diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index da16302..43126db 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -15,7 +15,7 @@ jobs:
       - name: Install dependencies
         run: |
           python3 -m pip install --upgrade pip poetry==1.2.2
-          poetry install --extras "settings"
+          poetry install
 
       - name: Linting
         run: |
@@ -33,7 +33,7 @@ jobs:
       - name: Install dependencies
         run: |
           python3 -m pip install --upgrade pip poetry==1.2.2
-          poetry install --extras "settings"
+          poetry install
 
       - name: Run tests
         env:
diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml
index 937d63f..8dadd8e 100644
--- a/.github/workflows/publish.yaml
+++ b/.github/workflows/publish.yaml
@@ -23,7 +23,7 @@ jobs:
         run: |
           python3 -m pip install --upgrade pip poetry==1.2.2
           poetry config virtualenvs.create false
-          poetry install --extras "settings"
+          poetry install
 
       - name: Linting
         run: |
@@ -41,7 +41,7 @@ jobs:
           poetry run pytest tests/ -v --cov-report xml:coverage.xml --cov
           poetry run coverage xml
 
-      - uses: codecov/codecov-action@v3
+      - uses: codecov/codecov-action@v4
         with:
           file: ./coverage.xml
 
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 1bda83f..35c45d8 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -25,6 +25,7 @@ repos:
     rev: 6.1.0
     hooks:
       - id: flake8
+        exclude: ^(vendor/)
         additional_dependencies: ["flake8-pyproject"]
   - repo: https://github.com/PyCQA/pydocstyle
     rev: 6.3.0
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7f8bba7..f09691b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -170,3 +170,17 @@ Changes are grouped as follows
 ### Fixed
 - Dependency updates for source code.
 - Updated Github actions to use the latest versions.
+
+## [0.3.4] - 2024-02-01
+
+### Fixed
+- Dependency updates for source code.
+- Added support for pydantic v1
+
+## [0.3.5] - 2024-02-01
+
+### Fixed
+- Dependency updates for source code.
+- Updated Github actions to use the latest versions of Code Cov.
+- Removed the multi-version support for pydantic.
+- Introduced vendoring for pydantic v1.
diff --git a/README.md b/README.md
index 9ba7a15..fa9cf35 100644
--- a/README.md
+++ b/README.md
@@ -50,5 +50,10 @@ Important steps to follow when working with CDF Files using the `fsspec` support
 
 Refer [cdffs.readthedocs.io](https://cdffs.readthedocs.io) for more details.
 
+## Vendoring
+
+`cdffs` uses pydandic.v1 package using vendoring. It was mainly introduced to overcome version conflicts
+related to the Cognite Notebooks.
+
 ## Contributing
 Want to contribute? Check out [CONTRIBUTING](CONTRIBUTING.md).
diff --git a/cognite/cdffs/__init__.py b/cognite/cdffs/__init__.py
index c24ec35..6ef35a8 100644
--- a/cognite/cdffs/__init__.py
+++ b/cognite/cdffs/__init__.py
@@ -4,7 +4,7 @@
 
 from .spec import CdfFileSystem
 
-__version__ = "0.3.4"
+__version__ = "0.3.5"
 __all__ = ["CdfFileSystem"]
 
 fsspec.register_implementation(CdfFileSystem.protocol, CdfFileSystem)
diff --git a/cognite/cdffs/credentials.py b/cognite/cdffs/credentials.py
index 7444672..808806f 100644
--- a/cognite/cdffs/credentials.py
+++ b/cognite/cdffs/credentials.py
@@ -6,15 +6,9 @@
 from cognite.client import ClientConfig, CogniteClient
 from cognite.client.credentials import OAuthClientCredentials, Token
 
-try:
-    from pydantic import BaseSettings, ConfigDict, SecretStr, validator
-
-    is_pydantic_v2 = False
-except ImportError:
-    from pydantic import ConfigDict, SecretStr, field_validator
-    from pydantic_settings import BaseSettings
-
-    is_pydantic_v2 = True
+from vendor.pydantic.class_validators import validator
+from vendor.pydantic.env_settings import BaseSettings
+from vendor.pydantic.types import SecretStr
 
 
 def validate_scopes(cls: Any, value: str) -> Optional[List]:
@@ -27,7 +21,14 @@ def validate_scopes(cls: Any, value: str) -> Optional[List]:
 class FsConfig(BaseSettings):
     """Base config to parse environment variables."""
 
-    model_config: ConfigDict = ConfigDict(env_nested_delimiter="__", env_file=".env", env_file_encoding="utf-8")
+    class Config:
+        """Global config for Base Settings."""
+
+        env_nested_delimiter = "__"
+        env_file = ".env"
+        env_file_encoding = "utf-8"
+
+    # model_config: ConfigDict = ConfigDict(env_nested_delimiter="__", env_file=".env", env_file_encoding="utf-8")
 
 
 class FsCredentials(FsConfig, ABC):
@@ -79,10 +80,7 @@ class FsOAuthCredentials(FsCredentials, FsConfig):
     scopes: Optional[Union[str, List]] = None
 
     # Validator
-    if is_pydantic_v2:
-        _scopes = field_validator("scopes")(validate_scopes)
-    else:
-        _scopes = validator("scopes")(validate_scopes)
+    _scopes = validator("scopes")(validate_scopes)
 
     def get_credentials(self) -> OAuthClientCredentials:
         """Construct credentials based on environment variables.
@@ -122,23 +120,13 @@ def get_credentials(self) -> Token:
 
 def get_connection_config(env_file: str) -> CogniteClient:
     """Construct Cognite Client from environment variables."""
-    credentials = FsOAuthCredentials(_env_file=env_file)
+    credentials = FsOAuthCredentials(_env_file=env_file)  # type:ignore
     connection_config = None
-
-    if is_pydantic_v2:
-        credentials_dump = credentials.model_dump().items()
-    else:
-        credentials_dump = credentials.dict().items()
-
-    if all(value is not None for _, value in credentials_dump):
+    if all(value is not None for _, value in credentials.dict().items()):
         connection_config = credentials.get_client_config()
     else:
-        token = FsToken(_env_file=env_file)
-        if is_pydantic_v2:
-            token_dump = token.model_dump().items()
-        else:
-            token_dump = token.dict().items()
-        if all(value is not None for _, value in token_dump):
+        token = FsToken(_env_file=env_file)  # type:ignore
+        if all(value is not None for _, value in token.dict().items()):
             connection_config = token.get_client_config()
 
     return connection_config
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 090ae18..c1f9c1d 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -9,13 +9,7 @@ the list of all supported/compatible python packages.
 
 Installation
 ^^^^^^^^^^^^
-To install this package(Recommended):
-
-  .. code-block:: bash
-
-   pip install cognite-cdffs[settings]
-
-If you need `cdffs` to be compatible with pydantic-v1, choose to install the expected pydantic-v1 (`^1.10.7`) and use,
+To install this package:
 
   .. code-block:: bash
 
diff --git a/mypy.ini b/mypy.ini
index a9ce278..26938eb 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -9,3 +9,7 @@ ignore_missing_imports = false
 disallow_untyped_defs = true
 follow_imports = normal
 show_error_codes = true
+
+
+[mypy-vendor.pydantic.*]
+ignore_errors = true
diff --git a/poetry.lock b/poetry.lock
index 8cc3580..74d3405 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -11,17 +11,6 @@ files = [
     {file = "alabaster-0.7.16.tar.gz", hash = "sha256:75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65"},
 ]
 
-[[package]]
-name = "annotated-types"
-version = "0.6.0"
-description = "Reusable constraint types to use with typing.Annotated"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
-    {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
-]
-
 [[package]]
 name = "asciitree"
 version = "0.3.3"
@@ -357,13 +346,13 @@ files = [
 
 [[package]]
 name = "cognite-sdk"
-version = "7.26.0"
+version = "7.28.2"
 description = "Cognite Python SDK"
 optional = false
 python-versions = ">=3.8,<4.0"
 files = [
-    {file = "cognite_sdk-7.26.0-py3-none-any.whl", hash = "sha256:38ae4c57e2e30383c882ddfcfb5fa1d4b702436ba4d785ae8fd0cdaa32044341"},
-    {file = "cognite_sdk-7.26.0.tar.gz", hash = "sha256:de5b99fbd820a05465baa0aa6ea8d138ae158baf2607173ad42a2b8d0709aa2a"},
+    {file = "cognite_sdk-7.28.2-py3-none-any.whl", hash = "sha256:5bd054f08c6dd68638d8eed80a3766b3a3111ccea7f4cf1fb08bd4fb257d9897"},
+    {file = "cognite_sdk-7.28.2.tar.gz", hash = "sha256:07bbf2b375e7486290d5b9dcd17aba59f20001e5b55af90b0bf46ca44719b4f5"},
 ]
 
 [package.dependencies]
@@ -397,63 +386,63 @@ files = [
 
 [[package]]
 name = "coverage"
-version = "7.4.3"
+version = "7.4.4"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -518,21 +507,24 @@ test-randomorder = ["pytest-randomly"]
 
 [[package]]
 name = "dask"
-version = "2024.2.1"
+version = "2024.3.1"
 description = "Parallel PyData with Task Scheduling"
 optional = false
 python-versions = ">=3.9"
 files = [
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 [package.dependencies]
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 cloudpickle = ">=1.5.0"
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 fsspec = ">=2021.09.0"
-importlib-metadata = ">=4.13.0"
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+numpy = {version = ">=1.21", optional = true, markers = "extra == \"array\""}
 packaging = ">=20.0"
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 partd = ">=1.2.0"
 pyyaml = ">=5.3.1"
 toolz = ">=0.10.0"
@@ -540,11 +532,27 @@ toolz = ">=0.10.0"
 [package.extras]
 array = ["numpy (>=1.21)"]
 complete = ["dask[array,dataframe,diagnostics,distributed]", "lz4 (>=4.3.2)", "pyarrow (>=7.0)", "pyarrow-hotfix"]
-dataframe = ["dask[array]", "pandas (>=1.3)"]
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 diagnostics = ["bokeh (>=2.4.2)", "jinja2 (>=2.10.3)"]
-distributed = ["distributed (==2024.2.1)"]
+distributed = ["distributed (==2024.3.1)"]
 test = ["pandas[test]", "pre-commit", "pytest", "pytest-cov", "pytest-rerunfailures", "pytest-timeout", "pytest-xdist"]
 
+[[package]]
+name = "dask-expr"
+version = "1.0.3"
+description = "High Level Expressions for Dask"
+optional = false
+python-versions = ">=3.9"
+files = [
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+
+[package.dependencies]
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+pandas = ">=2"
+pyarrow = ">=7.0.0"
+
 [[package]]
 name = "distlib"
 version = "0.3.8"
@@ -610,35 +618,35 @@ typing = ["typing-extensions (>=4.8)"]
 
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 ]
 
 [package.dependencies]
@@ -648,14 +656,13 @@ click = ">=8.0,<9.0"
 click-plugins = ">=1.0"
 cligj = ">=0.5"
 importlib-metadata = {version = "*", markers = "python_version < \"3.10\""}
-setuptools = "*"
 six = "*"
 
 [package.extras]
-all = ["Fiona[calc,s3,test]"]
+all = ["fiona[calc,s3,test]"]
 calc = ["shapely"]
 s3 = ["boto3 (>=1.3.1)"]
-test = ["Fiona[s3]", "pytest (>=7)", "pytest-cov", "pytz"]
+test = ["fiona[s3]", "pytest (>=7)", "pytest-cov", "pytz"]
 
 [[package]]
 name = "flake8"
@@ -795,22 +802,22 @@ files = [
 
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-version = "7.0.1"
+version = "7.0.2"
 description = "Read metadata from Python packages"
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
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 perf = ["ipython"]
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 [[package]]
 name = "iniconfig"
@@ -1205,13 +1212,13 @@ signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
 
 [[package]]
 name = "packaging"
-version = "23.2"
+version = "24.0"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.7"
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 ]
 
 [[package]]
@@ -1401,67 +1408,67 @@ virtualenv = ">=20.10.0"
 
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-    {file = "pydantic_core-2.16.3.tar.gz", hash = "sha256:1cac689f80a3abab2d3c0048b29eea5751114054f032a941a32de4c852c59cad"},
-]
-
-[package.dependencies]
-typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
-
-[[package]]
-name = "pydantic-settings"
-version = "2.2.1"
-description = "Settings management using Pydantic"
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "pydantic_settings-2.2.1-py3-none-any.whl", hash = "sha256:0235391d26db4d2190cb9b31051c4b46882d28a51533f97440867f012d4da091"},
-    {file = "pydantic_settings-2.2.1.tar.gz", hash = "sha256:00b9f6a5e95553590434c0fa01ead0b216c3e10bc54ae02e37f359948643c5ed"},
-]
-
-[package.dependencies]
-pydantic = ">=2.3.0"
-python-dotenv = ">=0.21.0"
-
-[package.extras]
-toml = ["tomli (>=2.0.1)"]
-yaml = ["pyyaml (>=6.0.1)"]
-
 [[package]]
 name = "pyflakes"
 version = "3.2.0"
@@ -1705,13 +1583,13 @@ certifi = "*"
 
 [[package]]
 name = "pytest"
-version = "8.1.0"
+version = "8.1.1"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.1.0-py3-none-any.whl", hash = "sha256:ee32db7af8de4629a455806befa90559f307424c07b8413ccfc30bf5b221dd7e"},
-    {file = "pytest-8.1.0.tar.gz", hash = "sha256:f8fa04ab8f98d185113ae60ea6d79c22f8143b14bc1caeced44a0ab844928323"},
+    {file = "pytest-8.1.1-py3-none-any.whl", hash = "sha256:2a8386cfc11fa9d2c50ee7b2a57e7d898ef90470a7a34c4b949ff59662bb78b7"},
+    {file = "pytest-8.1.1.tar.gz", hash = "sha256:ac978141a75948948817d360297b7aae0fcb9d6ff6bc9ec6d514b85d5a65c044"},
 ]
 
 [package.dependencies]
@@ -1894,13 +1772,13 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "requests-oauthlib"
-version = "1.3.1"
+version = "1.4.0"
 description = "OAuthlib authentication support for Requests."
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
-    {file = "requests-oauthlib-1.3.1.tar.gz", hash = "sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a"},
-    {file = "requests_oauthlib-1.3.1-py2.py3-none-any.whl", hash = "sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5"},
+    {file = "requests-oauthlib-1.4.0.tar.gz", hash = "sha256:acee623221e4a39abcbb919312c8ff04bd44e7e417087fb4bd5e2a2f53d5e79a"},
+    {file = "requests_oauthlib-1.4.0-py2.py3-none-any.whl", hash = "sha256:7a3130d94a17520169e38db6c8d75f2c974643788465ecc2e4b36d288bf13033"},
 ]
 
 [package.dependencies]
@@ -1992,18 +1870,18 @@ jeepney = ">=0.6"
 
 [[package]]
 name = "setuptools"
-version = "69.1.1"
+version = "69.2.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "setuptools-69.1.1-py3-none-any.whl", hash = "sha256:02fa291a0471b3a18b2b2481ed902af520c69e8ae0919c13da936542754b4c56"},
-    {file = "setuptools-69.1.1.tar.gz", hash = "sha256:5c0806c7d9af348e6dd3777b4f4dbb42c7ad85b190104837488eab9a7c945cf8"},
+    {file = "setuptools-69.2.0-py3-none-any.whl", hash = "sha256:c21c49fb1042386df081cb5d86759792ab89efca84cf114889191cd09aacc80c"},
+    {file = "setuptools-69.2.0.tar.gz", hash = "sha256:0ff4183f8f42cd8fa3acea16c45205521a4ef28f73c6391d8a25e92893134f2e"},
 ]
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
@@ -2296,13 +2174,13 @@ files = [
 
 [[package]]
 name = "twine"
-version = "4.0.2"
+version = "5.0.0"
 description = "Collection of utilities for publishing packages on PyPI"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "twine-4.0.2-py3-none-any.whl", hash = "sha256:929bc3c280033347a00f847236564d1c52a3e61b1ac2516c97c48f3ceab756d8"},
-    {file = "twine-4.0.2.tar.gz", hash = "sha256:9e102ef5fdd5a20661eb88fad46338806c3bd32cf1db729603fe3697b1bc83c8"},
+    {file = "twine-5.0.0-py3-none-any.whl", hash = "sha256:a262933de0b484c53408f9edae2e7821c1c45a3314ff2df9bdd343aa7ab8edc0"},
+    {file = "twine-5.0.0.tar.gz", hash = "sha256:89b0cc7d370a4b66421cc6102f269aa910fe0f1861c124f573cf2ddedbc10cf4"},
 ]
 
 [package.dependencies]
@@ -2318,13 +2196,13 @@ urllib3 = ">=1.26.0"
 
 [[package]]
 name = "types-requests"
-version = "2.31.0.20240218"
+version = "2.31.0.20240311"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-requests-2.31.0.20240218.tar.gz", hash = "sha256:f1721dba8385958f504a5386240b92de4734e047a08a40751c1654d1ac3349c5"},
-    {file = "types_requests-2.31.0.20240218-py3-none-any.whl", hash = "sha256:a82807ec6ddce8f00fe0e949da6d6bc1fbf1715420218a9640d695f70a9e5a9b"},
+    {file = "types-requests-2.31.0.20240311.tar.gz", hash = "sha256:b1c1b66abfb7fa79aae09097a811c4aa97130eb8831c60e47aee4ca344731ca5"},
+    {file = "types_requests-2.31.0.20240311-py3-none-any.whl", hash = "sha256:47872893d65a38e282ee9f277a4ee50d1b28bd592040df7d1fdaffdf3779937d"},
 ]
 
 [package.dependencies]
@@ -2415,13 +2293,13 @@ viz = ["matplotlib", "nc-time-axis", "seaborn"]
 
 [[package]]
 name = "zarr"
-version = "2.17.0"
+version = "2.17.1"
 description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "zarr-2.17.0-py3-none-any.whl", hash = "sha256:d287cb61019c4a0a0f386f76eeaa7f0b1160b1cb90cf96173a4b6cbc135df6e1"},
-    {file = "zarr-2.17.0.tar.gz", hash = "sha256:6390a2b8af31babaab4c963efc45bf1da7f9500c9aafac193f84cf019a7c66b0"},
+    {file = "zarr-2.17.1-py3-none-any.whl", hash = "sha256:e25df2741a6e92645f3890f30f3136d5b57a0f8f831094b024bbcab5f2797bc7"},
+    {file = "zarr-2.17.1.tar.gz", hash = "sha256:564b3aa072122546fe69a0fa21736f466b20fad41754334b62619f088ce46261"},
 ]
 
 [package.dependencies]
@@ -2436,23 +2314,20 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
 
 [[package]]
 name = "zipp"
-version = "3.17.0"
+version = "3.18.1"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
-    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
+    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
+    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
-
-[extras]
-settings = ["pydantic-settings"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9.10,<3.13"
-content-hash = "990136263ea0f942fc51d3aa453cc9d5658688df950bfe1973fea81ad5308b64"
+content-hash = "180cb6bb307be19deade6939d4ea43e081f80d6b13e61acdffa05609cd2298fd"
diff --git a/pyproject.toml b/pyproject.toml
index 53883d3..b475cbc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "cognite-cdffs"
-version = "0.3.4"
+version = "0.3.5"
 description = "File System Interface for CDF Files"
 license = "Apache-2.0"
 authors = ["Infant Alex <infant.alex@cognite.com>"]
@@ -8,11 +8,12 @@ readme = "README.md"
 packages = [
     { include="cognite", from="." },
 ]
+
 [tool.poetry.group.test.dependencies]
 pandas = "^2.2.1"
-pyarrow = "^15.0.0"
-zarr = "^2.17.0"
-dask = "^2024.2.1"
+pyarrow = "^15.0.1"
+zarr = "^2.17.1"
+dask = {version = "^2024.3.1", extras = ["dataframe"]}
 xarray = "^2024.2.0"
 geodatasets = "^2023.12.0"
 geopandas = "^0.14.3"
@@ -67,17 +68,13 @@ commands =
 
 [tool.poetry.dependencies]
 python = ">=3.9.10,<3.13"
-cognite-sdk = "^7.26.0"
+cognite-sdk = "^7.28.2"
 fsspec = "^2024.2.0"
 requests = "^2.31.0"
-twine = "^4.0.2"
-pydantic = {version =">=1.10.7"}
+twine = "^5.0.0"
 python-dotenv = "^1.0.1"
-pydantic-settings = { version = "^2.0.3", optional = true}
 tenacity = "^8.2.3"
 
-[tool.poetry.extras]
-settings = ["pydantic-settings"]
 
 [build-system]
 requires = ["poetry>=0.12"]
diff --git a/vendor/__init__.py b/vendor/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/vendor/pydantic/__init__.py b/vendor/pydantic/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/vendor/pydantic/annotated_types.py b/vendor/pydantic/annotated_types.py
new file mode 100644
index 0000000..f6fda80
--- /dev/null
+++ b/vendor/pydantic/annotated_types.py
@@ -0,0 +1,72 @@
+import sys
+from typing import TYPE_CHECKING, Any, Dict, FrozenSet, NamedTuple, Type
+
+from .fields import Required
+from .main import BaseModel, create_model
+from .typing import is_typeddict, is_typeddict_special
+
+if TYPE_CHECKING:
+    from typing_extensions import TypedDict
+
+if sys.version_info < (3, 11):
+
+    def is_legacy_typeddict(typeddict_cls: Type["TypedDict"]) -> bool:  # type: ignore[valid-type]
+        return is_typeddict(typeddict_cls) and type(typeddict_cls).__module__ == "typing"
+
+else:
+
+    def is_legacy_typeddict(_: Any) -> Any:
+        return False
+
+
+def create_model_from_typeddict(
+    # Mypy bug: `Type[TypedDict]` is resolved as `Any` https://github.com/python/mypy/issues/11030
+    typeddict_cls: Type["TypedDict"],  # type: ignore[valid-type]
+    **kwargs: Any,
+) -> Type["BaseModel"]:
+    """
+    Create a `BaseModel` based on the fields of a `TypedDict`.
+    Since `typing.TypedDict` in Python 3.8 does not store runtime information about optional keys,
+    we raise an error if this happens (see https://bugs.python.org/issue38834).
+    """
+    field_definitions: Dict[str, Any]
+
+    # Best case scenario: with python 3.9+ or when `TypedDict` is imported from `typing_extensions`
+    if not hasattr(typeddict_cls, "__required_keys__"):
+        raise TypeError(
+            "You should use `typing_extensions.TypedDict` instead of `typing.TypedDict` with Python < 3.9.2. "
+            "Without it, there is no way to differentiate required and optional fields when subclassed."
+        )
+
+    if is_legacy_typeddict(typeddict_cls) and any(
+        is_typeddict_special(t) for t in typeddict_cls.__annotations__.values()
+    ):
+        raise TypeError(
+            "You should use `typing_extensions.TypedDict` instead of `typing.TypedDict` with Python < 3.11. "
+            "Without it, there is no way to reflect Required/NotRequired keys."
+        )
+
+    required_keys: FrozenSet[str] = typeddict_cls.__required_keys__  # type: ignore[attr-defined]
+    field_definitions = {
+        field_name: (field_type, Required if field_name in required_keys else None)
+        for field_name, field_type in typeddict_cls.__annotations__.items()
+    }
+
+    return create_model(typeddict_cls.__name__, **kwargs, **field_definitions)
+
+
+def create_model_from_namedtuple(namedtuple_cls: Type["NamedTuple"], **kwargs: Any) -> Type["BaseModel"]:
+    """
+    Create a `BaseModel` based on the fields of a named tuple.
+    A named tuple can be created with `typing.NamedTuple` and declared annotations
+    but also with `collections.namedtuple`, in this case we consider all fields
+    to have type `Any`.
+    """
+    # With python 3.10+, `__annotations__` always exists but can be empty hence the `getattr... or...` logic
+    namedtuple_annotations: Dict[str, Type[Any]] = getattr(namedtuple_cls, "__annotations__", None) or {
+        k: Any for k in namedtuple_cls._fields
+    }
+    field_definitions: Dict[str, Any] = {
+        field_name: (field_type, Required) for field_name, field_type in namedtuple_annotations.items()
+    }
+    return create_model(namedtuple_cls.__name__, **kwargs, **field_definitions)
diff --git a/vendor/pydantic/class_validators.py b/vendor/pydantic/class_validators.py
new file mode 100644
index 0000000..a316b3f
--- /dev/null
+++ b/vendor/pydantic/class_validators.py
@@ -0,0 +1,361 @@
+import warnings
+from collections import ChainMap
+from functools import partial, partialmethod, wraps
+from itertools import chain
+from types import FunctionType
+from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union, overload
+
+from .errors import ConfigError
+from .typing import AnyCallable
+from .utils import ROOT_KEY, in_ipython
+
+if TYPE_CHECKING:
+    from .typing import AnyClassMethod
+
+
+class Validator:
+    __slots__ = "func", "pre", "each_item", "always", "check_fields", "skip_on_failure"
+
+    def __init__(
+        self,
+        func: AnyCallable,
+        pre: bool = False,
+        each_item: bool = False,
+        always: bool = False,
+        check_fields: bool = False,
+        skip_on_failure: bool = False,
+    ):
+        self.func = func
+        self.pre = pre
+        self.each_item = each_item
+        self.always = always
+        self.check_fields = check_fields
+        self.skip_on_failure = skip_on_failure
+
+
+if TYPE_CHECKING:
+    from inspect import Signature
+
+    from .config import BaseConfig
+    from .fields import ModelField
+    from .types import ModelOrDc
+
+    ValidatorCallable = Callable[[Optional[ModelOrDc], Any, Dict[str, Any], ModelField, Type[BaseConfig]], Any]
+    ValidatorsList = List[ValidatorCallable]
+    ValidatorListDict = Dict[str, List[Validator]]
+
+_FUNCS: Set[str] = set()
+VALIDATOR_CONFIG_KEY = "__validator_config__"
+ROOT_VALIDATOR_CONFIG_KEY = "__root_validator_config__"
+
+
+def validator(
+    *fields: str,
+    pre: bool = False,
+    each_item: bool = False,
+    always: bool = False,
+    check_fields: bool = True,
+    whole: Optional[bool] = None,
+    allow_reuse: bool = False,
+) -> Callable[[AnyCallable], "AnyClassMethod"]:
+    """
+    Decorate methods on the class indicating that they should be used to validate fields
+    :param fields: which field(s) the method should be called on
+    :param pre: whether or not this validator should be called before the standard validators (else after)
+    :param each_item: for complex objects (sets, lists etc.) whether to validate individual elements rather than the
+      whole object
+    :param always: whether this method and other validators should be called even if the value is missing
+    :param check_fields: whether to check that the fields actually exist on the model
+    :param allow_reuse: whether to track and raise an error if another validator refers to the decorated function
+    """
+    if not fields:
+        raise ConfigError("validator with no fields specified")
+    elif isinstance(fields[0], FunctionType):
+        raise ConfigError(
+            "validators should be used with fields and keyword arguments, not bare. "  # noqa: Q000
+            "E.g. usage should be `@validator('<field_name>', ...)`"
+        )
+    elif not all(isinstance(field, str) for field in fields):
+        raise ConfigError(
+            "validator fields should be passed as separate string args. "  # noqa: Q000
+            "E.g. usage should be `@validator('<field_name_1>', '<field_name_2>', ...)`"
+        )
+
+    if whole is not None:
+        warnings.warn(
+            'The "whole" keyword argument is deprecated, use "each_item" (inverse meaning, default False) instead',
+            DeprecationWarning,
+        )
+        assert each_item is False, '"each_item" and "whole" conflict, remove "whole"'
+        each_item = not whole
+
+    def dec(f: AnyCallable) -> "AnyClassMethod":
+        f_cls = _prepare_validator(f, allow_reuse)
+        setattr(
+            f_cls,
+            VALIDATOR_CONFIG_KEY,
+            (
+                fields,
+                Validator(func=f_cls.__func__, pre=pre, each_item=each_item, always=always, check_fields=check_fields),
+            ),
+        )
+        return f_cls
+
+    return dec
+
+
+@overload
+def root_validator(_func: AnyCallable) -> "AnyClassMethod":
+    ...
+
+
+@overload
+def root_validator(
+    *, pre: bool = False, allow_reuse: bool = False, skip_on_failure: bool = False
+) -> Callable[[AnyCallable], "AnyClassMethod"]:
+    ...
+
+
+def root_validator(
+    _func: Optional[AnyCallable] = None, *, pre: bool = False, allow_reuse: bool = False, skip_on_failure: bool = False
+) -> Union["AnyClassMethod", Callable[[AnyCallable], "AnyClassMethod"]]:
+    """
+    Decorate methods on a model indicating that they should be used to validate (and perhaps modify) data either
+    before or after standard model parsing/validation is performed.
+    """
+    if _func:
+        f_cls = _prepare_validator(_func, allow_reuse)
+        setattr(
+            f_cls, ROOT_VALIDATOR_CONFIG_KEY, Validator(func=f_cls.__func__, pre=pre, skip_on_failure=skip_on_failure)
+        )
+        return f_cls
+
+    def dec(f: AnyCallable) -> "AnyClassMethod":
+        f_cls = _prepare_validator(f, allow_reuse)
+        setattr(
+            f_cls, ROOT_VALIDATOR_CONFIG_KEY, Validator(func=f_cls.__func__, pre=pre, skip_on_failure=skip_on_failure)
+        )
+        return f_cls
+
+    return dec
+
+
+def _prepare_validator(function: AnyCallable, allow_reuse: bool) -> "AnyClassMethod":
+    """
+    Avoid validators with duplicated names since without this, validators can be overwritten silently
+    which generally isn't the intended behaviour, don't run in ipython (see #312) or if allow_reuse is False.
+    """
+    f_cls = function if isinstance(function, classmethod) else classmethod(function)
+    if not in_ipython() and not allow_reuse:
+        ref = (
+            getattr(f_cls.__func__, "__module__", "<No __module__>")
+            + "."
+            + getattr(f_cls.__func__, "__qualname__", f"<No __qualname__: id:{id(f_cls.__func__)}>")
+        )
+        if ref in _FUNCS:
+            raise ConfigError(f'duplicate validator function "{ref}"; if this is intended, set `allow_reuse=True`')
+        _FUNCS.add(ref)
+    return f_cls
+
+
+class ValidatorGroup:
+    def __init__(self, validators: "ValidatorListDict") -> None:
+        self.validators = validators
+        self.used_validators = {"*"}
+
+    def get_validators(self, name: str) -> Optional[Dict[str, Validator]]:
+        self.used_validators.add(name)
+        validators = self.validators.get(name, [])
+        if name != ROOT_KEY:
+            validators += self.validators.get("*", [])
+        if validators:
+            return {getattr(v.func, "__name__", f"<No __name__: id:{id(v.func)}>"): v for v in validators}
+        else:
+            return None
+
+    def check_for_unused(self) -> None:
+        unused_validators = set(
+            chain.from_iterable(
+                (
+                    getattr(v.func, "__name__", f"<No __name__: id:{id(v.func)}>")
+                    for v in self.validators[f]
+                    if v.check_fields
+                )
+                for f in (self.validators.keys() - self.used_validators)
+            )
+        )
+        if unused_validators:
+            fn = ", ".join(unused_validators)
+            raise ConfigError(
+                f"Validators defined with incorrect fields: {fn} "  # noqa: Q000
+                f"(use check_fields=False if you're inheriting from the model and intended this)"
+            )
+
+
+def extract_validators(namespace: Dict[str, Any]) -> Dict[str, List[Validator]]:
+    validators: Dict[str, List[Validator]] = {}
+    for var_name, value in namespace.items():
+        validator_config = getattr(value, VALIDATOR_CONFIG_KEY, None)
+        if validator_config:
+            fields, v = validator_config
+            for field in fields:
+                if field in validators:
+                    validators[field].append(v)
+                else:
+                    validators[field] = [v]
+    return validators
+
+
+def extract_root_validators(namespace: Dict[str, Any]) -> Tuple[List[AnyCallable], List[Tuple[bool, AnyCallable]]]:
+    from inspect import signature
+
+    pre_validators: List[AnyCallable] = []
+    post_validators: List[Tuple[bool, AnyCallable]] = []
+    for name, value in namespace.items():
+        validator_config: Optional[Validator] = getattr(value, ROOT_VALIDATOR_CONFIG_KEY, None)
+        if validator_config:
+            sig = signature(validator_config.func)
+            args = list(sig.parameters.keys())
+            if args[0] == "self":
+                raise ConfigError(
+                    f'Invalid signature for root validator {name}: {sig}, "self" not permitted as first argument, '
+                    f"should be: (cls, values)."
+                )
+            if len(args) != 2:
+                raise ConfigError(f"Invalid signature for root validator {name}: {sig}, should be: (cls, values).")
+            # check function signature
+            if validator_config.pre:
+                pre_validators.append(validator_config.func)
+            else:
+                post_validators.append((validator_config.skip_on_failure, validator_config.func))
+    return pre_validators, post_validators
+
+
+def inherit_validators(base_validators: "ValidatorListDict", validators: "ValidatorListDict") -> "ValidatorListDict":
+    for field, field_validators in base_validators.items():
+        if field not in validators:
+            validators[field] = []
+        validators[field] += field_validators
+    return validators
+
+
+def make_generic_validator(validator: AnyCallable) -> "ValidatorCallable":
+    """
+    Make a generic function which calls a validator with the right arguments.
+
+    Unfortunately other approaches (eg. return a partial of a function that builds the arguments) is slow,
+    hence this laborious way of doing things.
+
+    It's done like this so validators don't all need **kwargs in their signature, eg. any combination of
+    the arguments "values", "fields" and/or "config" are permitted.
+    """
+    from inspect import signature
+
+    if not isinstance(validator, (partial, partialmethod)):
+        # This should be the default case, so overhead is reduced
+        sig = signature(validator)
+        args = list(sig.parameters.keys())
+    else:
+        # Fix the generated argument lists of partial methods
+        sig = signature(validator.func)
+        args = [
+            k
+            for k in signature(validator.func).parameters.keys()
+            if k not in validator.args | validator.keywords.keys()
+        ]
+
+    first_arg = args.pop(0)
+    if first_arg == "self":
+        raise ConfigError(
+            f'Invalid signature for validator {validator}: {sig}, "self" not permitted as first argument, '
+            f'should be: (cls, value, values, config, field), "values", "config" and "field" are all optional.'
+        )
+    elif first_arg == "cls":
+        # assume the second argument is value
+        return wraps(validator)(_generic_validator_cls(validator, sig, set(args[1:])))
+    else:
+        # assume the first argument was value which has already been removed
+        return wraps(validator)(_generic_validator_basic(validator, sig, set(args)))
+
+
+def prep_validators(v_funcs: Iterable[AnyCallable]) -> "ValidatorsList":
+    return [make_generic_validator(f) for f in v_funcs if f]
+
+
+all_kwargs = {"values", "field", "config"}
+
+
+def _generic_validator_cls(validator: AnyCallable, sig: "Signature", args: Set[str]) -> "ValidatorCallable":
+    # assume the first argument is value
+    has_kwargs = False
+    if "kwargs" in args:
+        has_kwargs = True
+        args -= {"kwargs"}
+
+    if not args.issubset(all_kwargs):
+        raise ConfigError(
+            f"Invalid signature for validator {validator}: {sig}, should be: "
+            f'(cls, value, values, config, field), "values", "config" and "field" are all optional.'
+        )
+
+    if has_kwargs:
+        return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field, config=config)
+    elif args == set():
+        return lambda cls, v, values, field, config: validator(cls, v)
+    elif args == {"values"}:
+        return lambda cls, v, values, field, config: validator(cls, v, values=values)
+    elif args == {"field"}:
+        return lambda cls, v, values, field, config: validator(cls, v, field=field)
+    elif args == {"config"}:
+        return lambda cls, v, values, field, config: validator(cls, v, config=config)
+    elif args == {"values", "field"}:
+        return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field)
+    elif args == {"values", "config"}:
+        return lambda cls, v, values, field, config: validator(cls, v, values=values, config=config)
+    elif args == {"field", "config"}:
+        return lambda cls, v, values, field, config: validator(cls, v, field=field, config=config)
+    else:
+        # args == {'values', 'field', 'config'}
+        return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field, config=config)
+
+
+def _generic_validator_basic(validator: AnyCallable, sig: "Signature", args: Set[str]) -> "ValidatorCallable":
+    has_kwargs = False
+    if "kwargs" in args:
+        has_kwargs = True
+        args -= {"kwargs"}
+
+    if not args.issubset(all_kwargs):
+        raise ConfigError(
+            f"Invalid signature for validator {validator}: {sig}, should be: "
+            f'(value, values, config, field), "values", "config" and "field" are all optional.'
+        )
+
+    if has_kwargs:
+        return lambda cls, v, values, field, config: validator(v, values=values, field=field, config=config)
+    elif args == set():
+        return lambda cls, v, values, field, config: validator(v)
+    elif args == {"values"}:
+        return lambda cls, v, values, field, config: validator(v, values=values)
+    elif args == {"field"}:
+        return lambda cls, v, values, field, config: validator(v, field=field)
+    elif args == {"config"}:
+        return lambda cls, v, values, field, config: validator(v, config=config)
+    elif args == {"values", "field"}:
+        return lambda cls, v, values, field, config: validator(v, values=values, field=field)
+    elif args == {"values", "config"}:
+        return lambda cls, v, values, field, config: validator(v, values=values, config=config)
+    elif args == {"field", "config"}:
+        return lambda cls, v, values, field, config: validator(v, field=field, config=config)
+    else:
+        # args == {'values', 'field', 'config'}
+        return lambda cls, v, values, field, config: validator(v, values=values, field=field, config=config)
+
+
+def gather_all_validators(type_: "ModelOrDc") -> Dict[str, "AnyClassMethod"]:
+    all_attributes = ChainMap(*[cls.__dict__ for cls in type_.__mro__])  # type: ignore[arg-type,var-annotated]
+    return {
+        k: v
+        for k, v in all_attributes.items()
+        if hasattr(v, VALIDATOR_CONFIG_KEY) or hasattr(v, ROOT_VALIDATOR_CONFIG_KEY)
+    }
diff --git a/vendor/pydantic/color.py b/vendor/pydantic/color.py
new file mode 100644
index 0000000..b214bbf
--- /dev/null
+++ b/vendor/pydantic/color.py
@@ -0,0 +1,494 @@
+"""
+Color definitions are  used as per CSS3 specification:
+http://www.w3.org/TR/css3-color/#svg-color
+
+A few colors have multiple names referring to the sames colors, eg. `grey` and `gray` or `aqua` and `cyan`.
+
+In these cases the LAST color when sorted alphabetically takes preferences,
+eg. Color((0, 255, 255)).as_named() == 'cyan' because "cyan" comes after "aqua".
+"""
+import math
+import re
+from colorsys import hls_to_rgb, rgb_to_hls
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union, cast
+
+from .errors import ColorError
+from .utils import Representation, almost_equal_floats
+
+if TYPE_CHECKING:
+    from .typing import CallableGenerator, ReprArgs
+
+ColorTuple = Union[Tuple[int, int, int], Tuple[int, int, int, float]]
+ColorType = Union[ColorTuple, str]
+HslColorTuple = Union[Tuple[float, float, float], Tuple[float, float, float, float]]
+
+
+class RGBA:
+    """
+    Internal use only as a representation of a color.
+    """
+
+    __slots__ = "r", "g", "b", "alpha", "_tuple"
+
+    def __init__(self, r: float, g: float, b: float, alpha: Optional[float]):
+        self.r = r
+        self.g = g
+        self.b = b
+        self.alpha = alpha
+
+        self._tuple: Tuple[float, float, float, Optional[float]] = (r, g, b, alpha)
+
+    def __getitem__(self, item: Any) -> Any:
+        return self._tuple[item]
+
+
+# these are not compiled here to avoid import slowdown, they'll be compiled the first time they're used, then cached
+r_hex_short = r"\s*(?:#|0x)?([0-9a-f])([0-9a-f])([0-9a-f])([0-9a-f])?\s*"
+r_hex_long = r"\s*(?:#|0x)?([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})?\s*"
+_r_255 = r"(\d{1,3}(?:\.\d+)?)"
+_r_comma = r"\s*,\s*"
+r_rgb = rf"\s*rgb\(\s*{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_255}\)\s*"
+_r_alpha = r"(\d(?:\.\d+)?|\.\d+|\d{1,2}%)"
+r_rgba = rf"\s*rgba\(\s*{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_alpha}\s*\)\s*"
+_r_h = r"(-?\d+(?:\.\d+)?|-?\.\d+)(deg|rad|turn)?"
+_r_sl = r"(\d{1,3}(?:\.\d+)?)%"
+r_hsl = rf"\s*hsl\(\s*{_r_h}{_r_comma}{_r_sl}{_r_comma}{_r_sl}\s*\)\s*"
+r_hsla = rf"\s*hsl\(\s*{_r_h}{_r_comma}{_r_sl}{_r_comma}{_r_sl}{_r_comma}{_r_alpha}\s*\)\s*"
+
+# colors where the two hex characters are the same, if all colors match this the short version of hex colors can be used
+repeat_colors = {int(c * 2, 16) for c in "0123456789abcdef"}
+rads = 2 * math.pi
+
+
+class Color(Representation):
+    __slots__ = "_original", "_rgba"
+
+    def __init__(self, value: ColorType) -> None:
+        self._rgba: RGBA
+        self._original: ColorType
+        if isinstance(value, (tuple, list)):
+            self._rgba = parse_tuple(value)
+        elif isinstance(value, str):
+            self._rgba = parse_str(value)
+        elif isinstance(value, Color):
+            self._rgba = value._rgba
+            value = value._original
+        else:
+            raise ColorError(reason="value must be a tuple, list or string")
+
+        # if we've got here value must be a valid color
+        self._original = value
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        field_schema.update(type="string", format="color")
+
+    def original(self) -> ColorType:
+        """
+        Original value passed to Color
+        """
+        return self._original
+
+    def as_named(self, *, fallback: bool = False) -> str:
+        if self._rgba.alpha is None:
+            rgb = cast(Tuple[int, int, int], self.as_rgb_tuple())
+            try:
+                return COLORS_BY_VALUE[rgb]
+            except KeyError as e:
+                if fallback:
+                    return self.as_hex()
+                else:
+                    raise ValueError("no named color found, use fallback=True, as_hex() or as_rgb()") from e
+        else:
+            return self.as_hex()
+
+    def as_hex(self) -> str:
+        """
+        Hex string representing the color can be 3, 4, 6 or 8 characters depending on whether the string
+        a "short" representation of the color is possible and whether there's an alpha channel.
+        """
+        values = [float_to_255(c) for c in self._rgba[:3]]
+        if self._rgba.alpha is not None:
+            values.append(float_to_255(self._rgba.alpha))
+
+        as_hex = "".join(f"{v:02x}" for v in values)
+        if all(c in repeat_colors for c in values):
+            as_hex = "".join(as_hex[c] for c in range(0, len(as_hex), 2))
+        return "#" + as_hex
+
+    def as_rgb(self) -> str:
+        """
+        Color as an rgb(<r>, <g>, <b>) or rgba(<r>, <g>, <b>, <a>) string.
+        """
+        if self._rgba.alpha is None:
+            return f"rgb({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)})"
+        else:
+            return (
+                f"rgba({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)}, "
+                f"{round(self._alpha_float(), 2)})"
+            )
+
+    def as_rgb_tuple(self, *, alpha: Optional[bool] = None) -> ColorTuple:
+        """
+        Color as an RGB or RGBA tuple; red, green and blue are in the range 0 to 255, alpha if included is
+        in the range 0 to 1.
+
+        :param alpha: whether to include the alpha channel, options are
+          None - (default) include alpha only if it's set (e.g. not None)
+          True - always include alpha,
+          False - always omit alpha,
+        """
+        r, g, b = (float_to_255(c) for c in self._rgba[:3])
+        if alpha is None:
+            if self._rgba.alpha is None:
+                return r, g, b
+            else:
+                return r, g, b, self._alpha_float()
+        elif alpha:
+            return r, g, b, self._alpha_float()
+        else:
+            # alpha is False
+            return r, g, b
+
+    def as_hsl(self) -> str:
+        """
+        Color as an hsl(<h>, <s>, <l>) or hsl(<h>, <s>, <l>, <a>) string.
+        """
+        if self._rgba.alpha is None:
+            h, s, li = self.as_hsl_tuple(alpha=False)  # type: ignore
+            return f"hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%})"
+        else:
+            h, s, li, a = self.as_hsl_tuple(alpha=True)  # type: ignore
+            return f"hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%}, {round(a, 2)})"
+
+    def as_hsl_tuple(self, *, alpha: Optional[bool] = None) -> HslColorTuple:
+        """
+        Color as an HSL or HSLA tuple, e.g. hue, saturation, lightness and optionally alpha; all elements are in
+        the range 0 to 1.
+
+        NOTE: this is HSL as used in HTML and most other places, not HLS as used in python's colorsys.
+
+        :param alpha: whether to include the alpha channel, options are
+          None - (default) include alpha only if it's set (e.g. not None)
+          True - always include alpha,
+          False - always omit alpha,
+        """
+        h, l, s = rgb_to_hls(self._rgba.r, self._rgba.g, self._rgba.b)
+        if alpha is None:
+            if self._rgba.alpha is None:
+                return h, s, l
+            else:
+                return h, s, l, self._alpha_float()
+        if alpha:
+            return h, s, l, self._alpha_float()
+        else:
+            # alpha is False
+            return h, s, l
+
+    def _alpha_float(self) -> float:
+        return 1 if self._rgba.alpha is None else self._rgba.alpha
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield cls
+
+    def __str__(self) -> str:
+        return self.as_named(fallback=True)
+
+    def __repr_args__(self) -> "ReprArgs":
+        return [(None, self.as_named(fallback=True))] + [("rgb", self.as_rgb_tuple())]  # type: ignore
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, Color) and self.as_rgb_tuple() == other.as_rgb_tuple()
+
+    def __hash__(self) -> int:
+        return hash(self.as_rgb_tuple())
+
+
+def parse_tuple(value: Tuple[Any, ...]) -> RGBA:
+    """
+    Parse a tuple or list as a color.
+    """
+    if len(value) == 3:
+        r, g, b = (parse_color_value(v) for v in value)
+        return RGBA(r, g, b, None)
+    elif len(value) == 4:
+        r, g, b = (parse_color_value(v) for v in value[:3])
+        return RGBA(r, g, b, parse_float_alpha(value[3]))
+    else:
+        raise ColorError(reason="tuples must have length 3 or 4")
+
+
+def parse_str(value: str) -> RGBA:
+    """
+    Parse a string to an RGBA tuple, trying the following formats (in this order):
+    * named color, see COLORS_BY_NAME below
+    * hex short eg. `<prefix>fff` (prefix can be `#`, `0x` or nothing)
+    * hex long eg. `<prefix>ffffff` (prefix can be `#`, `0x` or nothing)
+    * `rgb(<r>, <g>, <b>) `
+    * `rgba(<r>, <g>, <b>, <a>)`
+    """
+    value_lower = value.lower()
+    try:
+        r, g, b = COLORS_BY_NAME[value_lower]
+    except KeyError:
+        pass
+    else:
+        return ints_to_rgba(r, g, b, None)
+
+    m = re.fullmatch(r_hex_short, value_lower)
+    if m:
+        *rgb, a = m.groups()
+        r, g, b = (int(v * 2, 16) for v in rgb)
+        if a:
+            alpha: Optional[float] = int(a * 2, 16) / 255
+        else:
+            alpha = None
+        return ints_to_rgba(r, g, b, alpha)
+
+    m = re.fullmatch(r_hex_long, value_lower)
+    if m:
+        *rgb, a = m.groups()
+        r, g, b = (int(v, 16) for v in rgb)
+        if a:
+            alpha = int(a, 16) / 255
+        else:
+            alpha = None
+        return ints_to_rgba(r, g, b, alpha)
+
+    m = re.fullmatch(r_rgb, value_lower)
+    if m:
+        return ints_to_rgba(*m.groups(), None)  # type: ignore
+
+    m = re.fullmatch(r_rgba, value_lower)
+    if m:
+        return ints_to_rgba(*m.groups())  # type: ignore
+
+    m = re.fullmatch(r_hsl, value_lower)
+    if m:
+        h, h_units, s, l_ = m.groups()
+        return parse_hsl(h, h_units, s, l_)
+
+    m = re.fullmatch(r_hsla, value_lower)
+    if m:
+        h, h_units, s, l_, a = m.groups()
+        return parse_hsl(h, h_units, s, l_, parse_float_alpha(a))
+
+    raise ColorError(reason="string not recognised as a valid color")
+
+
+def ints_to_rgba(r: Union[int, str], g: Union[int, str], b: Union[int, str], alpha: Optional[float]) -> RGBA:
+    return RGBA(parse_color_value(r), parse_color_value(g), parse_color_value(b), parse_float_alpha(alpha))
+
+
+def parse_color_value(value: Union[int, str], max_val: int = 255) -> float:
+    """
+    Parse a value checking it's a valid int in the range 0 to max_val and divide by max_val to give a number
+    in the range 0 to 1
+    """
+    try:
+        color = float(value)
+    except ValueError:
+        raise ColorError(reason="color values must be a valid number")
+    if 0 <= color <= max_val:
+        return color / max_val
+    else:
+        raise ColorError(reason=f"color values must be in the range 0 to {max_val}")
+
+
+def parse_float_alpha(value: Union[None, str, float, int]) -> Optional[float]:
+    """
+    Parse a value checking it's a valid float in the range 0 to 1
+    """
+    if value is None:
+        return None
+    try:
+        if isinstance(value, str) and value.endswith("%"):
+            alpha = float(value[:-1]) / 100
+        else:
+            alpha = float(value)
+    except ValueError:
+        raise ColorError(reason="alpha values must be a valid float")
+
+    if almost_equal_floats(alpha, 1):
+        return None
+    elif 0 <= alpha <= 1:
+        return alpha
+    else:
+        raise ColorError(reason="alpha values must be in the range 0 to 1")
+
+
+def parse_hsl(h: str, h_units: str, sat: str, light: str, alpha: Optional[float] = None) -> RGBA:
+    """
+    Parse raw hue, saturation, lightness and alpha values and convert to RGBA.
+    """
+    s_value, l_value = parse_color_value(sat, 100), parse_color_value(light, 100)
+
+    h_value = float(h)
+    if h_units in {None, "deg"}:
+        h_value = h_value % 360 / 360
+    elif h_units == "rad":
+        h_value = h_value % rads / rads
+    else:
+        # turns
+        h_value = h_value % 1
+
+    r, g, b = hls_to_rgb(h_value, l_value, s_value)
+    return RGBA(r, g, b, alpha)
+
+
+def float_to_255(c: float) -> int:
+    return int(round(c * 255))
+
+
+COLORS_BY_NAME = {
+    "aliceblue": (240, 248, 255),
+    "antiquewhite": (250, 235, 215),
+    "aqua": (0, 255, 255),
+    "aquamarine": (127, 255, 212),
+    "azure": (240, 255, 255),
+    "beige": (245, 245, 220),
+    "bisque": (255, 228, 196),
+    "black": (0, 0, 0),
+    "blanchedalmond": (255, 235, 205),
+    "blue": (0, 0, 255),
+    "blueviolet": (138, 43, 226),
+    "brown": (165, 42, 42),
+    "burlywood": (222, 184, 135),
+    "cadetblue": (95, 158, 160),
+    "chartreuse": (127, 255, 0),
+    "chocolate": (210, 105, 30),
+    "coral": (255, 127, 80),
+    "cornflowerblue": (100, 149, 237),
+    "cornsilk": (255, 248, 220),
+    "crimson": (220, 20, 60),
+    "cyan": (0, 255, 255),
+    "darkblue": (0, 0, 139),
+    "darkcyan": (0, 139, 139),
+    "darkgoldenrod": (184, 134, 11),
+    "darkgray": (169, 169, 169),
+    "darkgreen": (0, 100, 0),
+    "darkgrey": (169, 169, 169),
+    "darkkhaki": (189, 183, 107),
+    "darkmagenta": (139, 0, 139),
+    "darkolivegreen": (85, 107, 47),
+    "darkorange": (255, 140, 0),
+    "darkorchid": (153, 50, 204),
+    "darkred": (139, 0, 0),
+    "darksalmon": (233, 150, 122),
+    "darkseagreen": (143, 188, 143),
+    "darkslateblue": (72, 61, 139),
+    "darkslategray": (47, 79, 79),
+    "darkslategrey": (47, 79, 79),
+    "darkturquoise": (0, 206, 209),
+    "darkviolet": (148, 0, 211),
+    "deeppink": (255, 20, 147),
+    "deepskyblue": (0, 191, 255),
+    "dimgray": (105, 105, 105),
+    "dimgrey": (105, 105, 105),
+    "dodgerblue": (30, 144, 255),
+    "firebrick": (178, 34, 34),
+    "floralwhite": (255, 250, 240),
+    "forestgreen": (34, 139, 34),
+    "fuchsia": (255, 0, 255),
+    "gainsboro": (220, 220, 220),
+    "ghostwhite": (248, 248, 255),
+    "gold": (255, 215, 0),
+    "goldenrod": (218, 165, 32),
+    "gray": (128, 128, 128),
+    "green": (0, 128, 0),
+    "greenyellow": (173, 255, 47),
+    "grey": (128, 128, 128),
+    "honeydew": (240, 255, 240),
+    "hotpink": (255, 105, 180),
+    "indianred": (205, 92, 92),
+    "indigo": (75, 0, 130),
+    "ivory": (255, 255, 240),
+    "khaki": (240, 230, 140),
+    "lavender": (230, 230, 250),
+    "lavenderblush": (255, 240, 245),
+    "lawngreen": (124, 252, 0),
+    "lemonchiffon": (255, 250, 205),
+    "lightblue": (173, 216, 230),
+    "lightcoral": (240, 128, 128),
+    "lightcyan": (224, 255, 255),
+    "lightgoldenrodyellow": (250, 250, 210),
+    "lightgray": (211, 211, 211),
+    "lightgreen": (144, 238, 144),
+    "lightgrey": (211, 211, 211),
+    "lightpink": (255, 182, 193),
+    "lightsalmon": (255, 160, 122),
+    "lightseagreen": (32, 178, 170),
+    "lightskyblue": (135, 206, 250),
+    "lightslategray": (119, 136, 153),
+    "lightslategrey": (119, 136, 153),
+    "lightsteelblue": (176, 196, 222),
+    "lightyellow": (255, 255, 224),
+    "lime": (0, 255, 0),
+    "limegreen": (50, 205, 50),
+    "linen": (250, 240, 230),
+    "magenta": (255, 0, 255),
+    "maroon": (128, 0, 0),
+    "mediumaquamarine": (102, 205, 170),
+    "mediumblue": (0, 0, 205),
+    "mediumorchid": (186, 85, 211),
+    "mediumpurple": (147, 112, 219),
+    "mediumseagreen": (60, 179, 113),
+    "mediumslateblue": (123, 104, 238),
+    "mediumspringgreen": (0, 250, 154),
+    "mediumturquoise": (72, 209, 204),
+    "mediumvioletred": (199, 21, 133),
+    "midnightblue": (25, 25, 112),
+    "mintcream": (245, 255, 250),
+    "mistyrose": (255, 228, 225),
+    "moccasin": (255, 228, 181),
+    "navajowhite": (255, 222, 173),
+    "navy": (0, 0, 128),
+    "oldlace": (253, 245, 230),
+    "olive": (128, 128, 0),
+    "olivedrab": (107, 142, 35),
+    "orange": (255, 165, 0),
+    "orangered": (255, 69, 0),
+    "orchid": (218, 112, 214),
+    "palegoldenrod": (238, 232, 170),
+    "palegreen": (152, 251, 152),
+    "paleturquoise": (175, 238, 238),
+    "palevioletred": (219, 112, 147),
+    "papayawhip": (255, 239, 213),
+    "peachpuff": (255, 218, 185),
+    "peru": (205, 133, 63),
+    "pink": (255, 192, 203),
+    "plum": (221, 160, 221),
+    "powderblue": (176, 224, 230),
+    "purple": (128, 0, 128),
+    "red": (255, 0, 0),
+    "rosybrown": (188, 143, 143),
+    "royalblue": (65, 105, 225),
+    "saddlebrown": (139, 69, 19),
+    "salmon": (250, 128, 114),
+    "sandybrown": (244, 164, 96),
+    "seagreen": (46, 139, 87),
+    "seashell": (255, 245, 238),
+    "sienna": (160, 82, 45),
+    "silver": (192, 192, 192),
+    "skyblue": (135, 206, 235),
+    "slateblue": (106, 90, 205),
+    "slategray": (112, 128, 144),
+    "slategrey": (112, 128, 144),
+    "snow": (255, 250, 250),
+    "springgreen": (0, 255, 127),
+    "steelblue": (70, 130, 180),
+    "tan": (210, 180, 140),
+    "teal": (0, 128, 128),
+    "thistle": (216, 191, 216),
+    "tomato": (255, 99, 71),
+    "turquoise": (64, 224, 208),
+    "violet": (238, 130, 238),
+    "wheat": (245, 222, 179),
+    "white": (255, 255, 255),
+    "whitesmoke": (245, 245, 245),
+    "yellow": (255, 255, 0),
+    "yellowgreen": (154, 205, 50),
+}
+
+COLORS_BY_VALUE = {v: k for k, v in COLORS_BY_NAME.items()}
diff --git a/vendor/pydantic/config.py b/vendor/pydantic/config.py
new file mode 100644
index 0000000..689d759
--- /dev/null
+++ b/vendor/pydantic/config.py
@@ -0,0 +1,190 @@
+import json
+from enum import Enum
+from typing import TYPE_CHECKING, Any, Callable, Dict, ForwardRef, Optional, Tuple, Type, Union
+
+from typing_extensions import Literal, Protocol
+
+from .typing import AnyArgTCallable, AnyCallable
+from .utils import GetterDict
+from .version import compiled
+
+if TYPE_CHECKING:
+    from typing import overload
+
+    from .fields import ModelField
+    from .main import BaseModel
+
+    ConfigType = Type["BaseConfig"]
+
+    class SchemaExtraCallable(Protocol):
+        @overload
+        def __call__(self, schema: Dict[str, Any]) -> None:
+            pass
+
+        @overload
+        def __call__(self, schema: Dict[str, Any], model_class: Type[BaseModel]) -> None:
+            pass
+
+else:
+    SchemaExtraCallable = Callable[..., None]
+
+__all__ = "BaseConfig", "ConfigDict", "get_config", "Extra", "inherit_config", "prepare_config"
+
+
+class Extra(str, Enum):
+    allow = "allow"
+    ignore = "ignore"
+    forbid = "forbid"
+
+
+# https://github.com/cython/cython/issues/4003
+# Fixed in Cython 3 and Pydantic v1 won't support Cython 3.
+# Pydantic v2 doesn't depend on Cython at all.
+if not compiled:
+    from typing_extensions import TypedDict
+
+    class ConfigDict(TypedDict, total=False):
+        title: Optional[str]
+        anystr_lower: bool
+        anystr_strip_whitespace: bool
+        min_anystr_length: int
+        max_anystr_length: Optional[int]
+        validate_all: bool
+        extra: Extra
+        allow_mutation: bool
+        frozen: bool
+        allow_population_by_field_name: bool
+        use_enum_values: bool
+        fields: Dict[str, Union[str, Dict[str, str]]]
+        validate_assignment: bool
+        error_msg_templates: Dict[str, str]
+        arbitrary_types_allowed: bool
+        orm_mode: bool
+        getter_dict: Type[GetterDict]
+        alias_generator: Optional[Callable[[str], str]]
+        keep_untouched: Tuple[type, ...]
+        schema_extra: Union[Dict[str, object], "SchemaExtraCallable"]
+        json_loads: Callable[[str], object]
+        json_dumps: AnyArgTCallable[str]
+        json_encoders: Dict[Type[object], AnyCallable]
+        underscore_attrs_are_private: bool
+        allow_inf_nan: bool
+        copy_on_model_validation: Literal["none", "deep", "shallow"]
+        # whether dataclass `__post_init__` should be run after validation
+        post_init_call: Literal["before_validation", "after_validation"]
+
+else:
+    ConfigDict = dict  # type: ignore
+
+
+class BaseConfig:
+    title: Optional[str] = None
+    anystr_lower: bool = False
+    anystr_upper: bool = False
+    anystr_strip_whitespace: bool = False
+    min_anystr_length: int = 0
+    max_anystr_length: Optional[int] = None
+    validate_all: bool = False
+    extra: Extra = Extra.ignore
+    allow_mutation: bool = True
+    frozen: bool = False
+    allow_population_by_field_name: bool = False
+    use_enum_values: bool = False
+    fields: Dict[str, Union[str, Dict[str, str]]] = {}
+    validate_assignment: bool = False
+    error_msg_templates: Dict[str, str] = {}
+    arbitrary_types_allowed: bool = False
+    orm_mode: bool = False
+    getter_dict: Type[GetterDict] = GetterDict
+    alias_generator: Optional[Callable[[str], str]] = None
+    keep_untouched: Tuple[type, ...] = ()
+    schema_extra: Union[Dict[str, Any], "SchemaExtraCallable"] = {}
+    json_loads: Callable[[str], Any] = json.loads
+    json_dumps: Callable[..., str] = json.dumps
+    json_encoders: Dict[Union[Type[Any], str, ForwardRef], AnyCallable] = {}
+    underscore_attrs_are_private: bool = False
+    allow_inf_nan: bool = True
+
+    # whether inherited models as fields should be reconstructed as base model,
+    # and whether such a copy should be shallow or deep
+    copy_on_model_validation: Literal["none", "deep", "shallow"] = "shallow"
+
+    # whether `Union` should check all allowed types before even trying to coerce
+    smart_union: bool = False
+    # whether dataclass `__post_init__` should be run before or after validation
+    post_init_call: Literal["before_validation", "after_validation"] = "before_validation"
+
+    @classmethod
+    def get_field_info(cls, name: str) -> Dict[str, Any]:
+        """
+        Get properties of FieldInfo from the `fields` property of the config class.
+        """
+
+        fields_value = cls.fields.get(name)
+
+        if isinstance(fields_value, str):
+            field_info: Dict[str, Any] = {"alias": fields_value}
+        elif isinstance(fields_value, dict):
+            field_info = fields_value
+        else:
+            field_info = {}
+
+        if "alias" in field_info:
+            field_info.setdefault("alias_priority", 2)
+
+        if field_info.get("alias_priority", 0) <= 1 and cls.alias_generator:
+            alias = cls.alias_generator(name)
+            if not isinstance(alias, str):
+                raise TypeError(f"Config.alias_generator must return str, not {alias.__class__}")
+            field_info.update(alias=alias, alias_priority=1)
+        return field_info
+
+    @classmethod
+    def prepare_field(cls, field: "ModelField") -> None:
+        """
+        Optional hook to check or modify fields during model creation.
+        """
+
+
+def get_config(config: Union[ConfigDict, Type[object], None]) -> Type[BaseConfig]:
+    if config is None:
+        return BaseConfig
+
+    else:
+        config_dict = (
+            config
+            if isinstance(config, dict)
+            else {k: getattr(config, k) for k in dir(config) if not k.startswith("__")}
+        )
+
+        class Config(BaseConfig):
+            ...
+
+        for k, v in config_dict.items():
+            setattr(Config, k, v)
+        return Config
+
+
+def inherit_config(self_config: "ConfigType", parent_config: "ConfigType", **namespace: Any) -> "ConfigType":
+    if not self_config:
+        base_classes: Tuple["ConfigType", ...] = (parent_config,)
+    elif self_config == parent_config:
+        base_classes = (self_config,)
+    else:
+        base_classes = self_config, parent_config
+
+    namespace["json_encoders"] = {
+        **getattr(parent_config, "json_encoders", {}),
+        **getattr(self_config, "json_encoders", {}),
+        **namespace.get("json_encoders", {}),
+    }
+
+    return type("Config", base_classes, namespace)
+
+
+def prepare_config(config: Type[BaseConfig], cls_name: str) -> None:
+    if not isinstance(config.extra, Extra):
+        try:
+            config.extra = Extra(config.extra)
+        except ValueError:
+            raise ValueError(f'"{cls_name}": {config.extra} is not a valid value for "extra"')
diff --git a/vendor/pydantic/dataclasses.py b/vendor/pydantic/dataclasses.py
new file mode 100644
index 0000000..c6f1af0
--- /dev/null
+++ b/vendor/pydantic/dataclasses.py
@@ -0,0 +1,500 @@
+"""
+The main purpose is to enhance stdlib dataclasses by adding validation
+A pydantic dataclass can be generated from scratch or from a stdlib one.
+
+Behind the scene, a pydantic dataclass is just like a regular one on which we attach
+a `BaseModel` and magic methods to trigger the validation of the data.
+`__init__` and `__post_init__` are hence overridden and have extra logic to be
+able to validate input data.
+
+When a pydantic dataclass is generated from scratch, it's just a plain dataclass
+with validation triggered at initialization
+
+The tricky part if for stdlib dataclasses that are converted after into pydantic ones e.g.
+
+```py
+@dataclasses.dataclass
+class M:
+    x: int
+
+ValidatedM = pydantic.dataclasses.dataclass(M)
+```
+
+We indeed still want to support equality, hashing, repr, ... as if it was the stdlib one!
+
+```py
+assert isinstance(ValidatedM(x=1), M)
+assert ValidatedM(x=1) == M(x=1)
+```
+
+This means we **don't want to create a new dataclass that inherits from it**
+The trick is to create a wrapper around `M` that will act as a proxy to trigger
+validation without altering default `M` behaviour.
+"""
+import copy
+import dataclasses
+import sys
+from contextlib import contextmanager
+from functools import wraps
+
+try:
+    from functools import cached_property
+except ImportError:
+    # cached_property available only for python3.8+
+    pass
+
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, Dict, Generator, Optional, Type, TypeVar, Union, overload
+
+from typing_extensions import dataclass_transform
+
+from .class_validators import gather_all_validators
+from .config import BaseConfig, ConfigDict, Extra, get_config
+from .error_wrappers import ValidationError
+from .errors import DataclassTypeError
+from .fields import Field, FieldInfo, Required, Undefined
+from .main import create_model, validate_model
+from .utils import ClassAttribute
+
+if TYPE_CHECKING:
+    from .main import BaseModel
+    from .typing import CallableGenerator, NoArgAnyCallable
+
+    DataclassT = TypeVar("DataclassT", bound="Dataclass")
+
+    DataclassClassOrWrapper = Union[Type["Dataclass"], "DataclassProxy"]
+
+    class Dataclass:
+        # stdlib attributes
+        __dataclass_fields__: ClassVar[Dict[str, Any]]
+        __dataclass_params__: ClassVar[Any]  # in reality `dataclasses._DataclassParams`
+        __post_init__: ClassVar[Callable[..., None]]
+
+        # Added by pydantic
+        __pydantic_run_validation__: ClassVar[bool]
+        __post_init_post_parse__: ClassVar[Callable[..., None]]
+        __pydantic_initialised__: ClassVar[bool]
+        __pydantic_model__: ClassVar[Type[BaseModel]]
+        __pydantic_validate_values__: ClassVar[Callable[["Dataclass"], None]]
+        __pydantic_has_field_info_default__: ClassVar[bool]  # whether a `pydantic.Field` is used as default value
+
+        def __init__(self, *args: object, **kwargs: object) -> None:
+            pass
+
+        @classmethod
+        def __get_validators__(cls: Type["Dataclass"]) -> "CallableGenerator":
+            pass
+
+        @classmethod
+        def __validate__(cls: Type["DataclassT"], v: Any) -> "DataclassT":
+            pass
+
+
+__all__ = [
+    "dataclass",
+    "set_validation",
+    "create_pydantic_model_from_dataclass",
+    "is_builtin_dataclass",
+    "make_dataclass_validator",
+]
+
+_T = TypeVar("_T")
+
+if sys.version_info >= (3, 10):
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field))
+    @overload
+    def dataclass(
+        *,
+        init: bool = True,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool = False,
+        config: Union[ConfigDict, Type[object], None] = None,
+        validate_on_init: Optional[bool] = None,
+        use_proxy: Optional[bool] = None,
+        kw_only: bool = ...,
+    ) -> Callable[[Type[_T]], "DataclassClassOrWrapper"]:
+        ...
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field))
+    @overload
+    def dataclass(
+        _cls: Type[_T],
+        *,
+        init: bool = True,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool = False,
+        config: Union[ConfigDict, Type[object], None] = None,
+        validate_on_init: Optional[bool] = None,
+        use_proxy: Optional[bool] = None,
+        kw_only: bool = ...,
+    ) -> "DataclassClassOrWrapper":
+        ...
+
+else:
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field))
+    @overload
+    def dataclass(
+        *,
+        init: bool = True,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool = False,
+        config: Union[ConfigDict, Type[object], None] = None,
+        validate_on_init: Optional[bool] = None,
+        use_proxy: Optional[bool] = None,
+    ) -> Callable[[Type[_T]], "DataclassClassOrWrapper"]:
+        ...
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field))
+    @overload
+    def dataclass(
+        _cls: Type[_T],
+        *,
+        init: bool = True,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool = False,
+        config: Union[ConfigDict, Type[object], None] = None,
+        validate_on_init: Optional[bool] = None,
+        use_proxy: Optional[bool] = None,
+    ) -> "DataclassClassOrWrapper":
+        ...
+
+
+@dataclass_transform(field_specifiers=(dataclasses.field, Field))
+def dataclass(
+    _cls: Optional[Type[_T]] = None,
+    *,
+    init: bool = True,
+    repr: bool = True,
+    eq: bool = True,
+    order: bool = False,
+    unsafe_hash: bool = False,
+    frozen: bool = False,
+    config: Union[ConfigDict, Type[object], None] = None,
+    validate_on_init: Optional[bool] = None,
+    use_proxy: Optional[bool] = None,
+    kw_only: bool = False,
+) -> Union[Callable[[Type[_T]], "DataclassClassOrWrapper"], "DataclassClassOrWrapper"]:
+    """
+    Like the python standard lib dataclasses but with type validation.
+    The result is either a pydantic dataclass that will validate input data
+    or a wrapper that will trigger validation around a stdlib dataclass
+    to avoid modifying it directly
+    """
+    the_config = get_config(config)
+
+    def wrap(cls: Type[Any]) -> "DataclassClassOrWrapper":
+        should_use_proxy = (
+            use_proxy
+            if use_proxy is not None
+            else (
+                is_builtin_dataclass(cls)
+                and (cls.__bases__[0] is object or set(dir(cls)) == set(dir(cls.__bases__[0])))
+            )
+        )
+        if should_use_proxy:
+            dc_cls_doc = ""
+            dc_cls = DataclassProxy(cls)
+            default_validate_on_init = False
+        else:
+            dc_cls_doc = cls.__doc__ or ""  # needs to be done before generating dataclass
+            if sys.version_info >= (3, 10):
+                dc_cls = dataclasses.dataclass(
+                    cls,
+                    init=init,
+                    repr=repr,
+                    eq=eq,
+                    order=order,
+                    unsafe_hash=unsafe_hash,
+                    frozen=frozen,
+                    kw_only=kw_only,
+                )
+            else:
+                dc_cls = dataclasses.dataclass(  # type: ignore
+                    cls, init=init, repr=repr, eq=eq, order=order, unsafe_hash=unsafe_hash, frozen=frozen
+                )
+            default_validate_on_init = True
+
+        should_validate_on_init = default_validate_on_init if validate_on_init is None else validate_on_init
+        _add_pydantic_validation_attributes(cls, the_config, should_validate_on_init, dc_cls_doc)
+        dc_cls.__pydantic_model__.__try_update_forward_refs__(**{cls.__name__: cls})
+        return dc_cls
+
+    if _cls is None:
+        return wrap
+
+    return wrap(_cls)
+
+
+@contextmanager
+def set_validation(cls: Type["DataclassT"], value: bool) -> Generator[Type["DataclassT"], None, None]:
+    original_run_validation = cls.__pydantic_run_validation__
+    try:
+        cls.__pydantic_run_validation__ = value
+        yield cls
+    finally:
+        cls.__pydantic_run_validation__ = original_run_validation
+
+
+class DataclassProxy:
+    __slots__ = "__dataclass__"
+
+    def __init__(self, dc_cls: Type["Dataclass"]) -> None:
+        object.__setattr__(self, "__dataclass__", dc_cls)
+
+    def __call__(self, *args: Any, **kwargs: Any) -> Any:
+        with set_validation(self.__dataclass__, True):
+            return self.__dataclass__(*args, **kwargs)
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self.__dataclass__, name)
+
+    def __setattr__(self, __name: str, __value: Any) -> None:
+        return setattr(self.__dataclass__, __name, __value)
+
+    def __instancecheck__(self, instance: Any) -> bool:
+        return isinstance(instance, self.__dataclass__)
+
+    def __copy__(self) -> "DataclassProxy":
+        return DataclassProxy(copy.copy(self.__dataclass__))
+
+    def __deepcopy__(self, memo: Any) -> "DataclassProxy":
+        return DataclassProxy(copy.deepcopy(self.__dataclass__, memo))
+
+
+def _add_pydantic_validation_attributes(  # noqa: C901 (ignore complexity)
+    dc_cls: Type["Dataclass"],
+    config: Type[BaseConfig],
+    validate_on_init: bool,
+    dc_cls_doc: str,
+) -> None:
+    """
+    We need to replace the right method. If no `__post_init__` has been set in the stdlib dataclass
+    it won't even exist (code is generated on the fly by `dataclasses`)
+    By default, we run validation after `__init__` or `__post_init__` if defined
+    """
+    init = dc_cls.__init__
+
+    @wraps(init)
+    def handle_extra_init(self: "Dataclass", *args: Any, **kwargs: Any) -> None:
+        if config.extra == Extra.ignore:
+            init(self, *args, **{k: v for k, v in kwargs.items() if k in self.__dataclass_fields__})
+
+        elif config.extra == Extra.allow:
+            for k, v in kwargs.items():
+                self.__dict__.setdefault(k, v)
+            init(self, *args, **{k: v for k, v in kwargs.items() if k in self.__dataclass_fields__})
+
+        else:
+            init(self, *args, **kwargs)
+
+    if hasattr(dc_cls, "__post_init__"):
+        try:
+            post_init = dc_cls.__post_init__.__wrapped__  # type: ignore[attr-defined]
+        except AttributeError:
+            post_init = dc_cls.__post_init__
+
+        @wraps(post_init)
+        def new_post_init(self: "Dataclass", *args: Any, **kwargs: Any) -> None:
+            if config.post_init_call == "before_validation":
+                post_init(self, *args, **kwargs)
+
+            if self.__class__.__pydantic_run_validation__:
+                self.__pydantic_validate_values__()
+                if hasattr(self, "__post_init_post_parse__"):
+                    self.__post_init_post_parse__(*args, **kwargs)
+
+            if config.post_init_call == "after_validation":
+                post_init(self, *args, **kwargs)
+
+        setattr(dc_cls, "__init__", handle_extra_init)
+        setattr(dc_cls, "__post_init__", new_post_init)
+
+    else:
+
+        @wraps(init)
+        def new_init(self: "Dataclass", *args: Any, **kwargs: Any) -> None:
+            handle_extra_init(self, *args, **kwargs)
+
+            if self.__class__.__pydantic_run_validation__:
+                self.__pydantic_validate_values__()
+
+            if hasattr(self, "__post_init_post_parse__"):
+                # We need to find again the initvars. To do that we use `__dataclass_fields__` instead of
+                # public method `dataclasses.fields`
+
+                # get all initvars and their default values
+                initvars_and_values: Dict[str, Any] = {}
+                for i, f in enumerate(self.__class__.__dataclass_fields__.values()):
+                    if f._field_type is dataclasses._FIELD_INITVAR:  # type: ignore[attr-defined]
+                        try:
+                            # set arg value by default
+                            initvars_and_values[f.name] = args[i]
+                        except IndexError:
+                            initvars_and_values[f.name] = kwargs.get(f.name, f.default)
+
+                self.__post_init_post_parse__(**initvars_and_values)
+
+        setattr(dc_cls, "__init__", new_init)
+
+    setattr(dc_cls, "__pydantic_run_validation__", ClassAttribute("__pydantic_run_validation__", validate_on_init))
+    setattr(dc_cls, "__pydantic_initialised__", False)
+    setattr(dc_cls, "__pydantic_model__", create_pydantic_model_from_dataclass(dc_cls, config, dc_cls_doc))
+    setattr(dc_cls, "__pydantic_validate_values__", _dataclass_validate_values)
+    setattr(dc_cls, "__validate__", classmethod(_validate_dataclass))
+    setattr(dc_cls, "__get_validators__", classmethod(_get_validators))
+
+    if dc_cls.__pydantic_model__.__config__.validate_assignment and not dc_cls.__dataclass_params__.frozen:
+        setattr(dc_cls, "__setattr__", _dataclass_validate_assignment_setattr)
+
+
+def _get_validators(cls: "DataclassClassOrWrapper") -> "CallableGenerator":
+    yield cls.__validate__
+
+
+def _validate_dataclass(cls: Type["DataclassT"], v: Any) -> "DataclassT":
+    with set_validation(cls, True):
+        if isinstance(v, cls):
+            v.__pydantic_validate_values__()
+            return v
+        elif isinstance(v, (list, tuple)):
+            return cls(*v)
+        elif isinstance(v, dict):
+            return cls(**v)
+        else:
+            raise DataclassTypeError(class_name=cls.__name__)
+
+
+def create_pydantic_model_from_dataclass(
+    dc_cls: Type["Dataclass"],
+    config: Type[Any] = BaseConfig,
+    dc_cls_doc: Optional[str] = None,
+) -> Type["BaseModel"]:
+    field_definitions: Dict[str, Any] = {}
+    for field in dataclasses.fields(dc_cls):
+        default: Any = Undefined
+        default_factory: Optional["NoArgAnyCallable"] = None
+        field_info: FieldInfo
+
+        if field.default is not dataclasses.MISSING:
+            default = field.default
+        elif field.default_factory is not dataclasses.MISSING:
+            default_factory = field.default_factory
+        else:
+            default = Required
+
+        if isinstance(default, FieldInfo):
+            field_info = default
+            dc_cls.__pydantic_has_field_info_default__ = True
+        else:
+            field_info = Field(default=default, default_factory=default_factory, **field.metadata)
+
+        field_definitions[field.name] = (field.type, field_info)
+
+    validators = gather_all_validators(dc_cls)
+    model: Type["BaseModel"] = create_model(
+        dc_cls.__name__,
+        __config__=config,
+        __module__=dc_cls.__module__,
+        __validators__=validators,
+        __cls_kwargs__={"__resolve_forward_refs__": False},
+        **field_definitions,
+    )
+    model.__doc__ = dc_cls_doc if dc_cls_doc is not None else dc_cls.__doc__ or ""
+    return model
+
+
+if sys.version_info >= (3, 8):
+
+    def _is_field_cached_property(obj: "Dataclass", k: str) -> bool:
+        return isinstance(getattr(type(obj), k, None), cached_property)
+
+else:
+
+    def _is_field_cached_property(obj: "Dataclass", k: str) -> bool:
+        return False
+
+
+def _dataclass_validate_values(self: "Dataclass") -> None:
+    # validation errors can occur if this function is called twice on an already initialised dataclass.
+    # for example if Extra.forbid is enabled, it would consider __pydantic_initialised__ an invalid extra property
+    if getattr(self, "__pydantic_initialised__"):
+        return
+    if getattr(self, "__pydantic_has_field_info_default__", False):
+        # We need to remove `FieldInfo` values since they are not valid as input
+        # It's ok to do that because they are obviously the default values!
+        input_data = {
+            k: v
+            for k, v in self.__dict__.items()
+            if not (isinstance(v, FieldInfo) or _is_field_cached_property(self, k))
+        }
+    else:
+        input_data = {k: v for k, v in self.__dict__.items() if not _is_field_cached_property(self, k)}
+    d, _, validation_error = validate_model(self.__pydantic_model__, input_data, cls=self.__class__)
+    if validation_error:
+        raise validation_error
+    self.__dict__.update(d)
+    object.__setattr__(self, "__pydantic_initialised__", True)
+
+
+def _dataclass_validate_assignment_setattr(self: "Dataclass", name: str, value: Any) -> None:
+    if self.__pydantic_initialised__:
+        d = dict(self.__dict__)
+        d.pop(name, None)
+        known_field = self.__pydantic_model__.__fields__.get(name, None)
+        if known_field:
+            value, error_ = known_field.validate(value, d, loc=name, cls=self.__class__)
+            if error_:
+                raise ValidationError([error_], self.__class__)
+
+    object.__setattr__(self, name, value)
+
+
+def is_builtin_dataclass(_cls: Type[Any]) -> bool:
+    """
+    Whether a class is a stdlib dataclass
+    (useful to discriminated a pydantic dataclass that is actually a wrapper around a stdlib dataclass)
+
+    we check that
+    - `_cls` is a dataclass
+    - `_cls` is not a processed pydantic dataclass (with a basemodel attached)
+    - `_cls` is not a pydantic dataclass inheriting directly from a stdlib dataclass
+    e.g.
+    ```
+    @dataclasses.dataclass
+    class A:
+        x: int
+
+    @pydantic.dataclasses.dataclass
+    class B(A):
+        y: int
+    ```
+    In this case, when we first check `B`, we make an extra check and look at the annotations ('y'),
+    which won't be a superset of all the dataclass fields (only the stdlib fields i.e. 'x')
+    """
+    return (
+        dataclasses.is_dataclass(_cls)
+        and not hasattr(_cls, "__pydantic_model__")
+        and set(_cls.__dataclass_fields__).issuperset(set(getattr(_cls, "__annotations__", {})))
+    )
+
+
+def make_dataclass_validator(dc_cls: Type["Dataclass"], config: Type[BaseConfig]) -> "CallableGenerator":
+    """
+    Create a pydantic.dataclass from a builtin dataclass to add type validation
+    and yield the validators
+    It retrieves the parameters of the dataclass and forwards them to the newly created dataclass
+    """
+    yield from _get_validators(dataclass(dc_cls, config=config, use_proxy=True))
diff --git a/vendor/pydantic/datetime_parse.py b/vendor/pydantic/datetime_parse.py
new file mode 100644
index 0000000..055941f
--- /dev/null
+++ b/vendor/pydantic/datetime_parse.py
@@ -0,0 +1,248 @@
+"""
+Functions to parse datetime objects.
+
+We're using regular expressions rather than time.strptime because:
+- They provide both validation and parsing.
+- They're more flexible for datetimes.
+- The date/datetime/time constructors produce friendlier error messages.
+
+Stolen from https://raw.githubusercontent.com/django/django/main/django/utils/dateparse.py at
+9718fa2e8abe430c3526a9278dd976443d4ae3c6
+
+Changed to:
+* use standard python datetime types not django.utils.timezone
+* raise ValueError when regex doesn't match rather than returning None
+* support parsing unix timestamps for dates and datetimes
+"""
+import re
+from datetime import date, datetime, time, timedelta, timezone
+from typing import Dict, Optional, Type, Union
+
+from . import errors
+
+date_expr = r"(?P<year>\d{4})-(?P<month>\d{1,2})-(?P<day>\d{1,2})"
+time_expr = (
+    r"(?P<hour>\d{1,2}):(?P<minute>\d{1,2})"
+    r"(?::(?P<second>\d{1,2})(?:\.(?P<microsecond>\d{1,6})\d{0,6})?)?"
+    r"(?P<tzinfo>Z|[+-]\d{2}(?::?\d{2})?)?$"
+)
+
+date_re = re.compile(f"{date_expr}$")
+time_re = re.compile(time_expr)
+datetime_re = re.compile(f"{date_expr}[T ]{time_expr}")
+
+standard_duration_re = re.compile(
+    r"^"
+    r"(?:(?P<days>-?\d+) (days?, )?)?"
+    r"((?:(?P<hours>-?\d+):)(?=\d+:\d+))?"
+    r"(?:(?P<minutes>-?\d+):)?"
+    r"(?P<seconds>-?\d+)"
+    r"(?:\.(?P<microseconds>\d{1,6})\d{0,6})?"
+    r"$"
+)
+
+# Support the sections of ISO 8601 date representation that are accepted by timedelta
+iso8601_duration_re = re.compile(
+    r"^(?P<sign>[-+]?)"
+    r"P"
+    r"(?:(?P<days>\d+(.\d+)?)D)?"
+    r"(?:T"
+    r"(?:(?P<hours>\d+(.\d+)?)H)?"
+    r"(?:(?P<minutes>\d+(.\d+)?)M)?"
+    r"(?:(?P<seconds>\d+(.\d+)?)S)?"
+    r")?"
+    r"$"
+)
+
+EPOCH = datetime(1970, 1, 1)
+# if greater than this, the number is in ms, if less than or equal it's in seconds
+# (in seconds this is 11th October 2603, in ms it's 20th August 1970)
+MS_WATERSHED = int(2e10)
+# slightly more than datetime.max in ns - (datetime.max - EPOCH).total_seconds() * 1e9
+MAX_NUMBER = int(3e20)
+StrBytesIntFloat = Union[str, bytes, int, float]
+
+
+def get_numeric(value: StrBytesIntFloat, native_expected_type: str) -> Union[None, int, float]:
+    if isinstance(value, (int, float)):
+        return value
+    try:
+        return float(value)
+    except ValueError:
+        return None
+    except TypeError:
+        raise TypeError(f"invalid type; expected {native_expected_type}, string, bytes, int or float")
+
+
+def from_unix_seconds(seconds: Union[int, float]) -> datetime:
+    if seconds > MAX_NUMBER:
+        return datetime.max
+    elif seconds < -MAX_NUMBER:
+        return datetime.min
+
+    while abs(seconds) > MS_WATERSHED:
+        seconds /= 1000
+    dt = EPOCH + timedelta(seconds=seconds)
+    return dt.replace(tzinfo=timezone.utc)
+
+
+def _parse_timezone(value: Optional[str], error: Type[Exception]) -> Union[None, int, timezone]:
+    if value == "Z":
+        return timezone.utc
+    elif value is not None:
+        offset_mins = int(value[-2:]) if len(value) > 3 else 0
+        offset = 60 * int(value[1:3]) + offset_mins
+        if value[0] == "-":
+            offset = -offset
+        try:
+            return timezone(timedelta(minutes=offset))
+        except ValueError:
+            raise error()
+    else:
+        return None
+
+
+def parse_date(value: Union[date, StrBytesIntFloat]) -> date:
+    """
+    Parse a date/int/float/string and return a datetime.date.
+
+    Raise ValueError if the input is well formatted but not a valid date.
+    Raise ValueError if the input isn't well formatted.
+    """
+    if isinstance(value, date):
+        if isinstance(value, datetime):
+            return value.date()
+        else:
+            return value
+
+    number = get_numeric(value, "date")
+    if number is not None:
+        return from_unix_seconds(number).date()
+
+    if isinstance(value, bytes):
+        value = value.decode()
+
+    match = date_re.match(value)  # type: ignore
+    if match is None:
+        raise errors.DateError()
+
+    kw = {k: int(v) for k, v in match.groupdict().items()}
+
+    try:
+        return date(**kw)
+    except ValueError:
+        raise errors.DateError()
+
+
+def parse_time(value: Union[time, StrBytesIntFloat]) -> time:
+    """
+    Parse a time/string and return a datetime.time.
+
+    Raise ValueError if the input is well formatted but not a valid time.
+    Raise ValueError if the input isn't well formatted, in particular if it contains an offset.
+    """
+    if isinstance(value, time):
+        return value
+
+    number = get_numeric(value, "time")
+    if number is not None:
+        if number >= 86400:
+            # doesn't make sense since the time time loop back around to 0
+            raise errors.TimeError()
+        return (datetime.min + timedelta(seconds=number)).time()
+
+    if isinstance(value, bytes):
+        value = value.decode()
+
+    match = time_re.match(value)  # type: ignore
+    if match is None:
+        raise errors.TimeError()
+
+    kw = match.groupdict()
+    if kw["microsecond"]:
+        kw["microsecond"] = kw["microsecond"].ljust(6, "0")
+
+    tzinfo = _parse_timezone(kw.pop("tzinfo"), errors.TimeError)
+    kw_: Dict[str, Union[None, int, timezone]] = {k: int(v) for k, v in kw.items() if v is not None}
+    kw_["tzinfo"] = tzinfo
+
+    try:
+        return time(**kw_)  # type: ignore
+    except ValueError:
+        raise errors.TimeError()
+
+
+def parse_datetime(value: Union[datetime, StrBytesIntFloat]) -> datetime:
+    """
+    Parse a datetime/int/float/string and return a datetime.datetime.
+
+    This function supports time zone offsets. When the input contains one,
+    the output uses a timezone with a fixed offset from UTC.
+
+    Raise ValueError if the input is well formatted but not a valid datetime.
+    Raise ValueError if the input isn't well formatted.
+    """
+    if isinstance(value, datetime):
+        return value
+
+    number = get_numeric(value, "datetime")
+    if number is not None:
+        return from_unix_seconds(number)
+
+    if isinstance(value, bytes):
+        value = value.decode()
+
+    match = datetime_re.match(value)  # type: ignore
+    if match is None:
+        raise errors.DateTimeError()
+
+    kw = match.groupdict()
+    if kw["microsecond"]:
+        kw["microsecond"] = kw["microsecond"].ljust(6, "0")
+
+    tzinfo = _parse_timezone(kw.pop("tzinfo"), errors.DateTimeError)
+    kw_: Dict[str, Union[None, int, timezone]] = {k: int(v) for k, v in kw.items() if v is not None}
+    kw_["tzinfo"] = tzinfo
+
+    try:
+        return datetime(**kw_)  # type: ignore
+    except ValueError:
+        raise errors.DateTimeError()
+
+
+def parse_duration(value: StrBytesIntFloat) -> timedelta:
+    """
+    Parse a duration int/float/string and return a datetime.timedelta.
+
+    The preferred format for durations in Django is '%d %H:%M:%S.%f'.
+
+    Also supports ISO 8601 representation.
+    """
+    if isinstance(value, timedelta):
+        return value
+
+    if isinstance(value, (int, float)):
+        # below code requires a string
+        value = f"{value:f}"
+    elif isinstance(value, bytes):
+        value = value.decode()
+
+    try:
+        match = standard_duration_re.match(value) or iso8601_duration_re.match(value)
+    except TypeError:
+        raise TypeError("invalid type; expected timedelta, string, bytes, int or float")
+
+    if not match:
+        raise errors.DurationError()
+
+    kw = match.groupdict()
+    sign = -1 if kw.pop("sign", "+") == "-" else 1
+    if kw.get("microseconds"):
+        kw["microseconds"] = kw["microseconds"].ljust(6, "0")
+
+    if kw.get("seconds") and kw.get("microseconds") and kw["seconds"].startswith("-"):
+        kw["microseconds"] = "-" + kw["microseconds"]
+
+    kw_ = {k: float(v) for k, v in kw.items() if v is not None}
+
+    return sign * timedelta(**kw_)
diff --git a/vendor/pydantic/env_settings.py b/vendor/pydantic/env_settings.py
new file mode 100644
index 0000000..110a75b
--- /dev/null
+++ b/vendor/pydantic/env_settings.py
@@ -0,0 +1,350 @@
+import os
+import warnings
+from pathlib import Path
+from typing import AbstractSet, Any, Callable, ClassVar, Dict, List, Mapping, Optional, Tuple, Type, Union
+
+from .config import BaseConfig, Extra
+from .fields import ModelField
+from .main import BaseModel
+from .types import JsonWrapper
+from .typing import StrPath, display_as_type, get_origin, is_union
+from .utils import deep_update, lenient_issubclass, path_type, sequence_like
+
+env_file_sentinel = str(object())
+
+SettingsSourceCallable = Callable[["BaseSettings"], Dict[str, Any]]
+DotenvType = Union[StrPath, List[StrPath], Tuple[StrPath, ...]]
+
+
+class SettingsError(ValueError):
+    pass
+
+
+class BaseSettings(BaseModel):
+    """
+    Base class for settings, allowing values to be overridden by environment variables.
+
+    This is useful in production for secrets you do not wish to save in code, it plays nicely with docker(-compose),
+    Heroku and any 12 factor app design.
+    """
+
+    def __init__(
+        __pydantic_self__,
+        _env_file: Optional[DotenvType] = env_file_sentinel,
+        _env_file_encoding: Optional[str] = None,
+        _env_nested_delimiter: Optional[str] = None,
+        _secrets_dir: Optional[StrPath] = None,
+        **values: Any,
+    ) -> None:
+        # Uses something other than `self` the first arg to allow "self" as a settable attribute
+        super().__init__(
+            **__pydantic_self__._build_values(
+                values,
+                _env_file=_env_file,
+                _env_file_encoding=_env_file_encoding,
+                _env_nested_delimiter=_env_nested_delimiter,
+                _secrets_dir=_secrets_dir,
+            )
+        )
+
+    def _build_values(
+        self,
+        init_kwargs: Dict[str, Any],
+        _env_file: Optional[DotenvType] = None,
+        _env_file_encoding: Optional[str] = None,
+        _env_nested_delimiter: Optional[str] = None,
+        _secrets_dir: Optional[StrPath] = None,
+    ) -> Dict[str, Any]:
+        # Configure built-in sources
+        init_settings = InitSettingsSource(init_kwargs=init_kwargs)
+        env_settings = EnvSettingsSource(
+            env_file=(_env_file if _env_file != env_file_sentinel else self.__config__.env_file),
+            env_file_encoding=(
+                _env_file_encoding if _env_file_encoding is not None else self.__config__.env_file_encoding
+            ),
+            env_nested_delimiter=(
+                _env_nested_delimiter if _env_nested_delimiter is not None else self.__config__.env_nested_delimiter
+            ),
+            env_prefix_len=len(self.__config__.env_prefix),
+        )
+        file_secret_settings = SecretsSettingsSource(secrets_dir=_secrets_dir or self.__config__.secrets_dir)
+        # Provide a hook to set built-in sources priority and add / remove sources
+        sources = self.__config__.customise_sources(
+            init_settings=init_settings, env_settings=env_settings, file_secret_settings=file_secret_settings
+        )
+        if sources:
+            return deep_update(*reversed([source(self) for source in sources]))
+        else:
+            # no one should mean to do this, but I think returning an empty dict is marginally preferable
+            # to an informative error and much better than a confusing error
+            return {}
+
+    class Config(BaseConfig):
+        env_prefix: str = ""
+        env_file: Optional[DotenvType] = None
+        env_file_encoding: Optional[str] = None
+        env_nested_delimiter: Optional[str] = None
+        secrets_dir: Optional[StrPath] = None
+        validate_all: bool = True
+        extra: Extra = Extra.forbid
+        arbitrary_types_allowed: bool = True
+        case_sensitive: bool = False
+
+        @classmethod
+        def prepare_field(cls, field: ModelField) -> None:
+            env_names: Union[List[str], AbstractSet[str]]
+            field_info_from_config = cls.get_field_info(field.name)
+
+            env = field_info_from_config.get("env") or field.field_info.extra.get("env")
+            if env is None:
+                if field.has_alias:
+                    warnings.warn(
+                        "aliases are no longer used by BaseSettings to define which environment variables to read. "
+                        'Instead use the "env" field setting. '
+                        "See https://pydantic-docs.helpmanual.io/usage/settings/#environment-variable-names",
+                        FutureWarning,
+                    )
+                env_names = {cls.env_prefix + field.name}
+            elif isinstance(env, str):
+                env_names = {env}
+            elif isinstance(env, (set, frozenset)):
+                env_names = env
+            elif sequence_like(env):
+                env_names = list(env)
+            else:
+                raise TypeError(f"invalid field env: {env!r} ({display_as_type(env)}); should be string, list or set")
+
+            if not cls.case_sensitive:
+                env_names = env_names.__class__(n.lower() for n in env_names)
+            field.field_info.extra["env_names"] = env_names
+
+        @classmethod
+        def customise_sources(
+            cls,
+            init_settings: SettingsSourceCallable,
+            env_settings: SettingsSourceCallable,
+            file_secret_settings: SettingsSourceCallable,
+        ) -> Tuple[SettingsSourceCallable, ...]:
+            return init_settings, env_settings, file_secret_settings
+
+        @classmethod
+        def parse_env_var(cls, field_name: str, raw_val: str) -> Any:
+            return cls.json_loads(raw_val)
+
+    # populated by the metaclass using the Config class defined above, annotated here to help IDEs only
+    __config__: ClassVar[Type[Config]]
+
+
+class InitSettingsSource:
+    __slots__ = ("init_kwargs",)
+
+    def __init__(self, init_kwargs: Dict[str, Any]):
+        self.init_kwargs = init_kwargs
+
+    def __call__(self, settings: BaseSettings) -> Dict[str, Any]:
+        return self.init_kwargs
+
+    def __repr__(self) -> str:
+        return f"InitSettingsSource(init_kwargs={self.init_kwargs!r})"
+
+
+class EnvSettingsSource:
+    __slots__ = ("env_file", "env_file_encoding", "env_nested_delimiter", "env_prefix_len")
+
+    def __init__(
+        self,
+        env_file: Optional[DotenvType],
+        env_file_encoding: Optional[str],
+        env_nested_delimiter: Optional[str] = None,
+        env_prefix_len: int = 0,
+    ):
+        self.env_file: Optional[DotenvType] = env_file
+        self.env_file_encoding: Optional[str] = env_file_encoding
+        self.env_nested_delimiter: Optional[str] = env_nested_delimiter
+        self.env_prefix_len: int = env_prefix_len
+
+    def __call__(self, settings: BaseSettings) -> Dict[str, Any]:  # noqa C901
+        """
+        Build environment variables suitable for passing to the Model.
+        """
+        d: Dict[str, Any] = {}
+
+        if settings.__config__.case_sensitive:
+            env_vars: Mapping[str, Optional[str]] = os.environ
+        else:
+            env_vars = {k.lower(): v for k, v in os.environ.items()}
+
+        dotenv_vars = self._read_env_files(settings.__config__.case_sensitive)
+        if dotenv_vars:
+            env_vars = {**dotenv_vars, **env_vars}
+
+        for field in settings.__fields__.values():
+            env_val: Optional[str] = None
+            for env_name in field.field_info.extra["env_names"]:
+                env_val = env_vars.get(env_name)
+                if env_val is not None:
+                    break
+
+            is_complex, allow_parse_failure = self.field_is_complex(field)
+            if is_complex:
+                if env_val is None:
+                    # field is complex but no value found so far, try explode_env_vars
+                    env_val_built = self.explode_env_vars(field, env_vars)
+                    if env_val_built:
+                        d[field.alias] = env_val_built
+                else:
+                    # field is complex and there's a value, decode that as JSON, then add explode_env_vars
+                    try:
+                        env_val = settings.__config__.parse_env_var(field.name, env_val)
+                    except ValueError as e:
+                        if not allow_parse_failure:
+                            raise SettingsError(f'error parsing env var "{env_name}"') from e
+
+                    if isinstance(env_val, dict):
+                        d[field.alias] = deep_update(env_val, self.explode_env_vars(field, env_vars))
+                    else:
+                        d[field.alias] = env_val
+            elif env_val is not None:
+                # simplest case, field is not complex, we only need to add the value if it was found
+                d[field.alias] = env_val
+
+        return d
+
+    def _read_env_files(self, case_sensitive: bool) -> Dict[str, Optional[str]]:
+        env_files = self.env_file
+        if env_files is None:
+            return {}
+
+        if isinstance(env_files, (str, os.PathLike)):
+            env_files = [env_files]
+
+        dotenv_vars = {}
+        for env_file in env_files:
+            env_path = Path(env_file).expanduser()
+            if env_path.is_file():
+                dotenv_vars.update(
+                    read_env_file(env_path, encoding=self.env_file_encoding, case_sensitive=case_sensitive)
+                )
+
+        return dotenv_vars
+
+    def field_is_complex(self, field: ModelField) -> Tuple[bool, bool]:
+        """
+        Find out if a field is complex, and if so whether JSON errors should be ignored
+        """
+        if lenient_issubclass(field.annotation, JsonWrapper):
+            return False, False
+
+        if field.is_complex():
+            allow_parse_failure = False
+        elif is_union(get_origin(field.type_)) and field.sub_fields and any(f.is_complex() for f in field.sub_fields):
+            allow_parse_failure = True
+        else:
+            return False, False
+
+        return True, allow_parse_failure
+
+    def explode_env_vars(self, field: ModelField, env_vars: Mapping[str, Optional[str]]) -> Dict[str, Any]:
+        """
+        Process env_vars and extract the values of keys containing env_nested_delimiter into nested dictionaries.
+
+        This is applied to a single field, hence filtering by env_var prefix.
+        """
+        prefixes = [f"{env_name}{self.env_nested_delimiter}" for env_name in field.field_info.extra["env_names"]]
+        result: Dict[str, Any] = {}
+        for env_name, env_val in env_vars.items():
+            if not any(env_name.startswith(prefix) for prefix in prefixes):
+                continue
+            # we remove the prefix before splitting in case the prefix has characters in common with the delimiter
+            env_name_without_prefix = env_name[self.env_prefix_len :]
+            _, *keys, last_key = env_name_without_prefix.split(self.env_nested_delimiter)
+            env_var = result
+            for key in keys:
+                env_var = env_var.setdefault(key, {})
+            env_var[last_key] = env_val
+
+        return result
+
+    def __repr__(self) -> str:
+        return (
+            f"EnvSettingsSource(env_file={self.env_file!r}, env_file_encoding={self.env_file_encoding!r}, "
+            f"env_nested_delimiter={self.env_nested_delimiter!r})"
+        )
+
+
+class SecretsSettingsSource:
+    __slots__ = ("secrets_dir",)
+
+    def __init__(self, secrets_dir: Optional[StrPath]):
+        self.secrets_dir: Optional[StrPath] = secrets_dir
+
+    def __call__(self, settings: BaseSettings) -> Dict[str, Any]:
+        """
+        Build fields from "secrets" files.
+        """
+        secrets: Dict[str, Optional[str]] = {}
+
+        if self.secrets_dir is None:
+            return secrets
+
+        secrets_path = Path(self.secrets_dir).expanduser()
+
+        if not secrets_path.exists():
+            warnings.warn(f'directory "{secrets_path}" does not exist')
+            return secrets
+
+        if not secrets_path.is_dir():
+            raise SettingsError(f"secrets_dir must reference a directory, not a {path_type(secrets_path)}")
+
+        for field in settings.__fields__.values():
+            for env_name in field.field_info.extra["env_names"]:
+                path = find_case_path(secrets_path, env_name, settings.__config__.case_sensitive)
+                if not path:
+                    # path does not exist, we currently don't return a warning for this
+                    continue
+
+                if path.is_file():
+                    secret_value = path.read_text().strip()
+                    if field.is_complex():
+                        try:
+                            secret_value = settings.__config__.parse_env_var(field.name, secret_value)
+                        except ValueError as e:
+                            raise SettingsError(f'error parsing env var "{env_name}"') from e
+
+                    secrets[field.alias] = secret_value
+                else:
+                    warnings.warn(
+                        f'attempted to load secret file "{path}" but found a {path_type(path)} instead.',
+                        stacklevel=4,
+                    )
+        return secrets
+
+    def __repr__(self) -> str:
+        return f"SecretsSettingsSource(secrets_dir={self.secrets_dir!r})"
+
+
+def read_env_file(
+    file_path: StrPath, *, encoding: str = None, case_sensitive: bool = False
+) -> Dict[str, Optional[str]]:
+    try:
+        from dotenv import dotenv_values
+    except ImportError as e:
+        raise ImportError("python-dotenv is not installed, run `pip install pydantic[dotenv]`") from e
+
+    file_vars: Dict[str, Optional[str]] = dotenv_values(file_path, encoding=encoding or "utf8")
+    if not case_sensitive:
+        return {k.lower(): v for k, v in file_vars.items()}
+    else:
+        return file_vars
+
+
+def find_case_path(dir_path: Path, file_name: str, case_sensitive: bool) -> Optional[Path]:
+    """
+    Find a file within path's directory matching filename, optionally ignoring case.
+    """
+    for f in dir_path.iterdir():
+        if f.name == file_name:
+            return f
+        elif not case_sensitive and f.name.lower() == file_name.lower():
+            return f
+    return None
diff --git a/vendor/pydantic/error_wrappers.py b/vendor/pydantic/error_wrappers.py
new file mode 100644
index 0000000..c2eb8ef
--- /dev/null
+++ b/vendor/pydantic/error_wrappers.py
@@ -0,0 +1,161 @@
+import json
+from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Sequence, Tuple, Type, Union
+
+from .json import pydantic_encoder
+from .utils import Representation
+
+if TYPE_CHECKING:
+    from typing_extensions import TypedDict
+
+    from .config import BaseConfig
+    from .types import ModelOrDc
+    from .typing import ReprArgs
+
+    Loc = Tuple[Union[int, str], ...]
+
+    class _ErrorDictRequired(TypedDict):
+        loc: Loc
+        msg: str
+        type: str
+
+    class ErrorDict(_ErrorDictRequired, total=False):
+        ctx: Dict[str, Any]
+
+
+__all__ = "ErrorWrapper", "ValidationError"
+
+
+class ErrorWrapper(Representation):
+    __slots__ = "exc", "_loc"
+
+    def __init__(self, exc: Exception, loc: Union[str, "Loc"]) -> None:
+        self.exc = exc
+        self._loc = loc
+
+    def loc_tuple(self) -> "Loc":
+        if isinstance(self._loc, tuple):
+            return self._loc
+        else:
+            return (self._loc,)
+
+    def __repr_args__(self) -> "ReprArgs":
+        return [("exc", self.exc), ("loc", self.loc_tuple())]
+
+
+# ErrorList is something like Union[List[Union[List[ErrorWrapper], ErrorWrapper]], ErrorWrapper]
+# but recursive, therefore just use:
+ErrorList = Union[Sequence[Any], ErrorWrapper]
+
+
+class ValidationError(Representation, ValueError):
+    __slots__ = "raw_errors", "model", "_error_cache"
+
+    def __init__(self, errors: Sequence[ErrorList], model: "ModelOrDc") -> None:
+        self.raw_errors = errors
+        self.model = model
+        self._error_cache: Optional[List["ErrorDict"]] = None
+
+    def errors(self) -> List["ErrorDict"]:
+        if self._error_cache is None:
+            try:
+                config = self.model.__config__  # type: ignore
+            except AttributeError:
+                config = self.model.__pydantic_model__.__config__  # type: ignore
+            self._error_cache = list(flatten_errors(self.raw_errors, config))
+        return self._error_cache
+
+    def json(self, *, indent: Union[None, int, str] = 2) -> str:
+        return json.dumps(self.errors(), indent=indent, default=pydantic_encoder)
+
+    def __str__(self) -> str:
+        errors = self.errors()
+        no_errors = len(errors)
+        return (
+            f'{no_errors} validation error{"" if no_errors == 1 else "s"} for {self.model.__name__}\n'
+            f"{display_errors(errors)}"
+        )
+
+    def __repr_args__(self) -> "ReprArgs":
+        return [("model", self.model.__name__), ("errors", self.errors())]
+
+
+def display_errors(errors: List["ErrorDict"]) -> str:
+    return "\n".join(f'{_display_error_loc(e)}\n  {e["msg"]} ({_display_error_type_and_ctx(e)})' for e in errors)
+
+
+def _display_error_loc(error: "ErrorDict") -> str:
+    return " -> ".join(str(e) for e in error["loc"])
+
+
+def _display_error_type_and_ctx(error: "ErrorDict") -> str:
+    t = "type=" + error["type"]
+    ctx = error.get("ctx")
+    if ctx:
+        return t + "".join(f"; {k}={v}" for k, v in ctx.items())
+    else:
+        return t
+
+
+def flatten_errors(
+    errors: Sequence[Any], config: Type["BaseConfig"], loc: Optional["Loc"] = None
+) -> Generator["ErrorDict", None, None]:
+    for error in errors:
+        if isinstance(error, ErrorWrapper):
+            if loc:
+                error_loc = loc + error.loc_tuple()
+            else:
+                error_loc = error.loc_tuple()
+
+            if isinstance(error.exc, ValidationError):
+                yield from flatten_errors(error.exc.raw_errors, config, error_loc)
+            else:
+                yield error_dict(error.exc, config, error_loc)
+        elif isinstance(error, list):
+            yield from flatten_errors(error, config, loc=loc)
+        else:
+            raise RuntimeError(f"Unknown error object: {error}")
+
+
+def error_dict(exc: Exception, config: Type["BaseConfig"], loc: "Loc") -> "ErrorDict":
+    type_ = get_exc_type(exc.__class__)
+    msg_template = config.error_msg_templates.get(type_) or getattr(exc, "msg_template", None)
+    ctx = exc.__dict__
+    if msg_template:
+        msg = msg_template.format(**ctx)
+    else:
+        msg = str(exc)
+
+    d: "ErrorDict" = {"loc": loc, "msg": msg, "type": type_}
+
+    if ctx:
+        d["ctx"] = ctx
+
+    return d
+
+
+_EXC_TYPE_CACHE: Dict[Type[Exception], str] = {}
+
+
+def get_exc_type(cls: Type[Exception]) -> str:
+    # slightly more efficient than using lru_cache since we don't need to worry about the cache filling up
+    try:
+        return _EXC_TYPE_CACHE[cls]
+    except KeyError:
+        r = _get_exc_type(cls)
+        _EXC_TYPE_CACHE[cls] = r
+        return r
+
+
+def _get_exc_type(cls: Type[Exception]) -> str:
+    if issubclass(cls, AssertionError):
+        return "assertion_error"
+
+    base_name = "type_error" if issubclass(cls, TypeError) else "value_error"
+    if cls in (TypeError, ValueError):
+        # just TypeError or ValueError, no extra code
+        return base_name
+
+    # if it's not a TypeError or ValueError, we just take the lowercase of the exception name
+    # no chaining or snake case logic, use "code" for more complex error types.
+    code = getattr(cls, "code", None) or cls.__name__.replace("Error", "").lower()
+    return base_name + "." + code
diff --git a/vendor/pydantic/errors.py b/vendor/pydantic/errors.py
new file mode 100644
index 0000000..b9c9ced
--- /dev/null
+++ b/vendor/pydantic/errors.py
@@ -0,0 +1,646 @@
+from decimal import Decimal
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Callable, Sequence, Set, Tuple, Type, Union
+
+from .typing import display_as_type
+
+if TYPE_CHECKING:
+    from .typing import DictStrAny
+
+# explicitly state exports to avoid "from .errors import *" also importing Decimal, Path etc.
+__all__ = (
+    "PydanticTypeError",
+    "PydanticValueError",
+    "ConfigError",
+    "MissingError",
+    "ExtraError",
+    "NoneIsNotAllowedError",
+    "NoneIsAllowedError",
+    "WrongConstantError",
+    "NotNoneError",
+    "BoolError",
+    "BytesError",
+    "DictError",
+    "EmailError",
+    "UrlError",
+    "UrlSchemeError",
+    "UrlSchemePermittedError",
+    "UrlUserInfoError",
+    "UrlHostError",
+    "UrlHostTldError",
+    "UrlPortError",
+    "UrlExtraError",
+    "EnumError",
+    "IntEnumError",
+    "EnumMemberError",
+    "IntegerError",
+    "FloatError",
+    "PathError",
+    "PathNotExistsError",
+    "PathNotAFileError",
+    "PathNotADirectoryError",
+    "PyObjectError",
+    "SequenceError",
+    "ListError",
+    "SetError",
+    "FrozenSetError",
+    "TupleError",
+    "TupleLengthError",
+    "ListMinLengthError",
+    "ListMaxLengthError",
+    "ListUniqueItemsError",
+    "SetMinLengthError",
+    "SetMaxLengthError",
+    "FrozenSetMinLengthError",
+    "FrozenSetMaxLengthError",
+    "AnyStrMinLengthError",
+    "AnyStrMaxLengthError",
+    "StrError",
+    "StrRegexError",
+    "NumberNotGtError",
+    "NumberNotGeError",
+    "NumberNotLtError",
+    "NumberNotLeError",
+    "NumberNotMultipleError",
+    "DecimalError",
+    "DecimalIsNotFiniteError",
+    "DecimalMaxDigitsError",
+    "DecimalMaxPlacesError",
+    "DecimalWholeDigitsError",
+    "DateTimeError",
+    "DateError",
+    "DateNotInThePastError",
+    "DateNotInTheFutureError",
+    "TimeError",
+    "DurationError",
+    "HashableError",
+    "UUIDError",
+    "UUIDVersionError",
+    "ArbitraryTypeError",
+    "ClassError",
+    "SubclassError",
+    "JsonError",
+    "JsonTypeError",
+    "PatternError",
+    "DataclassTypeError",
+    "CallableError",
+    "IPvAnyAddressError",
+    "IPvAnyInterfaceError",
+    "IPvAnyNetworkError",
+    "IPv4AddressError",
+    "IPv6AddressError",
+    "IPv4NetworkError",
+    "IPv6NetworkError",
+    "IPv4InterfaceError",
+    "IPv6InterfaceError",
+    "ColorError",
+    "StrictBoolError",
+    "NotDigitError",
+    "LuhnValidationError",
+    "InvalidLengthForBrand",
+    "InvalidByteSize",
+    "InvalidByteSizeUnit",
+    "MissingDiscriminator",
+    "InvalidDiscriminator",
+)
+
+
+def cls_kwargs(cls: Type["PydanticErrorMixin"], ctx: "DictStrAny") -> "PydanticErrorMixin":
+    """
+    For built-in exceptions like ValueError or TypeError, we need to implement
+    __reduce__ to override the default behaviour (instead of __getstate__/__setstate__)
+    By default pickle protocol 2 calls `cls.__new__(cls, *args)`.
+    Since we only use kwargs, we need a little constructor to change that.
+    Note: the callable can't be a lambda as pickle looks in the namespace to find it
+    """
+    return cls(**ctx)
+
+
+class PydanticErrorMixin:
+    code: str
+    msg_template: str
+
+    def __init__(self, **ctx: Any) -> None:
+        self.__dict__ = ctx
+
+    def __str__(self) -> str:
+        return self.msg_template.format(**self.__dict__)
+
+    def __reduce__(self) -> Tuple[Callable[..., "PydanticErrorMixin"], Tuple[Type["PydanticErrorMixin"], "DictStrAny"]]:
+        return cls_kwargs, (self.__class__, self.__dict__)
+
+
+class PydanticTypeError(PydanticErrorMixin, TypeError):
+    pass
+
+
+class PydanticValueError(PydanticErrorMixin, ValueError):
+    pass
+
+
+class ConfigError(RuntimeError):
+    pass
+
+
+class MissingError(PydanticValueError):
+    msg_template = "field required"
+
+
+class ExtraError(PydanticValueError):
+    msg_template = "extra fields not permitted"
+
+
+class NoneIsNotAllowedError(PydanticTypeError):
+    code = "none.not_allowed"
+    msg_template = "none is not an allowed value"
+
+
+class NoneIsAllowedError(PydanticTypeError):
+    code = "none.allowed"
+    msg_template = "value is not none"
+
+
+class WrongConstantError(PydanticValueError):
+    code = "const"
+
+    def __str__(self) -> str:
+        permitted = ", ".join(repr(v) for v in self.permitted)  # type: ignore
+        return f"unexpected value; permitted: {permitted}"
+
+
+class NotNoneError(PydanticTypeError):
+    code = "not_none"
+    msg_template = "value is not None"
+
+
+class BoolError(PydanticTypeError):
+    msg_template = "value could not be parsed to a boolean"
+
+
+class BytesError(PydanticTypeError):
+    msg_template = "byte type expected"
+
+
+class DictError(PydanticTypeError):
+    msg_template = "value is not a valid dict"
+
+
+class EmailError(PydanticValueError):
+    msg_template = "value is not a valid email address"
+
+
+class UrlError(PydanticValueError):
+    code = "url"
+
+
+class UrlSchemeError(UrlError):
+    code = "url.scheme"
+    msg_template = "invalid or missing URL scheme"
+
+
+class UrlSchemePermittedError(UrlError):
+    code = "url.scheme"
+    msg_template = "URL scheme not permitted"
+
+    def __init__(self, allowed_schemes: Set[str]):
+        super().__init__(allowed_schemes=allowed_schemes)
+
+
+class UrlUserInfoError(UrlError):
+    code = "url.userinfo"
+    msg_template = "userinfo required in URL but missing"
+
+
+class UrlHostError(UrlError):
+    code = "url.host"
+    msg_template = "URL host invalid"
+
+
+class UrlHostTldError(UrlError):
+    code = "url.host"
+    msg_template = "URL host invalid, top level domain required"
+
+
+class UrlPortError(UrlError):
+    code = "url.port"
+    msg_template = "URL port invalid, port cannot exceed 65535"
+
+
+class UrlExtraError(UrlError):
+    code = "url.extra"
+    msg_template = "URL invalid, extra characters found after valid URL: {extra!r}"
+
+
+class EnumMemberError(PydanticTypeError):
+    code = "enum"
+
+    def __str__(self) -> str:
+        permitted = ", ".join(repr(v.value) for v in self.enum_values)  # type: ignore
+        return f"value is not a valid enumeration member; permitted: {permitted}"
+
+
+class IntegerError(PydanticTypeError):
+    msg_template = "value is not a valid integer"
+
+
+class FloatError(PydanticTypeError):
+    msg_template = "value is not a valid float"
+
+
+class PathError(PydanticTypeError):
+    msg_template = "value is not a valid path"
+
+
+class _PathValueError(PydanticValueError):
+    def __init__(self, *, path: Path) -> None:
+        super().__init__(path=str(path))
+
+
+class PathNotExistsError(_PathValueError):
+    code = "path.not_exists"
+    msg_template = 'file or directory at path "{path}" does not exist'
+
+
+class PathNotAFileError(_PathValueError):
+    code = "path.not_a_file"
+    msg_template = 'path "{path}" does not point to a file'
+
+
+class PathNotADirectoryError(_PathValueError):
+    code = "path.not_a_directory"
+    msg_template = 'path "{path}" does not point to a directory'
+
+
+class PyObjectError(PydanticTypeError):
+    msg_template = "ensure this value contains valid import path or valid callable: {error_message}"
+
+
+class SequenceError(PydanticTypeError):
+    msg_template = "value is not a valid sequence"
+
+
+class IterableError(PydanticTypeError):
+    msg_template = "value is not a valid iterable"
+
+
+class ListError(PydanticTypeError):
+    msg_template = "value is not a valid list"
+
+
+class SetError(PydanticTypeError):
+    msg_template = "value is not a valid set"
+
+
+class FrozenSetError(PydanticTypeError):
+    msg_template = "value is not a valid frozenset"
+
+
+class DequeError(PydanticTypeError):
+    msg_template = "value is not a valid deque"
+
+
+class TupleError(PydanticTypeError):
+    msg_template = "value is not a valid tuple"
+
+
+class TupleLengthError(PydanticValueError):
+    code = "tuple.length"
+    msg_template = "wrong tuple length {actual_length}, expected {expected_length}"
+
+    def __init__(self, *, actual_length: int, expected_length: int) -> None:
+        super().__init__(actual_length=actual_length, expected_length=expected_length)
+
+
+class ListMinLengthError(PydanticValueError):
+    code = "list.min_items"
+    msg_template = "ensure this value has at least {limit_value} items"
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class ListMaxLengthError(PydanticValueError):
+    code = "list.max_items"
+    msg_template = "ensure this value has at most {limit_value} items"
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class ListUniqueItemsError(PydanticValueError):
+    code = "list.unique_items"
+    msg_template = "the list has duplicated items"
+
+
+class SetMinLengthError(PydanticValueError):
+    code = "set.min_items"
+    msg_template = "ensure this value has at least {limit_value} items"
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class SetMaxLengthError(PydanticValueError):
+    code = "set.max_items"
+    msg_template = "ensure this value has at most {limit_value} items"
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class FrozenSetMinLengthError(PydanticValueError):
+    code = "frozenset.min_items"
+    msg_template = "ensure this value has at least {limit_value} items"
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class FrozenSetMaxLengthError(PydanticValueError):
+    code = "frozenset.max_items"
+    msg_template = "ensure this value has at most {limit_value} items"
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class AnyStrMinLengthError(PydanticValueError):
+    code = "any_str.min_length"
+    msg_template = "ensure this value has at least {limit_value} characters"
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class AnyStrMaxLengthError(PydanticValueError):
+    code = "any_str.max_length"
+    msg_template = "ensure this value has at most {limit_value} characters"
+
+    def __init__(self, *, limit_value: int) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class StrError(PydanticTypeError):
+    msg_template = "str type expected"
+
+
+class StrRegexError(PydanticValueError):
+    code = "str.regex"
+    msg_template = 'string does not match regex "{pattern}"'
+
+    def __init__(self, *, pattern: str) -> None:
+        super().__init__(pattern=pattern)
+
+
+class _NumberBoundError(PydanticValueError):
+    def __init__(self, *, limit_value: Union[int, float, Decimal]) -> None:
+        super().__init__(limit_value=limit_value)
+
+
+class NumberNotGtError(_NumberBoundError):
+    code = "number.not_gt"
+    msg_template = "ensure this value is greater than {limit_value}"
+
+
+class NumberNotGeError(_NumberBoundError):
+    code = "number.not_ge"
+    msg_template = "ensure this value is greater than or equal to {limit_value}"
+
+
+class NumberNotLtError(_NumberBoundError):
+    code = "number.not_lt"
+    msg_template = "ensure this value is less than {limit_value}"
+
+
+class NumberNotLeError(_NumberBoundError):
+    code = "number.not_le"
+    msg_template = "ensure this value is less than or equal to {limit_value}"
+
+
+class NumberNotFiniteError(PydanticValueError):
+    code = "number.not_finite_number"
+    msg_template = "ensure this value is a finite number"
+
+
+class NumberNotMultipleError(PydanticValueError):
+    code = "number.not_multiple"
+    msg_template = "ensure this value is a multiple of {multiple_of}"
+
+    def __init__(self, *, multiple_of: Union[int, float, Decimal]) -> None:
+        super().__init__(multiple_of=multiple_of)
+
+
+class DecimalError(PydanticTypeError):
+    msg_template = "value is not a valid decimal"
+
+
+class DecimalIsNotFiniteError(PydanticValueError):
+    code = "decimal.not_finite"
+    msg_template = "value is not a valid decimal"
+
+
+class DecimalMaxDigitsError(PydanticValueError):
+    code = "decimal.max_digits"
+    msg_template = "ensure that there are no more than {max_digits} digits in total"
+
+    def __init__(self, *, max_digits: int) -> None:
+        super().__init__(max_digits=max_digits)
+
+
+class DecimalMaxPlacesError(PydanticValueError):
+    code = "decimal.max_places"
+    msg_template = "ensure that there are no more than {decimal_places} decimal places"
+
+    def __init__(self, *, decimal_places: int) -> None:
+        super().__init__(decimal_places=decimal_places)
+
+
+class DecimalWholeDigitsError(PydanticValueError):
+    code = "decimal.whole_digits"
+    msg_template = "ensure that there are no more than {whole_digits} digits before the decimal point"
+
+    def __init__(self, *, whole_digits: int) -> None:
+        super().__init__(whole_digits=whole_digits)
+
+
+class DateTimeError(PydanticValueError):
+    msg_template = "invalid datetime format"
+
+
+class DateError(PydanticValueError):
+    msg_template = "invalid date format"
+
+
+class DateNotInThePastError(PydanticValueError):
+    code = "date.not_in_the_past"
+    msg_template = "date is not in the past"
+
+
+class DateNotInTheFutureError(PydanticValueError):
+    code = "date.not_in_the_future"
+    msg_template = "date is not in the future"
+
+
+class TimeError(PydanticValueError):
+    msg_template = "invalid time format"
+
+
+class DurationError(PydanticValueError):
+    msg_template = "invalid duration format"
+
+
+class HashableError(PydanticTypeError):
+    msg_template = "value is not a valid hashable"
+
+
+class UUIDError(PydanticTypeError):
+    msg_template = "value is not a valid uuid"
+
+
+class UUIDVersionError(PydanticValueError):
+    code = "uuid.version"
+    msg_template = "uuid version {required_version} expected"
+
+    def __init__(self, *, required_version: int) -> None:
+        super().__init__(required_version=required_version)
+
+
+class ArbitraryTypeError(PydanticTypeError):
+    code = "arbitrary_type"
+    msg_template = "instance of {expected_arbitrary_type} expected"
+
+    def __init__(self, *, expected_arbitrary_type: Type[Any]) -> None:
+        super().__init__(expected_arbitrary_type=display_as_type(expected_arbitrary_type))
+
+
+class ClassError(PydanticTypeError):
+    code = "class"
+    msg_template = "a class is expected"
+
+
+class SubclassError(PydanticTypeError):
+    code = "subclass"
+    msg_template = "subclass of {expected_class} expected"
+
+    def __init__(self, *, expected_class: Type[Any]) -> None:
+        super().__init__(expected_class=display_as_type(expected_class))
+
+
+class JsonError(PydanticValueError):
+    msg_template = "Invalid JSON"
+
+
+class JsonTypeError(PydanticTypeError):
+    code = "json"
+    msg_template = "JSON object must be str, bytes or bytearray"
+
+
+class PatternError(PydanticValueError):
+    code = "regex_pattern"
+    msg_template = "Invalid regular expression"
+
+
+class DataclassTypeError(PydanticTypeError):
+    code = "dataclass"
+    msg_template = "instance of {class_name}, tuple or dict expected"
+
+
+class CallableError(PydanticTypeError):
+    msg_template = "{value} is not callable"
+
+
+class EnumError(PydanticTypeError):
+    code = "enum_instance"
+    msg_template = "{value} is not a valid Enum instance"
+
+
+class IntEnumError(PydanticTypeError):
+    code = "int_enum_instance"
+    msg_template = "{value} is not a valid IntEnum instance"
+
+
+class IPvAnyAddressError(PydanticValueError):
+    msg_template = "value is not a valid IPv4 or IPv6 address"
+
+
+class IPvAnyInterfaceError(PydanticValueError):
+    msg_template = "value is not a valid IPv4 or IPv6 interface"
+
+
+class IPvAnyNetworkError(PydanticValueError):
+    msg_template = "value is not a valid IPv4 or IPv6 network"
+
+
+class IPv4AddressError(PydanticValueError):
+    msg_template = "value is not a valid IPv4 address"
+
+
+class IPv6AddressError(PydanticValueError):
+    msg_template = "value is not a valid IPv6 address"
+
+
+class IPv4NetworkError(PydanticValueError):
+    msg_template = "value is not a valid IPv4 network"
+
+
+class IPv6NetworkError(PydanticValueError):
+    msg_template = "value is not a valid IPv6 network"
+
+
+class IPv4InterfaceError(PydanticValueError):
+    msg_template = "value is not a valid IPv4 interface"
+
+
+class IPv6InterfaceError(PydanticValueError):
+    msg_template = "value is not a valid IPv6 interface"
+
+
+class ColorError(PydanticValueError):
+    msg_template = "value is not a valid color: {reason}"
+
+
+class StrictBoolError(PydanticValueError):
+    msg_template = "value is not a valid boolean"
+
+
+class NotDigitError(PydanticValueError):
+    code = "payment_card_number.digits"
+    msg_template = "card number is not all digits"
+
+
+class LuhnValidationError(PydanticValueError):
+    code = "payment_card_number.luhn_check"
+    msg_template = "card number is not luhn valid"
+
+
+class InvalidLengthForBrand(PydanticValueError):
+    code = "payment_card_number.invalid_length_for_brand"
+    msg_template = "Length for a {brand} card must be {required_length}"
+
+
+class InvalidByteSize(PydanticValueError):
+    msg_template = "could not parse value and unit from byte string"
+
+
+class InvalidByteSizeUnit(PydanticValueError):
+    msg_template = "could not interpret byte unit: {unit}"
+
+
+class MissingDiscriminator(PydanticValueError):
+    code = "discriminated_union.missing_discriminator"
+    msg_template = "Discriminator {discriminator_key!r} is missing in value"
+
+
+class InvalidDiscriminator(PydanticValueError):
+    code = "discriminated_union.invalid_discriminator"
+    msg_template = (
+        "No match for discriminator {discriminator_key!r} and value {discriminator_value!r} "
+        "(allowed values: {allowed_values})"
+    )
+
+    def __init__(self, *, discriminator_key: str, discriminator_value: Any, allowed_values: Sequence[Any]) -> None:
+        super().__init__(
+            discriminator_key=discriminator_key,
+            discriminator_value=discriminator_value,
+            allowed_values=", ".join(map(repr, allowed_values)),
+        )
diff --git a/vendor/pydantic/fields.py b/vendor/pydantic/fields.py
new file mode 100644
index 0000000..85b7054
--- /dev/null
+++ b/vendor/pydantic/fields.py
@@ -0,0 +1,1256 @@
+import copy
+import re
+from collections import Counter as CollectionCounter
+from collections import defaultdict, deque
+from collections.abc import Callable
+from collections.abc import Hashable as CollectionsHashable
+from collections.abc import Iterable as CollectionsIterable
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Counter,
+    DefaultDict,
+    Deque,
+    Dict,
+    ForwardRef,
+    FrozenSet,
+    Generator,
+    Iterable,
+    Iterator,
+    List,
+    Mapping,
+    Optional,
+    Pattern,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+)
+
+from typing_extensions import Annotated, Final
+
+from . import errors as errors_
+from .class_validators import Validator, make_generic_validator, prep_validators
+from .error_wrappers import ErrorWrapper
+from .errors import ConfigError, InvalidDiscriminator, MissingDiscriminator, NoneIsNotAllowedError
+from .types import Json, JsonWrapper
+from .typing import (
+    NoArgAnyCallable,
+    convert_generics,
+    display_as_type,
+    get_args,
+    get_origin,
+    is_finalvar,
+    is_literal_type,
+    is_new_type,
+    is_none_type,
+    is_typeddict,
+    is_typeddict_special,
+    is_union,
+    new_type_supertype,
+)
+from .utils import (
+    PyObjectStr,
+    Representation,
+    ValueItems,
+    get_discriminator_alias_and_values,
+    get_unique_discriminator_alias,
+    lenient_isinstance,
+    lenient_issubclass,
+    sequence_like,
+    smart_deepcopy,
+)
+from .validators import constant_validator, dict_validator, find_validators, validate_json
+
+Required: Any = Ellipsis
+
+T = TypeVar("T")
+
+
+class UndefinedType:
+    def __repr__(self) -> str:
+        return "PydanticUndefined"
+
+    def __copy__(self: T) -> T:
+        return self
+
+    def __reduce__(self) -> str:
+        return "Undefined"
+
+    def __deepcopy__(self: T, _: Any) -> T:
+        return self
+
+
+Undefined = UndefinedType()
+
+if TYPE_CHECKING:
+    from .class_validators import ValidatorsList
+    from .config import BaseConfig
+    from .error_wrappers import ErrorList
+    from .types import ModelOrDc
+    from .typing import AbstractSetIntStr, MappingIntStrAny, ReprArgs
+
+    ValidateReturn = Tuple[Optional[Any], Optional[ErrorList]]
+    LocStr = Union[Tuple[Union[int, str], ...], str]
+    BoolUndefined = Union[bool, UndefinedType]
+
+
+class FieldInfo(Representation):
+    """
+    Captures extra information about a field.
+    """
+
+    __slots__ = (
+        "default",
+        "default_factory",
+        "alias",
+        "alias_priority",
+        "title",
+        "description",
+        "exclude",
+        "include",
+        "const",
+        "gt",
+        "ge",
+        "lt",
+        "le",
+        "multiple_of",
+        "allow_inf_nan",
+        "max_digits",
+        "decimal_places",
+        "min_items",
+        "max_items",
+        "unique_items",
+        "min_length",
+        "max_length",
+        "allow_mutation",
+        "repr",
+        "regex",
+        "discriminator",
+        "extra",
+    )
+
+    # field constraints with the default value, it's also used in update_from_config below
+    __field_constraints__ = {
+        "min_length": None,
+        "max_length": None,
+        "regex": None,
+        "gt": None,
+        "lt": None,
+        "ge": None,
+        "le": None,
+        "multiple_of": None,
+        "allow_inf_nan": None,
+        "max_digits": None,
+        "decimal_places": None,
+        "min_items": None,
+        "max_items": None,
+        "unique_items": None,
+        "allow_mutation": True,
+    }
+
+    def __init__(self, default: Any = Undefined, **kwargs: Any) -> None:
+        self.default = default
+        self.default_factory = kwargs.pop("default_factory", None)
+        self.alias = kwargs.pop("alias", None)
+        self.alias_priority = kwargs.pop("alias_priority", 2 if self.alias is not None else None)
+        self.title = kwargs.pop("title", None)
+        self.description = kwargs.pop("description", None)
+        self.exclude = kwargs.pop("exclude", None)
+        self.include = kwargs.pop("include", None)
+        self.const = kwargs.pop("const", None)
+        self.gt = kwargs.pop("gt", None)
+        self.ge = kwargs.pop("ge", None)
+        self.lt = kwargs.pop("lt", None)
+        self.le = kwargs.pop("le", None)
+        self.multiple_of = kwargs.pop("multiple_of", None)
+        self.allow_inf_nan = kwargs.pop("allow_inf_nan", None)
+        self.max_digits = kwargs.pop("max_digits", None)
+        self.decimal_places = kwargs.pop("decimal_places", None)
+        self.min_items = kwargs.pop("min_items", None)
+        self.max_items = kwargs.pop("max_items", None)
+        self.unique_items = kwargs.pop("unique_items", None)
+        self.min_length = kwargs.pop("min_length", None)
+        self.max_length = kwargs.pop("max_length", None)
+        self.allow_mutation = kwargs.pop("allow_mutation", True)
+        self.regex = kwargs.pop("regex", None)
+        self.discriminator = kwargs.pop("discriminator", None)
+        self.repr = kwargs.pop("repr", True)
+        self.extra = kwargs
+
+    def __repr_args__(self) -> "ReprArgs":
+        field_defaults_to_hide: Dict[str, Any] = {
+            "repr": True,
+            **self.__field_constraints__,
+        }
+
+        attrs = ((s, getattr(self, s)) for s in self.__slots__)
+        return [(a, v) for a, v in attrs if v != field_defaults_to_hide.get(a, None)]
+
+    def get_constraints(self) -> Set[str]:
+        """
+        Gets the constraints set on the field by comparing the constraint value with its default value
+
+        :return: the constraints set on field_info
+        """
+        return {attr for attr, default in self.__field_constraints__.items() if getattr(self, attr) != default}
+
+    def update_from_config(self, from_config: Dict[str, Any]) -> None:
+        """
+        Update this FieldInfo based on a dict from get_field_info, only fields which have not been set are dated.
+        """
+        for attr_name, value in from_config.items():
+            try:
+                current_value = getattr(self, attr_name)
+            except AttributeError:
+                # attr_name is not an attribute of FieldInfo, it should therefore be added to extra
+                # (except if extra already has this value!)
+                self.extra.setdefault(attr_name, value)
+            else:
+                if current_value is self.__field_constraints__.get(attr_name, None):
+                    setattr(self, attr_name, value)
+                elif attr_name == "exclude":
+                    self.exclude = ValueItems.merge(value, current_value)
+                elif attr_name == "include":
+                    self.include = ValueItems.merge(value, current_value, intersect=True)
+
+    def _validate(self) -> None:
+        if self.default is not Undefined and self.default_factory is not None:
+            raise ValueError("cannot specify both default and default_factory")
+
+
+def Field(
+    default: Any = Undefined,
+    *,
+    default_factory: Optional[NoArgAnyCallable] = None,
+    alias: Optional[str] = None,
+    title: Optional[str] = None,
+    description: Optional[str] = None,
+    exclude: Optional[Union["AbstractSetIntStr", "MappingIntStrAny", Any]] = None,
+    include: Optional[Union["AbstractSetIntStr", "MappingIntStrAny", Any]] = None,
+    const: Optional[bool] = None,
+    gt: Optional[float] = None,
+    ge: Optional[float] = None,
+    lt: Optional[float] = None,
+    le: Optional[float] = None,
+    multiple_of: Optional[float] = None,
+    allow_inf_nan: Optional[bool] = None,
+    max_digits: Optional[int] = None,
+    decimal_places: Optional[int] = None,
+    min_items: Optional[int] = None,
+    max_items: Optional[int] = None,
+    unique_items: Optional[bool] = None,
+    min_length: Optional[int] = None,
+    max_length: Optional[int] = None,
+    allow_mutation: bool = True,
+    regex: Optional[str] = None,
+    discriminator: Optional[str] = None,
+    repr: bool = True,
+    **extra: Any,
+) -> Any:
+    """
+    Used to provide extra information about a field, either for the model schema or complex validation. Some arguments
+    apply only to number fields (``int``, ``float``, ``Decimal``) and some apply only to ``str``.
+
+    :param default: since this is replacing the field’s default, its first argument is used
+      to set the default, use ellipsis (``...``) to indicate the field is required
+    :param default_factory: callable that will be called when a default value is needed for this field
+      If both `default` and `default_factory` are set, an error is raised.
+    :param alias: the public name of the field
+    :param title: can be any string, used in the schema
+    :param description: can be any string, used in the schema
+    :param exclude: exclude this field while dumping.
+      Takes same values as the ``include`` and ``exclude`` arguments on the ``.dict`` method.
+    :param include: include this field while dumping.
+      Takes same values as the ``include`` and ``exclude`` arguments on the ``.dict`` method.
+    :param const: this field is required and *must* take it's default value
+    :param gt: only applies to numbers, requires the field to be "greater than". The schema
+      will have an ``exclusiveMinimum`` validation keyword
+    :param ge: only applies to numbers, requires the field to be "greater than or equal to". The
+      schema will have a ``minimum`` validation keyword
+    :param lt: only applies to numbers, requires the field to be "less than". The schema
+      will have an ``exclusiveMaximum`` validation keyword
+    :param le: only applies to numbers, requires the field to be "less than or equal to". The
+      schema will have a ``maximum`` validation keyword
+    :param multiple_of: only applies to numbers, requires the field to be "a multiple of". The
+      schema will have a ``multipleOf`` validation keyword
+    :param allow_inf_nan: only applies to numbers, allows the field to be NaN or infinity (+inf or -inf),
+        which is a valid Python float. Default True, set to False for compatibility with JSON.
+    :param max_digits: only applies to Decimals, requires the field to have a maximum number
+      of digits within the decimal. It does not include a zero before the decimal point or trailing decimal zeroes.
+    :param decimal_places: only applies to Decimals, requires the field to have at most a number of decimal places
+      allowed. It does not include trailing decimal zeroes.
+    :param min_items: only applies to lists, requires the field to have a minimum number of
+      elements. The schema will have a ``minItems`` validation keyword
+    :param max_items: only applies to lists, requires the field to have a maximum number of
+      elements. The schema will have a ``maxItems`` validation keyword
+    :param unique_items: only applies to lists, requires the field not to have duplicated
+      elements. The schema will have a ``uniqueItems`` validation keyword
+    :param min_length: only applies to strings, requires the field to have a minimum length. The
+      schema will have a ``minLength`` validation keyword
+    :param max_length: only applies to strings, requires the field to have a maximum length. The
+      schema will have a ``maxLength`` validation keyword
+    :param allow_mutation: a boolean which defaults to True. When False, the field raises a TypeError if the field is
+      assigned on an instance.  The BaseModel Config must set validate_assignment to True
+    :param regex: only applies to strings, requires the field match against a regular expression
+      pattern string. The schema will have a ``pattern`` validation keyword
+    :param discriminator: only useful with a (discriminated a.k.a. tagged) `Union` of sub models with a common field.
+      The `discriminator` is the name of this common field to shorten validation and improve generated schema
+    :param repr: show this field in the representation
+    :param **extra: any additional keyword arguments will be added as is to the schema
+    """
+    field_info = FieldInfo(
+        default,
+        default_factory=default_factory,
+        alias=alias,
+        title=title,
+        description=description,
+        exclude=exclude,
+        include=include,
+        const=const,
+        gt=gt,
+        ge=ge,
+        lt=lt,
+        le=le,
+        multiple_of=multiple_of,
+        allow_inf_nan=allow_inf_nan,
+        max_digits=max_digits,
+        decimal_places=decimal_places,
+        min_items=min_items,
+        max_items=max_items,
+        unique_items=unique_items,
+        min_length=min_length,
+        max_length=max_length,
+        allow_mutation=allow_mutation,
+        regex=regex,
+        discriminator=discriminator,
+        repr=repr,
+        **extra,
+    )
+    field_info._validate()
+    return field_info
+
+
+# used to be an enum but changed to int's for small performance improvement as less access overhead
+SHAPE_SINGLETON = 1
+SHAPE_LIST = 2
+SHAPE_SET = 3
+SHAPE_MAPPING = 4
+SHAPE_TUPLE = 5
+SHAPE_TUPLE_ELLIPSIS = 6
+SHAPE_SEQUENCE = 7
+SHAPE_FROZENSET = 8
+SHAPE_ITERABLE = 9
+SHAPE_GENERIC = 10
+SHAPE_DEQUE = 11
+SHAPE_DICT = 12
+SHAPE_DEFAULTDICT = 13
+SHAPE_COUNTER = 14
+SHAPE_NAME_LOOKUP = {
+    SHAPE_LIST: "List[{}]",
+    SHAPE_SET: "Set[{}]",
+    SHAPE_TUPLE_ELLIPSIS: "Tuple[{}, ...]",
+    SHAPE_SEQUENCE: "Sequence[{}]",
+    SHAPE_FROZENSET: "FrozenSet[{}]",
+    SHAPE_ITERABLE: "Iterable[{}]",
+    SHAPE_DEQUE: "Deque[{}]",
+    SHAPE_DICT: "Dict[{}]",
+    SHAPE_DEFAULTDICT: "DefaultDict[{}]",
+    SHAPE_COUNTER: "Counter[{}]",
+}
+
+MAPPING_LIKE_SHAPES: Set[int] = {SHAPE_DEFAULTDICT, SHAPE_DICT, SHAPE_MAPPING, SHAPE_COUNTER}
+
+
+class ModelField(Representation):
+    __slots__ = (
+        "type_",
+        "outer_type_",
+        "annotation",
+        "sub_fields",
+        "sub_fields_mapping",
+        "key_field",
+        "validators",
+        "pre_validators",
+        "post_validators",
+        "default",
+        "default_factory",
+        "required",
+        "final",
+        "model_config",
+        "name",
+        "alias",
+        "has_alias",
+        "field_info",
+        "discriminator_key",
+        "discriminator_alias",
+        "validate_always",
+        "allow_none",
+        "shape",
+        "class_validators",
+        "parse_json",
+    )
+
+    def __init__(
+        self,
+        *,
+        name: str,
+        type_: Type[Any],
+        class_validators: Optional[Dict[str, Validator]],
+        model_config: Type["BaseConfig"],
+        default: Any = None,
+        default_factory: Optional[NoArgAnyCallable] = None,
+        required: "BoolUndefined" = Undefined,
+        final: bool = False,
+        alias: Optional[str] = None,
+        field_info: Optional[FieldInfo] = None,
+    ) -> None:
+        self.name: str = name
+        self.has_alias: bool = alias is not None
+        self.alias: str = alias if alias is not None else name
+        self.annotation = type_
+        self.type_: Any = convert_generics(type_)
+        self.outer_type_: Any = type_
+        self.class_validators = class_validators or {}
+        self.default: Any = default
+        self.default_factory: Optional[NoArgAnyCallable] = default_factory
+        self.required: "BoolUndefined" = required
+        self.final: bool = final
+        self.model_config = model_config
+        self.field_info: FieldInfo = field_info or FieldInfo(default)
+        self.discriminator_key: Optional[str] = self.field_info.discriminator
+        self.discriminator_alias: Optional[str] = self.discriminator_key
+
+        self.allow_none: bool = False
+        self.validate_always: bool = False
+        self.sub_fields: Optional[List[ModelField]] = None
+        self.sub_fields_mapping: Optional[Dict[str, "ModelField"]] = None  # used for discriminated union
+        self.key_field: Optional[ModelField] = None
+        self.validators: "ValidatorsList" = []
+        self.pre_validators: Optional["ValidatorsList"] = None
+        self.post_validators: Optional["ValidatorsList"] = None
+        self.parse_json: bool = False
+        self.shape: int = SHAPE_SINGLETON
+        self.model_config.prepare_field(self)
+        self.prepare()
+
+    def get_default(self) -> Any:
+        return smart_deepcopy(self.default) if self.default_factory is None else self.default_factory()
+
+    @staticmethod
+    def _get_field_info(
+        field_name: str, annotation: Any, value: Any, config: Type["BaseConfig"]
+    ) -> Tuple[FieldInfo, Any]:
+        """
+        Get a FieldInfo from a root typing.Annotated annotation, value, or config default.
+
+        The FieldInfo may be set in typing.Annotated or the value, but not both. If neither contain
+        a FieldInfo, a new one will be created using the config.
+
+        :param field_name: name of the field for use in error messages
+        :param annotation: a type hint such as `str` or `Annotated[str, Field(..., min_length=5)]`
+        :param value: the field's assigned value
+        :param config: the model's config object
+        :return: the FieldInfo contained in the `annotation`, the value, or a new one from the config.
+        """
+        field_info_from_config = config.get_field_info(field_name)
+
+        field_info = None
+        if get_origin(annotation) is Annotated:
+            field_infos = [arg for arg in get_args(annotation)[1:] if isinstance(arg, FieldInfo)]
+            if len(field_infos) > 1:
+                raise ValueError(f"cannot specify multiple `Annotated` `Field`s for {field_name!r}")
+            field_info = next(iter(field_infos), None)
+            if field_info is not None:
+                field_info = copy.copy(field_info)
+                field_info.update_from_config(field_info_from_config)
+                if field_info.default not in (Undefined, Required):
+                    raise ValueError(f"`Field` default cannot be set in `Annotated` for {field_name!r}")
+                if value is not Undefined and value is not Required:
+                    # check also `Required` because of `validate_arguments` that sets `...` as default value
+                    field_info.default = value
+
+        if isinstance(value, FieldInfo):
+            if field_info is not None:
+                raise ValueError(f"cannot specify `Annotated` and value `Field`s together for {field_name!r}")
+            field_info = value
+            field_info.update_from_config(field_info_from_config)
+        elif field_info is None:
+            field_info = FieldInfo(value, **field_info_from_config)
+        value = None if field_info.default_factory is not None else field_info.default
+        field_info._validate()
+        return field_info, value
+
+    @classmethod
+    def infer(
+        cls,
+        *,
+        name: str,
+        value: Any,
+        annotation: Any,
+        class_validators: Optional[Dict[str, Validator]],
+        config: Type["BaseConfig"],
+    ) -> "ModelField":
+        from .schema import get_annotation_from_field_info
+
+        field_info, value = cls._get_field_info(name, annotation, value, config)
+        required: "BoolUndefined" = Undefined
+        if value is Required:
+            required = True
+            value = None
+        elif value is not Undefined:
+            required = False
+        annotation = get_annotation_from_field_info(annotation, field_info, name, config.validate_assignment)
+
+        return cls(
+            name=name,
+            type_=annotation,
+            alias=field_info.alias,
+            class_validators=class_validators,
+            default=value,
+            default_factory=field_info.default_factory,
+            required=required,
+            model_config=config,
+            field_info=field_info,
+        )
+
+    def set_config(self, config: Type["BaseConfig"]) -> None:
+        self.model_config = config
+        info_from_config = config.get_field_info(self.name)
+        config.prepare_field(self)
+        new_alias = info_from_config.get("alias")
+        new_alias_priority = info_from_config.get("alias_priority") or 0
+        if new_alias and new_alias_priority >= (self.field_info.alias_priority or 0):
+            self.field_info.alias = new_alias
+            self.field_info.alias_priority = new_alias_priority
+            self.alias = new_alias
+        new_exclude = info_from_config.get("exclude")
+        if new_exclude is not None:
+            self.field_info.exclude = ValueItems.merge(self.field_info.exclude, new_exclude)
+        new_include = info_from_config.get("include")
+        if new_include is not None:
+            self.field_info.include = ValueItems.merge(self.field_info.include, new_include, intersect=True)
+
+    @property
+    def alt_alias(self) -> bool:
+        return self.name != self.alias
+
+    def prepare(self) -> None:
+        """
+        Prepare the field but inspecting self.default, self.type_ etc.
+
+        Note: this method is **not** idempotent (because _type_analysis is not idempotent),
+        e.g. calling it it multiple times may modify the field and configure it incorrectly.
+        """
+        self._set_default_and_type()
+        if self.type_.__class__ is ForwardRef or self.type_.__class__ is DeferredType:
+            # self.type_ is currently a ForwardRef and there's nothing we can do now,
+            # user will need to call model.update_forward_refs()
+            return
+
+        self._type_analysis()
+        if self.required is Undefined:
+            self.required = True
+        if self.default is Undefined and self.default_factory is None:
+            self.default = None
+        self.populate_validators()
+
+    def _set_default_and_type(self) -> None:
+        """
+        Set the default value, infer the type if needed and check if `None` value is valid.
+        """
+        if self.default_factory is not None:
+            if self.type_ is Undefined:
+                raise errors_.ConfigError(
+                    f"you need to set the type of field {self.name!r} when using `default_factory`"
+                )
+            return
+
+        default_value = self.get_default()
+
+        if default_value is not None and self.type_ is Undefined:
+            self.type_ = default_value.__class__
+            self.outer_type_ = self.type_
+            self.annotation = self.type_
+
+        if self.type_ is Undefined:
+            raise errors_.ConfigError(f'unable to infer type for attribute "{self.name}"')
+
+        if self.required is False and default_value is None:
+            self.allow_none = True
+
+    def _type_analysis(self) -> None:  # noqa: C901 (ignore complexity)
+        # typing interface is horrible, we have to do some ugly checks
+        if lenient_issubclass(self.type_, JsonWrapper):
+            self.type_ = self.type_.inner_type
+            self.parse_json = True
+        elif lenient_issubclass(self.type_, Json):
+            self.type_ = Any
+            self.parse_json = True
+        elif isinstance(self.type_, TypeVar):
+            if self.type_.__bound__:
+                self.type_ = self.type_.__bound__
+            elif self.type_.__constraints__:
+                self.type_ = Union[self.type_.__constraints__]
+            else:
+                self.type_ = Any
+        elif is_new_type(self.type_):
+            self.type_ = new_type_supertype(self.type_)
+
+        if self.type_ is Any or self.type_ is object:
+            if self.required is Undefined:
+                self.required = False
+            self.allow_none = True
+            return
+        elif self.type_ is Pattern or self.type_ is re.Pattern:
+            # python 3.7 only, Pattern is a typing object but without sub fields
+            return
+        elif is_literal_type(self.type_):
+            return
+        elif is_typeddict(self.type_):
+            return
+
+        if is_finalvar(self.type_):
+            self.final = True
+
+            if self.type_ is Final:
+                self.type_ = Any
+            else:
+                self.type_ = get_args(self.type_)[0]
+
+            self._type_analysis()
+            return
+
+        origin = get_origin(self.type_)
+
+        if origin is Annotated or is_typeddict_special(origin):
+            self.type_ = get_args(self.type_)[0]
+            self._type_analysis()
+            return
+
+        if self.discriminator_key is not None and not is_union(origin):
+            raise TypeError("`discriminator` can only be used with `Union` type with more than one variant")
+
+        # add extra check for `collections.abc.Hashable` for python 3.10+ where origin is not `None`
+        if origin is None or origin is CollectionsHashable:
+            # field is not "typing" object eg. Union, Dict, List etc.
+            # allow None for virtual superclasses of NoneType, e.g. Hashable
+            if isinstance(self.type_, type) and isinstance(None, self.type_):
+                self.allow_none = True
+            return
+        elif origin is Callable:
+            return
+        elif is_union(origin):
+            types_ = []
+            for type_ in get_args(self.type_):
+                if is_none_type(type_) or type_ is Any or type_ is object:
+                    if self.required is Undefined:
+                        self.required = False
+                    self.allow_none = True
+                if is_none_type(type_):
+                    continue
+                types_.append(type_)
+
+            if len(types_) == 1:
+                # Optional[]
+                self.type_ = types_[0]
+                # this is the one case where the "outer type" isn't just the original type
+                self.outer_type_ = self.type_
+                # re-run to correctly interpret the new self.type_
+                self._type_analysis()
+            else:
+                self.sub_fields = [self._create_sub_type(t, f"{self.name}_{display_as_type(t)}") for t in types_]
+
+                if self.discriminator_key is not None:
+                    self.prepare_discriminated_union_sub_fields()
+            return
+        elif issubclass(origin, Tuple):  # type: ignore
+            # origin == Tuple without item type
+            args = get_args(self.type_)
+            if not args:  # plain tuple
+                self.type_ = Any
+                self.shape = SHAPE_TUPLE_ELLIPSIS
+            elif len(args) == 2 and args[1] is Ellipsis:  # e.g. Tuple[int, ...]
+                self.type_ = args[0]
+                self.shape = SHAPE_TUPLE_ELLIPSIS
+                self.sub_fields = [self._create_sub_type(args[0], f"{self.name}_0")]
+            elif args == ((),):  # Tuple[()] means empty tuple
+                self.shape = SHAPE_TUPLE
+                self.type_ = Any
+                self.sub_fields = []
+            else:
+                self.shape = SHAPE_TUPLE
+                self.sub_fields = [self._create_sub_type(t, f"{self.name}_{i}") for i, t in enumerate(args)]
+            return
+        elif issubclass(origin, List):
+            # Create self validators
+            get_validators = getattr(self.type_, "__get_validators__", None)
+            if get_validators:
+                self.class_validators.update(
+                    {f"list_{i}": Validator(validator, pre=True) for i, validator in enumerate(get_validators())}
+                )
+
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_LIST
+        elif issubclass(origin, Set):
+            # Create self validators
+            get_validators = getattr(self.type_, "__get_validators__", None)
+            if get_validators:
+                self.class_validators.update(
+                    {f"set_{i}": Validator(validator, pre=True) for i, validator in enumerate(get_validators())}
+                )
+
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_SET
+        elif issubclass(origin, FrozenSet):
+            # Create self validators
+            get_validators = getattr(self.type_, "__get_validators__", None)
+            if get_validators:
+                self.class_validators.update(
+                    {f"frozenset_{i}": Validator(validator, pre=True) for i, validator in enumerate(get_validators())}
+                )
+
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_FROZENSET
+        elif issubclass(origin, Deque):
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_DEQUE
+        elif issubclass(origin, Sequence):
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_SEQUENCE
+        # priority to most common mapping: dict
+        elif origin is dict or origin is Dict:
+            self.key_field = self._create_sub_type(get_args(self.type_)[0], "key_" + self.name, for_keys=True)
+            self.type_ = get_args(self.type_)[1]
+            self.shape = SHAPE_DICT
+        elif issubclass(origin, DefaultDict):
+            self.key_field = self._create_sub_type(get_args(self.type_)[0], "key_" + self.name, for_keys=True)
+            self.type_ = get_args(self.type_)[1]
+            self.shape = SHAPE_DEFAULTDICT
+        elif issubclass(origin, Counter):
+            self.key_field = self._create_sub_type(get_args(self.type_)[0], "key_" + self.name, for_keys=True)
+            self.type_ = int
+            self.shape = SHAPE_COUNTER
+        elif issubclass(origin, Mapping):
+            self.key_field = self._create_sub_type(get_args(self.type_)[0], "key_" + self.name, for_keys=True)
+            self.type_ = get_args(self.type_)[1]
+            self.shape = SHAPE_MAPPING
+        # Equality check as almost everything inherits form Iterable, including str
+        # check for Iterable and CollectionsIterable, as it could receive one even when declared with the other
+        elif origin in {Iterable, CollectionsIterable}:
+            self.type_ = get_args(self.type_)[0]
+            self.shape = SHAPE_ITERABLE
+            self.sub_fields = [self._create_sub_type(self.type_, f"{self.name}_type")]
+        elif issubclass(origin, Type):  # type: ignore
+            return
+        elif hasattr(origin, "__get_validators__") or self.model_config.arbitrary_types_allowed:
+            # Is a Pydantic-compatible generic that handles itself
+            # or we have arbitrary_types_allowed = True
+            self.shape = SHAPE_GENERIC
+            self.sub_fields = [self._create_sub_type(t, f"{self.name}_{i}") for i, t in enumerate(get_args(self.type_))]
+            self.type_ = origin
+            return
+        else:
+            raise TypeError(f'Fields of type "{origin}" are not supported.')
+
+        # type_ has been refined eg. as the type of a List and sub_fields needs to be populated
+        self.sub_fields = [self._create_sub_type(self.type_, "_" + self.name)]
+
+    def prepare_discriminated_union_sub_fields(self) -> None:
+        """
+        Prepare the mapping <discriminator key> -> <ModelField> and update `sub_fields`
+        Note that this process can be aborted if a `ForwardRef` is encountered
+        """
+        assert self.discriminator_key is not None
+
+        if self.type_.__class__ is DeferredType:
+            return
+
+        assert self.sub_fields is not None
+        sub_fields_mapping: Dict[str, "ModelField"] = {}
+        all_aliases: Set[str] = set()
+
+        for sub_field in self.sub_fields:
+            t = sub_field.type_
+            if t.__class__ is ForwardRef:
+                # Stopping everything...will need to call `update_forward_refs`
+                return
+
+            alias, discriminator_values = get_discriminator_alias_and_values(t, self.discriminator_key)
+            all_aliases.add(alias)
+            for discriminator_value in discriminator_values:
+                sub_fields_mapping[discriminator_value] = sub_field
+
+        self.sub_fields_mapping = sub_fields_mapping
+        self.discriminator_alias = get_unique_discriminator_alias(all_aliases, self.discriminator_key)
+
+    def _create_sub_type(self, type_: Type[Any], name: str, *, for_keys: bool = False) -> "ModelField":
+        if for_keys:
+            class_validators = None
+        else:
+            # validators for sub items should not have `each_item` as we want to check only the first sublevel
+            class_validators = {
+                k: Validator(
+                    func=v.func,
+                    pre=v.pre,
+                    each_item=False,
+                    always=v.always,
+                    check_fields=v.check_fields,
+                    skip_on_failure=v.skip_on_failure,
+                )
+                for k, v in self.class_validators.items()
+                if v.each_item
+            }
+
+        field_info, _ = self._get_field_info(name, type_, None, self.model_config)
+
+        return self.__class__(
+            type_=type_,
+            name=name,
+            class_validators=class_validators,
+            model_config=self.model_config,
+            field_info=field_info,
+        )
+
+    def populate_validators(self) -> None:
+        """
+        Prepare self.pre_validators, self.validators, and self.post_validators based on self.type_'s  __get_validators__
+        and class validators. This method should be idempotent, e.g. it should be safe to call multiple times
+        without mis-configuring the field.
+        """
+        self.validate_always = getattr(self.type_, "validate_always", False) or any(
+            v.always for v in self.class_validators.values()
+        )
+
+        class_validators_ = self.class_validators.values()
+        if not self.sub_fields or self.shape == SHAPE_GENERIC:
+            get_validators = getattr(self.type_, "__get_validators__", None)
+            v_funcs = (
+                *[v.func for v in class_validators_ if v.each_item and v.pre],
+                *(get_validators() if get_validators else list(find_validators(self.type_, self.model_config))),
+                *[v.func for v in class_validators_ if v.each_item and not v.pre],
+            )
+            self.validators = prep_validators(v_funcs)
+
+        self.pre_validators = []
+        self.post_validators = []
+
+        if self.field_info and self.field_info.const:
+            self.post_validators.append(make_generic_validator(constant_validator))
+
+        if class_validators_:
+            self.pre_validators += prep_validators(v.func for v in class_validators_ if not v.each_item and v.pre)
+            self.post_validators += prep_validators(v.func for v in class_validators_ if not v.each_item and not v.pre)
+
+        if self.parse_json:
+            self.pre_validators.append(make_generic_validator(validate_json))
+
+        self.pre_validators = self.pre_validators or None
+        self.post_validators = self.post_validators or None
+
+    def validate(
+        self, v: Any, values: Dict[str, Any], *, loc: "LocStr", cls: Optional["ModelOrDc"] = None
+    ) -> "ValidateReturn":
+        assert self.type_.__class__ is not DeferredType
+
+        if self.type_.__class__ is ForwardRef:
+            assert cls is not None
+            raise ConfigError(
+                f'field "{self.name}" not yet prepared so type is still a ForwardRef, '
+                f"you might need to call {cls.__name__}.update_forward_refs()."
+            )
+
+        errors: Optional["ErrorList"]
+        if self.pre_validators:
+            v, errors = self._apply_validators(v, values, loc, cls, self.pre_validators)
+            if errors:
+                return v, errors
+
+        if v is None:
+            if is_none_type(self.type_):
+                # keep validating
+                pass
+            elif self.allow_none:
+                if self.post_validators:
+                    return self._apply_validators(v, values, loc, cls, self.post_validators)
+                else:
+                    return None, None
+            else:
+                return v, ErrorWrapper(NoneIsNotAllowedError(), loc)
+
+        if self.shape == SHAPE_SINGLETON:
+            v, errors = self._validate_singleton(v, values, loc, cls)
+        elif self.shape in MAPPING_LIKE_SHAPES:
+            v, errors = self._validate_mapping_like(v, values, loc, cls)
+        elif self.shape == SHAPE_TUPLE:
+            v, errors = self._validate_tuple(v, values, loc, cls)
+        elif self.shape == SHAPE_ITERABLE:
+            v, errors = self._validate_iterable(v, values, loc, cls)
+        elif self.shape == SHAPE_GENERIC:
+            v, errors = self._apply_validators(v, values, loc, cls, self.validators)
+        else:
+            #  sequence, list, set, generator, tuple with ellipsis, frozen set
+            v, errors = self._validate_sequence_like(v, values, loc, cls)
+
+        if not errors and self.post_validators:
+            v, errors = self._apply_validators(v, values, loc, cls, self.post_validators)
+        return v, errors
+
+    def _validate_sequence_like(  # noqa: C901 (ignore complexity)
+        self, v: Any, values: Dict[str, Any], loc: "LocStr", cls: Optional["ModelOrDc"]
+    ) -> "ValidateReturn":
+        """
+        Validate sequence-like containers: lists, tuples, sets and generators
+        Note that large if-else blocks are necessary to enable Cython
+        optimization, which is why we disable the complexity check above.
+        """
+        if not sequence_like(v):
+            e: errors_.PydanticTypeError
+            if self.shape == SHAPE_LIST:
+                e = errors_.ListError()
+            elif self.shape in (SHAPE_TUPLE, SHAPE_TUPLE_ELLIPSIS):
+                e = errors_.TupleError()
+            elif self.shape == SHAPE_SET:
+                e = errors_.SetError()
+            elif self.shape == SHAPE_FROZENSET:
+                e = errors_.FrozenSetError()
+            else:
+                e = errors_.SequenceError()
+            return v, ErrorWrapper(e, loc)
+
+        loc = loc if isinstance(loc, tuple) else (loc,)
+        result = []
+        errors: List[ErrorList] = []
+        for i, v_ in enumerate(v):
+            v_loc = *loc, i
+            r, ee = self._validate_singleton(v_, values, v_loc, cls)
+            if ee:
+                errors.append(ee)
+            else:
+                result.append(r)
+
+        if errors:
+            return v, errors
+
+        converted: Union[List[Any], Set[Any], FrozenSet[Any], Tuple[Any, ...], Iterator[Any], Deque[Any]] = result
+
+        if self.shape == SHAPE_SET:
+            converted = set(result)
+        elif self.shape == SHAPE_FROZENSET:
+            converted = frozenset(result)
+        elif self.shape == SHAPE_TUPLE_ELLIPSIS:
+            converted = tuple(result)
+        elif self.shape == SHAPE_DEQUE:
+            converted = deque(result, maxlen=getattr(v, "maxlen", None))
+        elif self.shape == SHAPE_SEQUENCE:
+            if isinstance(v, tuple):
+                converted = tuple(result)
+            elif isinstance(v, set):
+                converted = set(result)
+            elif isinstance(v, Generator):
+                converted = iter(result)
+            elif isinstance(v, deque):
+                converted = deque(result, maxlen=getattr(v, "maxlen", None))
+        return converted, None
+
+    def _validate_iterable(
+        self, v: Any, values: Dict[str, Any], loc: "LocStr", cls: Optional["ModelOrDc"]
+    ) -> "ValidateReturn":
+        """
+        Validate Iterables.
+
+        This intentionally doesn't validate values to allow infinite generators.
+        """
+
+        try:
+            iterable = iter(v)
+        except TypeError:
+            return v, ErrorWrapper(errors_.IterableError(), loc)
+        return iterable, None
+
+    def _validate_tuple(
+        self, v: Any, values: Dict[str, Any], loc: "LocStr", cls: Optional["ModelOrDc"]
+    ) -> "ValidateReturn":
+        e: Optional[Exception] = None
+        if not sequence_like(v):
+            e = errors_.TupleError()
+        else:
+            actual_length, expected_length = len(v), len(self.sub_fields)  # type: ignore
+            if actual_length != expected_length:
+                e = errors_.TupleLengthError(actual_length=actual_length, expected_length=expected_length)
+
+        if e:
+            return v, ErrorWrapper(e, loc)
+
+        loc = loc if isinstance(loc, tuple) else (loc,)
+        result = []
+        errors: List[ErrorList] = []
+        for i, (v_, field) in enumerate(zip(v, self.sub_fields)):  # type: ignore
+            v_loc = *loc, i
+            r, ee = field.validate(v_, values, loc=v_loc, cls=cls)
+            if ee:
+                errors.append(ee)
+            else:
+                result.append(r)
+
+        if errors:
+            return v, errors
+        else:
+            return tuple(result), None
+
+    def _validate_mapping_like(
+        self, v: Any, values: Dict[str, Any], loc: "LocStr", cls: Optional["ModelOrDc"]
+    ) -> "ValidateReturn":
+        try:
+            v_iter = dict_validator(v)
+        except TypeError as exc:
+            return v, ErrorWrapper(exc, loc)
+
+        loc = loc if isinstance(loc, tuple) else (loc,)
+        result, errors = {}, []
+        for k, v_ in v_iter.items():
+            v_loc = *loc, "__key__"
+            key_result, key_errors = self.key_field.validate(k, values, loc=v_loc, cls=cls)  # type: ignore
+            if key_errors:
+                errors.append(key_errors)
+                continue
+
+            v_loc = *loc, k
+            value_result, value_errors = self._validate_singleton(v_, values, v_loc, cls)
+            if value_errors:
+                errors.append(value_errors)
+                continue
+
+            result[key_result] = value_result
+        if errors:
+            return v, errors
+        elif self.shape == SHAPE_DICT:
+            return result, None
+        elif self.shape == SHAPE_DEFAULTDICT:
+            return defaultdict(self.type_, result), None
+        elif self.shape == SHAPE_COUNTER:
+            return CollectionCounter(result), None
+        else:
+            return self._get_mapping_value(v, result), None
+
+    def _get_mapping_value(self, original: T, converted: Dict[Any, Any]) -> Union[T, Dict[Any, Any]]:
+        """
+        When type is `Mapping[KT, KV]` (or another unsupported mapping), we try to avoid
+        coercing to `dict` unwillingly.
+        """
+        original_cls = original.__class__
+
+        if original_cls == dict or original_cls == Dict:
+            return converted
+        elif original_cls in {defaultdict, DefaultDict}:
+            return defaultdict(self.type_, converted)
+        else:
+            try:
+                # Counter, OrderedDict, UserDict, ...
+                return original_cls(converted)  # type: ignore
+            except TypeError:
+                raise RuntimeError(f"Could not convert dictionary to {original_cls.__name__!r}") from None
+
+    def _validate_singleton(
+        self, v: Any, values: Dict[str, Any], loc: "LocStr", cls: Optional["ModelOrDc"]
+    ) -> "ValidateReturn":
+        if self.sub_fields:
+            if self.discriminator_key is not None:
+                return self._validate_discriminated_union(v, values, loc, cls)
+
+            errors = []
+
+            if self.model_config.smart_union and is_union(get_origin(self.type_)):
+                # 1st pass: check if the value is an exact instance of one of the Union types
+                # (e.g. to avoid coercing a bool into an int)
+                for field in self.sub_fields:
+                    if v.__class__ is field.outer_type_:
+                        return v, None
+
+                # 2nd pass: check if the value is an instance of any subclass of the Union types
+                for field in self.sub_fields:
+                    # This whole logic will be improved later on to support more complex `isinstance` checks
+                    # It will probably be done once a strict mode is added and be something like:
+                    # ```
+                    #     value, error = field.validate(v, values, strict=True)
+                    #     if error is None:
+                    #         return value, None
+                    # ```
+                    try:
+                        if isinstance(v, field.outer_type_):
+                            return v, None
+                    except TypeError:
+                        # compound type
+                        if lenient_isinstance(v, get_origin(field.outer_type_)):
+                            value, error = field.validate(v, values, loc=loc, cls=cls)
+                            if not error:
+                                return value, None
+
+            # 1st pass by default or 3rd pass with `smart_union` enabled:
+            # check if the value can be coerced into one of the Union types
+            for field in self.sub_fields:
+                value, error = field.validate(v, values, loc=loc, cls=cls)
+                if error:
+                    errors.append(error)
+                else:
+                    return value, None
+            return v, errors
+        else:
+            return self._apply_validators(v, values, loc, cls, self.validators)
+
+    def _validate_discriminated_union(
+        self, v: Any, values: Dict[str, Any], loc: "LocStr", cls: Optional["ModelOrDc"]
+    ) -> "ValidateReturn":
+        assert self.discriminator_key is not None
+        assert self.discriminator_alias is not None
+
+        try:
+            try:
+                discriminator_value = v[self.discriminator_alias]
+            except KeyError:
+                if self.model_config.allow_population_by_field_name:
+                    discriminator_value = v[self.discriminator_key]
+                else:
+                    raise
+        except KeyError:
+            return v, ErrorWrapper(MissingDiscriminator(discriminator_key=self.discriminator_key), loc)
+        except TypeError:
+            try:
+                # BaseModel or dataclass
+                discriminator_value = getattr(v, self.discriminator_key)
+            except (AttributeError, TypeError):
+                return v, ErrorWrapper(MissingDiscriminator(discriminator_key=self.discriminator_key), loc)
+
+        if self.sub_fields_mapping is None:
+            assert cls is not None
+            raise ConfigError(
+                f'field "{self.name}" not yet prepared so type is still a ForwardRef, '
+                f"you might need to call {cls.__name__}.update_forward_refs()."
+            )
+
+        try:
+            sub_field = self.sub_fields_mapping[discriminator_value]
+        except (KeyError, TypeError):
+            # KeyError: `discriminator_value` is not in the dictionary.
+            # TypeError: `discriminator_value` is unhashable.
+            assert self.sub_fields_mapping is not None
+            return v, ErrorWrapper(
+                InvalidDiscriminator(
+                    discriminator_key=self.discriminator_key,
+                    discriminator_value=discriminator_value,
+                    allowed_values=list(self.sub_fields_mapping),
+                ),
+                loc,
+            )
+        else:
+            if not isinstance(loc, tuple):
+                loc = (loc,)
+            return sub_field.validate(v, values, loc=(*loc, display_as_type(sub_field.type_)), cls=cls)
+
+    def _apply_validators(
+        self, v: Any, values: Dict[str, Any], loc: "LocStr", cls: Optional["ModelOrDc"], validators: "ValidatorsList"
+    ) -> "ValidateReturn":
+        for validator in validators:
+            try:
+                v = validator(cls, v, values, self, self.model_config)
+            except (ValueError, TypeError, AssertionError) as exc:
+                return v, ErrorWrapper(exc, loc)
+        return v, None
+
+    def is_complex(self) -> bool:
+        """
+        Whether the field is "complex" eg. env variables should be parsed as JSON.
+        """
+        from .main import BaseModel
+
+        return (
+            self.shape != SHAPE_SINGLETON
+            or hasattr(self.type_, "__pydantic_model__")
+            or lenient_issubclass(self.type_, (BaseModel, list, set, frozenset, dict))
+        )
+
+    def _type_display(self) -> PyObjectStr:
+        t = display_as_type(self.type_)
+
+        if self.shape in MAPPING_LIKE_SHAPES:
+            t = f"Mapping[{display_as_type(self.key_field.type_)}, {t}]"  # type: ignore
+        elif self.shape == SHAPE_TUPLE:
+            t = "Tuple[{}]".format(", ".join(display_as_type(f.type_) for f in self.sub_fields))  # type: ignore
+        elif self.shape == SHAPE_GENERIC:
+            assert self.sub_fields
+            t = "{}[{}]".format(
+                display_as_type(self.type_), ", ".join(display_as_type(f.type_) for f in self.sub_fields)
+            )
+        elif self.shape != SHAPE_SINGLETON:
+            t = SHAPE_NAME_LOOKUP[self.shape].format(t)
+
+        if self.allow_none and (self.shape != SHAPE_SINGLETON or not self.sub_fields):
+            t = f"Optional[{t}]"
+        return PyObjectStr(t)
+
+    def __repr_args__(self) -> "ReprArgs":
+        args = [("name", self.name), ("type", self._type_display()), ("required", self.required)]
+
+        if not self.required:
+            if self.default_factory is not None:
+                args.append(("default_factory", f"<function {self.default_factory.__name__}>"))
+            else:
+                args.append(("default", self.default))
+
+        if self.alt_alias:
+            args.append(("alias", self.alias))
+        return args
+
+
+class ModelPrivateAttr(Representation):
+    __slots__ = ("default", "default_factory")
+
+    def __init__(self, default: Any = Undefined, *, default_factory: Optional[NoArgAnyCallable] = None) -> None:
+        self.default = default
+        self.default_factory = default_factory
+
+    def get_default(self) -> Any:
+        return smart_deepcopy(self.default) if self.default_factory is None else self.default_factory()
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, self.__class__) and (self.default, self.default_factory) == (
+            other.default,
+            other.default_factory,
+        )
+
+
+def PrivateAttr(
+    default: Any = Undefined,
+    *,
+    default_factory: Optional[NoArgAnyCallable] = None,
+) -> Any:
+    """
+    Indicates that attribute is only used internally and never mixed with regular fields.
+
+    Types or values of private attrs are not checked by pydantic and it's up to you to keep them relevant.
+
+    Private attrs are stored in model __slots__.
+
+    :param default: the attribute’s default value
+    :param default_factory: callable that will be called when a default value is needed for this attribute
+      If both `default` and `default_factory` are set, an error is raised.
+    """
+    if default is not Undefined and default_factory is not None:
+        raise ValueError("cannot specify both default and default_factory")
+
+    return ModelPrivateAttr(
+        default,
+        default_factory=default_factory,
+    )
+
+
+class DeferredType:
+    """
+    Used to postpone field preparation, while creating recursive generic models.
+    """
+
+
+def is_finalvar_with_default_val(type_: Type[Any], val: Any) -> bool:
+    return is_finalvar(type_) and val is not Undefined and not isinstance(val, FieldInfo)
diff --git a/vendor/pydantic/json.py b/vendor/pydantic/json.py
new file mode 100644
index 0000000..7004966
--- /dev/null
+++ b/vendor/pydantic/json.py
@@ -0,0 +1,112 @@
+import datetime
+from collections import deque
+from decimal import Decimal
+from enum import Enum
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from pathlib import Path
+from re import Pattern
+from types import GeneratorType
+from typing import Any, Callable, Dict, Type, Union
+from uuid import UUID
+
+from .color import Color
+from .networks import NameEmail
+from .types import SecretBytes, SecretStr
+
+__all__ = "pydantic_encoder", "custom_pydantic_encoder", "timedelta_isoformat"
+
+
+def isoformat(o: Union[datetime.date, datetime.time]) -> str:
+    return o.isoformat()
+
+
+def decimal_encoder(dec_value: Decimal) -> Union[int, float]:
+    """
+    Encodes a Decimal as int of there's no exponent, otherwise float
+
+    This is useful when we use ConstrainedDecimal to represent Numeric(x,0)
+    where a integer (but not int typed) is used. Encoding this as a float
+    results in failed round-tripping between encode and parse.
+    Our Id type is a prime example of this.
+
+    >>> decimal_encoder(Decimal("1.0"))
+    1.0
+
+    >>> decimal_encoder(Decimal("1"))
+    1
+    """
+    if dec_value.as_tuple().exponent >= 0:
+        return int(dec_value)
+    else:
+        return float(dec_value)
+
+
+ENCODERS_BY_TYPE: Dict[Type[Any], Callable[[Any], Any]] = {
+    bytes: lambda o: o.decode(),
+    Color: str,
+    datetime.date: isoformat,
+    datetime.datetime: isoformat,
+    datetime.time: isoformat,
+    datetime.timedelta: lambda td: td.total_seconds(),
+    Decimal: decimal_encoder,
+    Enum: lambda o: o.value,
+    frozenset: list,
+    deque: list,
+    GeneratorType: list,
+    IPv4Address: str,
+    IPv4Interface: str,
+    IPv4Network: str,
+    IPv6Address: str,
+    IPv6Interface: str,
+    IPv6Network: str,
+    NameEmail: str,
+    Path: str,
+    Pattern: lambda o: o.pattern,
+    SecretBytes: str,
+    SecretStr: str,
+    set: list,
+    UUID: str,
+}
+
+
+def pydantic_encoder(obj: Any) -> Any:
+    from dataclasses import asdict, is_dataclass
+
+    from .main import BaseModel
+
+    if isinstance(obj, BaseModel):
+        return obj.dict()
+    elif is_dataclass(obj):
+        return asdict(obj)
+
+    # Check the class type and its superclasses for a matching encoder
+    for base in obj.__class__.__mro__[:-1]:
+        try:
+            encoder = ENCODERS_BY_TYPE[base]
+        except KeyError:
+            continue
+        return encoder(obj)
+    else:  # We have exited the for loop without finding a suitable encoder
+        raise TypeError(f"Object of type '{obj.__class__.__name__}' is not JSON serializable")
+
+
+def custom_pydantic_encoder(type_encoders: Dict[Any, Callable[[Type[Any]], Any]], obj: Any) -> Any:
+    # Check the class type and its superclasses for a matching encoder
+    for base in obj.__class__.__mro__[:-1]:
+        try:
+            encoder = type_encoders[base]
+        except KeyError:
+            continue
+
+        return encoder(obj)
+    else:  # We have exited the for loop without finding a suitable encoder
+        return pydantic_encoder(obj)
+
+
+def timedelta_isoformat(td: datetime.timedelta) -> str:
+    """
+    ISO 8601 encoding for Python timedelta object.
+    """
+    minutes, seconds = divmod(td.seconds, 60)
+    hours, minutes = divmod(minutes, 60)
+    return f'{"-" if td.days < 0 else ""}P{abs(td.days)}DT{hours:d}H{minutes:d}M{seconds:d}.{td.microseconds:06d}S'
diff --git a/vendor/pydantic/main.py b/vendor/pydantic/main.py
new file mode 100644
index 0000000..c810f51
--- /dev/null
+++ b/vendor/pydantic/main.py
@@ -0,0 +1,1107 @@
+import warnings
+from abc import ABCMeta
+from copy import deepcopy
+from enum import Enum
+from functools import partial
+from pathlib import Path
+from types import FunctionType, prepare_class, resolve_bases
+from typing import (
+    TYPE_CHECKING,
+    AbstractSet,
+    Any,
+    Callable,
+    ClassVar,
+    Dict,
+    List,
+    Mapping,
+    Optional,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+    no_type_check,
+    overload,
+)
+
+from typing_extensions import dataclass_transform
+
+from .class_validators import ValidatorGroup, extract_root_validators, extract_validators, inherit_validators
+from .config import BaseConfig, Extra, inherit_config, prepare_config
+from .error_wrappers import ErrorWrapper, ValidationError
+from .errors import ConfigError, DictError, ExtraError, MissingError
+from .fields import (
+    MAPPING_LIKE_SHAPES,
+    Field,
+    ModelField,
+    ModelPrivateAttr,
+    PrivateAttr,
+    Undefined,
+    is_finalvar_with_default_val,
+)
+from .json import custom_pydantic_encoder, pydantic_encoder
+from .parse import Protocol, load_file, load_str_bytes
+from .schema import default_ref_template, model_schema
+from .types import PyObject, StrBytes
+from .typing import (
+    AnyCallable,
+    get_args,
+    get_origin,
+    is_classvar,
+    is_namedtuple,
+    is_union,
+    resolve_annotations,
+    update_model_forward_refs,
+)
+from .utils import (
+    DUNDER_ATTRIBUTES,
+    ROOT_KEY,
+    ClassAttribute,
+    GetterDict,
+    Representation,
+    ValueItems,
+    generate_model_signature,
+    is_valid_field,
+    is_valid_private_name,
+    lenient_issubclass,
+    sequence_like,
+    smart_deepcopy,
+    unique_list,
+    validate_field_name,
+)
+
+if TYPE_CHECKING:
+    from inspect import Signature
+
+    from .class_validators import ValidatorListDict
+    from .types import ModelOrDc
+    from .typing import (
+        AbstractSetIntStr,
+        AnyClassMethod,
+        CallableGenerator,
+        DictAny,
+        DictStrAny,
+        MappingIntStrAny,
+        ReprArgs,
+        SetStr,
+        TupleGenerator,
+    )
+
+    Model = TypeVar("Model", bound="BaseModel")
+
+__all__ = "BaseModel", "create_model", "validate_model"
+
+_T = TypeVar("_T")
+
+
+def validate_custom_root_type(fields: Dict[str, ModelField]) -> None:
+    if len(fields) > 1:
+        raise ValueError(f"{ROOT_KEY} cannot be mixed with other fields")
+
+
+def generate_hash_function(frozen: bool) -> Optional[Callable[[Any], int]]:
+    def hash_function(self_: Any) -> int:
+        return hash(self_.__class__) + hash(tuple(self_.__dict__.values()))
+
+    return hash_function if frozen else None
+
+
+# If a field is of type `Callable`, its default value should be a function and cannot to ignored.
+ANNOTATED_FIELD_UNTOUCHED_TYPES: Tuple[Any, ...] = (property, type, classmethod, staticmethod)
+# When creating a `BaseModel` instance, we bypass all the methods, properties... added to the model
+UNTOUCHED_TYPES: Tuple[Any, ...] = (FunctionType,) + ANNOTATED_FIELD_UNTOUCHED_TYPES
+# Note `ModelMetaclass` refers to `BaseModel`, but is also used to *create* `BaseModel`, so we need to add this extra
+# (somewhat hacky) boolean to keep track of whether we've created the `BaseModel` class yet, and therefore whether it's
+# safe to refer to it. If it *hasn't* been created, we assume that the `__new__` call we're in the middle of is for
+# the `BaseModel` class, since that's defined immediately after the metaclass.
+_is_base_model_class_defined = False
+
+
+@dataclass_transform(kw_only_default=True, field_specifiers=(Field,))
+class ModelMetaclass(ABCMeta):
+    @no_type_check  # noqa C901
+    def __new__(mcs, name, bases, namespace, **kwargs):  # noqa C901
+        fields: Dict[str, ModelField] = {}
+        config = BaseConfig
+        validators: "ValidatorListDict" = {}
+
+        pre_root_validators, post_root_validators = [], []
+        private_attributes: Dict[str, ModelPrivateAttr] = {}
+        base_private_attributes: Dict[str, ModelPrivateAttr] = {}
+        slots: SetStr = namespace.get("__slots__", ())
+        slots = {slots} if isinstance(slots, str) else set(slots)
+        class_vars: SetStr = set()
+        hash_func: Optional[Callable[[Any], int]] = None
+
+        for base in reversed(bases):
+            if _is_base_model_class_defined and issubclass(base, BaseModel) and base != BaseModel:
+                fields.update(smart_deepcopy(base.__fields__))
+                config = inherit_config(base.__config__, config)
+                validators = inherit_validators(base.__validators__, validators)
+                pre_root_validators += base.__pre_root_validators__
+                post_root_validators += base.__post_root_validators__
+                base_private_attributes.update(base.__private_attributes__)
+                class_vars.update(base.__class_vars__)
+                hash_func = base.__hash__
+
+        resolve_forward_refs = kwargs.pop("__resolve_forward_refs__", True)
+        allowed_config_kwargs: SetStr = {
+            key
+            for key in dir(config)
+            if not (key.startswith("__") and key.endswith("__"))  # skip dunder methods and attributes
+        }
+        config_kwargs = {key: kwargs.pop(key) for key in kwargs.keys() & allowed_config_kwargs}
+        config_from_namespace = namespace.get("Config")
+        if config_kwargs and config_from_namespace:
+            raise TypeError("Specifying config in two places is ambiguous, use either Config attribute or class kwargs")
+        config = inherit_config(config_from_namespace, config, **config_kwargs)
+
+        validators = inherit_validators(extract_validators(namespace), validators)
+        vg = ValidatorGroup(validators)
+
+        for f in fields.values():
+            f.set_config(config)
+            extra_validators = vg.get_validators(f.name)
+            if extra_validators:
+                f.class_validators.update(extra_validators)
+                # re-run prepare to add extra validators
+                f.populate_validators()
+
+        prepare_config(config, name)
+
+        untouched_types = ANNOTATED_FIELD_UNTOUCHED_TYPES
+
+        def is_untouched(v: Any) -> bool:
+            return isinstance(v, untouched_types) or v.__class__.__name__ == "cython_function_or_method"
+
+        if (namespace.get("__module__"), namespace.get("__qualname__")) != ("pydantic.main", "BaseModel"):
+            annotations = resolve_annotations(namespace.get("__annotations__", {}), namespace.get("__module__", None))
+            # annotation only fields need to come first in fields
+            for ann_name, ann_type in annotations.items():
+                if is_classvar(ann_type):
+                    class_vars.add(ann_name)
+                elif is_finalvar_with_default_val(ann_type, namespace.get(ann_name, Undefined)):
+                    class_vars.add(ann_name)
+                elif is_valid_field(ann_name):
+                    validate_field_name(bases, ann_name)
+                    value = namespace.get(ann_name, Undefined)
+                    allowed_types = get_args(ann_type) if is_union(get_origin(ann_type)) else (ann_type,)
+                    if (
+                        is_untouched(value)
+                        and ann_type != PyObject
+                        and not any(
+                            lenient_issubclass(get_origin(allowed_type), Type) for allowed_type in allowed_types
+                        )
+                    ):
+                        continue
+                    fields[ann_name] = ModelField.infer(
+                        name=ann_name,
+                        value=value,
+                        annotation=ann_type,
+                        class_validators=vg.get_validators(ann_name),
+                        config=config,
+                    )
+                elif ann_name not in namespace and config.underscore_attrs_are_private:
+                    private_attributes[ann_name] = PrivateAttr()
+
+            untouched_types = UNTOUCHED_TYPES + config.keep_untouched
+            for var_name, value in namespace.items():
+                can_be_changed = var_name not in class_vars and not is_untouched(value)
+                if isinstance(value, ModelPrivateAttr):
+                    if not is_valid_private_name(var_name):
+                        raise NameError(
+                            f'Private attributes "{var_name}" must not be a valid field name; '
+                            f'Use sunder or dunder names, e. g. "_{var_name}" or "__{var_name}__"'
+                        )
+                    private_attributes[var_name] = value
+                elif config.underscore_attrs_are_private and is_valid_private_name(var_name) and can_be_changed:
+                    private_attributes[var_name] = PrivateAttr(default=value)
+                elif is_valid_field(var_name) and var_name not in annotations and can_be_changed:
+                    validate_field_name(bases, var_name)
+                    inferred = ModelField.infer(
+                        name=var_name,
+                        value=value,
+                        annotation=annotations.get(var_name, Undefined),
+                        class_validators=vg.get_validators(var_name),
+                        config=config,
+                    )
+                    if var_name in fields:
+                        if lenient_issubclass(inferred.type_, fields[var_name].type_):
+                            inferred.type_ = fields[var_name].type_
+                        else:
+                            raise TypeError(
+                                f"The type of {name}.{var_name} differs from the new default value; "
+                                f"if you wish to change the type of this field, please use a type annotation"
+                            )
+                    fields[var_name] = inferred
+
+        _custom_root_type = ROOT_KEY in fields
+        if _custom_root_type:
+            validate_custom_root_type(fields)
+        vg.check_for_unused()
+        if config.json_encoders:
+            json_encoder = partial(custom_pydantic_encoder, config.json_encoders)
+        else:
+            json_encoder = pydantic_encoder
+        pre_rv_new, post_rv_new = extract_root_validators(namespace)
+
+        if hash_func is None:
+            hash_func = generate_hash_function(config.frozen)
+
+        exclude_from_namespace = fields | private_attributes.keys() | {"__slots__"}
+        new_namespace = {
+            "__config__": config,
+            "__fields__": fields,
+            "__exclude_fields__": {
+                name: field.field_info.exclude for name, field in fields.items() if field.field_info.exclude is not None
+            }
+            or None,
+            "__include_fields__": {
+                name: field.field_info.include for name, field in fields.items() if field.field_info.include is not None
+            }
+            or None,
+            "__validators__": vg.validators,
+            "__pre_root_validators__": unique_list(
+                pre_root_validators + pre_rv_new,
+                name_factory=lambda v: v.__name__,
+            ),
+            "__post_root_validators__": unique_list(
+                post_root_validators + post_rv_new,
+                name_factory=lambda skip_on_failure_and_v: skip_on_failure_and_v[1].__name__,
+            ),
+            "__schema_cache__": {},
+            "__json_encoder__": staticmethod(json_encoder),
+            "__custom_root_type__": _custom_root_type,
+            "__private_attributes__": {**base_private_attributes, **private_attributes},
+            "__slots__": slots | private_attributes.keys(),
+            "__hash__": hash_func,
+            "__class_vars__": class_vars,
+            **{n: v for n, v in namespace.items() if n not in exclude_from_namespace},
+        }
+
+        cls = super().__new__(mcs, name, bases, new_namespace, **kwargs)
+        # set __signature__ attr only for model class, but not for its instances
+        cls.__signature__ = ClassAttribute("__signature__", generate_model_signature(cls.__init__, fields, config))
+        if resolve_forward_refs:
+            cls.__try_update_forward_refs__()
+
+        # preserve `__set_name__` protocol defined in https://peps.python.org/pep-0487
+        # for attributes not in `new_namespace` (e.g. private attributes)
+        for name, obj in namespace.items():
+            if name not in new_namespace:
+                set_name = getattr(obj, "__set_name__", None)
+                if callable(set_name):
+                    set_name(cls, name)
+
+        return cls
+
+    def __instancecheck__(self, instance: Any) -> bool:
+        """
+        Avoid calling ABC _abc_subclasscheck unless we're pretty sure.
+
+        See #3829 and python/cpython#92810
+        """
+        return hasattr(instance, "__fields__") and super().__instancecheck__(instance)
+
+
+object_setattr = object.__setattr__
+
+
+class BaseModel(Representation, metaclass=ModelMetaclass):
+    if TYPE_CHECKING:
+        # populated by the metaclass, defined here to help IDEs only
+        __fields__: ClassVar[Dict[str, ModelField]] = {}
+        __include_fields__: ClassVar[Optional[Mapping[str, Any]]] = None
+        __exclude_fields__: ClassVar[Optional[Mapping[str, Any]]] = None
+        __validators__: ClassVar[Dict[str, AnyCallable]] = {}
+        __pre_root_validators__: ClassVar[List[AnyCallable]]
+        __post_root_validators__: ClassVar[List[Tuple[bool, AnyCallable]]]
+        __config__: ClassVar[Type[BaseConfig]] = BaseConfig
+        __json_encoder__: ClassVar[Callable[[Any], Any]] = lambda x: x
+        __schema_cache__: ClassVar["DictAny"] = {}
+        __custom_root_type__: ClassVar[bool] = False
+        __signature__: ClassVar["Signature"]
+        __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]]
+        __class_vars__: ClassVar[SetStr]
+        __fields_set__: ClassVar[SetStr] = set()
+
+    Config = BaseConfig
+    __slots__ = ("__dict__", "__fields_set__")
+    __doc__ = ""  # Null out the Representation docstring
+
+    def __init__(__pydantic_self__, **data: Any) -> None:
+        """
+        Create a new model by parsing and validating input data from keyword arguments.
+
+        Raises ValidationError if the input data cannot be parsed to form a valid model.
+        """
+        # Uses something other than `self` the first arg to allow "self" as a settable attribute
+        values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
+        if validation_error:
+            raise validation_error
+        try:
+            object_setattr(__pydantic_self__, "__dict__", values)
+        except TypeError as e:
+            raise TypeError(
+                "Model values must be a dict; you may not have returned a dictionary from a root validator"
+            ) from e
+        object_setattr(__pydantic_self__, "__fields_set__", fields_set)
+        __pydantic_self__._init_private_attributes()
+
+    @no_type_check
+    def __setattr__(self, name, value):  # noqa: C901 (ignore complexity)
+        if name in self.__private_attributes__ or name in DUNDER_ATTRIBUTES:
+            return object_setattr(self, name, value)
+
+        if self.__config__.extra is not Extra.allow and name not in self.__fields__:
+            raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"')
+        elif not self.__config__.allow_mutation or self.__config__.frozen:
+            raise TypeError(f'"{self.__class__.__name__}" is immutable and does not support item assignment')
+        elif name in self.__fields__ and self.__fields__[name].final:
+            raise TypeError(
+                f'"{self.__class__.__name__}" object "{name}" field is final and does not support reassignment'
+            )
+        elif self.__config__.validate_assignment:
+            new_values = {**self.__dict__, name: value}
+
+            for validator in self.__pre_root_validators__:
+                try:
+                    new_values = validator(self.__class__, new_values)
+                except (ValueError, TypeError, AssertionError) as exc:
+                    raise ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], self.__class__)
+
+            known_field = self.__fields__.get(name, None)
+            if known_field:
+                # We want to
+                # - make sure validators are called without the current value for this field inside `values`
+                # - keep other values (e.g. submodels) untouched (using `BaseModel.dict()` will change them into dicts)
+                # - keep the order of the fields
+                if not known_field.field_info.allow_mutation:
+                    raise TypeError(f'"{known_field.name}" has allow_mutation set to False and cannot be assigned')
+                dict_without_original_value = {k: v for k, v in self.__dict__.items() if k != name}
+                value, error_ = known_field.validate(value, dict_without_original_value, loc=name, cls=self.__class__)
+                if error_:
+                    raise ValidationError([error_], self.__class__)
+                else:
+                    new_values[name] = value
+
+            errors = []
+            for skip_on_failure, validator in self.__post_root_validators__:
+                if skip_on_failure and errors:
+                    continue
+                try:
+                    new_values = validator(self.__class__, new_values)
+                except (ValueError, TypeError, AssertionError) as exc:
+                    errors.append(ErrorWrapper(exc, loc=ROOT_KEY))
+            if errors:
+                raise ValidationError(errors, self.__class__)
+
+            # update the whole __dict__ as other values than just `value`
+            # may be changed (e.g. with `root_validator`)
+            object_setattr(self, "__dict__", new_values)
+        else:
+            self.__dict__[name] = value
+
+        self.__fields_set__.add(name)
+
+    def __getstate__(self) -> "DictAny":
+        private_attrs = ((k, getattr(self, k, Undefined)) for k in self.__private_attributes__)
+        return {
+            "__dict__": self.__dict__,
+            "__fields_set__": self.__fields_set__,
+            "__private_attribute_values__": {k: v for k, v in private_attrs if v is not Undefined},
+        }
+
+    def __setstate__(self, state: "DictAny") -> None:
+        object_setattr(self, "__dict__", state["__dict__"])
+        object_setattr(self, "__fields_set__", state["__fields_set__"])
+        for name, value in state.get("__private_attribute_values__", {}).items():
+            object_setattr(self, name, value)
+
+    def _init_private_attributes(self) -> None:
+        for name, private_attr in self.__private_attributes__.items():
+            default = private_attr.get_default()
+            if default is not Undefined:
+                object_setattr(self, name, default)
+
+    def dict(
+        self,
+        *,
+        include: Optional[Union["AbstractSetIntStr", "MappingIntStrAny"]] = None,
+        exclude: Optional[Union["AbstractSetIntStr", "MappingIntStrAny"]] = None,
+        by_alias: bool = False,
+        skip_defaults: Optional[bool] = None,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+    ) -> "DictStrAny":
+        """
+        Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
+
+        """
+        if skip_defaults is not None:
+            warnings.warn(
+                f'{self.__class__.__name__}.dict(): "skip_defaults" is deprecated and replaced by "exclude_unset"',
+                DeprecationWarning,
+            )
+            exclude_unset = skip_defaults
+
+        return dict(
+            self._iter(
+                to_dict=True,
+                by_alias=by_alias,
+                include=include,
+                exclude=exclude,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                exclude_none=exclude_none,
+            )
+        )
+
+    def json(
+        self,
+        *,
+        include: Optional[Union["AbstractSetIntStr", "MappingIntStrAny"]] = None,
+        exclude: Optional[Union["AbstractSetIntStr", "MappingIntStrAny"]] = None,
+        by_alias: bool = False,
+        skip_defaults: Optional[bool] = None,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        encoder: Optional[Callable[[Any], Any]] = None,
+        models_as_dict: bool = True,
+        **dumps_kwargs: Any,
+    ) -> str:
+        """
+        Generate a JSON representation of the model, `include` and `exclude` arguments as per `dict()`.
+
+        `encoder` is an optional function to supply as `default` to json.dumps(), other arguments as per `json.dumps()`.
+        """
+        if skip_defaults is not None:
+            warnings.warn(
+                f'{self.__class__.__name__}.json(): "skip_defaults" is deprecated and replaced by "exclude_unset"',
+                DeprecationWarning,
+            )
+            exclude_unset = skip_defaults
+        encoder = cast(Callable[[Any], Any], encoder or self.__json_encoder__)
+
+        # We don't directly call `self.dict()`, which does exactly this with `to_dict=True`
+        # because we want to be able to keep raw `BaseModel` instances and not as `dict`.
+        # This allows users to write custom JSON encoders for given `BaseModel` classes.
+        data = dict(
+            self._iter(
+                to_dict=models_as_dict,
+                by_alias=by_alias,
+                include=include,
+                exclude=exclude,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                exclude_none=exclude_none,
+            )
+        )
+        if self.__custom_root_type__:
+            data = data[ROOT_KEY]
+        return self.__config__.json_dumps(data, default=encoder, **dumps_kwargs)
+
+    @classmethod
+    def _enforce_dict_if_root(cls, obj: Any) -> Any:
+        if cls.__custom_root_type__ and (
+            not (isinstance(obj, dict) and obj.keys() == {ROOT_KEY})
+            and not (isinstance(obj, BaseModel) and obj.__fields__.keys() == {ROOT_KEY})
+            or cls.__fields__[ROOT_KEY].shape in MAPPING_LIKE_SHAPES
+        ):
+            return {ROOT_KEY: obj}
+        else:
+            return obj
+
+    @classmethod
+    def parse_obj(cls: Type["Model"], obj: Any) -> "Model":
+        obj = cls._enforce_dict_if_root(obj)
+        if not isinstance(obj, dict):
+            try:
+                obj = dict(obj)
+            except (TypeError, ValueError) as e:
+                exc = TypeError(f"{cls.__name__} expected dict not {obj.__class__.__name__}")
+                raise ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], cls) from e
+        return cls(**obj)
+
+    @classmethod
+    def parse_raw(
+        cls: Type["Model"],
+        b: StrBytes,
+        *,
+        content_type: str = None,
+        encoding: str = "utf8",
+        proto: Protocol = None,
+        allow_pickle: bool = False,
+    ) -> "Model":
+        try:
+            obj = load_str_bytes(
+                b,
+                proto=proto,
+                content_type=content_type,
+                encoding=encoding,
+                allow_pickle=allow_pickle,
+                json_loads=cls.__config__.json_loads,
+            )
+        except (ValueError, TypeError, UnicodeDecodeError) as e:
+            raise ValidationError([ErrorWrapper(e, loc=ROOT_KEY)], cls)
+        return cls.parse_obj(obj)
+
+    @classmethod
+    def parse_file(
+        cls: Type["Model"],
+        path: Union[str, Path],
+        *,
+        content_type: str = None,
+        encoding: str = "utf8",
+        proto: Protocol = None,
+        allow_pickle: bool = False,
+    ) -> "Model":
+        obj = load_file(
+            path,
+            proto=proto,
+            content_type=content_type,
+            encoding=encoding,
+            allow_pickle=allow_pickle,
+            json_loads=cls.__config__.json_loads,
+        )
+        return cls.parse_obj(obj)
+
+    @classmethod
+    def from_orm(cls: Type["Model"], obj: Any) -> "Model":
+        if not cls.__config__.orm_mode:
+            raise ConfigError("You must have the config attribute orm_mode=True to use from_orm")
+        obj = {ROOT_KEY: obj} if cls.__custom_root_type__ else cls._decompose_class(obj)
+        m = cls.__new__(cls)
+        values, fields_set, validation_error = validate_model(cls, obj)
+        if validation_error:
+            raise validation_error
+        object_setattr(m, "__dict__", values)
+        object_setattr(m, "__fields_set__", fields_set)
+        m._init_private_attributes()
+        return m
+
+    @classmethod
+    def construct(cls: Type["Model"], _fields_set: Optional["SetStr"] = None, **values: Any) -> "Model":
+        """
+        Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
+        Default values are respected, but no other validation is performed.
+        Behaves as if `Config.extra = 'allow'` was set since it adds all passed values
+        """
+        m = cls.__new__(cls)
+        fields_values: Dict[str, Any] = {}
+        for name, field in cls.__fields__.items():
+            if field.alt_alias and field.alias in values:
+                fields_values[name] = values[field.alias]
+            elif name in values:
+                fields_values[name] = values[name]
+            elif not field.required:
+                fields_values[name] = field.get_default()
+        fields_values.update(values)
+        object_setattr(m, "__dict__", fields_values)
+        if _fields_set is None:
+            _fields_set = set(values.keys())
+        object_setattr(m, "__fields_set__", _fields_set)
+        m._init_private_attributes()
+        return m
+
+    def _copy_and_set_values(self: "Model", values: "DictStrAny", fields_set: "SetStr", *, deep: bool) -> "Model":
+        if deep:
+            # chances of having empty dict here are quite low for using smart_deepcopy
+            values = deepcopy(values)
+
+        cls = self.__class__
+        m = cls.__new__(cls)
+        object_setattr(m, "__dict__", values)
+        object_setattr(m, "__fields_set__", fields_set)
+        for name in self.__private_attributes__:
+            value = getattr(self, name, Undefined)
+            if value is not Undefined:
+                if deep:
+                    value = deepcopy(value)
+                object_setattr(m, name, value)
+
+        return m
+
+    def copy(
+        self: "Model",
+        *,
+        include: Optional[Union["AbstractSetIntStr", "MappingIntStrAny"]] = None,
+        exclude: Optional[Union["AbstractSetIntStr", "MappingIntStrAny"]] = None,
+        update: Optional["DictStrAny"] = None,
+        deep: bool = False,
+    ) -> "Model":
+        """
+        Duplicate a model, optionally choose which fields to include, exclude and change.
+
+        :param include: fields to include in new model
+        :param exclude: fields to exclude from new model, as with values this takes precedence over include
+        :param update: values to change/add in the new model. Note: the data is not validated before creating
+            the new model: you should trust this data
+        :param deep: set to `True` to make a deep copy of the model
+        :return: new model instance
+        """
+
+        values = dict(
+            self._iter(to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False),
+            **(update or {}),
+        )
+
+        # new `__fields_set__` can have unset optional fields with a set value in `update` kwarg
+        if update:
+            fields_set = self.__fields_set__ | update.keys()
+        else:
+            fields_set = set(self.__fields_set__)
+
+        return self._copy_and_set_values(values, fields_set, deep=deep)
+
+    @classmethod
+    def schema(cls, by_alias: bool = True, ref_template: str = default_ref_template) -> "DictStrAny":
+        cached = cls.__schema_cache__.get((by_alias, ref_template))
+        if cached is not None:
+            return cached
+        s = model_schema(cls, by_alias=by_alias, ref_template=ref_template)
+        cls.__schema_cache__[(by_alias, ref_template)] = s
+        return s
+
+    @classmethod
+    def schema_json(
+        cls, *, by_alias: bool = True, ref_template: str = default_ref_template, **dumps_kwargs: Any
+    ) -> str:
+        from .json import pydantic_encoder
+
+        return cls.__config__.json_dumps(
+            cls.schema(by_alias=by_alias, ref_template=ref_template), default=pydantic_encoder, **dumps_kwargs
+        )
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield cls.validate
+
+    @classmethod
+    def validate(cls: Type["Model"], value: Any) -> "Model":
+        if isinstance(value, cls):
+            copy_on_model_validation = cls.__config__.copy_on_model_validation
+            # whether to deep or shallow copy the model on validation, None means do not copy
+            deep_copy: Optional[bool] = None
+            if copy_on_model_validation not in {"deep", "shallow", "none"}:
+                # Warn about deprecated behavior
+                warnings.warn(
+                    "`copy_on_model_validation` should be a string: 'deep', 'shallow' or 'none'", DeprecationWarning
+                )
+                if copy_on_model_validation:
+                    deep_copy = False
+
+            if copy_on_model_validation == "shallow":
+                # shallow copy
+                deep_copy = False
+            elif copy_on_model_validation == "deep":
+                # deep copy
+                deep_copy = True
+
+            if deep_copy is None:
+                return value
+            else:
+                return value._copy_and_set_values(value.__dict__, value.__fields_set__, deep=deep_copy)
+
+        value = cls._enforce_dict_if_root(value)
+
+        if isinstance(value, dict):
+            return cls(**value)
+        elif cls.__config__.orm_mode:
+            return cls.from_orm(value)
+        else:
+            try:
+                value_as_dict = dict(value)
+            except (TypeError, ValueError) as e:
+                raise DictError() from e
+            return cls(**value_as_dict)
+
+    @classmethod
+    def _decompose_class(cls: Type["Model"], obj: Any) -> GetterDict:
+        if isinstance(obj, GetterDict):
+            return obj
+        return cls.__config__.getter_dict(obj)
+
+    @classmethod
+    @no_type_check
+    def _get_value(
+        cls,
+        v: Any,
+        to_dict: bool,
+        by_alias: bool,
+        include: Optional[Union["AbstractSetIntStr", "MappingIntStrAny"]],
+        exclude: Optional[Union["AbstractSetIntStr", "MappingIntStrAny"]],
+        exclude_unset: bool,
+        exclude_defaults: bool,
+        exclude_none: bool,
+    ) -> Any:
+        if isinstance(v, BaseModel):
+            if to_dict:
+                v_dict = v.dict(
+                    by_alias=by_alias,
+                    exclude_unset=exclude_unset,
+                    exclude_defaults=exclude_defaults,
+                    include=include,
+                    exclude=exclude,
+                    exclude_none=exclude_none,
+                )
+                if ROOT_KEY in v_dict:
+                    return v_dict[ROOT_KEY]
+                return v_dict
+            else:
+                return v.copy(include=include, exclude=exclude)
+
+        value_exclude = ValueItems(v, exclude) if exclude else None
+        value_include = ValueItems(v, include) if include else None
+
+        if isinstance(v, dict):
+            return {
+                k_: cls._get_value(
+                    v_,
+                    to_dict=to_dict,
+                    by_alias=by_alias,
+                    exclude_unset=exclude_unset,
+                    exclude_defaults=exclude_defaults,
+                    include=value_include and value_include.for_element(k_),
+                    exclude=value_exclude and value_exclude.for_element(k_),
+                    exclude_none=exclude_none,
+                )
+                for k_, v_ in v.items()
+                if (not value_exclude or not value_exclude.is_excluded(k_))
+                and (not value_include or value_include.is_included(k_))
+            }
+
+        elif sequence_like(v):
+            seq_args = (
+                cls._get_value(
+                    v_,
+                    to_dict=to_dict,
+                    by_alias=by_alias,
+                    exclude_unset=exclude_unset,
+                    exclude_defaults=exclude_defaults,
+                    include=value_include and value_include.for_element(i),
+                    exclude=value_exclude and value_exclude.for_element(i),
+                    exclude_none=exclude_none,
+                )
+                for i, v_ in enumerate(v)
+                if (not value_exclude or not value_exclude.is_excluded(i))
+                and (not value_include or value_include.is_included(i))
+            )
+
+            return v.__class__(*seq_args) if is_namedtuple(v.__class__) else v.__class__(seq_args)
+
+        elif isinstance(v, Enum) and getattr(cls.Config, "use_enum_values", False):
+            return v.value
+
+        else:
+            return v
+
+    @classmethod
+    def __try_update_forward_refs__(cls, **localns: Any) -> None:
+        """
+        Same as update_forward_refs but will not raise exception
+        when forward references are not defined.
+        """
+        update_model_forward_refs(cls, cls.__fields__.values(), cls.__config__.json_encoders, localns, (NameError,))
+
+    @classmethod
+    def update_forward_refs(cls, **localns: Any) -> None:
+        """
+        Try to update ForwardRefs on fields based on this Model, globalns and localns.
+        """
+        update_model_forward_refs(cls, cls.__fields__.values(), cls.__config__.json_encoders, localns)
+
+    def __iter__(self) -> "TupleGenerator":
+        """
+        so `dict(model)` works
+        """
+        yield from self.__dict__.items()
+
+    def _iter(
+        self,
+        to_dict: bool = False,
+        by_alias: bool = False,
+        include: Optional[Union["AbstractSetIntStr", "MappingIntStrAny"]] = None,
+        exclude: Optional[Union["AbstractSetIntStr", "MappingIntStrAny"]] = None,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+    ) -> "TupleGenerator":
+        # Merge field set excludes with explicit exclude parameter with explicit overriding field set options.
+        # The extra "is not None" guards are not logically necessary but optimizes performance for the simple case.
+        if exclude is not None or self.__exclude_fields__ is not None:
+            exclude = ValueItems.merge(self.__exclude_fields__, exclude)
+
+        if include is not None or self.__include_fields__ is not None:
+            include = ValueItems.merge(self.__include_fields__, include, intersect=True)
+
+        allowed_keys = self._calculate_keys(
+            include=include, exclude=exclude, exclude_unset=exclude_unset  # type: ignore
+        )
+        if allowed_keys is None and not (to_dict or by_alias or exclude_unset or exclude_defaults or exclude_none):
+            # huge boost for plain _iter()
+            yield from self.__dict__.items()
+            return
+
+        value_exclude = ValueItems(self, exclude) if exclude is not None else None
+        value_include = ValueItems(self, include) if include is not None else None
+
+        for field_key, v in self.__dict__.items():
+            if (allowed_keys is not None and field_key not in allowed_keys) or (exclude_none and v is None):
+                continue
+
+            if exclude_defaults:
+                model_field = self.__fields__.get(field_key)
+                if not getattr(model_field, "required", True) and getattr(model_field, "default", _missing) == v:
+                    continue
+
+            if by_alias and field_key in self.__fields__:
+                dict_key = self.__fields__[field_key].alias
+            else:
+                dict_key = field_key
+
+            if to_dict or value_include or value_exclude:
+                v = self._get_value(
+                    v,
+                    to_dict=to_dict,
+                    by_alias=by_alias,
+                    include=value_include and value_include.for_element(field_key),
+                    exclude=value_exclude and value_exclude.for_element(field_key),
+                    exclude_unset=exclude_unset,
+                    exclude_defaults=exclude_defaults,
+                    exclude_none=exclude_none,
+                )
+            yield dict_key, v
+
+    def _calculate_keys(
+        self,
+        include: Optional["MappingIntStrAny"],
+        exclude: Optional["MappingIntStrAny"],
+        exclude_unset: bool,
+        update: Optional["DictStrAny"] = None,
+    ) -> Optional[AbstractSet[str]]:
+        if include is None and exclude is None and exclude_unset is False:
+            return None
+
+        keys: AbstractSet[str]
+        if exclude_unset:
+            keys = self.__fields_set__.copy()
+        else:
+            keys = self.__dict__.keys()
+
+        if include is not None:
+            keys &= include.keys()
+
+        if update:
+            keys -= update.keys()
+
+        if exclude:
+            keys -= {k for k, v in exclude.items() if ValueItems.is_true(v)}
+
+        return keys
+
+    def __eq__(self, other: Any) -> bool:
+        if isinstance(other, BaseModel):
+            return self.dict() == other.dict()
+        else:
+            return self.dict() == other
+
+    def __repr_args__(self) -> "ReprArgs":
+        return [
+            (k, v)
+            for k, v in self.__dict__.items()
+            if k not in DUNDER_ATTRIBUTES and (k not in self.__fields__ or self.__fields__[k].field_info.repr)
+        ]
+
+
+_is_base_model_class_defined = True
+
+
+@overload
+def create_model(
+    __model_name: str,
+    *,
+    __config__: Optional[Type[BaseConfig]] = None,
+    __base__: None = None,
+    __module__: str = __name__,
+    __validators__: Dict[str, "AnyClassMethod"] = None,
+    __cls_kwargs__: Dict[str, Any] = None,
+    **field_definitions: Any,
+) -> Type["BaseModel"]:
+    ...
+
+
+@overload
+def create_model(
+    __model_name: str,
+    *,
+    __config__: Optional[Type[BaseConfig]] = None,
+    __base__: Union[Type["Model"], Tuple[Type["Model"], ...]],
+    __module__: str = __name__,
+    __validators__: Dict[str, "AnyClassMethod"] = None,
+    __cls_kwargs__: Dict[str, Any] = None,
+    **field_definitions: Any,
+) -> Type["Model"]:
+    ...
+
+
+def create_model(
+    __model_name: str,
+    *,
+    __config__: Optional[Type[BaseConfig]] = None,
+    __base__: Union[None, Type["Model"], Tuple[Type["Model"], ...]] = None,
+    __module__: str = __name__,
+    __validators__: Dict[str, "AnyClassMethod"] = None,
+    __cls_kwargs__: Dict[str, Any] = None,
+    __slots__: Optional[Tuple[str, ...]] = None,
+    **field_definitions: Any,
+) -> Type["Model"]:
+    """
+    Dynamically create a model.
+    :param __model_name: name of the created model
+    :param __config__: config class to use for the new model
+    :param __base__: base class for the new model to inherit from
+    :param __module__: module of the created model
+    :param __validators__: a dict of method names and @validator class methods
+    :param __cls_kwargs__: a dict for class creation
+    :param __slots__: Deprecated, `__slots__` should not be passed to `create_model`
+    :param field_definitions: fields of the model (or extra fields if a base is supplied)
+        in the format `<name>=(<type>, <default default>)` or `<name>=<default value>, e.g.
+        `foobar=(str, ...)` or `foobar=123`, or, for complex use-cases, in the format
+        `<name>=<Field>` or `<name>=(<type>, <FieldInfo>)`, e.g.
+        `foo=Field(datetime, default_factory=datetime.utcnow, alias='bar')` or
+        `foo=(str, FieldInfo(title='Foo'))`
+    """
+    if __slots__ is not None:
+        # __slots__ will be ignored from here on
+        warnings.warn("__slots__ should not be passed to create_model", RuntimeWarning)
+
+    if __base__ is not None:
+        if __config__ is not None:
+            raise ConfigError("to avoid confusion __config__ and __base__ cannot be used together")
+        if not isinstance(__base__, tuple):
+            __base__ = (__base__,)
+    else:
+        __base__ = (cast(Type["Model"], BaseModel),)
+
+    __cls_kwargs__ = __cls_kwargs__ or {}
+
+    fields = {}
+    annotations = {}
+
+    for f_name, f_def in field_definitions.items():
+        if not is_valid_field(f_name):
+            warnings.warn(f'fields may not start with an underscore, ignoring "{f_name}"', RuntimeWarning)
+        if isinstance(f_def, tuple):
+            try:
+                f_annotation, f_value = f_def
+            except ValueError as e:
+                raise ConfigError(
+                    "field definitions should either be a tuple of (<type>, <default>) or just a "
+                    "default value, unfortunately this means tuples as "
+                    "default values are not allowed"
+                ) from e
+        else:
+            f_annotation, f_value = None, f_def
+
+        if f_annotation:
+            annotations[f_name] = f_annotation
+        fields[f_name] = f_value
+
+    namespace: "DictStrAny" = {"__annotations__": annotations, "__module__": __module__}
+    if __validators__:
+        namespace.update(__validators__)
+    namespace.update(fields)
+    if __config__:
+        namespace["Config"] = inherit_config(__config__, BaseConfig)
+    resolved_bases = resolve_bases(__base__)
+    meta, ns, kwds = prepare_class(__model_name, resolved_bases, kwds=__cls_kwargs__)
+    if resolved_bases is not __base__:
+        ns["__orig_bases__"] = __base__
+    namespace.update(ns)
+    return meta(__model_name, resolved_bases, namespace, **kwds)
+
+
+_missing = object()
+
+
+def validate_model(  # noqa: C901 (ignore complexity)
+    model: Type[BaseModel], input_data: "DictStrAny", cls: "ModelOrDc" = None
+) -> Tuple["DictStrAny", "SetStr", Optional[ValidationError]]:
+    """
+    validate data against a model.
+    """
+    values = {}
+    errors = []
+    # input_data names, possibly alias
+    names_used = set()
+    # field names, never aliases
+    fields_set = set()
+    config = model.__config__
+    check_extra = config.extra is not Extra.ignore
+    cls_ = cls or model
+
+    for validator in model.__pre_root_validators__:
+        try:
+            input_data = validator(cls_, input_data)
+        except (ValueError, TypeError, AssertionError) as exc:
+            return {}, set(), ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], cls_)
+
+    for name, field in model.__fields__.items():
+        value = input_data.get(field.alias, _missing)
+        using_name = False
+        if value is _missing and config.allow_population_by_field_name and field.alt_alias:
+            value = input_data.get(field.name, _missing)
+            using_name = True
+
+        if value is _missing:
+            if field.required:
+                errors.append(ErrorWrapper(MissingError(), loc=field.alias))
+                continue
+
+            value = field.get_default()
+
+            if not config.validate_all and not field.validate_always:
+                values[name] = value
+                continue
+        else:
+            fields_set.add(name)
+            if check_extra:
+                names_used.add(field.name if using_name else field.alias)
+
+        v_, errors_ = field.validate(value, values, loc=field.alias, cls=cls_)
+        if isinstance(errors_, ErrorWrapper):
+            errors.append(errors_)
+        elif isinstance(errors_, list):
+            errors.extend(errors_)
+        else:
+            values[name] = v_
+
+    if check_extra:
+        if isinstance(input_data, GetterDict):
+            extra = input_data.extra_keys() - names_used
+        else:
+            extra = input_data.keys() - names_used
+        if extra:
+            fields_set |= extra
+            if config.extra is Extra.allow:
+                for f in extra:
+                    values[f] = input_data[f]
+            else:
+                for f in sorted(extra):
+                    errors.append(ErrorWrapper(ExtraError(), loc=f))
+
+    for skip_on_failure, validator in model.__post_root_validators__:
+        if skip_on_failure and errors:
+            continue
+        try:
+            values = validator(cls_, values)
+        except (ValueError, TypeError, AssertionError) as exc:
+            errors.append(ErrorWrapper(exc, loc=ROOT_KEY))
+
+    if errors:
+        return values, fields_set, ValidationError(errors, cls_)
+    else:
+        return values, fields_set, None
diff --git a/vendor/pydantic/networks.py b/vendor/pydantic/networks.py
new file mode 100644
index 0000000..d83ffd4
--- /dev/null
+++ b/vendor/pydantic/networks.py
@@ -0,0 +1,747 @@
+import re
+from ipaddress import (
+    IPv4Address,
+    IPv4Interface,
+    IPv4Network,
+    IPv6Address,
+    IPv6Interface,
+    IPv6Network,
+    _BaseAddress,
+    _BaseNetwork,
+)
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Collection,
+    Dict,
+    Generator,
+    List,
+    Match,
+    Optional,
+    Pattern,
+    Set,
+    Tuple,
+    Type,
+    Union,
+    cast,
+    no_type_check,
+)
+
+from . import errors
+from .utils import Representation, update_not_none
+from .validators import constr_length_validator, str_validator
+
+if TYPE_CHECKING:
+    import email_validator
+    from typing_extensions import TypedDict
+
+    from .config import BaseConfig
+    from .fields import ModelField
+    from .typing import AnyCallable
+
+    CallableGenerator = Generator[AnyCallable, None, None]
+
+    class Parts(TypedDict, total=False):
+        scheme: str
+        user: Optional[str]
+        password: Optional[str]
+        ipv4: Optional[str]
+        ipv6: Optional[str]
+        domain: Optional[str]
+        port: Optional[str]
+        path: Optional[str]
+        query: Optional[str]
+        fragment: Optional[str]
+
+    class HostParts(TypedDict, total=False):
+        host: str
+        tld: Optional[str]
+        host_type: Optional[str]
+        port: Optional[str]
+        rebuild: bool
+
+else:
+    email_validator = None
+
+    class Parts(dict):
+        pass
+
+
+NetworkType = Union[str, bytes, int, Tuple[Union[str, bytes, int], Union[str, int]]]
+
+__all__ = [
+    "AnyUrl",
+    "AnyHttpUrl",
+    "FileUrl",
+    "HttpUrl",
+    "stricturl",
+    "EmailStr",
+    "NameEmail",
+    "IPvAnyAddress",
+    "IPvAnyInterface",
+    "IPvAnyNetwork",
+    "PostgresDsn",
+    "CockroachDsn",
+    "AmqpDsn",
+    "RedisDsn",
+    "MongoDsn",
+    "KafkaDsn",
+    "validate_email",
+]
+
+_url_regex_cache = None
+_multi_host_url_regex_cache = None
+_ascii_domain_regex_cache = None
+_int_domain_regex_cache = None
+_host_regex_cache = None
+
+_host_regex = (
+    r"(?:"
+    r"(?P<ipv4>(?:\d{1,3}\.){3}\d{1,3})(?=$|[/:#?])|"  # ipv4
+    r"(?P<ipv6>\[[A-F0-9]*:[A-F0-9:]+\])(?=$|[/:#?])|"  # ipv6
+    r"(?P<domain>[^\s/:?#]+)"  # domain, validation occurs later
+    r")?"
+    r"(?::(?P<port>\d+))?"  # port
+)
+_scheme_regex = r"(?:(?P<scheme>[a-z][a-z0-9+\-.]+)://)?"  # scheme https://tools.ietf.org/html/rfc3986#appendix-A
+_user_info_regex = r"(?:(?P<user>[^\s:/]*)(?::(?P<password>[^\s/]*))?@)?"
+_path_regex = r"(?P<path>/[^\s?#]*)?"
+_query_regex = r"(?:\?(?P<query>[^\s#]*))?"
+_fragment_regex = r"(?:#(?P<fragment>[^\s#]*))?"
+
+
+def url_regex() -> Pattern[str]:
+    global _url_regex_cache
+    if _url_regex_cache is None:
+        _url_regex_cache = re.compile(
+            rf"{_scheme_regex}{_user_info_regex}{_host_regex}{_path_regex}{_query_regex}{_fragment_regex}",
+            re.IGNORECASE,
+        )
+    return _url_regex_cache
+
+
+def multi_host_url_regex() -> Pattern[str]:
+    """
+    Compiled multi host url regex.
+
+    Additionally to `url_regex` it allows to match multiple hosts.
+    E.g. host1.db.net,host2.db.net
+    """
+    global _multi_host_url_regex_cache
+    if _multi_host_url_regex_cache is None:
+        _multi_host_url_regex_cache = re.compile(
+            rf"{_scheme_regex}{_user_info_regex}"
+            r"(?P<hosts>([^/]*))"  # validation occurs later
+            rf"{_path_regex}{_query_regex}{_fragment_regex}",
+            re.IGNORECASE,
+        )
+    return _multi_host_url_regex_cache
+
+
+def ascii_domain_regex() -> Pattern[str]:
+    global _ascii_domain_regex_cache
+    if _ascii_domain_regex_cache is None:
+        ascii_chunk = r"[_0-9a-z](?:[-_0-9a-z]{0,61}[_0-9a-z])?"
+        ascii_domain_ending = r"(?P<tld>\.[a-z]{2,63})?\.?"
+        _ascii_domain_regex_cache = re.compile(
+            rf"(?:{ascii_chunk}\.)*?{ascii_chunk}{ascii_domain_ending}", re.IGNORECASE
+        )
+    return _ascii_domain_regex_cache
+
+
+def int_domain_regex() -> Pattern[str]:
+    global _int_domain_regex_cache
+    if _int_domain_regex_cache is None:
+        int_chunk = r"[_0-9a-\U00040000](?:[-_0-9a-\U00040000]{0,61}[_0-9a-\U00040000])?"
+        int_domain_ending = r"(?P<tld>(\.[^\W\d_]{2,63})|(\.(?:xn--)[_0-9a-z-]{2,63}))?\.?"
+        _int_domain_regex_cache = re.compile(rf"(?:{int_chunk}\.)*?{int_chunk}{int_domain_ending}", re.IGNORECASE)
+    return _int_domain_regex_cache
+
+
+def host_regex() -> Pattern[str]:
+    global _host_regex_cache
+    if _host_regex_cache is None:
+        _host_regex_cache = re.compile(
+            _host_regex,
+            re.IGNORECASE,
+        )
+    return _host_regex_cache
+
+
+class AnyUrl(str):
+    strip_whitespace = True
+    min_length = 1
+    max_length = 2**16
+    allowed_schemes: Optional[Collection[str]] = None
+    tld_required: bool = False
+    user_required: bool = False
+    host_required: bool = True
+    hidden_parts: Set[str] = set()
+
+    __slots__ = ("scheme", "user", "password", "host", "tld", "host_type", "port", "path", "query", "fragment")
+
+    @no_type_check
+    def __new__(cls, url: Optional[str], **kwargs) -> object:
+        return str.__new__(cls, cls.build(**kwargs) if url is None else url)
+
+    def __init__(
+        self,
+        url: str,
+        *,
+        scheme: str,
+        user: Optional[str] = None,
+        password: Optional[str] = None,
+        host: Optional[str] = None,
+        tld: Optional[str] = None,
+        host_type: str = "domain",
+        port: Optional[str] = None,
+        path: Optional[str] = None,
+        query: Optional[str] = None,
+        fragment: Optional[str] = None,
+    ) -> None:
+        str.__init__(url)
+        self.scheme = scheme
+        self.user = user
+        self.password = password
+        self.host = host
+        self.tld = tld
+        self.host_type = host_type
+        self.port = port
+        self.path = path
+        self.query = query
+        self.fragment = fragment
+
+    @classmethod
+    def build(
+        cls,
+        *,
+        scheme: str,
+        user: Optional[str] = None,
+        password: Optional[str] = None,
+        host: str,
+        port: Optional[str] = None,
+        path: Optional[str] = None,
+        query: Optional[str] = None,
+        fragment: Optional[str] = None,
+        **_kwargs: str,
+    ) -> str:
+        parts = Parts(
+            scheme=scheme,
+            user=user,
+            password=password,
+            host=host,
+            port=port,
+            path=path,
+            query=query,
+            fragment=fragment,
+            **_kwargs,  # type: ignore[misc]
+        )
+
+        url = scheme + "://"
+        if user:
+            url += user
+        if password:
+            url += ":" + password
+        if user or password:
+            url += "@"
+        url += host
+        if port and ("port" not in cls.hidden_parts or cls.get_default_parts(parts).get("port") != port):
+            url += ":" + port
+        if path:
+            url += path
+        if query:
+            url += "?" + query
+        if fragment:
+            url += "#" + fragment
+        return url
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(field_schema, minLength=cls.min_length, maxLength=cls.max_length, format="uri")
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: Any, field: "ModelField", config: "BaseConfig") -> "AnyUrl":
+        if value.__class__ == cls:
+            return value
+        value = str_validator(value)
+        if cls.strip_whitespace:
+            value = value.strip()
+        url: str = cast(str, constr_length_validator(value, field, config))
+
+        m = cls._match_url(url)
+        # the regex should always match, if it doesn't please report with details of the URL tried
+        assert m, "URL regex failed unexpectedly"
+
+        original_parts = cast("Parts", m.groupdict())
+        parts = cls.apply_default_parts(original_parts)
+        parts = cls.validate_parts(parts)
+
+        if m.end() != len(url):
+            raise errors.UrlExtraError(extra=url[m.end() :])
+
+        return cls._build_url(m, url, parts)
+
+    @classmethod
+    def _build_url(cls, m: Match[str], url: str, parts: "Parts") -> "AnyUrl":
+        """
+        Validate hosts and build the AnyUrl object. Split from `validate` so this method
+        can be altered in `MultiHostDsn`.
+        """
+        host, tld, host_type, rebuild = cls.validate_host(parts)
+
+        return cls(
+            None if rebuild else url,
+            scheme=parts["scheme"],
+            user=parts["user"],
+            password=parts["password"],
+            host=host,
+            tld=tld,
+            host_type=host_type,
+            port=parts["port"],
+            path=parts["path"],
+            query=parts["query"],
+            fragment=parts["fragment"],
+        )
+
+    @staticmethod
+    def _match_url(url: str) -> Optional[Match[str]]:
+        return url_regex().match(url)
+
+    @staticmethod
+    def _validate_port(port: Optional[str]) -> None:
+        if port is not None and int(port) > 65_535:
+            raise errors.UrlPortError()
+
+    @classmethod
+    def validate_parts(cls, parts: "Parts", validate_port: bool = True) -> "Parts":
+        """
+        A method used to validate parts of a URL.
+        Could be overridden to set default values for parts if missing
+        """
+        scheme = parts["scheme"]
+        if scheme is None:
+            raise errors.UrlSchemeError()
+
+        if cls.allowed_schemes and scheme.lower() not in cls.allowed_schemes:
+            raise errors.UrlSchemePermittedError(set(cls.allowed_schemes))
+
+        if validate_port:
+            cls._validate_port(parts["port"])
+
+        user = parts["user"]
+        if cls.user_required and user is None:
+            raise errors.UrlUserInfoError()
+
+        return parts
+
+    @classmethod
+    def validate_host(cls, parts: "Parts") -> Tuple[str, Optional[str], str, bool]:
+        tld, host_type, rebuild = None, None, False
+        for f in ("domain", "ipv4", "ipv6"):
+            host = parts[f]  # type: ignore[literal-required]
+            if host:
+                host_type = f
+                break
+
+        if host is None:
+            if cls.host_required:
+                raise errors.UrlHostError()
+        elif host_type == "domain":
+            is_international = False
+            d = ascii_domain_regex().fullmatch(host)
+            if d is None:
+                d = int_domain_regex().fullmatch(host)
+                if d is None:
+                    raise errors.UrlHostError()
+                is_international = True
+
+            tld = d.group("tld")
+            if tld is None and not is_international:
+                d = int_domain_regex().fullmatch(host)
+                assert d is not None
+                tld = d.group("tld")
+                is_international = True
+
+            if tld is not None:
+                tld = tld[1:]
+            elif cls.tld_required:
+                raise errors.UrlHostTldError()
+
+            if is_international:
+                host_type = "int_domain"
+                rebuild = True
+                host = host.encode("idna").decode("ascii")
+                if tld is not None:
+                    tld = tld.encode("idna").decode("ascii")
+
+        return host, tld, host_type, rebuild  # type: ignore
+
+    @staticmethod
+    def get_default_parts(parts: "Parts") -> "Parts":
+        return {}
+
+    @classmethod
+    def apply_default_parts(cls, parts: "Parts") -> "Parts":
+        for key, value in cls.get_default_parts(parts).items():
+            if not parts[key]:  # type: ignore[literal-required]
+                parts[key] = value  # type: ignore[literal-required]
+        return parts
+
+    def __repr__(self) -> str:
+        extra = ", ".join(f"{n}={getattr(self, n)!r}" for n in self.__slots__ if getattr(self, n) is not None)
+        return f"{self.__class__.__name__}({super().__repr__()}, {extra})"
+
+
+class AnyHttpUrl(AnyUrl):
+    allowed_schemes = {"http", "https"}
+
+    __slots__ = ()
+
+
+class HttpUrl(AnyHttpUrl):
+    tld_required = True
+    # https://stackoverflow.com/questions/417142/what-is-the-maximum-length-of-a-url-in-different-browsers
+    max_length = 2083
+    hidden_parts = {"port"}
+
+    @staticmethod
+    def get_default_parts(parts: "Parts") -> "Parts":
+        return {"port": "80" if parts["scheme"] == "http" else "443"}
+
+
+class FileUrl(AnyUrl):
+    allowed_schemes = {"file"}
+    host_required = False
+
+    __slots__ = ()
+
+
+class MultiHostDsn(AnyUrl):
+    __slots__ = AnyUrl.__slots__ + ("hosts",)
+
+    def __init__(self, *args: Any, hosts: Optional[List["HostParts"]] = None, **kwargs: Any):
+        super().__init__(*args, **kwargs)
+        self.hosts = hosts
+
+    @staticmethod
+    def _match_url(url: str) -> Optional[Match[str]]:
+        return multi_host_url_regex().match(url)
+
+    @classmethod
+    def validate_parts(cls, parts: "Parts", validate_port: bool = True) -> "Parts":
+        return super().validate_parts(parts, validate_port=False)
+
+    @classmethod
+    def _build_url(cls, m: Match[str], url: str, parts: "Parts") -> "MultiHostDsn":
+        hosts_parts: List["HostParts"] = []
+        host_re = host_regex()
+        for host in m.groupdict()["hosts"].split(","):
+            d: Parts = host_re.match(host).groupdict()  # type: ignore
+            host, tld, host_type, rebuild = cls.validate_host(d)
+            port = d.get("port")
+            cls._validate_port(port)
+            hosts_parts.append(
+                {
+                    "host": host,
+                    "host_type": host_type,
+                    "tld": tld,
+                    "rebuild": rebuild,
+                    "port": port,
+                }
+            )
+
+        if len(hosts_parts) > 1:
+            return cls(
+                None if any([hp["rebuild"] for hp in hosts_parts]) else url,
+                scheme=parts["scheme"],
+                user=parts["user"],
+                password=parts["password"],
+                path=parts["path"],
+                query=parts["query"],
+                fragment=parts["fragment"],
+                host_type=None,
+                hosts=hosts_parts,
+            )
+        else:
+            # backwards compatibility with single host
+            host_part = hosts_parts[0]
+            return cls(
+                None if host_part["rebuild"] else url,
+                scheme=parts["scheme"],
+                user=parts["user"],
+                password=parts["password"],
+                host=host_part["host"],
+                tld=host_part["tld"],
+                host_type=host_part["host_type"],
+                port=host_part.get("port"),
+                path=parts["path"],
+                query=parts["query"],
+                fragment=parts["fragment"],
+            )
+
+
+class PostgresDsn(MultiHostDsn):
+    allowed_schemes = {
+        "postgres",
+        "postgresql",
+        "postgresql+asyncpg",
+        "postgresql+pg8000",
+        "postgresql+psycopg",
+        "postgresql+psycopg2",
+        "postgresql+psycopg2cffi",
+        "postgresql+py-postgresql",
+        "postgresql+pygresql",
+    }
+    user_required = True
+
+    __slots__ = ()
+
+
+class CockroachDsn(AnyUrl):
+    allowed_schemes = {
+        "cockroachdb",
+        "cockroachdb+psycopg2",
+        "cockroachdb+asyncpg",
+    }
+    user_required = True
+
+
+class AmqpDsn(AnyUrl):
+    allowed_schemes = {"amqp", "amqps"}
+    host_required = False
+
+
+class RedisDsn(AnyUrl):
+    __slots__ = ()
+    allowed_schemes = {"redis", "rediss"}
+    host_required = False
+
+    @staticmethod
+    def get_default_parts(parts: "Parts") -> "Parts":
+        return {
+            "domain": "localhost" if not (parts["ipv4"] or parts["ipv6"]) else "",
+            "port": "6379",
+            "path": "/0",
+        }
+
+
+class MongoDsn(AnyUrl):
+    allowed_schemes = {"mongodb"}
+
+    # TODO: Needed to generic "Parts" for "Replica Set", "Sharded Cluster", and other mongodb deployment modes
+    @staticmethod
+    def get_default_parts(parts: "Parts") -> "Parts":
+        return {
+            "port": "27017",
+        }
+
+
+class KafkaDsn(AnyUrl):
+    allowed_schemes = {"kafka"}
+
+    @staticmethod
+    def get_default_parts(parts: "Parts") -> "Parts":
+        return {
+            "domain": "localhost",
+            "port": "9092",
+        }
+
+
+def stricturl(
+    *,
+    strip_whitespace: bool = True,
+    min_length: int = 1,
+    max_length: int = 2**16,
+    tld_required: bool = True,
+    host_required: bool = True,
+    allowed_schemes: Optional[Collection[str]] = None,
+) -> Type[AnyUrl]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(
+        strip_whitespace=strip_whitespace,
+        min_length=min_length,
+        max_length=max_length,
+        tld_required=tld_required,
+        host_required=host_required,
+        allowed_schemes=allowed_schemes,
+    )
+    return type("UrlValue", (AnyUrl,), namespace)
+
+
+def import_email_validator() -> None:
+    global email_validator
+    try:
+        import email_validator
+    except ImportError as e:
+        raise ImportError("email-validator is not installed, run `pip install pydantic[email]`") from e
+
+
+class EmailStr(str):
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        field_schema.update(type="string", format="email")
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        # included here and below so the error happens straight away
+        import_email_validator()
+
+        yield str_validator
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: Union[str]) -> str:
+        return validate_email(value)[1]
+
+
+class NameEmail(Representation):
+    __slots__ = "name", "email"
+
+    def __init__(self, name: str, email: str):
+        self.name = name
+        self.email = email
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, NameEmail) and (self.name, self.email) == (other.name, other.email)
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        field_schema.update(type="string", format="name-email")
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        import_email_validator()
+
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: Any) -> "NameEmail":
+        if value.__class__ == cls:
+            return value
+        value = str_validator(value)
+        return cls(*validate_email(value))
+
+    def __str__(self) -> str:
+        return f"{self.name} <{self.email}>"
+
+
+class IPvAnyAddress(_BaseAddress):
+    __slots__ = ()
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        field_schema.update(type="string", format="ipvanyaddress")
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: Union[str, bytes, int]) -> Union[IPv4Address, IPv6Address]:
+        try:
+            return IPv4Address(value)
+        except ValueError:
+            pass
+
+        try:
+            return IPv6Address(value)
+        except ValueError:
+            raise errors.IPvAnyAddressError()
+
+
+class IPvAnyInterface(_BaseAddress):
+    __slots__ = ()
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        field_schema.update(type="string", format="ipvanyinterface")
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: NetworkType) -> Union[IPv4Interface, IPv6Interface]:
+        try:
+            return IPv4Interface(value)
+        except ValueError:
+            pass
+
+        try:
+            return IPv6Interface(value)
+        except ValueError:
+            raise errors.IPvAnyInterfaceError()
+
+
+class IPvAnyNetwork(_BaseNetwork):  # type: ignore
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        field_schema.update(type="string", format="ipvanynetwork")
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: NetworkType) -> Union[IPv4Network, IPv6Network]:
+        # Assume IP Network is defined with a default value for ``strict`` argument.
+        # Define your own class if you want to specify network address check strictness.
+        try:
+            return IPv4Network(value)
+        except ValueError:
+            pass
+
+        try:
+            return IPv6Network(value)
+        except ValueError:
+            raise errors.IPvAnyNetworkError()
+
+
+pretty_email_regex = re.compile(r"([\w ]*?) *<(.*)> *")
+MAX_EMAIL_LENGTH = 2048
+"""Maximum length for an email.
+A somewhat arbitrary but very generous number compared to what is allowed by most implementations.
+"""
+
+
+def validate_email(value: Union[str]) -> Tuple[str, str]:
+    """
+    Email address validation using https://pypi.org/project/email-validator/
+    Notes:
+    * raw ip address (literal) domain parts are not allowed.
+    * "John Doe <local_part@domain.com>" style "pretty" email addresses are processed
+    * spaces are striped from the beginning and end of addresses but no error is raised
+    """
+    if email_validator is None:
+        import_email_validator()
+
+    if len(value) > MAX_EMAIL_LENGTH:
+        raise errors.EmailError()
+
+    m = pretty_email_regex.fullmatch(value)
+    name: Union[str, None] = None
+    if m:
+        name, value = m.groups()
+    email = value.strip()
+    try:
+        parts = email_validator.validate_email(email, check_deliverability=False)
+    except email_validator.EmailNotValidError as e:
+        raise errors.EmailError from e
+
+    if hasattr(parts, "normalized"):
+        # email-validator >= 2
+        email = parts.normalized
+        assert email is not None
+        name = name or parts.local_part
+        return name, email
+    else:
+        # email-validator >1, <2
+        at_index = email.index("@")
+        local_part = email[:at_index]  # RFC 5321, local part must be case-sensitive.
+        global_part = email[at_index:].lower()
+
+        return name or local_part, local_part + global_part
diff --git a/vendor/pydantic/parse.py b/vendor/pydantic/parse.py
new file mode 100644
index 0000000..b32ddc4
--- /dev/null
+++ b/vendor/pydantic/parse.py
@@ -0,0 +1,66 @@
+import json
+import pickle
+from enum import Enum
+from pathlib import Path
+from typing import Any, Callable, Union
+
+from .types import StrBytes
+
+
+class Protocol(str, Enum):
+    json = "json"
+    pickle = "pickle"
+
+
+def load_str_bytes(
+    b: StrBytes,
+    *,
+    content_type: str = None,
+    encoding: str = "utf8",
+    proto: Protocol = None,
+    allow_pickle: bool = False,
+    json_loads: Callable[[str], Any] = json.loads,
+) -> Any:
+    if proto is None and content_type:
+        if content_type.endswith(("json", "javascript")):
+            pass
+        elif allow_pickle and content_type.endswith("pickle"):
+            proto = Protocol.pickle
+        else:
+            raise TypeError(f"Unknown content-type: {content_type}")
+
+    proto = proto or Protocol.json
+
+    if proto == Protocol.json:
+        if isinstance(b, bytes):
+            b = b.decode(encoding)
+        return json_loads(b)
+    elif proto == Protocol.pickle:
+        if not allow_pickle:
+            raise RuntimeError("Trying to decode with pickle with allow_pickle=False")
+        bb = b if isinstance(b, bytes) else b.encode()
+        return pickle.loads(bb)
+    else:
+        raise TypeError(f"Unknown protocol: {proto}")
+
+
+def load_file(
+    path: Union[str, Path],
+    *,
+    content_type: str = None,
+    encoding: str = "utf8",
+    proto: Protocol = None,
+    allow_pickle: bool = False,
+    json_loads: Callable[[str], Any] = json.loads,
+) -> Any:
+    path = Path(path)
+    b = path.read_bytes()
+    if content_type is None:
+        if path.suffix in (".js", ".json"):
+            proto = Protocol.json
+        elif path.suffix == ".pkl":
+            proto = Protocol.pickle
+
+    return load_str_bytes(
+        b, proto=proto, content_type=content_type, encoding=encoding, allow_pickle=allow_pickle, json_loads=json_loads
+    )
diff --git a/vendor/pydantic/schema.py b/vendor/pydantic/schema.py
new file mode 100644
index 0000000..0f3d38f
--- /dev/null
+++ b/vendor/pydantic/schema.py
@@ -0,0 +1,1163 @@
+import re
+import warnings
+from collections import defaultdict
+from dataclasses import is_dataclass
+from datetime import date, datetime, time, timedelta
+from decimal import Decimal
+from enum import Enum
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from pathlib import Path
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    ForwardRef,
+    FrozenSet,
+    Generic,
+    Iterable,
+    List,
+    Optional,
+    Pattern,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
+from uuid import UUID
+
+from typing_extensions import Annotated, Literal
+
+from .fields import (
+    MAPPING_LIKE_SHAPES,
+    SHAPE_DEQUE,
+    SHAPE_FROZENSET,
+    SHAPE_GENERIC,
+    SHAPE_ITERABLE,
+    SHAPE_LIST,
+    SHAPE_SEQUENCE,
+    SHAPE_SET,
+    SHAPE_SINGLETON,
+    SHAPE_TUPLE,
+    SHAPE_TUPLE_ELLIPSIS,
+    FieldInfo,
+    ModelField,
+)
+from .json import pydantic_encoder
+from .networks import AnyUrl, EmailStr
+from .types import (
+    ConstrainedDecimal,
+    ConstrainedFloat,
+    ConstrainedFrozenSet,
+    ConstrainedInt,
+    ConstrainedList,
+    ConstrainedSet,
+    ConstrainedStr,
+    SecretBytes,
+    SecretStr,
+    StrictBytes,
+    StrictStr,
+    conbytes,
+    condecimal,
+    confloat,
+    confrozenset,
+    conint,
+    conlist,
+    conset,
+    constr,
+)
+from .typing import (
+    all_literal_values,
+    get_args,
+    get_origin,
+    get_sub_types,
+    is_callable_type,
+    is_literal_type,
+    is_namedtuple,
+    is_none_type,
+    is_union,
+)
+from .utils import ROOT_KEY, get_model, lenient_issubclass
+
+if TYPE_CHECKING:
+    from .dataclasses import Dataclass
+    from .main import BaseModel
+
+default_prefix = "#/definitions/"
+default_ref_template = "#/definitions/{model}"
+
+TypeModelOrEnum = Union[Type["BaseModel"], Type[Enum]]
+TypeModelSet = Set[TypeModelOrEnum]
+
+
+def _apply_modify_schema(
+    modify_schema: Callable[..., None], field: Optional[ModelField], field_schema: Dict[str, Any]
+) -> None:
+    from inspect import signature
+
+    sig = signature(modify_schema)
+    args = set(sig.parameters.keys())
+    if "field" in args or "kwargs" in args:
+        modify_schema(field_schema, field=field)
+    else:
+        modify_schema(field_schema)
+
+
+def schema(
+    models: Sequence[Union[Type["BaseModel"], Type["Dataclass"]]],
+    *,
+    by_alias: bool = True,
+    title: Optional[str] = None,
+    description: Optional[str] = None,
+    ref_prefix: Optional[str] = None,
+    ref_template: str = default_ref_template,
+) -> Dict[str, Any]:
+    """
+    Process a list of models and generate a single JSON Schema with all of them defined in the ``definitions``
+    top-level JSON key, including their sub-models.
+
+    :param models: a list of models to include in the generated JSON Schema
+    :param by_alias: generate the schemas using the aliases defined, if any
+    :param title: title for the generated schema that includes the definitions
+    :param description: description for the generated schema
+    :param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the
+      default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere
+      else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the
+      top-level key ``definitions``, so you can extract them from there. But all the references will have the set
+      prefix.
+    :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful
+      for references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For
+      a sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
+    :return: dict with the JSON Schema with a ``definitions`` top-level key including the schema definitions for
+      the models and sub-models passed in ``models``.
+    """
+    clean_models = [get_model(model) for model in models]
+    flat_models = get_flat_models_from_models(clean_models)
+    model_name_map = get_model_name_map(flat_models)
+    definitions = {}
+    output_schema: Dict[str, Any] = {}
+    if title:
+        output_schema["title"] = title
+    if description:
+        output_schema["description"] = description
+    for model in clean_models:
+        m_schema, m_definitions, m_nested_models = model_process_schema(
+            model,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+        )
+        definitions.update(m_definitions)
+        model_name = model_name_map[model]
+        definitions[model_name] = m_schema
+    if definitions:
+        output_schema["definitions"] = definitions
+    return output_schema
+
+
+def model_schema(
+    model: Union[Type["BaseModel"], Type["Dataclass"]],
+    by_alias: bool = True,
+    ref_prefix: Optional[str] = None,
+    ref_template: str = default_ref_template,
+) -> Dict[str, Any]:
+    """
+    Generate a JSON Schema for one model. With all the sub-models defined in the ``definitions`` top-level
+    JSON key.
+
+    :param model: a Pydantic model (a class that inherits from BaseModel)
+    :param by_alias: generate the schemas using the aliases defined, if any
+    :param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the
+      default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere
+      else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the
+      top-level key ``definitions``, so you can extract them from there. But all the references will have the set
+      prefix.
+    :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for
+      references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a
+      sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
+    :return: dict with the JSON Schema for the passed ``model``
+    """
+    model = get_model(model)
+    flat_models = get_flat_models_from_model(model)
+    model_name_map = get_model_name_map(flat_models)
+    model_name = model_name_map[model]
+    m_schema, m_definitions, nested_models = model_process_schema(
+        model, by_alias=by_alias, model_name_map=model_name_map, ref_prefix=ref_prefix, ref_template=ref_template
+    )
+    if model_name in nested_models:
+        # model_name is in Nested models, it has circular references
+        m_definitions[model_name] = m_schema
+        m_schema = get_schema_ref(model_name, ref_prefix, ref_template, False)
+    if m_definitions:
+        m_schema.update({"definitions": m_definitions})
+    return m_schema
+
+
+def get_field_info_schema(field: ModelField, schema_overrides: bool = False) -> Tuple[Dict[str, Any], bool]:
+    # If no title is explicitly set, we don't set title in the schema for enums.
+    # The behaviour is the same as `BaseModel` reference, where the default title
+    # is in the definitions part of the schema.
+    schema_: Dict[str, Any] = {}
+    if field.field_info.title or not lenient_issubclass(field.type_, Enum):
+        schema_["title"] = field.field_info.title or field.alias.title().replace("_", " ")
+
+    if field.field_info.title:
+        schema_overrides = True
+
+    if field.field_info.description:
+        schema_["description"] = field.field_info.description
+        schema_overrides = True
+
+    if not field.required and field.default is not None and not is_callable_type(field.outer_type_):
+        schema_["default"] = encode_default(field.default)
+        schema_overrides = True
+
+    return schema_, schema_overrides
+
+
+def field_schema(
+    field: ModelField,
+    *,
+    by_alias: bool = True,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_prefix: Optional[str] = None,
+    ref_template: str = default_ref_template,
+    known_models: Optional[TypeModelSet] = None,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    Process a Pydantic field and return a tuple with a JSON Schema for it as the first item.
+    Also return a dictionary of definitions with models as keys and their schemas as values. If the passed field
+    is a model and has sub-models, and those sub-models don't have overrides (as ``title``, ``default``, etc), they
+    will be included in the definitions and referenced in the schema instead of included recursively.
+
+    :param field: a Pydantic ``ModelField``
+    :param by_alias: use the defined alias (if any) in the returned schema
+    :param model_name_map: used to generate the JSON Schema references to other models included in the definitions
+    :param ref_prefix: the JSON Pointer prefix to use for references to other schemas, if None, the default of
+      #/definitions/ will be used
+    :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for
+      references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a
+      sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
+    :param known_models: used to solve circular references
+    :return: tuple of the schema for this field and additional definitions
+    """
+    s, schema_overrides = get_field_info_schema(field)
+
+    validation_schema = get_field_schema_validations(field)
+    if validation_schema:
+        s.update(validation_schema)
+        schema_overrides = True
+
+    f_schema, f_definitions, f_nested_models = field_type_schema(
+        field,
+        by_alias=by_alias,
+        model_name_map=model_name_map,
+        schema_overrides=schema_overrides,
+        ref_prefix=ref_prefix,
+        ref_template=ref_template,
+        known_models=known_models or set(),
+    )
+
+    # $ref will only be returned when there are no schema_overrides
+    if "$ref" in f_schema:
+        return f_schema, f_definitions, f_nested_models
+    else:
+        s.update(f_schema)
+        return s, f_definitions, f_nested_models
+
+
+numeric_types = (int, float, Decimal)
+_str_types_attrs: Tuple[Tuple[str, Union[type, Tuple[type, ...]], str], ...] = (
+    ("max_length", numeric_types, "maxLength"),
+    ("min_length", numeric_types, "minLength"),
+    ("regex", str, "pattern"),
+)
+
+_numeric_types_attrs: Tuple[Tuple[str, Union[type, Tuple[type, ...]], str], ...] = (
+    ("gt", numeric_types, "exclusiveMinimum"),
+    ("lt", numeric_types, "exclusiveMaximum"),
+    ("ge", numeric_types, "minimum"),
+    ("le", numeric_types, "maximum"),
+    ("multiple_of", numeric_types, "multipleOf"),
+)
+
+
+def get_field_schema_validations(field: ModelField) -> Dict[str, Any]:
+    """
+    Get the JSON Schema validation keywords for a ``field`` with an annotation of
+    a Pydantic ``FieldInfo`` with validation arguments.
+    """
+    f_schema: Dict[str, Any] = {}
+
+    if lenient_issubclass(field.type_, Enum):
+        # schema is already updated by `enum_process_schema`; just update with field extra
+        if field.field_info.extra:
+            f_schema.update(field.field_info.extra)
+        return f_schema
+
+    if lenient_issubclass(field.type_, (str, bytes)):
+        for attr_name, t, keyword in _str_types_attrs:
+            attr = getattr(field.field_info, attr_name, None)
+            if isinstance(attr, t):
+                f_schema[keyword] = attr
+    if lenient_issubclass(field.type_, numeric_types) and not issubclass(field.type_, bool):
+        for attr_name, t, keyword in _numeric_types_attrs:
+            attr = getattr(field.field_info, attr_name, None)
+            if isinstance(attr, t):
+                f_schema[keyword] = attr
+    if field.field_info is not None and field.field_info.const:
+        f_schema["const"] = field.default
+    if field.field_info.extra:
+        f_schema.update(field.field_info.extra)
+    modify_schema = getattr(field.outer_type_, "__modify_schema__", None)
+    if modify_schema:
+        _apply_modify_schema(modify_schema, field, f_schema)
+    return f_schema
+
+
+def get_model_name_map(unique_models: TypeModelSet) -> Dict[TypeModelOrEnum, str]:
+    """
+    Process a set of models and generate unique names for them to be used as keys in the JSON Schema
+    definitions. By default the names are the same as the class name. But if two models in different Python
+    modules have the same name (e.g. "users.Model" and "items.Model"), the generated names will be
+    based on the Python module path for those conflicting models to prevent name collisions.
+
+    :param unique_models: a Python set of models
+    :return: dict mapping models to names
+    """
+    name_model_map = {}
+    conflicting_names: Set[str] = set()
+    for model in unique_models:
+        model_name = normalize_name(model.__name__)
+        if model_name in conflicting_names:
+            model_name = get_long_model_name(model)
+            name_model_map[model_name] = model
+        elif model_name in name_model_map:
+            conflicting_names.add(model_name)
+            conflicting_model = name_model_map.pop(model_name)
+            name_model_map[get_long_model_name(conflicting_model)] = conflicting_model
+            name_model_map[get_long_model_name(model)] = model
+        else:
+            name_model_map[model_name] = model
+    return {v: k for k, v in name_model_map.items()}
+
+
+def get_flat_models_from_model(model: Type["BaseModel"], known_models: Optional[TypeModelSet] = None) -> TypeModelSet:
+    """
+    Take a single ``model`` and generate a set with itself and all the sub-models in the tree. I.e. if you pass
+    model ``Foo`` (subclass of Pydantic ``BaseModel``) as ``model``, and it has a field of type ``Bar`` (also
+    subclass of ``BaseModel``) and that model ``Bar`` has a field of type ``Baz`` (also subclass of ``BaseModel``),
+    the return value will be ``set([Foo, Bar, Baz])``.
+
+    :param model: a Pydantic ``BaseModel`` subclass
+    :param known_models: used to solve circular references
+    :return: a set with the initial model and all its sub-models
+    """
+    known_models = known_models or set()
+    flat_models: TypeModelSet = set()
+    flat_models.add(model)
+    known_models |= flat_models
+    fields = cast(Sequence[ModelField], model.__fields__.values())
+    flat_models |= get_flat_models_from_fields(fields, known_models=known_models)
+    return flat_models
+
+
+def get_flat_models_from_field(field: ModelField, known_models: TypeModelSet) -> TypeModelSet:
+    """
+    Take a single Pydantic ``ModelField`` (from a model) that could have been declared as a subclass of BaseModel
+    (so, it could be a submodel), and generate a set with its model and all the sub-models in the tree.
+    I.e. if you pass a field that was declared to be of type ``Foo`` (subclass of BaseModel) as ``field``, and that
+    model ``Foo`` has a field of type ``Bar`` (also subclass of ``BaseModel``) and that model ``Bar`` has a field of
+    type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``.
+
+    :param field: a Pydantic ``ModelField``
+    :param known_models: used to solve circular references
+    :return: a set with the model used in the declaration for this field, if any, and all its sub-models
+    """
+    from .main import BaseModel
+
+    flat_models: TypeModelSet = set()
+
+    field_type = field.type_
+    if lenient_issubclass(getattr(field_type, "__pydantic_model__", None), BaseModel):
+        field_type = field_type.__pydantic_model__
+
+    if field.sub_fields and not lenient_issubclass(field_type, BaseModel):
+        flat_models |= get_flat_models_from_fields(field.sub_fields, known_models=known_models)
+    elif lenient_issubclass(field_type, BaseModel) and field_type not in known_models:
+        flat_models |= get_flat_models_from_model(field_type, known_models=known_models)
+    elif lenient_issubclass(field_type, Enum):
+        flat_models.add(field_type)
+    return flat_models
+
+
+def get_flat_models_from_fields(fields: Sequence[ModelField], known_models: TypeModelSet) -> TypeModelSet:
+    """
+    Take a list of Pydantic  ``ModelField``s (from a model) that could have been declared as subclasses of ``BaseModel``
+    (so, any of them could be a submodel), and generate a set with their models and all the sub-models in the tree.
+    I.e. if you pass a the fields of a model ``Foo`` (subclass of ``BaseModel``) as ``fields``, and on of them has a
+    field of type ``Bar`` (also subclass of ``BaseModel``) and that model ``Bar`` has a field of type ``Baz`` (also
+    subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``.
+
+    :param fields: a list of Pydantic ``ModelField``s
+    :param known_models: used to solve circular references
+    :return: a set with any model declared in the fields, and all their sub-models
+    """
+    flat_models: TypeModelSet = set()
+    for field in fields:
+        flat_models |= get_flat_models_from_field(field, known_models=known_models)
+    return flat_models
+
+
+def get_flat_models_from_models(models: Sequence[Type["BaseModel"]]) -> TypeModelSet:
+    """
+    Take a list of ``models`` and generate a set with them and all their sub-models in their trees. I.e. if you pass
+    a list of two models, ``Foo`` and ``Bar``, both subclasses of Pydantic ``BaseModel`` as models, and ``Bar`` has
+    a field of type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``.
+    """
+    flat_models: TypeModelSet = set()
+    for model in models:
+        flat_models |= get_flat_models_from_model(model)
+    return flat_models
+
+
+def get_long_model_name(model: TypeModelOrEnum) -> str:
+    return f"{model.__module__}__{model.__qualname__}".replace(".", "__")
+
+
+def field_type_schema(
+    field: ModelField,
+    *,
+    by_alias: bool,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_template: str,
+    schema_overrides: bool = False,
+    ref_prefix: Optional[str] = None,
+    known_models: TypeModelSet,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    Used by ``field_schema()``, you probably should be using that function.
+
+    Take a single ``field`` and generate the schema for its type only, not including additional
+    information as title, etc. Also return additional schema definitions, from sub-models.
+    """
+    from .main import BaseModel  # noqa: F811
+
+    definitions = {}
+    nested_models: Set[str] = set()
+    f_schema: Dict[str, Any]
+    if field.shape in {
+        SHAPE_LIST,
+        SHAPE_TUPLE_ELLIPSIS,
+        SHAPE_SEQUENCE,
+        SHAPE_SET,
+        SHAPE_FROZENSET,
+        SHAPE_ITERABLE,
+        SHAPE_DEQUE,
+    }:
+        items_schema, f_definitions, f_nested_models = field_singleton_schema(
+            field,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+        definitions.update(f_definitions)
+        nested_models.update(f_nested_models)
+        f_schema = {"type": "array", "items": items_schema}
+        if field.shape in {SHAPE_SET, SHAPE_FROZENSET}:
+            f_schema["uniqueItems"] = True
+
+    elif field.shape in MAPPING_LIKE_SHAPES:
+        f_schema = {"type": "object"}
+        key_field = cast(ModelField, field.key_field)
+        regex = getattr(key_field.type_, "regex", None)
+        items_schema, f_definitions, f_nested_models = field_singleton_schema(
+            field,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+        definitions.update(f_definitions)
+        nested_models.update(f_nested_models)
+        if regex:
+            # Dict keys have a regex pattern
+            # items_schema might be a schema or empty dict, add it either way
+            f_schema["patternProperties"] = {ConstrainedStr._get_pattern(regex): items_schema}
+        if items_schema:
+            # The dict values are not simply Any, so they need a schema
+            f_schema["additionalProperties"] = items_schema
+    elif field.shape == SHAPE_TUPLE or (field.shape == SHAPE_GENERIC and not issubclass(field.type_, BaseModel)):
+        sub_schema = []
+        sub_fields = cast(List[ModelField], field.sub_fields)
+        for sf in sub_fields:
+            sf_schema, sf_definitions, sf_nested_models = field_type_schema(
+                sf,
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+            )
+            definitions.update(sf_definitions)
+            nested_models.update(sf_nested_models)
+            sub_schema.append(sf_schema)
+
+        sub_fields_len = len(sub_fields)
+        if field.shape == SHAPE_GENERIC:
+            all_of_schemas = sub_schema[0] if sub_fields_len == 1 else {"type": "array", "items": sub_schema}
+            f_schema = {"allOf": [all_of_schemas]}
+        else:
+            f_schema = {
+                "type": "array",
+                "minItems": sub_fields_len,
+                "maxItems": sub_fields_len,
+            }
+            if sub_fields_len >= 1:
+                f_schema["items"] = sub_schema
+    else:
+        assert field.shape in {SHAPE_SINGLETON, SHAPE_GENERIC}, field.shape
+        f_schema, f_definitions, f_nested_models = field_singleton_schema(
+            field,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            schema_overrides=schema_overrides,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+        definitions.update(f_definitions)
+        nested_models.update(f_nested_models)
+
+    # check field type to avoid repeated calls to the same __modify_schema__ method
+    if field.type_ != field.outer_type_:
+        if field.shape == SHAPE_GENERIC:
+            field_type = field.type_
+        else:
+            field_type = field.outer_type_
+        modify_schema = getattr(field_type, "__modify_schema__", None)
+        if modify_schema:
+            _apply_modify_schema(modify_schema, field, f_schema)
+    return f_schema, definitions, nested_models
+
+
+def model_process_schema(
+    model: TypeModelOrEnum,
+    *,
+    by_alias: bool = True,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_prefix: Optional[str] = None,
+    ref_template: str = default_ref_template,
+    known_models: Optional[TypeModelSet] = None,
+    field: Optional[ModelField] = None,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    Used by ``model_schema()``, you probably should be using that function.
+
+    Take a single ``model`` and generate its schema. Also return additional schema definitions, from sub-models. The
+    sub-models of the returned schema will be referenced, but their definitions will not be included in the schema. All
+    the definitions are returned as the second value.
+    """
+    from inspect import getdoc, signature
+
+    known_models = known_models or set()
+    if lenient_issubclass(model, Enum):
+        model = cast(Type[Enum], model)
+        s = enum_process_schema(model, field=field)
+        return s, {}, set()
+    model = cast(Type["BaseModel"], model)
+    s = {"title": model.__config__.title or model.__name__}
+    doc = getdoc(model)
+    if doc:
+        s["description"] = doc
+    known_models.add(model)
+    m_schema, m_definitions, nested_models = model_type_schema(
+        model,
+        by_alias=by_alias,
+        model_name_map=model_name_map,
+        ref_prefix=ref_prefix,
+        ref_template=ref_template,
+        known_models=known_models,
+    )
+    s.update(m_schema)
+    schema_extra = model.__config__.schema_extra
+    if callable(schema_extra):
+        if len(signature(schema_extra).parameters) == 1:
+            schema_extra(s)
+        else:
+            schema_extra(s, model)
+    else:
+        s.update(schema_extra)
+    return s, m_definitions, nested_models
+
+
+def model_type_schema(
+    model: Type["BaseModel"],
+    *,
+    by_alias: bool,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_template: str,
+    ref_prefix: Optional[str] = None,
+    known_models: TypeModelSet,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    You probably should be using ``model_schema()``, this function is indirectly used by that function.
+
+    Take a single ``model`` and generate the schema for its type only, not including additional
+    information as title, etc. Also return additional schema definitions, from sub-models.
+    """
+    properties = {}
+    required = []
+    definitions: Dict[str, Any] = {}
+    nested_models: Set[str] = set()
+    for k, f in model.__fields__.items():
+        try:
+            f_schema, f_definitions, f_nested_models = field_schema(
+                f,
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+            )
+        except SkipField as skip:
+            warnings.warn(skip.message, UserWarning)
+            continue
+        definitions.update(f_definitions)
+        nested_models.update(f_nested_models)
+        if by_alias:
+            properties[f.alias] = f_schema
+            if f.required:
+                required.append(f.alias)
+        else:
+            properties[k] = f_schema
+            if f.required:
+                required.append(k)
+    if ROOT_KEY in properties:
+        out_schema = properties[ROOT_KEY]
+        out_schema["title"] = model.__config__.title or model.__name__
+    else:
+        out_schema = {"type": "object", "properties": properties}
+        if required:
+            out_schema["required"] = required
+    if model.__config__.extra == "forbid":
+        out_schema["additionalProperties"] = False
+    return out_schema, definitions, nested_models
+
+
+def enum_process_schema(enum: Type[Enum], *, field: Optional[ModelField] = None) -> Dict[str, Any]:
+    """
+    Take a single `enum` and generate its schema.
+
+    This is similar to the `model_process_schema` function, but applies to ``Enum`` objects.
+    """
+    import inspect
+
+    schema_: Dict[str, Any] = {
+        "title": enum.__name__,
+        # Python assigns all enums a default docstring value of 'An enumeration', so
+        # all enums will have a description field even if not explicitly provided.
+        "description": inspect.cleandoc(enum.__doc__ or "An enumeration."),
+        # Add enum values and the enum field type to the schema.
+        "enum": [item.value for item in cast(Iterable[Enum], enum)],
+    }
+
+    add_field_type_to_schema(enum, schema_)
+
+    modify_schema = getattr(enum, "__modify_schema__", None)
+    if modify_schema:
+        _apply_modify_schema(modify_schema, field, schema_)
+
+    return schema_
+
+
+def field_singleton_sub_fields_schema(
+    field: ModelField,
+    *,
+    by_alias: bool,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_template: str,
+    schema_overrides: bool = False,
+    ref_prefix: Optional[str] = None,
+    known_models: TypeModelSet,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    This function is indirectly used by ``field_schema()``, you probably should be using that function.
+
+    Take a list of Pydantic ``ModelField`` from the declaration of a type with parameters, and generate their
+    schema. I.e., fields used as "type parameters", like ``str`` and ``int`` in ``Tuple[str, int]``.
+    """
+    sub_fields = cast(List[ModelField], field.sub_fields)
+    definitions = {}
+    nested_models: Set[str] = set()
+    if len(sub_fields) == 1:
+        return field_type_schema(
+            sub_fields[0],
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            schema_overrides=schema_overrides,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+    else:
+        s: Dict[str, Any] = {}
+        # https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.2.md#discriminator-object
+        field_has_discriminator: bool = field.discriminator_key is not None
+        if field_has_discriminator:
+            assert field.sub_fields_mapping is not None
+
+            discriminator_models_refs: Dict[str, Union[str, Dict[str, Any]]] = {}
+
+            for discriminator_value, sub_field in field.sub_fields_mapping.items():
+                if isinstance(discriminator_value, Enum):
+                    discriminator_value = str(discriminator_value.value)
+                # sub_field is either a `BaseModel` or directly an `Annotated` `Union` of many
+                if is_union(get_origin(sub_field.type_)):
+                    sub_models = get_sub_types(sub_field.type_)
+                    discriminator_models_refs[discriminator_value] = {
+                        model_name_map[sub_model]: get_schema_ref(
+                            model_name_map[sub_model], ref_prefix, ref_template, False
+                        )
+                        for sub_model in sub_models
+                    }
+                else:
+                    sub_field_type = sub_field.type_
+                    if hasattr(sub_field_type, "__pydantic_model__"):
+                        sub_field_type = sub_field_type.__pydantic_model__
+
+                    discriminator_model_name = model_name_map[sub_field_type]
+                    discriminator_model_ref = get_schema_ref(discriminator_model_name, ref_prefix, ref_template, False)
+                    discriminator_models_refs[discriminator_value] = discriminator_model_ref["$ref"]
+
+            s["discriminator"] = {
+                "propertyName": field.discriminator_alias,
+                "mapping": discriminator_models_refs,
+            }
+
+        sub_field_schemas = []
+        for sf in sub_fields:
+            sub_schema, sub_definitions, sub_nested_models = field_type_schema(
+                sf,
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                schema_overrides=schema_overrides,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+            )
+            definitions.update(sub_definitions)
+            if schema_overrides and "allOf" in sub_schema:
+                # if the sub_field is a referenced schema we only need the referenced
+                # object. Otherwise we will end up with several allOf inside anyOf/oneOf.
+                # See https://github.com/pydantic/pydantic/issues/1209
+                sub_schema = sub_schema["allOf"][0]
+
+            if sub_schema.keys() == {"discriminator", "oneOf"}:
+                # we don't want discriminator information inside oneOf choices, this is dealt with elsewhere
+                sub_schema.pop("discriminator")
+            sub_field_schemas.append(sub_schema)
+            nested_models.update(sub_nested_models)
+        s["oneOf" if field_has_discriminator else "anyOf"] = sub_field_schemas
+        return s, definitions, nested_models
+
+
+# Order is important, e.g. subclasses of str must go before str
+# this is used only for standard library types, custom types should use __modify_schema__ instead
+field_class_to_schema: Tuple[Tuple[Any, Dict[str, Any]], ...] = (
+    (Path, {"type": "string", "format": "path"}),
+    (datetime, {"type": "string", "format": "date-time"}),
+    (date, {"type": "string", "format": "date"}),
+    (time, {"type": "string", "format": "time"}),
+    (timedelta, {"type": "number", "format": "time-delta"}),
+    (IPv4Network, {"type": "string", "format": "ipv4network"}),
+    (IPv6Network, {"type": "string", "format": "ipv6network"}),
+    (IPv4Interface, {"type": "string", "format": "ipv4interface"}),
+    (IPv6Interface, {"type": "string", "format": "ipv6interface"}),
+    (IPv4Address, {"type": "string", "format": "ipv4"}),
+    (IPv6Address, {"type": "string", "format": "ipv6"}),
+    (Pattern, {"type": "string", "format": "regex"}),
+    (str, {"type": "string"}),
+    (bytes, {"type": "string", "format": "binary"}),
+    (bool, {"type": "boolean"}),
+    (int, {"type": "integer"}),
+    (float, {"type": "number"}),
+    (Decimal, {"type": "number"}),
+    (UUID, {"type": "string", "format": "uuid"}),
+    (dict, {"type": "object"}),
+    (list, {"type": "array", "items": {}}),
+    (tuple, {"type": "array", "items": {}}),
+    (set, {"type": "array", "items": {}, "uniqueItems": True}),
+    (frozenset, {"type": "array", "items": {}, "uniqueItems": True}),
+)
+
+json_scheme = {"type": "string", "format": "json-string"}
+
+
+def add_field_type_to_schema(field_type: Any, schema_: Dict[str, Any]) -> None:
+    """
+    Update the given `schema` with the type-specific metadata for the given `field_type`.
+
+    This function looks through `field_class_to_schema` for a class that matches the given `field_type`,
+    and then modifies the given `schema` with the information from that type.
+    """
+    for type_, t_schema in field_class_to_schema:
+        # Fallback for `typing.Pattern` and `re.Pattern` as they are not a valid class
+        if lenient_issubclass(field_type, type_) or field_type is type_ is Pattern:
+            schema_.update(t_schema)
+            break
+
+
+def get_schema_ref(name: str, ref_prefix: Optional[str], ref_template: str, schema_overrides: bool) -> Dict[str, Any]:
+    if ref_prefix:
+        schema_ref = {"$ref": ref_prefix + name}
+    else:
+        schema_ref = {"$ref": ref_template.format(model=name)}
+    return {"allOf": [schema_ref]} if schema_overrides else schema_ref
+
+
+def field_singleton_schema(  # noqa: C901 (ignore complexity)
+    field: ModelField,
+    *,
+    by_alias: bool,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_template: str,
+    schema_overrides: bool = False,
+    ref_prefix: Optional[str] = None,
+    known_models: TypeModelSet,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    This function is indirectly used by ``field_schema()``, you should probably be using that function.
+
+    Take a single Pydantic ``ModelField``, and return its schema and any additional definitions from sub-models.
+    """
+    from .main import BaseModel
+
+    definitions: Dict[str, Any] = {}
+    nested_models: Set[str] = set()
+    field_type = field.type_
+
+    # Recurse into this field if it contains sub_fields and is NOT a
+    # BaseModel OR that BaseModel is a const
+    if field.sub_fields and (
+        (field.field_info and field.field_info.const) or not lenient_issubclass(field_type, BaseModel)
+    ):
+        return field_singleton_sub_fields_schema(
+            field,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            schema_overrides=schema_overrides,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+    if field_type is Any or field_type is object or field_type.__class__ == TypeVar or get_origin(field_type) is type:
+        return {}, definitions, nested_models  # no restrictions
+    if is_none_type(field_type):
+        return {"type": "null"}, definitions, nested_models
+    if is_callable_type(field_type):
+        raise SkipField(f"Callable {field.name} was excluded from schema since JSON schema has no equivalent type.")
+    f_schema: Dict[str, Any] = {}
+    if field.field_info is not None and field.field_info.const:
+        f_schema["const"] = field.default
+
+    if is_literal_type(field_type):
+        values = tuple(x.value if isinstance(x, Enum) else x for x in all_literal_values(field_type))
+
+        if len({v.__class__ for v in values}) > 1:
+            return field_schema(
+                multitypes_literal_field_for_schema(values, field),
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+            )
+
+        # All values have the same type
+        field_type = values[0].__class__
+        f_schema["enum"] = list(values)
+        add_field_type_to_schema(field_type, f_schema)
+    elif lenient_issubclass(field_type, Enum):
+        enum_name = model_name_map[field_type]
+        f_schema, schema_overrides = get_field_info_schema(field, schema_overrides)
+        f_schema.update(get_schema_ref(enum_name, ref_prefix, ref_template, schema_overrides))
+        definitions[enum_name] = enum_process_schema(field_type, field=field)
+    elif is_namedtuple(field_type):
+        sub_schema, *_ = model_process_schema(
+            field_type.__pydantic_model__,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+            field=field,
+        )
+        items_schemas = list(sub_schema["properties"].values())
+        f_schema.update(
+            {
+                "type": "array",
+                "items": items_schemas,
+                "minItems": len(items_schemas),
+                "maxItems": len(items_schemas),
+            }
+        )
+    elif not hasattr(field_type, "__pydantic_model__"):
+        add_field_type_to_schema(field_type, f_schema)
+
+        modify_schema = getattr(field_type, "__modify_schema__", None)
+        if modify_schema:
+            _apply_modify_schema(modify_schema, field, f_schema)
+
+    if f_schema:
+        return f_schema, definitions, nested_models
+
+    # Handle dataclass-based models
+    if lenient_issubclass(getattr(field_type, "__pydantic_model__", None), BaseModel):
+        field_type = field_type.__pydantic_model__
+
+    if issubclass(field_type, BaseModel):
+        model_name = model_name_map[field_type]
+        if field_type not in known_models:
+            sub_schema, sub_definitions, sub_nested_models = model_process_schema(
+                field_type,
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+                field=field,
+            )
+            definitions.update(sub_definitions)
+            definitions[model_name] = sub_schema
+            nested_models.update(sub_nested_models)
+        else:
+            nested_models.add(model_name)
+        schema_ref = get_schema_ref(model_name, ref_prefix, ref_template, schema_overrides)
+        return schema_ref, definitions, nested_models
+
+    # For generics with no args
+    args = get_args(field_type)
+    if args is not None and not args and Generic in field_type.__bases__:
+        return f_schema, definitions, nested_models
+
+    raise ValueError(f"Value not declarable with JSON Schema, field: {field}")
+
+
+def multitypes_literal_field_for_schema(values: Tuple[Any, ...], field: ModelField) -> ModelField:
+    """
+    To support `Literal` with values of different types, we split it into multiple `Literal` with same type
+    e.g. `Literal['qwe', 'asd', 1, 2]` becomes `Union[Literal['qwe', 'asd'], Literal[1, 2]]`
+    """
+    literal_distinct_types = defaultdict(list)
+    for v in values:
+        literal_distinct_types[v.__class__].append(v)
+    distinct_literals = (Literal[tuple(same_type_values)] for same_type_values in literal_distinct_types.values())
+
+    return ModelField(
+        name=field.name,
+        type_=Union[tuple(distinct_literals)],  # type: ignore
+        class_validators=field.class_validators,
+        model_config=field.model_config,
+        default=field.default,
+        required=field.required,
+        alias=field.alias,
+        field_info=field.field_info,
+    )
+
+
+def encode_default(dft: Any) -> Any:
+    from .main import BaseModel
+
+    if isinstance(dft, BaseModel) or is_dataclass(dft):
+        dft = cast("dict[str, Any]", pydantic_encoder(dft))
+
+    if isinstance(dft, dict):
+        return {encode_default(k): encode_default(v) for k, v in dft.items()}
+    elif isinstance(dft, Enum):
+        return dft.value
+    elif isinstance(dft, (int, float, str)):
+        return dft
+    elif isinstance(dft, (list, tuple)):
+        t = dft.__class__
+        seq_args = (encode_default(v) for v in dft)
+        return t(*seq_args) if is_namedtuple(t) else t(seq_args)
+    elif dft is None:
+        return None
+    else:
+        return pydantic_encoder(dft)
+
+
+_map_types_constraint: Dict[Any, Callable[..., type]] = {int: conint, float: confloat, Decimal: condecimal}
+
+
+def get_annotation_from_field_info(
+    annotation: Any, field_info: FieldInfo, field_name: str, validate_assignment: bool = False
+) -> Type[Any]:
+    """
+    Get an annotation with validation implemented for numbers and strings based on the field_info.
+    :param annotation: an annotation from a field specification, as ``str``, ``ConstrainedStr``
+    :param field_info: an instance of FieldInfo, possibly with declarations for validations and JSON Schema
+    :param field_name: name of the field for use in error messages
+    :param validate_assignment: default False, flag for BaseModel Config value of validate_assignment
+    :return: the same ``annotation`` if unmodified or a new annotation with validation in place
+    """
+    constraints = field_info.get_constraints()
+    used_constraints: Set[str] = set()
+    if constraints:
+        annotation, used_constraints = get_annotation_with_constraints(annotation, field_info)
+    if validate_assignment:
+        used_constraints.add("allow_mutation")
+
+    unused_constraints = constraints - used_constraints
+    if unused_constraints:
+        raise ValueError(
+            f'On field "{field_name}" the following field constraints are set but not enforced: '
+            f'{", ".join(unused_constraints)}. '
+            f"\nFor more details see https://docs.pydantic.dev/usage/schema/#unenforced-field-constraints"
+        )
+
+    return annotation
+
+
+def get_annotation_with_constraints(annotation: Any, field_info: FieldInfo) -> Tuple[Type[Any], Set[str]]:  # noqa: C901
+    """
+    Get an annotation with used constraints implemented for numbers and strings based on the field_info.
+
+    :param annotation: an annotation from a field specification, as ``str``, ``ConstrainedStr``
+    :param field_info: an instance of FieldInfo, possibly with declarations for validations and JSON Schema
+    :return: the same ``annotation`` if unmodified or a new annotation along with the used constraints.
+    """
+    used_constraints: Set[str] = set()
+
+    def go(type_: Any) -> Type[Any]:
+        if (
+            is_literal_type(type_)
+            or isinstance(type_, ForwardRef)
+            or lenient_issubclass(type_, (ConstrainedList, ConstrainedSet, ConstrainedFrozenSet))
+        ):
+            return type_
+        origin = get_origin(type_)
+        if origin is not None:
+            args: Tuple[Any, ...] = get_args(type_)
+            if any(isinstance(a, ForwardRef) for a in args):
+                # forward refs cause infinite recursion below
+                return type_
+
+            if origin is Annotated:
+                return go(args[0])
+            if is_union(origin):
+                return Union[tuple(go(a) for a in args)]  # type: ignore
+
+            if issubclass(origin, List) and (
+                field_info.min_items is not None
+                or field_info.max_items is not None
+                or field_info.unique_items is not None
+            ):
+                used_constraints.update({"min_items", "max_items", "unique_items"})
+                return conlist(
+                    go(args[0]),
+                    min_items=field_info.min_items,
+                    max_items=field_info.max_items,
+                    unique_items=field_info.unique_items,
+                )
+
+            if issubclass(origin, Set) and (field_info.min_items is not None or field_info.max_items is not None):
+                used_constraints.update({"min_items", "max_items"})
+                return conset(go(args[0]), min_items=field_info.min_items, max_items=field_info.max_items)
+
+            if issubclass(origin, FrozenSet) and (field_info.min_items is not None or field_info.max_items is not None):
+                used_constraints.update({"min_items", "max_items"})
+                return confrozenset(go(args[0]), min_items=field_info.min_items, max_items=field_info.max_items)
+
+            for t in (Tuple, List, Set, FrozenSet, Sequence):
+                if issubclass(origin, t):  # type: ignore
+                    return t[tuple(go(a) for a in args)]  # type: ignore
+
+            if issubclass(origin, Dict):
+                return Dict[args[0], go(args[1])]  # type: ignore
+
+        attrs: Optional[Tuple[str, ...]] = None
+        constraint_func: Optional[Callable[..., type]] = None
+        if isinstance(type_, type):
+            if issubclass(type_, (SecretStr, SecretBytes)):
+                attrs = ("max_length", "min_length")
+
+                def constraint_func(**kw: Any) -> Type[Any]:
+                    return type(type_.__name__, (type_,), kw)
+
+            elif issubclass(type_, str) and not issubclass(type_, (EmailStr, AnyUrl)):
+                attrs = ("max_length", "min_length", "regex")
+                if issubclass(type_, StrictStr):
+
+                    def constraint_func(**kw: Any) -> Type[Any]:
+                        return type(type_.__name__, (type_,), kw)
+
+                else:
+                    constraint_func = constr
+            elif issubclass(type_, bytes):
+                attrs = ("max_length", "min_length", "regex")
+                if issubclass(type_, StrictBytes):
+
+                    def constraint_func(**kw: Any) -> Type[Any]:
+                        return type(type_.__name__, (type_,), kw)
+
+                else:
+                    constraint_func = conbytes
+            elif issubclass(type_, numeric_types) and not issubclass(
+                type_,
+                (
+                    ConstrainedInt,
+                    ConstrainedFloat,
+                    ConstrainedDecimal,
+                    ConstrainedList,
+                    ConstrainedSet,
+                    ConstrainedFrozenSet,
+                    bool,
+                ),
+            ):
+                # Is numeric type
+                attrs = ("gt", "lt", "ge", "le", "multiple_of")
+                if issubclass(type_, float):
+                    attrs += ("allow_inf_nan",)
+                if issubclass(type_, Decimal):
+                    attrs += ("max_digits", "decimal_places")
+                numeric_type = next(t for t in numeric_types if issubclass(type_, t))  # pragma: no branch
+                constraint_func = _map_types_constraint[numeric_type]
+
+        if attrs:
+            used_constraints.update(set(attrs))
+            kwargs = {
+                attr_name: attr
+                for attr_name, attr in ((attr_name, getattr(field_info, attr_name)) for attr_name in attrs)
+                if attr is not None
+            }
+            if kwargs:
+                constraint_func = cast(Callable[..., type], constraint_func)
+                return constraint_func(**kwargs)
+        return type_
+
+    return go(annotation), used_constraints
+
+
+def normalize_name(name: str) -> str:
+    """
+    Normalizes the given name. This can be applied to either a model *or* enum.
+    """
+    return re.sub(r"[^a-zA-Z0-9.\-_]", "_", name)
+
+
+class SkipField(Exception):
+    """
+    Utility exception used to exclude fields from schema.
+    """
+
+    def __init__(self, message: str) -> None:
+        self.message = message
diff --git a/vendor/pydantic/types.py b/vendor/pydantic/types.py
new file mode 100644
index 0000000..7eea0a5
--- /dev/null
+++ b/vendor/pydantic/types.py
@@ -0,0 +1,1205 @@
+import abc
+import math
+import re
+import warnings
+from datetime import date
+from decimal import Decimal, InvalidOperation
+from enum import Enum
+from pathlib import Path
+from types import new_class
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    ClassVar,
+    Dict,
+    FrozenSet,
+    List,
+    Optional,
+    Pattern,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+    overload,
+)
+from uuid import UUID
+from weakref import WeakSet
+
+from . import errors
+from .datetime_parse import parse_date
+from .utils import import_string, update_not_none
+from .validators import (
+    bytes_validator,
+    constr_length_validator,
+    constr_lower,
+    constr_strip_whitespace,
+    constr_upper,
+    decimal_validator,
+    float_finite_validator,
+    float_validator,
+    frozenset_validator,
+    int_validator,
+    list_validator,
+    number_multiple_validator,
+    number_size_validator,
+    path_exists_validator,
+    path_validator,
+    set_validator,
+    str_validator,
+    strict_bytes_validator,
+    strict_float_validator,
+    strict_int_validator,
+    strict_str_validator,
+)
+
+__all__ = [
+    "NoneStr",
+    "NoneBytes",
+    "StrBytes",
+    "NoneStrBytes",
+    "StrictStr",
+    "ConstrainedBytes",
+    "conbytes",
+    "ConstrainedList",
+    "conlist",
+    "ConstrainedSet",
+    "conset",
+    "ConstrainedFrozenSet",
+    "confrozenset",
+    "ConstrainedStr",
+    "constr",
+    "PyObject",
+    "ConstrainedInt",
+    "conint",
+    "PositiveInt",
+    "NegativeInt",
+    "NonNegativeInt",
+    "NonPositiveInt",
+    "ConstrainedFloat",
+    "confloat",
+    "PositiveFloat",
+    "NegativeFloat",
+    "NonNegativeFloat",
+    "NonPositiveFloat",
+    "FiniteFloat",
+    "ConstrainedDecimal",
+    "condecimal",
+    "UUID1",
+    "UUID3",
+    "UUID4",
+    "UUID5",
+    "FilePath",
+    "DirectoryPath",
+    "Json",
+    "JsonWrapper",
+    "SecretField",
+    "SecretStr",
+    "SecretBytes",
+    "StrictBool",
+    "StrictBytes",
+    "StrictInt",
+    "StrictFloat",
+    "PaymentCardNumber",
+    "ByteSize",
+    "PastDate",
+    "FutureDate",
+    "ConstrainedDate",
+    "condate",
+]
+
+NoneStr = Optional[str]
+NoneBytes = Optional[bytes]
+StrBytes = Union[str, bytes]
+NoneStrBytes = Optional[StrBytes]
+OptionalInt = Optional[int]
+OptionalIntFloat = Union[OptionalInt, float]
+OptionalIntFloatDecimal = Union[OptionalIntFloat, Decimal]
+OptionalDate = Optional[date]
+StrIntFloat = Union[str, int, float]
+
+if TYPE_CHECKING:
+    from typing_extensions import Annotated
+
+    from .dataclasses import Dataclass
+    from .main import BaseModel
+    from .typing import CallableGenerator
+
+    ModelOrDc = Type[Union[BaseModel, Dataclass]]
+
+T = TypeVar("T")
+_DEFINED_TYPES: "WeakSet[type]" = WeakSet()
+
+
+@overload
+def _registered(typ: Type[T]) -> Type[T]:
+    pass
+
+
+@overload
+def _registered(typ: "ConstrainedNumberMeta") -> "ConstrainedNumberMeta":
+    pass
+
+
+def _registered(typ: Union[Type[T], "ConstrainedNumberMeta"]) -> Union[Type[T], "ConstrainedNumberMeta"]:
+    # In order to generate valid examples of constrained types, Hypothesis needs
+    # to inspect the type object - so we keep a weakref to each contype object
+    # until it can be registered.  When (or if) our Hypothesis plugin is loaded,
+    # it monkeypatches this function.
+    # If Hypothesis is never used, the total effect is to keep a weak reference
+    # which has minimal memory usage and doesn't even affect garbage collection.
+    _DEFINED_TYPES.add(typ)
+    return typ
+
+
+class ConstrainedNumberMeta(type):
+    def __new__(cls, name: str, bases: Any, dct: Dict[str, Any]) -> "ConstrainedInt":  # type: ignore
+        new_cls = cast("ConstrainedInt", type.__new__(cls, name, bases, dct))
+
+        if new_cls.gt is not None and new_cls.ge is not None:
+            raise errors.ConfigError("bounds gt and ge cannot be specified at the same time")
+        if new_cls.lt is not None and new_cls.le is not None:
+            raise errors.ConfigError("bounds lt and le cannot be specified at the same time")
+
+        return _registered(new_cls)  # type: ignore
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BOOLEAN TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+if TYPE_CHECKING:
+    StrictBool = bool
+else:
+
+    class StrictBool(int):
+        """
+        StrictBool to allow for bools which are not type-coerced.
+        """
+
+        @classmethod
+        def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+            field_schema.update(type="boolean")
+
+        @classmethod
+        def __get_validators__(cls) -> "CallableGenerator":
+            yield cls.validate
+
+        @classmethod
+        def validate(cls, value: Any) -> bool:
+            """
+            Ensure that we only allow bools.
+            """
+            if isinstance(value, bool):
+                return value
+
+            raise errors.StrictBoolError()
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ INTEGER TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class ConstrainedInt(int, metaclass=ConstrainedNumberMeta):
+    strict: bool = False
+    gt: OptionalInt = None
+    ge: OptionalInt = None
+    lt: OptionalInt = None
+    le: OptionalInt = None
+    multiple_of: OptionalInt = None
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(
+            field_schema,
+            exclusiveMinimum=cls.gt,
+            exclusiveMaximum=cls.lt,
+            minimum=cls.ge,
+            maximum=cls.le,
+            multipleOf=cls.multiple_of,
+        )
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield strict_int_validator if cls.strict else int_validator
+        yield number_size_validator
+        yield number_multiple_validator
+
+
+def conint(
+    *,
+    strict: bool = False,
+    gt: Optional[int] = None,
+    ge: Optional[int] = None,
+    lt: Optional[int] = None,
+    le: Optional[int] = None,
+    multiple_of: Optional[int] = None,
+) -> Type[int]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(strict=strict, gt=gt, ge=ge, lt=lt, le=le, multiple_of=multiple_of)
+    return type("ConstrainedIntValue", (ConstrainedInt,), namespace)
+
+
+if TYPE_CHECKING:
+    PositiveInt = int
+    NegativeInt = int
+    NonPositiveInt = int
+    NonNegativeInt = int
+    StrictInt = int
+else:
+
+    class PositiveInt(ConstrainedInt):
+        gt = 0
+
+    class NegativeInt(ConstrainedInt):
+        lt = 0
+
+    class NonPositiveInt(ConstrainedInt):
+        le = 0
+
+    class NonNegativeInt(ConstrainedInt):
+        ge = 0
+
+    class StrictInt(ConstrainedInt):
+        strict = True
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ FLOAT TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class ConstrainedFloat(float, metaclass=ConstrainedNumberMeta):
+    strict: bool = False
+    gt: OptionalIntFloat = None
+    ge: OptionalIntFloat = None
+    lt: OptionalIntFloat = None
+    le: OptionalIntFloat = None
+    multiple_of: OptionalIntFloat = None
+    allow_inf_nan: Optional[bool] = None
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(
+            field_schema,
+            exclusiveMinimum=cls.gt,
+            exclusiveMaximum=cls.lt,
+            minimum=cls.ge,
+            maximum=cls.le,
+            multipleOf=cls.multiple_of,
+        )
+        # Modify constraints to account for differences between IEEE floats and JSON
+        if field_schema.get("exclusiveMinimum") == -math.inf:
+            del field_schema["exclusiveMinimum"]
+        if field_schema.get("minimum") == -math.inf:
+            del field_schema["minimum"]
+        if field_schema.get("exclusiveMaximum") == math.inf:
+            del field_schema["exclusiveMaximum"]
+        if field_schema.get("maximum") == math.inf:
+            del field_schema["maximum"]
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield strict_float_validator if cls.strict else float_validator
+        yield number_size_validator
+        yield number_multiple_validator
+        yield float_finite_validator
+
+
+def confloat(
+    *,
+    strict: bool = False,
+    gt: float = None,
+    ge: float = None,
+    lt: float = None,
+    le: float = None,
+    multiple_of: float = None,
+    allow_inf_nan: Optional[bool] = None,
+) -> Type[float]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(strict=strict, gt=gt, ge=ge, lt=lt, le=le, multiple_of=multiple_of, allow_inf_nan=allow_inf_nan)
+    return type("ConstrainedFloatValue", (ConstrainedFloat,), namespace)
+
+
+if TYPE_CHECKING:
+    PositiveFloat = float
+    NegativeFloat = float
+    NonPositiveFloat = float
+    NonNegativeFloat = float
+    StrictFloat = float
+    FiniteFloat = float
+else:
+
+    class PositiveFloat(ConstrainedFloat):
+        gt = 0
+
+    class NegativeFloat(ConstrainedFloat):
+        lt = 0
+
+    class NonPositiveFloat(ConstrainedFloat):
+        le = 0
+
+    class NonNegativeFloat(ConstrainedFloat):
+        ge = 0
+
+    class StrictFloat(ConstrainedFloat):
+        strict = True
+
+    class FiniteFloat(ConstrainedFloat):
+        allow_inf_nan = False
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BYTES TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class ConstrainedBytes(bytes):
+    strip_whitespace = False
+    to_upper = False
+    to_lower = False
+    min_length: OptionalInt = None
+    max_length: OptionalInt = None
+    strict: bool = False
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(field_schema, minLength=cls.min_length, maxLength=cls.max_length)
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield strict_bytes_validator if cls.strict else bytes_validator
+        yield constr_strip_whitespace
+        yield constr_upper
+        yield constr_lower
+        yield constr_length_validator
+
+
+def conbytes(
+    *,
+    strip_whitespace: bool = False,
+    to_upper: bool = False,
+    to_lower: bool = False,
+    min_length: Optional[int] = None,
+    max_length: Optional[int] = None,
+    strict: bool = False,
+) -> Type[bytes]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(
+        strip_whitespace=strip_whitespace,
+        to_upper=to_upper,
+        to_lower=to_lower,
+        min_length=min_length,
+        max_length=max_length,
+        strict=strict,
+    )
+    return _registered(type("ConstrainedBytesValue", (ConstrainedBytes,), namespace))
+
+
+if TYPE_CHECKING:
+    StrictBytes = bytes
+else:
+
+    class StrictBytes(ConstrainedBytes):
+        strict = True
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ STRING TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class ConstrainedStr(str):
+    strip_whitespace = False
+    to_upper = False
+    to_lower = False
+    min_length: OptionalInt = None
+    max_length: OptionalInt = None
+    curtail_length: OptionalInt = None
+    regex: Optional[Union[str, Pattern[str]]] = None
+    strict = False
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(
+            field_schema,
+            minLength=cls.min_length,
+            maxLength=cls.max_length,
+            pattern=cls.regex and cls._get_pattern(cls.regex),
+        )
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield strict_str_validator if cls.strict else str_validator
+        yield constr_strip_whitespace
+        yield constr_upper
+        yield constr_lower
+        yield constr_length_validator
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: Union[str]) -> Union[str]:
+        if cls.curtail_length and len(value) > cls.curtail_length:
+            value = value[: cls.curtail_length]
+
+        if cls.regex:
+            if not re.match(cls.regex, value):
+                raise errors.StrRegexError(pattern=cls._get_pattern(cls.regex))
+
+        return value
+
+    @staticmethod
+    def _get_pattern(regex: Union[str, Pattern[str]]) -> str:
+        return regex if isinstance(regex, str) else regex.pattern
+
+
+def constr(
+    *,
+    strip_whitespace: bool = False,
+    to_upper: bool = False,
+    to_lower: bool = False,
+    strict: bool = False,
+    min_length: Optional[int] = None,
+    max_length: Optional[int] = None,
+    curtail_length: Optional[int] = None,
+    regex: Optional[str] = None,
+) -> Type[str]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(
+        strip_whitespace=strip_whitespace,
+        to_upper=to_upper,
+        to_lower=to_lower,
+        strict=strict,
+        min_length=min_length,
+        max_length=max_length,
+        curtail_length=curtail_length,
+        regex=regex and re.compile(regex),
+    )
+    return _registered(type("ConstrainedStrValue", (ConstrainedStr,), namespace))
+
+
+if TYPE_CHECKING:
+    StrictStr = str
+else:
+
+    class StrictStr(ConstrainedStr):
+        strict = True
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ SET TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+# This types superclass should be Set[T], but cython chokes on that...
+class ConstrainedSet(set):  # type: ignore
+    # Needed for pydantic to detect that this is a set
+    __origin__ = set
+    __args__: Set[Type[T]]  # type: ignore
+
+    min_items: Optional[int] = None
+    max_items: Optional[int] = None
+    item_type: Type[T]  # type: ignore
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield cls.set_length_validator
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(field_schema, minItems=cls.min_items, maxItems=cls.max_items)
+
+    @classmethod
+    def set_length_validator(cls, v: "Optional[Set[T]]") -> "Optional[Set[T]]":
+        if v is None:
+            return None
+
+        v = set_validator(v)
+        v_len = len(v)
+
+        if cls.min_items is not None and v_len < cls.min_items:
+            raise errors.SetMinLengthError(limit_value=cls.min_items)
+
+        if cls.max_items is not None and v_len > cls.max_items:
+            raise errors.SetMaxLengthError(limit_value=cls.max_items)
+
+        return v
+
+
+def conset(item_type: Type[T], *, min_items: Optional[int] = None, max_items: Optional[int] = None) -> Type[Set[T]]:
+    # __args__ is needed to conform to typing generics api
+    namespace = {"min_items": min_items, "max_items": max_items, "item_type": item_type, "__args__": [item_type]}
+    # We use new_class to be able to deal with Generic types
+    return new_class("ConstrainedSetValue", (ConstrainedSet,), {}, lambda ns: ns.update(namespace))
+
+
+# This types superclass should be FrozenSet[T], but cython chokes on that...
+class ConstrainedFrozenSet(frozenset):  # type: ignore
+    # Needed for pydantic to detect that this is a set
+    __origin__ = frozenset
+    __args__: FrozenSet[Type[T]]  # type: ignore
+
+    min_items: Optional[int] = None
+    max_items: Optional[int] = None
+    item_type: Type[T]  # type: ignore
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield cls.frozenset_length_validator
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(field_schema, minItems=cls.min_items, maxItems=cls.max_items)
+
+    @classmethod
+    def frozenset_length_validator(cls, v: "Optional[FrozenSet[T]]") -> "Optional[FrozenSet[T]]":
+        if v is None:
+            return None
+
+        v = frozenset_validator(v)
+        v_len = len(v)
+
+        if cls.min_items is not None and v_len < cls.min_items:
+            raise errors.FrozenSetMinLengthError(limit_value=cls.min_items)
+
+        if cls.max_items is not None and v_len > cls.max_items:
+            raise errors.FrozenSetMaxLengthError(limit_value=cls.max_items)
+
+        return v
+
+
+def confrozenset(
+    item_type: Type[T], *, min_items: Optional[int] = None, max_items: Optional[int] = None
+) -> Type[FrozenSet[T]]:
+    # __args__ is needed to conform to typing generics api
+    namespace = {"min_items": min_items, "max_items": max_items, "item_type": item_type, "__args__": [item_type]}
+    # We use new_class to be able to deal with Generic types
+    return new_class("ConstrainedFrozenSetValue", (ConstrainedFrozenSet,), {}, lambda ns: ns.update(namespace))
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ LIST TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+# This types superclass should be List[T], but cython chokes on that...
+class ConstrainedList(list):  # type: ignore
+    # Needed for pydantic to detect that this is a list
+    __origin__ = list
+    __args__: Tuple[Type[T], ...]  # type: ignore
+
+    min_items: Optional[int] = None
+    max_items: Optional[int] = None
+    unique_items: Optional[bool] = None
+    item_type: Type[T]  # type: ignore
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield cls.list_length_validator
+        if cls.unique_items:
+            yield cls.unique_items_validator
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(field_schema, minItems=cls.min_items, maxItems=cls.max_items, uniqueItems=cls.unique_items)
+
+    @classmethod
+    def list_length_validator(cls, v: "Optional[List[T]]") -> "Optional[List[T]]":
+        if v is None:
+            return None
+
+        v = list_validator(v)
+        v_len = len(v)
+
+        if cls.min_items is not None and v_len < cls.min_items:
+            raise errors.ListMinLengthError(limit_value=cls.min_items)
+
+        if cls.max_items is not None and v_len > cls.max_items:
+            raise errors.ListMaxLengthError(limit_value=cls.max_items)
+
+        return v
+
+    @classmethod
+    def unique_items_validator(cls, v: "Optional[List[T]]") -> "Optional[List[T]]":
+        if v is None:
+            return None
+
+        for i, value in enumerate(v, start=1):
+            if value in v[i:]:
+                raise errors.ListUniqueItemsError()
+
+        return v
+
+
+def conlist(
+    item_type: Type[T], *, min_items: Optional[int] = None, max_items: Optional[int] = None, unique_items: bool = None
+) -> Type[List[T]]:
+    # __args__ is needed to conform to typing generics api
+    namespace = dict(
+        min_items=min_items, max_items=max_items, unique_items=unique_items, item_type=item_type, __args__=(item_type,)
+    )
+    # We use new_class to be able to deal with Generic types
+    return new_class("ConstrainedListValue", (ConstrainedList,), {}, lambda ns: ns.update(namespace))
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PYOBJECT TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+if TYPE_CHECKING:
+    PyObject = Callable[..., Any]
+else:
+
+    class PyObject:
+        validate_always = True
+
+        @classmethod
+        def __get_validators__(cls) -> "CallableGenerator":
+            yield cls.validate
+
+        @classmethod
+        def validate(cls, value: Any) -> Any:
+            if isinstance(value, Callable):
+                return value
+
+            try:
+                value = str_validator(value)
+            except errors.StrError:
+                raise errors.PyObjectError(error_message="value is neither a valid import path not a valid callable")
+
+            try:
+                return import_string(value)
+            except ImportError as e:
+                raise errors.PyObjectError(error_message=str(e))
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DECIMAL TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class ConstrainedDecimal(Decimal, metaclass=ConstrainedNumberMeta):
+    gt: OptionalIntFloatDecimal = None
+    ge: OptionalIntFloatDecimal = None
+    lt: OptionalIntFloatDecimal = None
+    le: OptionalIntFloatDecimal = None
+    max_digits: OptionalInt = None
+    decimal_places: OptionalInt = None
+    multiple_of: OptionalIntFloatDecimal = None
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(
+            field_schema,
+            exclusiveMinimum=cls.gt,
+            exclusiveMaximum=cls.lt,
+            minimum=cls.ge,
+            maximum=cls.le,
+            multipleOf=cls.multiple_of,
+        )
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield decimal_validator
+        yield number_size_validator
+        yield number_multiple_validator
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value: Decimal) -> Decimal:
+        try:
+            normalized_value = value.normalize()
+        except InvalidOperation:
+            normalized_value = value
+        digit_tuple, exponent = normalized_value.as_tuple()[1:]
+        if exponent in {"F", "n", "N"}:
+            raise errors.DecimalIsNotFiniteError()
+
+        if exponent >= 0:
+            # A positive exponent adds that many trailing zeros.
+            digits = len(digit_tuple) + exponent
+            decimals = 0
+        else:
+            # If the absolute value of the negative exponent is larger than the
+            # number of digits, then it's the same as the number of digits,
+            # because it'll consume all of the digits in digit_tuple and then
+            # add abs(exponent) - len(digit_tuple) leading zeros after the
+            # decimal point.
+            if abs(exponent) > len(digit_tuple):
+                digits = decimals = abs(exponent)
+            else:
+                digits = len(digit_tuple)
+                decimals = abs(exponent)
+        whole_digits = digits - decimals
+
+        if cls.max_digits is not None and digits > cls.max_digits:
+            raise errors.DecimalMaxDigitsError(max_digits=cls.max_digits)
+
+        if cls.decimal_places is not None and decimals > cls.decimal_places:
+            raise errors.DecimalMaxPlacesError(decimal_places=cls.decimal_places)
+
+        if cls.max_digits is not None and cls.decimal_places is not None:
+            expected = cls.max_digits - cls.decimal_places
+            if whole_digits > expected:
+                raise errors.DecimalWholeDigitsError(whole_digits=expected)
+
+        return value
+
+
+def condecimal(
+    *,
+    gt: Decimal = None,
+    ge: Decimal = None,
+    lt: Decimal = None,
+    le: Decimal = None,
+    max_digits: Optional[int] = None,
+    decimal_places: Optional[int] = None,
+    multiple_of: Decimal = None,
+) -> Type[Decimal]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(
+        gt=gt, ge=ge, lt=lt, le=le, max_digits=max_digits, decimal_places=decimal_places, multiple_of=multiple_of
+    )
+    return type("ConstrainedDecimalValue", (ConstrainedDecimal,), namespace)
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ UUID TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+if TYPE_CHECKING:
+    UUID1 = UUID
+    UUID3 = UUID
+    UUID4 = UUID
+    UUID5 = UUID
+else:
+
+    class UUID1(UUID):
+        _required_version = 1
+
+        @classmethod
+        def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+            field_schema.update(type="string", format=f"uuid{cls._required_version}")
+
+    class UUID3(UUID1):
+        _required_version = 3
+
+    class UUID4(UUID1):
+        _required_version = 4
+
+    class UUID5(UUID1):
+        _required_version = 5
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PATH TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+if TYPE_CHECKING:
+    FilePath = Path
+    DirectoryPath = Path
+else:
+
+    class FilePath(Path):
+        @classmethod
+        def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+            field_schema.update(format="file-path")
+
+        @classmethod
+        def __get_validators__(cls) -> "CallableGenerator":
+            yield path_validator
+            yield path_exists_validator
+            yield cls.validate
+
+        @classmethod
+        def validate(cls, value: Path) -> Path:
+            if not value.is_file():
+                raise errors.PathNotAFileError(path=value)
+
+            return value
+
+    class DirectoryPath(Path):
+        @classmethod
+        def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+            field_schema.update(format="directory-path")
+
+        @classmethod
+        def __get_validators__(cls) -> "CallableGenerator":
+            yield path_validator
+            yield path_exists_validator
+            yield cls.validate
+
+        @classmethod
+        def validate(cls, value: Path) -> Path:
+            if not value.is_dir():
+                raise errors.PathNotADirectoryError(path=value)
+
+            return value
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ JSON TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class JsonWrapper:
+    pass
+
+
+class JsonMeta(type):
+    def __getitem__(self, t: Type[Any]) -> Type[JsonWrapper]:
+        if t is Any:
+            return Json  # allow Json[Any] to replecate plain Json
+        return _registered(type("JsonWrapperValue", (JsonWrapper,), {"inner_type": t}))
+
+
+if TYPE_CHECKING:
+    Json = Annotated[T, ...]  # Json[list[str]] will be recognized by type checkers as list[str]
+
+else:
+
+    class Json(metaclass=JsonMeta):
+        @classmethod
+        def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+            field_schema.update(type="string", format="json-string")
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ SECRET TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class SecretField(abc.ABC):
+    """
+    Note: this should be implemented as a generic like `SecretField(ABC, Generic[T])`,
+          the `__init__()` should be part of the abstract class and the
+          `get_secret_value()` method should use the generic `T` type.
+
+          However Cython doesn't support very well generics at the moment and
+          the generated code fails to be imported (see
+          https://github.com/cython/cython/issues/2753).
+    """
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, self.__class__) and self.get_secret_value() == other.get_secret_value()
+
+    def __str__(self) -> str:
+        return "**********" if self.get_secret_value() else ""
+
+    def __hash__(self) -> int:
+        return hash(self.get_secret_value())
+
+    @abc.abstractmethod
+    def get_secret_value(self) -> Any:  # pragma: no cover
+        ...
+
+
+class SecretStr(SecretField):
+    min_length: OptionalInt = None
+    max_length: OptionalInt = None
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(
+            field_schema,
+            type="string",
+            writeOnly=True,
+            format="password",
+            minLength=cls.min_length,
+            maxLength=cls.max_length,
+        )
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield cls.validate
+        yield constr_length_validator
+
+    @classmethod
+    def validate(cls, value: Any) -> "SecretStr":
+        if isinstance(value, cls):
+            return value
+        value = str_validator(value)
+        return cls(value)
+
+    def __init__(self, value: str):
+        self._secret_value = value
+
+    def __repr__(self) -> str:
+        return f"SecretStr('{self}')"
+
+    def __len__(self) -> int:
+        return len(self._secret_value)
+
+    def display(self) -> str:
+        warnings.warn("`secret_str.display()` is deprecated, use `str(secret_str)` instead", DeprecationWarning)
+        return str(self)
+
+    def get_secret_value(self) -> str:
+        return self._secret_value
+
+
+class SecretBytes(SecretField):
+    min_length: OptionalInt = None
+    max_length: OptionalInt = None
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(
+            field_schema,
+            type="string",
+            writeOnly=True,
+            format="password",
+            minLength=cls.min_length,
+            maxLength=cls.max_length,
+        )
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield cls.validate
+        yield constr_length_validator
+
+    @classmethod
+    def validate(cls, value: Any) -> "SecretBytes":
+        if isinstance(value, cls):
+            return value
+        value = bytes_validator(value)
+        return cls(value)
+
+    def __init__(self, value: bytes):
+        self._secret_value = value
+
+    def __repr__(self) -> str:
+        return f"SecretBytes(b'{self}')"
+
+    def __len__(self) -> int:
+        return len(self._secret_value)
+
+    def display(self) -> str:
+        warnings.warn("`secret_bytes.display()` is deprecated, use `str(secret_bytes)` instead", DeprecationWarning)
+        return str(self)
+
+    def get_secret_value(self) -> bytes:
+        return self._secret_value
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PAYMENT CARD TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+class PaymentCardBrand(str, Enum):
+    # If you add another card type, please also add it to the
+    # Hypothesis strategy in `pydantic._hypothesis_plugin`.
+    amex = "American Express"
+    mastercard = "Mastercard"
+    visa = "Visa"
+    other = "other"
+
+    def __str__(self) -> str:
+        return self.value
+
+
+class PaymentCardNumber(str):
+    """
+    Based on: https://en.wikipedia.org/wiki/Payment_card_number
+    """
+
+    strip_whitespace: ClassVar[bool] = True
+    min_length: ClassVar[int] = 12
+    max_length: ClassVar[int] = 19
+    bin: str
+    last4: str
+    brand: PaymentCardBrand
+
+    def __init__(self, card_number: str):
+        self.bin = card_number[:6]
+        self.last4 = card_number[-4:]
+        self.brand = self._get_brand(card_number)
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield str_validator
+        yield constr_strip_whitespace
+        yield constr_length_validator
+        yield cls.validate_digits
+        yield cls.validate_luhn_check_digit
+        yield cls
+        yield cls.validate_length_for_brand
+
+    @property
+    def masked(self) -> str:
+        num_masked = len(self) - 10  # len(bin) + len(last4) == 10
+        return f'{self.bin}{"*" * num_masked}{self.last4}'
+
+    @classmethod
+    def validate_digits(cls, card_number: str) -> str:
+        if not card_number.isdigit():
+            raise errors.NotDigitError
+        return card_number
+
+    @classmethod
+    def validate_luhn_check_digit(cls, card_number: str) -> str:
+        """
+        Based on: https://en.wikipedia.org/wiki/Luhn_algorithm
+        """
+        sum_ = int(card_number[-1])
+        length = len(card_number)
+        parity = length % 2
+        for i in range(length - 1):
+            digit = int(card_number[i])
+            if i % 2 == parity:
+                digit *= 2
+            if digit > 9:
+                digit -= 9
+            sum_ += digit
+        valid = sum_ % 10 == 0
+        if not valid:
+            raise errors.LuhnValidationError
+        return card_number
+
+    @classmethod
+    def validate_length_for_brand(cls, card_number: "PaymentCardNumber") -> "PaymentCardNumber":
+        """
+        Validate length based on BIN for major brands:
+        https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN)
+        """
+        required_length: Union[None, int, str] = None
+        if card_number.brand in PaymentCardBrand.mastercard:
+            required_length = 16
+            valid = len(card_number) == required_length
+        elif card_number.brand == PaymentCardBrand.visa:
+            required_length = "13, 16 or 19"
+            valid = len(card_number) in {13, 16, 19}
+        elif card_number.brand == PaymentCardBrand.amex:
+            required_length = 15
+            valid = len(card_number) == required_length
+        else:
+            valid = True
+        if not valid:
+            raise errors.InvalidLengthForBrand(brand=card_number.brand, required_length=required_length)
+        return card_number
+
+    @staticmethod
+    def _get_brand(card_number: str) -> PaymentCardBrand:
+        if card_number[0] == "4":
+            brand = PaymentCardBrand.visa
+        elif 51 <= int(card_number[:2]) <= 55:
+            brand = PaymentCardBrand.mastercard
+        elif card_number[:2] in {"34", "37"}:
+            brand = PaymentCardBrand.amex
+        else:
+            brand = PaymentCardBrand.other
+        return brand
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BYTE SIZE TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+BYTE_SIZES = {
+    "b": 1,
+    "kb": 10**3,
+    "mb": 10**6,
+    "gb": 10**9,
+    "tb": 10**12,
+    "pb": 10**15,
+    "eb": 10**18,
+    "kib": 2**10,
+    "mib": 2**20,
+    "gib": 2**30,
+    "tib": 2**40,
+    "pib": 2**50,
+    "eib": 2**60,
+}
+BYTE_SIZES.update({k.lower()[0]: v for k, v in BYTE_SIZES.items() if "i" not in k})
+byte_string_re = re.compile(r"^\s*(\d*\.?\d+)\s*(\w+)?", re.IGNORECASE)
+
+
+class ByteSize(int):
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, v: StrIntFloat) -> "ByteSize":
+        try:
+            return cls(int(v))
+        except ValueError:
+            pass
+
+        str_match = byte_string_re.match(str(v))
+        if str_match is None:
+            raise errors.InvalidByteSize()
+
+        scalar, unit = str_match.groups()
+        if unit is None:
+            unit = "b"
+
+        try:
+            unit_mult = BYTE_SIZES[unit.lower()]
+        except KeyError:
+            raise errors.InvalidByteSizeUnit(unit=unit)
+
+        return cls(int(float(scalar) * unit_mult))
+
+    def human_readable(self, decimal: bool = False) -> str:
+        if decimal:
+            divisor = 1000
+            units = ["B", "KB", "MB", "GB", "TB", "PB"]
+            final_unit = "EB"
+        else:
+            divisor = 1024
+            units = ["B", "KiB", "MiB", "GiB", "TiB", "PiB"]
+            final_unit = "EiB"
+
+        num = float(self)
+        for unit in units:
+            if abs(num) < divisor:
+                return f"{num:0.1f}{unit}"
+            num /= divisor
+
+        return f"{num:0.1f}{final_unit}"
+
+    def to(self, unit: str) -> float:
+        try:
+            unit_div = BYTE_SIZES[unit.lower()]
+        except KeyError:
+            raise errors.InvalidByteSizeUnit(unit=unit)
+
+        return self / unit_div
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DATE TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+if TYPE_CHECKING:
+    PastDate = date
+    FutureDate = date
+else:
+
+    class PastDate(date):
+        @classmethod
+        def __get_validators__(cls) -> "CallableGenerator":
+            yield parse_date
+            yield cls.validate
+
+        @classmethod
+        def validate(cls, value: date) -> date:
+            if value >= date.today():
+                raise errors.DateNotInThePastError()
+
+            return value
+
+    class FutureDate(date):
+        @classmethod
+        def __get_validators__(cls) -> "CallableGenerator":
+            yield parse_date
+            yield cls.validate
+
+        @classmethod
+        def validate(cls, value: date) -> date:
+            if value <= date.today():
+                raise errors.DateNotInTheFutureError()
+
+            return value
+
+
+class ConstrainedDate(date, metaclass=ConstrainedNumberMeta):
+    gt: OptionalDate = None
+    ge: OptionalDate = None
+    lt: OptionalDate = None
+    le: OptionalDate = None
+
+    @classmethod
+    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:
+        update_not_none(field_schema, exclusiveMinimum=cls.gt, exclusiveMaximum=cls.lt, minimum=cls.ge, maximum=cls.le)
+
+    @classmethod
+    def __get_validators__(cls) -> "CallableGenerator":
+        yield parse_date
+        yield number_size_validator
+
+
+def condate(
+    *,
+    gt: date = None,
+    ge: date = None,
+    lt: date = None,
+    le: date = None,
+) -> Type[date]:
+    # use kwargs then define conf in a dict to aid with IDE type hinting
+    namespace = dict(gt=gt, ge=ge, lt=lt, le=le)
+    return type("ConstrainedDateValue", (ConstrainedDate,), namespace)
diff --git a/vendor/pydantic/typing.py b/vendor/pydantic/typing.py
new file mode 100644
index 0000000..d3c0b6a
--- /dev/null
+++ b/vendor/pydantic/typing.py
@@ -0,0 +1,597 @@
+import sys
+import typing
+from collections.abc import Callable
+from os import PathLike
+from typing import TYPE_CHECKING, AbstractSet, Any
+from typing import Callable as TypingCallable  # type: ignore
+from typing import (
+    ClassVar,
+    Dict,
+    ForwardRef,
+    Generator,
+    Iterable,
+    List,
+    Mapping,
+    NewType,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    _eval_type,
+    cast,
+    get_type_hints,
+)
+
+from typing_extensions import Annotated, Final, Literal
+from typing_extensions import NotRequired as TypedDictNotRequired
+from typing_extensions import Required as TypedDictRequired
+
+try:
+    from typing import _TypingBase as typing_base  # type: ignore
+except ImportError:
+    from typing import _Final as typing_base  # type: ignore
+
+try:
+    from typing import GenericAlias as TypingGenericAlias  # type: ignore
+except ImportError:
+    # python < 3.9 does not have GenericAlias (list[int], tuple[str, ...] and so on)
+    TypingGenericAlias = ()
+
+try:
+    from types import UnionType as TypesUnionType  # type: ignore
+except ImportError:
+    # python < 3.10 does not have UnionType (str | int, byte | bool and so on)
+    TypesUnionType = ()
+
+
+if sys.version_info < (3, 9):
+
+    def evaluate_forwardref(type_: ForwardRef, globalns: Any, localns: Any) -> Any:
+        return type_._evaluate(globalns, localns)
+
+else:
+
+    def evaluate_forwardref(type_: ForwardRef, globalns: Any, localns: Any) -> Any:
+        # Even though it is the right signature for python 3.9, mypy complains with
+        # `error: Too many arguments for "_evaluate" of "ForwardRef"` hence the cast...
+        return cast(Any, type_)._evaluate(globalns, localns, set())
+
+
+if sys.version_info < (3, 9):
+    # Ensure we always get all the whole `Annotated` hint, not just the annotated type.
+    # For 3.7 to 3.8, `get_type_hints` doesn't recognize `typing_extensions.Annotated`,
+    # so it already returns the full annotation
+    get_all_type_hints = get_type_hints
+
+else:
+
+    def get_all_type_hints(obj: Any, globalns: Any = None, localns: Any = None) -> Any:
+        return get_type_hints(obj, globalns, localns, include_extras=True)
+
+
+_T = TypeVar("_T")
+
+AnyCallable = TypingCallable[..., Any]
+NoArgAnyCallable = TypingCallable[[], Any]
+
+# workaround for https://github.com/python/mypy/issues/9496
+AnyArgTCallable = TypingCallable[..., _T]
+
+
+# Annotated[...] is implemented by returning an instance of one of these classes, depending on
+# python/typing_extensions version.
+AnnotatedTypeNames = {"AnnotatedMeta", "_AnnotatedAlias"}
+
+
+LITERAL_TYPES: Set[Any] = {Literal}
+if hasattr(typing, "Literal"):
+    LITERAL_TYPES.add(typing.Literal)
+
+
+if sys.version_info < (3, 8):
+
+    def get_origin(t: Type[Any]) -> Optional[Type[Any]]:
+        if type(t).__name__ in AnnotatedTypeNames:
+            # weirdly this is a runtime requirement, as well as for mypy
+            return cast(Type[Any], Annotated)
+        return getattr(t, "__origin__", None)
+
+else:
+    from typing import get_origin as _typing_get_origin
+
+    def get_origin(tp: Type[Any]) -> Optional[Type[Any]]:
+        """
+        We can't directly use `typing.get_origin` since we need a fallback to support
+        custom generic classes like `ConstrainedList`
+        It should be useless once https://github.com/cython/cython/issues/3537 is
+        solved and https://github.com/pydantic/pydantic/pull/1753 is merged.
+        """
+        if type(tp).__name__ in AnnotatedTypeNames:
+            return cast(Type[Any], Annotated)  # mypy complains about _SpecialForm
+        return _typing_get_origin(tp) or getattr(tp, "__origin__", None)
+
+
+if sys.version_info < (3, 8):
+    from typing import _GenericAlias
+
+    def get_args(t: Type[Any]) -> Tuple[Any, ...]:
+        """Compatibility version of get_args for python 3.7.
+
+        Mostly compatible with the python 3.8 `typing` module version
+        and able to handle almost all use cases.
+        """
+        if type(t).__name__ in AnnotatedTypeNames:
+            return t.__args__ + t.__metadata__
+        if isinstance(t, _GenericAlias):
+            res = t.__args__
+            if t.__origin__ is Callable and res and res[0] is not Ellipsis:
+                res = (list(res[:-1]), res[-1])
+            return res
+        return getattr(t, "__args__", ())
+
+else:
+    from typing import get_args as _typing_get_args
+
+    def _generic_get_args(tp: Type[Any]) -> Tuple[Any, ...]:
+        """
+        In python 3.9, `typing.Dict`, `typing.List`, ...
+        do have an empty `__args__` by default (instead of the generic ~T for example).
+        In order to still support `Dict` for example and consider it as `Dict[Any, Any]`,
+        we retrieve the `_nparams` value that tells us how many parameters it needs.
+        """
+        if hasattr(tp, "_nparams"):
+            return (Any,) * tp._nparams
+        # Special case for `tuple[()]`, which used to return ((),) with `typing.Tuple`
+        # in python 3.10- but now returns () for `tuple` and `Tuple`.
+        # This will probably be clarified in pydantic v2
+        try:
+            if tp == Tuple[()] or sys.version_info >= (3, 9) and tp == tuple[()]:  # type: ignore[misc]
+                return ((),)
+        # there is a TypeError when compiled with cython
+        except TypeError:  # pragma: no cover
+            pass
+        return ()
+
+    def get_args(tp: Type[Any]) -> Tuple[Any, ...]:
+        """Get type arguments with all substitutions performed.
+
+        For unions, basic simplifications used by Union constructor are performed.
+        Examples::
+            get_args(Dict[str, int]) == (str, int)
+            get_args(int) == ()
+            get_args(Union[int, Union[T, int], str][int]) == (int, str)
+            get_args(Union[int, Tuple[T, int]][str]) == (int, Tuple[str, int])
+            get_args(Callable[[], T][int]) == ([], int)
+        """
+        if type(tp).__name__ in AnnotatedTypeNames:
+            return tp.__args__ + tp.__metadata__
+        # the fallback is needed for the same reasons as `get_origin` (see above)
+        return _typing_get_args(tp) or getattr(tp, "__args__", ()) or _generic_get_args(tp)
+
+
+if sys.version_info < (3, 9):
+
+    def convert_generics(tp: Type[Any]) -> Type[Any]:
+        """Python 3.9 and older only supports generics from `typing` module.
+        They convert strings to ForwardRef automatically.
+
+        Examples::
+            typing.List['Hero'] == typing.List[ForwardRef('Hero')]
+        """
+        return tp
+
+else:
+    from typing import _UnionGenericAlias  # type: ignore
+
+    from typing_extensions import _AnnotatedAlias
+
+    def convert_generics(tp: Type[Any]) -> Type[Any]:
+        """
+        Recursively searches for `str` type hints and replaces them with ForwardRef.
+
+        Examples::
+            convert_generics(list['Hero']) == list[ForwardRef('Hero')]
+            convert_generics(dict['Hero', 'Team']) == dict[ForwardRef('Hero'), ForwardRef('Team')]
+            convert_generics(typing.Dict['Hero', 'Team']) == typing.Dict[ForwardRef('Hero'), ForwardRef('Team')]
+            convert_generics(list[str | 'Hero'] | int) == list[str | ForwardRef('Hero')] | int
+        """
+        origin = get_origin(tp)
+        if not origin or not hasattr(tp, "__args__"):
+            return tp
+
+        args = get_args(tp)
+
+        # typing.Annotated needs special treatment
+        if origin is Annotated:
+            return _AnnotatedAlias(convert_generics(args[0]), args[1:])
+
+        # recursively replace `str` instances inside of `GenericAlias` with `ForwardRef(arg)`
+        converted = tuple(
+            ForwardRef(arg) if isinstance(arg, str) and isinstance(tp, TypingGenericAlias) else convert_generics(arg)
+            for arg in args
+        )
+
+        if converted == args:
+            return tp
+        elif isinstance(tp, TypingGenericAlias):
+            return TypingGenericAlias(origin, converted)
+        elif isinstance(tp, TypesUnionType):
+            # recreate types.UnionType (PEP604, Python >= 3.10)
+            return _UnionGenericAlias(origin, converted)
+        else:
+            try:
+                setattr(tp, "__args__", converted)
+            except AttributeError:
+                pass
+            return tp
+
+
+if sys.version_info < (3, 10):
+
+    def is_union(tp: Optional[Type[Any]]) -> bool:
+        return tp is Union
+
+    WithArgsTypes = (TypingGenericAlias,)
+
+else:
+    import types
+    import typing
+
+    def is_union(tp: Optional[Type[Any]]) -> bool:
+        return tp is Union or tp is types.UnionType  # noqa: E721
+
+    WithArgsTypes = (typing._GenericAlias, types.GenericAlias, types.UnionType)
+
+
+StrPath = Union[str, PathLike]
+
+
+if TYPE_CHECKING:
+    from .fields import ModelField
+
+    TupleGenerator = Generator[Tuple[str, Any], None, None]
+    DictStrAny = Dict[str, Any]
+    DictAny = Dict[Any, Any]
+    SetStr = Set[str]
+    ListStr = List[str]
+    IntStr = Union[int, str]
+    AbstractSetIntStr = AbstractSet[IntStr]
+    DictIntStrAny = Dict[IntStr, Any]
+    MappingIntStrAny = Mapping[IntStr, Any]
+    CallableGenerator = Generator[AnyCallable, None, None]
+    ReprArgs = Sequence[Tuple[Optional[str], Any]]
+
+    MYPY = False
+    if MYPY:
+        AnyClassMethod = classmethod[Any]
+    else:
+        # classmethod[TargetType, CallableParamSpecType, CallableReturnType]
+        AnyClassMethod = classmethod[Any, Any, Any]
+
+__all__ = (
+    "AnyCallable",
+    "NoArgAnyCallable",
+    "NoneType",
+    "is_none_type",
+    "display_as_type",
+    "resolve_annotations",
+    "is_callable_type",
+    "is_literal_type",
+    "all_literal_values",
+    "is_namedtuple",
+    "is_typeddict",
+    "is_typeddict_special",
+    "is_new_type",
+    "new_type_supertype",
+    "is_classvar",
+    "is_finalvar",
+    "update_field_forward_refs",
+    "update_model_forward_refs",
+    "TupleGenerator",
+    "DictStrAny",
+    "DictAny",
+    "SetStr",
+    "ListStr",
+    "IntStr",
+    "AbstractSetIntStr",
+    "DictIntStrAny",
+    "CallableGenerator",
+    "ReprArgs",
+    "AnyClassMethod",
+    "CallableGenerator",
+    "WithArgsTypes",
+    "get_args",
+    "get_origin",
+    "get_sub_types",
+    "typing_base",
+    "get_all_type_hints",
+    "is_union",
+    "StrPath",
+    "MappingIntStrAny",
+)
+
+
+NoneType = None.__class__
+
+
+NONE_TYPES: Tuple[Any, Any, Any] = (None, NoneType, Literal[None])
+
+
+if sys.version_info < (3, 8):
+    # Even though this implementation is slower, we need it for python 3.7:
+    # In python 3.7 "Literal" is not a builtin type and uses a different
+    # mechanism.
+    # for this reason `Literal[None] is Literal[None]` evaluates to `False`,
+    # breaking the faster implementation used for the other python versions.
+
+    def is_none_type(type_: Any) -> bool:
+        return type_ in NONE_TYPES
+
+elif sys.version_info[:2] == (3, 8):
+
+    def is_none_type(type_: Any) -> bool:
+        for none_type in NONE_TYPES:
+            if type_ is none_type:
+                return True
+        # With python 3.8, specifically 3.8.10, Literal "is" check sare very flakey
+        # can change on very subtle changes like use of types in other modules,
+        # hopefully this check avoids that issue.
+        if is_literal_type(type_):  # pragma: no cover
+            return all_literal_values(type_) == (None,)
+        return False
+
+else:
+
+    def is_none_type(type_: Any) -> bool:
+        return type_ in NONE_TYPES
+
+
+def display_as_type(v: Type[Any]) -> str:
+    if not isinstance(v, typing_base) and not isinstance(v, WithArgsTypes) and not isinstance(v, type):
+        v = v.__class__
+
+    if is_union(get_origin(v)):
+        return f'Union[{", ".join(map(display_as_type, get_args(v)))}]'
+
+    if isinstance(v, WithArgsTypes):
+        # Generic alias are constructs like `list[int]`
+        return str(v).replace("typing.", "")
+
+    try:
+        return v.__name__
+    except AttributeError:
+        # happens with typing objects
+        return str(v).replace("typing.", "")
+
+
+def resolve_annotations(raw_annotations: Dict[str, Type[Any]], module_name: Optional[str]) -> Dict[str, Type[Any]]:
+    """
+    Partially taken from typing.get_type_hints.
+
+    Resolve string or ForwardRef annotations into type objects if possible.
+    """
+    base_globals: Optional[Dict[str, Any]] = None
+    if module_name:
+        try:
+            module = sys.modules[module_name]
+        except KeyError:
+            # happens occasionally, see https://github.com/pydantic/pydantic/issues/2363
+            pass
+        else:
+            base_globals = module.__dict__
+
+    annotations = {}
+    for name, value in raw_annotations.items():
+        if isinstance(value, str):
+            if (3, 10) > sys.version_info >= (3, 9, 8) or sys.version_info >= (3, 10, 1):
+                value = ForwardRef(value, is_argument=False, is_class=True)
+            else:
+                value = ForwardRef(value, is_argument=False)
+        try:
+            value = _eval_type(value, base_globals, None)
+        except NameError:
+            # this is ok, it can be fixed with update_forward_refs
+            pass
+        annotations[name] = value
+    return annotations
+
+
+def is_callable_type(type_: Type[Any]) -> bool:
+    return type_ is Callable or get_origin(type_) is Callable
+
+
+def is_literal_type(type_: Type[Any]) -> bool:
+    return Literal is not None and get_origin(type_) in LITERAL_TYPES
+
+
+def literal_values(type_: Type[Any]) -> Tuple[Any, ...]:
+    return get_args(type_)
+
+
+def all_literal_values(type_: Type[Any]) -> Tuple[Any, ...]:
+    """
+    This method is used to retrieve all Literal values as
+    Literal can be used recursively (see https://www.python.org/dev/peps/pep-0586)
+    e.g. `Literal[Literal[Literal[1, 2, 3], "foo"], 5, None]`
+    """
+    if not is_literal_type(type_):
+        return (type_,)
+
+    values = literal_values(type_)
+    return tuple(x for value in values for x in all_literal_values(value))
+
+
+def is_namedtuple(type_: Type[Any]) -> bool:
+    """
+    Check if a given class is a named tuple.
+    It can be either a `typing.NamedTuple` or `collections.namedtuple`
+    """
+    from .utils import lenient_issubclass
+
+    return lenient_issubclass(type_, tuple) and hasattr(type_, "_fields")
+
+
+def is_typeddict(type_: Type[Any]) -> bool:
+    """
+    Check if a given class is a typed dict (from `typing` or `typing_extensions`)
+    In 3.10, there will be a public method (https://docs.python.org/3.10/library/typing.html#typing.is_typeddict)
+    """
+    from .utils import lenient_issubclass
+
+    return lenient_issubclass(type_, dict) and hasattr(type_, "__total__")
+
+
+def _check_typeddict_special(type_: Any) -> bool:
+    return type_ is TypedDictRequired or type_ is TypedDictNotRequired
+
+
+def is_typeddict_special(type_: Any) -> bool:
+    """
+    Check if type is a TypedDict special form (Required or NotRequired).
+    """
+    return _check_typeddict_special(type_) or _check_typeddict_special(get_origin(type_))
+
+
+test_type = NewType("test_type", str)
+
+
+def is_new_type(type_: Type[Any]) -> bool:
+    """
+    Check whether type_ was created using typing.NewType
+    """
+    return isinstance(type_, test_type.__class__) and hasattr(type_, "__supertype__")  # type: ignore
+
+
+def new_type_supertype(type_: Type[Any]) -> Type[Any]:
+    while hasattr(type_, "__supertype__"):
+        type_ = type_.__supertype__
+    return type_
+
+
+def _check_classvar(v: Optional[Type[Any]]) -> bool:
+    if v is None:
+        return False
+
+    return v.__class__ == ClassVar.__class__ and getattr(v, "_name", None) == "ClassVar"
+
+
+def _check_finalvar(v: Optional[Type[Any]]) -> bool:
+    """
+    Check if a given type is a `typing.Final` type.
+    """
+    if v is None:
+        return False
+
+    return v.__class__ == Final.__class__ and (sys.version_info < (3, 8) or getattr(v, "_name", None) == "Final")
+
+
+def is_classvar(ann_type: Type[Any]) -> bool:
+    if _check_classvar(ann_type) or _check_classvar(get_origin(ann_type)):
+        return True
+
+    # this is an ugly workaround for class vars that contain forward references and are therefore themselves
+    # forward references, see #3679
+    if ann_type.__class__ == ForwardRef and ann_type.__forward_arg__.startswith("ClassVar["):
+        return True
+
+    return False
+
+
+def is_finalvar(ann_type: Type[Any]) -> bool:
+    return _check_finalvar(ann_type) or _check_finalvar(get_origin(ann_type))
+
+
+def update_field_forward_refs(field: "ModelField", globalns: Any, localns: Any) -> None:
+    """
+    Try to update ForwardRefs on fields based on this ModelField, globalns and localns.
+    """
+    prepare = False
+    if field.type_.__class__ == ForwardRef:
+        prepare = True
+        field.type_ = evaluate_forwardref(field.type_, globalns, localns or None)
+    if field.outer_type_.__class__ == ForwardRef:
+        prepare = True
+        field.outer_type_ = evaluate_forwardref(field.outer_type_, globalns, localns or None)
+    if prepare:
+        field.prepare()
+
+    if field.sub_fields:
+        for sub_f in field.sub_fields:
+            update_field_forward_refs(sub_f, globalns=globalns, localns=localns)
+
+    if field.discriminator_key is not None:
+        field.prepare_discriminated_union_sub_fields()
+
+
+def update_model_forward_refs(
+    model: Type[Any],
+    fields: Iterable["ModelField"],
+    json_encoders: Dict[Union[Type[Any], str, ForwardRef], AnyCallable],
+    localns: "DictStrAny",
+    exc_to_suppress: Tuple[Type[BaseException], ...] = (),
+) -> None:
+    """
+    Try to update model fields ForwardRefs based on model and localns.
+    """
+    if model.__module__ in sys.modules:
+        globalns = sys.modules[model.__module__].__dict__.copy()
+    else:
+        globalns = {}
+
+    globalns.setdefault(model.__name__, model)
+
+    for f in fields:
+        try:
+            update_field_forward_refs(f, globalns=globalns, localns=localns)
+        except exc_to_suppress:
+            pass
+
+    for key in set(json_encoders.keys()):
+        if isinstance(key, str):
+            fr: ForwardRef = ForwardRef(key)
+        elif isinstance(key, ForwardRef):
+            fr = key
+        else:
+            continue
+
+        try:
+            new_key = evaluate_forwardref(fr, globalns, localns or None)
+        except exc_to_suppress:  # pragma: no cover
+            continue
+
+        json_encoders[new_key] = json_encoders.pop(key)
+
+
+def get_class(type_: Type[Any]) -> Union[None, bool, Type[Any]]:
+    """
+    Tries to get the class of a Type[T] annotation. Returns True if Type is used
+    without brackets. Otherwise returns None.
+    """
+    if type_ is type:
+        return True
+
+    if get_origin(type_) is None:
+        return None
+
+    args = get_args(type_)
+    if not args or not isinstance(args[0], type):
+        return True
+    else:
+        return args[0]
+
+
+def get_sub_types(tp: Any) -> List[Any]:
+    """
+    Return all the types that are allowed by type `tp`
+    `tp` can be a `Union` of allowed types or an `Annotated` type
+    """
+    origin = get_origin(tp)
+    if origin is Annotated:
+        return get_sub_types(get_args(tp)[0])
+    elif is_union(origin):
+        return [x for t in get_args(tp) for x in get_sub_types(t)]
+    else:
+        return [tp]
diff --git a/vendor/pydantic/utils.py b/vendor/pydantic/utils.py
new file mode 100644
index 0000000..26a07cf
--- /dev/null
+++ b/vendor/pydantic/utils.py
@@ -0,0 +1,803 @@
+import keyword
+import warnings
+import weakref
+from collections import OrderedDict, defaultdict, deque
+from copy import deepcopy
+from itertools import islice, zip_longest
+from types import BuiltinFunctionType, CodeType, FunctionType, GeneratorType, LambdaType, ModuleType
+from typing import (
+    TYPE_CHECKING,
+    AbstractSet,
+    Any,
+    Callable,
+    Collection,
+    Dict,
+    Generator,
+    Iterable,
+    Iterator,
+    List,
+    Mapping,
+    NoReturn,
+    Optional,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+)
+
+from typing_extensions import Annotated
+
+from .errors import ConfigError
+from .typing import (
+    NoneType,
+    WithArgsTypes,
+    all_literal_values,
+    display_as_type,
+    get_args,
+    get_origin,
+    is_literal_type,
+    is_union,
+)
+from .version import version_info
+
+if TYPE_CHECKING:
+    from inspect import Signature
+    from pathlib import Path
+
+    from .config import BaseConfig
+    from .dataclasses import Dataclass
+    from .fields import ModelField
+    from .main import BaseModel
+    from .typing import AbstractSetIntStr, DictIntStrAny, IntStr, MappingIntStrAny, ReprArgs
+
+    RichReprResult = Iterable[Union[Any, Tuple[Any], Tuple[str, Any], Tuple[str, Any, Any]]]
+
+__all__ = (
+    "import_string",
+    "sequence_like",
+    "validate_field_name",
+    "lenient_isinstance",
+    "lenient_issubclass",
+    "in_ipython",
+    "is_valid_identifier",
+    "deep_update",
+    "update_not_none",
+    "almost_equal_floats",
+    "get_model",
+    "to_camel",
+    "is_valid_field",
+    "smart_deepcopy",
+    "PyObjectStr",
+    "Representation",
+    "GetterDict",
+    "ValueItems",
+    "version_info",  # required here to match behaviour in v1.3
+    "ClassAttribute",
+    "path_type",
+    "ROOT_KEY",
+    "get_unique_discriminator_alias",
+    "get_discriminator_alias_and_values",
+    "DUNDER_ATTRIBUTES",
+)
+
+ROOT_KEY = "__root__"
+# these are types that are returned unchanged by deepcopy
+IMMUTABLE_NON_COLLECTIONS_TYPES: Set[Type[Any]] = {
+    int,
+    float,
+    complex,
+    str,
+    bool,
+    bytes,
+    type,
+    NoneType,
+    FunctionType,
+    BuiltinFunctionType,
+    LambdaType,
+    weakref.ref,
+    CodeType,
+    # note: including ModuleType will differ from behaviour of deepcopy by not producing error.
+    # It might be not a good idea in general, but considering that this function used only internally
+    # against default values of fields, this will allow to actually have a field with module as default value
+    ModuleType,
+    NotImplemented.__class__,
+    Ellipsis.__class__,
+}
+
+# these are types that if empty, might be copied with simple copy() instead of deepcopy()
+BUILTIN_COLLECTIONS: Set[Type[Any]] = {
+    list,
+    set,
+    tuple,
+    frozenset,
+    dict,
+    OrderedDict,
+    defaultdict,
+    deque,
+}
+
+
+def import_string(dotted_path: str) -> Any:
+    """
+    Stolen approximately from django. Import a dotted module path and return the attribute/class designated by the
+    last name in the path. Raise ImportError if the import fails.
+    """
+    from importlib import import_module
+
+    try:
+        module_path, class_name = dotted_path.strip(" ").rsplit(".", 1)
+    except ValueError as e:
+        raise ImportError(f'"{dotted_path}" doesn\'t look like a module path') from e
+
+    module = import_module(module_path)
+    try:
+        return getattr(module, class_name)
+    except AttributeError as e:
+        raise ImportError(f'Module "{module_path}" does not define a "{class_name}" attribute') from e
+
+
+def truncate(v: Union[str], *, max_len: int = 80) -> str:
+    """
+    Truncate a value and add a unicode ellipsis (three dots) to the end if it was too long
+    """
+    warnings.warn("`truncate` is no-longer used by pydantic and is deprecated", DeprecationWarning)
+    if isinstance(v, str) and len(v) > (max_len - 2):
+        # -3 so quote + string + … + quote has correct length
+        return (v[: (max_len - 3)] + "…").__repr__()
+    try:
+        v = v.__repr__()
+    except TypeError:
+        v = v.__class__.__repr__(v)  # in case v is a type
+    if len(v) > max_len:
+        v = v[: max_len - 1] + "…"
+    return v
+
+
+def sequence_like(v: Any) -> bool:
+    return isinstance(v, (list, tuple, set, frozenset, GeneratorType, deque))
+
+
+def validate_field_name(bases: List[Type["BaseModel"]], field_name: str) -> None:
+    """
+    Ensure that the field's name does not shadow an existing attribute of the model.
+    """
+    for base in bases:
+        if getattr(base, field_name, None):
+            raise NameError(
+                f'Field name "{field_name}" shadows a BaseModel attribute; '
+                f"use a different field name with \"alias='{field_name}'\"."
+            )
+
+
+def lenient_isinstance(o: Any, class_or_tuple: Union[Type[Any], Tuple[Type[Any], ...], None]) -> bool:
+    try:
+        return isinstance(o, class_or_tuple)  # type: ignore[arg-type]
+    except TypeError:
+        return False
+
+
+def lenient_issubclass(cls: Any, class_or_tuple: Union[Type[Any], Tuple[Type[Any], ...], None]) -> bool:
+    try:
+        return isinstance(cls, type) and issubclass(cls, class_or_tuple)  # type: ignore[arg-type]
+    except TypeError:
+        if isinstance(cls, WithArgsTypes):
+            return False
+        raise  # pragma: no cover
+
+
+def in_ipython() -> bool:
+    """
+    Check whether we're in an ipython environment, including jupyter notebooks.
+    """
+    try:
+        eval("__IPYTHON__")
+    except NameError:
+        return False
+    else:  # pragma: no cover
+        return True
+
+
+def is_valid_identifier(identifier: str) -> bool:
+    """
+    Checks that a string is a valid identifier and not a Python keyword.
+    :param identifier: The identifier to test.
+    :return: True if the identifier is valid.
+    """
+    return identifier.isidentifier() and not keyword.iskeyword(identifier)
+
+
+KeyType = TypeVar("KeyType")
+
+
+def deep_update(mapping: Dict[KeyType, Any], *updating_mappings: Dict[KeyType, Any]) -> Dict[KeyType, Any]:
+    updated_mapping = mapping.copy()
+    for updating_mapping in updating_mappings:
+        for k, v in updating_mapping.items():
+            if k in updated_mapping and isinstance(updated_mapping[k], dict) and isinstance(v, dict):
+                updated_mapping[k] = deep_update(updated_mapping[k], v)
+            else:
+                updated_mapping[k] = v
+    return updated_mapping
+
+
+def update_not_none(mapping: Dict[Any, Any], **update: Any) -> None:
+    mapping.update({k: v for k, v in update.items() if v is not None})
+
+
+def almost_equal_floats(value_1: float, value_2: float, *, delta: float = 1e-8) -> bool:
+    """
+    Return True if two floats are almost equal
+    """
+    return abs(value_1 - value_2) <= delta
+
+
+def generate_model_signature(
+    init: Callable[..., None], fields: Dict[str, "ModelField"], config: Type["BaseConfig"]
+) -> "Signature":
+    """
+    Generate signature for model based on its fields
+    """
+    from inspect import Parameter, Signature, signature
+
+    from .config import Extra
+
+    present_params = signature(init).parameters.values()
+    merged_params: Dict[str, Parameter] = {}
+    var_kw = None
+    use_var_kw = False
+
+    for param in islice(present_params, 1, None):  # skip self arg
+        if param.kind is param.VAR_KEYWORD:
+            var_kw = param
+            continue
+        merged_params[param.name] = param
+
+    if var_kw:  # if custom init has no var_kw, fields which are not declared in it cannot be passed through
+        allow_names = config.allow_population_by_field_name
+        for field_name, field in fields.items():
+            param_name = field.alias
+            if field_name in merged_params or param_name in merged_params:
+                continue
+            elif not is_valid_identifier(param_name):
+                if allow_names and is_valid_identifier(field_name):
+                    param_name = field_name
+                else:
+                    use_var_kw = True
+                    continue
+
+            # TODO: replace annotation with actual expected types once #1055 solved
+            kwargs = {"default": field.default} if not field.required else {}
+            merged_params[param_name] = Parameter(
+                param_name, Parameter.KEYWORD_ONLY, annotation=field.annotation, **kwargs
+            )
+
+    if config.extra is Extra.allow:
+        use_var_kw = True
+
+    if var_kw and use_var_kw:
+        # Make sure the parameter for extra kwargs
+        # does not have the same name as a field
+        default_model_signature = [
+            ("__pydantic_self__", Parameter.POSITIONAL_OR_KEYWORD),
+            ("data", Parameter.VAR_KEYWORD),
+        ]
+        if [(p.name, p.kind) for p in present_params] == default_model_signature:
+            # if this is the standard model signature, use extra_data as the extra args name
+            var_kw_name = "extra_data"
+        else:
+            # else start from var_kw
+            var_kw_name = var_kw.name
+
+        # generate a name that's definitely unique
+        while var_kw_name in fields:
+            var_kw_name += "_"
+        merged_params[var_kw_name] = var_kw.replace(name=var_kw_name)
+
+    return Signature(parameters=list(merged_params.values()), return_annotation=None)
+
+
+def get_model(obj: Union[Type["BaseModel"], Type["Dataclass"]]) -> Type["BaseModel"]:
+    from .main import BaseModel
+
+    try:
+        model_cls = obj.__pydantic_model__  # type: ignore
+    except AttributeError:
+        model_cls = obj
+
+    if not issubclass(model_cls, BaseModel):
+        raise TypeError("Unsupported type, must be either BaseModel or dataclass")
+    return model_cls
+
+
+def to_camel(string: str) -> str:
+    return "".join(word.capitalize() for word in string.split("_"))
+
+
+def to_lower_camel(string: str) -> str:
+    if len(string) >= 1:
+        pascal_string = to_camel(string)
+        return pascal_string[0].lower() + pascal_string[1:]
+    return string.lower()
+
+
+T = TypeVar("T")
+
+
+def unique_list(
+    input_list: Union[List[T], Tuple[T, ...]],
+    *,
+    name_factory: Callable[[T], str] = str,
+) -> List[T]:
+    """
+    Make a list unique while maintaining order.
+    We update the list if another one with the same name is set
+    (e.g. root validator overridden in subclass)
+    """
+    result: List[T] = []
+    result_names: List[str] = []
+    for v in input_list:
+        v_name = name_factory(v)
+        if v_name not in result_names:
+            result_names.append(v_name)
+            result.append(v)
+        else:
+            result[result_names.index(v_name)] = v
+
+    return result
+
+
+class PyObjectStr(str):
+    """
+    String class where repr doesn't include quotes. Useful with Representation when you want to return a string
+    representation of something that valid (or pseudo-valid) python.
+    """
+
+    def __repr__(self) -> str:
+        return str(self)
+
+
+class Representation:
+    """
+    Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.
+
+    __pretty__ is used by [devtools](https://python-devtools.helpmanual.io/) to provide human readable representations
+    of objects.
+    """
+
+    __slots__: Tuple[str, ...] = tuple()
+
+    def __repr_args__(self) -> "ReprArgs":
+        """
+        Returns the attributes to show in __str__, __repr__, and __pretty__ this is generally overridden.
+
+        Can either return:
+        * name - value pairs, e.g.: `[('foo_name', 'foo'), ('bar_name', ['b', 'a', 'r'])]`
+        * or, just values, e.g.: `[(None, 'foo'), (None, ['b', 'a', 'r'])]`
+        """
+        attrs = ((s, getattr(self, s)) for s in self.__slots__)
+        return [(a, v) for a, v in attrs if v is not None]
+
+    def __repr_name__(self) -> str:
+        """
+        Name of the instance's class, used in __repr__.
+        """
+        return self.__class__.__name__
+
+    def __repr_str__(self, join_str: str) -> str:
+        return join_str.join(repr(v) if a is None else f"{a}={v!r}" for a, v in self.__repr_args__())
+
+    def __pretty__(self, fmt: Callable[[Any], Any], **kwargs: Any) -> Generator[Any, None, None]:
+        """
+        Used by devtools (https://python-devtools.helpmanual.io/) to provide a human readable representations of objects
+        """
+        yield self.__repr_name__() + "("
+        yield 1
+        for name, value in self.__repr_args__():
+            if name is not None:
+                yield name + "="
+            yield fmt(value)
+            yield ","
+            yield 0
+        yield -1
+        yield ")"
+
+    def __str__(self) -> str:
+        return self.__repr_str__(" ")
+
+    def __repr__(self) -> str:
+        return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
+
+    def __rich_repr__(self) -> "RichReprResult":
+        """Get fields for Rich library"""
+        for name, field_repr in self.__repr_args__():
+            if name is None:
+                yield field_repr
+            else:
+                yield name, field_repr
+
+
+class GetterDict(Representation):
+    """
+    Hack to make object's smell just enough like dicts for validate_model.
+
+    We can't inherit from Mapping[str, Any] because it upsets cython so we have to implement all methods ourselves.
+    """
+
+    __slots__ = ("_obj",)
+
+    def __init__(self, obj: Any):
+        self._obj = obj
+
+    def __getitem__(self, key: str) -> Any:
+        try:
+            return getattr(self._obj, key)
+        except AttributeError as e:
+            raise KeyError(key) from e
+
+    def get(self, key: Any, default: Any = None) -> Any:
+        return getattr(self._obj, key, default)
+
+    def extra_keys(self) -> Set[Any]:
+        """
+        We don't want to get any other attributes of obj if the model didn't explicitly ask for them
+        """
+        return set()
+
+    def keys(self) -> List[Any]:
+        """
+        Keys of the pseudo dictionary, uses a list not set so order information can be maintained like python
+        dictionaries.
+        """
+        return list(self)
+
+    def values(self) -> List[Any]:
+        return [self[k] for k in self]
+
+    def items(self) -> Iterator[Tuple[str, Any]]:
+        for k in self:
+            yield k, self.get(k)
+
+    def __iter__(self) -> Iterator[str]:
+        for name in dir(self._obj):
+            if not name.startswith("_"):
+                yield name
+
+    def __len__(self) -> int:
+        return sum(1 for _ in self)
+
+    def __contains__(self, item: Any) -> bool:
+        return item in self.keys()
+
+    def __eq__(self, other: Any) -> bool:
+        return dict(self) == dict(other.items())
+
+    def __repr_args__(self) -> "ReprArgs":
+        return [(None, dict(self))]
+
+    def __repr_name__(self) -> str:
+        return f"GetterDict[{display_as_type(self._obj)}]"
+
+
+class ValueItems(Representation):
+    """
+    Class for more convenient calculation of excluded or included fields on values.
+    """
+
+    __slots__ = ("_items", "_type")
+
+    def __init__(self, value: Any, items: Union["AbstractSetIntStr", "MappingIntStrAny"]) -> None:
+        items = self._coerce_items(items)
+
+        if isinstance(value, (list, tuple)):
+            items = self._normalize_indexes(items, len(value))
+
+        self._items: "MappingIntStrAny" = items
+
+    def is_excluded(self, item: Any) -> bool:
+        """
+        Check if item is fully excluded.
+
+        :param item: key or index of a value
+        """
+        return self.is_true(self._items.get(item))
+
+    def is_included(self, item: Any) -> bool:
+        """
+        Check if value is contained in self._items
+
+        :param item: key or index of value
+        """
+        return item in self._items
+
+    def for_element(self, e: "IntStr") -> Optional[Union["AbstractSetIntStr", "MappingIntStrAny"]]:
+        """
+        :param e: key or index of element on value
+        :return: raw values for element if self._items is dict and contain needed element
+        """
+
+        item = self._items.get(e)
+        return item if not self.is_true(item) else None
+
+    def _normalize_indexes(self, items: "MappingIntStrAny", v_length: int) -> "DictIntStrAny":
+        """
+        :param items: dict or set of indexes which will be normalized
+        :param v_length: length of sequence indexes of which will be
+
+        >>> self._normalize_indexes({0: True, -2: True, -1: True}, 4)
+        {0: True, 2: True, 3: True}
+        >>> self._normalize_indexes({'__all__': True}, 4)
+        {0: True, 1: True, 2: True, 3: True}
+        """
+
+        normalized_items: "DictIntStrAny" = {}
+        all_items = None
+        for i, v in items.items():
+            if not (isinstance(v, Mapping) or isinstance(v, AbstractSet) or self.is_true(v)):
+                raise TypeError(f'Unexpected type of exclude value for index "{i}" {v.__class__}')
+            if i == "__all__":
+                all_items = self._coerce_value(v)
+                continue
+            if not isinstance(i, int):
+                raise TypeError(
+                    "Excluding fields from a sequence of sub-models or dicts must be performed index-wise: "
+                    'expected integer keys or keyword "__all__"'
+                )
+            normalized_i = v_length + i if i < 0 else i
+            normalized_items[normalized_i] = self.merge(v, normalized_items.get(normalized_i))
+
+        if not all_items:
+            return normalized_items
+        if self.is_true(all_items):
+            for i in range(v_length):
+                normalized_items.setdefault(i, ...)
+            return normalized_items
+        for i in range(v_length):
+            normalized_item = normalized_items.setdefault(i, {})
+            if not self.is_true(normalized_item):
+                normalized_items[i] = self.merge(all_items, normalized_item)
+        return normalized_items
+
+    @classmethod
+    def merge(cls, base: Any, override: Any, intersect: bool = False) -> Any:
+        """
+        Merge a ``base`` item with an ``override`` item.
+
+        Both ``base`` and ``override`` are converted to dictionaries if possible.
+        Sets are converted to dictionaries with the sets entries as keys and
+        Ellipsis as values.
+
+        Each key-value pair existing in ``base`` is merged with ``override``,
+        while the rest of the key-value pairs are updated recursively with this function.
+
+        Merging takes place based on the "union" of keys if ``intersect`` is
+        set to ``False`` (default) and on the intersection of keys if
+        ``intersect`` is set to ``True``.
+        """
+        override = cls._coerce_value(override)
+        base = cls._coerce_value(base)
+        if override is None:
+            return base
+        if cls.is_true(base) or base is None:
+            return override
+        if cls.is_true(override):
+            return base if intersect else override
+
+        # intersection or union of keys while preserving ordering:
+        if intersect:
+            merge_keys = [k for k in base if k in override] + [k for k in override if k in base]
+        else:
+            merge_keys = list(base) + [k for k in override if k not in base]
+
+        merged: "DictIntStrAny" = {}
+        for k in merge_keys:
+            merged_item = cls.merge(base.get(k), override.get(k), intersect=intersect)
+            if merged_item is not None:
+                merged[k] = merged_item
+
+        return merged
+
+    @staticmethod
+    def _coerce_items(items: Union["AbstractSetIntStr", "MappingIntStrAny"]) -> "MappingIntStrAny":
+        if isinstance(items, Mapping):
+            pass
+        elif isinstance(items, AbstractSet):
+            items = dict.fromkeys(items, ...)
+        else:
+            class_name = getattr(items, "__class__", "???")
+            assert_never(
+                items,
+                f"Unexpected type of exclude value {class_name}",
+            )
+        return items
+
+    @classmethod
+    def _coerce_value(cls, value: Any) -> Any:
+        if value is None or cls.is_true(value):
+            return value
+        return cls._coerce_items(value)
+
+    @staticmethod
+    def is_true(v: Any) -> bool:
+        return v is True or v is ...
+
+    def __repr_args__(self) -> "ReprArgs":
+        return [(None, self._items)]
+
+
+class ClassAttribute:
+    """
+    Hide class attribute from its instances
+    """
+
+    __slots__ = (
+        "name",
+        "value",
+    )
+
+    def __init__(self, name: str, value: Any) -> None:
+        self.name = name
+        self.value = value
+
+    def __get__(self, instance: Any, owner: Type[Any]) -> None:
+        if instance is None:
+            return self.value
+        raise AttributeError(f"{self.name!r} attribute of {owner.__name__!r} is class-only")
+
+
+path_types = {
+    "is_dir": "directory",
+    "is_file": "file",
+    "is_mount": "mount point",
+    "is_symlink": "symlink",
+    "is_block_device": "block device",
+    "is_char_device": "char device",
+    "is_fifo": "FIFO",
+    "is_socket": "socket",
+}
+
+
+def path_type(p: "Path") -> str:
+    """
+    Find out what sort of thing a path is.
+    """
+    assert p.exists(), "path does not exist"
+    for method, name in path_types.items():
+        if getattr(p, method)():
+            return name
+
+    return "unknown"
+
+
+Obj = TypeVar("Obj")
+
+
+def smart_deepcopy(obj: Obj) -> Obj:
+    """
+    Return type as is for immutable built-in types
+    Use obj.copy() for built-in empty collections
+    Use copy.deepcopy() for non-empty collections and unknown objects
+    """
+
+    obj_type = obj.__class__
+    if obj_type in IMMUTABLE_NON_COLLECTIONS_TYPES:
+        return obj  # fastest case: obj is immutable and not collection therefore will not be copied anyway
+    try:
+        if not obj and obj_type in BUILTIN_COLLECTIONS:
+            # faster way for empty collections, no need to copy its members
+            return obj if obj_type is tuple else obj.copy()  # type: ignore  # tuple doesn't have copy method
+    except (TypeError, ValueError, RuntimeError):
+        # do we really dare to catch ALL errors? Seems a bit risky
+        pass
+
+    return deepcopy(obj)  # slowest way when we actually might need a deepcopy
+
+
+def is_valid_field(name: str) -> bool:
+    if not name.startswith("_"):
+        return True
+    return ROOT_KEY == name
+
+
+DUNDER_ATTRIBUTES = {
+    "__annotations__",
+    "__classcell__",
+    "__doc__",
+    "__module__",
+    "__orig_bases__",
+    "__orig_class__",
+    "__qualname__",
+}
+
+
+def is_valid_private_name(name: str) -> bool:
+    return not is_valid_field(name) and name not in DUNDER_ATTRIBUTES
+
+
+_EMPTY = object()
+
+
+def all_identical(left: Iterable[Any], right: Iterable[Any]) -> bool:
+    """
+    Check that the items of `left` are the same objects as those in `right`.
+
+    >>> a, b = object(), object()
+    >>> all_identical([a, b, a], [a, b, a])
+    True
+    >>> all_identical([a, b, [a]], [a, b, [a]])  # new list object, while "equal" is not "identical"
+    False
+    """
+    for left_item, right_item in zip_longest(left, right, fillvalue=_EMPTY):
+        if left_item is not right_item:
+            return False
+    return True
+
+
+def assert_never(obj: NoReturn, msg: str) -> NoReturn:
+    """
+    Helper to make sure that we have covered all possible types.
+
+    This is mostly useful for ``mypy``, docs:
+    https://mypy.readthedocs.io/en/latest/literal_types.html#exhaustive-checks
+    """
+    raise TypeError(msg)
+
+
+def get_unique_discriminator_alias(all_aliases: Collection[str], discriminator_key: str) -> str:
+    """Validate that all aliases are the same and if that's the case return the alias"""
+    unique_aliases = set(all_aliases)
+    if len(unique_aliases) > 1:
+        raise ConfigError(
+            f'Aliases for discriminator {discriminator_key!r} must be the same (got {", ".join(sorted(all_aliases))})'
+        )
+    return unique_aliases.pop()
+
+
+def get_discriminator_alias_and_values(tp: Any, discriminator_key: str) -> Tuple[str, Tuple[str, ...]]:
+    """
+    Get alias and all valid values in the `Literal` type of the discriminator field
+    `tp` can be a `BaseModel` class or directly an `Annotated` `Union` of many.
+    """
+    is_root_model = getattr(tp, "__custom_root_type__", False)
+
+    if get_origin(tp) is Annotated:
+        tp = get_args(tp)[0]
+
+    if hasattr(tp, "__pydantic_model__"):
+        tp = tp.__pydantic_model__
+
+    if is_union(get_origin(tp)):
+        alias, all_values = _get_union_alias_and_all_values(tp, discriminator_key)
+        return alias, tuple(v for values in all_values for v in values)
+    elif is_root_model:
+        union_type = tp.__fields__[ROOT_KEY].type_
+        alias, all_values = _get_union_alias_and_all_values(union_type, discriminator_key)
+
+        if len(set(all_values)) > 1:
+            raise ConfigError(
+                f"Field {discriminator_key!r} is not the same for all submodels of {display_as_type(tp)!r}"
+            )
+
+        return alias, all_values[0]
+
+    else:
+        try:
+            t_discriminator_type = tp.__fields__[discriminator_key].type_
+        except AttributeError as e:
+            raise TypeError(f"Type {tp.__name__!r} is not a valid `BaseModel` or `dataclass`") from e
+        except KeyError as e:
+            raise ConfigError(f"Model {tp.__name__!r} needs a discriminator field for key {discriminator_key!r}") from e
+
+        if not is_literal_type(t_discriminator_type):
+            raise ConfigError(f"Field {discriminator_key!r} of model {tp.__name__!r} needs to be a `Literal`")
+
+        return tp.__fields__[discriminator_key].alias, all_literal_values(t_discriminator_type)
+
+
+def _get_union_alias_and_all_values(
+    union_type: Type[Any], discriminator_key: str
+) -> Tuple[str, Tuple[Tuple[str, ...], ...]]:
+    zipped_aliases_values = [get_discriminator_alias_and_values(t, discriminator_key) for t in get_args(union_type)]
+    # unzip: [('alias_a',('v1', 'v2)), ('alias_b', ('v3',))] => [('alias_a', 'alias_b'), (('v1', 'v2'), ('v3',))]
+    all_aliases, all_values = zip(*zipped_aliases_values)
+    return get_unique_discriminator_alias(all_aliases, discriminator_key), all_values
diff --git a/vendor/pydantic/validators.py b/vendor/pydantic/validators.py
new file mode 100644
index 0000000..bf0f1cc
--- /dev/null
+++ b/vendor/pydantic/validators.py
@@ -0,0 +1,765 @@
+import math
+import re
+from collections import OrderedDict, deque
+from collections.abc import Hashable as CollectionsHashable
+from datetime import date, datetime, time, timedelta
+from decimal import Decimal, DecimalException
+from enum import Enum, IntEnum
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from pathlib import Path
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Deque,
+    Dict,
+    ForwardRef,
+    FrozenSet,
+    Generator,
+    Hashable,
+    List,
+    NamedTuple,
+    Pattern,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+)
+from uuid import UUID
+
+from . import errors
+from .datetime_parse import parse_date, parse_datetime, parse_duration, parse_time
+from .typing import (
+    AnyCallable,
+    all_literal_values,
+    display_as_type,
+    get_class,
+    is_callable_type,
+    is_literal_type,
+    is_namedtuple,
+    is_none_type,
+    is_typeddict,
+)
+from .utils import almost_equal_floats, lenient_issubclass, sequence_like
+
+if TYPE_CHECKING:
+    from typing_extensions import Literal, TypedDict
+
+    from .config import BaseConfig
+    from .fields import ModelField
+    from .types import ConstrainedDecimal, ConstrainedFloat, ConstrainedInt
+
+    ConstrainedNumber = Union[ConstrainedDecimal, ConstrainedFloat, ConstrainedInt]
+    AnyOrderedDict = OrderedDict[Any, Any]
+    Number = Union[int, float, Decimal]
+    StrBytes = Union[str, bytes]
+
+
+def str_validator(v: Any) -> Union[str]:
+    if isinstance(v, str):
+        if isinstance(v, Enum):
+            return v.value
+        else:
+            return v
+    elif isinstance(v, (float, int, Decimal)):
+        # is there anything else we want to add here? If you think so, create an issue.
+        return str(v)
+    elif isinstance(v, (bytes, bytearray)):
+        return v.decode()
+    else:
+        raise errors.StrError()
+
+
+def strict_str_validator(v: Any) -> Union[str]:
+    if isinstance(v, str) and not isinstance(v, Enum):
+        return v
+    raise errors.StrError()
+
+
+def bytes_validator(v: Any) -> Union[bytes]:
+    if isinstance(v, bytes):
+        return v
+    elif isinstance(v, bytearray):
+        return bytes(v)
+    elif isinstance(v, str):
+        return v.encode()
+    elif isinstance(v, (float, int, Decimal)):
+        return str(v).encode()
+    else:
+        raise errors.BytesError()
+
+
+def strict_bytes_validator(v: Any) -> Union[bytes]:
+    if isinstance(v, bytes):
+        return v
+    elif isinstance(v, bytearray):
+        return bytes(v)
+    else:
+        raise errors.BytesError()
+
+
+BOOL_FALSE = {0, "0", "off", "f", "false", "n", "no"}
+BOOL_TRUE = {1, "1", "on", "t", "true", "y", "yes"}
+
+
+def bool_validator(v: Any) -> bool:
+    if v is True or v is False:
+        return v
+    if isinstance(v, bytes):
+        v = v.decode()
+    if isinstance(v, str):
+        v = v.lower()
+    try:
+        if v in BOOL_TRUE:
+            return True
+        if v in BOOL_FALSE:
+            return False
+    except TypeError:
+        raise errors.BoolError()
+    raise errors.BoolError()
+
+
+# matches the default limit cpython, see https://github.com/python/cpython/pull/96500
+max_str_int = 4_300
+
+
+def int_validator(v: Any) -> int:
+    if isinstance(v, int) and not (v is True or v is False):
+        return v
+
+    # see https://github.com/pydantic/pydantic/issues/1477 and in turn, https://github.com/python/cpython/issues/95778
+    # this check should be unnecessary once patch releases are out for 3.7, 3.8, 3.9 and 3.10
+    # but better to check here until then.
+    # NOTICE: this does not fully protect user from the DOS risk since the standard library JSON implementation
+    # (and other std lib modules like xml) use `int()` and are likely called before this, the best workaround is to
+    # 1. update to the latest patch release of python once released, 2. use a different JSON library like ujson
+    if isinstance(v, (str, bytes, bytearray)) and len(v) > max_str_int:
+        raise errors.IntegerError()
+
+    try:
+        return int(v)
+    except (TypeError, ValueError, OverflowError):
+        raise errors.IntegerError()
+
+
+def strict_int_validator(v: Any) -> int:
+    if isinstance(v, int) and not (v is True or v is False):
+        return v
+    raise errors.IntegerError()
+
+
+def float_validator(v: Any) -> float:
+    if isinstance(v, float):
+        return v
+
+    try:
+        return float(v)
+    except (TypeError, ValueError):
+        raise errors.FloatError()
+
+
+def strict_float_validator(v: Any) -> float:
+    if isinstance(v, float):
+        return v
+    raise errors.FloatError()
+
+
+def float_finite_validator(v: "Number", field: "ModelField", config: "BaseConfig") -> "Number":
+    allow_inf_nan = getattr(field.type_, "allow_inf_nan", None)
+    if allow_inf_nan is None:
+        allow_inf_nan = config.allow_inf_nan
+
+    if allow_inf_nan is False and (math.isnan(v) or math.isinf(v)):
+        raise errors.NumberNotFiniteError()
+    return v
+
+
+def number_multiple_validator(v: "Number", field: "ModelField") -> "Number":
+    field_type: ConstrainedNumber = field.type_
+    if field_type.multiple_of is not None:
+        mod = float(v) / float(field_type.multiple_of) % 1
+        if not almost_equal_floats(mod, 0.0) and not almost_equal_floats(mod, 1.0):
+            raise errors.NumberNotMultipleError(multiple_of=field_type.multiple_of)
+    return v
+
+
+def number_size_validator(v: "Number", field: "ModelField") -> "Number":
+    field_type: ConstrainedNumber = field.type_
+    if field_type.gt is not None and not v > field_type.gt:
+        raise errors.NumberNotGtError(limit_value=field_type.gt)
+    elif field_type.ge is not None and not v >= field_type.ge:
+        raise errors.NumberNotGeError(limit_value=field_type.ge)
+
+    if field_type.lt is not None and not v < field_type.lt:
+        raise errors.NumberNotLtError(limit_value=field_type.lt)
+    if field_type.le is not None and not v <= field_type.le:
+        raise errors.NumberNotLeError(limit_value=field_type.le)
+
+    return v
+
+
+def constant_validator(v: "Any", field: "ModelField") -> "Any":
+    """Validate ``const`` fields.
+
+    The value provided for a ``const`` field must be equal to the default value
+    of the field. This is to support the keyword of the same name in JSON
+    Schema.
+    """
+    if v != field.default:
+        raise errors.WrongConstantError(given=v, permitted=[field.default])
+
+    return v
+
+
+def anystr_length_validator(v: "StrBytes", config: "BaseConfig") -> "StrBytes":
+    v_len = len(v)
+
+    min_length = config.min_anystr_length
+    if v_len < min_length:
+        raise errors.AnyStrMinLengthError(limit_value=min_length)
+
+    max_length = config.max_anystr_length
+    if max_length is not None and v_len > max_length:
+        raise errors.AnyStrMaxLengthError(limit_value=max_length)
+
+    return v
+
+
+def anystr_strip_whitespace(v: "StrBytes") -> "StrBytes":
+    return v.strip()
+
+
+def anystr_upper(v: "StrBytes") -> "StrBytes":
+    return v.upper()
+
+
+def anystr_lower(v: "StrBytes") -> "StrBytes":
+    return v.lower()
+
+
+def ordered_dict_validator(v: Any) -> "AnyOrderedDict":
+    if isinstance(v, OrderedDict):
+        return v
+
+    try:
+        return OrderedDict(v)
+    except (TypeError, ValueError):
+        raise errors.DictError()
+
+
+def dict_validator(v: Any) -> Dict[Any, Any]:
+    if isinstance(v, dict):
+        return v
+
+    try:
+        return dict(v)
+    except (TypeError, ValueError):
+        raise errors.DictError()
+
+
+def list_validator(v: Any) -> List[Any]:
+    if isinstance(v, list):
+        return v
+    elif sequence_like(v):
+        return list(v)
+    else:
+        raise errors.ListError()
+
+
+def tuple_validator(v: Any) -> Tuple[Any, ...]:
+    if isinstance(v, tuple):
+        return v
+    elif sequence_like(v):
+        return tuple(v)
+    else:
+        raise errors.TupleError()
+
+
+def set_validator(v: Any) -> Set[Any]:
+    if isinstance(v, set):
+        return v
+    elif sequence_like(v):
+        return set(v)
+    else:
+        raise errors.SetError()
+
+
+def frozenset_validator(v: Any) -> FrozenSet[Any]:
+    if isinstance(v, frozenset):
+        return v
+    elif sequence_like(v):
+        return frozenset(v)
+    else:
+        raise errors.FrozenSetError()
+
+
+def deque_validator(v: Any) -> Deque[Any]:
+    if isinstance(v, deque):
+        return v
+    elif sequence_like(v):
+        return deque(v)
+    else:
+        raise errors.DequeError()
+
+
+def enum_member_validator(v: Any, field: "ModelField", config: "BaseConfig") -> Enum:
+    try:
+        enum_v = field.type_(v)
+    except ValueError:
+        # field.type_ should be an enum, so will be iterable
+        raise errors.EnumMemberError(enum_values=list(field.type_))
+    return enum_v.value if config.use_enum_values else enum_v
+
+
+def uuid_validator(v: Any, field: "ModelField") -> UUID:
+    try:
+        if isinstance(v, str):
+            v = UUID(v)
+        elif isinstance(v, (bytes, bytearray)):
+            try:
+                v = UUID(v.decode())
+            except ValueError:
+                # 16 bytes in big-endian order as the bytes argument fail
+                # the above check
+                v = UUID(bytes=v)
+    except ValueError:
+        raise errors.UUIDError()
+
+    if not isinstance(v, UUID):
+        raise errors.UUIDError()
+
+    required_version = getattr(field.type_, "_required_version", None)
+    if required_version and v.version != required_version:
+        raise errors.UUIDVersionError(required_version=required_version)
+
+    return v
+
+
+def decimal_validator(v: Any) -> Decimal:
+    if isinstance(v, Decimal):
+        return v
+    elif isinstance(v, (bytes, bytearray)):
+        v = v.decode()
+
+    v = str(v).strip()
+
+    try:
+        v = Decimal(v)
+    except DecimalException:
+        raise errors.DecimalError()
+
+    if not v.is_finite():
+        raise errors.DecimalIsNotFiniteError()
+
+    return v
+
+
+def hashable_validator(v: Any) -> Hashable:
+    if isinstance(v, Hashable):
+        return v
+
+    raise errors.HashableError()
+
+
+def ip_v4_address_validator(v: Any) -> IPv4Address:
+    if isinstance(v, IPv4Address):
+        return v
+
+    try:
+        return IPv4Address(v)
+    except ValueError:
+        raise errors.IPv4AddressError()
+
+
+def ip_v6_address_validator(v: Any) -> IPv6Address:
+    if isinstance(v, IPv6Address):
+        return v
+
+    try:
+        return IPv6Address(v)
+    except ValueError:
+        raise errors.IPv6AddressError()
+
+
+def ip_v4_network_validator(v: Any) -> IPv4Network:
+    """
+    Assume IPv4Network initialised with a default ``strict`` argument
+
+    See more:
+    https://docs.python.org/library/ipaddress.html#ipaddress.IPv4Network
+    """
+    if isinstance(v, IPv4Network):
+        return v
+
+    try:
+        return IPv4Network(v)
+    except ValueError:
+        raise errors.IPv4NetworkError()
+
+
+def ip_v6_network_validator(v: Any) -> IPv6Network:
+    """
+    Assume IPv6Network initialised with a default ``strict`` argument
+
+    See more:
+    https://docs.python.org/library/ipaddress.html#ipaddress.IPv6Network
+    """
+    if isinstance(v, IPv6Network):
+        return v
+
+    try:
+        return IPv6Network(v)
+    except ValueError:
+        raise errors.IPv6NetworkError()
+
+
+def ip_v4_interface_validator(v: Any) -> IPv4Interface:
+    if isinstance(v, IPv4Interface):
+        return v
+
+    try:
+        return IPv4Interface(v)
+    except ValueError:
+        raise errors.IPv4InterfaceError()
+
+
+def ip_v6_interface_validator(v: Any) -> IPv6Interface:
+    if isinstance(v, IPv6Interface):
+        return v
+
+    try:
+        return IPv6Interface(v)
+    except ValueError:
+        raise errors.IPv6InterfaceError()
+
+
+def path_validator(v: Any) -> Path:
+    if isinstance(v, Path):
+        return v
+
+    try:
+        return Path(v)
+    except TypeError:
+        raise errors.PathError()
+
+
+def path_exists_validator(v: Any) -> Path:
+    if not v.exists():
+        raise errors.PathNotExistsError(path=v)
+
+    return v
+
+
+def callable_validator(v: Any) -> AnyCallable:
+    """
+    Perform a simple check if the value is callable.
+
+    Note: complete matching of argument type hints and return types is not performed
+    """
+    if callable(v):
+        return v
+
+    raise errors.CallableError(value=v)
+
+
+def enum_validator(v: Any) -> Enum:
+    if isinstance(v, Enum):
+        return v
+
+    raise errors.EnumError(value=v)
+
+
+def int_enum_validator(v: Any) -> IntEnum:
+    if isinstance(v, IntEnum):
+        return v
+
+    raise errors.IntEnumError(value=v)
+
+
+def make_literal_validator(type_: Any) -> Callable[[Any], Any]:
+    permitted_choices = all_literal_values(type_)
+
+    # To have a O(1) complexity and still return one of the values set inside the `Literal`,
+    # we create a dict with the set values (a set causes some problems with the way intersection works).
+    # In some cases the set value and checked value can indeed be different (see `test_literal_validator_str_enum`)
+    allowed_choices = {v: v for v in permitted_choices}
+
+    def literal_validator(v: Any) -> Any:
+        try:
+            return allowed_choices[v]
+        except (KeyError, TypeError):
+            raise errors.WrongConstantError(given=v, permitted=permitted_choices)
+
+    return literal_validator
+
+
+def constr_length_validator(v: "StrBytes", field: "ModelField", config: "BaseConfig") -> "StrBytes":
+    v_len = len(v)
+
+    min_length = field.type_.min_length if field.type_.min_length is not None else config.min_anystr_length
+    if v_len < min_length:
+        raise errors.AnyStrMinLengthError(limit_value=min_length)
+
+    max_length = field.type_.max_length if field.type_.max_length is not None else config.max_anystr_length
+    if max_length is not None and v_len > max_length:
+        raise errors.AnyStrMaxLengthError(limit_value=max_length)
+
+    return v
+
+
+def constr_strip_whitespace(v: "StrBytes", field: "ModelField", config: "BaseConfig") -> "StrBytes":
+    strip_whitespace = field.type_.strip_whitespace or config.anystr_strip_whitespace
+    if strip_whitespace:
+        v = v.strip()
+
+    return v
+
+
+def constr_upper(v: "StrBytes", field: "ModelField", config: "BaseConfig") -> "StrBytes":
+    upper = field.type_.to_upper or config.anystr_upper
+    if upper:
+        v = v.upper()
+
+    return v
+
+
+def constr_lower(v: "StrBytes", field: "ModelField", config: "BaseConfig") -> "StrBytes":
+    lower = field.type_.to_lower or config.anystr_lower
+    if lower:
+        v = v.lower()
+    return v
+
+
+def validate_json(v: Any, config: "BaseConfig") -> Any:
+    if v is None:
+        # pass None through to other validators
+        return v
+    try:
+        return config.json_loads(v)  # type: ignore
+    except ValueError:
+        raise errors.JsonError()
+    except TypeError:
+        raise errors.JsonTypeError()
+
+
+T = TypeVar("T")
+
+
+def make_arbitrary_type_validator(type_: Type[T]) -> Callable[[T], T]:
+    def arbitrary_type_validator(v: Any) -> T:
+        if isinstance(v, type_):
+            return v
+        raise errors.ArbitraryTypeError(expected_arbitrary_type=type_)
+
+    return arbitrary_type_validator
+
+
+def make_class_validator(type_: Type[T]) -> Callable[[Any], Type[T]]:
+    def class_validator(v: Any) -> Type[T]:
+        if lenient_issubclass(v, type_):
+            return v
+        raise errors.SubclassError(expected_class=type_)
+
+    return class_validator
+
+
+def any_class_validator(v: Any) -> Type[T]:
+    if isinstance(v, type):
+        return v
+    raise errors.ClassError()
+
+
+def none_validator(v: Any) -> "Literal[None]":
+    if v is None:
+        return v
+    raise errors.NotNoneError()
+
+
+def pattern_validator(v: Any) -> Pattern[str]:
+    if isinstance(v, Pattern):
+        return v
+
+    str_value = str_validator(v)
+
+    try:
+        return re.compile(str_value)
+    except re.error:
+        raise errors.PatternError()
+
+
+NamedTupleT = TypeVar("NamedTupleT", bound=NamedTuple)
+
+
+def make_namedtuple_validator(
+    namedtuple_cls: Type[NamedTupleT], config: Type["BaseConfig"]
+) -> Callable[[Tuple[Any, ...]], NamedTupleT]:
+    from .annotated_types import create_model_from_namedtuple
+
+    NamedTupleModel = create_model_from_namedtuple(
+        namedtuple_cls,
+        __config__=config,
+        __module__=namedtuple_cls.__module__,
+    )
+    namedtuple_cls.__pydantic_model__ = NamedTupleModel  # type: ignore[attr-defined]
+
+    def namedtuple_validator(values: Tuple[Any, ...]) -> NamedTupleT:
+        annotations = NamedTupleModel.__annotations__
+
+        if len(values) > len(annotations):
+            raise errors.ListMaxLengthError(limit_value=len(annotations))
+
+        dict_values: Dict[str, Any] = dict(zip(annotations, values))
+        validated_dict_values: Dict[str, Any] = dict(NamedTupleModel(**dict_values))
+        return namedtuple_cls(**validated_dict_values)
+
+    return namedtuple_validator
+
+
+def make_typeddict_validator(
+    typeddict_cls: Type["TypedDict"], config: Type["BaseConfig"]  # type: ignore[valid-type]
+) -> Callable[[Any], Dict[str, Any]]:
+    from .annotated_types import create_model_from_typeddict
+
+    TypedDictModel = create_model_from_typeddict(
+        typeddict_cls,
+        __config__=config,
+        __module__=typeddict_cls.__module__,
+    )
+    typeddict_cls.__pydantic_model__ = TypedDictModel  # type: ignore[attr-defined]
+
+    def typeddict_validator(values: "TypedDict") -> Dict[str, Any]:  # type: ignore[valid-type]
+        return TypedDictModel.parse_obj(values).dict(exclude_unset=True)
+
+    return typeddict_validator
+
+
+class IfConfig:
+    def __init__(self, validator: AnyCallable, *config_attr_names: str, ignored_value: Any = False) -> None:
+        self.validator = validator
+        self.config_attr_names = config_attr_names
+        self.ignored_value = ignored_value
+
+    def check(self, config: Type["BaseConfig"]) -> bool:
+        return any(getattr(config, name) not in {None, self.ignored_value} for name in self.config_attr_names)
+
+
+# order is important here, for example: bool is a subclass of int so has to come first, datetime before date same,
+# IPv4Interface before IPv4Address, etc
+_VALIDATORS: List[Tuple[Type[Any], List[Any]]] = [
+    (IntEnum, [int_validator, enum_member_validator]),
+    (Enum, [enum_member_validator]),
+    (
+        str,
+        [
+            str_validator,
+            IfConfig(anystr_strip_whitespace, "anystr_strip_whitespace"),
+            IfConfig(anystr_upper, "anystr_upper"),
+            IfConfig(anystr_lower, "anystr_lower"),
+            IfConfig(anystr_length_validator, "min_anystr_length", "max_anystr_length"),
+        ],
+    ),
+    (
+        bytes,
+        [
+            bytes_validator,
+            IfConfig(anystr_strip_whitespace, "anystr_strip_whitespace"),
+            IfConfig(anystr_upper, "anystr_upper"),
+            IfConfig(anystr_lower, "anystr_lower"),
+            IfConfig(anystr_length_validator, "min_anystr_length", "max_anystr_length"),
+        ],
+    ),
+    (bool, [bool_validator]),
+    (int, [int_validator]),
+    (float, [float_validator, IfConfig(float_finite_validator, "allow_inf_nan", ignored_value=True)]),
+    (Path, [path_validator]),
+    (datetime, [parse_datetime]),
+    (date, [parse_date]),
+    (time, [parse_time]),
+    (timedelta, [parse_duration]),
+    (OrderedDict, [ordered_dict_validator]),
+    (dict, [dict_validator]),
+    (list, [list_validator]),
+    (tuple, [tuple_validator]),
+    (set, [set_validator]),
+    (frozenset, [frozenset_validator]),
+    (deque, [deque_validator]),
+    (UUID, [uuid_validator]),
+    (Decimal, [decimal_validator]),
+    (IPv4Interface, [ip_v4_interface_validator]),
+    (IPv6Interface, [ip_v6_interface_validator]),
+    (IPv4Address, [ip_v4_address_validator]),
+    (IPv6Address, [ip_v6_address_validator]),
+    (IPv4Network, [ip_v4_network_validator]),
+    (IPv6Network, [ip_v6_network_validator]),
+]
+
+
+def find_validators(  # noqa: C901 (ignore complexity)
+    type_: Type[Any], config: Type["BaseConfig"]
+) -> Generator[AnyCallable, None, None]:
+    from .dataclasses import is_builtin_dataclass, make_dataclass_validator
+
+    if type_ is Any or type_ is object:
+        return
+    type_type = type_.__class__
+    if type_type == ForwardRef or type_type == TypeVar:
+        return
+
+    if is_none_type(type_):
+        yield none_validator
+        return
+    if type_ is Pattern or type_ is re.Pattern:
+        yield pattern_validator
+        return
+    if type_ is Hashable or type_ is CollectionsHashable:
+        yield hashable_validator
+        return
+    if is_callable_type(type_):
+        yield callable_validator
+        return
+    if is_literal_type(type_):
+        yield make_literal_validator(type_)
+        return
+    if is_builtin_dataclass(type_):
+        yield from make_dataclass_validator(type_, config)
+        return
+    if type_ is Enum:
+        yield enum_validator
+        return
+    if type_ is IntEnum:
+        yield int_enum_validator
+        return
+    if is_namedtuple(type_):
+        yield tuple_validator
+        yield make_namedtuple_validator(type_, config)
+        return
+    if is_typeddict(type_):
+        yield make_typeddict_validator(type_, config)
+        return
+
+    class_ = get_class(type_)
+    if class_ is not None:
+        if class_ is not Any and isinstance(class_, type):
+            yield make_class_validator(class_)
+        else:
+            yield any_class_validator
+        return
+
+    for val_type, validators in _VALIDATORS:
+        try:
+            if issubclass(type_, val_type):
+                for v in validators:
+                    if isinstance(v, IfConfig):
+                        if v.check(config):
+                            yield v.validator
+                    else:
+                        yield v
+                return
+        except TypeError:
+            raise RuntimeError(f"error checking inheritance of {type_!r} (type: {display_as_type(type_)})")
+
+    if config.arbitrary_types_allowed:
+        yield make_arbitrary_type_validator(type_)
+    else:
+        raise RuntimeError(f"no validator found for {type_}, see `arbitrary_types_allowed` in Config")
diff --git a/vendor/pydantic/version.py b/vendor/pydantic/version.py
new file mode 100644
index 0000000..8bea34e
--- /dev/null
+++ b/vendor/pydantic/version.py
@@ -0,0 +1,38 @@
+__all__ = "compiled", "VERSION", "version_info"
+
+VERSION = "1.10.14"
+
+try:
+    import cython  # type: ignore
+except ImportError:
+    compiled: bool = False
+else:  # pragma: no cover
+    try:
+        compiled = cython.compiled
+    except AttributeError:
+        compiled = False
+
+
+def version_info() -> str:
+    import platform
+    import sys
+    from importlib import import_module
+    from pathlib import Path
+
+    optional_deps = []
+    for p in ("devtools", "dotenv", "email-validator", "typing-extensions"):
+        try:
+            import_module(p.replace("-", "_"))
+        except ImportError:
+            continue
+        optional_deps.append(p)
+
+    info = {
+        "pydantic version": VERSION,
+        "pydantic compiled": compiled,
+        "install path": Path(__file__).resolve().parent,
+        "python version": sys.version,
+        "platform": platform.platform(),
+        "optional deps. installed": optional_deps,
+    }
+    return "\n".join("{:>30} {}".format(k + ":", str(v).replace("\n", " ")) for k, v in info.items())