1210f5a7b78b8a20009ecced6b12818f455b1e9fa", size = 1895938 },
+    { url = "https://files.pythonhosted.org/packages/71/2a/185aff24ce844e39abb8dd680f4e959f0006944f4a8a0ea372d9f9ae2e53/pydantic_core-2.27.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:491a2b73db93fab69731eaee494f320faa4e093dbed776be1a829c2eb222c34c", size = 1815684 },
+    { url = "https://files.pythonhosted.org/packages/c3/43/fafabd3d94d159d4f1ed62e383e264f146a17dd4d48453319fd782e7979e/pydantic_core-2.27.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7969e133a6f183be60e9f6f56bfae753585680f3b7307a8e555a948d443cc05a", size = 1829169 },
+    { url = "https://files.pythonhosted.org/packages/a2/d1/f2dfe1a2a637ce6800b799aa086d079998959f6f1215eb4497966efd2274/pydantic_core-2.27.2-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3de9961f2a346257caf0aa508a4da705467f53778e9ef6fe744c038119737ef5", size = 1867227 },
+    { url = "https://files.pythonhosted.org/packages/7d/39/e06fcbcc1c785daa3160ccf6c1c38fea31f5754b756e34b65f74e99780b5/pydantic_core-2.27.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e2bb4d3e5873c37bb3dd58714d4cd0b0e6238cebc4177ac8fe878f8b3aa8e74c", size = 2037695 },
+    { url = "https://files.pythonhosted.org/packages/7a/67/61291ee98e07f0650eb756d44998214231f50751ba7e13f4f325d95249ab/pydantic_core-2.27.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:280d219beebb0752699480fe8f1dc61ab6615c2046d76b7ab7ee38858de0a4e7", size = 2741662 },
+    { url = "https://files.pythonhosted.org/packages/32/90/3b15e31b88ca39e9e626630b4c4a1f5a0dfd09076366f4219429e6786076/pydantic_core-2.27.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:47956ae78b6422cbd46f772f1746799cbb862de838fd8d1fbd34a82e05b0983a", size = 1993370 },
+    { url = "https://files.pythonhosted.org/packages/ff/83/c06d333ee3a67e2e13e07794995c1535565132940715931c1c43bfc85b11/pydantic_core-2.27.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:14d4a5c49d2f009d62a2a7140d3064f686d17a5d1a268bc641954ba181880236", size = 1996813 },
+    { url = "https://files.pythonhosted.org/packages/7c/f7/89be1c8deb6e22618a74f0ca0d933fdcb8baa254753b26b25ad3acff8f74/pydantic_core-2.27.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:337b443af21d488716f8d0b6164de833e788aa6bd7e3a39c005febc1284f4962", size = 2005287 },
+    { url = "https://files.pythonhosted.org/packages/b7/7d/8eb3e23206c00ef7feee17b83a4ffa0a623eb1a9d382e56e4aa46fd15ff2/pydantic_core-2.27.2-cp310-cp310-musllinux_1_1_armv7l.whl", hash = "sha256:03d0f86ea3184a12f41a2d23f7ccb79cdb5a18e06993f8a45baa8dfec746f0e9", size = 2128414 },
+    { url = "https://files.pythonhosted.org/packages/4e/99/fe80f3ff8dd71a3ea15763878d464476e6cb0a2db95ff1c5c554133b6b83/pydantic_core-2.27.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:7041c36f5680c6e0f08d922aed302e98b3745d97fe1589db0a3eebf6624523af", size = 2155301 },
+    { url = "https://files.pythonhosted.org/packages/2b/a3/e50460b9a5789ca1451b70d4f52546fa9e2b420ba3bfa6100105c0559238/pydantic_core-2.27.2-cp310-cp310-win32.whl", hash = "sha256:50a68f3e3819077be2c98110c1f9dcb3817e93f267ba80a2c05bb4f8799e2ff4", size = 1816685 },
+    { url = "https://files.pythonhosted.org/packages/57/4c/a8838731cb0f2c2a39d3535376466de6049034d7b239c0202a64aaa05533/pydantic_core-2.27.2-cp310-cp310-win_amd64.whl", hash = "sha256:e0fd26b16394ead34a424eecf8a31a1f5137094cabe84a1bcb10fa6ba39d3d31", size = 1982876 },
+    { url = "https://files.pythonhosted.org/packages/c2/89/f3450af9d09d44eea1f2c369f49e8f181d742f28220f88cc4dfaae91ea6e/pydantic_core-2.27.2-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:8e10c99ef58cfdf2a66fc15d66b16c4a04f62bca39db589ae8cba08bc55331bc", size = 1893421 },
+    { url = "https://files.pythonhosted.org/packages/9e/e3/71fe85af2021f3f386da42d291412e5baf6ce7716bd7101ea49c810eda90/pydantic_core-2.27.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:26f32e0adf166a84d0cb63be85c562ca8a6fa8de28e5f0d92250c6b7e9e2aff7", size = 1814998 },
+    { url = "https://files.pythonhosted.org/packages/a6/3c/724039e0d848fd69dbf5806894e26479577316c6f0f112bacaf67aa889ac/pydantic_core-2.27.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8c19d1ea0673cd13cc2f872f6c9ab42acc4e4f492a7ca9d3795ce2b112dd7e15", size = 1826167 },
+    { url = "https://files.pythonhosted.org/packages/2b/5b/1b29e8c1fb5f3199a9a57c1452004ff39f494bbe9bdbe9a81e18172e40d3/pydantic_core-2.27.2-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5e68c4446fe0810e959cdff46ab0a41ce2f2c86d227d96dc3847af0ba7def306", size = 1865071 },
+    { url = "https://files.pythonhosted.org/packages/89/6c/3985203863d76bb7d7266e36970d7e3b6385148c18a68cc8915fd8c84d57/pydantic_core-2.27.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d9640b0059ff4f14d1f37321b94061c6db164fbe49b334b31643e0528d100d99", size = 2036244 },
+    { url = "https://files.pythonhosted.org/packages/0e/41/f15316858a246b5d723f7d7f599f79e37493b2e84bfc789e58d88c209f8a/pydantic_core-2.27.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:40d02e7d45c9f8af700f3452f329ead92da4c5f4317ca9b896de7ce7199ea459", size = 2737470 },
+    { url = "https://files.pythonhosted.org/packages/a8/7c/b860618c25678bbd6d1d99dbdfdf0510ccb50790099b963ff78a124b754f/pydantic_core-2.27.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1c1fd185014191700554795c99b347d64f2bb637966c4cfc16998a0ca700d048", size = 1992291 },
+    { url = "https://files.pythonhosted.org/packages/bf/73/42c3742a391eccbeab39f15213ecda3104ae8682ba3c0c28069fbcb8c10d/pydantic_core-2.27.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d81d2068e1c1228a565af076598f9e7451712700b673de8f502f0334f281387d", size = 1994613 },
+    { url = "https://files.pythonhosted.org/packages/94/7a/941e89096d1175d56f59340f3a8ebaf20762fef222c298ea96d36a6328c5/pydantic_core-2.27.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:1a4207639fb02ec2dbb76227d7c751a20b1a6b4bc52850568e52260cae64ca3b", size = 2002355 },
+    { url = "https://files.pythonhosted.org/packages/6e/95/2359937a73d49e336a5a19848713555605d4d8d6940c3ec6c6c0ca4dcf25/pydantic_core-2.27.2-cp311-cp311-musllinux_1_1_armv7l.whl", hash = "sha256:3de3ce3c9ddc8bbd88f6e0e304dea0e66d843ec9de1b0042b0911c1663ffd474", size = 2126661 },
+    { url = "https://files.pythonhosted.org/packages/2b/4c/ca02b7bdb6012a1adef21a50625b14f43ed4d11f1fc237f9d7490aa5078c/pydantic_core-2.27.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:30c5f68ded0c36466acede341551106821043e9afaad516adfb6e8fa80a4e6a6", size = 2153261 },
+    { url = "https://files.pythonhosted.org/packages/72/9d/a241db83f973049a1092a079272ffe2e3e82e98561ef6214ab53fe53b1c7/pydantic_core-2.27.2-cp311-cp311-win32.whl", hash = "sha256:c70c26d2c99f78b125a3459f8afe1aed4d9687c24fd677c6a4436bc042e50d6c", size = 1812361 },
+    { url = "https://files.pythonhosted.org/packages/e8/ef/013f07248041b74abd48a385e2110aa3a9bbfef0fbd97d4e6d07d2f5b89a/pydantic_core-2.27.2-cp311-cp311-win_amd64.whl", hash = "sha256:08e125dbdc505fa69ca7d9c499639ab6407cfa909214d500897d02afb816e7cc", size = 1982484 },
+    { url = "https://files.pythonhosted.org/packages/10/1c/16b3a3e3398fd29dca77cea0a1d998d6bde3902fa2706985191e2313cc76/pydantic_core-2.27.2-cp311-cp311-win_arm64.whl", hash = "sha256:26f0d68d4b235a2bae0c3fc585c585b4ecc51382db0e3ba402a22cbc440915e4", size = 1867102 },
+    { url = "https://files.pythonhosted.org/packages/d6/74/51c8a5482ca447871c93e142d9d4a92ead74de6c8dc5e66733e22c9bba89/pydantic_core-2.27.2-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:9e0c8cfefa0ef83b4da9588448b6d8d2a2bf1a53c3f1ae5fca39eb3061e2f0b0", size = 1893127 },
+    { url = "https://files.pythonhosted.org/packages/d3/f3/c97e80721735868313c58b89d2de85fa80fe8dfeeed84dc51598b92a135e/pydantic_core-2.27.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:83097677b8e3bd7eaa6775720ec8e0405f1575015a463285a92bfdfe254529ef", size = 1811340 },
+    { url = "https://files.pythonhosted.org/packages/9e/91/840ec1375e686dbae1bd80a9e46c26a1e0083e1186abc610efa3d9a36180/pydantic_core-2.27.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:172fce187655fece0c90d90a678424b013f8fbb0ca8b036ac266749c09438cb7", size = 1822900 },
+    { url = "https://files.pythonhosted.org/packages/f6/31/4240bc96025035500c18adc149aa6ffdf1a0062a4b525c932065ceb4d868/pydantic_core-2.27.2-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:519f29f5213271eeeeb3093f662ba2fd512b91c5f188f3bb7b27bc5973816934", size = 1869177 },
+    { url = "https://files.pythonhosted.org/packages/fa/20/02fbaadb7808be578317015c462655c317a77a7c8f0ef274bc016a784c54/pydantic_core-2.27.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:05e3a55d124407fffba0dd6b0c0cd056d10e983ceb4e5dbd10dda135c31071d6", size = 2038046 },
+    { url = "https://files.pythonhosted.org/packages/06/86/7f306b904e6c9eccf0668248b3f272090e49c275bc488a7b88b0823444a4/pydantic_core-2.27.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9c3ed807c7b91de05e63930188f19e921d1fe90de6b4f5cd43ee7fcc3525cb8c", size = 2685386 },
+    { url = "https://files.pythonhosted.org/packages/8d/f0/49129b27c43396581a635d8710dae54a791b17dfc50c70164866bbf865e3/pydantic_core-2.27.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6fb4aadc0b9a0c063206846d603b92030eb6f03069151a625667f982887153e2", size = 1997060 },
+    { url = "https://files.pythonhosted.org/packages/0d/0f/943b4af7cd416c477fd40b187036c4f89b416a33d3cc0ab7b82708a667aa/pydantic_core-2.27.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:28ccb213807e037460326424ceb8b5245acb88f32f3d2777427476e1b32c48c4", size = 2004870 },
+    { url = "https://files.pythonhosted.org/packages/35/40/aea70b5b1a63911c53a4c8117c0a828d6790483f858041f47bab0b779f44/pydantic_core-2.27.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:de3cd1899e2c279b140adde9357c4495ed9d47131b4a4eaff9052f23398076b3", size = 1999822 },
+    { url = "https://files.pythonhosted.org/packages/f2/b3/807b94fd337d58effc5498fd1a7a4d9d59af4133e83e32ae39a96fddec9d/pydantic_core-2.27.2-cp312-cp312-musllinux_1_1_armv7l.whl", hash = "sha256:220f892729375e2d736b97d0e51466252ad84c51857d4d15f5e9692f9ef12be4", size = 2130364 },
+    { url = "https://files.pythonhosted.org/packages/fc/df/791c827cd4ee6efd59248dca9369fb35e80a9484462c33c6649a8d02b565/pydantic_core-2.27.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:a0fcd29cd6b4e74fe8ddd2c90330fd8edf2e30cb52acda47f06dd615ae72da57", size = 2158303 },
+    { url = "https://files.pythonhosted.org/packages/9b/67/4e197c300976af185b7cef4c02203e175fb127e414125916bf1128b639a9/pydantic_core-2.27.2-cp312-cp312-win32.whl", hash = "sha256:1e2cb691ed9834cd6a8be61228471d0a503731abfb42f82458ff27be7b2186fc", size = 1834064 },
+    { url = "https://files.pythonhosted.org/packages/1f/ea/cd7209a889163b8dcca139fe32b9687dd05249161a3edda62860430457a5/pydantic_core-2.27.2-cp312-cp312-win_amd64.whl", hash = "sha256:cc3f1a99a4f4f9dd1de4fe0312c114e740b5ddead65bb4102884b384c15d8bc9", size = 1989046 },
+    { url = "https://files.pythonhosted.org/packages/bc/49/c54baab2f4658c26ac633d798dab66b4c3a9bbf47cff5284e9c182f4137a/pydantic_core-2.27.2-cp312-cp312-win_arm64.whl", hash = "sha256:3911ac9284cd8a1792d3cb26a2da18f3ca26c6908cc434a18f730dc0db7bfa3b", size = 1885092 },
+    { url = "https://files.pythonhosted.org/packages/41/b1/9bc383f48f8002f99104e3acff6cba1231b29ef76cfa45d1506a5cad1f84/pydantic_core-2.27.2-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:7d14bd329640e63852364c306f4d23eb744e0f8193148d4044dd3dacdaacbd8b", size = 1892709 },
+    { url = "https://files.pythonhosted.org/packages/10/6c/e62b8657b834f3eb2961b49ec8e301eb99946245e70bf42c8817350cbefc/pydantic_core-2.27.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:82f91663004eb8ed30ff478d77c4d1179b3563df6cdb15c0817cd1cdaf34d154", size = 1811273 },
+    { url = "https://files.pythonhosted.org/packages/ba/15/52cfe49c8c986e081b863b102d6b859d9defc63446b642ccbbb3742bf371/pydantic_core-2.27.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:71b24c7d61131bb83df10cc7e687433609963a944ccf45190cfc21e0887b08c9", size = 1823027 },
+    { url = "https://files.pythonhosted.org/packages/b1/1c/b6f402cfc18ec0024120602bdbcebc7bdd5b856528c013bd4d13865ca473/pydantic_core-2.27.2-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fa8e459d4954f608fa26116118bb67f56b93b209c39b008277ace29937453dc9", size = 1868888 },
+    { url = "https://files.pythonhosted.org/packages/bd/7b/8cb75b66ac37bc2975a3b7de99f3c6f355fcc4d89820b61dffa8f1e81677/pydantic_core-2.27.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ce8918cbebc8da707ba805b7fd0b382816858728ae7fe19a942080c24e5b7cd1", size = 2037738 },
+    { url = "https://files.pythonhosted.org/packages/c8/f1/786d8fe78970a06f61df22cba58e365ce304bf9b9f46cc71c8c424e0c334/pydantic_core-2.27.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:eda3f5c2a021bbc5d976107bb302e0131351c2ba54343f8a496dc8783d3d3a6a", size = 2685138 },
+    { url = "https://files.pythonhosted.org/packages/a6/74/d12b2cd841d8724dc8ffb13fc5cef86566a53ed358103150209ecd5d1999/pydantic_core-2.27.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bd8086fa684c4775c27f03f062cbb9eaa6e17f064307e86b21b9e0abc9c0f02e", size = 1997025 },
+    { url = "https://files.pythonhosted.org/packages/a0/6e/940bcd631bc4d9a06c9539b51f070b66e8f370ed0933f392db6ff350d873/pydantic_core-2.27.2-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:8d9b3388db186ba0c099a6d20f0604a44eabdeef1777ddd94786cdae158729e4", size = 2004633 },
+    { url = "https://files.pythonhosted.org/packages/50/cc/a46b34f1708d82498c227d5d80ce615b2dd502ddcfd8376fc14a36655af1/pydantic_core-2.27.2-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:7a66efda2387de898c8f38c0cf7f14fca0b51a8ef0b24bfea5849f1b3c95af27", size = 1999404 },
+    { url = "https://files.pythonhosted.org/packages/ca/2d/c365cfa930ed23bc58c41463bae347d1005537dc8db79e998af8ba28d35e/pydantic_core-2.27.2-cp313-cp313-musllinux_1_1_armv7l.whl", hash = "sha256:18a101c168e4e092ab40dbc2503bdc0f62010e95d292b27827871dc85450d7ee", size = 2130130 },
+    { url = "https://files.pythonhosted.org/packages/f4/d7/eb64d015c350b7cdb371145b54d96c919d4db516817f31cd1c650cae3b21/pydantic_core-2.27.2-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:ba5dd002f88b78a4215ed2f8ddbdf85e8513382820ba15ad5ad8955ce0ca19a1", size = 2157946 },
+    { url = "https://files.pythonhosted.org/packages/a4/99/bddde3ddde76c03b65dfd5a66ab436c4e58ffc42927d4ff1198ffbf96f5f/pydantic_core-2.27.2-cp313-cp313-win32.whl", hash = "sha256:1ebaf1d0481914d004a573394f4be3a7616334be70261007e47c2a6fe7e50130", size = 1834387 },
+    { url = "https://files.pythonhosted.org/packages/71/47/82b5e846e01b26ac6f1893d3c5f9f3a2eb6ba79be26eef0b759b4fe72946/pydantic_core-2.27.2-cp313-cp313-win_amd64.whl", hash = "sha256:953101387ecf2f5652883208769a79e48db18c6df442568a0b5ccd8c2723abee", size = 1990453 },
+    { url = "https://files.pythonhosted.org/packages/51/b2/b2b50d5ecf21acf870190ae5d093602d95f66c9c31f9d5de6062eb329ad1/pydantic_core-2.27.2-cp313-cp313-win_arm64.whl", hash = "sha256:ac4dbfd1691affb8f48c2c13241a2e3b60ff23247cbcf981759c768b6633cf8b", size = 1885186 },
+    { url = "https://files.pythonhosted.org/packages/46/72/af70981a341500419e67d5cb45abe552a7c74b66326ac8877588488da1ac/pydantic_core-2.27.2-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:2bf14caea37e91198329b828eae1618c068dfb8ef17bb33287a7ad4b61ac314e", size = 1891159 },
+    { url = "https://files.pythonhosted.org/packages/ad/3d/c5913cccdef93e0a6a95c2d057d2c2cba347815c845cda79ddd3c0f5e17d/pydantic_core-2.27.2-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:b0cb791f5b45307caae8810c2023a184c74605ec3bcbb67d13846c28ff731ff8", size = 1768331 },
+    { url = "https://files.pythonhosted.org/packages/f6/f0/a3ae8fbee269e4934f14e2e0e00928f9346c5943174f2811193113e58252/pydantic_core-2.27.2-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:688d3fd9fcb71f41c4c015c023d12a79d1c4c0732ec9eb35d96e3388a120dcf3", size = 1822467 },
+    { url = "https://files.pythonhosted.org/packages/d7/7a/7bbf241a04e9f9ea24cd5874354a83526d639b02674648af3f350554276c/pydantic_core-2.27.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3d591580c34f4d731592f0e9fe40f9cc1b430d297eecc70b962e93c5c668f15f", size = 1979797 },
+    { url = "https://files.pythonhosted.org/packages/4f/5f/4784c6107731f89e0005a92ecb8a2efeafdb55eb992b8e9d0a2be5199335/pydantic_core-2.27.2-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:82f986faf4e644ffc189a7f1aafc86e46ef70372bb153e7001e8afccc6e54133", size = 1987839 },
+    { url = "https://files.pythonhosted.org/packages/6d/a7/61246562b651dff00de86a5f01b6e4befb518df314c54dec187a78d81c84/pydantic_core-2.27.2-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:bec317a27290e2537f922639cafd54990551725fc844249e64c523301d0822fc", size = 1998861 },
+    { url = "https://files.pythonhosted.org/packages/86/aa/837821ecf0c022bbb74ca132e117c358321e72e7f9702d1b6a03758545e2/pydantic_core-2.27.2-pp310-pypy310_pp73-musllinux_1_1_armv7l.whl", hash = "sha256:0296abcb83a797db256b773f45773da397da75a08f5fcaef41f2044adec05f50", size = 2116582 },
+    { url = "https://files.pythonhosted.org/packages/81/b0/5e74656e95623cbaa0a6278d16cf15e10a51f6002e3ec126541e95c29ea3/pydantic_core-2.27.2-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:0d75070718e369e452075a6017fbf187f788e17ed67a3abd47fa934d001863d9", size = 2151985 },
+    { url = "https://files.pythonhosted.org/packages/63/37/3e32eeb2a451fddaa3898e2163746b0cffbbdbb4740d38372db0490d67f3/pydantic_core-2.27.2-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:7e17b560be3c98a8e3aa66ce828bdebb9e9ac6ad5466fba92eb74c4c95cb1151", size = 2004715 },
+]
+
+[[package]]
+name = "pygments"
+version = "2.19.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7c/2d/c3338d48ea6cc0feb8446d8e6937e1408088a72a39937982cc6111d17f84/pygments-2.19.1.tar.gz", hash = "sha256:61c16d2a8576dc0649d9f39e089b5f02bcd27fba10d8fb4dcc28173f7a45151f", size = 4968581 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8a/0b/9fcc47d19c48b59121088dd6da2488a49d5f72dacf8262e2790a1d2c7d15/pygments-2.19.1-py3-none-any.whl", hash = "sha256:9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c", size = 1225293 },
+]
+
+[[package]]
+name = "pyparsing"
+version = "3.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8b/1a/3544f4f299a47911c2ab3710f534e52fea62a633c96806995da5d25be4b2/pyparsing-3.2.1.tar.gz", hash = "sha256:61980854fd66de3a90028d679a954d5f2623e83144b5afe5ee86f43d762e5f0a", size = 1067694 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1c/a7/c8a2d361bf89c0d9577c934ebb7421b25dc84bf3a8e3ac0a40aed9acc547/pyparsing-3.2.1-py3-none-any.whl", hash = "sha256:506ff4f4386c4cec0590ec19e6302d3aedb992fdc02c761e90416f158dacf8e1", size = 107716 },
+]
+
+[[package]]
+name = "pytest"
+version = "8.3.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
+    { name = "iniconfig" },
+    { name = "packaging" },
+    { name = "pluggy" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ae/3c/c9d525a414d506893f0cd8a8d0de7706446213181570cdbd766691164e40/pytest-8.3.5.tar.gz", hash = "sha256:f4efe70cc14e511565ac476b57c279e12a855b11f48f212af1080ef2263d3845", size = 1450891 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/30/3d/64ad57c803f1fa1e963a7946b6e0fea4a70df53c1a7fed304586539c2bac/pytest-8.3.5-py3-none-any.whl", hash = "sha256:c69214aa47deac29fad6c2a4f590b9c4a9fdb16a403176fe154b79c0b4d4d820", size = 343634 },
+]
+
+[[package]]
+name = "pytest-cov"
+version = "6.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "coverage", extra = ["toml"] },
+    { name = "pytest" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/be/45/9b538de8cef30e17c7b45ef42f538a94889ed6a16f2387a6c89e73220651/pytest-cov-6.0.0.tar.gz", hash = "sha256:fde0b595ca248bb8e2d76f020b465f3b107c9632e6a1d1705f17834c89dcadc0", size = 66945 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl", hash = "sha256:eee6f1b9e61008bd34975a4d5bab25801eb31898b032dd55addc93e96fcaaa35", size = 22949 },
+]
+
+[[package]]
+name = "pytest-randomly"
+version = "3.16.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pytest" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c0/68/d221ed7f4a2a49a664da721b8e87b52af6dd317af2a6cb51549cf17ac4b8/pytest_randomly-3.16.0.tar.gz", hash = "sha256:11bf4d23a26484de7860d82f726c0629837cf4064b79157bd18ec9d41d7feb26", size = 13367 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/22/70/b31577d7c46d8e2f9baccfed5067dd8475262a2331ffb0bfdf19361c9bde/pytest_randomly-3.16.0-py3-none-any.whl", hash = "sha256:8633d332635a1a0983d3bba19342196807f6afb17c3eef78e02c2f85dade45d6", size = 8396 },
+]
+
+[[package]]
+name = "pytest-sugar"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "packaging" },
+    { name = "pytest" },
+    { name = "termcolor" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f5/ac/5754f5edd6d508bc6493bc37d74b928f102a5fff82d9a80347e180998f08/pytest-sugar-1.0.0.tar.gz", hash = "sha256:6422e83258f5b0c04ce7c632176c7732cab5fdb909cb39cca5c9139f81276c0a", size = 14992 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/92/fb/889f1b69da2f13691de09a111c16c4766a433382d44aa0ecf221deded44a/pytest_sugar-1.0.0-py3-none-any.whl", hash = "sha256:70ebcd8fc5795dc457ff8b69d266a4e2e8a74ae0c3edc749381c64b5246c8dfd", size = 10171 },
+]
+
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892 },
+]
+
+[[package]]
+name = "pytz"
+version = "2025.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f8/bf/abbd3cdfb8fbc7fb3d4d38d320f2441b1e7cbe29be4f23797b4a2b5d8aac/pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3", size = 320884 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00", size = 509225 },
+]
+
+[[package]]
+name = "pywin32"
+version = "310"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/95/da/a5f38fffbba2fb99aa4aa905480ac4b8e83ca486659ac8c95bce47fb5276/pywin32-310-cp310-cp310-win32.whl", hash = "sha256:6dd97011efc8bf51d6793a82292419eba2c71cf8e7250cfac03bba284454abc1", size = 8848240 },
+    { url = "https://files.pythonhosted.org/packages/aa/fe/d873a773324fa565619ba555a82c9dabd677301720f3660a731a5d07e49a/pywin32-310-cp310-cp310-win_amd64.whl", hash = "sha256:c3e78706e4229b915a0821941a84e7ef420bf2b77e08c9dae3c76fd03fd2ae3d", size = 9601854 },
+    { url = "https://files.pythonhosted.org/packages/3c/84/1a8e3d7a15490d28a5d816efa229ecb4999cdc51a7c30dd8914f669093b8/pywin32-310-cp310-cp310-win_arm64.whl", hash = "sha256:33babed0cf0c92a6f94cc6cc13546ab24ee13e3e800e61ed87609ab91e4c8213", size = 8522963 },
+    { url = "https://files.pythonhosted.org/packages/f7/b1/68aa2986129fb1011dabbe95f0136f44509afaf072b12b8f815905a39f33/pywin32-310-cp311-cp311-win32.whl", hash = "sha256:1e765f9564e83011a63321bb9d27ec456a0ed90d3732c4b2e312b855365ed8bd", size = 8784284 },
+    { url = "https://files.pythonhosted.org/packages/b3/bd/d1592635992dd8db5bb8ace0551bc3a769de1ac8850200cfa517e72739fb/pywin32-310-cp311-cp311-win_amd64.whl", hash = "sha256:126298077a9d7c95c53823934f000599f66ec9296b09167810eb24875f32689c", size = 9520748 },
+    { url = "https://files.pythonhosted.org/packages/90/b1/ac8b1ffce6603849eb45a91cf126c0fa5431f186c2e768bf56889c46f51c/pywin32-310-cp311-cp311-win_arm64.whl", hash = "sha256:19ec5fc9b1d51c4350be7bb00760ffce46e6c95eaf2f0b2f1150657b1a43c582", size = 8455941 },
+    { url = "https://files.pythonhosted.org/packages/6b/ec/4fdbe47932f671d6e348474ea35ed94227fb5df56a7c30cbbb42cd396ed0/pywin32-310-cp312-cp312-win32.whl", hash = "sha256:8a75a5cc3893e83a108c05d82198880704c44bbaee4d06e442e471d3c9ea4f3d", size = 8796239 },
+    { url = "https://files.pythonhosted.org/packages/e3/e5/b0627f8bb84e06991bea89ad8153a9e50ace40b2e1195d68e9dff6b03d0f/pywin32-310-cp312-cp312-win_amd64.whl", hash = "sha256:bf5c397c9a9a19a6f62f3fb821fbf36cac08f03770056711f765ec1503972060", size = 9503839 },
+    { url = "https://files.pythonhosted.org/packages/1f/32/9ccf53748df72301a89713936645a664ec001abd35ecc8578beda593d37d/pywin32-310-cp312-cp312-win_arm64.whl", hash = "sha256:2349cc906eae872d0663d4d6290d13b90621eaf78964bb1578632ff20e152966", size = 8459470 },
+    { url = "https://files.pythonhosted.org/packages/1c/09/9c1b978ffc4ae53999e89c19c77ba882d9fce476729f23ef55211ea1c034/pywin32-310-cp313-cp313-win32.whl", hash = "sha256:5d241a659c496ada3253cd01cfaa779b048e90ce4b2b38cd44168ad555ce74ab", size = 8794384 },
+    { url = "https://files.pythonhosted.org/packages/45/3c/b4640f740ffebadd5d34df35fecba0e1cfef8fde9f3e594df91c28ad9b50/pywin32-310-cp313-cp313-win_amd64.whl", hash = "sha256:667827eb3a90208ddbdcc9e860c81bde63a135710e21e4cb3348968e4bd5249e", size = 9503039 },
+    { url = "https://files.pythonhosted.org/packages/b4/f4/f785020090fb050e7fb6d34b780f2231f302609dc964672f72bfaeb59a28/pywin32-310-cp313-cp313-win_arm64.whl", hash = "sha256:e308f831de771482b7cf692a1f308f8fca701b2d8f9dde6cc440c7da17e47b33", size = 8458152 },
+]
+
+[[package]]
+name = "pyyaml"
+version = "6.0.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/54/ed/79a089b6be93607fa5cdaedf301d7dfb23af5f25c398d5ead2525b063e17/pyyaml-6.0.2.tar.gz", hash = "sha256:d584d9ec91ad65861cc08d42e834324ef890a082e591037abe114850ff7bbc3e", size = 130631 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9b/95/a3fac87cb7158e231b5a6012e438c647e1a87f09f8e0d123acec8ab8bf71/PyYAML-6.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086", size = 184199 },
+    { url = "https://files.pythonhosted.org/packages/c7/7a/68bd47624dab8fd4afbfd3c48e3b79efe09098ae941de5b58abcbadff5cb/PyYAML-6.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:29717114e51c84ddfba879543fb232a6ed60086602313ca38cce623c1d62cfbf", size = 171758 },
+    { url = "https://files.pythonhosted.org/packages/49/ee/14c54df452143b9ee9f0f29074d7ca5516a36edb0b4cc40c3f280131656f/PyYAML-6.0.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8824b5a04a04a047e72eea5cec3bc266db09e35de6bdfe34c9436ac5ee27d237", size = 718463 },
+    { url = "https://files.pythonhosted.org/packages/4d/61/de363a97476e766574650d742205be468921a7b532aa2499fcd886b62530/PyYAML-6.0.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7c36280e6fb8385e520936c3cb3b8042851904eba0e58d277dca80a5cfed590b", size = 719280 },
+    { url = "https://files.pythonhosted.org/packages/6b/4e/1523cb902fd98355e2e9ea5e5eb237cbc5f3ad5f3075fa65087aa0ecb669/PyYAML-6.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ec031d5d2feb36d1d1a24380e4db6d43695f3748343d99434e6f5f9156aaa2ed", size = 751239 },
+    { url = "https://files.pythonhosted.org/packages/b7/33/5504b3a9a4464893c32f118a9cc045190a91637b119a9c881da1cf6b7a72/PyYAML-6.0.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:936d68689298c36b53b29f23c6dbb74de12b4ac12ca6cfe0e047bedceea56180", size = 695802 },
+    { url = "https://files.pythonhosted.org/packages/5c/20/8347dcabd41ef3a3cdc4f7b7a2aff3d06598c8779faa189cdbf878b626a4/PyYAML-6.0.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:23502f431948090f597378482b4812b0caae32c22213aecf3b55325e049a6c68", size = 720527 },
+    { url = "https://files.pythonhosted.org/packages/be/aa/5afe99233fb360d0ff37377145a949ae258aaab831bde4792b32650a4378/PyYAML-6.0.2-cp310-cp310-win32.whl", hash = "sha256:2e99c6826ffa974fe6e27cdb5ed0021786b03fc98e5ee3c5bfe1fd5015f42b99", size = 144052 },
+    { url = "https://files.pythonhosted.org/packages/b5/84/0fa4b06f6d6c958d207620fc60005e241ecedceee58931bb20138e1e5776/PyYAML-6.0.2-cp310-cp310-win_amd64.whl", hash = "sha256:a4d3091415f010369ae4ed1fc6b79def9416358877534caf6a0fdd2146c87a3e", size = 161774 },
+    { url = "https://files.pythonhosted.org/packages/f8/aa/7af4e81f7acba21a4c6be026da38fd2b872ca46226673c89a758ebdc4fd2/PyYAML-6.0.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:cc1c1159b3d456576af7a3e4d1ba7e6924cb39de8f67111c735f6fc832082774", size = 184612 },
+    { url = "https://files.pythonhosted.org/packages/8b/62/b9faa998fd185f65c1371643678e4d58254add437edb764a08c5a98fb986/PyYAML-6.0.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1e2120ef853f59c7419231f3bf4e7021f1b936f6ebd222406c3b60212205d2ee", size = 172040 },
+    { url = "https://files.pythonhosted.org/packages/ad/0c/c804f5f922a9a6563bab712d8dcc70251e8af811fce4524d57c2c0fd49a4/PyYAML-6.0.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5d225db5a45f21e78dd9358e58a98702a0302f2659a3c6cd320564b75b86f47c", size = 736829 },
+    { url = "https://files.pythonhosted.org/packages/51/16/6af8d6a6b210c8e54f1406a6b9481febf9c64a3109c541567e35a49aa2e7/PyYAML-6.0.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5ac9328ec4831237bec75defaf839f7d4564be1e6b25ac710bd1a96321cc8317", size = 764167 },
+    { url = "https://files.pythonhosted.org/packages/75/e4/2c27590dfc9992f73aabbeb9241ae20220bd9452df27483b6e56d3975cc5/PyYAML-6.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3ad2a3decf9aaba3d29c8f537ac4b243e36bef957511b4766cb0057d32b0be85", size = 762952 },
+    { url = "https://files.pythonhosted.org/packages/9b/97/ecc1abf4a823f5ac61941a9c00fe501b02ac3ab0e373c3857f7d4b83e2b6/PyYAML-6.0.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:ff3824dc5261f50c9b0dfb3be22b4567a6f938ccce4587b38952d85fd9e9afe4", size = 735301 },
+    { url = "https://files.pythonhosted.org/packages/45/73/0f49dacd6e82c9430e46f4a027baa4ca205e8b0a9dce1397f44edc23559d/PyYAML-6.0.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:797b4f722ffa07cc8d62053e4cff1486fa6dc094105d13fea7b1de7d8bf71c9e", size = 756638 },
+    { url = "https://files.pythonhosted.org/packages/22/5f/956f0f9fc65223a58fbc14459bf34b4cc48dec52e00535c79b8db361aabd/PyYAML-6.0.2-cp311-cp311-win32.whl", hash = "sha256:11d8f3dd2b9c1207dcaf2ee0bbbfd5991f571186ec9cc78427ba5bd32afae4b5", size = 143850 },
+    { url = "https://files.pythonhosted.org/packages/ed/23/8da0bbe2ab9dcdd11f4f4557ccaf95c10b9811b13ecced089d43ce59c3c8/PyYAML-6.0.2-cp311-cp311-win_amd64.whl", hash = "sha256:e10ce637b18caea04431ce14fabcf5c64a1c61ec9c56b071a4b7ca131ca52d44", size = 161980 },
+    { url = "https://files.pythonhosted.org/packages/86/0c/c581167fc46d6d6d7ddcfb8c843a4de25bdd27e4466938109ca68492292c/PyYAML-6.0.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:c70c95198c015b85feafc136515252a261a84561b7b1d51e3384e0655ddf25ab", size = 183873 },
+    { url = "https://files.pythonhosted.org/packages/a8/0c/38374f5bb272c051e2a69281d71cba6fdb983413e6758b84482905e29a5d/PyYAML-6.0.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ce826d6ef20b1bc864f0a68340c8b3287705cae2f8b4b1d932177dcc76721725", size = 173302 },
+    { url = "https://files.pythonhosted.org/packages/c3/93/9916574aa8c00aa06bbac729972eb1071d002b8e158bd0e83a3b9a20a1f7/PyYAML-6.0.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1f71ea527786de97d1a0cc0eacd1defc0985dcf6b3f17bb77dcfc8c34bec4dc5", size = 739154 },
+    { url = "https://files.pythonhosted.org/packages/95/0f/b8938f1cbd09739c6da569d172531567dbcc9789e0029aa070856f123984/PyYAML-6.0.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9b22676e8097e9e22e36d6b7bda33190d0d400f345f23d4065d48f4ca7ae0425", size = 766223 },
+    { url = "https://files.pythonhosted.org/packages/b9/2b/614b4752f2e127db5cc206abc23a8c19678e92b23c3db30fc86ab731d3bd/PyYAML-6.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:80bab7bfc629882493af4aa31a4cfa43a4c57c83813253626916b8c7ada83476", size = 767542 },
+    { url = "https://files.pythonhosted.org/packages/d4/00/dd137d5bcc7efea1836d6264f049359861cf548469d18da90cd8216cf05f/PyYAML-6.0.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:0833f8694549e586547b576dcfaba4a6b55b9e96098b36cdc7ebefe667dfed48", size = 731164 },
+    { url = "https://files.pythonhosted.org/packages/c9/1f/4f998c900485e5c0ef43838363ba4a9723ac0ad73a9dc42068b12aaba4e4/PyYAML-6.0.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8b9c7197f7cb2738065c481a0461e50ad02f18c78cd75775628afb4d7137fb3b", size = 756611 },
+    { url = "https://files.pythonhosted.org/packages/df/d1/f5a275fdb252768b7a11ec63585bc38d0e87c9e05668a139fea92b80634c/PyYAML-6.0.2-cp312-cp312-win32.whl", hash = "sha256:ef6107725bd54b262d6dedcc2af448a266975032bc85ef0172c5f059da6325b4", size = 140591 },
+    { url = "https://files.pythonhosted.org/packages/0c/e8/4f648c598b17c3d06e8753d7d13d57542b30d56e6c2dedf9c331ae56312e/PyYAML-6.0.2-cp312-cp312-win_amd64.whl", hash = "sha256:7e7401d0de89a9a855c839bc697c079a4af81cf878373abd7dc625847d25cbd8", size = 156338 },
+    { url = "https://files.pythonhosted.org/packages/ef/e3/3af305b830494fa85d95f6d95ef7fa73f2ee1cc8ef5b495c7c3269fb835f/PyYAML-6.0.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:efdca5630322a10774e8e98e1af481aad470dd62c3170801852d752aa7a783ba", size = 181309 },
+    { url = "https://files.pythonhosted.org/packages/45/9f/3b1c20a0b7a3200524eb0076cc027a970d320bd3a6592873c85c92a08731/PyYAML-6.0.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:50187695423ffe49e2deacb8cd10510bc361faac997de9efef88badc3bb9e2d1", size = 171679 },
+    { url = "https://files.pythonhosted.org/packages/7c/9a/337322f27005c33bcb656c655fa78325b730324c78620e8328ae28b64d0c/PyYAML-6.0.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0ffe8360bab4910ef1b9e87fb812d8bc0a308b0d0eef8c8f44e0254ab3b07133", size = 733428 },
+    { url = "https://files.pythonhosted.org/packages/a3/69/864fbe19e6c18ea3cc196cbe5d392175b4cf3d5d0ac1403ec3f2d237ebb5/PyYAML-6.0.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:17e311b6c678207928d649faa7cb0d7b4c26a0ba73d41e99c4fff6b6c3276484", size = 763361 },
+    { url = "https://files.pythonhosted.org/packages/04/24/b7721e4845c2f162d26f50521b825fb061bc0a5afcf9a386840f23ea19fa/PyYAML-6.0.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:70b189594dbe54f75ab3a1acec5f1e3faa7e8cf2f1e08d9b561cb41b845f69d5", size = 759523 },
+    { url = "https://files.pythonhosted.org/packages/2b/b2/e3234f59ba06559c6ff63c4e10baea10e5e7df868092bf9ab40e5b9c56b6/PyYAML-6.0.2-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:41e4e3953a79407c794916fa277a82531dd93aad34e29c2a514c2c0c5fe971cc", size = 726660 },
+    { url = "https://files.pythonhosted.org/packages/fe/0f/25911a9f080464c59fab9027482f822b86bf0608957a5fcc6eaac85aa515/PyYAML-6.0.2-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:68ccc6023a3400877818152ad9a1033e3db8625d899c72eacb5a668902e4d652", size = 751597 },
+    { url = "https://files.pythonhosted.org/packages/14/0d/e2c3b43bbce3cf6bd97c840b46088a3031085179e596d4929729d8d68270/PyYAML-6.0.2-cp313-cp313-win32.whl", hash = "sha256:bc2fa7c6b47d6bc618dd7fb02ef6fdedb1090ec036abab80d4681424b84c1183", size = 140527 },
+    { url = "https://files.pythonhosted.org/packages/fa/de/02b54f42487e3d3c6efb3f89428677074ca7bf43aae402517bc7cca949f3/PyYAML-6.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:8388ee1976c416731879ac16da0aff3f63b286ffdd57cdeb95f3f2e085687563", size = 156446 },
+]
+
+[[package]]
+name = "pyzmq"
+version = "26.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cffi", marker = "implementation_name == 'pypy'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3a/ed/c3876f3b3e8beba336214ce44e1efa1792dd537027cef24192ac2b077d7c/pyzmq-26.3.0.tar.gz", hash = "sha256:f1cd68b8236faab78138a8fc703f7ca0ad431b17a3fcac696358600d4e6243b3", size = 276733 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/41/a8/cc21dcd6f0f96dbd636fcaab345f9664cd54e6577a21a74694202479d3fa/pyzmq-26.3.0-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:1586944f4736515af5c6d3a5b150c7e8ca2a2d6e46b23057320584d6f2438f4a", size = 1345312 },
+    { url = "https://files.pythonhosted.org/packages/0b/6d/7e0e52798697536d572a105849c4ab621ca00511674b6ce694cb05e437fc/pyzmq-26.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aa7efc695d1fc9f72d91bf9b6c6fe2d7e1b4193836ec530a98faf7d7a7577a58", size = 678336 },
+    { url = "https://files.pythonhosted.org/packages/91/86/8914875e2341a40da460feaa9cace727e50a6b640a20ac36186686bde7d9/pyzmq-26.3.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bd84441e4021cec6e4dd040550386cd9c9ea1d9418ea1a8002dbb7b576026b2b", size = 916965 },
+    { url = "https://files.pythonhosted.org/packages/9a/59/72b390b31ed0cc825881435f21baaae9d57e263aba526fa833863b90d667/pyzmq-26.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9176856f36c34a8aa5c0b35ddf52a5d5cd8abeece57c2cd904cfddae3fd9acd3", size = 874003 },
+    { url = "https://files.pythonhosted.org/packages/97/d4/4dd152dbbaac35d4e1fe8e8fd26d73640fcd84ec9c3915b545692df1ffb7/pyzmq-26.3.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:49334faa749d55b77f084389a80654bf2e68ab5191c0235066f0140c1b670d64", size = 867989 },
+    { url = "https://files.pythonhosted.org/packages/a4/22/1c5dc761dff13981d27d8225aedb19e70ce9149d16cf0c97c7547570e986/pyzmq-26.3.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:fd30fc80fe96efb06bea21667c5793bbd65c0dc793187feb39b8f96990680b00", size = 1207989 },
+    { url = "https://files.pythonhosted.org/packages/03/89/227ffb9e30b3fbe8196e7c97704345feb750b468e852ab64b0d19fa89e1a/pyzmq-26.3.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:b2eddfbbfb473a62c3a251bb737a6d58d91907f6e1d95791431ebe556f47d916", size = 1520523 },
+    { url = "https://files.pythonhosted.org/packages/29/d3/e9b99b8404b6a470762cb947bc342e462a853a22ce0b0f2982c65a9b698f/pyzmq-26.3.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:70b3acb9ad729a53d4e751dace35404a024f188aad406013454216aba5485b4e", size = 1419912 },
+    { url = "https://files.pythonhosted.org/packages/bb/69/074e2cde8135cae9452778e644ea5c91493bc536367d956005fe83072f63/pyzmq-26.3.0-cp310-cp310-win32.whl", hash = "sha256:c1bd75d692cd7c6d862a98013bfdf06702783b75cffbf5dae06d718fecefe8f2", size = 583733 },
+    { url = "https://files.pythonhosted.org/packages/00/f0/55e57d40f6e21877e96507c0c2dd7e32afffc37b0dde7b834df1170cd749/pyzmq-26.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:d7165bcda0dbf203e5ad04d79955d223d84b2263df4db92f525ba370b03a12ab", size = 647229 },
+    { url = "https://files.pythonhosted.org/packages/38/1d/6e935b5f06d674c931540b29932a0dd5e1b9d29d047c2764a9c8c6f3ce08/pyzmq-26.3.0-cp310-cp310-win_arm64.whl", hash = "sha256:e34a63f71d2ecffb3c643909ad2d488251afeb5ef3635602b3448e609611a7ed", size = 561038 },
+    { url = "https://files.pythonhosted.org/packages/22/75/774e9a4a4291864dd37a03a7bfaf46a82d61cd36c16edd33a5739ad49be3/pyzmq-26.3.0-cp311-cp311-macosx_10_15_universal2.whl", hash = "sha256:2833602d9d42c94b9d0d2a44d2b382d3d3a4485be018ba19dddc401a464c617a", size = 1345893 },
+    { url = "https://files.pythonhosted.org/packages/ca/51/d3eedd2bd46ef851bea528d8a2688a5091183b27fc238801fcac70e80dbb/pyzmq-26.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d8270d104ec7caa0bdac246d31d48d94472033ceab5ba142881704350b28159c", size = 678261 },
+    { url = "https://files.pythonhosted.org/packages/de/5e/521d7c6613769dcc3ed5e44e7082938b6dab27fffe02755784e54e98e17b/pyzmq-26.3.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c208a977843d18d3bd185f323e4eaa912eb4869cb230947dc6edd8a27a4e558a", size = 915311 },
+    { url = "https://files.pythonhosted.org/packages/78/db/3be86dd82adc638a2eb07c3028c1747ead49a71d7d334980b007f593fd9f/pyzmq-26.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eddc2be28a379c218e0d92e4a432805dcb0ca5870156a90b54c03cd9799f9f8a", size = 873193 },
+    { url = "https://files.pythonhosted.org/packages/63/1a/81a31920d5113113ccd50271649dd2d0cfcfe46925d8f8a196fe560ed0e6/pyzmq-26.3.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:c0b519fa2159c42272f8a244354a0e110d65175647e5185b04008ec00df9f079", size = 867648 },
+    { url = "https://files.pythonhosted.org/packages/55/79/bbf57979ff2d89b5465d7205db08de7222d2560edc11272eb054c5a68cb5/pyzmq-26.3.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:1595533de3a80bf8363372c20bafa963ec4bf9f2b8f539b1d9a5017f430b84c9", size = 1208475 },
+    { url = "https://files.pythonhosted.org/packages/50/fc/1246dfc4b165e7ff97ac3c4117bdd3747e03ebb62269f71f65e216bfac8b/pyzmq-26.3.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:bbef99eb8d18ba9a40f00e8836b8040cdcf0f2fa649684cf7a66339599919d21", size = 1519428 },
+    { url = "https://files.pythonhosted.org/packages/5f/9a/143aacb6b372b0e2d812aec73a06fc5df3e169a361d4302226f8563954c6/pyzmq-26.3.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:979486d444ca3c469cd1c7f6a619ce48ff08b3b595d451937db543754bfacb65", size = 1419530 },
+    { url = "https://files.pythonhosted.org/packages/47/f7/b437e77d496089e17e77866eb126dd97ea47041b58e53892f57e82869198/pyzmq-26.3.0-cp311-cp311-win32.whl", hash = "sha256:4b127cfe10b4c56e4285b69fd4b38ea1d368099ea4273d8fb349163fce3cd598", size = 582538 },
+    { url = "https://files.pythonhosted.org/packages/a1/2c/99a01a2d7865aaf44e47c2182cbdbc15da1f2e4cfee92dc8e1fb5114f993/pyzmq-26.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:cf736cc1298ef15280d9fcf7a25c09b05af016656856dc6fe5626fd8912658dd", size = 647989 },
+    { url = "https://files.pythonhosted.org/packages/60/b3/36ac1cb8fafeadff09935f4bdc1232e511af8f8893d6cebc7ceb93c6753a/pyzmq-26.3.0-cp311-cp311-win_arm64.whl", hash = "sha256:2dc46ec09f5d36f606ac8393303149e69d17121beee13c8dac25e2a2078e31c4", size = 561533 },
+    { url = "https://files.pythonhosted.org/packages/7b/03/7170c3814bb9106c1bca67700c731aaf1cd990fd2f0097c754acb600330e/pyzmq-26.3.0-cp312-cp312-macosx_10_15_universal2.whl", hash = "sha256:c80653332c6136da7f4d4e143975e74ac0fa14f851f716d90583bc19e8945cea", size = 1348354 },
+    { url = "https://files.pythonhosted.org/packages/74/f3/908b17f9111cdc764aef1de3d36026a2984c46ed90c3c2c85f28b66142f0/pyzmq-26.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6e317ee1d4528a03506cb1c282cd9db73660a35b3564096de37de7350e7d87a7", size = 671056 },
+    { url = "https://files.pythonhosted.org/packages/02/ad/afcb8484b65ceacd1609f709c2caeed31bd6c49261a7507cd5c175cc105f/pyzmq-26.3.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:943a22ebb3daacb45f76a9bcca9a7b74e7d94608c0c0505da30af900b998ca8d", size = 908597 },
+    { url = "https://files.pythonhosted.org/packages/a1/b5/4eeeae0aaaa6ef0c74cfa8b2273b53382bd858df6d99485f2fc8211e7002/pyzmq-26.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3fc9e71490d989144981ea21ef4fdfaa7b6aa84aff9632d91c736441ce2f6b00", size = 865260 },
+    { url = "https://files.pythonhosted.org/packages/74/6a/63db856e93e3a3c3dc98a1de28a902cf1b21c7b0d3856cd5931d7cfd30af/pyzmq-26.3.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:e281a8071a06888575a4eb523c4deeefdcd2f5fe4a2d47e02ac8bf3a5b49f695", size = 859916 },
+    { url = "https://files.pythonhosted.org/packages/e1/ce/d522c9b46ee3746d4b98c81969c568c2c6296e931a65f2c87104b645654c/pyzmq-26.3.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:be77efd735bb1064605be8dec6e721141c1421ef0b115ef54e493a64e50e9a52", size = 1201368 },
+    { url = "https://files.pythonhosted.org/packages/5a/56/29dcd3647a39e933eb489fda261a1e2700a59d4a9432889a85166e15651c/pyzmq-26.3.0-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:7a4ac2ffa34f1212dd586af90f4ba894e424f0cabb3a49cdcff944925640f6ac", size = 1512663 },
+    { url = "https://files.pythonhosted.org/packages/6b/36/7c570698127a43398ed1b1832dada59496e633115016addbce5eda9938a6/pyzmq-26.3.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:ba698c7c252af83b6bba9775035263f0df5f807f0404019916d4b71af8161f66", size = 1411693 },
+    { url = "https://files.pythonhosted.org/packages/de/54/51d39bef85a7cdbca36227f7defdbfcdc5011b8361a3bfc0e8df431f5a5d/pyzmq-26.3.0-cp312-cp312-win32.whl", hash = "sha256:214038aaa88e801e54c2ef0cfdb2e6df27eb05f67b477380a452b595c5ecfa37", size = 581244 },
+    { url = "https://files.pythonhosted.org/packages/f2/6a/9512b11a1d0c5648534f03d5ab0c3222f55dc9c192029c1cb00a0ca044e2/pyzmq-26.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:bad7fe0372e505442482ca3ccbc0d6f38dae81b1650f57a0aa6bbee18e7df495", size = 643559 },
+    { url = "https://files.pythonhosted.org/packages/27/9f/faf5c9cf91b61eeb82a5e919d024d3ac28a795c92cce817be264ccd757d3/pyzmq-26.3.0-cp312-cp312-win_arm64.whl", hash = "sha256:b7b578d604e79e99aa39495becea013fd043fa9f36e4b490efa951f3d847a24d", size = 557664 },
+    { url = "https://files.pythonhosted.org/packages/37/16/97b8c5107bfccb39120e611671a452c9ff6e8626fb3f8d4c15afd652b6ae/pyzmq-26.3.0-cp313-cp313-macosx_10_15_universal2.whl", hash = "sha256:fa85953df84beb7b8b73cb3ec3f5d92b62687a09a8e71525c6734e020edf56fd", size = 1345691 },
+    { url = "https://files.pythonhosted.org/packages/a5/61/d5572d95040c0bb5b31eed5b23f3f0f992d94e4e0de0cea62e3c7f3a85c1/pyzmq-26.3.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:209d09f0ab6ddbcebe64630d1e6ca940687e736f443c265ae15bc4bfad833597", size = 670622 },
+    { url = "https://files.pythonhosted.org/packages/1c/0c/f0235d27388aacf4ed8bcc1d574f6f2f629da0a20610faa0a8e9d363c2b0/pyzmq-26.3.0-cp313-cp313-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d35cc1086f1d4f907df85c6cceb2245cb39a04f69c3f375993363216134d76d4", size = 908683 },
+    { url = "https://files.pythonhosted.org/packages/cb/52/664828f9586c396b857eec088d208230463e3dc991a24df6adbad98fbaa3/pyzmq-26.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b380e9087078ba91e45fb18cdd0c25275ffaa045cf63c947be0ddae6186bc9d9", size = 865212 },
+    { url = "https://files.pythonhosted.org/packages/2b/14/213b2967030b7d7aecc32dd453830f98799b3cbf2b10a40232e9f22a6520/pyzmq-26.3.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:6d64e74143587efe7c9522bb74d1448128fdf9897cc9b6d8b9927490922fd558", size = 860068 },
+    { url = "https://files.pythonhosted.org/packages/aa/e5/ff50c8fade69d1c0469652832c626d1910668697642c10cb0e1b6183ef9a/pyzmq-26.3.0-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:efba4f53ac7752eea6d8ca38a4ddac579e6e742fba78d1e99c12c95cd2acfc64", size = 1201303 },
+    { url = "https://files.pythonhosted.org/packages/9a/e2/fff5e483be95ccc11a05781323e001e63ec15daec1d0f6f08de72ca534db/pyzmq-26.3.0-cp313-cp313-musllinux_1_1_i686.whl", hash = "sha256:9b0137a1c40da3b7989839f9b78a44de642cdd1ce20dcef341de174c8d04aa53", size = 1512892 },
+    { url = "https://files.pythonhosted.org/packages/21/75/cc44d276e43136e5692e487c3c019f816e11ed445261e434217c28cc98c4/pyzmq-26.3.0-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:a995404bd3982c089e57b428c74edd5bfc3b0616b3dbcd6a8e270f1ee2110f36", size = 1411736 },
+    { url = "https://files.pythonhosted.org/packages/ee/1c/d070cbc9a7961fe772641c51bb3798d88cb1f8e20ca718407363462624cf/pyzmq-26.3.0-cp313-cp313-win32.whl", hash = "sha256:240b1634b9e530ef6a277d95cbca1a6922f44dfddc5f0a3cd6c722a8de867f14", size = 581214 },
+    { url = "https://files.pythonhosted.org/packages/38/d3/91082f1151ff5b54e0bed40eb1a26f418530ab07ecaec4dbb83e3d9fa9a9/pyzmq-26.3.0-cp313-cp313-win_amd64.whl", hash = "sha256:fe67291775ea4c2883764ba467eb389c29c308c56b86c1e19e49c9e1ed0cbeca", size = 643412 },
+    { url = "https://files.pythonhosted.org/packages/e0/cf/dabe68dfdf3e67bea6152eeec4b251cf899ee5b853cfb5c97e4719f9e6e9/pyzmq-26.3.0-cp313-cp313-win_arm64.whl", hash = "sha256:73ca9ae9a9011b714cf7650450cd9c8b61a135180b708904f1f0a05004543dce", size = 557444 },
+    { url = "https://files.pythonhosted.org/packages/c0/56/e7576ac71c1566da4f4ec586351462a2bb202143fb074bf56df8fe85dcc3/pyzmq-26.3.0-cp313-cp313t-macosx_10_15_universal2.whl", hash = "sha256:fea7efbd7e49af9d7e5ed6c506dfc7de3d1a628790bd3a35fd0e3c904dc7d464", size = 1340288 },
+    { url = "https://files.pythonhosted.org/packages/f1/ab/0bca97e94d420b5908968bc479e51c3686a9f80d8893450eefcd673b1b1d/pyzmq-26.3.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c4430c7cba23bb0e2ee203eee7851c1654167d956fc6d4b3a87909ccaf3c5825", size = 662462 },
+    { url = "https://files.pythonhosted.org/packages/ee/be/99e89b55863808da322ac3ab52d8e135dcf2241094aaa468bfe2923d5194/pyzmq-26.3.0-cp313-cp313t-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:016d89bee8c7d566fad75516b4e53ec7c81018c062d4c51cd061badf9539be52", size = 896464 },
+    { url = "https://files.pythonhosted.org/packages/38/d4/a4be06a313c8d6a5fe1d92975db30aca85f502e867fca392532e06a28c3c/pyzmq-26.3.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:04bfe59852d76d56736bfd10ac1d49d421ab8ed11030b4a0332900691507f557", size = 853432 },
+    { url = "https://files.pythonhosted.org/packages/12/e6/e608b4c34106bbf5b3b382662ea90a43b2e23df0aa9c1f0fd4e21168d523/pyzmq-26.3.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:1fe05bd0d633a0f672bb28cb8b4743358d196792e1caf04973b7898a0d70b046", size = 845884 },
+    { url = "https://files.pythonhosted.org/packages/c3/a9/d5e6355308ba529d9cd3576ee8bb3b2e2b726571748f515fbb8559401f5b/pyzmq-26.3.0-cp313-cp313t-musllinux_1_1_aarch64.whl", hash = "sha256:2aa1a9f236d5b835fb8642f27de95f9edcfd276c4bc1b6ffc84f27c6fb2e2981", size = 1191454 },
+    { url = "https://files.pythonhosted.org/packages/6a/9a/a21dc6c73ac242e425709c1e0049368d8f5db5de7c1102a45f93f5c492b3/pyzmq-26.3.0-cp313-cp313t-musllinux_1_1_i686.whl", hash = "sha256:21399b31753bf321043ea60c360ed5052cc7be20739785b1dff1820f819e35b3", size = 1500397 },
+    { url = "https://files.pythonhosted.org/packages/87/88/0236056156da0278c9ca2e2562463643597808b5bbd6c34009ba217e7e92/pyzmq-26.3.0-cp313-cp313t-musllinux_1_1_x86_64.whl", hash = "sha256:d015efcd96aca8882057e7e6f06224f79eecd22cad193d3e6a0a91ec67590d1f", size = 1398401 },
+    { url = "https://files.pythonhosted.org/packages/7e/ec/2e02dde6b1a436b02a6c0e3cb64c779bf6e76cc41c12131f29d9b10a088f/pyzmq-26.3.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:ad03f4252d9041b0635c37528dfa3f44b39f46024ae28c8567f7423676ee409b", size = 835672 },
+    { url = "https://files.pythonhosted.org/packages/22/ee/30c2c3f162912cff31af2b9d87295533d16f867e7621bd6f9ed62d9cc807/pyzmq-26.3.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0f3dfb68cf7bf4cfdf34283a75848e077c5defa4907506327282afe92780084d", size = 570837 },
+    { url = "https://files.pythonhosted.org/packages/51/a5/5aead624f5f1033dab9bdaf3e2bc692a8042fcb59355c919a2c042061780/pyzmq-26.3.0-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:356ec0e39c5a9cda872b65aca1fd8a5d296ffdadf8e2442b70ff32e73ef597b1", size = 799508 },
+    { url = "https://files.pythonhosted.org/packages/ca/8a/dcc0a24cfed80cc004abcba710077147ec9178a12865914e73a60a70cb62/pyzmq-26.3.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:749d671b0eec8e738bbf0b361168369d8c682b94fcd458c20741dc4d69ef5278", size = 758001 },
+    { url = "https://files.pythonhosted.org/packages/1a/74/f18e63540340f5c740396eb6408d154a84e9f0e9e1ae931b192bf2aa7425/pyzmq-26.3.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:f950f17ae608e0786298340163cac25a4c5543ef25362dd5ddb6dcb10b547be9", size = 556425 },
+    { url = "https://files.pythonhosted.org/packages/f4/c6/e36b2a2ff6534cb1d1f6b3fb37901ac54675caf7b2e1239613aa40d1d217/pyzmq-26.3.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:b4fc9903a73c25be9d5fe45c87faababcf3879445efa16140146b08fccfac017", size = 835670 },
+    { url = "https://files.pythonhosted.org/packages/1d/b9/8059c5af94b245068e7f7379c08c7e409ec854139d6021aecf2c111d8547/pyzmq-26.3.0-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c15b69af22030960ac63567e98ad8221cddf5d720d9cf03d85021dfd452324ef", size = 570838 },
+    { url = "https://files.pythonhosted.org/packages/80/a4/f0a4266ff2d94a87f7c32895b1716f9ac0edc0471d518462beeb0a9a94b5/pyzmq-26.3.0-pp311-pypy311_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2cf9ab0dff4dbaa2e893eb608373c97eb908e53b7d9793ad00ccbd082c0ee12f", size = 799507 },
+    { url = "https://files.pythonhosted.org/packages/78/14/3d7d459f496fab8e487b23423ccba57abf7153a4fde0c3e000500fa02ff8/pyzmq-26.3.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3ec332675f6a138db57aad93ae6387953763f85419bdbd18e914cb279ee1c451", size = 758002 },
+    { url = "https://files.pythonhosted.org/packages/22/65/cc1f0e1db1290770285430e36d51767e620487523e6a04094be637e55698/pyzmq-26.3.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:eb96568a22fe070590942cd4780950e2172e00fb033a8b76e47692583b1bd97c", size = 556425 },
+]
+
+[[package]]
+name = "quartodoc"
+version = "0.9.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "black" },
+    { name = "click" },
+    { name = "griffe" },
+    { name = "importlib-metadata" },
+    { name = "importlib-resources" },
+    { name = "plum-dispatch" },
+    { name = "pydantic" },
+    { name = "pyyaml" },
+    { name = "requests" },
+    { name = "sphobjinv" },
+    { name = "tabulate" },
+    { name = "typing-extensions" },
+    { name = "watchdog" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f4/ad/1bdc6745d0d0527da0a2f30a235850873e98442a2c1afdd67ac7e164e0c4/quartodoc-0.9.1.tar.gz", hash = "sha256:418d9ff0f2d4481a87e926e3a3e5bc31efbe556c7f36912aa0a3f716c0eafa9c", size = 772465 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0e/47/de4c28e2002653226de3e34c9a3e989619796e5f0fe771284c570a016cec/quartodoc-0.9.1-py3-none-any.whl", hash = "sha256:78345002f75f086be6e1b1ccf3b4e3ada2fa3affb8a8fc0dbdb37890a5da03fa", size = 84275 },
+]
+
+[[package]]
+name = "referencing"
+version = "0.36.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "attrs" },
+    { name = "rpds-py" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/2f/db/98b5c277be99dd18bfd91dd04e1b759cad18d1a338188c936e92f921c7e2/referencing-0.36.2.tar.gz", hash = "sha256:df2e89862cd09deabbdba16944cc3f10feb6b3e6f18e902f7cc25609a34775aa", size = 74744 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c1/b1/3baf80dc6d2b7bc27a95a67752d0208e410351e3feb4eb78de5f77454d8d/referencing-0.36.2-py3-none-any.whl", hash = "sha256:e8699adbbf8b5c7de96d8ffa0eb5c158b3beafce084968e2ea8bb08c6794dcd0", size = 26775 },
+]
+
+[[package]]
+name = "requests"
+version = "2.32.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "certifi" },
+    { name = "charset-normalizer" },
+    { name = "idna" },
+    { name = "urllib3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/63/70/2bf7780ad2d390a8d301ad0b550f1581eadbd9a20f896afe06353c2a2913/requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760", size = 131218 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6", size = 64928 },
+]
+
+[[package]]
+name = "rich"
+version = "13.9.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown-it-py" },
+    { name = "pygments" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ab/3a/0316b28d0761c6734d6bc14e770d85506c986c85ffb239e688eeaab2c2bc/rich-13.9.4.tar.gz", hash = "sha256:439594978a49a09530cff7ebc4b5c7103ef57baf48d5ea3184f21d9a2befa098", size = 223149 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/19/71/39c7c0d87f8d4e6c020a393182060eaefeeae6c01dab6a84ec346f2567df/rich-13.9.4-py3-none-any.whl", hash = "sha256:6049d5e6ec054bf2779ab3358186963bac2ea89175919d699e378b99738c2a90", size = 242424 },
+]
+
+[[package]]
+name = "rpds-py"
+version = "0.23.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0a/79/2ce611b18c4fd83d9e3aecb5cba93e1917c050f556db39842889fa69b79f/rpds_py-0.23.1.tar.gz", hash = "sha256:7f3240dcfa14d198dba24b8b9cb3b108c06b68d45b7babd9eefc1038fdf7e707", size = 26806 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/34/fe/e5326459863bd525122f4e9c80ac8d7c6cfa171b7518d04cc27c12c209b0/rpds_py-0.23.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:2a54027554ce9b129fc3d633c92fa33b30de9f08bc61b32c053dc9b537266fed", size = 372123 },
+    { url = "https://files.pythonhosted.org/packages/f9/db/f10a3795f7a89fb27594934012d21c61019bbeb516c5bdcfbbe9e9e617a7/rpds_py-0.23.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b5ef909a37e9738d146519657a1aab4584018746a18f71c692f2f22168ece40c", size = 356778 },
+    { url = "https://files.pythonhosted.org/packages/21/27/0d3678ad7f432fa86f8fac5f5fc6496a4d2da85682a710d605219be20063/rpds_py-0.23.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3ee9d6f0b38efb22ad94c3b68ffebe4c47865cdf4b17f6806d6c674e1feb4246", size = 385775 },
+    { url = "https://files.pythonhosted.org/packages/99/a0/1786defa125b2ad228027f22dff26312ce7d1fee3c7c3c2682f403db2062/rpds_py-0.23.1-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:f7356a6da0562190558c4fcc14f0281db191cdf4cb96e7604c06acfcee96df15", size = 391181 },
+    { url = "https://files.pythonhosted.org/packages/f1/5c/1240934050a7ffd020a915486d0cc4c7f6e7a2442a77aedf13664db55d36/rpds_py-0.23.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9441af1d25aed96901f97ad83d5c3e35e6cd21a25ca5e4916c82d7dd0490a4fa", size = 444607 },
+    { url = "https://files.pythonhosted.org/packages/b7/1b/cee6905b47817fd0a377716dbe4df35295de46df46ee2ff704538cc371b0/rpds_py-0.23.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3d8abf7896a91fb97e7977d1aadfcc2c80415d6dc2f1d0fca5b8d0df247248f3", size = 445550 },
+    { url = "https://files.pythonhosted.org/packages/54/f7/f0821ca34032892d7a67fcd5042f50074ff2de64e771e10df01085c88d47/rpds_py-0.23.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1b08027489ba8fedde72ddd233a5ea411b85a6ed78175f40285bd401bde7466d", size = 386148 },
+    { url = "https://files.pythonhosted.org/packages/eb/ef/2afe53bc857c4bcba336acfd2629883a5746e7291023e017ac7fc98d85aa/rpds_py-0.23.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fee513135b5a58f3bb6d89e48326cd5aa308e4bcdf2f7d59f67c861ada482bf8", size = 416780 },
+    { url = "https://files.pythonhosted.org/packages/ae/9a/38d2236cf669789b8a3e1a014c9b6a8d7b8925b952c92e7839ae2749f9ac/rpds_py-0.23.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:35d5631ce0af26318dba0ae0ac941c534453e42f569011585cb323b7774502a5", size = 558265 },
+    { url = "https://files.pythonhosted.org/packages/e6/0a/f2705530c42578f20ed0b5b90135eecb30eef6e2ba73e7ba69087fad2dba/rpds_py-0.23.1-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:a20cb698c4a59c534c6701b1c24a968ff2768b18ea2991f886bd8985ce17a89f", size = 585270 },
+    { url = "https://files.pythonhosted.org/packages/29/4e/3b597dc84ed82c3d757ac9aa620de224a94e06d2e102069795ae7e81c015/rpds_py-0.23.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:5e9c206a1abc27e0588cf8b7c8246e51f1a16a103734f7750830a1ccb63f557a", size = 553850 },
+    { url = "https://files.pythonhosted.org/packages/00/cc/6498b6f79e4375e6737247661e52a2d18f6accf4910e0c8da978674b4241/rpds_py-0.23.1-cp310-cp310-win32.whl", hash = "sha256:d9f75a06ecc68f159d5d7603b734e1ff6daa9497a929150f794013aa9f6e3f12", size = 220660 },
+    { url = "https://files.pythonhosted.org/packages/17/2b/08db023d23e8c7032c99d8d2a70d32e450a868ab73d16e3ff5290308a665/rpds_py-0.23.1-cp310-cp310-win_amd64.whl", hash = "sha256:f35eff113ad430b5272bbfc18ba111c66ff525828f24898b4e146eb479a2cdda", size = 232551 },
+    { url = "https://files.pythonhosted.org/packages/1c/67/6e5d4234bb9dee062ffca2a5f3c7cd38716317d6760ec235b175eed4de2c/rpds_py-0.23.1-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:b79f5ced71efd70414a9a80bbbfaa7160da307723166f09b69773153bf17c590", size = 372264 },
+    { url = "https://files.pythonhosted.org/packages/a7/0a/3dedb2daee8e783622427f5064e2d112751d8276ee73aa5409f000a132f4/rpds_py-0.23.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c9e799dac1ffbe7b10c1fd42fe4cd51371a549c6e108249bde9cd1200e8f59b4", size = 356883 },
+    { url = "https://files.pythonhosted.org/packages/ed/fc/e1acef44f9c24b05fe5434b235f165a63a52959ac655e3f7a55726cee1a4/rpds_py-0.23.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:721f9c4011b443b6e84505fc00cc7aadc9d1743f1c988e4c89353e19c4a968ee", size = 385624 },
+    { url = "https://files.pythonhosted.org/packages/97/0a/a05951f6465d01622720c03ef6ef31adfbe865653e05ed7c45837492f25e/rpds_py-0.23.1-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:f88626e3f5e57432e6191cd0c5d6d6b319b635e70b40be2ffba713053e5147dd", size = 391500 },
+    { url = "https://files.pythonhosted.org/packages/ea/2e/cca0583ec0690ea441dceae23c0673b99755710ea22f40bccf1e78f41481/rpds_py-0.23.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:285019078537949cecd0190f3690a0b0125ff743d6a53dfeb7a4e6787af154f5", size = 444869 },
+    { url = "https://files.pythonhosted.org/packages/cc/e6/95cda68b33a6d814d1e96b0e406d231ed16629101460d1740e92f03365e6/rpds_py-0.23.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b92f5654157de1379c509b15acec9d12ecf6e3bc1996571b6cb82a4302060447", size = 444930 },
+    { url = "https://files.pythonhosted.org/packages/5f/a7/e94cdb73411ae9c11414d3c7c9a6ad75d22ad4a8d094fb45a345ba9e3018/rpds_py-0.23.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e768267cbe051dd8d1c5305ba690bb153204a09bf2e3de3ae530de955f5b5580", size = 386254 },
+    { url = "https://files.pythonhosted.org/packages/dd/c5/a4a943d90a39e85efd1e04b1ad5129936786f9a9aa27bb7be8fc5d9d50c9/rpds_py-0.23.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c5334a71f7dc1160382d45997e29f2637c02f8a26af41073189d79b95d3321f1", size = 417090 },
+    { url = "https://files.pythonhosted.org/packages/0c/a0/80d0013b12428d1fce0ab4e71829400b0a32caec12733c79e6109f843342/rpds_py-0.23.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d6adb81564af0cd428910f83fa7da46ce9ad47c56c0b22b50872bc4515d91966", size = 557639 },
+    { url = "https://files.pythonhosted.org/packages/a6/92/ec2e6980afb964a2cd7a99cbdef1f6c01116abe94b42cbe336ac93dd11c2/rpds_py-0.23.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:cafa48f2133d4daa028473ede7d81cd1b9f9e6925e9e4003ebdf77010ee02f35", size = 584572 },
+    { url = "https://files.pythonhosted.org/packages/3d/ce/75b6054db34a390789a82523790717b27c1bd735e453abb429a87c4f0f26/rpds_py-0.23.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:0fced9fd4a07a1ded1bac7e961ddd9753dd5d8b755ba8e05acba54a21f5f1522", size = 553028 },
+    { url = "https://files.pythonhosted.org/packages/cc/24/f45abe0418c06a5cba0f846e967aa27bac765acd927aabd857c21319b8cc/rpds_py-0.23.1-cp311-cp311-win32.whl", hash = "sha256:243241c95174b5fb7204c04595852fe3943cc41f47aa14c3828bc18cd9d3b2d6", size = 220862 },
+    { url = "https://files.pythonhosted.org/packages/2d/a6/3c0880e8bbfc36451ef30dc416266f6d2934705e468db5d21c8ba0ab6400/rpds_py-0.23.1-cp311-cp311-win_amd64.whl", hash = "sha256:11dd60b2ffddba85715d8a66bb39b95ddbe389ad2cfcf42c833f1bcde0878eaf", size = 232953 },
+    { url = "https://files.pythonhosted.org/packages/f3/8c/d17efccb9f5b9137ddea706664aebae694384ae1d5997c0202093e37185a/rpds_py-0.23.1-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:3902df19540e9af4cc0c3ae75974c65d2c156b9257e91f5101a51f99136d834c", size = 364369 },
+    { url = "https://files.pythonhosted.org/packages/6e/c0/ab030f696b5c573107115a88d8d73d80f03309e60952b64c584c70c659af/rpds_py-0.23.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:66f8d2a17e5838dd6fb9be6baaba8e75ae2f5fa6b6b755d597184bfcd3cb0eba", size = 349965 },
+    { url = "https://files.pythonhosted.org/packages/b3/55/b40170f5a079c4fb0b6a82b299689e66e744edca3c3375a8b160fb797660/rpds_py-0.23.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:112b8774b0b4ee22368fec42749b94366bd9b536f8f74c3d4175d4395f5cbd31", size = 389064 },
+    { url = "https://files.pythonhosted.org/packages/ab/1c/b03a912c59ec7c1e16b26e587b9dfa8ddff3b07851e781e8c46e908a365a/rpds_py-0.23.1-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e0df046f2266e8586cf09d00588302a32923eb6386ced0ca5c9deade6af9a149", size = 397741 },
+    { url = "https://files.pythonhosted.org/packages/52/6f/151b90792b62fb6f87099bcc9044c626881fdd54e31bf98541f830b15cea/rpds_py-0.23.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:0f3288930b947cbebe767f84cf618d2cbe0b13be476e749da0e6a009f986248c", size = 448784 },
+    { url = "https://files.pythonhosted.org/packages/71/2a/6de67c0c97ec7857e0e9e5cd7c52405af931b303eb1e5b9eff6c50fd9a2e/rpds_py-0.23.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ce473a2351c018b06dd8d30d5da8ab5a0831056cc53b2006e2a8028172c37ce5", size = 440203 },
+    { url = "https://files.pythonhosted.org/packages/db/5e/e759cd1c276d98a4b1f464b17a9bf66c65d29f8f85754e27e1467feaa7c3/rpds_py-0.23.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d550d7e9e7d8676b183b37d65b5cd8de13676a738973d330b59dc8312df9c5dc", size = 391611 },
+    { url = "https://files.pythonhosted.org/packages/1c/1e/2900358efcc0d9408c7289769cba4c0974d9db314aa884028ed7f7364f61/rpds_py-0.23.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:e14f86b871ea74c3fddc9a40e947d6a5d09def5adc2076ee61fb910a9014fb35", size = 423306 },
+    { url = "https://files.pythonhosted.org/packages/23/07/6c177e6d059f5d39689352d6c69a926ee4805ffdb6f06203570234d3d8f7/rpds_py-0.23.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1bf5be5ba34e19be579ae873da515a2836a2166d8d7ee43be6ff909eda42b72b", size = 562323 },
+    { url = "https://files.pythonhosted.org/packages/70/e4/f9097fd1c02b516fff9850792161eb9fc20a2fd54762f3c69eae0bdb67cb/rpds_py-0.23.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:d7031d493c4465dbc8d40bd6cafefef4bd472b17db0ab94c53e7909ee781b9ef", size = 588351 },
+    { url = "https://files.pythonhosted.org/packages/87/39/5db3c6f326bfbe4576ae2af6435bd7555867d20ae690c786ff33659f293b/rpds_py-0.23.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:55ff4151cfd4bc635e51cfb1c59ac9f7196b256b12e3a57deb9e5742e65941ad", size = 557252 },
+    { url = "https://files.pythonhosted.org/packages/fd/14/2d5ad292f144fa79bafb78d2eb5b8a3a91c358b6065443cb9c49b5d1fedf/rpds_py-0.23.1-cp312-cp312-win32.whl", hash = "sha256:a9d3b728f5a5873d84cba997b9d617c6090ca5721caaa691f3b1a78c60adc057", size = 222181 },
+    { url = "https://files.pythonhosted.org/packages/a3/4f/0fce63e0f5cdd658e71e21abd17ac1bc9312741ebb8b3f74eeed2ebdf771/rpds_py-0.23.1-cp312-cp312-win_amd64.whl", hash = "sha256:b03a8d50b137ee758e4c73638b10747b7c39988eb8e6cd11abb7084266455165", size = 237426 },
+    { url = "https://files.pythonhosted.org/packages/13/9d/b8b2c0edffb0bed15be17b6d5ab06216f2f47f9ee49259c7e96a3ad4ca42/rpds_py-0.23.1-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:4caafd1a22e5eaa3732acb7672a497123354bef79a9d7ceed43387d25025e935", size = 363672 },
+    { url = "https://files.pythonhosted.org/packages/bd/c2/5056fa29e6894144d7ba4c938b9b0445f75836b87d2dd00ed4999dc45a8c/rpds_py-0.23.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:178f8a60fc24511c0eb756af741c476b87b610dba83270fce1e5a430204566a4", size = 349602 },
+    { url = "https://files.pythonhosted.org/packages/b0/bc/33779a1bb0ee32d8d706b173825aab75c628521d23ce72a7c1e6a6852f86/rpds_py-0.23.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c632419c3870507ca20a37c8f8f5352317aca097639e524ad129f58c125c61c6", size = 388746 },
+    { url = "https://files.pythonhosted.org/packages/62/0b/71db3e36b7780a619698ec82a9c87ab44ad7ca7f5480913e8a59ff76f050/rpds_py-0.23.1-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:698a79d295626ee292d1730bc2ef6e70a3ab135b1d79ada8fde3ed0047b65a10", size = 397076 },
+    { url = "https://files.pythonhosted.org/packages/bb/2e/494398f613edf77ba10a916b1ddea2acce42ab0e3b62e2c70ffc0757ce00/rpds_py-0.23.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:271fa2184cf28bdded86bb6217c8e08d3a169fe0bbe9be5e8d96e8476b707122", size = 448399 },
+    { url = "https://files.pythonhosted.org/packages/dd/53/4bd7f5779b1f463243ee5fdc83da04dd58a08f86e639dbffa7a35f969a84/rpds_py-0.23.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b91cceb5add79ee563bd1f70b30896bd63bc5f78a11c1f00a1e931729ca4f1f4", size = 439764 },
+    { url = "https://files.pythonhosted.org/packages/f6/55/b3c18c04a460d951bf8e91f2abf46ce5b6426fb69784166a6a25827cb90a/rpds_py-0.23.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3a6cb95074777f1ecda2ca4fa7717caa9ee6e534f42b7575a8f0d4cb0c24013", size = 390662 },
+    { url = "https://files.pythonhosted.org/packages/2a/65/cc463044a3cbd616029b2aa87a651cdee8288d2fdd7780b2244845e934c1/rpds_py-0.23.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:50fb62f8d8364978478b12d5f03bf028c6bc2af04082479299139dc26edf4c64", size = 422680 },
+    { url = "https://files.pythonhosted.org/packages/fa/8e/1fa52990c7836d72e8d70cd7753f2362c72fbb0a49c1462e8c60e7176d0b/rpds_py-0.23.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c8f7e90b948dc9dcfff8003f1ea3af08b29c062f681c05fd798e36daa3f7e3e8", size = 561792 },
+    { url = "https://files.pythonhosted.org/packages/57/b8/fe3b612979b1a29d0c77f8585903d8b3a292604b26d4b300e228b8ac6360/rpds_py-0.23.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:5b98b6c953e5c2bda51ab4d5b4f172617d462eebc7f4bfdc7c7e6b423f6da957", size = 588127 },
+    { url = "https://files.pythonhosted.org/packages/44/2d/fde474de516bbc4b9b230f43c98e7f8acc5da7fc50ceed8e7af27553d346/rpds_py-0.23.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:2893d778d4671ee627bac4037a075168b2673c57186fb1a57e993465dbd79a93", size = 556981 },
+    { url = "https://files.pythonhosted.org/packages/18/57/767deeb27b81370bbab8f74ef6e68d26c4ea99018f3c71a570e506fede85/rpds_py-0.23.1-cp313-cp313-win32.whl", hash = "sha256:2cfa07c346a7ad07019c33fb9a63cf3acb1f5363c33bc73014e20d9fe8b01cdd", size = 221936 },
+    { url = "https://files.pythonhosted.org/packages/7d/6c/3474cfdd3cafe243f97ab8474ea8949236eb2a1a341ca55e75ce00cd03da/rpds_py-0.23.1-cp313-cp313-win_amd64.whl", hash = "sha256:3aaf141d39f45322e44fc2c742e4b8b4098ead5317e5f884770c8df0c332da70", size = 237145 },
+    { url = "https://files.pythonhosted.org/packages/ec/77/e985064c624230f61efa0423759bb066da56ebe40c654f8b5ba225bd5d63/rpds_py-0.23.1-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:759462b2d0aa5a04be5b3e37fb8183615f47014ae6b116e17036b131985cb731", size = 359623 },
+    { url = "https://files.pythonhosted.org/packages/62/d9/a33dcbf62b29e40559e012d525bae7d516757cf042cc9234bd34ca4b6aeb/rpds_py-0.23.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3e9212f52074fc9d72cf242a84063787ab8e21e0950d4d6709886fb62bcb91d5", size = 345900 },
+    { url = "https://files.pythonhosted.org/packages/92/eb/f81a4be6397861adb2cb868bb6a28a33292c2dcac567d1dc575226055e55/rpds_py-0.23.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9e9f3a3ac919406bc0414bbbd76c6af99253c507150191ea79fab42fdb35982a", size = 386426 },
+    { url = "https://files.pythonhosted.org/packages/09/47/1f810c9b5e83be005341201b5389f1d240dfa440346ea7189f9b3fd6961d/rpds_py-0.23.1-cp313-cp313t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c04ca91dda8a61584165825907f5c967ca09e9c65fe8966ee753a3f2b019fe1e", size = 392314 },
+    { url = "https://files.pythonhosted.org/packages/83/bd/bc95831432fd6c46ed8001f01af26de0763a059d6d7e6d69e3c5bf02917a/rpds_py-0.23.1-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4ab923167cfd945abb9b51a407407cf19f5bee35001221f2911dc85ffd35ff4f", size = 447706 },
+    { url = "https://files.pythonhosted.org/packages/19/3e/567c04c226b1802dc6dc82cad3d53e1fa0a773258571c74ac5d8fbde97ed/rpds_py-0.23.1-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ed6f011bedca8585787e5082cce081bac3d30f54520097b2411351b3574e1219", size = 437060 },
+    { url = "https://files.pythonhosted.org/packages/fe/77/a77d2c6afe27ae7d0d55fc32f6841502648070dc8d549fcc1e6d47ff8975/rpds_py-0.23.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6959bb9928c5c999aba4a3f5a6799d571ddc2c59ff49917ecf55be2bbb4e3722", size = 389347 },
+    { url = "https://files.pythonhosted.org/packages/3f/47/6b256ff20a74cfebeac790ab05586e0ac91f88e331125d4740a6c86fc26f/rpds_py-0.23.1-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1ed7de3c86721b4e83ac440751329ec6a1102229aa18163f84c75b06b525ad7e", size = 415554 },
+    { url = "https://files.pythonhosted.org/packages/fc/29/d4572469a245bc9fc81e35166dca19fc5298d5c43e1a6dd64bf145045193/rpds_py-0.23.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:5fb89edee2fa237584e532fbf78f0ddd1e49a47c7c8cfa153ab4849dc72a35e6", size = 557418 },
+    { url = "https://files.pythonhosted.org/packages/9c/0a/68cf7228895b1a3f6f39f51b15830e62456795e61193d2c8b87fd48c60db/rpds_py-0.23.1-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:7e5413d2e2d86025e73f05510ad23dad5950ab8417b7fc6beaad99be8077138b", size = 583033 },
+    { url = "https://files.pythonhosted.org/packages/14/18/017ab41dcd6649ad5db7d00155b4c212b31ab05bd857d5ba73a1617984eb/rpds_py-0.23.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:d31ed4987d72aabdf521eddfb6a72988703c091cfc0064330b9e5f8d6a042ff5", size = 554880 },
+    { url = "https://files.pythonhosted.org/packages/2e/dd/17de89431268da8819d8d51ce67beac28d9b22fccf437bc5d6d2bcd1acdb/rpds_py-0.23.1-cp313-cp313t-win32.whl", hash = "sha256:f3429fb8e15b20961efca8c8b21432623d85db2228cc73fe22756c6637aa39e7", size = 219743 },
+    { url = "https://files.pythonhosted.org/packages/68/15/6d22d07e063ce5e9bfbd96db9ec2fbb4693591b4503e3a76996639474d02/rpds_py-0.23.1-cp313-cp313t-win_amd64.whl", hash = "sha256:d6f6512a90bd5cd9030a6237f5346f046c6f0e40af98657568fa45695d4de59d", size = 235415 },
+    { url = "https://files.pythonhosted.org/packages/95/a9/6fafd35fc6bac05f59bcbc800b57cef877911ff1c015397c519fec888642/rpds_py-0.23.1-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:c1f8afa346ccd59e4e5630d5abb67aba6a9812fddf764fd7eb11f382a345f8cc", size = 373463 },
+    { url = "https://files.pythonhosted.org/packages/5b/ac/44f00029b8fbe0903a19e9a87a9b86063bf8700df2cc58868373d378418c/rpds_py-0.23.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:fad784a31869747df4ac968a351e070c06ca377549e4ace94775aaa3ab33ee06", size = 358400 },
+    { url = "https://files.pythonhosted.org/packages/5e/9c/3da199346c68d785f10dccab123b74c8c5f73be3f742c9e33d1116e07931/rpds_py-0.23.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b5a96fcac2f18e5a0a23a75cd27ce2656c66c11c127b0318e508aab436b77428", size = 386815 },
+    { url = "https://files.pythonhosted.org/packages/d3/45/8f6533c33c0d33da8c2c8b2fb8f2ee90b23c05c679b86b0ac6aee4653749/rpds_py-0.23.1-pp310-pypy310_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3e77febf227a1dc3220159355dba68faa13f8dca9335d97504abf428469fb18b", size = 392974 },
+    { url = "https://files.pythonhosted.org/packages/ca/56/6a9ac1bf0455ba07385d8fe98c571c519b4f2000cff6581487bf9fab9272/rpds_py-0.23.1-pp310-pypy310_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:26bb3e8de93443d55e2e748e9fd87deb5f8075ca7bc0502cfc8be8687d69a2ec", size = 446019 },
+    { url = "https://files.pythonhosted.org/packages/f4/83/5d9a3f9731cdccf49088bcc4ce821a5cf50bd1737cdad83e9959a7b9054d/rpds_py-0.23.1-pp310-pypy310_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:db7707dde9143a67b8812c7e66aeb2d843fe33cc8e374170f4d2c50bd8f2472d", size = 445811 },
+    { url = "https://files.pythonhosted.org/packages/44/50/f2e0a98c62fc1fe68b176caca587714dc5c8bb2c3d1dd1eeb2bd4cc787ac/rpds_py-0.23.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1eedaaccc9bb66581d4ae7c50e15856e335e57ef2734dbc5fd8ba3e2a4ab3cb6", size = 388070 },
+    { url = "https://files.pythonhosted.org/packages/f2/d0/4981878f8f157e6dbea01d95e0119bf3d6b4c2c884fe64a9e6987f941104/rpds_py-0.23.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:28358c54fffadf0ae893f6c1050e8f8853e45df22483b7fff2f6ab6152f5d8bf", size = 419173 },
+    { url = "https://files.pythonhosted.org/packages/ce/13/fc971c470da96b270d2f64fedee987351bd935dc3016932a5cdcb1a88a2a/rpds_py-0.23.1-pp310-pypy310_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:633462ef7e61d839171bf206551d5ab42b30b71cac8f10a64a662536e057fdef", size = 559048 },
+    { url = "https://files.pythonhosted.org/packages/42/02/be91e1de139ec8b4f9fec4192fd779ba48af281cfc762c0ca4c15b945484/rpds_py-0.23.1-pp310-pypy310_pp73-musllinux_1_2_i686.whl", hash = "sha256:a98f510d86f689fcb486dc59e6e363af04151e5260ad1bdddb5625c10f1e95f8", size = 584773 },
+    { url = "https://files.pythonhosted.org/packages/27/28/3af8a1956df3edc41d884267d766dc096496dafc83f02f764a475eca0b4a/rpds_py-0.23.1-pp310-pypy310_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:e0397dd0b3955c61ef9b22838144aa4bef6f0796ba5cc8edfc64d468b93798b4", size = 555153 },
+    { url = "https://files.pythonhosted.org/packages/5e/bb/e45f51c4e1327dea3c72b846c6de129eebacb7a6cb309af7af35d0578c80/rpds_py-0.23.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:75307599f0d25bf6937248e5ac4e3bde5ea72ae6618623b86146ccc7845ed00b", size = 233827 },
+]
+
+[[package]]
+name = "ruff"
+version = "0.11.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a0/ab/be73f46695c350147ffbfd98d42f34bfe370e6b4146bb0bfdc5b3c636fc7/ruff-0.11.1.tar.gz", hash = "sha256:f2e209a283c9fa423e268cad015ec4fb249178608f755fb67491ff175ecbffbf", size = 3850423 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/de/09/fa72b32fb7a1f670c8d3cb925910447c496117850c6aa1601365ad11cd00/ruff-0.11.1-py3-none-linux_armv6l.whl", hash = "sha256:9c833671aaefcbe280aa54da387264402ffbb1e513ff3420c9c7265ea56d6c5c", size = 10114855 },
+    { url = "https://files.pythonhosted.org/packages/fa/c2/544da7ee0bf0a28117218065ccac69683d8d7a885544e6c84076cf72351b/ruff-0.11.1-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:a5a57cd457764228c73066b832040728b02a3837c53c8a781a960b68129c4e0b", size = 10870287 },
+    { url = "https://files.pythonhosted.org/packages/ee/56/0d1c0a3f3ab644e813f365c7eee5d0f9b75e755c5642400cd6ad7070f730/ruff-0.11.1-py3-none-macosx_11_0_arm64.whl", hash = "sha256:da91da0d42e70cd8bda8e6687fab2afd28513a3cc9434539f4149610e63baf8f", size = 10223642 },
+    { url = "https://files.pythonhosted.org/packages/d6/7c/88f06c23018ca02b8b0f78b62e4c701faa2ef6413d9f5e82277bc094d2e9/ruff-0.11.1-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:429a2e533e3a0dba2ba7e0608a736e728150aa9b6d179245aa11a1339baa968b", size = 10384382 },
+    { url = "https://files.pythonhosted.org/packages/67/55/15d2a355e4c64a712938df4c650d7239128865981eb8314a49782a53ef45/ruff-0.11.1-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:6bbcc2984a4d5cbc0f7b10409e74a00a043be45d813e5e81eb58e707455df7f1", size = 9965192 },
+    { url = "https://files.pythonhosted.org/packages/39/e8/4312a514fac213e90e5b45120339dbaa95a396f4cf3ab740e757fed4fa35/ruff-0.11.1-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:88d9c283ebc88faa5bc23fa33f399b6d47a93f5980c92edcddf1f2127aa376b3", size = 11563312 },
+    { url = "https://files.pythonhosted.org/packages/69/ff/4da09bea58e1d784f45169fd281643422727a1c0776802cd6d4b42a0037d/ruff-0.11.1-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:1f2b03d504516d6b22065ce7fac2564dac15d79a6a776452dabfdd7673a45b07", size = 12223815 },
+    { url = "https://files.pythonhosted.org/packages/ec/ba/7e3550f1478a53c4f7efcb0c31a03256eb032a871026da61324ad06dcd0b/ruff-0.11.1-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:52b95a9071f5ad8552af890bd814c6a04daf5b27297ac1054e3667016f3ab739", size = 11662551 },
+    { url = "https://files.pythonhosted.org/packages/7d/69/7e2f1c8a0c76de18f9c054382d31c6d5f9b55a3e476b2630fefe289d31a6/ruff-0.11.1-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28e2d89e7ba8a1525cdb50bc86c07aba35e7bbeef86dad93781b14ad94dc732c", size = 13792940 },
+    { url = "https://files.pythonhosted.org/packages/02/4b/14f54bbf8f8dd03cb9a8a0294e7de1dc41f3be1806af07b8a104761db597/ruff-0.11.1-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e76be5a98dc6c29d85dfa72eb419e8d9276ee96ccf5c33f2b6828001907dcb17", size = 11328700 },
+    { url = "https://files.pythonhosted.org/packages/f5/0c/40c1ba7e9a8c2c7325267c214746b80da1b54fb7e70a9265608213f4b2fb/ruff-0.11.1-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:441f94c44fe250691c92382ef84f40acef290766fb3e819a9035e83eadd4dbbe", size = 10272650 },
+    { url = "https://files.pythonhosted.org/packages/a3/27/f86b786e0153c5b52768370f395086c4523dee1af37757a8ef73779b9fbd/ruff-0.11.1-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:62882a4cc7c0a48c2f34189bd4c7ba45f3d0efb990e02413eeb180aa042a39ca", size = 9973454 },
+    { url = "https://files.pythonhosted.org/packages/ab/46/47bf0c7ee80b0476226865d724ee7682323a2cb20aa5d134b8a665f58793/ruff-0.11.1-py3-none-musllinux_1_2_i686.whl", hash = "sha256:111dbad1706d8200a7138237b4766b45ba7ee45cc8299c02102f4327624f86a2", size = 10975303 },
+    { url = "https://files.pythonhosted.org/packages/9d/9d/deea8ece99f31aef07f378f83b5571baca74dff0d28fec720e54b171b862/ruff-0.11.1-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:e2df41763d7a9fd438b6b7bde7b75eb3a92ef2f4682ed2d8e4b997b5f0c76ca9", size = 11402605 },
+    { url = "https://files.pythonhosted.org/packages/b8/8c/f11e21f4a5ce0858c35745d43e9a5abd870ed33e151ada23c124bf433e71/ruff-0.11.1-py3-none-win32.whl", hash = "sha256:e17b85919d461583aa7e0171bb4f419a6545b261ca080984db49b1f8dced1d4b", size = 10335500 },
+    { url = "https://files.pythonhosted.org/packages/21/c9/ac2784bad48c7a8702d2f13851b69b2a08534b59657ee2b724f42aaaf861/ruff-0.11.1-py3-none-win_amd64.whl", hash = "sha256:caa872941b876f7ad73abc60144f9a7f6efb575e4f91c4fc1517f0339bcea01e", size = 11365097 },
+    { url = "https://files.pythonhosted.org/packages/9e/7d/f37251c102cf155cdf664322f1f362f4fd8fe1eb6870e9154255d3cbf0d8/ruff-0.11.1-py3-none-win_arm64.whl", hash = "sha256:7aa939fa57ef6770d18bd5cf0d6de77198dd762a559bd0d4a8763bdae4c8cc16", size = 10510306 },
+]
+
+[[package]]
+name = "scipy"
+version = "1.15.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b7/b9/31ba9cd990e626574baf93fbc1ac61cf9ed54faafd04c479117517661637/scipy-1.15.2.tar.gz", hash = "sha256:cd58a314d92838f7e6f755c8a2167ead4f27e1fd5c1251fd54289569ef3495ec", size = 59417316 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/95/df/ef233fff6838fe6f7840d69b5ef9f20d2b5c912a8727b21ebf876cb15d54/scipy-1.15.2-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:a2ec871edaa863e8213ea5df811cd600734f6400b4af272e1c011e69401218e9", size = 38692502 },
+    { url = "https://files.pythonhosted.org/packages/5c/20/acdd4efb8a68b842968f7bc5611b1aeb819794508771ad104de418701422/scipy-1.15.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:6f223753c6ea76983af380787611ae1291e3ceb23917393079dcc746ba60cfb5", size = 30085508 },
+    { url = "https://files.pythonhosted.org/packages/42/55/39cf96ca7126f1e78ee72a6344ebdc6702fc47d037319ad93221063e6cf4/scipy-1.15.2-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:ecf797d2d798cf7c838c6d98321061eb3e72a74710e6c40540f0e8087e3b499e", size = 22359166 },
+    { url = "https://files.pythonhosted.org/packages/51/48/708d26a4ab8a1441536bf2dfcad1df0ca14a69f010fba3ccbdfc02df7185/scipy-1.15.2-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:9b18aa747da280664642997e65aab1dd19d0c3d17068a04b3fe34e2559196cb9", size = 25112047 },
+    { url = "https://files.pythonhosted.org/packages/dd/65/f9c5755b995ad892020381b8ae11f16d18616208e388621dfacc11df6de6/scipy-1.15.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:87994da02e73549dfecaed9e09a4f9d58a045a053865679aeb8d6d43747d4df3", size = 35536214 },
+    { url = "https://files.pythonhosted.org/packages/de/3c/c96d904b9892beec978562f64d8cc43f9cca0842e65bd3cd1b7f7389b0ba/scipy-1.15.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:69ea6e56d00977f355c0f84eba69877b6df084516c602d93a33812aa04d90a3d", size = 37646981 },
+    { url = "https://files.pythonhosted.org/packages/3d/74/c2d8a24d18acdeae69ed02e132b9bc1bb67b7bee90feee1afe05a68f9d67/scipy-1.15.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:888307125ea0c4466287191e5606a2c910963405ce9671448ff9c81c53f85f58", size = 37230048 },
+    { url = "https://files.pythonhosted.org/packages/42/19/0aa4ce80eca82d487987eff0bc754f014dec10d20de2f66754fa4ea70204/scipy-1.15.2-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:9412f5e408b397ff5641080ed1e798623dbe1ec0d78e72c9eca8992976fa65aa", size = 40010322 },
+    { url = "https://files.pythonhosted.org/packages/d0/d2/f0683b7e992be44d1475cc144d1f1eeae63c73a14f862974b4db64af635e/scipy-1.15.2-cp310-cp310-win_amd64.whl", hash = "sha256:b5e025e903b4f166ea03b109bb241355b9c42c279ea694d8864d033727205e65", size = 41233385 },
+    { url = "https://files.pythonhosted.org/packages/40/1f/bf0a5f338bda7c35c08b4ed0df797e7bafe8a78a97275e9f439aceb46193/scipy-1.15.2-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:92233b2df6938147be6fa8824b8136f29a18f016ecde986666be5f4d686a91a4", size = 38703651 },
+    { url = "https://files.pythonhosted.org/packages/de/54/db126aad3874601048c2c20ae3d8a433dbfd7ba8381551e6f62606d9bd8e/scipy-1.15.2-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:62ca1ff3eb513e09ed17a5736929429189adf16d2d740f44e53270cc800ecff1", size = 30102038 },
+    { url = "https://files.pythonhosted.org/packages/61/d8/84da3fffefb6c7d5a16968fe5b9f24c98606b165bb801bb0b8bc3985200f/scipy-1.15.2-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:4c6676490ad76d1c2894d77f976144b41bd1a4052107902238047fb6a473e971", size = 22375518 },
+    { url = "https://files.pythonhosted.org/packages/44/78/25535a6e63d3b9c4c90147371aedb5d04c72f3aee3a34451f2dc27c0c07f/scipy-1.15.2-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:a8bf5cb4a25046ac61d38f8d3c3426ec11ebc350246a4642f2f315fe95bda655", size = 25142523 },
+    { url = "https://files.pythonhosted.org/packages/e0/22/4b4a26fe1cd9ed0bc2b2cb87b17d57e32ab72c346949eaf9288001f8aa8e/scipy-1.15.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6a8e34cf4c188b6dd004654f88586d78f95639e48a25dfae9c5e34a6dc34547e", size = 35491547 },
+    { url = "https://files.pythonhosted.org/packages/32/ea/564bacc26b676c06a00266a3f25fdfe91a9d9a2532ccea7ce6dd394541bc/scipy-1.15.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:28a0d2c2075946346e4408b211240764759e0fabaeb08d871639b5f3b1aca8a0", size = 37634077 },
+    { url = "https://files.pythonhosted.org/packages/43/c2/bfd4e60668897a303b0ffb7191e965a5da4056f0d98acfb6ba529678f0fb/scipy-1.15.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:42dabaaa798e987c425ed76062794e93a243be8f0f20fff6e7a89f4d61cb3d40", size = 37231657 },
+    { url = "https://files.pythonhosted.org/packages/4a/75/5f13050bf4f84c931bcab4f4e83c212a36876c3c2244475db34e4b5fe1a6/scipy-1.15.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:6f5e296ec63c5da6ba6fa0343ea73fd51b8b3e1a300b0a8cae3ed4b1122c7462", size = 40035857 },
+    { url = "https://files.pythonhosted.org/packages/b9/8b/7ec1832b09dbc88f3db411f8cdd47db04505c4b72c99b11c920a8f0479c3/scipy-1.15.2-cp311-cp311-win_amd64.whl", hash = "sha256:597a0c7008b21c035831c39927406c6181bcf8f60a73f36219b69d010aa04737", size = 41217654 },
+    { url = "https://files.pythonhosted.org/packages/4b/5d/3c78815cbab499610f26b5bae6aed33e227225a9fa5290008a733a64f6fc/scipy-1.15.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c4697a10da8f8765bb7c83e24a470da5797e37041edfd77fd95ba3811a47c4fd", size = 38756184 },
+    { url = "https://files.pythonhosted.org/packages/37/20/3d04eb066b471b6e171827548b9ddb3c21c6bbea72a4d84fc5989933910b/scipy-1.15.2-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:869269b767d5ee7ea6991ed7e22b3ca1f22de73ab9a49c44bad338b725603301", size = 30163558 },
+    { url = "https://files.pythonhosted.org/packages/a4/98/e5c964526c929ef1f795d4c343b2ff98634ad2051bd2bbadfef9e772e413/scipy-1.15.2-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:bad78d580270a4d32470563ea86c6590b465cb98f83d760ff5b0990cb5518a93", size = 22437211 },
+    { url = "https://files.pythonhosted.org/packages/1d/cd/1dc7371e29195ecbf5222f9afeedb210e0a75057d8afbd942aa6cf8c8eca/scipy-1.15.2-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:b09ae80010f52efddb15551025f9016c910296cf70adbf03ce2a8704f3a5ad20", size = 25232260 },
+    { url = "https://files.pythonhosted.org/packages/f0/24/1a181a9e5050090e0b5138c5f496fee33293c342b788d02586bc410c6477/scipy-1.15.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5a6fd6eac1ce74a9f77a7fc724080d507c5812d61e72bd5e4c489b042455865e", size = 35198095 },
+    { url = "https://files.pythonhosted.org/packages/c0/53/eaada1a414c026673eb983f8b4a55fe5eb172725d33d62c1b21f63ff6ca4/scipy-1.15.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2b871df1fe1a3ba85d90e22742b93584f8d2b8e6124f8372ab15c71b73e428b8", size = 37297371 },
+    { url = "https://files.pythonhosted.org/packages/e9/06/0449b744892ed22b7e7b9a1994a866e64895363572677a316a9042af1fe5/scipy-1.15.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:03205d57a28e18dfd39f0377d5002725bf1f19a46f444108c29bdb246b6c8a11", size = 36872390 },
+    { url = "https://files.pythonhosted.org/packages/6a/6f/a8ac3cfd9505ec695c1bc35edc034d13afbd2fc1882a7c6b473e280397bb/scipy-1.15.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:601881dfb761311045b03114c5fe718a12634e5608c3b403737ae463c9885d53", size = 39700276 },
+    { url = "https://files.pythonhosted.org/packages/f5/6f/e6e5aff77ea2a48dd96808bb51d7450875af154ee7cbe72188afb0b37929/scipy-1.15.2-cp312-cp312-win_amd64.whl", hash = "sha256:e7c68b6a43259ba0aab737237876e5c2c549a031ddb7abc28c7b47f22e202ded", size = 40942317 },
+    { url = "https://files.pythonhosted.org/packages/53/40/09319f6e0f276ea2754196185f95cd191cb852288440ce035d5c3a931ea2/scipy-1.15.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:01edfac9f0798ad6b46d9c4c9ca0e0ad23dbf0b1eb70e96adb9fa7f525eff0bf", size = 38717587 },
+    { url = "https://files.pythonhosted.org/packages/fe/c3/2854f40ecd19585d65afaef601e5e1f8dbf6758b2f95b5ea93d38655a2c6/scipy-1.15.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:08b57a9336b8e79b305a143c3655cc5bdbe6d5ece3378578888d2afbb51c4e37", size = 30100266 },
+    { url = "https://files.pythonhosted.org/packages/dd/b1/f9fe6e3c828cb5930b5fe74cb479de5f3d66d682fa8adb77249acaf545b8/scipy-1.15.2-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:54c462098484e7466362a9f1672d20888f724911a74c22ae35b61f9c5919183d", size = 22373768 },
+    { url = "https://files.pythonhosted.org/packages/15/9d/a60db8c795700414c3f681908a2b911e031e024d93214f2d23c6dae174ab/scipy-1.15.2-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:cf72ff559a53a6a6d77bd8eefd12a17995ffa44ad86c77a5df96f533d4e6c6bb", size = 25154719 },
+    { url = "https://files.pythonhosted.org/packages/37/3b/9bda92a85cd93f19f9ed90ade84aa1e51657e29988317fabdd44544f1dd4/scipy-1.15.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9de9d1416b3d9e7df9923ab23cd2fe714244af10b763975bea9e4f2e81cebd27", size = 35163195 },
+    { url = "https://files.pythonhosted.org/packages/03/5a/fc34bf1aa14dc7c0e701691fa8685f3faec80e57d816615e3625f28feb43/scipy-1.15.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fb530e4794fc8ea76a4a21ccb67dea33e5e0e60f07fc38a49e821e1eae3b71a0", size = 37255404 },
+    { url = "https://files.pythonhosted.org/packages/4a/71/472eac45440cee134c8a180dbe4c01b3ec247e0338b7c759e6cd71f199a7/scipy-1.15.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5ea7ed46d437fc52350b028b1d44e002646e28f3e8ddc714011aaf87330f2f32", size = 36860011 },
+    { url = "https://files.pythonhosted.org/packages/01/b3/21f890f4f42daf20e4d3aaa18182dddb9192771cd47445aaae2e318f6738/scipy-1.15.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:11e7ad32cf184b74380f43d3c0a706f49358b904fa7d5345f16ddf993609184d", size = 39657406 },
+    { url = "https://files.pythonhosted.org/packages/0d/76/77cf2ac1f2a9cc00c073d49e1e16244e389dd88e2490c91d84e1e3e4d126/scipy-1.15.2-cp313-cp313-win_amd64.whl", hash = "sha256:a5080a79dfb9b78b768cebf3c9dcbc7b665c5875793569f48bf0e2b1d7f68f6f", size = 40961243 },
+    { url = "https://files.pythonhosted.org/packages/4c/4b/a57f8ddcf48e129e6054fa9899a2a86d1fc6b07a0e15c7eebff7ca94533f/scipy-1.15.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:447ce30cee6a9d5d1379087c9e474628dab3db4a67484be1b7dc3196bfb2fac9", size = 38870286 },
+    { url = "https://files.pythonhosted.org/packages/0c/43/c304d69a56c91ad5f188c0714f6a97b9c1fed93128c691148621274a3a68/scipy-1.15.2-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:c90ebe8aaa4397eaefa8455a8182b164a6cc1d59ad53f79943f266d99f68687f", size = 30141634 },
+    { url = "https://files.pythonhosted.org/packages/44/1a/6c21b45d2548eb73be9b9bff421aaaa7e85e22c1f9b3bc44b23485dfce0a/scipy-1.15.2-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:def751dd08243934c884a3221156d63e15234a3155cf25978b0a668409d45eb6", size = 22415179 },
+    { url = "https://files.pythonhosted.org/packages/74/4b/aefac4bba80ef815b64f55da06f62f92be5d03b467f2ce3668071799429a/scipy-1.15.2-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:302093e7dfb120e55515936cb55618ee0b895f8bcaf18ff81eca086c17bd80af", size = 25126412 },
+    { url = "https://files.pythonhosted.org/packages/b1/53/1cbb148e6e8f1660aacd9f0a9dfa2b05e9ff1cb54b4386fe868477972ac2/scipy-1.15.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7cd5b77413e1855351cdde594eca99c1f4a588c2d63711388b6a1f1c01f62274", size = 34952867 },
+    { url = "https://files.pythonhosted.org/packages/2c/23/e0eb7f31a9c13cf2dca083828b97992dd22f8184c6ce4fec5deec0c81fcf/scipy-1.15.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d0194c37037707b2afa7a2f2a924cf7bac3dc292d51b6a925e5fcb89bc5c776", size = 36890009 },
+    { url = "https://files.pythonhosted.org/packages/03/f3/e699e19cabe96bbac5189c04aaa970718f0105cff03d458dc5e2b6bd1e8c/scipy-1.15.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:bae43364d600fdc3ac327db99659dcb79e6e7ecd279a75fe1266669d9a652828", size = 36545159 },
+    { url = "https://files.pythonhosted.org/packages/af/f5/ab3838e56fe5cc22383d6fcf2336e48c8fe33e944b9037fbf6cbdf5a11f8/scipy-1.15.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f031846580d9acccd0044efd1a90e6f4df3a6e12b4b6bd694a7bc03a89892b28", size = 39136566 },
+    { url = "https://files.pythonhosted.org/packages/0a/c8/b3f566db71461cabd4b2d5b39bcc24a7e1c119535c8361f81426be39bb47/scipy-1.15.2-cp313-cp313t-win_amd64.whl", hash = "sha256:fe8a9eb875d430d81755472c5ba75e84acc980e4a8f6204d402849234d3017db", size = 40477705 },
+]
+
+[[package]]
+name = "six"
+version = "1.17.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050 },
+]
+
+[[package]]
+name = "snowballstemmer"
+version = "2.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/44/7b/af302bebf22c749c56c9c3e8ae13190b5b5db37a33d9068652e8f73b7089/snowballstemmer-2.2.0.tar.gz", hash = "sha256:09b16deb8547d3412ad7b590689584cd0fe25ec8db3be37788be3810cbf19cb1", size = 86699 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl", hash = "sha256:c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a", size = 93002 },
+]
+
+[[package]]
+name = "sortedcontainers"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e8/c4/ba2f8066cceb6f23394729afe52f3bf7adec04bf9ed2c820b39e19299111/sortedcontainers-2.4.0.tar.gz", hash = "sha256:25caa5a06cc30b6b83d11423433f65d1f9d76c4c6a0c90e3379eaa43b9bfdb88", size = 30594 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/32/46/9cb0e58b2deb7f82b84065f37f3bffeb12413f947f9388e4cac22c4621ce/sortedcontainers-2.4.0-py2.py3-none-any.whl", hash = "sha256:a163dcaede0f1c021485e957a39245190e74249897e2ae4b2aa38595db237ee0", size = 29575 },
+]
+
+[[package]]
+name = "soupsieve"
+version = "2.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d7/ce/fbaeed4f9fb8b2daa961f90591662df6a86c1abf25c548329a86920aedfb/soupsieve-2.6.tar.gz", hash = "sha256:e2e68417777af359ec65daac1057404a3c8a5455bb8abc36f1a9866ab1a51abb", size = 101569 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d1/c2/fe97d779f3ef3b15f05c94a2f1e3d21732574ed441687474db9d342a7315/soupsieve-2.6-py3-none-any.whl", hash = "sha256:e72c4ff06e4fb6e4b5a9f0f55fe6e81514581fca1515028625d0f299c602ccc9", size = 36186 },
+]
+
+[[package]]
+name = "sphinx"
+version = "8.1.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "alabaster" },
+    { name = "babel" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "docutils" },
+    { name = "imagesize" },
+    { name = "jinja2" },
+    { name = "packaging" },
+    { name = "pygments" },
+    { name = "requests" },
+    { name = "snowballstemmer" },
+    { name = "sphinxcontrib-applehelp" },
+    { name = "sphinxcontrib-devhelp" },
+    { name = "sphinxcontrib-htmlhelp" },
+    { name = "sphinxcontrib-jsmath" },
+    { name = "sphinxcontrib-qthelp" },
+    { name = "sphinxcontrib-serializinghtml" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/6f/6d/be0b61178fe2cdcb67e2a92fc9ebb488e3c51c4f74a36a7824c0adf23425/sphinx-8.1.3.tar.gz", hash = "sha256:43c1911eecb0d3e161ad78611bc905d1ad0e523e4ddc202a58a821773dc4c927", size = 8184611 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/26/60/1ddff83a56d33aaf6f10ec8ce84b4c007d9368b21008876fceda7e7381ef/sphinx-8.1.3-py3-none-any.whl", hash = "sha256:09719015511837b76bf6e03e42eb7595ac8c2e41eeb9c29c5b755c6b677992a2", size = 3487125 },
+]
+
+[[package]]
+name = "sphinxcontrib-applehelp"
+version = "2.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ba/6e/b837e84a1a704953c62ef8776d45c3e8d759876b4a84fe14eba2859106fe/sphinxcontrib_applehelp-2.0.0.tar.gz", hash = "sha256:2f29ef331735ce958efa4734873f084941970894c6090408b079c61b2e1c06d1", size = 20053 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5d/85/9ebeae2f76e9e77b952f4b274c27238156eae7979c5421fba91a28f4970d/sphinxcontrib_applehelp-2.0.0-py3-none-any.whl", hash = "sha256:4cd3f0ec4ac5dd9c17ec65e9ab272c9b867ea77425228e68ecf08d6b28ddbdb5", size = 119300 },
+]
+
+[[package]]
+name = "sphinxcontrib-devhelp"
+version = "2.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f6/d2/5beee64d3e4e747f316bae86b55943f51e82bb86ecd325883ef65741e7da/sphinxcontrib_devhelp-2.0.0.tar.gz", hash = "sha256:411f5d96d445d1d73bb5d52133377b4248ec79db5c793ce7dbe59e074b4dd1ad", size = 12967 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/35/7a/987e583882f985fe4d7323774889ec58049171828b58c2217e7f79cdf44e/sphinxcontrib_devhelp-2.0.0-py3-none-any.whl", hash = "sha256:aefb8b83854e4b0998877524d1029fd3e6879210422ee3780459e28a1f03a8a2", size = 82530 },
+]
+
+[[package]]
+name = "sphinxcontrib-htmlhelp"
+version = "2.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/43/93/983afd9aa001e5201eab16b5a444ed5b9b0a7a010541e0ddfbbfd0b2470c/sphinxcontrib_htmlhelp-2.1.0.tar.gz", hash = "sha256:c9e2916ace8aad64cc13a0d233ee22317f2b9025b9cf3295249fa985cc7082e9", size = 22617 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0a/7b/18a8c0bcec9182c05a0b3ec2a776bba4ead82750a55ff798e8d406dae604/sphinxcontrib_htmlhelp-2.1.0-py3-none-any.whl", hash = "sha256:166759820b47002d22914d64a075ce08f4c46818e17cfc9470a9786b759b19f8", size = 98705 },
+]
+
+[[package]]
+name = "sphinxcontrib-jsmath"
+version = "1.0.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b2/e8/9ed3830aeed71f17c026a07a5097edcf44b692850ef215b161b8ad875729/sphinxcontrib-jsmath-1.0.1.tar.gz", hash = "sha256:a9925e4a4587247ed2191a22df5f6970656cb8ca2bd6284309578f2153e0c4b8", size = 5787 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c2/42/4c8646762ee83602e3fb3fbe774c2fac12f317deb0b5dbeeedd2d3ba4b77/sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl", hash = "sha256:2ec2eaebfb78f3f2078e73666b1415417a116cc848b72e5172e596c871103178", size = 5071 },
+]
+
+[[package]]
+name = "sphinxcontrib-qthelp"
+version = "2.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/68/bc/9104308fc285eb3e0b31b67688235db556cd5b0ef31d96f30e45f2e51cae/sphinxcontrib_qthelp-2.0.0.tar.gz", hash = "sha256:4fe7d0ac8fc171045be623aba3e2a8f613f8682731f9153bb2e40ece16b9bbab", size = 17165 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/27/83/859ecdd180cacc13b1f7e857abf8582a64552ea7a061057a6c716e790fce/sphinxcontrib_qthelp-2.0.0-py3-none-any.whl", hash = "sha256:b18a828cdba941ccd6ee8445dbe72ffa3ef8cbe7505d8cd1fa0d42d3f2d5f3eb", size = 88743 },
+]
+
+[[package]]
+name = "sphinxcontrib-serializinghtml"
+version = "2.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3b/44/6716b257b0aa6bfd51a1b31665d1c205fb12cb5ad56de752dfa15657de2f/sphinxcontrib_serializinghtml-2.0.0.tar.gz", hash = "sha256:e9d912827f872c029017a53f0ef2180b327c3f7fd23c87229f7a8e8b70031d4d", size = 16080 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/52/a7/d2782e4e3f77c8450f727ba74a8f12756d5ba823d81b941f1b04da9d033a/sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl", hash = "sha256:6e2cb0eef194e10c27ec0023bfeb25badbbb5868244cf5bc5bdc04e4464bf331", size = 92072 },
+]
+
+[[package]]
+name = "sphobjinv"
+version = "2.3.1.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "attrs" },
+    { name = "certifi" },
+    { name = "jsonschema" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8c/5e/cf2c54fcc6cc999f64d3a34edeee1ca69d6e1f8aee40772c5f3f036296dc/sphobjinv-2.3.1.2.tar.gz", hash = "sha256:1c874a368460851352d45c92afa172b5c42653b47e2e70d78d61dac93308aa9d", size = 268026 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9c/4d/103e541e2533df159e1070cd4372b447a0b689e08a49d271b7b950e21f92/sphobjinv-2.3.1.2-py3-none-any.whl", hash = "sha256:66478d1787d28ef3ebeeedad57c592fdea04cf10eeed0df56307c85ab4eee789", size = 50820 },
+]
+
+[[package]]
+name = "stack-data"
+version = "0.6.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "asttokens" },
+    { name = "executing" },
+    { name = "pure-eval" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/28/e3/55dcc2cfbc3ca9c29519eb6884dd1415ecb53b0e934862d3559ddcb7e20b/stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9", size = 44707 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695", size = 24521 },
+]
+
+[[package]]
+name = "tabulate"
+version = "0.9.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ec/fe/802052aecb21e3797b8f7902564ab6ea0d60ff8ca23952079064155d1ae1/tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c", size = 81090 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f", size = 35252 },
+]
+
+[[package]]
+name = "termcolor"
+version = "2.5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/37/72/88311445fd44c455c7d553e61f95412cf89054308a1aa2434ab835075fc5/termcolor-2.5.0.tar.gz", hash = "sha256:998d8d27da6d48442e8e1f016119076b690d962507531df4890fcd2db2ef8a6f", size = 13057 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7f/be/df630c387a0a054815d60be6a97eb4e8f17385d5d6fe660e1c02750062b4/termcolor-2.5.0-py3-none-any.whl", hash = "sha256:37b17b5fc1e604945c2642c872a3764b5d547a48009871aea3edd3afa180afb8", size = 7755 },
+]
+
+[[package]]
+name = "tinycss2"
+version = "1.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "webencodings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7a/fd/7a5ee21fd08ff70d3d33a5781c255cbe779659bd03278feb98b19ee550f4/tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7", size = 87085 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e6/34/ebdc18bae6aa14fbee1a08b63c015c72b64868ff7dae68808ab500c492e2/tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289", size = 26610 },
+]
+
+[[package]]
+name = "tomli"
+version = "2.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/18/87/302344fed471e44a87289cf4967697d07e532f2421fdaf868a303cbae4ff/tomli-2.2.1.tar.gz", hash = "sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff", size = 17175 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/43/ca/75707e6efa2b37c77dadb324ae7d9571cb424e61ea73fad7c56c2d14527f/tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249", size = 131077 },
+    { url = "https://files.pythonhosted.org/packages/c7/16/51ae563a8615d472fdbffc43a3f3d46588c264ac4f024f63f01283becfbb/tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6", size = 123429 },
+    { url = "https://files.pythonhosted.org/packages/f1/dd/4f6cd1e7b160041db83c694abc78e100473c15d54620083dbd5aae7b990e/tomli-2.2.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ece47d672db52ac607a3d9599a9d48dcb2f2f735c6c2d1f34130085bb12b112a", size = 226067 },
+    { url = "https://files.pythonhosted.org/packages/a9/6b/c54ede5dc70d648cc6361eaf429304b02f2871a345bbdd51e993d6cdf550/tomli-2.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6972ca9c9cc9f0acaa56a8ca1ff51e7af152a9f87fb64623e31d5c83700080ee", size = 236030 },
+    { url = "https://files.pythonhosted.org/packages/1f/47/999514fa49cfaf7a92c805a86c3c43f4215621855d151b61c602abb38091/tomli-2.2.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c954d2250168d28797dd4e3ac5cf812a406cd5a92674ee4c8f123c889786aa8e", size = 240898 },
+    { url = "https://files.pythonhosted.org/packages/73/41/0a01279a7ae09ee1573b423318e7934674ce06eb33f50936655071d81a24/tomli-2.2.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8dd28b3e155b80f4d54beb40a441d366adcfe740969820caf156c019fb5c7ec4", size = 229894 },
+    { url = "https://files.pythonhosted.org/packages/55/18/5d8bc5b0a0362311ce4d18830a5d28943667599a60d20118074ea1b01bb7/tomli-2.2.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:e59e304978767a54663af13c07b3d1af22ddee3bb2fb0618ca1593e4f593a106", size = 245319 },
+    { url = "https://files.pythonhosted.org/packages/92/a3/7ade0576d17f3cdf5ff44d61390d4b3febb8a9fc2b480c75c47ea048c646/tomli-2.2.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:33580bccab0338d00994d7f16f4c4ec25b776af3ffaac1ed74e0b3fc95e885a8", size = 238273 },
+    { url = "https://files.pythonhosted.org/packages/72/6f/fa64ef058ac1446a1e51110c375339b3ec6be245af9d14c87c4a6412dd32/tomli-2.2.1-cp311-cp311-win32.whl", hash = "sha256:465af0e0875402f1d226519c9904f37254b3045fc5084697cefb9bdde1ff99ff", size = 98310 },
+    { url = "https://files.pythonhosted.org/packages/6a/1c/4a2dcde4a51b81be3530565e92eda625d94dafb46dbeb15069df4caffc34/tomli-2.2.1-cp311-cp311-win_amd64.whl", hash = "sha256:2d0f2fdd22b02c6d81637a3c95f8cd77f995846af7414c5c4b8d0545afa1bc4b", size = 108309 },
+    { url = "https://files.pythonhosted.org/packages/52/e1/f8af4c2fcde17500422858155aeb0d7e93477a0d59a98e56cbfe75070fd0/tomli-2.2.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:4a8f6e44de52d5e6c657c9fe83b562f5f4256d8ebbfe4ff922c495620a7f6cea", size = 132762 },
+    { url = "https://files.pythonhosted.org/packages/03/b8/152c68bb84fc00396b83e7bbddd5ec0bd3dd409db4195e2a9b3e398ad2e3/tomli-2.2.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8d57ca8095a641b8237d5b079147646153d22552f1c637fd3ba7f4b0b29167a8", size = 123453 },
+    { url = "https://files.pythonhosted.org/packages/c8/d6/fc9267af9166f79ac528ff7e8c55c8181ded34eb4b0e93daa767b8841573/tomli-2.2.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4e340144ad7ae1533cb897d406382b4b6fede8890a03738ff1683af800d54192", size = 233486 },
+    { url = "https://files.pythonhosted.org/packages/5c/51/51c3f2884d7bab89af25f678447ea7d297b53b5a3b5730a7cb2ef6069f07/tomli-2.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:db2b95f9de79181805df90bedc5a5ab4c165e6ec3fe99f970d0e302f384ad222", size = 242349 },
+    { url = "https://files.pythonhosted.org/packages/ab/df/bfa89627d13a5cc22402e441e8a931ef2108403db390ff3345c05253935e/tomli-2.2.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:40741994320b232529c802f8bc86da4e1aa9f413db394617b9a256ae0f9a7f77", size = 252159 },
+    { url = "https://files.pythonhosted.org/packages/9e/6e/fa2b916dced65763a5168c6ccb91066f7639bdc88b48adda990db10c8c0b/tomli-2.2.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:400e720fe168c0f8521520190686ef8ef033fb19fc493da09779e592861b78c6", size = 237243 },
+    { url = "https://files.pythonhosted.org/packages/b4/04/885d3b1f650e1153cbb93a6a9782c58a972b94ea4483ae4ac5cedd5e4a09/tomli-2.2.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:02abe224de6ae62c19f090f68da4e27b10af2b93213d36cf44e6e1c5abd19fdd", size = 259645 },
+    { url = "https://files.pythonhosted.org/packages/9c/de/6b432d66e986e501586da298e28ebeefd3edc2c780f3ad73d22566034239/tomli-2.2.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b82ebccc8c8a36f2094e969560a1b836758481f3dc360ce9a3277c65f374285e", size = 244584 },
+    { url = "https://files.pythonhosted.org/packages/1c/9a/47c0449b98e6e7d1be6cbac02f93dd79003234ddc4aaab6ba07a9a7482e2/tomli-2.2.1-cp312-cp312-win32.whl", hash = "sha256:889f80ef92701b9dbb224e49ec87c645ce5df3fa2cc548664eb8a25e03127a98", size = 98875 },
+    { url = "https://files.pythonhosted.org/packages/ef/60/9b9638f081c6f1261e2688bd487625cd1e660d0a85bd469e91d8db969734/tomli-2.2.1-cp312-cp312-win_amd64.whl", hash = "sha256:7fc04e92e1d624a4a63c76474610238576942d6b8950a2d7f908a340494e67e4", size = 109418 },
+    { url = "https://files.pythonhosted.org/packages/04/90/2ee5f2e0362cb8a0b6499dc44f4d7d48f8fff06d28ba46e6f1eaa61a1388/tomli-2.2.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f4039b9cbc3048b2416cc57ab3bda989a6fcf9b36cf8937f01a6e731b64f80d7", size = 132708 },
+    { url = "https://files.pythonhosted.org/packages/c0/ec/46b4108816de6b385141f082ba99e315501ccd0a2ea23db4a100dd3990ea/tomli-2.2.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:286f0ca2ffeeb5b9bd4fcc8d6c330534323ec51b2f52da063b11c502da16f30c", size = 123582 },
+    { url = "https://files.pythonhosted.org/packages/a0/bd/b470466d0137b37b68d24556c38a0cc819e8febe392d5b199dcd7f578365/tomli-2.2.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a92ef1a44547e894e2a17d24e7557a5e85a9e1d0048b0b5e7541f76c5032cb13", size = 232543 },
+    { url = "https://files.pythonhosted.org/packages/d9/e5/82e80ff3b751373f7cead2815bcbe2d51c895b3c990686741a8e56ec42ab/tomli-2.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9316dc65bed1684c9a98ee68759ceaed29d229e985297003e494aa825ebb0281", size = 241691 },
+    { url = "https://files.pythonhosted.org/packages/05/7e/2a110bc2713557d6a1bfb06af23dd01e7dde52b6ee7dadc589868f9abfac/tomli-2.2.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e85e99945e688e32d5a35c1ff38ed0b3f41f43fad8df0bdf79f72b2ba7bc5272", size = 251170 },
+    { url = "https://files.pythonhosted.org/packages/64/7b/22d713946efe00e0adbcdfd6d1aa119ae03fd0b60ebed51ebb3fa9f5a2e5/tomli-2.2.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ac065718db92ca818f8d6141b5f66369833d4a80a9d74435a268c52bdfa73140", size = 236530 },
+    { url = "https://files.pythonhosted.org/packages/38/31/3a76f67da4b0cf37b742ca76beaf819dca0ebef26d78fc794a576e08accf/tomli-2.2.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:d920f33822747519673ee656a4b6ac33e382eca9d331c87770faa3eef562aeb2", size = 258666 },
+    { url = "https://files.pythonhosted.org/packages/07/10/5af1293da642aded87e8a988753945d0cf7e00a9452d3911dd3bb354c9e2/tomli-2.2.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a198f10c4d1b1375d7687bc25294306e551bf1abfa4eace6650070a5c1ae2744", size = 243954 },
+    { url = "https://files.pythonhosted.org/packages/5b/b9/1ed31d167be802da0fc95020d04cd27b7d7065cc6fbefdd2f9186f60d7bd/tomli-2.2.1-cp313-cp313-win32.whl", hash = "sha256:d3f5614314d758649ab2ab3a62d4f2004c825922f9e370b29416484086b264ec", size = 98724 },
+    { url = "https://files.pythonhosted.org/packages/c7/32/b0963458706accd9afcfeb867c0f9175a741bf7b19cd424230714d722198/tomli-2.2.1-cp313-cp313-win_amd64.whl", hash = "sha256:a38aa0308e754b0e3c67e344754dff64999ff9b513e691d0e786265c93583c69", size = 109383 },
+    { url = "https://files.pythonhosted.org/packages/6e/c2/61d3e0f47e2b74ef40a68b9e6ad5984f6241a942f7cd3bbfbdbd03861ea9/tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc", size = 14257 },
+]
+
+[[package]]
+name = "tornado"
+version = "6.4.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/59/45/a0daf161f7d6f36c3ea5fc0c2de619746cc3dd4c76402e9db545bd920f63/tornado-6.4.2.tar.gz", hash = "sha256:92bad5b4746e9879fd7bf1eb21dce4e3fc5128d71601f80005afa39237ad620b", size = 501135 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/26/7e/71f604d8cea1b58f82ba3590290b66da1e72d840aeb37e0d5f7291bd30db/tornado-6.4.2-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:e828cce1123e9e44ae2a50a9de3055497ab1d0aeb440c5ac23064d9e44880da1", size = 436299 },
+    { url = "https://files.pythonhosted.org/packages/96/44/87543a3b99016d0bf54fdaab30d24bf0af2e848f1d13d34a3a5380aabe16/tornado-6.4.2-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:072ce12ada169c5b00b7d92a99ba089447ccc993ea2143c9ede887e0937aa803", size = 434253 },
+    { url = "https://files.pythonhosted.org/packages/cb/fb/fdf679b4ce51bcb7210801ef4f11fdac96e9885daa402861751353beea6e/tornado-6.4.2-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1a017d239bd1bb0919f72af256a970624241f070496635784d9bf0db640d3fec", size = 437602 },
+    { url = "https://files.pythonhosted.org/packages/4f/3b/e31aeffffc22b475a64dbeb273026a21b5b566f74dee48742817626c47dc/tornado-6.4.2-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c36e62ce8f63409301537222faffcef7dfc5284f27eec227389f2ad11b09d946", size = 436972 },
+    { url = "https://files.pythonhosted.org/packages/22/55/b78a464de78051a30599ceb6983b01d8f732e6f69bf37b4ed07f642ac0fc/tornado-6.4.2-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bca9eb02196e789c9cb5c3c7c0f04fb447dc2adffd95265b2c7223a8a615ccbf", size = 437173 },
+    { url = "https://files.pythonhosted.org/packages/79/5e/be4fb0d1684eb822c9a62fb18a3e44a06188f78aa466b2ad991d2ee31104/tornado-6.4.2-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:304463bd0772442ff4d0f5149c6f1c2135a1fae045adf070821c6cdc76980634", size = 437892 },
+    { url = "https://files.pythonhosted.org/packages/f5/33/4f91fdd94ea36e1d796147003b490fe60a0215ac5737b6f9c65e160d4fe0/tornado-6.4.2-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:c82c46813ba483a385ab2a99caeaedf92585a1f90defb5693351fa7e4ea0bf73", size = 437334 },
+    { url = "https://files.pythonhosted.org/packages/2b/ae/c1b22d4524b0e10da2f29a176fb2890386f7bd1f63aacf186444873a88a0/tornado-6.4.2-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:932d195ca9015956fa502c6b56af9eb06106140d844a335590c1ec7f5277d10c", size = 437261 },
+    { url = "https://files.pythonhosted.org/packages/b5/25/36dbd49ab6d179bcfc4c6c093a51795a4f3bed380543a8242ac3517a1751/tornado-6.4.2-cp38-abi3-win32.whl", hash = "sha256:2876cef82e6c5978fde1e0d5b1f919d756968d5b4282418f3146b79b58556482", size = 438463 },
+    { url = "https://files.pythonhosted.org/packages/61/cc/58b1adeb1bb46228442081e746fcdbc4540905c87e8add7c277540934edb/tornado-6.4.2-cp38-abi3-win_amd64.whl", hash = "sha256:908b71bf3ff37d81073356a5fadcc660eb10c1476ee6e2725588626ce7e5ca38", size = 438907 },
+]
+
+[[package]]
+name = "traitlets"
+version = "5.14.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7", size = 161621 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f", size = 85359 },
+]
+
+[[package]]
+name = "typing-extensions"
+version = "4.12.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/df/db/f35a00659bc03fec321ba8bce9420de607a1d37f8342eee1863174c69557/typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8", size = 85321 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/26/9f/ad63fc0248c5379346306f8668cda6e2e2e9c95e01216d2b8ffd9ff037d0/typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d", size = 37438 },
+]
+
+[[package]]
+name = "tzdata"
+version = "2025.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/95/32/1a225d6164441be760d75c2c42e2780dc0873fe382da3e98a2e1e48361e5/tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9", size = 196380 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839 },
+]
+
+[[package]]
+name = "urllib3"
+version = "2.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/aa/63/e53da845320b757bf29ef6a9062f5c669fe997973f966045cb019c3f4b66/urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d", size = 307268 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c8/19/4ec628951a74043532ca2cf5d97b7b14863931476d117c471e8e2b1eb39f/urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df", size = 128369 },
+]
+
+[[package]]
+name = "watchdog"
+version = "6.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/db/7d/7f3d619e951c88ed75c6037b246ddcf2d322812ee8ea189be89511721d54/watchdog-6.0.0.tar.gz", hash = "sha256:9ddf7c82fda3ae8e24decda1338ede66e1c99883db93711d8fb941eaa2d8c282", size = 131220 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0c/56/90994d789c61df619bfc5ce2ecdabd5eeff564e1eb47512bd01b5e019569/watchdog-6.0.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d1cdb490583ebd691c012b3d6dae011000fe42edb7a82ece80965b42abd61f26", size = 96390 },
+    { url = "https://files.pythonhosted.org/packages/55/46/9a67ee697342ddf3c6daa97e3a587a56d6c4052f881ed926a849fcf7371c/watchdog-6.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bc64ab3bdb6a04d69d4023b29422170b74681784ffb9463ed4870cf2f3e66112", size = 88389 },
+    { url = "https://files.pythonhosted.org/packages/44/65/91b0985747c52064d8701e1075eb96f8c40a79df889e59a399453adfb882/watchdog-6.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c897ac1b55c5a1461e16dae288d22bb2e412ba9807df8397a635d88f671d36c3", size = 89020 },
+    { url = "https://files.pythonhosted.org/packages/e0/24/d9be5cd6642a6aa68352ded4b4b10fb0d7889cb7f45814fb92cecd35f101/watchdog-6.0.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6eb11feb5a0d452ee41f824e271ca311a09e250441c262ca2fd7ebcf2461a06c", size = 96393 },
+    { url = "https://files.pythonhosted.org/packages/63/7a/6013b0d8dbc56adca7fdd4f0beed381c59f6752341b12fa0886fa7afc78b/watchdog-6.0.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:ef810fbf7b781a5a593894e4f439773830bdecb885e6880d957d5b9382a960d2", size = 88392 },
+    { url = "https://files.pythonhosted.org/packages/d1/40/b75381494851556de56281e053700e46bff5b37bf4c7267e858640af5a7f/watchdog-6.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:afd0fe1b2270917c5e23c2a65ce50c2a4abb63daafb0d419fde368e272a76b7c", size = 89019 },
+    { url = "https://files.pythonhosted.org/packages/39/ea/3930d07dafc9e286ed356a679aa02d777c06e9bfd1164fa7c19c288a5483/watchdog-6.0.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:bdd4e6f14b8b18c334febb9c4425a878a2ac20efd1e0b231978e7b150f92a948", size = 96471 },
+    { url = "https://files.pythonhosted.org/packages/12/87/48361531f70b1f87928b045df868a9fd4e253d9ae087fa4cf3f7113be363/watchdog-6.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c7c15dda13c4eb00d6fb6fc508b3c0ed88b9d5d374056b239c4ad1611125c860", size = 88449 },
+    { url = "https://files.pythonhosted.org/packages/5b/7e/8f322f5e600812e6f9a31b75d242631068ca8f4ef0582dd3ae6e72daecc8/watchdog-6.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6f10cb2d5902447c7d0da897e2c6768bca89174d0c6e1e30abec5421af97a5b0", size = 89054 },
+    { url = "https://files.pythonhosted.org/packages/68/98/b0345cabdce2041a01293ba483333582891a3bd5769b08eceb0d406056ef/watchdog-6.0.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:490ab2ef84f11129844c23fb14ecf30ef3d8a6abafd3754a6f75ca1e6654136c", size = 96480 },
+    { url = "https://files.pythonhosted.org/packages/85/83/cdf13902c626b28eedef7ec4f10745c52aad8a8fe7eb04ed7b1f111ca20e/watchdog-6.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:76aae96b00ae814b181bb25b1b98076d5fc84e8a53cd8885a318b42b6d3a5134", size = 88451 },
+    { url = "https://files.pythonhosted.org/packages/fe/c4/225c87bae08c8b9ec99030cd48ae9c4eca050a59bf5c2255853e18c87b50/watchdog-6.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a175f755fc2279e0b7312c0035d52e27211a5bc39719dd529625b1930917345b", size = 89057 },
+    { url = "https://files.pythonhosted.org/packages/30/ad/d17b5d42e28a8b91f8ed01cb949da092827afb9995d4559fd448d0472763/watchdog-6.0.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:c7ac31a19f4545dd92fc25d200694098f42c9a8e391bc00bdd362c5736dbf881", size = 87902 },
+    { url = "https://files.pythonhosted.org/packages/5c/ca/c3649991d140ff6ab67bfc85ab42b165ead119c9e12211e08089d763ece5/watchdog-6.0.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:9513f27a1a582d9808cf21a07dae516f0fab1cf2d7683a742c498b93eedabb11", size = 88380 },
+    { url = "https://files.pythonhosted.org/packages/a9/c7/ca4bf3e518cb57a686b2feb4f55a1892fd9a3dd13f470fca14e00f80ea36/watchdog-6.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7607498efa04a3542ae3e05e64da8202e58159aa1fa4acddf7678d34a35d4f13", size = 79079 },
+    { url = "https://files.pythonhosted.org/packages/5c/51/d46dc9332f9a647593c947b4b88e2381c8dfc0942d15b8edc0310fa4abb1/watchdog-6.0.0-py3-none-manylinux2014_armv7l.whl", hash = "sha256:9041567ee8953024c83343288ccc458fd0a2d811d6a0fd68c4c22609e3490379", size = 79078 },
+    { url = "https://files.pythonhosted.org/packages/d4/57/04edbf5e169cd318d5f07b4766fee38e825d64b6913ca157ca32d1a42267/watchdog-6.0.0-py3-none-manylinux2014_i686.whl", hash = "sha256:82dc3e3143c7e38ec49d61af98d6558288c415eac98486a5c581726e0737c00e", size = 79076 },
+    { url = "https://files.pythonhosted.org/packages/ab/cc/da8422b300e13cb187d2203f20b9253e91058aaf7db65b74142013478e66/watchdog-6.0.0-py3-none-manylinux2014_ppc64.whl", hash = "sha256:212ac9b8bf1161dc91bd09c048048a95ca3a4c4f5e5d4a7d1b1a7d5752a7f96f", size = 79077 },
+    { url = "https://files.pythonhosted.org/packages/2c/3b/b8964e04ae1a025c44ba8e4291f86e97fac443bca31de8bd98d3263d2fcf/watchdog-6.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:e3df4cbb9a450c6d49318f6d14f4bbc80d763fa587ba46ec86f99f9e6876bb26", size = 79078 },
+    { url = "https://files.pythonhosted.org/packages/62/ae/a696eb424bedff7407801c257d4b1afda455fe40821a2be430e173660e81/watchdog-6.0.0-py3-none-manylinux2014_s390x.whl", hash = "sha256:2cce7cfc2008eb51feb6aab51251fd79b85d9894e98ba847408f662b3395ca3c", size = 79077 },
+    { url = "https://files.pythonhosted.org/packages/b5/e8/dbf020b4d98251a9860752a094d09a65e1b436ad181faf929983f697048f/watchdog-6.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:20ffe5b202af80ab4266dcd3e91aae72bf2da48c0d33bdb15c66658e685e94e2", size = 79078 },
+    { url = "https://files.pythonhosted.org/packages/07/f6/d0e5b343768e8bcb4cda79f0f2f55051bf26177ecd5651f84c07567461cf/watchdog-6.0.0-py3-none-win32.whl", hash = "sha256:07df1fdd701c5d4c8e55ef6cf55b8f0120fe1aef7ef39a1c6fc6bc2e606d517a", size = 79065 },
+    { url = "https://files.pythonhosted.org/packages/db/d9/c495884c6e548fce18a8f40568ff120bc3a4b7b99813081c8ac0c936fa64/watchdog-6.0.0-py3-none-win_amd64.whl", hash = "sha256:cbafb470cf848d93b5d013e2ecb245d4aa1c8fd0504e863ccefa32445359d680", size = 79070 },
+    { url = "https://files.pythonhosted.org/packages/33/e8/e40370e6d74ddba47f002a32919d91310d6074130fe4e17dabcafc15cbf1/watchdog-6.0.0-py3-none-win_ia64.whl", hash = "sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f", size = 79067 },
+]
+
+[[package]]
+name = "wcwidth"
+version = "0.2.13"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/63/53559446a878410fc5a5974feb13d31d78d752eb18aeba59c7fef1af7598/wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5", size = 101301 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fd/84/fd2ba7aafacbad3c4201d395674fc6348826569da3c0937e75505ead3528/wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859", size = 34166 },
+]
+
+[[package]]
+name = "webencodings"
+version = "0.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/02/ae6ceac1baeda530866a85075641cec12989bd8d31af6d5ab4a3e8c92f47/webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923", size = 9721 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", size = 11774 },
+]
+
+[[package]]
+name = "zipp"
+version = "3.21.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3f/50/bad581df71744867e9468ebd0bcd6505de3b275e06f202c2cb016e3ff56f/zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4", size = 24545 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b7/1a/7e4798e9339adc931158c9d69ecc34f5e6791489d469f5e50ec15e35f458/zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931", size = 9630 },
+]