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micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic-2.13.4.dist-info/licenses/LICENSE

@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2017 to present Pydantic Services Inc. and individual contributors.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_config.py

@@ -0,0 +1,386 @@
+from __future__ import annotations as _annotations
+
+import warnings
+from contextlib import contextmanager
+from re import Pattern
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+)
+
+from pydantic_core import core_schema
+from typing_extensions import Self
+
+from ..aliases import AliasGenerator
+from ..config import ConfigDict, ExtraValues, JsonDict, JsonEncoder, JsonSchemaExtraCallable
+from ..errors import PydanticUserError
+from ..warnings import PydanticDeprecatedSince20, PydanticDeprecatedSince210
+
+if TYPE_CHECKING:
+    from .._internal._schema_generation_shared import GenerateSchema
+    from ..fields import ComputedFieldInfo, FieldInfo
+
+DEPRECATION_MESSAGE = 'Support for class-based `config` is deprecated, use ConfigDict instead.'
+
+
+class ConfigWrapper:
+    """Internal wrapper for Config which exposes ConfigDict items as attributes."""
+
+    __slots__ = ('config_dict',)
+
+    config_dict: ConfigDict
+
+    # all annotations are copied directly from ConfigDict, and should be kept up to date, a test will fail if they
+    # stop matching
+    title: str | None
+    str_to_lower: bool
+    str_to_upper: bool
+    str_strip_whitespace: bool
+    str_min_length: int
+    str_max_length: int | None
+    extra: ExtraValues | None
+    frozen: bool
+    populate_by_name: bool
+    use_enum_values: bool
+    validate_assignment: bool
+    arbitrary_types_allowed: bool
+    from_attributes: bool
+    # whether to use the actual key provided in the data (e.g. alias or first alias for "field required" errors) instead of field_names
+    # to construct error `loc`s, default `True`
+    loc_by_alias: bool
+    alias_generator: Callable[[str], str] | AliasGenerator | None
+    model_title_generator: Callable[[type], str] | None
+    field_title_generator: Callable[[str, FieldInfo | ComputedFieldInfo], str] | None
+    ignored_types: tuple[type, ...]
+    allow_inf_nan: bool
+    json_schema_extra: JsonDict | JsonSchemaExtraCallable | None
+    json_encoders: dict[type[object], JsonEncoder] | None
+
+    # new in V2
+    strict: bool
+    # whether instances of models and dataclasses (including subclass instances) should re-validate, default 'never'
+    revalidate_instances: Literal['always', 'never', 'subclass-instances']
+    ser_json_timedelta: Literal['iso8601', 'float']
+    ser_json_temporal: Literal['iso8601', 'seconds', 'milliseconds']
+    val_temporal_unit: Literal['seconds', 'milliseconds', 'infer']
+    ser_json_bytes: Literal['utf8', 'base64', 'hex']
+    val_json_bytes: Literal['utf8', 'base64', 'hex']
+    ser_json_inf_nan: Literal['null', 'constants', 'strings']
+    # whether to validate default values during validation, default False
+    validate_default: bool
+    validate_return: bool
+    protected_namespaces: tuple[str | Pattern[str], ...]
+    hide_input_in_errors: bool
+    defer_build: bool
+    plugin_settings: dict[str, object] | None
+    schema_generator: type[GenerateSchema] | None
+    json_schema_serialization_defaults_required: bool
+    json_schema_mode_override: Literal['validation', 'serialization', None]
+    coerce_numbers_to_str: bool
+    regex_engine: Literal['rust-regex', 'python-re']
+    validation_error_cause: bool
+    use_attribute_docstrings: bool
+    cache_strings: bool | Literal['all', 'keys', 'none']
+    validate_by_alias: bool
+    validate_by_name: bool
+    serialize_by_alias: bool
+    url_preserve_empty_path: bool
+    polymorphic_serialization: bool
+
+    def __init__(self, config: ConfigDict | dict[str, Any] | type[Any] | None, *, check: bool = True):
+        if check:
+            self.config_dict = prepare_config(config)
+        else:
+            self.config_dict = cast(ConfigDict, config)
+
+    @classmethod
+    def for_model(
+        cls,
+        bases: tuple[type[Any], ...],
+        namespace: dict[str, Any],
+        raw_annotations: dict[str, Any],
+        kwargs: dict[str, Any],
+    ) -> Self:
+        """Build a new `ConfigWrapper` instance for a `BaseModel`.
+
+        The config wrapper built based on (in descending order of priority):
+        - options from `kwargs`
+        - options from the `namespace`
+        - options from the base classes (`bases`)
+
+        Args:
+            bases: A tuple of base classes.
+            namespace: The namespace of the class being created.
+            raw_annotations: The (non-evaluated) annotations of the model.
+            kwargs: The kwargs passed to the class being created.
+
+        Returns:
+            A `ConfigWrapper` instance for `BaseModel`.
+        """
+        config_new = ConfigDict()
+        for base in bases:
+            config = getattr(base, 'model_config', None)
+            if config:
+                config_new.update(config.copy())
+
+        config_class_from_namespace = namespace.get('Config')
+        config_dict_from_namespace = namespace.get('model_config')
+
+        if raw_annotations.get('model_config') and config_dict_from_namespace is None:
+            raise PydanticUserError(
+                '`model_config` cannot be used as a model field name. Use `model_config` for model configuration.',
+                code='model-config-invalid-field-name',
+            )
+
+        if config_class_from_namespace and config_dict_from_namespace:
+            raise PydanticUserError('"Config" and "model_config" cannot be used together', code='config-both')
+
+        config_from_namespace = config_dict_from_namespace or prepare_config(config_class_from_namespace)
+
+        config_new.update(config_from_namespace)
+
+        for k in list(kwargs.keys()):
+            if k in config_keys:
+                config_new[k] = kwargs.pop(k)
+
+        return cls(config_new)
+
+    # we don't show `__getattr__` to type checkers so missing attributes cause errors
+    if not TYPE_CHECKING:  # pragma: no branch
+
+        def __getattr__(self, name: str) -> Any:
+            try:
+                return self.config_dict[name]
+            except KeyError:
+                try:
+                    return config_defaults[name]
+                except KeyError:
+                    raise AttributeError(f'Config has no attribute {name!r}') from None
+
+    def core_config(self, title: str | None) -> core_schema.CoreConfig:
+        """Create a pydantic-core config.
+
+        We don't use getattr here since we don't want to populate with defaults.
+
+        Args:
+            title: The title to use if not set in config.
+
+        Returns:
+            A `CoreConfig` object created from config.
+        """
+        config = self.config_dict
+
+        if config.get('schema_generator') is not None:
+            warnings.warn(
+                'The `schema_generator` setting has been deprecated since v2.10. This setting no longer has any effect.',
+                PydanticDeprecatedSince210,
+                stacklevel=2,
+            )
+
+        if (populate_by_name := config.get('populate_by_name')) is not None:
+            # We include this patch for backwards compatibility purposes, but this config setting will be deprecated in v3.0, and likely removed in v4.0.
+            # Thus, the above warning and this patch can be removed then as well.
+            if config.get('validate_by_name') is None:
+                config['validate_by_alias'] = True
+                config['validate_by_name'] = populate_by_name
+
+        # We dynamically patch validate_by_name to be True if validate_by_alias is set to False
+        # and validate_by_name is not explicitly set.
+        if config.get('validate_by_alias') is False and config.get('validate_by_name') is None:
+            config['validate_by_name'] = True
+
+        if (not config.get('validate_by_alias', True)) and (not config.get('validate_by_name', False)):
+            raise PydanticUserError(
+                'At least one of `validate_by_alias` or `validate_by_name` must be set to True.',
+                code='validate-by-alias-and-name-false',
+            )
+
+        return core_schema.CoreConfig(
+            **{  # pyright: ignore[reportArgumentType]
+                k: v
+                for k, v in (
+                    ('title', config.get('title') or title or None),
+                    ('extra_fields_behavior', config.get('extra')),
+                    ('allow_inf_nan', config.get('allow_inf_nan')),
+                    ('str_strip_whitespace', config.get('str_strip_whitespace')),
+                    ('str_to_lower', config.get('str_to_lower')),
+                    ('str_to_upper', config.get('str_to_upper')),
+                    ('strict', config.get('strict')),
+                    ('ser_json_timedelta', config.get('ser_json_timedelta')),
+                    ('ser_json_temporal', config.get('ser_json_temporal')),
+                    ('val_temporal_unit', config.get('val_temporal_unit')),
+                    ('ser_json_bytes', config.get('ser_json_bytes')),
+                    ('val_json_bytes', config.get('val_json_bytes')),
+                    ('ser_json_inf_nan', config.get('ser_json_inf_nan')),
+                    ('from_attributes', config.get('from_attributes')),
+                    ('loc_by_alias', config.get('loc_by_alias')),
+                    ('revalidate_instances', config.get('revalidate_instances')),
+                    ('validate_default', config.get('validate_default')),
+                    ('str_max_length', config.get('str_max_length')),
+                    ('str_min_length', config.get('str_min_length')),
+                    ('hide_input_in_errors', config.get('hide_input_in_errors')),
+                    ('coerce_numbers_to_str', config.get('coerce_numbers_to_str')),
+                    ('regex_engine', config.get('regex_engine')),
+                    ('validation_error_cause', config.get('validation_error_cause')),
+                    ('cache_strings', config.get('cache_strings')),
+                    ('validate_by_alias', config.get('validate_by_alias')),
+                    ('validate_by_name', config.get('validate_by_name')),
+                    ('serialize_by_alias', config.get('serialize_by_alias')),
+                    ('url_preserve_empty_path', config.get('url_preserve_empty_path')),
+                    ('polymorphic_serialization', config.get('polymorphic_serialization')),
+                )
+                if v is not None
+            }
+        )
+
+    def __repr__(self):
+        c = ', '.join(f'{k}={v!r}' for k, v in self.config_dict.items())
+        return f'ConfigWrapper({c})'
+
+
+class ConfigWrapperStack:
+    """A stack of `ConfigWrapper` instances."""
+
+    def __init__(self, config_wrapper: ConfigWrapper):
+        self._config_wrapper_stack: list[ConfigWrapper] = [config_wrapper]
+
+    @property
+    def tail(self) -> ConfigWrapper:
+        return self._config_wrapper_stack[-1]
+
+    @contextmanager
+    def push(self, config_wrapper: ConfigWrapper | ConfigDict | None):
+        if config_wrapper is None:
+            yield
+            return
+
+        if not isinstance(config_wrapper, ConfigWrapper):
+            config_wrapper = ConfigWrapper(config_wrapper, check=False)
+
+        self._config_wrapper_stack.append(config_wrapper)
+        try:
+            yield
+        finally:
+            self._config_wrapper_stack.pop()
+
+
+config_defaults = ConfigDict(
+    title=None,
+    str_to_lower=False,
+    str_to_upper=False,
+    str_strip_whitespace=False,
+    str_min_length=0,
+    str_max_length=None,
+    # let the model / dataclass decide how to handle it
+    extra=None,
+    frozen=False,
+    populate_by_name=False,
+    use_enum_values=False,
+    validate_assignment=False,
+    arbitrary_types_allowed=False,
+    from_attributes=False,
+    loc_by_alias=True,
+    alias_generator=None,
+    model_title_generator=None,
+    field_title_generator=None,
+    ignored_types=(),
+    allow_inf_nan=True,
+    json_schema_extra=None,
+    strict=False,
+    revalidate_instances='never',
+    ser_json_timedelta='iso8601',
+    ser_json_temporal='iso8601',
+    val_temporal_unit='infer',
+    ser_json_bytes='utf8',
+    val_json_bytes='utf8',
+    ser_json_inf_nan='null',
+    validate_default=False,
+    validate_return=False,
+    protected_namespaces=('model_validate', 'model_dump'),
+    hide_input_in_errors=False,
+    json_encoders=None,
+    defer_build=False,
+    schema_generator=None,
+    plugin_settings=None,
+    json_schema_serialization_defaults_required=False,
+    json_schema_mode_override=None,
+    coerce_numbers_to_str=False,
+    regex_engine='rust-regex',
+    validation_error_cause=False,
+    use_attribute_docstrings=False,
+    cache_strings=True,
+    validate_by_alias=True,
+    validate_by_name=False,
+    serialize_by_alias=False,
+    url_preserve_empty_path=False,
+    polymorphic_serialization=False,
+)
+
+
+def prepare_config(config: ConfigDict | dict[str, Any] | type[Any] | None) -> ConfigDict:
+    """Create a `ConfigDict` instance from an existing dict, a class (e.g. old class-based config) or None.
+
+    Args:
+        config: The input config.
+
+    Returns:
+        A ConfigDict object created from config.
+    """
+    if config is None:
+        return ConfigDict()
+
+    if not isinstance(config, dict):
+        warnings.warn(DEPRECATION_MESSAGE, PydanticDeprecatedSince20, stacklevel=4)
+        config = {k: getattr(config, k) for k in dir(config) if not k.startswith('__')}
+
+    config_dict = cast(ConfigDict, config)
+    check_deprecated(config_dict)
+    return config_dict
+
+
+config_keys = set(ConfigDict.__annotations__.keys())
+
+
+V2_REMOVED_KEYS = {
+    'allow_mutation',
+    'error_msg_templates',
+    'fields',
+    'getter_dict',
+    'smart_union',
+    'underscore_attrs_are_private',
+    'json_loads',
+    'json_dumps',
+    'copy_on_model_validation',
+    'post_init_call',
+}
+V2_RENAMED_KEYS = {
+    'allow_population_by_field_name': 'validate_by_name',
+    'anystr_lower': 'str_to_lower',
+    'anystr_strip_whitespace': 'str_strip_whitespace',
+    'anystr_upper': 'str_to_upper',
+    'keep_untouched': 'ignored_types',
+    'max_anystr_length': 'str_max_length',
+    'min_anystr_length': 'str_min_length',
+    'orm_mode': 'from_attributes',
+    'schema_extra': 'json_schema_extra',
+    'validate_all': 'validate_default',
+}
+
+
+def check_deprecated(config_dict: ConfigDict) -> None:
+    """Check for deprecated config keys and warn the user.
+
+    Args:
+        config_dict: The input config.
+    """
+    deprecated_removed_keys = V2_REMOVED_KEYS & config_dict.keys()
+    deprecated_renamed_keys = V2_RENAMED_KEYS.keys() & config_dict.keys()
+    if deprecated_removed_keys or deprecated_renamed_keys:
+        renamings = {k: V2_RENAMED_KEYS[k] for k in sorted(deprecated_renamed_keys)}
+        renamed_bullets = [f'* {k!r} has been renamed to {v!r}' for k, v in renamings.items()]
+        removed_bullets = [f'* {k!r} has been removed' for k in sorted(deprecated_removed_keys)]
+        message = '\n'.join(['Valid config keys have changed in V2:'] + renamed_bullets + removed_bullets)
+        warnings.warn(message, UserWarning)

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_core_metadata.py

@@ -0,0 +1,97 @@
+from __future__ import annotations as _annotations
+
+from typing import TYPE_CHECKING, Any, TypedDict, cast
+from warnings import warn
+
+if TYPE_CHECKING:
+    from ..config import JsonDict, JsonSchemaExtraCallable
+    from ._schema_generation_shared import (
+        GetJsonSchemaFunction,
+    )
+
+
+class CoreMetadata(TypedDict, total=False):
+    """A `TypedDict` for holding the metadata dict of the schema.
+
+    Attributes:
+        pydantic_js_functions: List of JSON schema functions that resolve refs during application.
+        pydantic_js_annotation_functions: List of JSON schema functions that don't resolve refs during application.
+        pydantic_js_prefer_positional_arguments: Whether JSON schema generator will
+            prefer positional over keyword arguments for an 'arguments' schema.
+            custom validation function. Only applies to before, plain, and wrap validators.
+        pydantic_js_updates: key / value pair updates to apply to the JSON schema for a type.
+        pydantic_js_extra: WIP, either key/value pair updates to apply to the JSON schema, or a custom callable.
+        pydantic_internal_union_tag_key: Used internally by the `Tag` metadata to specify the tag used for a discriminated union.
+        pydantic_internal_union_discriminator: Used internally to specify the discriminator value for a discriminated union
+            when the discriminator was applied to a `'definition-ref'` schema, and that reference was missing at the time
+            of the annotation application.
+
+    TODO: Perhaps we should move this structure to pydantic-core. At the moment, though,
+    it's easier to iterate on if we leave it in pydantic until we feel there is a semi-stable API.
+
+    TODO: It's unfortunate how functionally oriented JSON schema generation is, especially that which occurs during
+    the core schema generation process. It's inevitable that we need to store some json schema related information
+    on core schemas, given that we generate JSON schemas directly from core schemas. That being said, debugging related
+    issues is quite difficult when JSON schema information is disguised via dynamically defined functions.
+    """
+
+    pydantic_js_functions: list[GetJsonSchemaFunction]
+    pydantic_js_annotation_functions: list[GetJsonSchemaFunction]
+    pydantic_js_prefer_positional_arguments: bool
+    pydantic_js_updates: JsonDict
+    pydantic_js_extra: JsonDict | JsonSchemaExtraCallable
+    pydantic_internal_union_tag_key: str
+    pydantic_internal_union_discriminator: str
+
+
+def update_core_metadata(
+    core_metadata: Any,
+    /,
+    *,
+    pydantic_js_functions: list[GetJsonSchemaFunction] | None = None,
+    pydantic_js_annotation_functions: list[GetJsonSchemaFunction] | None = None,
+    pydantic_js_updates: JsonDict | None = None,
+    pydantic_js_extra: JsonDict | JsonSchemaExtraCallable | None = None,
+) -> None:
+    from ..json_schema import PydanticJsonSchemaWarning
+
+    """Update CoreMetadata instance in place. When we make modifications in this function, they
+    take effect on the `core_metadata` reference passed in as the first (and only) positional argument.
+
+    First, cast to `CoreMetadata`, then finish with a cast to `dict[str, Any]` for core schema compatibility.
+    We do this here, instead of before / after each call to this function so that this typing hack
+    can be easily removed if/when we move `CoreMetadata` to `pydantic-core`.
+
+    For parameter descriptions, see `CoreMetadata` above.
+    """
+    core_metadata = cast(CoreMetadata, core_metadata)
+
+    if pydantic_js_functions:
+        core_metadata.setdefault('pydantic_js_functions', []).extend(pydantic_js_functions)
+
+    if pydantic_js_annotation_functions:
+        core_metadata.setdefault('pydantic_js_annotation_functions', []).extend(pydantic_js_annotation_functions)
+
+    if pydantic_js_updates:
+        if (existing_updates := core_metadata.get('pydantic_js_updates')) is not None:
+            core_metadata['pydantic_js_updates'] = {**existing_updates, **pydantic_js_updates}
+        else:
+            core_metadata['pydantic_js_updates'] = pydantic_js_updates
+
+    if pydantic_js_extra is not None:
+        existing_pydantic_js_extra = core_metadata.get('pydantic_js_extra')
+        if existing_pydantic_js_extra is None:
+            core_metadata['pydantic_js_extra'] = pydantic_js_extra
+        if isinstance(existing_pydantic_js_extra, dict):
+            if isinstance(pydantic_js_extra, dict):
+                core_metadata['pydantic_js_extra'] = {**existing_pydantic_js_extra, **pydantic_js_extra}
+            if callable(pydantic_js_extra):
+                warn(
+                    'Composing `dict` and `callable` type `json_schema_extra` is not supported.'
+                    'The `callable` type is being ignored.'
+                    "If you'd like support for this behavior, please open an issue on pydantic.",
+                    PydanticJsonSchemaWarning,
+                )
+        if callable(existing_pydantic_js_extra):
+            # if ever there's a case of a callable, we'll just keep the last json schema extra spec
+            core_metadata['pydantic_js_extra'] = pydantic_js_extra

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_core_utils.py

@@ -0,0 +1,174 @@
+from __future__ import annotations
+
+import inspect
+from collections.abc import Mapping, Sequence
+from typing import TYPE_CHECKING, Any, Union
+
+from pydantic_core import CoreSchema, core_schema
+from typing_extensions import TypeGuard, get_args, get_origin
+from typing_inspection import typing_objects
+
+from . import _repr
+from ._typing_extra import is_generic_alias
+
+if TYPE_CHECKING:
+    from rich.console import Console
+
+AnyFunctionSchema = Union[
+    core_schema.AfterValidatorFunctionSchema,
+    core_schema.BeforeValidatorFunctionSchema,
+    core_schema.WrapValidatorFunctionSchema,
+    core_schema.PlainValidatorFunctionSchema,
+]
+
+
+FunctionSchemaWithInnerSchema = Union[
+    core_schema.AfterValidatorFunctionSchema,
+    core_schema.BeforeValidatorFunctionSchema,
+    core_schema.WrapValidatorFunctionSchema,
+]
+
+CoreSchemaField = Union[
+    core_schema.ModelField, core_schema.DataclassField, core_schema.TypedDictField, core_schema.ComputedField
+]
+CoreSchemaOrField = Union[core_schema.CoreSchema, CoreSchemaField]
+
+_CORE_SCHEMA_FIELD_TYPES = {'typed-dict-field', 'dataclass-field', 'model-field', 'computed-field'}
+_FUNCTION_WITH_INNER_SCHEMA_TYPES = {'function-before', 'function-after', 'function-wrap'}
+_LIST_LIKE_SCHEMA_WITH_ITEMS_TYPES = {'list', 'set', 'frozenset'}
+
+
+def is_core_schema(
+    schema: CoreSchemaOrField,
+) -> TypeGuard[CoreSchema]:
+    return schema['type'] not in _CORE_SCHEMA_FIELD_TYPES
+
+
+def is_core_schema_field(
+    schema: CoreSchemaOrField,
+) -> TypeGuard[CoreSchemaField]:
+    return schema['type'] in _CORE_SCHEMA_FIELD_TYPES
+
+
+def is_function_with_inner_schema(
+    schema: CoreSchemaOrField,
+) -> TypeGuard[FunctionSchemaWithInnerSchema]:
+    return schema['type'] in _FUNCTION_WITH_INNER_SCHEMA_TYPES
+
+
+def is_list_like_schema_with_items_schema(
+    schema: CoreSchema,
+) -> TypeGuard[core_schema.ListSchema | core_schema.SetSchema | core_schema.FrozenSetSchema]:
+    return schema['type'] in _LIST_LIKE_SCHEMA_WITH_ITEMS_TYPES
+
+
+def get_type_ref(type_: Any, args_override: tuple[type[Any], ...] | None = None) -> str:
+    """Produces the ref to be used for this type by pydantic_core's core schemas.
+
+    This `args_override` argument was added for the purpose of creating valid recursive references
+    when creating generic models without needing to create a concrete class.
+    """
+    origin = get_origin(type_) or type_
+
+    args = get_args(type_) if is_generic_alias(type_) else (args_override or ())
+    generic_metadata = getattr(type_, '__pydantic_generic_metadata__', None)
+    if generic_metadata:
+        origin = generic_metadata['origin'] or origin
+        args = generic_metadata['args'] or args
+
+    module_name = getattr(origin, '__module__', '<No __module__>')
+    if typing_objects.is_typealiastype(origin):
+        type_ref = f'{module_name}.{origin.__name__}:{id(origin)}'
+    else:
+        try:
+            qualname = getattr(origin, '__qualname__', f'<No __qualname__: {origin}>')
+        except Exception:
+            qualname = getattr(origin, '__qualname__', '<No __qualname__>')
+        type_ref = f'{module_name}.{qualname}:{id(origin)}'
+
+    arg_refs: list[str] = []
+    for arg in args:
+        if isinstance(arg, str):
+            # Handle string literals as a special case; we may be able to remove this special handling if we
+            # wrap them in a ForwardRef at some point.
+            arg_ref = f'{arg}:str-{id(arg)}'
+        else:
+            arg_ref = f'{_repr.display_as_type(arg)}:{id(arg)}'
+        arg_refs.append(arg_ref)
+    if arg_refs:
+        type_ref = f'{type_ref}[{",".join(arg_refs)}]'
+    return type_ref
+
+
+def get_ref(s: core_schema.CoreSchema) -> None | str:
+    """Get the ref from the schema if it has one.
+    This exists just for type checking to work correctly.
+    """
+    return s.get('ref', None)
+
+
+def _clean_schema_for_pretty_print(obj: Any, strip_metadata: bool = True) -> Any:  # pragma: no cover
+    """A utility function to remove irrelevant information from a core schema."""
+    if isinstance(obj, Mapping):
+        new_dct = {}
+        for k, v in obj.items():
+            if k == 'metadata' and strip_metadata:
+                new_metadata = {}
+
+                for meta_k, meta_v in v.items():
+                    if meta_k in ('pydantic_js_functions', 'pydantic_js_annotation_functions'):
+                        new_metadata['js_metadata'] = '<stripped>'
+                    else:
+                        new_metadata[meta_k] = _clean_schema_for_pretty_print(meta_v, strip_metadata=strip_metadata)
+
+                if list(new_metadata.keys()) == ['js_metadata']:
+                    new_metadata = {'<stripped>'}
+
+                new_dct[k] = new_metadata
+            # Remove some defaults:
+            elif k in ('custom_init', 'root_model') and not v:
+                continue
+            else:
+                new_dct[k] = _clean_schema_for_pretty_print(v, strip_metadata=strip_metadata)
+
+        return new_dct
+    elif isinstance(obj, Sequence) and not isinstance(obj, str):
+        return [_clean_schema_for_pretty_print(v, strip_metadata=strip_metadata) for v in obj]
+    else:
+        return obj
+
+
+def pretty_print_core_schema(
+    val: Any,
+    *,
+    console: Console | None = None,
+    max_depth: int | None = None,
+    strip_metadata: bool = True,
+) -> None:  # pragma: no cover
+    """Pretty-print a core schema using the `rich` library.
+
+    Args:
+        val: The core schema to print, or a Pydantic model/dataclass/type adapter
+            (in which case the cached core schema is fetched and printed).
+        console: A rich console to use when printing. Defaults to the global rich console instance.
+        max_depth: The number of nesting levels which may be printed.
+        strip_metadata: Whether to strip metadata in the output. If `True` any known core metadata
+            attributes will be stripped (but custom attributes are kept). Defaults to `True`.
+    """
+    # lazy import:
+    from rich.pretty import pprint
+
+    # circ. imports:
+    from pydantic import BaseModel, TypeAdapter
+    from pydantic.dataclasses import is_pydantic_dataclass
+
+    if (inspect.isclass(val) and issubclass(val, BaseModel)) or is_pydantic_dataclass(val):
+        val = val.__pydantic_core_schema__
+    if isinstance(val, TypeAdapter):
+        val = val.core_schema
+    cleaned_schema = _clean_schema_for_pretty_print(val, strip_metadata=strip_metadata)
+
+    pprint(cleaned_schema, console=console, max_depth=max_depth)
+
+
+pps = pretty_print_core_schema

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_dataclasses.py

@@ -0,0 +1,315 @@
+"""Private logic for creating pydantic dataclasses."""
+
+from __future__ import annotations as _annotations
+
+import copy
+import dataclasses
+import sys
+import warnings
+from collections.abc import Generator
+from contextlib import contextmanager
+from functools import partial
+from typing import TYPE_CHECKING, Any, ClassVar, Protocol, cast
+
+from pydantic_core import (
+    ArgsKwargs,
+    SchemaSerializer,
+    SchemaValidator,
+    core_schema,
+)
+from typing_extensions import TypeAlias, TypeIs
+
+from ..errors import PydanticUndefinedAnnotation
+from ..fields import FieldInfo
+from ..plugin._schema_validator import PluggableSchemaValidator, create_schema_validator
+from ..warnings import PydanticDeprecatedSince20
+from . import _config, _decorators
+from ._fields import collect_dataclass_fields
+from ._generate_schema import GenerateSchema, InvalidSchemaError
+from ._generics import get_standard_typevars_map
+from ._mock_val_ser import set_dataclass_mocks
+from ._namespace_utils import NsResolver
+from ._signature import generate_pydantic_signature
+from ._utils import LazyClassAttribute
+
+if TYPE_CHECKING:
+    from _typeshed import DataclassInstance as StandardDataclass
+
+    from ..config import ConfigDict
+
+    class PydanticDataclass(StandardDataclass, Protocol):
+        """A protocol containing attributes only available once a class has been decorated as a Pydantic dataclass.
+
+        Attributes:
+            __pydantic_config__: Pydantic-specific configuration settings for the dataclass.
+            __pydantic_complete__: Whether dataclass building is completed, or if there are still undefined fields.
+            __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
+            __pydantic_decorators__: Metadata containing the decorators defined on the dataclass.
+            __pydantic_fields__: Metadata about the fields defined on the dataclass.
+            __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the dataclass.
+            __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the dataclass.
+        """
+
+        __pydantic_config__: ClassVar[ConfigDict]
+        __pydantic_complete__: ClassVar[bool]
+        __pydantic_core_schema__: ClassVar[core_schema.CoreSchema]
+        __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos]
+        __pydantic_fields__: ClassVar[dict[str, FieldInfo]]
+        __pydantic_serializer__: ClassVar[SchemaSerializer]
+        __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator]
+
+        @classmethod
+        def __pydantic_fields_complete__(cls) -> bool: ...
+
+
+def set_dataclass_fields(
+    cls: type[StandardDataclass],
+    config_wrapper: _config.ConfigWrapper,
+    ns_resolver: NsResolver | None = None,
+) -> None:
+    """Collect and set `cls.__pydantic_fields__`.
+
+    Args:
+        cls: The class.
+        config_wrapper: The config wrapper instance.
+        ns_resolver: Namespace resolver to use when getting dataclass annotations.
+    """
+    typevars_map = get_standard_typevars_map(cls)
+    fields = collect_dataclass_fields(
+        cls, ns_resolver=ns_resolver, typevars_map=typevars_map, config_wrapper=config_wrapper
+    )
+
+    cls.__pydantic_fields__ = fields  # type: ignore
+
+
+def complete_dataclass(
+    cls: type[Any],
+    config_wrapper: _config.ConfigWrapper,
+    *,
+    raise_errors: bool = True,
+    ns_resolver: NsResolver | None = None,
+    _force_build: bool = False,
+) -> bool:
+    """Finish building a pydantic dataclass.
+
+    This logic is called on a class which has already been wrapped in `dataclasses.dataclass()`.
+
+    This is somewhat analogous to `pydantic._internal._model_construction.complete_model_class`.
+
+    Args:
+        cls: The class.
+        config_wrapper: The config wrapper instance.
+        raise_errors: Whether to raise errors, defaults to `True`.
+        ns_resolver: The namespace resolver instance to use when collecting dataclass fields
+            and during schema building.
+        _force_build: Whether to force building the dataclass, no matter if
+            [`defer_build`][pydantic.config.ConfigDict.defer_build] is set.
+
+    Returns:
+        `True` if building a pydantic dataclass is successfully completed, `False` otherwise.
+
+    Raises:
+        PydanticUndefinedAnnotation: If `raise_error` is `True` and there is an undefined annotations.
+    """
+    original_init = cls.__init__
+
+    # dataclass.__init__ must be defined here so its `__qualname__` can be changed since functions can't be copied,
+    # and so that the mock validator is used if building was deferred:
+    def __init__(__dataclass_self__: PydanticDataclass, *args: Any, **kwargs: Any) -> None:
+        __tracebackhide__ = True
+        s = __dataclass_self__
+        s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s)
+
+    __init__.__qualname__ = f'{cls.__qualname__}.__init__'
+
+    cls.__init__ = __init__  # type: ignore
+    cls.__pydantic_config__ = config_wrapper.config_dict  # type: ignore
+
+    set_dataclass_fields(cls, config_wrapper=config_wrapper, ns_resolver=ns_resolver)
+
+    if not _force_build and config_wrapper.defer_build:
+        set_dataclass_mocks(cls)
+        return False
+
+    if hasattr(cls, '__post_init_post_parse__'):
+        warnings.warn(
+            'Support for `__post_init_post_parse__` has been dropped, the method will not be called',
+            PydanticDeprecatedSince20,
+        )
+
+    typevars_map = get_standard_typevars_map(cls)
+    gen_schema = GenerateSchema(
+        config_wrapper,
+        ns_resolver=ns_resolver,
+        typevars_map=typevars_map,
+    )
+
+    # set __signature__ attr only for the class, but not for its instances
+    # (because instances can define `__call__`, and `inspect.signature` shouldn't
+    # use the `__signature__` attribute and instead generate from `__call__`).
+    cls.__signature__ = LazyClassAttribute(
+        '__signature__',
+        partial(
+            generate_pydantic_signature,
+            # It's important that we reference the `original_init` here,
+            # as it is the one synthesized by the stdlib `dataclass` module:
+            init=original_init,
+            fields=cls.__pydantic_fields__,  # type: ignore
+            validate_by_name=config_wrapper.validate_by_name,
+            extra=config_wrapper.extra,
+            is_dataclass=True,
+        ),
+    )
+
+    try:
+        schema = gen_schema.generate_schema(cls)
+    except PydanticUndefinedAnnotation as e:
+        if raise_errors:
+            raise
+        set_dataclass_mocks(cls, f'`{e.name}`')
+        return False
+
+    core_config = config_wrapper.core_config(title=cls.__name__)
+
+    try:
+        schema = gen_schema.clean_schema(schema)
+    except InvalidSchemaError:
+        set_dataclass_mocks(cls)
+        return False
+
+    # We are about to set all the remaining required properties expected for this cast;
+    # __pydantic_decorators__ and __pydantic_fields__ should already be set
+    cls = cast('type[PydanticDataclass]', cls)
+
+    cls.__pydantic_core_schema__ = schema
+    cls.__pydantic_validator__ = create_schema_validator(
+        schema, cls, cls.__module__, cls.__qualname__, 'dataclass', core_config, config_wrapper.plugin_settings
+    )
+    cls.__pydantic_serializer__ = SchemaSerializer(schema, core_config)
+    cls.__pydantic_complete__ = True
+    return True
+
+
+def is_stdlib_dataclass(cls: type[Any], /) -> TypeIs[type[StandardDataclass]]:
+    """Returns `True` if the class is a stdlib dataclass and *not* a Pydantic dataclass.
+
+    Unlike the stdlib `dataclasses.is_dataclass()` function, this does *not* include subclasses
+    of a dataclass that are themselves not dataclasses.
+
+    Args:
+        cls: The class.
+
+    Returns:
+        `True` if the class is a stdlib dataclass, `False` otherwise.
+    """
+    return '__dataclass_fields__' in cls.__dict__ and not hasattr(cls, '__pydantic_validator__')
+
+
+def as_dataclass_field(pydantic_field: FieldInfo) -> dataclasses.Field[Any]:
+    field_args: dict[str, Any] = {'default': pydantic_field}
+
+    # Needed because if `doc` is set, the dataclass slots will be a dict (field name -> doc) instead of a tuple:
+    if sys.version_info >= (3, 14) and pydantic_field.description is not None:
+        field_args['doc'] = pydantic_field.description
+
+    # Needed as the stdlib dataclass module processes kw_only in a specific way during class construction:
+    if sys.version_info >= (3, 10) and pydantic_field.kw_only is not None:
+        field_args['kw_only'] = pydantic_field.kw_only
+
+    # Needed as the stdlib dataclass modules generates `__repr__()` during class construction:
+    if pydantic_field.repr is not True:
+        field_args['repr'] = pydantic_field.repr
+
+    return dataclasses.field(**field_args)
+
+
+DcFields: TypeAlias = dict[str, dataclasses.Field[Any]]
+
+
+@contextmanager
+def patch_base_fields(cls: type[Any]) -> Generator[None]:
+    """Temporarily patch the stdlib dataclasses bases of `cls` if the Pydantic `Field()` function is used.
+
+    When creating a Pydantic dataclass, it is possible to inherit from stdlib dataclasses, where
+    the Pydantic `Field()` function is used. To create this Pydantic dataclass, we first apply
+    the stdlib `@dataclass` decorator on it. During the construction of the stdlib dataclass,
+    the `kw_only` and `repr` field arguments need to be understood by the stdlib *during* the
+    dataclass construction. To do so, we temporarily patch the fields dictionary of the affected
+    bases.
+
+    For instance, with the following example:
+
+    ```python {test="skip" lint="skip"}
+    import dataclasses as stdlib_dc
+
+    import pydantic
+    import pydantic.dataclasses as pydantic_dc
+
+    @stdlib_dc.dataclass
+    class A:
+        a: int = pydantic.Field(repr=False)
+
+    # Notice that the `repr` attribute of the dataclass field is `True`:
+    A.__dataclass_fields__['a']
+    #> dataclass.Field(default=FieldInfo(repr=False), repr=True, ...)
+
+    @pydantic_dc.dataclass
+    class B(A):
+        b: int = pydantic.Field(repr=False)
+    ```
+
+    When passing `B` to the stdlib `@dataclass` decorator, it will look for fields in the parent classes
+    and reuse them directly. When this context manager is active, `A` will be temporarily patched to be
+    equivalent to:
+
+    ```python {test="skip" lint="skip"}
+    @stdlib_dc.dataclass
+    class A:
+        a: int = stdlib_dc.field(default=Field(repr=False), repr=False)
+    ```
+
+    !!! note
+        This is only applied to the bases of `cls`, and not `cls` itself. The reason is that the Pydantic
+        dataclass decorator "owns" `cls` (in the previous example, `B`). As such, we instead modify the fields
+        directly (in the previous example, we simply do `setattr(B, 'b', as_dataclass_field(pydantic_field))`).
+
+    !!! note
+        This approach is far from ideal, and can probably be the source of unwanted side effects/race conditions.
+        The previous implemented approach was mutating the `__annotations__` dict of `cls`, which is no longer a
+        safe operation in Python 3.14+, and resulted in unexpected behavior with field ordering anyway.
+    """
+    # A list of two-tuples, the first element being a reference to the
+    # dataclass fields dictionary, the second element being a mapping between
+    # the field names that were modified, and their original `Field`:
+    original_fields_list: list[tuple[DcFields, DcFields]] = []
+
+    for base in cls.__mro__[1:]:
+        dc_fields: dict[str, dataclasses.Field[Any]] = base.__dict__.get('__dataclass_fields__', {})
+        dc_fields_with_pydantic_field_defaults = {
+            field_name: field
+            for field_name, field in dc_fields.items()
+            if isinstance(field.default, FieldInfo)
+            # Only do the patching if one of the affected attributes is set:
+            and (field.default.description is not None or field.default.kw_only or field.default.repr is not True)
+        }
+        if dc_fields_with_pydantic_field_defaults:
+            original_fields_list.append((dc_fields, dc_fields_with_pydantic_field_defaults))
+            for field_name, field in dc_fields_with_pydantic_field_defaults.items():
+                default = cast(FieldInfo, field.default)
+                # `dataclasses.Field` isn't documented as working with `copy.copy()`.
+                # It is a class with `__slots__`, so should work (and we hope for the best):
+                new_dc_field = copy.copy(field)
+                # For base fields, no need to set `doc` from `FieldInfo.description`, this is only relevant
+                # for the class under construction and handled in `as_dataclass_field()`.
+                if sys.version_info >= (3, 10) and default.kw_only:
+                    new_dc_field.kw_only = True
+                if default.repr is not True:
+                    new_dc_field.repr = default.repr
+                dc_fields[field_name] = new_dc_field
+
+    try:
+        yield
+    finally:
+        for fields, original_fields in original_fields_list:
+            for field_name, original_field in original_fields.items():
+                fields[field_name] = original_field

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_decorators.py

@@ -0,0 +1,873 @@
+"""Logic related to validators applied to models etc. via the `@field_validator` and `@model_validator` decorators."""
+
+from __future__ import annotations as _annotations
+
+import types
+from collections import deque
+from collections.abc import Iterable
+from copy import copy
+from dataclasses import dataclass, field
+from functools import cached_property, partial, partialmethod
+from inspect import Parameter, Signature, isdatadescriptor, ismethoddescriptor
+from itertools import islice
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, Generic, Literal, TypeVar, Union
+
+from pydantic_core import PydanticUndefined, PydanticUndefinedType, core_schema
+from typing_extensions import Self, TypeAlias, is_typeddict
+
+from ..errors import PydanticUserError
+from ._core_utils import get_type_ref
+from ._internal_dataclass import slots_true
+from ._namespace_utils import GlobalsNamespace, MappingNamespace
+from ._typing_extra import get_function_type_hints, signature_no_eval
+from ._utils import can_be_positional
+
+if TYPE_CHECKING:
+    from ..fields import ComputedFieldInfo
+    from ..functional_validators import FieldValidatorModes
+    from ._config import ConfigWrapper
+
+
+@dataclass(**slots_true)
+class ValidatorDecoratorInfo:
+    """A container for data from `@validator` so that we can access it
+    while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@validator'.
+        fields: A tuple of field names the validator should be called on.
+        mode: The proposed validator mode.
+        each_item: For complex objects (sets, lists etc.) whether to validate individual
+            elements rather than the whole object.
+        always: Whether this method and other validators should be called even if the value is missing.
+        check_fields: Whether to check that the fields actually exist on the model.
+    """
+
+    decorator_repr: ClassVar[str] = '@validator'
+
+    fields: tuple[str, ...]
+    mode: Literal['before', 'after']
+    each_item: bool
+    always: bool
+    check_fields: bool | None
+
+
+@dataclass(**slots_true)
+class FieldValidatorDecoratorInfo:
+    """A container for data from `@field_validator` so that we can access it
+    while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@field_validator'.
+        fields: A tuple of field names the validator should be called on.
+        mode: The proposed validator mode.
+        check_fields: Whether to check that the fields actually exist on the model.
+        json_schema_input_type: The input type of the function. This is only used to generate
+            the appropriate JSON Schema (in validation mode) and can only specified
+            when `mode` is either `'before'`, `'plain'` or `'wrap'`.
+    """
+
+    decorator_repr: ClassVar[str] = '@field_validator'
+
+    fields: tuple[str, ...]
+    mode: FieldValidatorModes
+    check_fields: bool | None
+    json_schema_input_type: Any
+
+
+@dataclass(**slots_true)
+class RootValidatorDecoratorInfo:
+    """A container for data from `@root_validator` so that we can access it
+    while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@root_validator'.
+        mode: The proposed validator mode.
+    """
+
+    decorator_repr: ClassVar[str] = '@root_validator'
+    mode: Literal['before', 'after']
+
+
+@dataclass(**slots_true)
+class FieldSerializerDecoratorInfo:
+    """A container for data from `@field_serializer` so that we can access it
+    while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@field_serializer'.
+        fields: A tuple of field names the serializer should be called on.
+        mode: The proposed serializer mode.
+        return_type: The type of the serializer's return value.
+        when_used: The serialization condition. Accepts a string with values `'always'`, `'unless-none'`, `'json'`,
+            and `'json-unless-none'`.
+        check_fields: Whether to check that the fields actually exist on the model.
+    """
+
+    decorator_repr: ClassVar[str] = '@field_serializer'
+    fields: tuple[str, ...]
+    mode: Literal['plain', 'wrap']
+    return_type: Any
+    when_used: core_schema.WhenUsed
+    check_fields: bool | None
+
+
+@dataclass(**slots_true)
+class ModelSerializerDecoratorInfo:
+    """A container for data from `@model_serializer` so that we can access it
+    while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@model_serializer'.
+        mode: The proposed serializer mode.
+        return_type: The type of the serializer's return value.
+        when_used: The serialization condition. Accepts a string with values `'always'`, `'unless-none'`, `'json'`,
+            and `'json-unless-none'`.
+    """
+
+    decorator_repr: ClassVar[str] = '@model_serializer'
+    mode: Literal['plain', 'wrap']
+    return_type: Any
+    when_used: core_schema.WhenUsed
+
+
+@dataclass(**slots_true)
+class ModelValidatorDecoratorInfo:
+    """A container for data from `@model_validator` so that we can access it
+    while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@model_validator'.
+        mode: The proposed serializer mode.
+    """
+
+    decorator_repr: ClassVar[str] = '@model_validator'
+    mode: Literal['wrap', 'before', 'after']
+
+
+DecoratorInfo: TypeAlias = """Union[
+    ValidatorDecoratorInfo,
+    FieldValidatorDecoratorInfo,
+    RootValidatorDecoratorInfo,
+    FieldSerializerDecoratorInfo,
+    ModelSerializerDecoratorInfo,
+    ModelValidatorDecoratorInfo,
+    ComputedFieldInfo,
+]"""
+
+ReturnType = TypeVar('ReturnType')
+DecoratedType: TypeAlias = (
+    'Union[classmethod[Any, Any, ReturnType], staticmethod[Any, ReturnType], Callable[..., ReturnType], property]'
+)
+
+
+@dataclass  # can't use slots here since we set attributes on `__post_init__`
+class PydanticDescriptorProxy(Generic[ReturnType]):
+    """Wrap a classmethod, staticmethod, property or unbound function
+    and act as a descriptor that allows us to detect decorated items
+    from the class' attributes.
+
+    This class' __get__ returns the wrapped item's __get__ result,
+    which makes it transparent for classmethods and staticmethods.
+
+    Attributes:
+        wrapped: The decorator that has to be wrapped.
+        decorator_info: The decorator info.
+        shim: A wrapper function to wrap V1 style function.
+    """
+
+    wrapped: DecoratedType[ReturnType]
+    decorator_info: DecoratorInfo
+    shim: Callable[[Callable[..., Any]], Callable[..., Any]] | None = None
+
+    def __post_init__(self):
+        for attr in 'setter', 'deleter':
+            if hasattr(self.wrapped, attr):
+                f = partial(self._call_wrapped_attr, name=attr)
+                setattr(self, attr, f)
+
+    def _call_wrapped_attr(self, func: Callable[[Any], None], *, name: str) -> PydanticDescriptorProxy[ReturnType]:
+        self.wrapped = getattr(self.wrapped, name)(func)
+        if isinstance(self.wrapped, property):
+            # update ComputedFieldInfo.wrapped_property
+            from ..fields import ComputedFieldInfo
+
+            if isinstance(self.decorator_info, ComputedFieldInfo):
+                self.decorator_info.wrapped_property = self.wrapped
+        return self
+
+    def __get__(self, obj: object | None, obj_type: type[object] | None = None) -> PydanticDescriptorProxy[ReturnType]:
+        try:
+            return self.wrapped.__get__(obj, obj_type)  # pyright: ignore[reportReturnType]
+        except AttributeError:
+            # not a descriptor, e.g. a partial object
+            return self.wrapped  # type: ignore[return-value]
+
+    def __set_name__(self, instance: Any, name: str) -> None:
+        if hasattr(self.wrapped, '__set_name__'):
+            self.wrapped.__set_name__(instance, name)  # pyright: ignore[reportFunctionMemberAccess]
+
+    def __getattr__(self, name: str, /) -> Any:
+        """Forward checks for __isabstractmethod__ and such."""
+        return getattr(self.wrapped, name)
+
+
+DecoratorInfoType = TypeVar('DecoratorInfoType', bound=DecoratorInfo)
+
+
+@dataclass(**slots_true)
+class Decorator(Generic[DecoratorInfoType]):
+    """A generic container class to join together the decorator metadata
+    (metadata from decorator itself, which we have when the
+    decorator is called but not when we are building the core-schema)
+    and the bound function (which we have after the class itself is created).
+
+    Attributes:
+        cls_ref: The class ref.
+        cls_var_name: The decorated function name.
+        func: The decorated function.
+        shim: A wrapper function to wrap V1 style function.
+        info: The decorator info.
+    """
+
+    cls_ref: str
+    cls_var_name: str
+    func: Callable[..., Any]
+    shim: Callable[[Any], Any] | None
+    info: DecoratorInfoType
+
+    @staticmethod
+    def build(
+        cls_: Any,
+        *,
+        cls_var_name: str,
+        shim: Callable[[Any], Any] | None,
+        info: DecoratorInfoType,
+    ) -> Decorator[DecoratorInfoType]:
+        """Build a new decorator.
+
+        Args:
+            cls_: The class.
+            cls_var_name: The decorated function name.
+            shim: A wrapper function to wrap V1 style function.
+            info: The decorator info.
+
+        Returns:
+            The new decorator instance.
+        """
+        func = get_attribute_from_bases(cls_, cls_var_name)
+        if shim is not None:
+            func = shim(func)
+        func = unwrap_wrapped_function(func, unwrap_partial=False)
+        if not callable(func):
+            # TODO most likely this branch can be removed when we drop support for Python 3.12:
+            # This branch will get hit for classmethod properties
+            attribute = get_attribute_from_base_dicts(cls_, cls_var_name)  # prevents the binding call to `__get__`
+            if isinstance(attribute, PydanticDescriptorProxy):
+                func = unwrap_wrapped_function(attribute.wrapped)
+        return Decorator(
+            cls_ref=get_type_ref(cls_),
+            cls_var_name=cls_var_name,
+            func=func,
+            shim=shim,
+            info=info,
+        )
+
+    def bind_to_cls(self, cls: Any) -> Decorator[DecoratorInfoType]:
+        """Bind the decorator to a class.
+
+        Args:
+            cls: the class.
+
+        Returns:
+            The new decorator instance.
+        """
+        return self.build(
+            cls,
+            cls_var_name=self.cls_var_name,
+            shim=self.shim,
+            info=copy(self.info),
+        )
+
+
+def get_bases(tp: type[Any]) -> tuple[type[Any], ...]:
+    """Get the base classes of a class or typeddict.
+
+    Args:
+        tp: The type or class to get the bases.
+
+    Returns:
+        The base classes.
+    """
+    if is_typeddict(tp):
+        return tp.__orig_bases__  # type: ignore
+    try:
+        return tp.__bases__
+    except AttributeError:
+        return ()
+
+
+def mro(tp: type[Any]) -> tuple[type[Any], ...]:
+    """Calculate the Method Resolution Order of bases using the C3 algorithm.
+
+    See https://www.python.org/download/releases/2.3/mro/
+    """
+    # try to use the existing mro, for performance mainly
+    # but also because it helps verify the implementation below
+    if not is_typeddict(tp):
+        try:
+            return tp.__mro__
+        except AttributeError:
+            # GenericAlias and some other cases
+            pass
+
+    bases = get_bases(tp)
+    return (tp,) + mro_for_bases(bases)
+
+
+def mro_for_bases(bases: tuple[type[Any], ...]) -> tuple[type[Any], ...]:
+    def merge_seqs(seqs: list[deque[type[Any]]]) -> Iterable[type[Any]]:
+        while True:
+            non_empty = [seq for seq in seqs if seq]
+            if not non_empty:
+                # Nothing left to process, we're done.
+                return
+            candidate: type[Any] | None = None
+            for seq in non_empty:  # Find merge candidates among seq heads.
+                candidate = seq[0]
+                not_head = [s for s in non_empty if candidate in islice(s, 1, None)]
+                if not_head:
+                    # Reject the candidate.
+                    candidate = None
+                else:
+                    break
+            if not candidate:
+                raise TypeError('Inconsistent hierarchy, no C3 MRO is possible')
+            yield candidate
+            for seq in non_empty:
+                # Remove candidate.
+                if seq[0] == candidate:
+                    seq.popleft()
+
+    seqs = [deque(mro(base)) for base in bases] + [deque(bases)]
+    return tuple(merge_seqs(seqs))
+
+
+_sentinel = object()
+
+
+def get_attribute_from_bases(tp: type[Any] | tuple[type[Any], ...], name: str) -> Any:
+    """Get the attribute from the next class in the MRO that has it,
+    aiming to simulate calling the method on the actual class.
+
+    The reason for iterating over the mro instead of just getting
+    the attribute (which would do that for us) is to support TypedDict,
+    which lacks a real __mro__, but can have a virtual one constructed
+    from its bases (as done here).
+
+    Args:
+        tp: The type or class to search for the attribute. If a tuple, this is treated as a set of base classes.
+        name: The name of the attribute to retrieve.
+
+    Returns:
+        Any: The attribute value, if found.
+
+    Raises:
+        AttributeError: If the attribute is not found in any class in the MRO.
+    """
+    if isinstance(tp, tuple):
+        for base in mro_for_bases(tp):
+            attribute = base.__dict__.get(name, _sentinel)
+            if attribute is not _sentinel:
+                attribute_get = getattr(attribute, '__get__', None)
+                if attribute_get is not None:
+                    return attribute_get(None, tp)
+                return attribute
+        raise AttributeError(f'{name} not found in {tp}')
+    else:
+        try:
+            return getattr(tp, name)
+        except AttributeError:
+            return get_attribute_from_bases(mro(tp), name)
+
+
+def get_attribute_from_base_dicts(tp: type[Any], name: str) -> Any:
+    """Get an attribute out of the `__dict__` following the MRO.
+    This prevents the call to `__get__` on the descriptor, and allows
+    us to get the original function for classmethod properties.
+
+    Args:
+        tp: The type or class to search for the attribute.
+        name: The name of the attribute to retrieve.
+
+    Returns:
+        Any: The attribute value, if found.
+
+    Raises:
+        KeyError: If the attribute is not found in any class's `__dict__` in the MRO.
+    """
+    for base in reversed(mro(tp)):
+        if name in base.__dict__:
+            return base.__dict__[name]
+    return tp.__dict__[name]  # raise the error
+
+
+@dataclass(**slots_true)
+class DecoratorInfos:
+    """Mapping of name in the class namespace to decorator info.
+
+    note that the name in the class namespace is the function or attribute name
+    not the field name!
+    """
+
+    validators: dict[str, Decorator[ValidatorDecoratorInfo]] = field(default_factory=dict)
+    field_validators: dict[str, Decorator[FieldValidatorDecoratorInfo]] = field(default_factory=dict)
+    root_validators: dict[str, Decorator[RootValidatorDecoratorInfo]] = field(default_factory=dict)
+    field_serializers: dict[str, Decorator[FieldSerializerDecoratorInfo]] = field(default_factory=dict)
+    model_serializers: dict[str, Decorator[ModelSerializerDecoratorInfo]] = field(default_factory=dict)
+    model_validators: dict[str, Decorator[ModelValidatorDecoratorInfo]] = field(default_factory=dict)
+    computed_fields: dict[str, Decorator[ComputedFieldInfo]] = field(default_factory=dict)
+
+    @classmethod
+    def build(
+        cls,
+        typ: type[Any],
+        # Default to `True` for backwards compatibility:
+        replace_wrapped_methods: bool = True,
+    ) -> Self:
+        """Build a `DecoratorInfos` instance for the given model, dataclass or `TypedDict` type.
+
+        Decorators from parent classes are included, including "bare" classes (e.g. if `typ`
+        is a Pydantic model, non Pydantic parent model classes are also taken into account).
+        The collection of the decorators happens by respecting the MRO.
+
+        If one of the bases has an `__pydantic_decorators__` attribute set, it is assumed to be
+        a `DecoratorInfos` instance and is used as-is. The `__pydantic_decorators__` attribute
+        is *not* being set on the provided `typ`.
+
+        Args:
+            typ: The model, dataclass or `TypedDict` type to use when building the `DecoratorInfos` instance.
+            replace_wrapped_methods: Whether to replace the decorator's wrapped methods on `typ`.
+                This is useful e.g. for field validators which are initially class methods. This should
+                only be set to `True` if `typ` is a Pydantic model or dataclass (otherwise this results
+                in mutations of classes Pydantic doesn't "own").
+        """
+        # reminder: dicts are ordered and replacement does not alter the order
+        res = cls()
+        # Iterate over the bases, without the actual `typ`.
+        # `1:-1` because we don't need to include `object`/`TypedDict`:
+        for base in reversed(mro(typ)[1:-1]):
+            existing: DecoratorInfos | None = base.__dict__.get('__pydantic_decorators__')
+            if existing is None:
+                existing, _ = _decorator_infos_for_class(base, collect_to_replace=False)
+            res.validators.update({k: v.bind_to_cls(typ) for k, v in existing.validators.items()})
+            res.field_validators.update({k: v.bind_to_cls(typ) for k, v in existing.field_validators.items()})
+            res.root_validators.update({k: v.bind_to_cls(typ) for k, v in existing.root_validators.items()})
+            res.field_serializers.update({k: v.bind_to_cls(typ) for k, v in existing.field_serializers.items()})
+            res.model_serializers.update({k: v.bind_to_cls(typ) for k, v in existing.model_serializers.items()})
+            res.model_validators.update({k: v.bind_to_cls(typ) for k, v in existing.model_validators.items()})
+            res.computed_fields.update({k: v.bind_to_cls(typ) for k, v in existing.computed_fields.items()})
+
+        decorator_infos, to_replace = _decorator_infos_for_class(typ, collect_to_replace=True)
+
+        res.validators.update(decorator_infos.validators)
+        res.field_validators.update(decorator_infos.field_validators)
+        res.root_validators.update(decorator_infos.root_validators)
+        res.field_serializers.update(decorator_infos.field_serializers)
+        res.model_serializers.update(decorator_infos.model_serializers)
+        res.model_validators.update(decorator_infos.model_validators)
+        res.computed_fields.update(decorator_infos.computed_fields)
+
+        if replace_wrapped_methods and to_replace:
+            for name, value in to_replace:
+                setattr(typ, name, value)
+
+        res._validate()
+        return res
+
+    def _validate(self) -> None:
+        seen: set[str] = set()
+        for field_ser in self.field_serializers.values():
+            for f_name in field_ser.info.fields:
+                if f_name in seen:
+                    raise PydanticUserError(
+                        f'Multiple field serializer functions were defined for field {f_name!r}, this is not allowed.',
+                        code='multiple-field-serializers',
+                    )
+                seen.add(f_name)
+
+    def update_from_config(self, config_wrapper: ConfigWrapper) -> None:
+        """Update the decorator infos from the configuration of the class they are attached to."""
+        for name, computed_field_dec in self.computed_fields.items():
+            computed_field_dec.info._update_from_config(config_wrapper, name)
+
+
+def _decorator_infos_for_class(
+    typ: type[Any],
+    *,
+    collect_to_replace: bool,
+) -> tuple[DecoratorInfos, list[tuple[str, Any]]]:
+    """Collect a `DecoratorInfos` for class, without looking into bases."""
+    res = DecoratorInfos()
+    to_replace: list[tuple[str, Any]] = []
+
+    for var_name, var_value in vars(typ).items():
+        if isinstance(var_value, PydanticDescriptorProxy):
+            info = var_value.decorator_info
+            if isinstance(info, ValidatorDecoratorInfo):
+                res.validators[var_name] = Decorator.build(typ, cls_var_name=var_name, shim=var_value.shim, info=info)
+            elif isinstance(info, FieldValidatorDecoratorInfo):
+                res.field_validators[var_name] = Decorator.build(
+                    typ, cls_var_name=var_name, shim=var_value.shim, info=info
+                )
+            elif isinstance(info, RootValidatorDecoratorInfo):
+                res.root_validators[var_name] = Decorator.build(
+                    typ, cls_var_name=var_name, shim=var_value.shim, info=info
+                )
+            elif isinstance(info, FieldSerializerDecoratorInfo):
+                res.field_serializers[var_name] = Decorator.build(
+                    typ, cls_var_name=var_name, shim=var_value.shim, info=info
+                )
+            elif isinstance(info, ModelValidatorDecoratorInfo):
+                res.model_validators[var_name] = Decorator.build(
+                    typ, cls_var_name=var_name, shim=var_value.shim, info=info
+                )
+            elif isinstance(info, ModelSerializerDecoratorInfo):
+                res.model_serializers[var_name] = Decorator.build(
+                    typ, cls_var_name=var_name, shim=var_value.shim, info=info
+                )
+            else:
+                from ..fields import ComputedFieldInfo
+
+                isinstance(var_value, ComputedFieldInfo)
+                res.computed_fields[var_name] = Decorator.build(typ, cls_var_name=var_name, shim=None, info=info)
+            if collect_to_replace:
+                to_replace.append((var_name, var_value.wrapped))
+
+    return res, to_replace
+
+
+def inspect_validator(
+    validator: Callable[..., Any], *, mode: FieldValidatorModes, type: Literal['field', 'model']
+) -> bool:
+    """Look at a field or model validator function and determine whether it takes an info argument.
+
+    An error is raised if the function has an invalid signature.
+
+    Args:
+        validator: The validator function to inspect.
+        mode: The proposed validator mode.
+        type: The type of validator, either 'field' or 'model'.
+
+    Returns:
+        Whether the validator takes an info argument.
+    """
+    try:
+        sig = signature_no_eval(validator)
+    except (ValueError, TypeError):
+        # `inspect.signature` might not be able to infer a signature, e.g. with C objects.
+        # In this case, we assume no info argument is present:
+        return False
+    n_positional = count_positional_required_params(sig)
+    if mode == 'wrap':
+        if n_positional == 3:
+            return True
+        elif n_positional == 2:
+            return False
+    else:
+        assert mode in {'before', 'after', 'plain'}, f"invalid mode: {mode!r}, expected 'before', 'after' or 'plain"
+        if n_positional == 2:
+            return True
+        elif n_positional == 1:
+            return False
+
+    raise PydanticUserError(
+        f'Unrecognized {type} validator function signature for {validator} with `mode={mode}`: {sig}',
+        code='validator-signature',
+    )
+
+
+def inspect_field_serializer(serializer: Callable[..., Any], mode: Literal['plain', 'wrap']) -> tuple[bool, bool]:
+    """Look at a field serializer function and determine if it is a field serializer,
+    and whether it takes an info argument.
+
+    An error is raised if the function has an invalid signature.
+
+    Args:
+        serializer: The serializer function to inspect.
+        mode: The serializer mode, either 'plain' or 'wrap'.
+
+    Returns:
+        Tuple of (is_field_serializer, info_arg).
+    """
+    try:
+        sig = signature_no_eval(serializer)
+    except (ValueError, TypeError):
+        # `inspect.signature` might not be able to infer a signature, e.g. with C objects.
+        # In this case, we assume no info argument is present and this is not a method:
+        return (False, False)
+
+    first = next(iter(sig.parameters.values()), None)
+    is_field_serializer = first is not None and first.name == 'self'
+
+    n_positional = count_positional_required_params(sig)
+    if is_field_serializer:
+        # -1 to correct for self parameter
+        info_arg = _serializer_info_arg(mode, n_positional - 1)
+    else:
+        info_arg = _serializer_info_arg(mode, n_positional)
+
+    if info_arg is None:
+        raise PydanticUserError(
+            f'Unrecognized field_serializer function signature for {serializer} with `mode={mode}`:{sig}',
+            code='field-serializer-signature',
+        )
+
+    return is_field_serializer, info_arg
+
+
+def inspect_annotated_serializer(serializer: Callable[..., Any], mode: Literal['plain', 'wrap']) -> bool:
+    """Look at a serializer function used via `Annotated` and determine whether it takes an info argument.
+
+    An error is raised if the function has an invalid signature.
+
+    Args:
+        serializer: The serializer function to check.
+        mode: The serializer mode, either 'plain' or 'wrap'.
+
+    Returns:
+        info_arg
+    """
+    try:
+        sig = signature_no_eval(serializer)
+    except (ValueError, TypeError):
+        # `inspect.signature` might not be able to infer a signature, e.g. with C objects.
+        # In this case, we assume no info argument is present:
+        return False
+    info_arg = _serializer_info_arg(mode, count_positional_required_params(sig))
+    if info_arg is None:
+        raise PydanticUserError(
+            f'Unrecognized field_serializer function signature for {serializer} with `mode={mode}`:{sig}',
+            code='field-serializer-signature',
+        )
+    else:
+        return info_arg
+
+
+def inspect_model_serializer(serializer: Callable[..., Any], mode: Literal['plain', 'wrap']) -> bool:
+    """Look at a model serializer function and determine whether it takes an info argument.
+
+    An error is raised if the function has an invalid signature.
+
+    Args:
+        serializer: The serializer function to check.
+        mode: The serializer mode, either 'plain' or 'wrap'.
+
+    Returns:
+        `info_arg` - whether the function expects an info argument.
+    """
+    if isinstance(serializer, (staticmethod, classmethod)) or not is_instance_method_from_sig(serializer):
+        raise PydanticUserError(
+            '`@model_serializer` must be applied to instance methods', code='model-serializer-instance-method'
+        )
+
+    sig = signature_no_eval(serializer)
+    info_arg = _serializer_info_arg(mode, count_positional_required_params(sig))
+    if info_arg is None:
+        raise PydanticUserError(
+            f'Unrecognized model_serializer function signature for {serializer} with `mode={mode}`:{sig}',
+            code='model-serializer-signature',
+        )
+    else:
+        return info_arg
+
+
+def _serializer_info_arg(mode: Literal['plain', 'wrap'], n_positional: int) -> bool | None:
+    if mode == 'plain':
+        if n_positional == 1:
+            # (input_value: Any, /) -> Any
+            return False
+        elif n_positional == 2:
+            # (model: Any, input_value: Any, /) -> Any
+            return True
+    else:
+        assert mode == 'wrap', f"invalid mode: {mode!r}, expected 'plain' or 'wrap'"
+        if n_positional == 2:
+            # (input_value: Any, serializer: SerializerFunctionWrapHandler, /) -> Any
+            return False
+        elif n_positional == 3:
+            # (input_value: Any, serializer: SerializerFunctionWrapHandler, info: SerializationInfo, /) -> Any
+            return True
+
+    return None
+
+
+AnyDecoratorCallable: TypeAlias = (
+    'Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any], Callable[..., Any]]'
+)
+
+
+def is_instance_method_from_sig(function: AnyDecoratorCallable) -> bool:
+    """Whether the function is an instance method.
+
+    It will consider a function as instance method if the first parameter of
+    function is `self`.
+
+    Args:
+        function: The function to check.
+
+    Returns:
+        `True` if the function is an instance method, `False` otherwise.
+    """
+    sig = signature_no_eval(unwrap_wrapped_function(function))
+    first = next(iter(sig.parameters.values()), None)
+    if first and first.name == 'self':
+        return True
+    return False
+
+
+def ensure_classmethod_based_on_signature(function: AnyDecoratorCallable) -> Any:
+    """Apply the `@classmethod` decorator on the function.
+
+    Args:
+        function: The function to apply the decorator on.
+
+    Return:
+        The `@classmethod` decorator applied function.
+    """
+    if not isinstance(
+        unwrap_wrapped_function(function, unwrap_class_static_method=False), classmethod
+    ) and _is_classmethod_from_sig(function):
+        return classmethod(function)  # type: ignore[arg-type]
+    return function
+
+
+def _is_classmethod_from_sig(function: AnyDecoratorCallable) -> bool:
+    sig = signature_no_eval(unwrap_wrapped_function(function))
+    first = next(iter(sig.parameters.values()), None)
+    if first and first.name == 'cls':
+        return True
+    return False
+
+
+def unwrap_wrapped_function(
+    func: Any,
+    *,
+    unwrap_partial: bool = True,
+    unwrap_class_static_method: bool = True,
+) -> Any:
+    """Recursively unwraps a wrapped function until the underlying function is reached.
+    This handles property, functools.partial, functools.partialmethod, staticmethod, and classmethod.
+
+    Args:
+        func: The function to unwrap.
+        unwrap_partial: If True (default), unwrap partial and partialmethod decorators.
+        unwrap_class_static_method: If True (default), also unwrap classmethod and staticmethod
+            decorators. If False, only unwrap partial and partialmethod decorators.
+
+    Returns:
+        The underlying function of the wrapped function.
+    """
+    # Define the types we want to check against as a single tuple.
+    unwrap_types = (
+        (property, cached_property)
+        + ((partial, partialmethod) if unwrap_partial else ())
+        + ((staticmethod, classmethod) if unwrap_class_static_method else ())
+    )
+
+    while isinstance(func, unwrap_types):
+        if unwrap_class_static_method and isinstance(func, (classmethod, staticmethod)):
+            func = func.__func__
+        elif isinstance(func, (partial, partialmethod)):
+            func = func.func
+        elif isinstance(func, property):
+            func = func.fget  # arbitrary choice, convenient for computed fields
+        else:
+            # Make coverage happy as it can only get here in the last possible case
+            assert isinstance(func, cached_property)
+            func = func.func  # type: ignore
+
+    return func
+
+
+_function_like = (
+    partial,
+    partialmethod,
+    types.FunctionType,
+    types.BuiltinFunctionType,
+    types.MethodType,
+    types.WrapperDescriptorType,
+    types.MethodWrapperType,
+    types.MemberDescriptorType,
+)
+
+
+def get_callable_return_type(
+    callable_obj: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> Any | PydanticUndefinedType:
+    """Get the callable return type.
+
+    Args:
+        callable_obj: The callable to analyze.
+        globalns: The globals namespace to use during type annotation evaluation.
+        localns: The locals namespace to use during type annotation evaluation.
+
+    Returns:
+        The function return type.
+    """
+    if isinstance(callable_obj, type):
+        # types are callables, and we assume the return type
+        # is the type itself (e.g. `int()` results in an instance of `int`).
+        return callable_obj
+
+    if not isinstance(callable_obj, _function_like):
+        call_func = getattr(type(callable_obj), '__call__', None)  # noqa: B004
+        if call_func is not None:
+            callable_obj = call_func
+
+    hints = get_function_type_hints(
+        unwrap_wrapped_function(callable_obj),
+        include_keys={'return'},
+        globalns=globalns,
+        localns=localns,
+    )
+    return hints.get('return', PydanticUndefined)
+
+
+def count_positional_required_params(sig: Signature) -> int:
+    """Get the number of positional (required) arguments of a signature.
+
+    This function should only be used to inspect signatures of validation and serialization functions.
+    The first argument (the value being serialized or validated) is counted as a required argument
+    even if a default value exists.
+
+    Returns:
+        The number of positional arguments of a signature.
+    """
+    parameters = list(sig.parameters.values())
+    return sum(
+        1
+        for param in parameters
+        if can_be_positional(param)
+        # First argument is the value being validated/serialized, and can have a default value
+        # (e.g. `float`, which has signature `(x=0, /)`). We assume other parameters (the info arg
+        # for instance) should be required, and thus without any default value.
+        and (param.default is Parameter.empty or param is parameters[0])
+    )
+
+
+def ensure_property(f: Any) -> Any:
+    """Ensure that a function is a `property` or `cached_property`, or is a valid descriptor.
+
+    Args:
+        f: The function to check.
+
+    Returns:
+        The function, or a `property` or `cached_property` instance wrapping the function.
+    """
+    if ismethoddescriptor(f) or isdatadescriptor(f):
+        return f
+    else:
+        return property(f)

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_decorators_v1.py

@@ -0,0 +1,174 @@
+"""Logic for V1 validators, e.g. `@validator` and `@root_validator`."""
+
+from __future__ import annotations as _annotations
+
+from inspect import Parameter, signature
+from typing import Any, Union, cast
+
+from pydantic_core import core_schema
+from typing_extensions import Protocol
+
+from ..errors import PydanticUserError
+from ._utils import can_be_positional
+
+
+class V1OnlyValueValidator(Protocol):
+    """A simple validator, supported for V1 validators and V2 validators."""
+
+    def __call__(self, __value: Any) -> Any: ...
+
+
+class V1ValidatorWithValues(Protocol):
+    """A validator with `values` argument, supported for V1 validators and V2 validators."""
+
+    def __call__(self, __value: Any, values: dict[str, Any]) -> Any: ...
+
+
+class V1ValidatorWithValuesKwOnly(Protocol):
+    """A validator with keyword only `values` argument, supported for V1 validators and V2 validators."""
+
+    def __call__(self, __value: Any, *, values: dict[str, Any]) -> Any: ...
+
+
+class V1ValidatorWithKwargs(Protocol):
+    """A validator with `kwargs` argument, supported for V1 validators and V2 validators."""
+
+    def __call__(self, __value: Any, **kwargs: Any) -> Any: ...
+
+
+class V1ValidatorWithValuesAndKwargs(Protocol):
+    """A validator with `values` and `kwargs` arguments, supported for V1 validators and V2 validators."""
+
+    def __call__(self, __value: Any, values: dict[str, Any], **kwargs: Any) -> Any: ...
+
+
+V1Validator = Union[
+    V1ValidatorWithValues, V1ValidatorWithValuesKwOnly, V1ValidatorWithKwargs, V1ValidatorWithValuesAndKwargs
+]
+
+
+def can_be_keyword(param: Parameter) -> bool:
+    return param.kind in (Parameter.POSITIONAL_OR_KEYWORD, Parameter.KEYWORD_ONLY)
+
+
+def make_generic_v1_field_validator(validator: V1Validator) -> core_schema.WithInfoValidatorFunction:
+    """Wrap a V1 style field validator for V2 compatibility.
+
+    Args:
+        validator: The V1 style field validator.
+
+    Returns:
+        A wrapped V2 style field validator.
+
+    Raises:
+        PydanticUserError: If the signature is not supported or the parameters are
+            not available in Pydantic V2.
+    """
+    sig = signature(validator)
+
+    needs_values_kw = False
+
+    for param_num, (param_name, parameter) in enumerate(sig.parameters.items()):
+        if can_be_keyword(parameter) and param_name in ('field', 'config'):
+            raise PydanticUserError(
+                'The `field` and `config` parameters are not available in Pydantic V2, '
+                'please use the `info` parameter instead.',
+                code='validator-field-config-info',
+            )
+        if parameter.kind is Parameter.VAR_KEYWORD:
+            needs_values_kw = True
+        elif can_be_keyword(parameter) and param_name == 'values':
+            needs_values_kw = True
+        elif can_be_positional(parameter) and param_num == 0:
+            # value
+            continue
+        elif parameter.default is Parameter.empty:  # ignore params with defaults e.g. bound by functools.partial
+            raise PydanticUserError(
+                f'Unsupported signature for V1 style validator {validator}: {sig} is not supported.',
+                code='validator-v1-signature',
+            )
+
+    if needs_values_kw:
+        # (v, **kwargs), (v, values, **kwargs), (v, *, values, **kwargs) or (v, *, values)
+        val1 = cast(V1ValidatorWithValues, validator)
+
+        def wrapper1(value: Any, info: core_schema.ValidationInfo) -> Any:
+            return val1(value, values=info.data)
+
+        return wrapper1
+    else:
+        val2 = cast(V1OnlyValueValidator, validator)
+
+        def wrapper2(value: Any, _: core_schema.ValidationInfo) -> Any:
+            return val2(value)
+
+        return wrapper2
+
+
+RootValidatorValues = dict[str, Any]
+# technically tuple[model_dict, model_extra, fields_set] | tuple[dataclass_dict, init_vars]
+RootValidatorFieldsTuple = tuple[Any, ...]
+
+
+class V1RootValidatorFunction(Protocol):
+    """A simple root validator, supported for V1 validators and V2 validators."""
+
+    def __call__(self, __values: RootValidatorValues) -> RootValidatorValues: ...
+
+
+class V2CoreBeforeRootValidator(Protocol):
+    """V2 validator with mode='before'."""
+
+    def __call__(self, __values: RootValidatorValues, __info: core_schema.ValidationInfo) -> RootValidatorValues: ...
+
+
+class V2CoreAfterRootValidator(Protocol):
+    """V2 validator with mode='after'."""
+
+    def __call__(
+        self, __fields_tuple: RootValidatorFieldsTuple, __info: core_schema.ValidationInfo
+    ) -> RootValidatorFieldsTuple: ...
+
+
+def make_v1_generic_root_validator(
+    validator: V1RootValidatorFunction, pre: bool
+) -> V2CoreBeforeRootValidator | V2CoreAfterRootValidator:
+    """Wrap a V1 style root validator for V2 compatibility.
+
+    Args:
+        validator: The V1 style field validator.
+        pre: Whether the validator is a pre validator.
+
+    Returns:
+        A wrapped V2 style validator.
+    """
+    if pre is True:
+        # mode='before' for pydantic-core
+        def _wrapper1(values: RootValidatorValues, _: core_schema.ValidationInfo) -> RootValidatorValues:
+            return validator(values)
+
+        return _wrapper1
+
+    # mode='after' for pydantic-core
+    def _wrapper2(fields_tuple: RootValidatorFieldsTuple, _: core_schema.ValidationInfo) -> RootValidatorFieldsTuple:
+        if len(fields_tuple) == 2:
+            # dataclass, this is easy
+            values, init_vars = fields_tuple
+            values = validator(values)
+            return values, init_vars
+        else:
+            # ugly hack: to match v1 behaviour, we merge values and model_extra, then split them up based on fields
+            # afterwards
+            model_dict, model_extra, fields_set = fields_tuple
+            if model_extra:
+                fields = set(model_dict.keys())
+                model_dict.update(model_extra)
+                model_dict_new = validator(model_dict)
+                for k in list(model_dict_new.keys()):
+                    if k not in fields:
+                        model_extra[k] = model_dict_new.pop(k)
+            else:
+                model_dict_new = validator(model_dict)
+            return model_dict_new, model_extra, fields_set
+
+    return _wrapper2

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_discriminated_union.py

@@ -0,0 +1,494 @@
+from __future__ import annotations as _annotations
+
+from collections.abc import Hashable, Sequence
+from typing import TYPE_CHECKING, Any, cast
+
+from pydantic_core import CoreSchema, core_schema
+
+from ..errors import PydanticUserError
+from . import _core_utils
+from ._core_utils import (
+    CoreSchemaField,
+)
+
+if TYPE_CHECKING:
+    from ..types import Discriminator
+    from ._core_metadata import CoreMetadata
+
+
+class MissingDefinitionForUnionRef(Exception):
+    """Raised when applying a discriminated union discriminator to a schema
+    requires a definition that is not yet defined
+    """
+
+    def __init__(self, ref: str) -> None:
+        self.ref = ref
+        super().__init__(f'Missing definition for ref {self.ref!r}')
+
+
+def set_discriminator_in_metadata(schema: CoreSchema, discriminator: Any) -> None:
+    metadata = cast('CoreMetadata', schema.setdefault('metadata', {}))
+    metadata['pydantic_internal_union_discriminator'] = discriminator
+
+
+def apply_discriminator(
+    schema: core_schema.CoreSchema,
+    discriminator: str | Discriminator,
+    definitions: dict[str, core_schema.CoreSchema] | None = None,
+) -> core_schema.CoreSchema:
+    """Applies the discriminator and returns a new core schema.
+
+    Args:
+        schema: The input schema.
+        discriminator: The name of the field which will serve as the discriminator.
+        definitions: A mapping of schema ref to schema.
+
+    Returns:
+        The new core schema.
+
+    Raises:
+        TypeError:
+            - If `discriminator` is used with invalid union variant.
+            - If `discriminator` is used with `Union` type with one variant.
+            - If `discriminator` value mapped to multiple choices.
+        MissingDefinitionForUnionRef:
+            If the definition for ref is missing.
+        PydanticUserError:
+            - If a model in union doesn't have a discriminator field.
+            - If discriminator field has a non-string alias.
+            - If discriminator fields have different aliases.
+            - If discriminator field not of type `Literal`.
+    """
+    from ..types import Discriminator
+
+    if isinstance(discriminator, Discriminator):
+        if isinstance(discriminator.discriminator, str):
+            discriminator = discriminator.discriminator
+        else:
+            return discriminator._convert_schema(schema)
+
+    return _ApplyInferredDiscriminator(discriminator, definitions or {}).apply(schema)
+
+
+class _ApplyInferredDiscriminator:
+    """This class is used to convert an input schema containing a union schema into one where that union is
+    replaced with a tagged-union, with all the associated debugging and performance benefits.
+
+    This is done by:
+    * Validating that the input schema is compatible with the provided discriminator
+    * Introspecting the schema to determine which discriminator values should map to which union choices
+    * Handling various edge cases such as 'definitions', 'default', 'nullable' schemas, and more
+
+    I have chosen to implement the conversion algorithm in this class, rather than a function,
+    to make it easier to maintain state while recursively walking the provided CoreSchema.
+    """
+
+    def __init__(self, discriminator: str, definitions: dict[str, core_schema.CoreSchema]):
+        # `discriminator` should be the name of the field which will serve as the discriminator.
+        # It must be the python name of the field, and *not* the field's alias. Note that as of now,
+        # all members of a discriminated union _must_ use a field with the same name as the discriminator.
+        # This may change if/when we expose a way to manually specify the TaggedUnionSchema's choices.
+        self.discriminator = discriminator
+
+        # `definitions` should contain a mapping of schema ref to schema for all schemas which might
+        # be referenced by some choice
+        self.definitions = definitions
+
+        # `_discriminator_alias` will hold the value, if present, of the alias for the discriminator
+        #
+        # Note: following the v1 implementation, we currently disallow the use of different aliases
+        # for different choices. This is not a limitation of pydantic_core, but if we try to handle
+        # this, the inference logic gets complicated very quickly, and could result in confusing
+        # debugging challenges for users making subtle mistakes.
+        #
+        # Rather than trying to do the most powerful inference possible, I think we should eventually
+        # expose a way to more-manually control the way the TaggedUnionSchema is constructed through
+        # the use of a new type which would be placed as an Annotation on the Union type. This would
+        # provide the full flexibility/power of pydantic_core's TaggedUnionSchema where necessary for
+        # more complex cases, without over-complicating the inference logic for the common cases.
+        self._discriminator_alias: str | None = None
+
+        # `_should_be_nullable` indicates whether the converted union has `None` as an allowed value.
+        # If `None` is an acceptable value of the (possibly-wrapped) union, we ignore it while
+        # constructing the TaggedUnionSchema, but set the `_should_be_nullable` attribute to True.
+        # Once we have constructed the TaggedUnionSchema, if `_should_be_nullable` is True, we ensure
+        # that the final schema gets wrapped as a NullableSchema. This has the same semantics on the
+        # python side, but resolves the issue that `None` cannot correspond to any discriminator values.
+        self._should_be_nullable = False
+
+        # `_is_nullable` is used to track if the final produced schema will definitely be nullable;
+        # we set it to True if the input schema is wrapped in a nullable schema that we know will be preserved
+        # as an indication that, even if None is discovered as one of the union choices, we will not need to wrap
+        # the final value in another nullable schema.
+        #
+        # This is more complicated than just checking for the final outermost schema having type 'nullable' thanks
+        # to the possible presence of other wrapper schemas such as DefinitionsSchema, WithDefaultSchema, etc.
+        self._is_nullable = False
+
+        # `_choices_to_handle` serves as a stack of choices to add to the tagged union. Initially, choices
+        # from the union in the wrapped schema will be appended to this list, and the recursive choice-handling
+        # algorithm may add more choices to this stack as (nested) unions are encountered.
+        self._choices_to_handle: list[core_schema.CoreSchema] = []
+
+        # `_tagged_union_choices` is built during the call to `apply`, and will hold the choices to be included
+        # in the output TaggedUnionSchema that will replace the union from the input schema
+        self._tagged_union_choices: dict[Hashable, core_schema.CoreSchema] = {}
+
+        # `_used` is changed to True after applying the discriminator to prevent accidental reuse
+        self._used = False
+
+    def apply(self, schema: core_schema.CoreSchema) -> core_schema.CoreSchema:
+        """Return a new CoreSchema based on `schema` that uses a tagged-union with the discriminator provided
+        to this class.
+
+        Args:
+            schema: The input schema.
+
+        Returns:
+            The new core schema.
+
+        Raises:
+            TypeError:
+                - If `discriminator` is used with invalid union variant.
+                - If `discriminator` is used with `Union` type with one variant.
+                - If `discriminator` value mapped to multiple choices.
+            ValueError:
+                If the definition for ref is missing.
+            PydanticUserError:
+                - If a model in union doesn't have a discriminator field.
+                - If discriminator field has a non-string alias.
+                - If discriminator fields have different aliases.
+                - If discriminator field not of type `Literal`.
+        """
+        assert not self._used
+        schema = self._apply_to_root(schema)
+        if self._should_be_nullable and not self._is_nullable:
+            schema = core_schema.nullable_schema(schema)
+        self._used = True
+        return schema
+
+    def _apply_to_root(self, schema: core_schema.CoreSchema) -> core_schema.CoreSchema:
+        """This method handles the outer-most stage of recursion over the input schema:
+        unwrapping nullable or definitions schemas, and calling the `_handle_choice`
+        method iteratively on the choices extracted (recursively) from the possibly-wrapped union.
+        """
+        if schema['type'] == 'nullable':
+            self._is_nullable = True
+            wrapped = self._apply_to_root(schema['schema'])
+            nullable_wrapper = schema.copy()
+            nullable_wrapper['schema'] = wrapped
+            return nullable_wrapper
+
+        if schema['type'] == 'definitions':
+            wrapped = self._apply_to_root(schema['schema'])
+            definitions_wrapper = schema.copy()
+            definitions_wrapper['schema'] = wrapped
+            return definitions_wrapper
+
+        if schema['type'] == 'definition-ref':
+            schema_ref = schema['schema_ref']
+            if schema_ref not in self.definitions:  # pragma: no cover
+                raise MissingDefinitionForUnionRef(schema_ref)
+
+            def_schema = self.definitions[schema_ref]
+            # If using a referenceable union as discriminated (e.g. `type Pet = Cat | Dog; field: Pet = Field(discriminator=...)`):
+            if def_schema['type'] == 'union':
+                schema = def_schema.copy()
+                schema.pop('ref')
+
+        if schema['type'] != 'union':
+            # If the schema is not a union, it probably means it just had a single member and
+            # was flattened by pydantic_core.
+            # However, it still may make sense to apply the discriminator to this schema,
+            # as a way to get discriminated-union-style error messages, so we allow this here.
+            schema = core_schema.union_schema([schema])
+
+        # Reverse the choices list before extending the stack so that they get handled in the order they occur
+        choices_schemas = [v[0] if isinstance(v, tuple) else v for v in schema['choices'][::-1]]
+        self._choices_to_handle.extend(choices_schemas)
+        while self._choices_to_handle:
+            choice = self._choices_to_handle.pop()
+            self._handle_choice(choice)
+
+        if self._discriminator_alias is not None and self._discriminator_alias != self.discriminator:
+            # * We need to annotate `discriminator` as a union here to handle both branches of this conditional
+            # * We need to annotate `discriminator` as list[list[str | int]] and not list[list[str]] due to the
+            #   invariance of list, and because list[list[str | int]] is the type of the discriminator argument
+            #   to tagged_union_schema below
+            # * See the docstring of pydantic_core.core_schema.tagged_union_schema for more details about how to
+            #   interpret the value of the discriminator argument to tagged_union_schema. (The list[list[str]] here
+            #   is the appropriate way to provide a list of fallback attributes to check for a discriminator value.)
+            discriminator: str | list[list[str | int]] = [[self.discriminator], [self._discriminator_alias]]
+        else:
+            discriminator = self.discriminator
+        return core_schema.tagged_union_schema(
+            choices=self._tagged_union_choices,
+            discriminator=discriminator,
+            custom_error_type=schema.get('custom_error_type'),
+            custom_error_message=schema.get('custom_error_message'),
+            custom_error_context=schema.get('custom_error_context'),
+            strict=False,
+            from_attributes=True,
+            ref=schema.get('ref'),
+            metadata=schema.get('metadata'),
+            serialization=schema.get('serialization'),
+        )
+
+    def _handle_choice(self, choice: core_schema.CoreSchema) -> None:
+        """This method handles the "middle" stage of recursion over the input schema.
+        Specifically, it is responsible for handling each choice of the outermost union
+        (and any "coalesced" choices obtained from inner unions).
+
+        Here, "handling" entails:
+        * Coalescing nested unions and compatible tagged-unions
+        * Tracking the presence of 'none' and 'nullable' schemas occurring as choices
+        * Validating that each allowed discriminator value maps to a unique choice
+        * Updating the _tagged_union_choices mapping that will ultimately be used to build the TaggedUnionSchema.
+        """
+        if choice['type'] == 'definition-ref':
+            if choice['schema_ref'] not in self.definitions:
+                raise MissingDefinitionForUnionRef(choice['schema_ref'])
+
+        if choice['type'] == 'none':
+            self._should_be_nullable = True
+        elif choice['type'] == 'definitions':
+            self._handle_choice(choice['schema'])
+        elif choice['type'] == 'nullable':
+            self._should_be_nullable = True
+            self._handle_choice(choice['schema'])  # unwrap the nullable schema
+        elif choice['type'] == 'union':
+            # Reverse the choices list before extending the stack so that they get handled in the order they occur
+            choices_schemas = [v[0] if isinstance(v, tuple) else v for v in choice['choices'][::-1]]
+            self._choices_to_handle.extend(choices_schemas)
+        elif choice['type'] not in {
+            'model',
+            'typed-dict',
+            'tagged-union',
+            'lax-or-strict',
+            'dataclass',
+            'dataclass-args',
+            'definition-ref',
+        } and not _core_utils.is_function_with_inner_schema(choice):
+            # We should eventually handle 'definition-ref' as well
+            err_str = f'The core schema type {choice["type"]!r} is not a valid discriminated union variant.'
+            if choice['type'] == 'list':
+                err_str += (
+                    ' If you are making use of a list of union types, make sure the discriminator is applied to the '
+                    'union type and not the list (e.g. `list[Annotated[<T> | <U>, Field(discriminator=...)]]`).'
+                )
+            raise TypeError(err_str)
+        else:
+            if choice['type'] == 'tagged-union' and self._is_discriminator_shared(choice):
+                # In this case, this inner tagged-union is compatible with the outer tagged-union,
+                # and its choices can be coalesced into the outer TaggedUnionSchema.
+                subchoices = [x for x in choice['choices'].values() if not isinstance(x, (str, int))]
+                # Reverse the choices list before extending the stack so that they get handled in the order they occur
+                self._choices_to_handle.extend(subchoices[::-1])
+                return
+
+            inferred_discriminator_values = self._infer_discriminator_values_for_choice(choice, source_name=None)
+            self._set_unique_choice_for_values(choice, inferred_discriminator_values)
+
+    def _is_discriminator_shared(self, choice: core_schema.TaggedUnionSchema) -> bool:
+        """This method returns a boolean indicating whether the discriminator for the `choice`
+        is the same as that being used for the outermost tagged union. This is used to
+        determine whether this TaggedUnionSchema choice should be "coalesced" into the top level,
+        or whether it should be treated as a separate (nested) choice.
+        """
+        inner_discriminator = choice['discriminator']
+        return inner_discriminator == self.discriminator or (
+            isinstance(inner_discriminator, list)
+            and (self.discriminator in inner_discriminator or [self.discriminator] in inner_discriminator)
+        )
+
+    def _infer_discriminator_values_for_choice(  # noqa C901
+        self, choice: core_schema.CoreSchema, source_name: str | None
+    ) -> list[str | int]:
+        """This function recurses over `choice`, extracting all discriminator values that should map to this choice.
+
+        `model_name` is accepted for the purpose of producing useful error messages.
+        """
+        if choice['type'] == 'definitions':
+            return self._infer_discriminator_values_for_choice(choice['schema'], source_name=source_name)
+
+        elif _core_utils.is_function_with_inner_schema(choice):
+            return self._infer_discriminator_values_for_choice(choice['schema'], source_name=source_name)
+
+        elif choice['type'] == 'lax-or-strict':
+            return sorted(
+                set(
+                    self._infer_discriminator_values_for_choice(choice['lax_schema'], source_name=None)
+                    + self._infer_discriminator_values_for_choice(choice['strict_schema'], source_name=None)
+                )
+            )
+
+        elif choice['type'] == 'tagged-union':
+            values: list[str | int] = []
+            # Ignore str/int "choices" since these are just references to other choices
+            subchoices = [x for x in choice['choices'].values() if not isinstance(x, (str, int))]
+            for subchoice in subchoices:
+                subchoice_values = self._infer_discriminator_values_for_choice(subchoice, source_name=None)
+                values.extend(subchoice_values)
+            return values
+
+        elif choice['type'] == 'union':
+            values = []
+            for subchoice in choice['choices']:
+                subchoice_schema = subchoice[0] if isinstance(subchoice, tuple) else subchoice
+                subchoice_values = self._infer_discriminator_values_for_choice(subchoice_schema, source_name=None)
+                values.extend(subchoice_values)
+            return values
+
+        elif choice['type'] == 'nullable':
+            self._should_be_nullable = True
+            return self._infer_discriminator_values_for_choice(choice['schema'], source_name=None)
+
+        elif choice['type'] == 'model':
+            return self._infer_discriminator_values_for_choice(choice['schema'], source_name=choice['cls'].__name__)
+
+        elif choice['type'] == 'dataclass':
+            return self._infer_discriminator_values_for_choice(choice['schema'], source_name=choice['cls'].__name__)
+
+        elif choice['type'] == 'model-fields':
+            return self._infer_discriminator_values_for_model_choice(choice, source_name=source_name)
+
+        elif choice['type'] == 'dataclass-args':
+            return self._infer_discriminator_values_for_dataclass_choice(choice, source_name=source_name)
+
+        elif choice['type'] == 'typed-dict':
+            return self._infer_discriminator_values_for_typed_dict_choice(choice, source_name=source_name)
+
+        elif choice['type'] == 'definition-ref':
+            schema_ref = choice['schema_ref']
+            if schema_ref not in self.definitions:
+                raise MissingDefinitionForUnionRef(schema_ref)
+            return self._infer_discriminator_values_for_choice(self.definitions[schema_ref], source_name=source_name)
+        else:
+            err_str = f'The core schema type {choice["type"]!r} is not a valid discriminated union variant.'
+            if choice['type'] == 'list':
+                err_str += (
+                    ' If you are making use of a list of union types, make sure the discriminator is applied to the '
+                    'union type and not the list (e.g. `list[Annotated[<T> | <U>, Field(discriminator=...)]]`).'
+                )
+            raise TypeError(err_str)
+
+    def _infer_discriminator_values_for_typed_dict_choice(
+        self, choice: core_schema.TypedDictSchema, source_name: str | None = None
+    ) -> list[str | int]:
+        """This method just extracts the _infer_discriminator_values_for_choice logic specific to TypedDictSchema
+        for the sake of readability.
+        """
+        source = 'TypedDict' if source_name is None else f'TypedDict {source_name!r}'
+        field = choice['fields'].get(self.discriminator)
+        if field is None:
+            raise PydanticUserError(
+                f'{source} needs a discriminator field for key {self.discriminator!r}', code='discriminator-no-field'
+            )
+        return self._infer_discriminator_values_for_field(field, source)
+
+    def _infer_discriminator_values_for_model_choice(
+        self, choice: core_schema.ModelFieldsSchema, source_name: str | None = None
+    ) -> list[str | int]:
+        source = 'ModelFields' if source_name is None else f'Model {source_name!r}'
+        field = choice['fields'].get(self.discriminator)
+        if field is None:
+            raise PydanticUserError(
+                f'{source} needs a discriminator field for key {self.discriminator!r}', code='discriminator-no-field'
+            )
+        return self._infer_discriminator_values_for_field(field, source)
+
+    def _infer_discriminator_values_for_dataclass_choice(
+        self, choice: core_schema.DataclassArgsSchema, source_name: str | None = None
+    ) -> list[str | int]:
+        source = 'DataclassArgs' if source_name is None else f'Dataclass {source_name!r}'
+        for field in choice['fields']:
+            if field['name'] == self.discriminator:
+                break
+        else:
+            raise PydanticUserError(
+                f'{source} needs a discriminator field for key {self.discriminator!r}', code='discriminator-no-field'
+            )
+        return self._infer_discriminator_values_for_field(field, source)
+
+    def _infer_discriminator_values_for_field(self, field: CoreSchemaField, source: str) -> list[str | int]:
+        if field['type'] == 'computed-field':
+            # This should never occur as a discriminator, as it is only relevant to serialization
+            return []
+        alias = field.get('validation_alias', self.discriminator)
+        if not isinstance(alias, str):
+            raise PydanticUserError(
+                f'Alias {alias!r} is not supported in a discriminated union', code='discriminator-alias-type'
+            )
+        if self._discriminator_alias is None:
+            self._discriminator_alias = alias
+        elif self._discriminator_alias != alias:
+            raise PydanticUserError(
+                f'Aliases for discriminator {self.discriminator!r} must be the same '
+                f'(got {alias}, {self._discriminator_alias})',
+                code='discriminator-alias',
+            )
+        return self._infer_discriminator_values_for_inner_schema(field['schema'], source)
+
+    def _infer_discriminator_values_for_inner_schema(
+        self, schema: core_schema.CoreSchema, source: str
+    ) -> list[str | int]:
+        """When inferring discriminator values for a field, we typically extract the expected values from a literal
+        schema. This function does that, but also handles nested unions and defaults.
+        """
+        if schema['type'] == 'literal':
+            return schema['expected']
+
+        elif schema['type'] == 'union':
+            # Generally when multiple values are allowed they should be placed in a single `Literal`, but
+            # we add this case to handle the situation where a field is annotated as a `Union` of `Literal`s.
+            # For example, this lets us handle `Union[Literal['key'], Union[Literal['Key'], Literal['KEY']]]`
+            values: list[Any] = []
+            for choice in schema['choices']:
+                choice_schema = choice[0] if isinstance(choice, tuple) else choice
+                choice_values = self._infer_discriminator_values_for_inner_schema(choice_schema, source)
+                values.extend(choice_values)
+            return values
+
+        elif schema['type'] == 'default':
+            # This will happen if the field has a default value; we ignore it while extracting the discriminator values
+            return self._infer_discriminator_values_for_inner_schema(schema['schema'], source)
+
+        elif schema['type'] == 'function-after':
+            # After validators don't affect the discriminator values
+            return self._infer_discriminator_values_for_inner_schema(schema['schema'], source)
+
+        elif schema['type'] == 'model' and schema.get('root_model'):
+            # Support RootModel[Literal[...]] as discriminator field type
+            return self._infer_discriminator_values_for_inner_schema(schema['schema'], source)
+
+        elif schema['type'] in {'function-before', 'function-wrap', 'function-plain'}:
+            validator_type = repr(schema['type'].split('-')[1])
+            raise PydanticUserError(
+                f'Cannot use a mode={validator_type} validator in the'
+                f' discriminator field {self.discriminator!r} of {source}',
+                code='discriminator-validator',
+            )
+
+        else:
+            raise PydanticUserError(
+                f'{source} needs field {self.discriminator!r} to be of type `Literal`',
+                code='discriminator-needs-literal',
+            )
+
+    def _set_unique_choice_for_values(self, choice: core_schema.CoreSchema, values: Sequence[str | int]) -> None:
+        """This method updates `self.tagged_union_choices` so that all provided (discriminator) `values` map to the
+        provided `choice`, validating that none of these values already map to another (different) choice.
+        """
+        for discriminator_value in values:
+            if discriminator_value in self._tagged_union_choices:
+                # It is okay if `value` is already in tagged_union_choices as long as it maps to the same value.
+                # Because tagged_union_choices may map values to other values, we need to walk the choices dict
+                # until we get to a "real" choice, and confirm that is equal to the one assigned.
+                existing_choice = self._tagged_union_choices[discriminator_value]
+                if existing_choice != choice:
+                    raise TypeError(
+                        f'Value {discriminator_value!r} for discriminator '
+                        f'{self.discriminator!r} mapped to multiple choices'
+                    )
+            else:
+                self._tagged_union_choices[discriminator_value] = choice

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_docs_extraction.py

@@ -0,0 +1,113 @@
+"""Utilities related to attribute docstring extraction."""
+
+from __future__ import annotations
+
+import ast
+import inspect
+import sys
+import textwrap
+from typing import Any
+
+
+class DocstringVisitor(ast.NodeVisitor):
+    def __init__(self) -> None:
+        super().__init__()
+
+        self.target: str | None = None
+        self.attrs: dict[str, str] = {}
+        self.previous_node_type: type[ast.AST] | None = None
+
+    def visit(self, node: ast.AST) -> Any:
+        node_result = super().visit(node)
+        self.previous_node_type = type(node)
+        return node_result
+
+    def visit_AnnAssign(self, node: ast.AnnAssign) -> Any:
+        if isinstance(node.target, ast.Name):
+            self.target = node.target.id
+
+    def visit_Expr(self, node: ast.Expr) -> Any:
+        if (
+            isinstance(node.value, ast.Constant)
+            and isinstance(node.value.value, str)
+            and self.previous_node_type is ast.AnnAssign
+        ):
+            docstring = inspect.cleandoc(node.value.value)
+            if self.target:
+                self.attrs[self.target] = docstring
+            self.target = None
+
+
+def _dedent_source_lines(source: list[str]) -> str:
+    # Required for nested class definitions, e.g. in a function block
+    dedent_source = textwrap.dedent(''.join(source))
+    if dedent_source.startswith((' ', '\t')):
+        # We are in the case where there's a dedented (usually multiline) string
+        # at a lower indentation level than the class itself. We wrap our class
+        # in a function as a workaround.
+        dedent_source = f'def dedent_workaround():\n{dedent_source}'
+    return dedent_source
+
+
+def _extract_source_from_frame(cls: type[Any]) -> list[str] | None:
+    frame = inspect.currentframe()
+
+    while frame:
+        if inspect.getmodule(frame) is inspect.getmodule(cls):
+            lnum = frame.f_lineno
+            try:
+                lines, _ = inspect.findsource(frame)
+            except OSError:  # pragma: no cover
+                # Source can't be retrieved (maybe because running in an interactive terminal),
+                # we don't want to error here.
+                pass
+            else:
+                block_lines = inspect.getblock(lines[lnum - 1 :])
+                dedent_source = _dedent_source_lines(block_lines)
+                try:
+                    block_tree = ast.parse(dedent_source)
+                except SyntaxError:
+                    pass
+                else:
+                    stmt = block_tree.body[0]
+                    if isinstance(stmt, ast.FunctionDef) and stmt.name == 'dedent_workaround':
+                        # `_dedent_source_lines` wrapped the class around the workaround function
+                        stmt = stmt.body[0]
+                    if isinstance(stmt, ast.ClassDef) and stmt.name == cls.__name__:
+                        return block_lines
+
+        frame = frame.f_back
+
+
+def extract_docstrings_from_cls(cls: type[Any], use_inspect: bool = False) -> dict[str, str]:
+    """Map model attributes and their corresponding docstring.
+
+    Args:
+        cls: The class of the Pydantic model to inspect.
+        use_inspect: Whether to skip usage of frames to find the object and use
+            the `inspect` module instead.
+
+    Returns:
+        A mapping containing attribute names and their corresponding docstring.
+    """
+    if use_inspect or sys.version_info >= (3, 13):
+        # On Python < 3.13, `inspect.getsourcelines()` might not work as expected
+        # if two classes have the same name in the same source file.
+        # On Python 3.13+, it will use the new `__firstlineno__` class attribute,
+        # making it way more robust.
+        try:
+            source, _ = inspect.getsourcelines(cls)
+        except OSError:  # pragma: no cover
+            return {}
+    else:
+        # TODO remove this implementation when we drop support for Python 3.12:
+        source = _extract_source_from_frame(cls)
+
+    if not source:
+        return {}
+
+    dedent_source = _dedent_source_lines(source)
+
+    visitor = DocstringVisitor()
+    visitor.visit(ast.parse(dedent_source))
+    return visitor.attrs

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_fields.py

@@ -0,0 +1,729 @@
+"""Private logic related to fields (the `Field()` function and `FieldInfo` class), and arguments to `Annotated`."""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import warnings
+from collections.abc import Mapping
+from functools import cache
+from inspect import Parameter, ismethoddescriptor
+from re import Pattern
+from typing import TYPE_CHECKING, Any, Callable, TypeVar, cast
+
+from pydantic_core import PydanticUndefined
+from typing_extensions import TypeIs
+from typing_inspection.introspection import AnnotationSource
+
+from pydantic import PydanticDeprecatedSince211
+from pydantic.errors import PydanticUserError
+
+from ..aliases import AliasGenerator
+from . import _generics, _typing_extra
+from ._config import ConfigWrapper
+from ._docs_extraction import extract_docstrings_from_cls
+from ._import_utils import import_cached_base_model, import_cached_field_info
+from ._internal_dataclass import slots_true
+from ._namespace_utils import NsResolver
+from ._repr import Representation
+from ._utils import can_be_positional, get_first_not_none
+
+if TYPE_CHECKING:
+    from annotated_types import BaseMetadata
+
+    from ..fields import FieldInfo
+    from ..main import BaseModel
+    from ._dataclasses import PydanticDataclass, StandardDataclass
+    from ._decorators import DecoratorInfos
+
+
+class PydanticMetadata(Representation):
+    """Base class for annotation markers like `Strict`."""
+
+    __slots__ = ()
+
+
+@dataclasses.dataclass(**slots_true)  # TODO: make kw_only when we drop support for 3.9.
+class PydanticExtraInfo:
+    # TODO: make use of PEP 747:
+    annotation: Any
+    complete: bool
+
+
+def pydantic_general_metadata(**metadata: Any) -> BaseMetadata:
+    """Create a new `_PydanticGeneralMetadata` class with the given metadata.
+
+    Args:
+        **metadata: The metadata to add.
+
+    Returns:
+        The new `_PydanticGeneralMetadata` class.
+    """
+    return _general_metadata_cls()(metadata)  # type: ignore
+
+
+@cache
+def _general_metadata_cls() -> type[BaseMetadata]:
+    """Do it this way to avoid importing `annotated_types` at import time."""
+    from annotated_types import BaseMetadata
+
+    class _PydanticGeneralMetadata(PydanticMetadata, BaseMetadata):
+        """Pydantic general metadata like `max_digits`."""
+
+        def __init__(self, metadata: Any):
+            self.__dict__ = metadata
+
+    return _PydanticGeneralMetadata  # type: ignore
+
+
+def _check_protected_namespaces(
+    protected_namespaces: tuple[str | Pattern[str], ...],
+    ann_name: str,
+    bases: tuple[type[Any], ...],
+    cls_name: str,
+) -> None:
+    BaseModel = import_cached_base_model()
+
+    for protected_namespace in protected_namespaces:
+        ns_violation = False
+        if isinstance(protected_namespace, Pattern):
+            ns_violation = protected_namespace.match(ann_name) is not None
+        elif isinstance(protected_namespace, str):
+            ns_violation = ann_name.startswith(protected_namespace)
+
+        if ns_violation:
+            for b in bases:
+                if hasattr(b, ann_name):
+                    if not (issubclass(b, BaseModel) and ann_name in getattr(b, '__pydantic_fields__', {})):
+                        raise ValueError(
+                            f'Field {ann_name!r} conflicts with member {getattr(b, ann_name)}'
+                            f' of protected namespace {protected_namespace!r}.'
+                        )
+            else:
+                valid_namespaces: list[str] = []
+                for pn in protected_namespaces:
+                    if isinstance(pn, Pattern):
+                        if not pn.match(ann_name):
+                            valid_namespaces.append(f're.compile({pn.pattern!r})')
+                    else:
+                        if not ann_name.startswith(pn):
+                            valid_namespaces.append(f"'{pn}'")
+
+                valid_namespaces_str = f'({", ".join(valid_namespaces)}{",)" if len(valid_namespaces) == 1 else ")"}'
+
+                warnings.warn(
+                    f'Field {ann_name!r} in {cls_name!r} conflicts with protected namespace {protected_namespace!r}.\n\n'
+                    f"You may be able to solve this by setting the 'protected_namespaces' configuration to {valid_namespaces_str}.",
+                    UserWarning,
+                    stacklevel=5,
+                )
+
+
+def _update_fields_from_docstrings(cls: type[Any], fields: dict[str, FieldInfo], use_inspect: bool = False) -> None:
+    fields_docs = extract_docstrings_from_cls(cls, use_inspect=use_inspect)
+    for ann_name, field_info in fields.items():
+        if field_info.description is None and ann_name in fields_docs:
+            field_info.description = fields_docs[ann_name]
+
+
+def _apply_field_title_generator_to_field_info(
+    title_generator: Callable[[str, FieldInfo], str],
+    field_name: str,
+    field_info: FieldInfo,
+):
+    if field_info.title is None:
+        title = title_generator(field_name, field_info)
+        if not isinstance(title, str):
+            raise TypeError(f'field_title_generator {title_generator} must return str, not {title.__class__}')
+
+        field_info.title = title
+
+
+def _apply_alias_generator_to_field_info(
+    alias_generator: Callable[[str], str] | AliasGenerator, field_name: str, field_info: FieldInfo
+):
+    """Apply an alias generator to aliases on a `FieldInfo` instance if appropriate.
+
+    Args:
+        alias_generator: A callable that takes a string and returns a string, or an `AliasGenerator` instance.
+        field_name: The name of the field from which to generate the alias.
+        field_info: The `FieldInfo` instance to which the alias generator is (maybe) applied.
+    """
+    # Apply an alias_generator if
+    # 1. An alias is not specified
+    # 2. An alias is specified, but the priority is <= 1
+    if (
+        field_info.alias_priority is None
+        or field_info.alias_priority <= 1
+        or field_info.alias is None
+        or field_info.validation_alias is None
+        or field_info.serialization_alias is None
+    ):
+        alias, validation_alias, serialization_alias = None, None, None
+
+        if isinstance(alias_generator, AliasGenerator):
+            alias, validation_alias, serialization_alias = alias_generator.generate_aliases(field_name)
+        elif callable(alias_generator):
+            alias = alias_generator(field_name)
+            if not isinstance(alias, str):
+                raise TypeError(f'alias_generator {alias_generator} must return str, not {alias.__class__}')
+
+        # if priority is not set, we set to 1
+        # which supports the case where the alias_generator from a child class is used
+        # to generate an alias for a field in a parent class
+        if field_info.alias_priority is None or field_info.alias_priority <= 1:
+            field_info.alias_priority = 1
+
+        # if the priority is 1, then we set the aliases to the generated alias
+        if field_info.alias_priority == 1:
+            field_info.serialization_alias = get_first_not_none(serialization_alias, alias)
+            field_info.validation_alias = get_first_not_none(validation_alias, alias)
+            field_info.alias = alias
+
+        # if any of the aliases are not set, then we set them to the corresponding generated alias
+        if field_info.alias is None:
+            field_info.alias = alias
+        if field_info.serialization_alias is None:
+            field_info.serialization_alias = get_first_not_none(serialization_alias, alias)
+        if field_info.validation_alias is None:
+            field_info.validation_alias = get_first_not_none(validation_alias, alias)
+
+
+def update_field_from_config(config_wrapper: ConfigWrapper, field_name: str, field_info: FieldInfo) -> None:
+    """Update the `FieldInfo` instance from the configuration set on the model it belongs to.
+
+    This will apply the title and alias generators from the configuration.
+
+    Args:
+        config_wrapper: The configuration from the model.
+        field_name: The field name the `FieldInfo` instance is attached to.
+        field_info: The `FieldInfo` instance to update.
+    """
+    field_title_generator = field_info.field_title_generator or config_wrapper.field_title_generator
+    if field_title_generator is not None:
+        _apply_field_title_generator_to_field_info(field_title_generator, field_name, field_info)
+    if config_wrapper.alias_generator is not None:
+        _apply_alias_generator_to_field_info(config_wrapper.alias_generator, field_name, field_info)
+
+
+_deprecated_method_names = {'dict', 'json', 'copy', '_iter', '_copy_and_set_values', '_calculate_keys'}
+
+_deprecated_classmethod_names = {
+    'parse_obj',
+    'parse_raw',
+    'parse_file',
+    'from_orm',
+    'construct',
+    'schema',
+    'schema_json',
+    'validate',
+    'update_forward_refs',
+    '_get_value',
+}
+
+
+def collect_model_fields(  # noqa: C901
+    cls: type[BaseModel],
+    config_wrapper: ConfigWrapper,
+    ns_resolver: NsResolver,
+    *,
+    typevars_map: Mapping[TypeVar, Any] | None = None,
+) -> tuple[dict[str, FieldInfo], PydanticExtraInfo | None, set[str]]:
+    """Collect the fields and class variables names of a nascent Pydantic model.
+
+    The fields collection process is *lenient*, meaning it won't error if string annotations
+    fail to evaluate. If this happens, the original annotation (and assigned value, if any)
+    is stored on the created `FieldInfo` instance.
+
+    The `rebuild_model_fields()` should be called at a later point (e.g. when rebuilding the model),
+    and will make use of these stored attributes.
+
+    Args:
+        cls: BaseModel or dataclass.
+        config_wrapper: The config wrapper instance.
+        ns_resolver: Namespace resolver to use when getting model annotations.
+        typevars_map: A dictionary mapping type variables to their concrete types.
+
+    Returns:
+        A three-tuple containing the model fields, the `PydanticExtraInfo` instance if the `__pydantic_extra__` annotation is set,
+        and class variables names.
+
+    Raises:
+        NameError:
+            - If there is a conflict between a field name and protected namespaces.
+            - If there is a field other than `root` in `RootModel`.
+            - If a field shadows an attribute in the parent model.
+    """
+    FieldInfo_ = import_cached_field_info()
+    BaseModel_ = import_cached_base_model()
+
+    bases = cls.__bases__
+    parent_fields_lookup: dict[str, FieldInfo] = {}
+    for base in reversed(bases):
+        if model_fields := getattr(base, '__pydantic_fields__', None):
+            parent_fields_lookup.update(model_fields)
+
+    type_hints = _typing_extra.get_model_type_hints(cls, ns_resolver=ns_resolver)
+
+    # `cls_annotations` is only used to determine if an annotation comes from a parent class
+    cls_annotations = _typing_extra.safe_get_annotations(cls)
+
+    fields: dict[str, FieldInfo] = {}
+
+    class_vars: set[str] = set()
+    for ann_name, (ann_type, evaluated) in type_hints.items():
+        if ann_name == 'model_config':
+            # We never want to treat `model_config` as a field
+            # Note: we may need to change this logic if/when we introduce a `BareModel` class with no
+            # protected namespaces (where `model_config` might be allowed as a field name)
+            continue
+
+        _check_protected_namespaces(
+            protected_namespaces=config_wrapper.protected_namespaces,
+            ann_name=ann_name,
+            bases=bases,
+            cls_name=cls.__name__,
+        )
+
+        if _typing_extra.is_classvar_annotation(ann_type):
+            class_vars.add(ann_name)
+            continue
+
+        assigned_value = getattr(cls, ann_name, PydanticUndefined)
+        if assigned_value is not PydanticUndefined and (
+            # One of the deprecated instance methods was used as a field name (e.g. `dict()`):
+            any(getattr(BaseModel_, depr_name, None) is assigned_value for depr_name in _deprecated_method_names)
+            # One of the deprecated class methods was used as a field name (e.g. `schema()`):
+            or (
+                hasattr(assigned_value, '__func__')
+                and any(
+                    getattr(getattr(BaseModel_, depr_name, None), '__func__', None) is assigned_value.__func__  # pyright: ignore[reportAttributeAccessIssue]
+                    for depr_name in _deprecated_classmethod_names
+                )
+            )
+        ):
+            # Then `assigned_value` would be the method, even though no default was specified:
+            assigned_value = PydanticUndefined
+
+        if not is_valid_field_name(ann_name):
+            continue
+        if cls.__pydantic_root_model__ and ann_name != 'root':
+            raise NameError(
+                f"Unexpected field with name {ann_name!r}; only 'root' is allowed as a field of a `RootModel`"
+            )
+
+        for base in bases:
+            if hasattr(base, ann_name):
+                if ann_name not in cls_annotations:
+                    # Don't warn when a field exists in a parent class but has not been defined in the current class
+                    continue
+
+                # when building a generic model with `MyModel[int]`, the generic_origin check makes sure we don't get
+                # "... shadows an attribute" warnings
+                generic_origin = getattr(cls, '__pydantic_generic_metadata__', {}).get('origin')
+                if base is generic_origin:
+                    # Don't warn when "shadowing" of attributes in parametrized generics
+                    continue
+
+                dataclass_fields = {
+                    field.name for field in (dataclasses.fields(base) if dataclasses.is_dataclass(base) else ())
+                }
+                if ann_name in dataclass_fields:
+                    # Don't warn when inheriting stdlib dataclasses whose fields are "shadowed" by defaults being set
+                    # on the class instance.
+                    continue
+
+                warnings.warn(
+                    f'Field name "{ann_name}" in "{cls.__qualname__}" shadows an attribute in parent '
+                    f'"{base.__qualname__}"',
+                    UserWarning,
+                    stacklevel=4,
+                )
+
+        if assigned_value is PydanticUndefined:  # no assignment, just a plain annotation
+            if ann_name in cls_annotations or ann_name not in parent_fields_lookup:
+                # field is either:
+                # - present in the current model's annotations (and *not* from parent classes)
+                # - not found on any base classes; this seems to be caused by fields not getting
+                #   generated due to models not being fully defined while initializing recursive models.
+                #   Nothing stops us from just creating a `FieldInfo` for this type hint, so we do this.
+                field_info = FieldInfo_.from_annotation(ann_type, _source=AnnotationSource.CLASS)
+                field_info._original_annotation = ann_type
+                if not evaluated:
+                    field_info._complete = False
+                    # Store the original annotation that should be used to rebuild
+                    # the field info later:
+            else:
+                # The field was present on one of the (possibly multiple) base classes, we make a copy directly from it.
+                parent_field_info = parent_fields_lookup[ann_name]._copy()
+
+                # The only case where substituting the type variables is relevant (i.e. when `typevars_map` is not empty)
+                # is when a generic class is parameterized (e.g. `MyGenericModel[int, str]`), which creates a new class object
+                # (unlike the stdlib genercis that create a generic alias). In this case, we are guaranteed to only have to copy
+                # from the origin/parent model (e.g. `MyGenericModel`).
+                if typevars_map:
+                    field_info = _recreate_field_info(
+                        parent_field_info, ns_resolver=ns_resolver, typevars_map=typevars_map, lenient=True
+                    )
+                else:
+                    field_info = parent_field_info
+
+        else:  # An assigned value is present (either the default value, or a `Field()` function)
+            if isinstance(assigned_value, FieldInfo_) and ismethoddescriptor(assigned_value.default):
+                # `assigned_value` was fetched using `getattr`, which triggers a call to `__get__`
+                # for descriptors, so we do the same if the `= field(default=...)` form is used.
+                # Note that we only do this for method descriptors for now, we might want to
+                # extend this to any descriptor in the future (by simply checking for
+                # `hasattr(assigned_value.default, '__get__')`).
+                default = assigned_value.default.__get__(None, cls)
+                assigned_value.default = default
+                assigned_value._attributes_set['default'] = default
+
+            field_info = FieldInfo_.from_annotated_attribute(ann_type, assigned_value, _source=AnnotationSource.CLASS)
+
+            # Store the original annotation and assignment value that could be used to rebuild the field info later.
+            field_info._original_assignment = assigned_value
+            field_info._original_annotation = ann_type
+            if not evaluated:
+                field_info._complete = False
+            elif 'final' in field_info._qualifiers and not field_info.is_required():
+                warnings.warn(
+                    f'Annotation {ann_name!r} is marked as final and has a default value. Pydantic treats {ann_name!r} as a '
+                    'class variable, but it will be considered as a normal field in V3 to be aligned with dataclasses. If you '
+                    f'still want {ann_name!r} to be considered as a class variable, annotate it as: `ClassVar[<type>] = <default>.`',
+                    category=PydanticDeprecatedSince211,
+                    # Incorrect when `create_model` is used, but the chance that final with a default is used is low in that case:
+                    stacklevel=4,
+                )
+                class_vars.add(ann_name)
+                continue
+
+            # attributes which are fields are removed from the class namespace:
+            # 1. To match the behaviour of annotation-only fields
+            # 2. To avoid false positives in the NameError check above
+            try:
+                delattr(cls, ann_name)
+            except AttributeError:
+                pass  # indicates the attribute was on a parent class
+
+        # Use cls.__dict__['__pydantic_decorators__'] instead of cls.__pydantic_decorators__
+        # to make sure the decorators have already been built for this exact class
+        decorators: DecoratorInfos = cls.__dict__['__pydantic_decorators__']
+        if ann_name in decorators.computed_fields:
+            raise TypeError(
+                f'Field {ann_name!r} of class {cls.__name__!r} overrides symbol of same name in a parent class. '
+                'This override with a computed_field is incompatible.'
+            )
+        fields[ann_name] = field_info
+
+        if field_info._complete:
+            # If not complete, this will be called in `rebuild_model_fields()`:
+            update_field_from_config(config_wrapper, ann_name, field_info)
+
+    if config_wrapper.use_attribute_docstrings:
+        _update_fields_from_docstrings(cls, fields)
+
+    pydantic_extra_info: PydanticExtraInfo | None = None
+    if '__pydantic_extra__' in type_hints:
+        ann, complete = type_hints['__pydantic_extra__']
+        pydantic_extra_info = PydanticExtraInfo(
+            annotation=ann,
+            complete=complete,
+        )
+
+    return fields, pydantic_extra_info, class_vars
+
+
+def rebuild_model_fields(
+    cls: type[BaseModel],
+    *,
+    config_wrapper: ConfigWrapper,
+    ns_resolver: NsResolver,
+    typevars_map: Mapping[TypeVar, Any],
+) -> tuple[dict[str, FieldInfo], PydanticExtraInfo | None]:
+    """Rebuild the (already present) model fields by trying to reevaluate annotations.
+
+    This function should be called whenever a model with incomplete fields is encountered.
+
+    Returns:
+        A two-tuple, the first element being the rebuilt fields, the second element being
+        the rebuild `PydanticExtraInfo` instance, if available.
+
+    Raises:
+        NameError: If one of the annotations failed to evaluate.
+
+    Note:
+        This function *doesn't* mutate the model fields in place, as it can be called during
+        schema generation, where you don't want to mutate other model's fields.
+    """
+    rebuilt_fields: dict[str, FieldInfo] = {}
+    with ns_resolver.push(cls):
+        for f_name, field_info in cls.__pydantic_fields__.items():
+            if field_info._complete:
+                rebuilt_fields[f_name] = field_info
+            else:
+                new_field = _recreate_field_info(
+                    field_info, ns_resolver=ns_resolver, typevars_map=typevars_map, lenient=False
+                )
+                update_field_from_config(config_wrapper, f_name, new_field)
+                rebuilt_fields[f_name] = new_field
+
+        if cls.__pydantic_extra_info__ is not None and not cls.__pydantic_extra_info__.complete:
+            rebuilt_extra_info = PydanticExtraInfo(
+                annotation=_typing_extra.eval_type(
+                    cls.__pydantic_extra_info__.annotation, *ns_resolver.types_namespace
+                ),
+                complete=True,
+            )
+        else:
+            rebuilt_extra_info = cls.__pydantic_extra_info__
+
+    return rebuilt_fields, rebuilt_extra_info
+
+
+def _recreate_field_info(
+    field_info: FieldInfo,
+    ns_resolver: NsResolver,
+    typevars_map: Mapping[TypeVar, Any],
+    *,
+    lenient: bool,
+) -> FieldInfo:
+    FieldInfo_ = import_cached_field_info()
+
+    existing_desc = field_info.description
+    if lenient:
+        ann = _generics.replace_types(field_info._original_annotation, typevars_map)
+        ann, evaluated = _typing_extra.try_eval_type(
+            ann,
+            *ns_resolver.types_namespace,
+        )
+    else:
+        # Not the best pattern, maybe we could ship our own `eval_type()`,
+        # that would replace the type variables on the fly during evaluation.
+        ann = _typing_extra.eval_type(
+            field_info._original_annotation,
+            *ns_resolver.types_namespace,
+        )
+        ann = _generics.replace_types(ann, typevars_map)
+        ann = _typing_extra.eval_type(
+            ann,
+            *ns_resolver.types_namespace,
+        )
+        evaluated = True
+
+    if (assign := field_info._original_assignment) is PydanticUndefined:
+        new_field = FieldInfo_.from_annotation(ann, _source=AnnotationSource.CLASS)
+    else:
+        new_field = FieldInfo_.from_annotated_attribute(ann, assign, _source=AnnotationSource.CLASS)
+        new_field._original_assignment = assign
+    new_field._original_annotation = ann
+    # The description might come from the docstring if `use_attribute_docstrings` was `True`:
+    new_field.description = new_field.description if new_field.description is not None else existing_desc
+    if not evaluated:
+        new_field._complete = False
+
+    return new_field
+
+
+def collect_dataclass_fields(
+    cls: type[StandardDataclass],
+    *,
+    config_wrapper: ConfigWrapper,
+    ns_resolver: NsResolver | None = None,
+    typevars_map: dict[Any, Any] | None = None,
+) -> dict[str, FieldInfo]:
+    """Collect the fields of a dataclass.
+
+    Args:
+        cls: dataclass.
+        config_wrapper: The config wrapper instance.
+        ns_resolver: Namespace resolver to use when getting dataclass annotations.
+            Defaults to an empty instance.
+        typevars_map: A dictionary mapping type variables to their concrete types.
+
+    Returns:
+        The dataclass fields.
+    """
+    FieldInfo_ = import_cached_field_info()
+
+    fields: dict[str, FieldInfo] = {}
+    ns_resolver = ns_resolver or NsResolver()
+    dataclass_fields = cls.__dataclass_fields__
+
+    # The logic here is similar to `_typing_extra.get_cls_type_hints`,
+    # although we do it manually as stdlib dataclasses already have annotations
+    # collected in each class:
+    for base in reversed(cls.__mro__):
+        if not dataclasses.is_dataclass(base):
+            continue
+
+        with ns_resolver.push(base):
+            for ann_name, dataclass_field in dataclass_fields.items():
+                base_anns = _typing_extra.safe_get_annotations(base)
+
+                if ann_name not in base_anns:
+                    # `__dataclass_fields__`contains every field, even the ones from base classes.
+                    # Only collect the ones defined on `base`.
+                    continue
+
+                globalns, localns = ns_resolver.types_namespace
+                ann_type, evaluated = _typing_extra.try_eval_type(dataclass_field.type, globalns, localns)
+
+                if _typing_extra.is_classvar_annotation(ann_type):
+                    continue
+
+                if (
+                    not dataclass_field.init
+                    and dataclass_field.default is dataclasses.MISSING
+                    and dataclass_field.default_factory is dataclasses.MISSING
+                ):
+                    # TODO: We should probably do something with this so that validate_assignment behaves properly
+                    #   Issue: https://github.com/pydantic/pydantic/issues/5470
+                    continue
+
+                if isinstance(dataclass_field.default, FieldInfo_):
+                    if dataclass_field.default.init_var:
+                        if dataclass_field.default.init is False:
+                            raise PydanticUserError(
+                                f'Dataclass field {ann_name} has init=False and init_var=True, but these are mutually exclusive.',
+                                code='clashing-init-and-init-var',
+                            )
+
+                        # TODO: same note as above re validate_assignment
+                        continue
+                    field_info = FieldInfo_.from_annotated_attribute(
+                        ann_type, dataclass_field.default, _source=AnnotationSource.DATACLASS
+                    )
+                    field_info._original_assignment = dataclass_field.default
+                else:
+                    field_info = FieldInfo_.from_annotated_attribute(
+                        ann_type, dataclass_field, _source=AnnotationSource.DATACLASS
+                    )
+                    field_info._original_assignment = dataclass_field
+
+                if not evaluated:
+                    field_info._complete = False
+                    field_info._original_annotation = ann_type
+
+                fields[ann_name] = field_info
+                update_field_from_config(config_wrapper, ann_name, field_info)
+
+                if field_info.default is not PydanticUndefined and isinstance(
+                    getattr(cls, ann_name, field_info), FieldInfo_
+                ):
+                    # We need this to fix the default when the "default" from __dataclass_fields__ is a pydantic.FieldInfo
+                    setattr(cls, ann_name, field_info.default)
+
+    if typevars_map:
+        for field in fields.values():
+            # We don't pass any ns, as `field.annotation`
+            # was already evaluated. TODO: is this method relevant?
+            # Can't we juste use `_generics.replace_types`?
+            field.apply_typevars_map(typevars_map)
+
+    if config_wrapper.use_attribute_docstrings:
+        _update_fields_from_docstrings(
+            cls,
+            fields,
+            # We can't rely on the (more reliable) frame inspection method
+            # for stdlib dataclasses:
+            use_inspect=not hasattr(cls, '__is_pydantic_dataclass__'),
+        )
+
+    return fields
+
+
+def rebuild_dataclass_fields(
+    cls: type[PydanticDataclass],
+    *,
+    config_wrapper: ConfigWrapper,
+    ns_resolver: NsResolver,
+    typevars_map: Mapping[TypeVar, Any],
+) -> dict[str, FieldInfo]:
+    """Rebuild the (already present) dataclass fields by trying to reevaluate annotations.
+
+    This function should be called whenever a dataclass with incomplete fields is encountered.
+
+    Raises:
+        NameError: If one of the annotations failed to evaluate.
+
+    Note:
+        This function *doesn't* mutate the dataclass fields in place, as it can be called during
+        schema generation, where you don't want to mutate other dataclass's fields.
+    """
+    FieldInfo_ = import_cached_field_info()
+
+    rebuilt_fields: dict[str, FieldInfo] = {}
+    with ns_resolver.push(cls):
+        for f_name, field_info in cls.__pydantic_fields__.items():
+            if field_info._complete:
+                rebuilt_fields[f_name] = field_info
+            else:
+                existing_desc = field_info.description
+                ann = _typing_extra.eval_type(
+                    field_info._original_annotation,
+                    *ns_resolver.types_namespace,
+                )
+                ann = _generics.replace_types(ann, typevars_map)
+                new_field = FieldInfo_.from_annotated_attribute(
+                    ann,
+                    field_info._original_assignment,
+                    _source=AnnotationSource.DATACLASS,
+                )
+
+                # The description might come from the docstring if `use_attribute_docstrings` was `True`:
+                new_field.description = new_field.description if new_field.description is not None else existing_desc
+                update_field_from_config(config_wrapper, f_name, new_field)
+                rebuilt_fields[f_name] = new_field
+
+    return rebuilt_fields
+
+
+def is_valid_field_name(name: str) -> bool:
+    return not name.startswith('_')
+
+
+def is_valid_privateattr_name(name: str) -> bool:
+    return name.startswith('_') and not name.startswith('__')
+
+
+def takes_validated_data_argument(
+    default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any],
+) -> TypeIs[Callable[[dict[str, Any]], Any]]:
+    """Whether the provided default factory callable has a validated data parameter."""
+    try:
+        sig = _typing_extra.signature_no_eval(default_factory)
+    except (ValueError, TypeError):
+        # `inspect.signature` might not be able to infer a signature, e.g. with C objects.
+        # In this case, we assume no data argument is present:
+        return False
+
+    parameters = list(sig.parameters.values())
+
+    return len(parameters) == 1 and can_be_positional(parameters[0]) and parameters[0].default is Parameter.empty
+
+
+def resolve_default_value(
+    default: Any,
+    default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None,
+    *,
+    validated_data: dict[str, Any] | None = None,
+    call_default_factory: bool = False,
+) -> Any:
+    """Resolve the default value using either a static default or a default_factory."""
+    from ._utils import smart_deepcopy
+
+    if default_factory is None:
+        return smart_deepcopy(default)
+    if call_default_factory:
+        if takes_validated_data_argument(default_factory=default_factory):
+            fac = cast('Callable[[dict[str, Any]], Any]', default_factory)
+            if validated_data is None:
+                raise ValueError(
+                    "The default factory requires the 'validated_data' argument, which was not provided when calling 'get_default()'."
+                )
+            return fac(validated_data)
+        else:
+            fac = cast('Callable[[], Any]', default_factory)
+            return fac()
+
+    return PydanticUndefined

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_forward_ref.py

@@ -0,0 +1,23 @@
+from __future__ import annotations as _annotations
+
+from dataclasses import dataclass
+from typing import Union
+
+
+@dataclass
+class PydanticRecursiveRef:
+    type_ref: str
+
+    __name__ = 'PydanticRecursiveRef'
+    __hash__ = object.__hash__
+
+    def __call__(self) -> None:
+        """Defining __call__ is necessary for the `typing` module to let you use an instance of
+        this class as the result of resolving a standard ForwardRef.
+        """
+
+    def __or__(self, other):
+        return Union[self, other]  # type: ignore
+
+    def __ror__(self, other):
+        return Union[other, self]  # type: ignore

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_generics.py

@@ -0,0 +1,530 @@
+from __future__ import annotations
+
+import operator
+import sys
+import types
+import typing
+from collections import ChainMap
+from collections.abc import Iterator, Mapping
+from contextlib import contextmanager
+from contextvars import ContextVar
+from functools import reduce
+from itertools import zip_longest
+from types import prepare_class
+from typing import TYPE_CHECKING, Annotated, Any, TypedDict, TypeVar, cast
+from weakref import WeakValueDictionary
+
+import typing_extensions
+from typing_inspection import typing_objects
+from typing_inspection.introspection import is_union_origin
+
+from . import _typing_extra
+from ._core_utils import get_type_ref
+from ._forward_ref import PydanticRecursiveRef
+from ._utils import all_identical, is_model_class
+
+if TYPE_CHECKING:
+    from ..main import BaseModel
+
+GenericTypesCacheKey = tuple[Any, Any, tuple[Any, ...]]
+
+# Note: We want to remove LimitedDict, but to do this, we'd need to improve the handling of generics caching.
+#   Right now, to handle recursive generics, we some types must remain cached for brief periods without references.
+#   By chaining the WeakValuesDict with a LimitedDict, we have a way to retain caching for all types with references,
+#   while also retaining a limited number of types even without references. This is generally enough to build
+#   specific recursive generic models without losing required items out of the cache.
+
+KT = TypeVar('KT')
+VT = TypeVar('VT')
+_LIMITED_DICT_SIZE = 100
+
+
+class LimitedDict(dict[KT, VT]):
+    def __init__(self, size_limit: int = _LIMITED_DICT_SIZE) -> None:
+        self.size_limit = size_limit
+        super().__init__()
+
+    def __setitem__(self, key: KT, value: VT, /) -> None:
+        super().__setitem__(key, value)
+        if len(self) > self.size_limit:
+            excess = len(self) - self.size_limit + self.size_limit // 10
+            to_remove = list(self.keys())[:excess]
+            for k in to_remove:
+                del self[k]
+
+
+# weak dictionaries allow the dynamically created parametrized versions of generic models to get collected
+# once they are no longer referenced by the caller.
+GenericTypesCache = WeakValueDictionary[GenericTypesCacheKey, 'type[BaseModel]']
+
+if TYPE_CHECKING:
+
+    class DeepChainMap(ChainMap[KT, VT]):  # type: ignore
+        ...
+
+else:
+
+    class DeepChainMap(ChainMap):
+        """Variant of ChainMap that allows direct updates to inner scopes.
+
+        Taken from https://docs.python.org/3/library/collections.html#collections.ChainMap,
+        with some light modifications for this use case.
+        """
+
+        def clear(self) -> None:
+            for mapping in self.maps:
+                mapping.clear()
+
+        def __setitem__(self, key: KT, value: VT) -> None:
+            for mapping in self.maps:
+                mapping[key] = value
+
+        def __delitem__(self, key: KT) -> None:
+            hit = False
+            for mapping in self.maps:
+                if key in mapping:
+                    del mapping[key]
+                    hit = True
+            if not hit:
+                raise KeyError(key)
+
+
+# Despite the fact that LimitedDict _seems_ no longer necessary, I'm very nervous to actually remove it
+# and discover later on that we need to re-add all this infrastructure...
+# _GENERIC_TYPES_CACHE = DeepChainMap(GenericTypesCache(), LimitedDict())
+
+_GENERIC_TYPES_CACHE = GenericTypesCache()
+
+
+class PydanticGenericMetadata(TypedDict):
+    origin: type[BaseModel] | None  # analogous to typing._GenericAlias.__origin__
+    args: tuple[Any, ...]  # analogous to typing._GenericAlias.__args__
+    parameters: tuple[TypeVar, ...]  # analogous to typing.Generic.__parameters__
+
+
+def create_generic_submodel(
+    model_name: str, origin: type[BaseModel], args: tuple[Any, ...], params: tuple[Any, ...]
+) -> type[BaseModel]:
+    """Dynamically create a submodel of a provided (generic) BaseModel.
+
+    This is used when producing concrete parametrizations of generic models. This function
+    only *creates* the new subclass; the schema/validators/serialization must be updated to
+    reflect a concrete parametrization elsewhere.
+
+    Args:
+        model_name: The name of the newly created model.
+        origin: The base class for the new model to inherit from.
+        args: A tuple of generic metadata arguments.
+        params: A tuple of generic metadata parameters.
+
+    Returns:
+        The created submodel.
+    """
+    namespace: dict[str, Any] = {'__module__': origin.__module__}
+    bases = (origin,)
+    meta, ns, kwds = prepare_class(model_name, bases)
+    namespace.update(ns)
+    created_model = meta(
+        model_name,
+        bases,
+        namespace,
+        __pydantic_generic_metadata__={
+            'origin': origin,
+            'args': args,
+            'parameters': params,
+        },
+        __pydantic_reset_parent_namespace__=False,
+        **kwds,
+    )
+
+    model_module, called_globally = _get_caller_frame_info(depth=3)
+    if called_globally:  # create global reference and therefore allow pickling
+        object_by_reference = None
+        reference_name = model_name
+        reference_module_globals = sys.modules[created_model.__module__].__dict__
+        while object_by_reference is not created_model:
+            object_by_reference = reference_module_globals.setdefault(reference_name, created_model)
+            reference_name += '_'
+
+    return created_model
+
+
+def _get_caller_frame_info(depth: int = 2) -> tuple[str | None, bool]:
+    """Used inside a function to check whether it was called globally.
+
+    Args:
+        depth: The depth to get the frame.
+
+    Returns:
+        A tuple contains `module_name` and `called_globally`.
+
+    Raises:
+        RuntimeError: If the function is not called inside a function.
+    """
+    try:
+        previous_caller_frame = sys._getframe(depth)
+    except ValueError as e:
+        raise RuntimeError('This function must be used inside another function') from e
+    except AttributeError:  # sys module does not have _getframe function, so there's nothing we can do about it
+        return None, False
+    frame_globals = previous_caller_frame.f_globals
+    return frame_globals.get('__name__'), previous_caller_frame.f_locals is frame_globals
+
+
+DictValues: type[Any] = {}.values().__class__
+
+
+def iter_contained_typevars(v: Any) -> Iterator[TypeVar]:
+    """Recursively iterate through all subtypes and type args of `v` and yield any typevars that are found.
+
+    This is inspired as an alternative to directly accessing the `__parameters__` attribute of a GenericAlias,
+    since __parameters__ of (nested) generic BaseModel subclasses won't show up in that list.
+    """
+    if isinstance(v, TypeVar):
+        yield v
+    elif is_model_class(v):
+        yield from v.__pydantic_generic_metadata__['parameters']
+    elif isinstance(v, (DictValues, list)):
+        for var in v:
+            yield from iter_contained_typevars(var)
+    else:
+        args = get_args(v)
+        for arg in args:
+            yield from iter_contained_typevars(arg)
+
+
+def get_args(v: Any) -> Any:
+    pydantic_generic_metadata: PydanticGenericMetadata | None = getattr(v, '__pydantic_generic_metadata__', None)
+    if pydantic_generic_metadata:
+        return pydantic_generic_metadata.get('args')
+    return typing_extensions.get_args(v)
+
+
+def get_origin(v: Any) -> Any:
+    pydantic_generic_metadata: PydanticGenericMetadata | None = getattr(v, '__pydantic_generic_metadata__', None)
+    if pydantic_generic_metadata:
+        return pydantic_generic_metadata.get('origin')
+    return typing_extensions.get_origin(v)
+
+
+def get_standard_typevars_map(cls: Any) -> dict[TypeVar, Any] | None:
+    """Package a generic type's typevars and parametrization (if present) into a dictionary compatible with the
+    `replace_types` function. Specifically, this works with standard typing generics and typing._GenericAlias.
+    """
+    origin = get_origin(cls)
+    if origin is None:
+        return None
+    if not hasattr(origin, '__parameters__'):
+        return None
+
+    # In this case, we know that cls is a _GenericAlias, and origin is the generic type
+    # So it is safe to access cls.__args__ and origin.__parameters__
+    args: tuple[Any, ...] = cls.__args__  # type: ignore
+    parameters: tuple[TypeVar, ...] = origin.__parameters__
+    return dict(zip(parameters, args))
+
+
+def get_model_typevars_map(cls: type[BaseModel]) -> dict[TypeVar, Any]:
+    """Package a generic BaseModel's typevars and concrete parametrization (if present) into a dictionary compatible
+    with the `replace_types` function.
+
+    Since BaseModel.__class_getitem__ does not produce a typing._GenericAlias, and the BaseModel generic info is
+    stored in the __pydantic_generic_metadata__ attribute, we need special handling here.
+    """
+    # TODO: This could be unified with `get_standard_typevars_map` if we stored the generic metadata
+    #   in the __origin__, __args__, and __parameters__ attributes of the model.
+    generic_metadata = cls.__pydantic_generic_metadata__
+    origin = generic_metadata['origin']
+    args = generic_metadata['args']
+    if not args:
+        # No need to go into `iter_contained_typevars`:
+        return {}
+    return dict(zip(iter_contained_typevars(origin), args))
+
+
+def replace_types(type_: Any, type_map: Mapping[TypeVar, Any] | None) -> Any:
+    """Return type with all occurrences of `type_map` keys recursively replaced with their values.
+
+    Args:
+        type_: The class or generic alias.
+        type_map: Mapping from `TypeVar` instance to concrete types.
+
+    Returns:
+        A new type representing the basic structure of `type_` with all
+        `typevar_map` keys recursively replaced.
+
+    Example:
+        ```python
+        from typing import Union
+
+        from pydantic._internal._generics import replace_types
+
+        replace_types(tuple[str, Union[list[str], float]], {str: int})
+        #> tuple[int, Union[list[int], float]]
+        ```
+    """
+    if not type_map:
+        return type_
+
+    type_args = get_args(type_)
+    origin_type = get_origin(type_)
+
+    if typing_objects.is_annotated(origin_type):
+        annotated_type, *annotations = type_args
+        annotated_type = replace_types(annotated_type, type_map)
+        # TODO remove parentheses when we drop support for Python 3.10:
+        return Annotated[(annotated_type, *annotations)]
+
+    # Having type args is a good indicator that this is a typing special form
+    # instance or a generic alias of some sort.
+    if type_args:
+        resolved_type_args = tuple(replace_types(arg, type_map) for arg in type_args)
+        if all_identical(type_args, resolved_type_args):
+            # If all arguments are the same, there is no need to modify the
+            # type or create a new object at all
+            return type_
+
+        if (
+            origin_type is not None
+            and isinstance(type_, _typing_extra.typing_base)
+            and not isinstance(origin_type, _typing_extra.typing_base)
+            and getattr(type_, '_name', None) is not None
+        ):
+            # In python < 3.9 generic aliases don't exist so any of these like `list`,
+            # `type` or `collections.abc.Callable` need to be translated.
+            # See: https://www.python.org/dev/peps/pep-0585
+            origin_type = getattr(typing, type_._name)
+        assert origin_type is not None
+
+        if is_union_origin(origin_type):
+            if any(typing_objects.is_any(arg) for arg in resolved_type_args):
+                # `Any | T` ~ `Any`:
+                resolved_type_args = (Any,)
+            # `Never | T` ~ `T`:
+            resolved_type_args = tuple(
+                arg
+                for arg in resolved_type_args
+                if not (typing_objects.is_noreturn(arg) or typing_objects.is_never(arg))
+            )
+
+        # PEP-604 syntax (e.g. `list | str`) is represented with a types.UnionType object that does not
+        # implement `__getitem__()`. In Python 3.14+, `typing.Union` and `types.UnionType` are the same,
+        # and we instead rely on `typing.Union` as it implicitly converts string annotations to `ForwardRef`
+        # instances (this is to avoid type errors as per https://github.com/python/cpython/pull/105366).
+        # TODO remove type ignore comment when we drop support for Python 3.9 (https://github.com/microsoft/pyright/issues/11241):
+        if (3, 10) <= sys.version_info < (3, 14) and origin_type is types.UnionType:  # pyright: ignore[reportAttributeAccessIssue]
+            return reduce(operator.or_, resolved_type_args)
+        # NotRequired[T] and Required[T] don't support tuple type resolved_type_args, hence the condition below
+        return origin_type[resolved_type_args[0] if len(resolved_type_args) == 1 else resolved_type_args]
+
+    # We handle pydantic generic models separately as they don't have the same
+    # semantics as "typing" classes or generic aliases
+
+    if not origin_type and is_model_class(type_):
+        parameters = type_.__pydantic_generic_metadata__['parameters']
+        if not parameters:
+            return type_
+        resolved_type_args = tuple(replace_types(t, type_map) for t in parameters)
+        if all_identical(parameters, resolved_type_args):
+            return type_
+        return type_[resolved_type_args]
+
+    # Handle special case for typehints that can have lists as arguments.
+    # `typing.Callable[[int, str], int]` is an example for this.
+    if isinstance(type_, list):
+        resolved_list = [replace_types(element, type_map) for element in type_]
+        if all_identical(type_, resolved_list):
+            return type_
+        return resolved_list
+
+    # If all else fails, we try to resolve the type directly and otherwise just
+    # return the input with no modifications.
+    return type_map.get(type_, type_)
+
+
+def map_generic_model_arguments(cls: type[BaseModel], args: tuple[Any, ...]) -> dict[TypeVar, Any]:
+    """Return a mapping between the parameters of a generic model and the provided arguments during parameterization.
+
+    Raises:
+        TypeError: If the number of arguments does not match the parameters (i.e. if providing too few or too many arguments).
+
+    Example:
+        ```python {test="skip" lint="skip"}
+        class Model[T, U, V = int](BaseModel): ...
+
+        map_generic_model_arguments(Model, (str, bytes))
+        #> {T: str, U: bytes, V: int}
+
+        map_generic_model_arguments(Model, (str,))
+        #> TypeError: Too few arguments for <class '__main__.Model'>; actual 1, expected at least 2
+
+        map_generic_model_arguments(Model, (str, bytes, int, complex))
+        #> TypeError: Too many arguments for <class '__main__.Model'>; actual 4, expected 3
+        ```
+
+    Note:
+        This function is analogous to the private `typing._check_generic_specialization` function.
+    """
+    parameters = cls.__pydantic_generic_metadata__['parameters']
+    expected_len = len(parameters)
+    typevars_map: dict[TypeVar, Any] = {}
+
+    _missing = object()
+    for parameter, argument in zip_longest(parameters, args, fillvalue=_missing):
+        if parameter is _missing:
+            raise TypeError(f'Too many arguments for {cls}; actual {len(args)}, expected {expected_len}')
+
+        if argument is _missing:
+            param = cast(TypeVar, parameter)
+            try:
+                has_default = param.has_default()  # pyright: ignore[reportAttributeAccessIssue]
+            except AttributeError:
+                # Happens if using `typing.TypeVar` (and not `typing_extensions`) on Python < 3.13.
+                has_default = False
+            if has_default:
+                # The default might refer to other type parameters. For an example, see:
+                # https://typing.python.org/en/latest/spec/generics.html#type-parameters-as-parameters-to-generics
+                typevars_map[param] = replace_types(param.__default__, typevars_map)  # pyright: ignore[reportAttributeAccessIssue]
+            else:
+                expected_len -= sum(hasattr(p, 'has_default') and p.has_default() for p in parameters)  # pyright: ignore[reportAttributeAccessIssue]
+                raise TypeError(f'Too few arguments for {cls}; actual {len(args)}, expected at least {expected_len}')
+        else:
+            param = cast(TypeVar, parameter)
+            typevars_map[param] = argument
+
+    return typevars_map
+
+
+_generic_recursion_cache: ContextVar[set[str] | None] = ContextVar('_generic_recursion_cache', default=None)
+
+
+@contextmanager
+def generic_recursion_self_type(
+    origin: type[BaseModel], args: tuple[Any, ...]
+) -> Iterator[PydanticRecursiveRef | None]:
+    """This contextmanager should be placed around the recursive calls used to build a generic type,
+    and accept as arguments the generic origin type and the type arguments being passed to it.
+
+    If the same origin and arguments are observed twice, it implies that a self-reference placeholder
+    can be used while building the core schema, and will produce a schema_ref that will be valid in the
+    final parent schema.
+    """
+    previously_seen_type_refs = _generic_recursion_cache.get()
+    if previously_seen_type_refs is None:
+        previously_seen_type_refs = set()
+        token = _generic_recursion_cache.set(previously_seen_type_refs)
+    else:
+        token = None
+
+    try:
+        type_ref = get_type_ref(origin, args_override=args)
+        if type_ref in previously_seen_type_refs:
+            self_type = PydanticRecursiveRef(type_ref=type_ref)
+            yield self_type
+        else:
+            previously_seen_type_refs.add(type_ref)
+            yield
+            previously_seen_type_refs.remove(type_ref)
+    finally:
+        if token:
+            _generic_recursion_cache.reset(token)
+
+
+def recursively_defined_type_refs() -> set[str]:
+    visited = _generic_recursion_cache.get()
+    if not visited:
+        return set()  # not in a generic recursion, so there are no types
+
+    return visited.copy()  # don't allow modifications
+
+
+def get_cached_generic_type_early(parent: type[BaseModel], typevar_values: Any) -> type[BaseModel] | None:
+    """The use of a two-stage cache lookup approach was necessary to have the highest performance possible for
+    repeated calls to `__class_getitem__` on generic types (which may happen in tighter loops during runtime),
+    while still ensuring that certain alternative parametrizations ultimately resolve to the same type.
+
+    As a concrete example, this approach was necessary to make Model[List[T]][int] equal to Model[List[int]].
+    The approach could be modified to not use two different cache keys at different points, but the
+    _early_cache_key is optimized to be as quick to compute as possible (for repeated-access speed), and the
+    _late_cache_key is optimized to be as "correct" as possible, so that two types that will ultimately be the
+    same after resolving the type arguments will always produce cache hits.
+
+    If we wanted to move to only using a single cache key per type, we would either need to always use the
+    slower/more computationally intensive logic associated with _late_cache_key, or would need to accept
+    that Model[List[T]][int] is a different type than Model[List[T]][int]. Because we rely on subclass relationships
+    during validation, I think it is worthwhile to ensure that types that are functionally equivalent are actually
+    equal.
+    """
+    return _GENERIC_TYPES_CACHE.get(_early_cache_key(parent, typevar_values))
+
+
+def get_cached_generic_type_late(
+    parent: type[BaseModel], typevar_values: Any, origin: type[BaseModel], args: tuple[Any, ...]
+) -> type[BaseModel] | None:
+    """See the docstring of `get_cached_generic_type_early` for more information about the two-stage cache lookup."""
+    cached = _GENERIC_TYPES_CACHE.get(_late_cache_key(origin, args, typevar_values))
+    if cached is not None:
+        set_cached_generic_type(parent, typevar_values, cached, origin, args)
+    return cached
+
+
+def set_cached_generic_type(
+    parent: type[BaseModel],
+    typevar_values: tuple[Any, ...],
+    type_: type[BaseModel],
+    origin: type[BaseModel] | None = None,
+    args: tuple[Any, ...] | None = None,
+) -> None:
+    """See the docstring of `get_cached_generic_type_early` for more information about why items are cached with
+    two different keys.
+    """
+    _GENERIC_TYPES_CACHE[_early_cache_key(parent, typevar_values)] = type_
+    if len(typevar_values) == 1:
+        _GENERIC_TYPES_CACHE[_early_cache_key(parent, typevar_values[0])] = type_
+    if origin and args:
+        _GENERIC_TYPES_CACHE[_late_cache_key(origin, args, typevar_values)] = type_
+
+
+def _union_orderings_key(typevar_values: Any) -> Any:
+    """This is intended to help differentiate between Union types with the same arguments in different order.
+
+    Thanks to caching internal to the `typing` module, it is not possible to distinguish between
+    List[Union[int, float]] and List[Union[float, int]] (and similarly for other "parent" origins besides List)
+    because `typing` considers Union[int, float] to be equal to Union[float, int].
+
+    However, you _can_ distinguish between (top-level) Union[int, float] vs. Union[float, int].
+    Because we parse items as the first Union type that is successful, we get slightly more consistent behavior
+    if we make an effort to distinguish the ordering of items in a union. It would be best if we could _always_
+    get the exact-correct order of items in the union, but that would require a change to the `typing` module itself.
+    (See https://github.com/python/cpython/issues/86483 for reference.)
+    """
+    if isinstance(typevar_values, tuple):
+        return tuple(_union_orderings_key(value) for value in typevar_values)
+    elif typing_objects.is_union(typing_extensions.get_origin(typevar_values)):
+        return get_args(typevar_values)
+    else:
+        return ()
+
+
+def _early_cache_key(cls: type[BaseModel], typevar_values: Any) -> GenericTypesCacheKey:
+    """This is intended for minimal computational overhead during lookups of cached types.
+
+    Note that this is overly simplistic, and it's possible that two different cls/typevar_values
+    inputs would ultimately result in the same type being created in BaseModel.__class_getitem__.
+    To handle this, we have a fallback _late_cache_key that is checked later if the _early_cache_key
+    lookup fails, and should result in a cache hit _precisely_ when the inputs to __class_getitem__
+    would result in the same type.
+    """
+    return cls, typevar_values, _union_orderings_key(typevar_values)
+
+
+def _late_cache_key(origin: type[BaseModel], args: tuple[Any, ...], typevar_values: Any) -> GenericTypesCacheKey:
+    """This is intended for use later in the process of creating a new type, when we have more information
+    about the exact args that will be passed. If it turns out that a different set of inputs to
+    __class_getitem__ resulted in the same inputs to the generic type creation process, we can still
+    return the cached type, and update the cache with the _early_cache_key as well.
+    """
+    # The _union_orderings_key is placed at the start here to ensure there cannot be a collision with an
+    # _early_cache_key, as that function will always produce a BaseModel subclass as the first item in the key,
+    # whereas this function will always produce a tuple as the first item in the key.
+    return _union_orderings_key(typevar_values), origin, args

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_git.py

@@ -0,0 +1,27 @@
+"""Git utilities, adopted from mypy's git utilities (https://github.com/python/mypy/blob/master/mypy/git.py)."""
+
+from __future__ import annotations
+
+import subprocess
+from pathlib import Path
+
+
+def is_git_repo(dir: Path) -> bool:
+    """Is the given directory version-controlled with git?"""
+    return dir.joinpath('.git').exists()
+
+
+def have_git() -> bool:  # pragma: no cover
+    """Can we run the git executable?"""
+    try:
+        subprocess.check_output(['git', '--help'])
+        return True
+    except subprocess.CalledProcessError:
+        return False
+    except OSError:
+        return False
+
+
+def git_revision(dir: Path) -> str:
+    """Get the SHA-1 of the HEAD of a git repository."""
+    return subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'], cwd=dir).decode('utf-8').strip()

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_import_utils.py

@@ -0,0 +1,20 @@
+from functools import cache
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from pydantic import BaseModel
+    from pydantic.fields import FieldInfo
+
+
+@cache
+def import_cached_base_model() -> type['BaseModel']:
+    from pydantic import BaseModel
+
+    return BaseModel
+
+
+@cache
+def import_cached_field_info() -> type['FieldInfo']:
+    from pydantic.fields import FieldInfo
+
+    return FieldInfo

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_internal_dataclass.py

@@ -0,0 +1,7 @@
+import sys
+
+# `slots` is available on Python >= 3.10
+if sys.version_info >= (3, 10):
+    slots_true = {'slots': True}
+else:
+    slots_true = {}

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_known_annotated_metadata.py

@@ -0,0 +1,403 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from collections.abc import Iterable
+from copy import copy
+from functools import lru_cache, partial
+from typing import TYPE_CHECKING, Any
+
+from pydantic_core import CoreSchema, PydanticCustomError, ValidationError, to_jsonable_python
+from pydantic_core import core_schema as cs
+
+from ._fields import PydanticMetadata
+from ._import_utils import import_cached_field_info
+
+if TYPE_CHECKING:
+    pass
+
+STRICT = {'strict'}
+FAIL_FAST = {'fail_fast'}
+LENGTH_CONSTRAINTS = {'min_length', 'max_length'}
+INEQUALITY = {'le', 'ge', 'lt', 'gt'}
+NUMERIC_CONSTRAINTS = {'multiple_of', *INEQUALITY}
+ALLOW_INF_NAN = {'allow_inf_nan'}
+
+STR_CONSTRAINTS = {
+    *LENGTH_CONSTRAINTS,
+    *STRICT,
+    'strip_whitespace',
+    'to_lower',
+    'to_upper',
+    'pattern',
+    'coerce_numbers_to_str',
+    'ascii_only',
+}
+BYTES_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT}
+
+LIST_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST}
+TUPLE_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST}
+SET_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST}
+DICT_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT}
+GENERATOR_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT}
+SEQUENCE_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *FAIL_FAST}
+
+FLOAT_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *ALLOW_INF_NAN, *STRICT}
+DECIMAL_CONSTRAINTS = {'max_digits', 'decimal_places', *FLOAT_CONSTRAINTS}
+INT_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *ALLOW_INF_NAN, *STRICT}
+BOOL_CONSTRAINTS = STRICT
+UUID_CONSTRAINTS = STRICT
+
+DATE_TIME_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT}
+TIMEDELTA_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT}
+TIME_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT}
+LAX_OR_STRICT_CONSTRAINTS = STRICT
+ENUM_CONSTRAINTS = STRICT
+COMPLEX_CONSTRAINTS = STRICT
+
+UNION_CONSTRAINTS = {'union_mode'}
+URL_CONSTRAINTS = {
+    'max_length',
+    'allowed_schemes',
+    'host_required',
+    'default_host',
+    'default_port',
+    'default_path',
+}
+
+TEXT_SCHEMA_TYPES = ('str', 'bytes', 'url', 'multi-host-url')
+SEQUENCE_SCHEMA_TYPES = ('list', 'tuple', 'set', 'frozenset', 'generator', *TEXT_SCHEMA_TYPES)
+NUMERIC_SCHEMA_TYPES = ('float', 'int', 'date', 'time', 'timedelta', 'datetime')
+
+CONSTRAINTS_TO_ALLOWED_SCHEMAS: dict[str, set[str]] = defaultdict(set)
+
+constraint_schema_pairings: list[tuple[set[str], tuple[str, ...]]] = [
+    (STR_CONSTRAINTS, TEXT_SCHEMA_TYPES),
+    (BYTES_CONSTRAINTS, ('bytes',)),
+    (LIST_CONSTRAINTS, ('list',)),
+    (TUPLE_CONSTRAINTS, ('tuple',)),
+    (SET_CONSTRAINTS, ('set', 'frozenset')),
+    (DICT_CONSTRAINTS, ('dict',)),
+    (GENERATOR_CONSTRAINTS, ('generator',)),
+    (FLOAT_CONSTRAINTS, ('float',)),
+    (INT_CONSTRAINTS, ('int',)),
+    (DATE_TIME_CONSTRAINTS, ('date', 'time', 'datetime', 'timedelta')),
+    # TODO: this is a bit redundant, we could probably avoid some of these
+    (STRICT, (*TEXT_SCHEMA_TYPES, *SEQUENCE_SCHEMA_TYPES, *NUMERIC_SCHEMA_TYPES, 'typed-dict', 'model')),
+    (UNION_CONSTRAINTS, ('union',)),
+    (URL_CONSTRAINTS, ('url', 'multi-host-url')),
+    (BOOL_CONSTRAINTS, ('bool',)),
+    (UUID_CONSTRAINTS, ('uuid',)),
+    (LAX_OR_STRICT_CONSTRAINTS, ('lax-or-strict',)),
+    (ENUM_CONSTRAINTS, ('enum',)),
+    (DECIMAL_CONSTRAINTS, ('decimal',)),
+    (COMPLEX_CONSTRAINTS, ('complex',)),
+]
+
+for constraints, schemas in constraint_schema_pairings:
+    for c in constraints:
+        CONSTRAINTS_TO_ALLOWED_SCHEMAS[c].update(schemas)
+
+
+def as_jsonable_value(v: Any) -> Any:
+    if type(v) not in (int, str, float, bytes, bool, type(None)):
+        return to_jsonable_python(v)
+    return v
+
+
+def expand_grouped_metadata(annotations: Iterable[Any]) -> Iterable[Any]:
+    """Expand the annotations.
+
+    Args:
+        annotations: An iterable of annotations.
+
+    Returns:
+        An iterable of expanded annotations.
+
+    Example:
+        ```python
+        from annotated_types import Ge, Len
+
+        from pydantic._internal._known_annotated_metadata import expand_grouped_metadata
+
+        print(list(expand_grouped_metadata([Ge(4), Len(5)])))
+        #> [Ge(ge=4), MinLen(min_length=5)]
+        ```
+    """
+    import annotated_types as at
+
+    FieldInfo = import_cached_field_info()
+
+    for annotation in annotations:
+        if isinstance(annotation, at.GroupedMetadata):
+            yield from annotation
+        elif isinstance(annotation, FieldInfo):
+            yield from annotation.metadata
+            # this is a bit problematic in that it results in duplicate metadata
+            # all of our "consumers" can handle it, but it is not ideal
+            # we probably should split up FieldInfo into:
+            # - annotated types metadata
+            # - individual metadata known only to Pydantic
+            annotation = copy(annotation)
+            annotation.metadata = []
+            yield annotation
+        else:
+            yield annotation
+
+
+@lru_cache
+def _get_at_to_constraint_map() -> dict[type, str]:
+    """Return a mapping of annotated types to constraints.
+
+    Normally, we would define a mapping like this in the module scope, but we can't do that
+    because we don't permit module level imports of `annotated_types`, in an attempt to speed up
+    the import time of `pydantic`. We still only want to have this dictionary defined in one place,
+    so we use this function to cache the result.
+    """
+    import annotated_types as at
+
+    return {
+        at.Gt: 'gt',
+        at.Ge: 'ge',
+        at.Lt: 'lt',
+        at.Le: 'le',
+        at.MultipleOf: 'multiple_of',
+        at.MinLen: 'min_length',
+        at.MaxLen: 'max_length',
+    }
+
+
+def apply_known_metadata(annotation: Any, schema: CoreSchema) -> CoreSchema | None:  # noqa: C901
+    """Apply `annotation` to `schema` if it is an annotation we know about (Gt, Le, etc.).
+    Otherwise return `None`.
+
+    This does not handle all known annotations. If / when it does, it can always
+    return a CoreSchema and return the unmodified schema if the annotation should be ignored.
+
+    Assumes that GroupedMetadata has already been expanded via `expand_grouped_metadata`.
+
+    Args:
+        annotation: The annotation.
+        schema: The schema.
+
+    Returns:
+        An updated schema with annotation if it is an annotation we know about, `None` otherwise.
+
+    Raises:
+        RuntimeError: If a constraint can't be applied to a specific schema type.
+        ValueError: If an unknown constraint is encountered.
+    """
+    import annotated_types as at
+
+    from ._validators import NUMERIC_VALIDATOR_LOOKUP, forbid_inf_nan_check
+
+    schema = schema.copy()
+    schema_update, other_metadata = collect_known_metadata([annotation])
+    schema_type = schema['type']
+
+    chain_schema_constraints: set[str] = {
+        'pattern',
+        'strip_whitespace',
+        'to_lower',
+        'to_upper',
+        'coerce_numbers_to_str',
+        'ascii_only',
+    }
+    chain_schema_steps: list[CoreSchema] = []
+
+    for constraint, value in schema_update.items():
+        if constraint not in CONSTRAINTS_TO_ALLOWED_SCHEMAS:
+            raise ValueError(f'Unknown constraint {constraint}')
+        allowed_schemas = CONSTRAINTS_TO_ALLOWED_SCHEMAS[constraint]
+
+        # if it becomes necessary to handle more than one constraint
+        # in this recursive case with function-after or function-wrap, we should refactor
+        # this is a bit challenging because we sometimes want to apply constraints to the inner schema,
+        # whereas other times we want to wrap the existing schema with a new one that enforces a new constraint.
+        if schema_type in {'function-before', 'function-wrap', 'function-after'} and constraint == 'strict':
+            schema['schema'] = apply_known_metadata(annotation, schema['schema'])  # type: ignore  # schema is function schema
+            return schema
+
+        # if we're allowed to apply constraint directly to the schema, like le to int, do that
+        if schema_type in allowed_schemas:
+            if constraint == 'union_mode' and schema_type == 'union':
+                schema['mode'] = value  # type: ignore  # schema is UnionSchema
+            else:
+                schema[constraint] = value
+            continue
+
+        #  else, apply a function after validator to the schema to enforce the corresponding constraint
+        if constraint in chain_schema_constraints:
+
+            def _apply_constraint_with_incompatibility_info(
+                value: Any, handler: cs.ValidatorFunctionWrapHandler
+            ) -> Any:
+                try:
+                    x = handler(value)
+                except ValidationError as ve:
+                    # if the error is about the type, it's likely that the constraint is incompatible the type of the field
+                    # for example, the following invalid schema wouldn't be caught during schema build, but rather at this point
+                    # with a cryptic 'string_type' error coming from the string validator,
+                    # that we'd rather express as a constraint incompatibility error (TypeError)
+                    # Annotated[list[int], Field(pattern='abc')]
+                    if 'type' in ve.errors()[0]['type']:
+                        raise TypeError(
+                            f"Unable to apply constraint '{constraint}' to supplied value {value} for schema of type '{schema_type}'"  # noqa: B023
+                        )
+                    raise ve
+                return x
+
+            chain_schema_steps.append(
+                cs.no_info_wrap_validator_function(
+                    _apply_constraint_with_incompatibility_info, cs.str_schema(**{constraint: value})
+                )
+            )
+        elif constraint in NUMERIC_VALIDATOR_LOOKUP:
+            if constraint in LENGTH_CONSTRAINTS:
+                inner_schema = schema
+                while inner_schema['type'] in {'function-before', 'function-wrap', 'function-after'}:
+                    inner_schema = inner_schema['schema']  # type: ignore
+                inner_schema_type = inner_schema['type']
+                if inner_schema_type == 'list' or (
+                    inner_schema_type == 'json-or-python' and inner_schema['json_schema']['type'] == 'list'  # type: ignore
+                ):
+                    js_constraint_key = 'minItems' if constraint == 'min_length' else 'maxItems'
+                else:
+                    js_constraint_key = 'minLength' if constraint == 'min_length' else 'maxLength'
+            else:
+                js_constraint_key = constraint
+
+            schema = cs.no_info_after_validator_function(
+                partial(NUMERIC_VALIDATOR_LOOKUP[constraint], **{constraint: value}), schema
+            )
+            metadata = schema.get('metadata', {})
+            if (existing_json_schema_updates := metadata.get('pydantic_js_updates')) is not None:
+                metadata['pydantic_js_updates'] = {
+                    **existing_json_schema_updates,
+                    **{js_constraint_key: as_jsonable_value(value)},
+                }
+            else:
+                metadata['pydantic_js_updates'] = {js_constraint_key: as_jsonable_value(value)}
+            schema['metadata'] = metadata
+        elif constraint == 'allow_inf_nan' and value is False:
+            schema = cs.no_info_after_validator_function(
+                forbid_inf_nan_check,
+                schema,
+            )
+        else:
+            # It's rare that we'd get here, but it's possible if we add a new constraint and forget to handle it
+            # Most constraint errors are caught at runtime during attempted application
+            raise RuntimeError(f"Unable to apply constraint '{constraint}' to schema of type '{schema_type}'")
+
+    for annotation in other_metadata:
+        if (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()):
+            constraint = at_to_constraint_map[annotation_type]
+            validator = NUMERIC_VALIDATOR_LOOKUP.get(constraint)
+            if validator is None:
+                raise ValueError(f'Unknown constraint {constraint}')
+            schema = cs.no_info_after_validator_function(
+                partial(validator, {constraint: getattr(annotation, constraint)}), schema
+            )
+            continue
+        elif isinstance(annotation, (at.Predicate, at.Not)):
+            predicate_name = f'{annotation.func.__qualname__!r} ' if hasattr(annotation.func, '__qualname__') else ''
+
+            # Note: B023 is ignored because even though we iterate over `other_metadata`, it is guaranteed
+            # to be of length 1. `apply_known_metadata()` is called from `GenerateSchema`, where annotations
+            # were already expanded via `expand_grouped_metadata()`. Confusing, but this falls into the annotations
+            # refactor.
+            if isinstance(annotation, at.Predicate):
+
+                def val_func(v: Any) -> Any:
+                    predicate_satisfied = annotation.func(v)  # noqa: B023
+                    if not predicate_satisfied:
+                        raise PydanticCustomError(
+                            'predicate_failed',
+                            f'Predicate {predicate_name}failed',  # pyright: ignore[reportArgumentType]  # noqa: B023
+                        )
+                    return v
+
+            else:
+
+                def val_func(v: Any) -> Any:
+                    predicate_satisfied = annotation.func(v)  # noqa: B023
+                    if predicate_satisfied:
+                        raise PydanticCustomError(
+                            'not_operation_failed',
+                            f'Not of {predicate_name}failed',  # pyright: ignore[reportArgumentType]  # noqa: B023
+                        )
+                    return v
+
+            schema = cs.no_info_after_validator_function(val_func, schema)
+        else:
+            # ignore any other unknown metadata
+            return None
+
+    if chain_schema_steps:
+        chain_schema_steps = [schema] + chain_schema_steps
+        return cs.chain_schema(chain_schema_steps)
+
+    return schema
+
+
+def collect_known_metadata(annotations: Iterable[Any]) -> tuple[dict[str, Any], list[Any]]:
+    """Split `annotations` into known metadata and unknown annotations.
+
+    Args:
+        annotations: An iterable of annotations.
+
+    Returns:
+        A tuple contains a dict of known metadata and a list of unknown annotations.
+
+    Example:
+        ```python
+        from annotated_types import Gt, Len
+
+        from pydantic._internal._known_annotated_metadata import collect_known_metadata
+
+        print(collect_known_metadata([Gt(1), Len(42), ...]))
+        #> ({'gt': 1, 'min_length': 42}, [Ellipsis])
+        ```
+    """
+    annotations = expand_grouped_metadata(annotations)
+
+    res: dict[str, Any] = {}
+    remaining: list[Any] = []
+
+    for annotation in annotations:
+        # isinstance(annotation, PydanticMetadata) also covers ._fields:_PydanticGeneralMetadata
+        if isinstance(annotation, PydanticMetadata):
+            res.update(annotation.__dict__)
+        # we don't use dataclasses.asdict because that recursively calls asdict on the field values
+        elif (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()):
+            constraint = at_to_constraint_map[annotation_type]
+            res[constraint] = getattr(annotation, constraint)
+        elif isinstance(annotation, type) and issubclass(annotation, PydanticMetadata):
+            # also support PydanticMetadata classes being used without initialisation,
+            # e.g. `Annotated[int, Strict]` as well as `Annotated[int, Strict()]`
+            res.update({k: v for k, v in vars(annotation).items() if not k.startswith('_')})
+        else:
+            remaining.append(annotation)
+    # Nones can sneak in but pydantic-core will reject them
+    # it'd be nice to clean things up so we don't put in None (we probably don't _need_ to, it was just easier)
+    # but this is simple enough to kick that can down the road
+    res = {k: v for k, v in res.items() if v is not None}
+    return res, remaining
+
+
+def check_metadata(metadata: dict[str, Any], allowed: Iterable[str], source_type: Any) -> None:
+    """A small utility function to validate that the given metadata can be applied to the target.
+    More than saving lines of code, this gives us a consistent error message for all of our internal implementations.
+
+    Args:
+        metadata: A dict of metadata.
+        allowed: An iterable of allowed metadata.
+        source_type: The source type.
+
+    Raises:
+        TypeError: If there is metadatas that can't be applied on source type.
+    """
+    unknown = metadata.keys() - set(allowed)
+    if unknown:
+        raise TypeError(
+            f'The following constraints cannot be applied to {source_type!r}: {", ".join([f"{k!r}" for k in unknown])}'
+        )

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_mock_val_ser.py

@@ -0,0 +1,228 @@
+from __future__ import annotations
+
+from collections.abc import Iterator, Mapping
+from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, TypeVar, Union
+
+from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator
+
+from ..errors import PydanticErrorCodes, PydanticUserError
+from ..plugin._schema_validator import PluggableSchemaValidator
+
+if TYPE_CHECKING:
+    from ..dataclasses import PydanticDataclass
+    from ..main import BaseModel
+    from ..type_adapter import TypeAdapter
+
+
+ValSer = TypeVar('ValSer', bound=Union[SchemaValidator, PluggableSchemaValidator, SchemaSerializer])
+T = TypeVar('T')
+
+
+class MockCoreSchema(Mapping[str, Any]):
+    """Mocker for `pydantic_core.CoreSchema` which optionally attempts to
+    rebuild the thing it's mocking when one of its methods is accessed and raises an error if that fails.
+    """
+
+    __slots__ = '_error_message', '_code', '_attempt_rebuild', '_built_memo'
+
+    def __init__(
+        self,
+        error_message: str,
+        *,
+        code: PydanticErrorCodes,
+        attempt_rebuild: Callable[[], CoreSchema | None] | None = None,
+    ) -> None:
+        self._error_message = error_message
+        self._code: PydanticErrorCodes = code
+        self._attempt_rebuild = attempt_rebuild
+        self._built_memo: CoreSchema | None = None
+
+    def __getitem__(self, key: str) -> Any:
+        return self._get_built().__getitem__(key)
+
+    def __len__(self) -> int:
+        return self._get_built().__len__()
+
+    def __iter__(self) -> Iterator[str]:
+        return self._get_built().__iter__()
+
+    def _get_built(self) -> CoreSchema:
+        if self._built_memo is not None:
+            return self._built_memo
+
+        if self._attempt_rebuild:
+            schema = self._attempt_rebuild()
+            if schema is not None:
+                self._built_memo = schema
+                return schema
+        raise PydanticUserError(self._error_message, code=self._code)
+
+    def rebuild(self) -> CoreSchema | None:
+        self._built_memo = None
+        if self._attempt_rebuild:
+            schema = self._attempt_rebuild()
+            if schema is not None:
+                return schema
+            else:
+                raise PydanticUserError(self._error_message, code=self._code)
+        return None
+
+
+class MockValSer(Generic[ValSer]):
+    """Mocker for `pydantic_core.SchemaValidator` or `pydantic_core.SchemaSerializer` which optionally attempts to
+    rebuild the thing it's mocking when one of its methods is accessed and raises an error if that fails.
+    """
+
+    __slots__ = '_error_message', '_code', '_val_or_ser', '_attempt_rebuild'
+
+    def __init__(
+        self,
+        error_message: str,
+        *,
+        code: PydanticErrorCodes,
+        val_or_ser: Literal['validator', 'serializer'],
+        attempt_rebuild: Callable[[], ValSer | None] | None = None,
+    ) -> None:
+        self._error_message = error_message
+        self._val_or_ser = SchemaValidator if val_or_ser == 'validator' else SchemaSerializer
+        self._code: PydanticErrorCodes = code
+        self._attempt_rebuild = attempt_rebuild
+
+    def __getattr__(self, item: str) -> None:
+        __tracebackhide__ = True
+        if self._attempt_rebuild:
+            val_ser = self._attempt_rebuild()
+            if val_ser is not None:
+                return getattr(val_ser, item)
+
+        # raise an AttributeError if `item` doesn't exist
+        getattr(self._val_or_ser, item)
+        raise PydanticUserError(self._error_message, code=self._code)
+
+    def rebuild(self) -> ValSer | None:
+        if self._attempt_rebuild:
+            val_ser = self._attempt_rebuild()
+            if val_ser is not None:
+                return val_ser
+            else:
+                raise PydanticUserError(self._error_message, code=self._code)
+        return None
+
+
+def set_type_adapter_mocks(adapter: TypeAdapter) -> None:
+    """Set `core_schema`, `validator` and `serializer` to mock core types on a type adapter instance.
+
+    Args:
+        adapter: The type adapter instance to set the mocks on
+    """
+    type_repr = str(adapter._type)
+    undefined_type_error_message = (
+        f'`TypeAdapter[{type_repr}]` is not fully defined; you should define `{type_repr}` and all referenced types,'
+        f' then call `.rebuild()` on the instance.'
+    )
+
+    def attempt_rebuild_fn(attr_fn: Callable[[TypeAdapter], T]) -> Callable[[], T | None]:
+        def handler() -> T | None:
+            if adapter.rebuild(raise_errors=False, _parent_namespace_depth=5) is not False:
+                return attr_fn(adapter)
+            return None
+
+        return handler
+
+    adapter.core_schema = MockCoreSchema(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.core_schema),
+    )
+    adapter.validator = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='validator',
+        attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.validator),
+    )
+    adapter.serializer = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='serializer',
+        attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.serializer),
+    )
+
+
+def set_model_mocks(cls: type[BaseModel], undefined_name: str = 'all referenced types') -> None:
+    """Set `__pydantic_core_schema__`, `__pydantic_validator__` and `__pydantic_serializer__` to mock core types on a model.
+
+    Args:
+        cls: The model class to set the mocks on
+        undefined_name: Name of the undefined thing, used in error messages
+    """
+    undefined_type_error_message = (
+        f'`{cls.__name__}` is not fully defined; you should define {undefined_name},'
+        f' then call `{cls.__name__}.model_rebuild()`.'
+    )
+
+    def attempt_rebuild_fn(attr_fn: Callable[[type[BaseModel]], T]) -> Callable[[], T | None]:
+        def handler() -> T | None:
+            if cls.model_rebuild(raise_errors=False, _parent_namespace_depth=5) is not False:
+                return attr_fn(cls)
+            return None
+
+        return handler
+
+    cls.__pydantic_core_schema__ = MockCoreSchema(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_core_schema__),
+    )
+    cls.__pydantic_validator__ = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='validator',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_validator__),
+    )
+    cls.__pydantic_serializer__ = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='serializer',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_serializer__),
+    )
+
+
+def set_dataclass_mocks(cls: type[PydanticDataclass], undefined_name: str = 'all referenced types') -> None:
+    """Set `__pydantic_validator__` and `__pydantic_serializer__` to `MockValSer`s on a dataclass.
+
+    Args:
+        cls: The model class to set the mocks on
+        undefined_name: Name of the undefined thing, used in error messages
+    """
+    from ..dataclasses import rebuild_dataclass
+
+    undefined_type_error_message = (
+        f'`{cls.__name__}` is not fully defined; you should define {undefined_name},'
+        f' then call `pydantic.dataclasses.rebuild_dataclass({cls.__name__})`.'
+    )
+
+    def attempt_rebuild_fn(attr_fn: Callable[[type[PydanticDataclass]], T]) -> Callable[[], T | None]:
+        def handler() -> T | None:
+            if rebuild_dataclass(cls, raise_errors=False, _parent_namespace_depth=5) is not False:
+                return attr_fn(cls)
+            return None
+
+        return handler
+
+    cls.__pydantic_core_schema__ = MockCoreSchema(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_core_schema__),
+    )
+    cls.__pydantic_validator__ = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='validator',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_validator__),
+    )
+    cls.__pydantic_serializer__ = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='serializer',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_serializer__),
+    )

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_model_construction.py

@@ -0,0 +1,868 @@
+"""Private logic for creating models."""
+
+from __future__ import annotations as _annotations
+
+import operator
+import sys
+import typing
+import warnings
+import weakref
+from abc import ABCMeta
+from functools import cache, partial, wraps
+from types import FunctionType
+from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, NoReturn, TypeVar, cast
+
+from pydantic_core import PydanticUndefined, SchemaSerializer
+from typing_extensions import TypeAliasType, dataclass_transform, deprecated, get_args, get_origin
+from typing_inspection import typing_objects
+
+from ..errors import PydanticUndefinedAnnotation, PydanticUserError
+from ..plugin._schema_validator import create_schema_validator
+from ..warnings import GenericBeforeBaseModelWarning, PydanticDeprecatedSince20
+from ._config import ConfigWrapper
+from ._decorators import DecoratorInfos, PydanticDescriptorProxy, get_attribute_from_bases, unwrap_wrapped_function
+from ._fields import collect_model_fields, is_valid_field_name, is_valid_privateattr_name, rebuild_model_fields
+from ._generate_schema import GenerateSchema, InvalidSchemaError
+from ._generics import PydanticGenericMetadata, get_model_typevars_map
+from ._import_utils import import_cached_base_model, import_cached_field_info
+from ._mock_val_ser import set_model_mocks
+from ._namespace_utils import NsResolver
+from ._signature import generate_pydantic_signature
+from ._typing_extra import (
+    _make_forward_ref,
+    eval_type_backport,
+    is_classvar_annotation,
+    parent_frame_namespace,
+)
+from ._utils import LazyClassAttribute, SafeGetItemProxy
+
+if TYPE_CHECKING:
+    from ..fields import Field as PydanticModelField
+    from ..fields import FieldInfo, ModelPrivateAttr
+    from ..fields import PrivateAttr as PydanticModelPrivateAttr
+    from ..main import BaseModel
+    from ._fields import PydanticExtraInfo
+else:
+    PydanticModelField = object()
+    PydanticModelPrivateAttr = object()
+
+object_setattr = object.__setattr__
+
+
+class _ModelNamespaceDict(dict):
+    """A dictionary subclass that intercepts attribute setting on model classes and
+    warns about overriding of decorators.
+    """
+
+    def __setitem__(self, k: str, v: object) -> None:
+        existing: Any = self.get(k, None)
+        if existing and v is not existing and isinstance(existing, PydanticDescriptorProxy):
+            warnings.warn(
+                f'`{k}` overrides an existing Pydantic `{existing.decorator_info.decorator_repr}` decorator',
+                stacklevel=2,
+            )
+
+        return super().__setitem__(k, v)
+
+
+def NoInitField(
+    *,
+    init: Literal[False] = False,
+) -> Any:
+    """Only for typing purposes. Used as default value of `__pydantic_fields_set__`,
+    `__pydantic_extra__`, `__pydantic_private__`, so they could be ignored when
+    synthesizing the `__init__` signature.
+    """
+
+
+# For ModelMetaclass.register():
+_T = TypeVar('_T')
+
+
+@dataclass_transform(kw_only_default=True, field_specifiers=(PydanticModelField, PydanticModelPrivateAttr, NoInitField))
+class ModelMetaclass(ABCMeta):
+    def __new__(
+        mcs,
+        cls_name: str,
+        bases: tuple[type[Any], ...],
+        namespace: dict[str, Any],
+        __pydantic_generic_metadata__: PydanticGenericMetadata | None = None,
+        __pydantic_reset_parent_namespace__: bool = True,
+        _create_model_module: str | None = None,
+        **kwargs: Any,
+    ) -> type:
+        """Metaclass for creating Pydantic models.
+
+        Args:
+            cls_name: The name of the class to be created.
+            bases: The base classes of the class to be created.
+            namespace: The attribute dictionary of the class to be created.
+            __pydantic_generic_metadata__: Metadata for generic models.
+            __pydantic_reset_parent_namespace__: Reset parent namespace.
+            _create_model_module: The module of the class to be created, if created by `create_model`.
+            **kwargs: Catch-all for any other keyword arguments.
+
+        Returns:
+            The new class created by the metaclass.
+        """
+        # Note `ModelMetaclass` refers to `BaseModel`, but is also used to *create* `BaseModel`, so we rely on the fact
+        # that `BaseModel` itself won't have any bases, but any subclass of it will, to determine whether the `__new__`
+        # call we're in the middle of is for the `BaseModel` class.
+        if bases:
+            raw_annotations: dict[str, Any]
+            if sys.version_info >= (3, 14):
+                if (
+                    '__annotations__' in namespace
+                ):  # `from __future__ import annotations` was used in the model's module
+                    raw_annotations = namespace['__annotations__']
+                else:
+                    # See https://docs.python.org/3.14/library/annotationlib.html#using-annotations-in-a-metaclass:
+                    from annotationlib import Format, call_annotate_function, get_annotate_from_class_namespace
+
+                    if annotate := get_annotate_from_class_namespace(namespace):
+                        raw_annotations = call_annotate_function(annotate, format=Format.FORWARDREF)
+                    else:
+                        raw_annotations = {}
+            else:
+                raw_annotations = namespace.get('__annotations__', {})
+
+            base_field_names, class_vars, base_private_attributes = mcs._collect_bases_data(bases)
+
+            config_wrapper = ConfigWrapper.for_model(bases, namespace, raw_annotations, kwargs)
+            namespace['model_config'] = config_wrapper.config_dict
+            private_attributes = inspect_namespace(
+                namespace, raw_annotations, config_wrapper.ignored_types, class_vars, base_field_names
+            )
+            if private_attributes or base_private_attributes:
+                original_model_post_init = get_model_post_init(namespace, bases)
+                if original_model_post_init is not None:
+                    # if there are private attributes and a model_post_init function, we handle both
+
+                    @wraps(original_model_post_init)
+                    def wrapped_model_post_init(self: BaseModel, context: Any, /) -> None:
+                        """We need to both initialize private attributes and call the user-defined model_post_init
+                        method.
+                        """
+                        init_private_attributes(self, context)
+                        original_model_post_init(self, context)
+
+                    namespace['model_post_init'] = wrapped_model_post_init
+                else:
+                    namespace['model_post_init'] = init_private_attributes
+
+            namespace['__class_vars__'] = class_vars
+            namespace['__private_attributes__'] = {**base_private_attributes, **private_attributes}
+
+            cls = cast('type[BaseModel]', super().__new__(mcs, cls_name, bases, namespace, **kwargs))
+            BaseModel_ = import_cached_base_model()
+
+            mro = cls.__mro__
+            if Generic in mro and mro.index(Generic) < mro.index(BaseModel_):
+                warnings.warn(
+                    GenericBeforeBaseModelWarning(
+                        'Classes should inherit from `BaseModel` before generic classes (e.g. `typing.Generic[T]`) '
+                        'for pydantic generics to work properly.'
+                    ),
+                    stacklevel=2,
+                )
+
+            cls.__pydantic_custom_init__ = not getattr(cls.__init__, '__pydantic_base_init__', False)
+            cls.__pydantic_post_init__ = (
+                None if cls.model_post_init is BaseModel_.model_post_init else 'model_post_init'
+            )
+
+            cls.__pydantic_setattr_handlers__ = {}
+
+            cls.__pydantic_decorators__ = DecoratorInfos.build(cls, replace_wrapped_methods=True)
+            cls.__pydantic_decorators__.update_from_config(config_wrapper)
+
+            # Use the getattr below to grab the __parameters__ from the `typing.Generic` parent class
+            if __pydantic_generic_metadata__:
+                cls.__pydantic_generic_metadata__ = __pydantic_generic_metadata__
+            else:
+                parent_parameters = getattr(cls, '__pydantic_generic_metadata__', {}).get('parameters', ())
+                parameters = getattr(cls, '__parameters__', None) or parent_parameters
+                if parameters and parent_parameters and not all(x in parameters for x in parent_parameters):
+                    from ..root_model import RootModelRootType
+
+                    missing_parameters = tuple(x for x in parameters if x not in parent_parameters)
+                    if RootModelRootType in parent_parameters and RootModelRootType not in parameters:
+                        # This is a special case where the user has subclassed RootModel, but has not parameterized
+                        # RootModel with the generic type identifiers being used. Ex:
+                        # class MyModel(RootModel, Generic[T]):
+                        #    root: T
+                        # Should instead just be:
+                        # class MyModel(RootModel[T]):
+                        #   root: T
+                        parameters_str = ', '.join([x.__name__ for x in missing_parameters])
+                        error_message = (
+                            f'{cls.__name__} is a subclass of `RootModel`, but does not include the generic type identifier(s) '
+                            f'{parameters_str} in its parameters. '
+                            f'You should parametrize RootModel directly, e.g., `class {cls.__name__}(RootModel[{parameters_str}]): ...`.'
+                        )
+                    else:
+                        combined_parameters = parent_parameters + missing_parameters
+                        parameters_str = ', '.join([str(x) for x in combined_parameters])
+                        generic_type_label = f'typing.Generic[{parameters_str}]'
+                        error_message = (
+                            f'All parameters must be present on typing.Generic;'
+                            f' you should inherit from {generic_type_label}.'
+                        )
+                        if Generic not in bases:  # pragma: no cover
+                            # We raise an error here not because it is desirable, but because some cases are mishandled.
+                            # It would be nice to remove this error and still have things behave as expected, it's just
+                            # challenging because we are using a custom `__class_getitem__` to parametrize generic models,
+                            # and not returning a typing._GenericAlias from it.
+                            bases_str = ', '.join([x.__name__ for x in bases] + [generic_type_label])
+                            error_message += (
+                                f' Note: `typing.Generic` must go last: `class {cls.__name__}({bases_str}): ...`)'
+                            )
+                    raise TypeError(error_message)
+
+                cls.__pydantic_generic_metadata__ = {
+                    'origin': None,
+                    'args': (),
+                    'parameters': parameters,
+                }
+
+            cls.__pydantic_complete__ = False  # Ensure this specific class gets completed
+
+            # 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 private_attributes.items():
+                obj.__set_name__(cls, name)
+
+            if __pydantic_reset_parent_namespace__:
+                cls.__pydantic_parent_namespace__ = build_lenient_weakvaluedict(parent_frame_namespace())
+            parent_namespace: dict[str, Any] | None = getattr(cls, '__pydantic_parent_namespace__', None)
+            if isinstance(parent_namespace, dict):
+                parent_namespace = unpack_lenient_weakvaluedict(parent_namespace)
+
+            ns_resolver = NsResolver(parent_namespace=parent_namespace)
+
+            set_model_fields(cls, config_wrapper=config_wrapper, ns_resolver=ns_resolver)
+
+            # This is also set in `complete_model_class()`, after schema gen because they are recreated.
+            # We set them here as well for backwards compatibility:
+            cls.__pydantic_computed_fields__ = {
+                k: v.info for k, v in cls.__pydantic_decorators__.computed_fields.items()
+            }
+
+            if config_wrapper.defer_build:
+                set_model_mocks(cls)
+            else:
+                # Any operation that requires accessing the field infos instances should be put inside
+                # `complete_model_class()`:
+                complete_model_class(
+                    cls,
+                    config_wrapper,
+                    ns_resolver,
+                    raise_errors=False,
+                    create_model_module=_create_model_module,
+                )
+
+            if config_wrapper.frozen and '__hash__' not in namespace:
+                set_default_hash_func(cls, bases)
+
+            # using super(cls, cls) on the next line ensures we only call the parent class's __pydantic_init_subclass__
+            # I believe the `type: ignore` is only necessary because mypy doesn't realize that this code branch is
+            # only hit for _proper_ subclasses of BaseModel
+            super(cls, cls).__pydantic_init_subclass__(**kwargs)  # type: ignore[misc]
+            return cls
+        else:
+            # These are instance variables, but have been assigned to `NoInitField` to trick the type checker.
+            for instance_slot in '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__':
+                namespace.pop(
+                    instance_slot,
+                    None,  # In case the metaclass is used with a class other than `BaseModel`.
+                )
+            namespace.get('__annotations__', {}).clear()
+            return super().__new__(mcs, cls_name, bases, namespace, **kwargs)
+
+    if not TYPE_CHECKING:  # pragma: no branch
+        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access
+
+        def __getattr__(self, item: str) -> Any:
+            """This is necessary to keep attribute access working for class attribute access."""
+            private_attributes = self.__dict__.get('__private_attributes__')
+            if private_attributes and item in private_attributes:
+                return private_attributes[item]
+            raise AttributeError(item)
+
+    @classmethod
+    def __prepare__(cls, *args: Any, **kwargs: Any) -> dict[str, object]:
+        return _ModelNamespaceDict()
+
+    # Due to performance and memory issues, in the ABCMeta.__subclasscheck__ implementation, we don't support
+    # registered virtual subclasses. See https://github.com/python/cpython/issues/92810#issuecomment-2762454345.
+    # This may change once CPython is fixed (possibly in 3.15), in which case we should conditionally
+    # define `register()`.
+    def register(self, subclass: type[_T]) -> type[_T]:
+        warnings.warn(
+            f"For performance reasons, virtual subclasses registered using '{self.__qualname__}.register()' "
+            "are not supported in 'isinstance()' and 'issubclass()' checks.",
+            stacklevel=2,
+        )
+        return super().register(subclass)
+
+    __instancecheck__ = type.__instancecheck__  # pyright: ignore[reportAssignmentType]
+    __subclasscheck__ = type.__subclasscheck__  # pyright: ignore[reportAssignmentType]
+
+    @staticmethod
+    def _collect_bases_data(bases: tuple[type[Any], ...]) -> tuple[set[str], set[str], dict[str, ModelPrivateAttr]]:
+        BaseModel = import_cached_base_model()
+
+        field_names: set[str] = set()
+        class_vars: set[str] = set()
+        private_attributes: dict[str, ModelPrivateAttr] = {}
+        for base in bases:
+            if issubclass(base, BaseModel) and base is not BaseModel:
+                # model_fields might not be defined yet in the case of generics, so we use getattr here:
+                field_names.update(getattr(base, '__pydantic_fields__', {}).keys())
+                class_vars.update(base.__class_vars__)
+                private_attributes.update(base.__private_attributes__)
+        return field_names, class_vars, private_attributes
+
+    @property
+    @deprecated(
+        'The `__fields__` attribute is deprecated, use the `model_fields` class property instead.', category=None
+    )
+    def __fields__(self) -> dict[str, FieldInfo]:
+        warnings.warn(
+            'The `__fields__` attribute is deprecated, use the `model_fields` class property instead.',
+            PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        return getattr(self, '__pydantic_fields__', {})
+
+    @property
+    def __pydantic_fields_complete__(self) -> bool:
+        """Whether the fields were successfully collected (i.e. type hints were successfully resolved).
+
+        This is a private attribute, not meant to be used outside Pydantic.
+        """
+        if '__pydantic_fields__' not in self.__dict__:
+            return False
+
+        field_infos = cast('dict[str, FieldInfo]', self.__pydantic_fields__)  # pyright: ignore[reportAttributeAccessIssue]
+
+        pydantic_extra_info = cast('PydanticExtraInfo | None', self.__pydantic_extra_info__)  # pyright: ignore[reportAttributeAccessIssue]
+        if pydantic_extra_info is not None:
+            extra_complete = pydantic_extra_info.complete
+        else:
+            extra_complete = True
+
+        return all(field_info._complete for field_info in field_infos.values()) and extra_complete
+
+    def __dir__(self) -> list[str]:
+        attributes = list(super().__dir__())
+        if '__fields__' in attributes:
+            attributes.remove('__fields__')
+        return attributes
+
+
+def init_private_attributes(self: BaseModel, context: Any, /) -> None:
+    """This function is meant to behave like a BaseModel method to initialize private attributes.
+
+    It takes context as an argument since that's what pydantic-core passes when calling it.
+
+    Args:
+        self: The BaseModel instance.
+        context: The context.
+    """
+    if getattr(self, '__pydantic_private__', None) is None:
+        pydantic_private = {}
+        for name, private_attr in self.__private_attributes__.items():
+            # Avoid needlessly creating a new dict for the validated data:
+            if private_attr.default_factory_takes_validated_data:
+                default = private_attr.get_default(
+                    call_default_factory=True, validated_data={**self.__dict__, **pydantic_private}
+                )
+            else:
+                default = private_attr.get_default(call_default_factory=True)
+            if default is not PydanticUndefined:
+                pydantic_private[name] = default
+        object_setattr(self, '__pydantic_private__', pydantic_private)
+
+
+def get_model_post_init(namespace: dict[str, Any], bases: tuple[type[Any], ...]) -> Callable[..., Any] | None:
+    """Get the `model_post_init` method from the namespace or the class bases, or `None` if not defined."""
+    if 'model_post_init' in namespace:
+        return namespace['model_post_init']
+
+    BaseModel = import_cached_base_model()
+
+    model_post_init = get_attribute_from_bases(bases, 'model_post_init')
+    if model_post_init is not BaseModel.model_post_init:
+        return model_post_init
+
+
+def inspect_namespace(  # noqa C901
+    namespace: dict[str, Any],
+    raw_annotations: dict[str, Any],
+    ignored_types: tuple[type[Any], ...],
+    base_class_vars: set[str],
+    base_class_fields: set[str],
+) -> dict[str, ModelPrivateAttr]:
+    """Iterate over the namespace and:
+    * gather private attributes
+    * check for items which look like fields but are not (e.g. have no annotation) and warn.
+
+    Args:
+        namespace: The attribute dictionary of the class to be created.
+        raw_annotations: The (non-evaluated) annotations of the model.
+        ignored_types: A tuple of ignore types.
+        base_class_vars: A set of base class class variables.
+        base_class_fields: A set of base class fields.
+
+    Returns:
+        A dict containing private attributes info.
+
+    Raises:
+        TypeError: If there is a `__root__` field in model.
+        NameError: If private attribute name is invalid.
+        PydanticUserError:
+            - If a field does not have a type annotation.
+            - If a field on base class was overridden by a non-annotated attribute.
+    """
+    from ..fields import ModelPrivateAttr, PrivateAttr
+
+    FieldInfo = import_cached_field_info()
+
+    all_ignored_types = ignored_types + default_ignored_types()
+
+    private_attributes: dict[str, ModelPrivateAttr] = {}
+
+    if '__root__' in raw_annotations or '__root__' in namespace:
+        raise TypeError("To define root models, use `pydantic.RootModel` rather than a field called '__root__'")
+
+    ignored_names: set[str] = set()
+    for var_name, value in list(namespace.items()):
+        if var_name == 'model_config' or var_name == '__pydantic_extra__':
+            continue
+        elif (
+            isinstance(value, type)
+            and value.__module__ == namespace['__module__']
+            and '__qualname__' in namespace
+            and value.__qualname__.startswith(f'{namespace["__qualname__"]}.')
+        ):
+            # `value` is a nested type defined in this namespace; don't error
+            continue
+        elif isinstance(value, all_ignored_types) or value.__class__.__module__ == 'functools':
+            ignored_names.add(var_name)
+            continue
+        elif isinstance(value, ModelPrivateAttr):
+            if var_name.startswith('__'):
+                raise NameError(
+                    'Private attributes must not use dunder names;'
+                    f' use a single underscore prefix instead of {var_name!r}.'
+                )
+            elif is_valid_field_name(var_name):
+                raise NameError(
+                    'Private attributes must not use valid field names;'
+                    f' use sunder names, e.g. {"_" + var_name!r} instead of {var_name!r}.'
+                )
+            private_attributes[var_name] = value
+            del namespace[var_name]
+        elif isinstance(value, FieldInfo) and not is_valid_field_name(var_name):
+            suggested_name = var_name.lstrip('_') or 'my_field'  # don't suggest '' for all-underscore name
+            raise NameError(
+                f'Fields must not use names with leading underscores;'
+                f' e.g., use {suggested_name!r} instead of {var_name!r}.'
+            )
+
+        elif var_name.startswith('__'):
+            continue
+        elif is_valid_privateattr_name(var_name):
+            if var_name not in raw_annotations or not is_classvar_annotation(raw_annotations[var_name]):
+                private_attributes[var_name] = cast(ModelPrivateAttr, PrivateAttr(default=value))
+                del namespace[var_name]
+        elif var_name in base_class_vars:
+            continue
+        elif var_name not in raw_annotations:
+            if var_name in base_class_fields:
+                raise PydanticUserError(
+                    f'Field {var_name!r} defined on a base class was overridden by a non-annotated attribute. '
+                    f'All field definitions, including overrides, require a type annotation.',
+                    code='model-field-overridden',
+                )
+            elif isinstance(value, FieldInfo):
+                raise PydanticUserError(
+                    f'Field {var_name!r} requires a type annotation', code='model-field-missing-annotation'
+                )
+            else:
+                raise PydanticUserError(
+                    f'A non-annotated attribute was detected: `{var_name} = {value!r}`. All model fields require a '
+                    f'type annotation; if `{var_name}` is not meant to be a field, you may be able to resolve this '
+                    f"error by annotating it as a `ClassVar` or updating `model_config['ignored_types']`.",
+                    code='model-field-missing-annotation',
+                )
+
+    for ann_name, ann_type in raw_annotations.items():
+        if (
+            is_valid_privateattr_name(ann_name)
+            and ann_name not in private_attributes
+            and ann_name not in ignored_names
+            # This condition can be a false negative when `ann_type` is stringified,
+            # but it is handled in most cases in `set_model_fields`:
+            and not is_classvar_annotation(ann_type)
+            and ann_type not in all_ignored_types
+            and getattr(ann_type, '__module__', None) != 'functools'
+        ):
+            if isinstance(ann_type, str):
+                # Walking up the frames to get the module namespace where the model is defined
+                # (as the model class wasn't created yet, we unfortunately can't use `cls.__module__`):
+                frame = sys._getframe(2)
+                if frame is not None:
+                    try:
+                        ann_type = eval_type_backport(
+                            _make_forward_ref(ann_type, is_argument=False, is_class=True),
+                            globalns=frame.f_globals,
+                            localns=frame.f_locals,
+                        )
+                    except (NameError, TypeError):
+                        pass
+
+            if typing_objects.is_annotated(get_origin(ann_type)):
+                _, *metadata = get_args(ann_type)
+                private_attr = next((v for v in metadata if isinstance(v, ModelPrivateAttr)), None)
+                if private_attr is not None:
+                    private_attributes[ann_name] = private_attr
+                    continue
+            private_attributes[ann_name] = PrivateAttr()
+
+    return private_attributes
+
+
+def set_default_hash_func(cls: type[BaseModel], bases: tuple[type[Any], ...]) -> None:
+    base_hash_func = get_attribute_from_bases(bases, '__hash__')
+    new_hash_func = make_hash_func(cls)
+    if base_hash_func in {None, object.__hash__} or getattr(base_hash_func, '__code__', None) == new_hash_func.__code__:
+        # If `__hash__` is some default, we generate a hash function.
+        # It will be `None` if not overridden from BaseModel.
+        # It may be `object.__hash__` if there is another
+        # parent class earlier in the bases which doesn't override `__hash__` (e.g. `typing.Generic`).
+        # It may be a value set by `set_default_hash_func` if `cls` is a subclass of another frozen model.
+        # In the last case we still need a new hash function to account for new `model_fields`.
+        cls.__hash__ = new_hash_func
+
+
+def make_hash_func(cls: type[BaseModel]) -> Any:
+    getter = operator.itemgetter(*cls.__pydantic_fields__.keys()) if cls.__pydantic_fields__ else lambda _: 0
+
+    def hash_func(self: Any) -> int:
+        try:
+            return hash(getter(self.__dict__))
+        except KeyError:
+            # In rare cases (such as when using the deprecated copy method), the __dict__ may not contain
+            # all model fields, which is how we can get here.
+            # getter(self.__dict__) is much faster than any 'safe' method that accounts for missing keys,
+            # and wrapping it in a `try` doesn't slow things down much in the common case.
+            return hash(getter(SafeGetItemProxy(self.__dict__)))
+
+    return hash_func
+
+
+def set_model_fields(
+    cls: type[BaseModel],
+    config_wrapper: ConfigWrapper,
+    ns_resolver: NsResolver,
+) -> None:
+    """Collect and set `cls.__pydantic_fields__` and `cls.__class_vars__`.
+
+    Args:
+        cls: BaseModel or dataclass.
+        config_wrapper: The config wrapper instance.
+        ns_resolver: Namespace resolver to use when getting model annotations.
+    """
+    typevars_map = get_model_typevars_map(cls)
+    fields, pydantic_extra_info, class_vars = collect_model_fields(
+        cls, config_wrapper, ns_resolver, typevars_map=typevars_map
+    )
+
+    cls.__pydantic_fields__ = fields
+    cls.__pydantic_extra_info__ = pydantic_extra_info
+    cls.__class_vars__.update(class_vars)
+
+    for k in class_vars:
+        # Class vars should not be private attributes
+        #     We remove them _here_ and not earlier because we rely on inspecting the class to determine its classvars,
+        #     but private attributes are determined by inspecting the namespace _prior_ to class creation.
+        #     In the case that a classvar with a leading-'_' is defined via a ForwardRef (e.g., when using
+        #     `__future__.annotations`), we want to remove the private attribute which was detected _before_ we knew it
+        #     evaluated to a classvar
+
+        value = cls.__private_attributes__.pop(k, None)
+        if value is not None and value.default is not PydanticUndefined:
+            setattr(cls, k, value.default)
+
+
+def complete_model_class(
+    cls: type[BaseModel],
+    config_wrapper: ConfigWrapper,
+    ns_resolver: NsResolver,
+    *,
+    raise_errors: bool = True,
+    call_on_complete_hook: bool = True,
+    create_model_module: str | None = None,
+    is_force_rebuild: bool = False,
+) -> bool:
+    """Finish building a model class.
+
+    This logic must be called after class has been created since validation functions must be bound
+    and `get_type_hints` requires a class object.
+
+    Args:
+        cls: BaseModel or dataclass.
+        config_wrapper: The config wrapper instance.
+        ns_resolver: The namespace resolver instance to use during schema building.
+        raise_errors: Whether to raise errors.
+        call_on_complete_hook: Whether to call the `__pydantic_on_complete__` hook.
+        create_model_module: The module of the class to be created, if created by `create_model`.
+        is_force_rebuild: Whether the model is being force-rebuilt (if True, pre-built serializers and
+                          validators are not used, to avoid stale references).
+
+    Returns:
+        `True` if the model is successfully completed, else `False`.
+
+    Raises:
+        PydanticUndefinedAnnotation: If PydanticUndefinedAnnotation occurs in __get_pydantic_core_schema__
+            and `raise_errors=True`.
+    """
+    typevars_map = get_model_typevars_map(cls)
+
+    if not cls.__pydantic_fields_complete__:
+        # Note: when coming from `ModelMetaclass.__new__()`, this results in fields being built twice.
+        # We do so a second time here so that we can get the ``NameError`` for the specific undefined annotation.
+        # Alternatively, we could let `GenerateSchema()` raise the error, but there are cases where incomplete
+        # fields are inherited in `collect_model_fields()` and can actually have their annotation resolved in the
+        # generate schema process. As we want to avoid having `__pydantic_fields_complete__` set to `False`
+        # when `__pydantic_complete__` is `True`, we rebuild here:
+        try:
+            cls.__pydantic_fields__, cls.__pydantic_extra_info__ = rebuild_model_fields(
+                cls,
+                config_wrapper=config_wrapper,
+                ns_resolver=ns_resolver,
+                typevars_map=typevars_map,
+            )
+        except NameError as e:
+            exc = PydanticUndefinedAnnotation.from_name_error(e)
+            set_model_mocks(cls, f'`{exc.name}`')
+            if raise_errors:
+                raise exc from e
+
+        if not raise_errors and not cls.__pydantic_fields_complete__:
+            # No need to continue with schema gen, it is guaranteed to fail
+            return False
+
+        assert cls.__pydantic_fields_complete__
+
+    gen_schema = GenerateSchema(
+        config_wrapper,
+        ns_resolver,
+        typevars_map,
+    )
+
+    try:
+        schema = gen_schema.generate_schema(cls)
+    except PydanticUndefinedAnnotation as e:
+        if raise_errors:
+            raise
+        set_model_mocks(cls, f'`{e.name}`')
+        return False
+
+    core_config = config_wrapper.core_config(title=cls.__name__)
+
+    try:
+        schema = gen_schema.clean_schema(schema)
+    except InvalidSchemaError:
+        set_model_mocks(cls)
+        return False
+
+    # This needs to happen *after* model schema generation, as the return types
+    # of the properties are evaluated and the `ComputedFieldInfo` are recreated:
+    cls.__pydantic_computed_fields__ = {k: v.info for k, v in cls.__pydantic_decorators__.computed_fields.items()}
+
+    set_deprecated_descriptors(cls)
+
+    cls.__pydantic_core_schema__ = schema
+
+    cls.__pydantic_validator__ = create_schema_validator(
+        schema,
+        cls,
+        create_model_module or cls.__module__,
+        cls.__qualname__,
+        'create_model' if create_model_module else 'BaseModel',
+        core_config,
+        config_wrapper.plugin_settings,
+        _use_prebuilt=not is_force_rebuild,
+    )
+    cls.__pydantic_serializer__ = SchemaSerializer(schema, core_config, _use_prebuilt=not is_force_rebuild)
+
+    # set __signature__ attr only for model class, but not for its instances
+    # (because instances can define `__call__`, and `inspect.signature` shouldn't
+    # use the `__signature__` attribute and instead generate from `__call__`).
+    cls.__signature__ = LazyClassAttribute(
+        '__signature__',
+        partial(
+            generate_pydantic_signature,
+            init=cls.__init__,
+            fields=cls.__pydantic_fields__,
+            validate_by_name=config_wrapper.validate_by_name,
+            extra=config_wrapper.extra,
+        ),
+    )
+
+    cls.__pydantic_complete__ = True
+
+    if call_on_complete_hook:
+        cls.__pydantic_on_complete__()
+
+    return True
+
+
+def set_deprecated_descriptors(cls: type[BaseModel]) -> None:
+    """Set data descriptors on the class for deprecated fields."""
+    for field, field_info in cls.__pydantic_fields__.items():
+        if (msg := field_info.deprecation_message) is not None:
+            desc = _DeprecatedFieldDescriptor(msg)
+            desc.__set_name__(cls, field)
+            setattr(cls, field, desc)
+
+    for field, computed_field_info in cls.__pydantic_computed_fields__.items():
+        if (
+            (msg := computed_field_info.deprecation_message) is not None
+            # Avoid having two warnings emitted:
+            and not hasattr(unwrap_wrapped_function(computed_field_info.wrapped_property), '__deprecated__')
+        ):
+            desc = _DeprecatedFieldDescriptor(msg, computed_field_info.wrapped_property)
+            desc.__set_name__(cls, field)
+            setattr(cls, field, desc)
+
+
+class _DeprecatedFieldDescriptor:
+    """Read-only data descriptor used to emit a runtime deprecation warning before accessing a deprecated field.
+
+    Attributes:
+        msg: The deprecation message to be emitted.
+        wrapped_property: The property instance if the deprecated field is a computed field, or `None`.
+        field_name: The name of the field being deprecated.
+    """
+
+    field_name: str
+
+    def __init__(self, msg: str, wrapped_property: property | None = None) -> None:
+        self.msg = msg
+        self.wrapped_property = wrapped_property
+
+    def __set_name__(self, cls: type[BaseModel], name: str) -> None:
+        self.field_name = name
+
+    def __get__(self, obj: BaseModel | None, obj_type: type[BaseModel] | None = None) -> Any:
+        if obj is None:
+            if self.wrapped_property is not None:
+                return self.wrapped_property.__get__(None, obj_type)
+            raise AttributeError(self.field_name)
+
+        warnings.warn(self.msg, DeprecationWarning, stacklevel=2)
+
+        if self.wrapped_property is not None:
+            return self.wrapped_property.__get__(obj, obj_type)
+        return obj.__dict__[self.field_name]
+
+    # Defined to make it a data descriptor and take precedence over the instance's dictionary.
+    # Note that it will not be called when setting a value on a model instance
+    # as `BaseModel.__setattr__` is defined and takes priority.
+    def __set__(self, obj: Any, value: Any) -> NoReturn:
+        raise AttributeError(self.field_name)
+
+
+class _PydanticWeakRef:
+    """Wrapper for `weakref.ref` that enables `pickle` serialization.
+
+    Cloudpickle fails to serialize weakref.ref objects due to an arcane error related to
+    to abstract base classes (`abc.ABC`). This class works around the issue by wrapping
+    `weakref.ref` instead of subclassing it.
+
+    See https://github.com/pydantic/pydantic/issues/6763 for context.
+
+    Semantics:
+        - If not pickled, behaves the same as a `weakref.ref`.
+        - If pickled along with the referenced object, the same `weakref.ref` behavior
+          will be maintained between them after unpickling.
+        - If pickled without the referenced object, after unpickling the underlying
+          reference will be cleared (`__call__` will always return `None`).
+    """
+
+    def __init__(self, obj: Any):
+        if obj is None:
+            # The object will be `None` upon deserialization if the serialized weakref
+            # had lost its underlying object.
+            self._wr = None
+        else:
+            self._wr = weakref.ref(obj)
+
+    def __call__(self) -> Any:
+        if self._wr is None:
+            return None
+        else:
+            return self._wr()
+
+    def __reduce__(self) -> tuple[Callable, tuple[weakref.ReferenceType | None]]:
+        return _PydanticWeakRef, (self(),)
+
+
+def build_lenient_weakvaluedict(d: dict[str, Any] | None) -> dict[str, Any] | None:
+    """Takes an input dictionary, and produces a new value that (invertibly) replaces the values with weakrefs.
+
+    We can't just use a WeakValueDictionary because many types (including int, str, etc.) can't be stored as values
+    in a WeakValueDictionary.
+
+    The `unpack_lenient_weakvaluedict` function can be used to reverse this operation.
+    """
+    if d is None:
+        return None
+    result = {}
+    for k, v in d.items():
+        try:
+            proxy = _PydanticWeakRef(v)
+        except TypeError:
+            proxy = v
+        result[k] = proxy
+    return result
+
+
+def unpack_lenient_weakvaluedict(d: dict[str, Any] | None) -> dict[str, Any] | None:
+    """Inverts the transform performed by `build_lenient_weakvaluedict`."""
+    if d is None:
+        return None
+
+    result = {}
+    for k, v in d.items():
+        if isinstance(v, _PydanticWeakRef):
+            v = v()
+            if v is not None:
+                result[k] = v
+        else:
+            result[k] = v
+    return result
+
+
+@cache
+def default_ignored_types() -> tuple[type[Any], ...]:
+    from ..fields import ComputedFieldInfo
+
+    ignored_types = [
+        FunctionType,
+        property,
+        classmethod,
+        staticmethod,
+        PydanticDescriptorProxy,
+        ComputedFieldInfo,
+        TypeAliasType,  # from `typing_extensions`
+    ]
+
+    if sys.version_info >= (3, 12):
+        ignored_types.append(typing.TypeAliasType)
+
+    return tuple(ignored_types)

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_namespace_utils.py

@@ -0,0 +1,293 @@
+from __future__ import annotations
+
+import sys
+from collections.abc import Generator, Iterator, Mapping
+from contextlib import contextmanager
+from functools import cached_property
+from typing import Any, Callable, NamedTuple, TypeVar
+
+from typing_extensions import ParamSpec, TypeAlias, TypeAliasType, TypeVarTuple
+
+GlobalsNamespace: TypeAlias = 'dict[str, Any]'
+"""A global namespace.
+
+In most cases, this is a reference to the `__dict__` attribute of a module.
+This namespace type is expected as the `globals` argument during annotations evaluation.
+"""
+
+MappingNamespace: TypeAlias = Mapping[str, Any]
+"""Any kind of namespace.
+
+In most cases, this is a local namespace (e.g. the `__dict__` attribute of a class,
+the [`f_locals`][frame.f_locals] attribute of a frame object, when dealing with types
+defined inside functions).
+This namespace type is expected as the `locals` argument during annotations evaluation.
+"""
+
+_TypeVarLike: TypeAlias = 'TypeVar | ParamSpec | TypeVarTuple'
+
+
+class NamespacesTuple(NamedTuple):
+    """A tuple of globals and locals to be used during annotations evaluation.
+
+    This datastructure is defined as a named tuple so that it can easily be unpacked:
+
+    ```python {lint="skip" test="skip"}
+    def eval_type(typ: type[Any], ns: NamespacesTuple) -> None:
+        return eval(typ, *ns)
+    ```
+    """
+
+    globals: GlobalsNamespace
+    """The namespace to be used as the `globals` argument during annotations evaluation."""
+
+    locals: MappingNamespace
+    """The namespace to be used as the `locals` argument during annotations evaluation."""
+
+
+def get_module_ns_of(obj: Any) -> dict[str, Any]:
+    """Get the namespace of the module where the object is defined.
+
+    Caution: this function does not return a copy of the module namespace, so the result
+    should not be mutated. The burden of enforcing this is on the caller.
+    """
+    module_name = getattr(obj, '__module__', None)
+    if module_name:
+        try:
+            return sys.modules[module_name].__dict__
+        except KeyError:
+            # happens occasionally, see https://github.com/pydantic/pydantic/issues/2363
+            return {}
+    return {}
+
+
+# Note that this class is almost identical to `collections.ChainMap`, but need to enforce
+# immutable mappings here:
+class LazyLocalNamespace(Mapping[str, Any]):
+    """A lazily evaluated mapping, to be used as the `locals` argument during annotations evaluation.
+
+    While the [`eval`][eval] function expects a mapping as the `locals` argument, it only
+    performs `__getitem__` calls. The [`Mapping`][collections.abc.Mapping] abstract base class
+    is fully implemented only for type checking purposes.
+
+    Args:
+        *namespaces: The namespaces to consider, in ascending order of priority.
+
+    Example:
+        ```python {lint="skip" test="skip"}
+        ns = LazyLocalNamespace({'a': 1, 'b': 2}, {'a': 3})
+        ns['a']
+        #> 3
+        ns['b']
+        #> 2
+        ```
+    """
+
+    def __init__(self, *namespaces: MappingNamespace) -> None:
+        self._namespaces = namespaces
+
+    @cached_property
+    def data(self) -> dict[str, Any]:
+        return {k: v for ns in self._namespaces for k, v in ns.items()}
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def __getitem__(self, key: str) -> Any:
+        return self.data[key]
+
+    def __contains__(self, key: object) -> bool:
+        return key in self.data
+
+    def __iter__(self) -> Iterator[str]:
+        return iter(self.data)
+
+
+def ns_for_function(obj: Callable[..., Any], parent_namespace: MappingNamespace | None = None) -> NamespacesTuple:
+    """Return the global and local namespaces to be used when evaluating annotations for the provided function.
+
+    The global namespace will be the `__dict__` attribute of the module the function was defined in.
+    The local namespace will contain the `__type_params__` introduced by PEP 695.
+
+    Args:
+        obj: The object to use when building namespaces.
+        parent_namespace: Optional namespace to be added with the lowest priority in the local namespace.
+            If the passed function is a method, the `parent_namespace` will be the namespace of the class
+            the method is defined in. Thus, we also fetch type `__type_params__` from there (i.e. the
+            class-scoped type variables).
+    """
+    locals_list: list[MappingNamespace] = []
+    if parent_namespace is not None:
+        locals_list.append(parent_namespace)
+
+    # Get the `__type_params__` attribute introduced by PEP 695.
+    # Note that the `typing._eval_type` function expects type params to be
+    # passed as a separate argument. However, internally, `_eval_type` calls
+    # `ForwardRef._evaluate` which will merge type params with the localns,
+    # essentially mimicking what we do here.
+    type_params: tuple[_TypeVarLike, ...] = getattr(obj, '__type_params__', ())
+    if parent_namespace is not None:
+        # We also fetch type params from the parent namespace. If present, it probably
+        # means the function was defined in a class. This is to support the following:
+        # https://github.com/python/cpython/issues/124089.
+        type_params += parent_namespace.get('__type_params__', ())
+
+    locals_list.append({t.__name__: t for t in type_params})
+
+    # What about short-circuiting to `obj.__globals__`?
+    globalns = get_module_ns_of(obj)
+
+    return NamespacesTuple(globalns, LazyLocalNamespace(*locals_list))
+
+
+class NsResolver:
+    """A class responsible for the namespaces resolving logic for annotations evaluation.
+
+    This class handles the namespace logic when evaluating annotations mainly for class objects.
+
+    It holds a stack of classes that are being inspected during the core schema building,
+    and the `types_namespace` property exposes the globals and locals to be used for
+    type annotation evaluation. Additionally -- if no class is present in the stack -- a
+    fallback globals and locals can be provided using the `namespaces_tuple` argument
+    (this is useful when generating a schema for a simple annotation, e.g. when using
+    `TypeAdapter`).
+
+    The namespace creation logic is unfortunately flawed in some cases, for backwards
+    compatibility reasons and to better support valid edge cases. See the description
+    for the `parent_namespace` argument and the example for more details.
+
+    Args:
+        namespaces_tuple: The default globals and locals to use if no class is present
+            on the stack. This can be useful when using the `GenerateSchema` class
+            with `TypeAdapter`, where the "type" being analyzed is a simple annotation.
+        parent_namespace: An optional parent namespace that will be added to the locals
+            with the lowest priority. For a given class defined in a function, the locals
+            of this function are usually used as the parent namespace:
+
+            ```python {lint="skip" test="skip"}
+            from pydantic import BaseModel
+
+            def func() -> None:
+                SomeType = int
+
+                class Model(BaseModel):
+                    f: 'SomeType'
+
+                # when collecting fields, an namespace resolver instance will be created
+                # this way:
+                # ns_resolver = NsResolver(parent_namespace={'SomeType': SomeType})
+            ```
+
+            For backwards compatibility reasons and to support valid edge cases, this parent
+            namespace will be used for *every* type being pushed to the stack. In the future,
+            we might want to be smarter by only doing so when the type being pushed is defined
+            in the same module as the parent namespace.
+
+    Example:
+        ```python {lint="skip" test="skip"}
+        ns_resolver = NsResolver(
+            parent_namespace={'fallback': 1},
+        )
+
+        class Sub:
+            m: 'Model'
+
+        class Model:
+            some_local = 1
+            sub: Sub
+
+        ns_resolver = NsResolver()
+
+        # This is roughly what happens when we build a core schema for `Model`:
+        with ns_resolver.push(Model):
+            ns_resolver.types_namespace
+            #> NamespacesTuple({'Sub': Sub}, {'Model': Model, 'some_local': 1})
+            # First thing to notice here, the model being pushed is added to the locals.
+            # Because `NsResolver` is being used during the model definition, it is not
+            # yet added to the globals. This is useful when resolving self-referencing annotations.
+
+            with ns_resolver.push(Sub):
+                ns_resolver.types_namespace
+                #> NamespacesTuple({'Sub': Sub}, {'Sub': Sub, 'Model': Model})
+                # Second thing to notice: `Sub` is present in both the globals and locals.
+                # This is not an issue, just that as described above, the model being pushed
+                # is added to the locals, but it happens to be present in the globals as well
+                # because it is already defined.
+                # Third thing to notice: `Model` is also added in locals. This is a backwards
+                # compatibility workaround that allows for `Sub` to be able to resolve `'Model'`
+                # correctly (as otherwise models would have to be rebuilt even though this
+                # doesn't look necessary).
+        ```
+    """
+
+    def __init__(
+        self,
+        namespaces_tuple: NamespacesTuple | None = None,
+        parent_namespace: MappingNamespace | None = None,
+    ) -> None:
+        self._base_ns_tuple = namespaces_tuple or NamespacesTuple({}, {})
+        self._parent_ns = parent_namespace
+        self._types_stack: list[type[Any] | TypeAliasType] = []
+
+    @cached_property
+    def types_namespace(self) -> NamespacesTuple:
+        """The current global and local namespaces to be used for annotations evaluation."""
+        if not self._types_stack:
+            # TODO: should we merge the parent namespace here?
+            # This is relevant for TypeAdapter, where there are no types on the stack, and we might
+            # need access to the parent_ns. Right now, we sidestep this in `type_adapter.py` by passing
+            # locals to both parent_ns and the base_ns_tuple, but this is a bit hacky.
+            # we might consider something like:
+            # if self._parent_ns is not None:
+            #     # Hacky workarounds, see class docstring:
+            #     # An optional parent namespace that will be added to the locals with the lowest priority
+            #     locals_list: list[MappingNamespace] = [self._parent_ns, self._base_ns_tuple.locals]
+            #     return NamespacesTuple(self._base_ns_tuple.globals, LazyLocalNamespace(*locals_list))
+            return self._base_ns_tuple
+
+        typ = self._types_stack[-1]
+
+        globalns = get_module_ns_of(typ)
+
+        locals_list: list[MappingNamespace] = []
+        # Hacky workarounds, see class docstring:
+        # An optional parent namespace that will be added to the locals with the lowest priority
+        if self._parent_ns is not None:
+            locals_list.append(self._parent_ns)
+        if len(self._types_stack) > 1:
+            first_type = self._types_stack[0]
+            locals_list.append({first_type.__name__: first_type})
+
+        # Adding `__type_params__` *before* `vars(typ)`, as the latter takes priority
+        # (see https://github.com/python/cpython/pull/120272).
+        # TODO `typ.__type_params__` when we drop support for Python 3.11:
+        type_params: tuple[_TypeVarLike, ...] = getattr(typ, '__type_params__', ())
+        if type_params:
+            # Adding `__type_params__` is mostly useful for generic classes defined using
+            # PEP 695 syntax *and* using forward annotations (see the example in
+            # https://github.com/python/cpython/issues/114053). For TypeAliasType instances,
+            # it is way less common, but still required if using a string annotation in the alias
+            # value, e.g. `type A[T] = 'T'` (which is not necessary in most cases).
+            locals_list.append({t.__name__: t for t in type_params})
+
+        # TypeAliasType instances don't have a `__dict__` attribute, so the check
+        # is necessary:
+        if hasattr(typ, '__dict__'):
+            locals_list.append(vars(typ))
+
+        # The `len(self._types_stack) > 1` check above prevents this from being added twice:
+        locals_list.append({typ.__name__: typ})
+
+        return NamespacesTuple(globalns, LazyLocalNamespace(*locals_list))
+
+    @contextmanager
+    def push(self, typ: type[Any] | TypeAliasType, /) -> Generator[None]:
+        """Push a type to the stack."""
+        self._types_stack.append(typ)
+        # Reset the cached property:
+        self.__dict__.pop('types_namespace', None)
+        try:
+            yield
+        finally:
+            self._types_stack.pop()
+            self.__dict__.pop('types_namespace', None)

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_repr.py

@@ -0,0 +1,124 @@
+"""Tools to provide pretty/human-readable display of objects."""
+
+from __future__ import annotations as _annotations
+
+import types
+from collections.abc import Callable, Collection, Generator, Iterable
+from typing import TYPE_CHECKING, Any, ForwardRef, cast
+
+import typing_extensions
+from typing_extensions import TypeAlias
+from typing_inspection import typing_objects
+from typing_inspection.introspection import is_union_origin
+
+from . import _typing_extra
+
+if TYPE_CHECKING:
+    # TODO remove type error comments when we drop support for Python 3.9
+    ReprArgs: TypeAlias = Iterable[tuple[str | None, Any]]  # pyright: ignore[reportGeneralTypeIssues]
+    RichReprResult: TypeAlias = Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]  # pyright: ignore[reportGeneralTypeIssues]
+
+
+class PlainRepr(str):
+    """String class where repr doesn't include quotes. Useful with Representation when you want to return a string
+    representation of something that is valid (or pseudo-valid) python.
+    """
+
+    def __repr__(self) -> str:
+        return str(self)
+
+
+class Representation:
+    # Mixin to provide `__str__`, `__repr__`, and `__pretty__` and `__rich_repr__` methods.
+    # `__pretty__` is used by [devtools](https://python-devtools.helpmanual.io/).
+    # `__rich_repr__` is used by [rich](https://rich.readthedocs.io/en/stable/pretty.html).
+    # (this is not a docstring to avoid adding a docstring to classes which inherit from Representation)
+
+    __slots__ = ()
+
+    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_names = cast(Collection[str], self.__slots__)
+        if not attrs_names and hasattr(self, '__dict__'):
+            attrs_names = self.__dict__.keys()
+        attrs = ((s, getattr(self, s)) for s in attrs_names)
+        return [(a, v if v is not self else self.__repr_recursion__(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_recursion__(self, object: Any) -> str:
+        """Returns the string representation of a recursive object."""
+        # This is copied over from the stdlib `pprint` module:
+        return f'<Recursion on {type(object).__name__} with id={id(object)}>'
+
+    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]:
+        """Used by devtools (https://python-devtools.helpmanual.io/) to pretty print 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 __rich_repr__(self) -> RichReprResult:
+        """Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects."""
+        for name, field_repr in self.__repr_args__():
+            if name is None:
+                yield field_repr
+            else:
+                yield name, field_repr
+
+    def __str__(self) -> str:
+        return self.__repr_str__(' ')
+
+    def __repr__(self) -> str:
+        return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
+
+
+def display_as_type(obj: Any) -> str:
+    """Pretty representation of a type, should be as close as possible to the original type definition string.
+
+    Takes some logic from `typing._type_repr`.
+    """
+    if isinstance(obj, (types.FunctionType, types.BuiltinFunctionType)):
+        return obj.__name__
+    elif obj is ...:
+        return '...'
+    elif isinstance(obj, Representation):
+        return repr(obj)
+    elif isinstance(obj, ForwardRef) or typing_objects.is_typealiastype(obj):
+        return str(obj)
+
+    if not isinstance(obj, (_typing_extra.typing_base, _typing_extra.WithArgsTypes, type)):
+        obj = obj.__class__
+
+    if is_union_origin(typing_extensions.get_origin(obj)):
+        args = ', '.join(map(display_as_type, typing_extensions.get_args(obj)))
+        return f'Union[{args}]'
+    elif isinstance(obj, _typing_extra.WithArgsTypes):
+        if typing_objects.is_literal(typing_extensions.get_origin(obj)):
+            args = ', '.join(map(repr, typing_extensions.get_args(obj)))
+        else:
+            args = ', '.join(map(display_as_type, typing_extensions.get_args(obj)))
+        try:
+            return f'{obj.__qualname__}[{args}]'
+        except AttributeError:
+            return str(obj).replace('typing.', '').replace('typing_extensions.', '')  # handles TypeAliasType in 3.12
+    elif isinstance(obj, type):
+        return obj.__qualname__
+    else:
+        return repr(obj).replace('typing.', '').replace('typing_extensions.', '')

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_schema_gather.py

@@ -0,0 +1,212 @@
+# pyright: reportTypedDictNotRequiredAccess=false, reportGeneralTypeIssues=false, reportArgumentType=false, reportAttributeAccessIssue=false
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import TypedDict
+
+from pydantic_core.core_schema import (
+    ComputedField,
+    CoreSchema,
+    DefinitionReferenceSchema,
+    SerSchema,
+    iter_union_choices,
+)
+from typing_extensions import TypeAlias
+
+AllSchemas: TypeAlias = 'CoreSchema | SerSchema | ComputedField'
+
+
+class GatherResult(TypedDict):
+    """Schema traversing result."""
+
+    collected_references: dict[str, DefinitionReferenceSchema | None]
+    """The collected definition references.
+
+    If a definition reference schema can be inlined, it means that there is
+    only one in the whole core schema. As such, it is stored as the value.
+    Otherwise, the value is set to `None`.
+    """
+
+    deferred_discriminator_schemas: list[CoreSchema]
+    """The list of core schemas having the discriminator application deferred."""
+
+
+class MissingDefinitionError(LookupError):
+    """A reference was pointing to a non-existing core schema."""
+
+    def __init__(self, schema_reference: str, /) -> None:
+        self.schema_reference = schema_reference
+
+
+@dataclass
+class GatherContext:
+    """The current context used during core schema traversing.
+
+    Context instances should only be used during schema traversing.
+    """
+
+    definitions: dict[str, CoreSchema]
+    """The available definitions."""
+
+    deferred_discriminator_schemas: list[CoreSchema] = field(init=False, default_factory=list)
+    """The list of core schemas having the discriminator application deferred.
+
+    Internally, these core schemas have a specific key set in the core metadata dict.
+    """
+
+    collected_references: dict[str, DefinitionReferenceSchema | None] = field(init=False, default_factory=dict)
+    """The collected definition references.
+
+    If a definition reference schema can be inlined, it means that there is
+    only one in the whole core schema. As such, it is stored as the value.
+    Otherwise, the value is set to `None`.
+
+    During schema traversing, definition reference schemas can be added as candidates, or removed
+    (by setting the value to `None`).
+    """
+
+
+def traverse_metadata(schema: AllSchemas, ctx: GatherContext) -> None:
+    meta = schema.get('metadata')
+    if meta is not None and 'pydantic_internal_union_discriminator' in meta:
+        ctx.deferred_discriminator_schemas.append(schema)  # pyright: ignore[reportArgumentType]
+
+
+def traverse_definition_ref(def_ref_schema: DefinitionReferenceSchema, ctx: GatherContext) -> None:
+    schema_ref = def_ref_schema['schema_ref']
+
+    if schema_ref not in ctx.collected_references:
+        definition = ctx.definitions.get(schema_ref)
+        if definition is None:
+            raise MissingDefinitionError(schema_ref)
+
+        # The `'definition-ref'` schema was only encountered once, make it
+        # a candidate to be inlined:
+        ctx.collected_references[schema_ref] = def_ref_schema
+        traverse_schema(definition, ctx)
+        if 'serialization' in def_ref_schema:
+            traverse_schema(def_ref_schema['serialization'], ctx)
+        traverse_metadata(def_ref_schema, ctx)
+    else:
+        # The `'definition-ref'` schema was already encountered, meaning
+        # the previously encountered schema (and this one) can't be inlined:
+        ctx.collected_references[schema_ref] = None
+
+
+def traverse_schema(schema: AllSchemas, context: GatherContext) -> None:
+    # TODO When we drop 3.9, use a match statement to get better type checking and remove
+    # file-level type ignore.
+    # (the `'type'` could also be fetched in every `if/elif` statement, but this alters performance).
+    schema_type = schema['type']
+
+    if schema_type == 'definition-ref':
+        traverse_definition_ref(schema, context)
+        # `traverse_definition_ref` handles the possible serialization and metadata schemas:
+        return
+    elif schema_type == 'definitions':
+        traverse_schema(schema['schema'], context)
+        for definition in schema['definitions']:
+            traverse_schema(definition, context)
+    elif schema_type in {'list', 'set', 'frozenset', 'generator'}:
+        if 'items_schema' in schema:
+            traverse_schema(schema['items_schema'], context)
+    elif schema_type == 'tuple':
+        if 'items_schema' in schema:
+            for s in schema['items_schema']:
+                traverse_schema(s, context)
+    elif schema_type == 'dict':
+        if 'keys_schema' in schema:
+            traverse_schema(schema['keys_schema'], context)
+        if 'values_schema' in schema:
+            traverse_schema(schema['values_schema'], context)
+    elif schema_type == 'union':
+        for choice in iter_union_choices(schema):
+            traverse_schema(choice, context)
+    elif schema_type == 'tagged-union':
+        for v in schema['choices'].values():
+            traverse_schema(v, context)
+    elif schema_type == 'chain':
+        for step in schema['steps']:
+            traverse_schema(step, context)
+    elif schema_type == 'lax-or-strict':
+        traverse_schema(schema['lax_schema'], context)
+        traverse_schema(schema['strict_schema'], context)
+    elif schema_type == 'json-or-python':
+        traverse_schema(schema['json_schema'], context)
+        traverse_schema(schema['python_schema'], context)
+    elif schema_type in {'model-fields', 'typed-dict'}:
+        if 'extras_schema' in schema:
+            traverse_schema(schema['extras_schema'], context)
+        if 'computed_fields' in schema:
+            for s in schema['computed_fields']:
+                traverse_schema(s, context)
+        for s in schema['fields'].values():
+            traverse_schema(s, context)
+    elif schema_type == 'dataclass-args':
+        if 'computed_fields' in schema:
+            for s in schema['computed_fields']:
+                traverse_schema(s, context)
+        for s in schema['fields']:
+            traverse_schema(s, context)
+    elif schema_type == 'arguments':
+        for s in schema['arguments_schema']:
+            traverse_schema(s['schema'], context)
+        if 'var_args_schema' in schema:
+            traverse_schema(schema['var_args_schema'], context)
+        if 'var_kwargs_schema' in schema:
+            traverse_schema(schema['var_kwargs_schema'], context)
+    elif schema_type == 'arguments-v3':
+        for s in schema['arguments_schema']:
+            traverse_schema(s['schema'], context)
+    elif schema_type == 'call':
+        traverse_schema(schema['arguments_schema'], context)
+        if 'return_schema' in schema:
+            traverse_schema(schema['return_schema'], context)
+    elif schema_type == 'computed-field':
+        traverse_schema(schema['return_schema'], context)
+    elif schema_type == 'function-before':
+        if 'schema' in schema:
+            traverse_schema(schema['schema'], context)
+        if 'json_schema_input_schema' in schema:
+            traverse_schema(schema['json_schema_input_schema'], context)
+    elif schema_type == 'function-plain':
+        # TODO duplicate schema types for serializers and validators, needs to be deduplicated.
+        if 'return_schema' in schema:
+            traverse_schema(schema['return_schema'], context)
+        if 'json_schema_input_schema' in schema:
+            traverse_schema(schema['json_schema_input_schema'], context)
+    elif schema_type == 'function-wrap':
+        # TODO duplicate schema types for serializers and validators, needs to be deduplicated.
+        if 'return_schema' in schema:
+            traverse_schema(schema['return_schema'], context)
+        if 'schema' in schema:
+            traverse_schema(schema['schema'], context)
+        if 'json_schema_input_schema' in schema:
+            traverse_schema(schema['json_schema_input_schema'], context)
+    else:
+        if 'schema' in schema:
+            traverse_schema(schema['schema'], context)
+
+    if 'serialization' in schema:
+        traverse_schema(schema['serialization'], context)
+    traverse_metadata(schema, context)
+
+
+def gather_schemas_for_cleaning(schema: CoreSchema, definitions: dict[str, CoreSchema]) -> GatherResult:
+    """Traverse the core schema and definitions and return the necessary information for schema cleaning.
+
+    During the core schema traversing, any `'definition-ref'` schema is:
+
+    - Validated: the reference must point to an existing definition. If this is not the case, a
+      `MissingDefinitionError` exception is raised.
+    - Stored in the context: the actual reference is stored in the context. Depending on whether
+      the `'definition-ref'` schema is encountered more that once, the schema itself is also
+      saved in the context to be inlined (i.e. replaced by the definition it points to).
+    """
+    context = GatherContext(definitions)
+    traverse_schema(schema, context)
+
+    return {
+        'collected_references': context.collected_references,
+        'deferred_discriminator_schemas': context.deferred_discriminator_schemas,
+    }

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_schema_generation_shared.py

@@ -0,0 +1,125 @@
+"""Types and utility functions used by various other internal tools."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Callable, Literal
+
+from pydantic_core import core_schema
+
+from ..annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler
+
+if TYPE_CHECKING:
+    from ..json_schema import GenerateJsonSchema, JsonSchemaValue
+    from ._core_utils import CoreSchemaOrField
+    from ._generate_schema import GenerateSchema
+    from ._namespace_utils import NamespacesTuple
+
+    GetJsonSchemaFunction = Callable[[CoreSchemaOrField, GetJsonSchemaHandler], JsonSchemaValue]
+    HandlerOverride = Callable[[CoreSchemaOrField], JsonSchemaValue]
+
+
+class GenerateJsonSchemaHandler(GetJsonSchemaHandler):
+    """JsonSchemaHandler implementation that doesn't do ref unwrapping by default.
+
+    This is used for any Annotated metadata so that we don't end up with conflicting
+    modifications to the definition schema.
+
+    Used internally by Pydantic, please do not rely on this implementation.
+    See `GetJsonSchemaHandler` for the handler API.
+    """
+
+    def __init__(self, generate_json_schema: GenerateJsonSchema, handler_override: HandlerOverride | None) -> None:
+        self.generate_json_schema = generate_json_schema
+        self.handler = handler_override or generate_json_schema.generate_inner
+        self.mode = generate_json_schema.mode
+
+    def __call__(self, core_schema: CoreSchemaOrField, /) -> JsonSchemaValue:
+        return self.handler(core_schema)
+
+    def resolve_ref_schema(self, maybe_ref_json_schema: JsonSchemaValue) -> JsonSchemaValue:
+        """Resolves `$ref` in the json schema.
+
+        This returns the input json schema if there is no `$ref` in json schema.
+
+        Args:
+            maybe_ref_json_schema: The input json schema that may contains `$ref`.
+
+        Returns:
+            Resolved json schema.
+
+        Raises:
+            LookupError: If it can't find the definition for `$ref`.
+        """
+        if '$ref' not in maybe_ref_json_schema:
+            return maybe_ref_json_schema
+        ref = maybe_ref_json_schema['$ref']
+        json_schema = self.generate_json_schema.get_schema_from_definitions(ref)
+        if json_schema is None:
+            raise LookupError(
+                f'Could not find a ref for {ref}.'
+                ' Maybe you tried to call resolve_ref_schema from within a recursive model?'
+            )
+        return json_schema
+
+
+class CallbackGetCoreSchemaHandler(GetCoreSchemaHandler):
+    """Wrapper to use an arbitrary function as a `GetCoreSchemaHandler`.
+
+    Used internally by Pydantic, please do not rely on this implementation.
+    See `GetCoreSchemaHandler` for the handler API.
+    """
+
+    def __init__(
+        self,
+        handler: Callable[[Any], core_schema.CoreSchema],
+        generate_schema: GenerateSchema,
+        ref_mode: Literal['to-def', 'unpack'] = 'to-def',
+    ) -> None:
+        self._handler = handler
+        self._generate_schema = generate_schema
+        self._ref_mode = ref_mode
+
+    def __call__(self, source_type: Any, /) -> core_schema.CoreSchema:
+        schema = self._handler(source_type)
+        if self._ref_mode == 'to-def':
+            ref = schema.get('ref')
+            if ref is not None:
+                return self._generate_schema.defs.create_definition_reference_schema(schema)
+            return schema
+        else:  # ref_mode = 'unpack'
+            return self.resolve_ref_schema(schema)
+
+    def _get_types_namespace(self) -> NamespacesTuple:
+        return self._generate_schema._types_namespace
+
+    def generate_schema(self, source_type: Any, /) -> core_schema.CoreSchema:
+        return self._generate_schema.generate_schema(source_type)
+
+    @property
+    def field_name(self) -> str | None:
+        return self._generate_schema.field_name_stack.get()
+
+    def resolve_ref_schema(self, maybe_ref_schema: core_schema.CoreSchema) -> core_schema.CoreSchema:
+        """Resolves reference in the core schema.
+
+        Args:
+            maybe_ref_schema: The input core schema that may contains reference.
+
+        Returns:
+            Resolved core schema.
+
+        Raises:
+            LookupError: If it can't find the definition for reference.
+        """
+        if maybe_ref_schema['type'] == 'definition-ref':
+            ref = maybe_ref_schema['schema_ref']
+            definition = self._generate_schema.defs.get_schema_from_ref(ref)
+            if definition is None:
+                raise LookupError(
+                    f'Could not find a ref for {ref}.'
+                    ' Maybe you tried to call resolve_ref_schema from within a recursive model?'
+                )
+            return definition
+        elif maybe_ref_schema['type'] == 'definitions':
+            return self.resolve_ref_schema(maybe_ref_schema['schema'])
+        return maybe_ref_schema

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_serializers.py

@@ -0,0 +1,53 @@
+from __future__ import annotations
+
+import collections
+import collections.abc
+import typing
+from typing import Any
+
+from pydantic_core import PydanticOmit, core_schema
+
+SEQUENCE_ORIGIN_MAP: dict[Any, Any] = {
+    typing.Deque: collections.deque,  # noqa: UP006
+    collections.deque: collections.deque,
+    list: list,
+    typing.List: list,  # noqa: UP006
+    tuple: tuple,
+    typing.Tuple: tuple,  # noqa: UP006
+    set: set,
+    typing.AbstractSet: set,
+    typing.Set: set,  # noqa: UP006
+    frozenset: frozenset,
+    typing.FrozenSet: frozenset,  # noqa: UP006
+    typing.Sequence: list,
+    typing.MutableSequence: list,
+    typing.MutableSet: set,
+    # this doesn't handle subclasses of these
+    # parametrized typing.Set creates one of these
+    collections.abc.MutableSet: set,
+    collections.abc.Set: frozenset,
+}
+
+
+def serialize_sequence_via_list(
+    v: Any, handler: core_schema.SerializerFunctionWrapHandler, info: core_schema.SerializationInfo
+) -> Any:
+    items: list[Any] = []
+
+    mapped_origin = SEQUENCE_ORIGIN_MAP.get(type(v), None)
+    if mapped_origin is None:
+        # we shouldn't hit this branch, should probably add a serialization error or something
+        return v
+
+    for index, item in enumerate(v):
+        try:
+            v = handler(item, index)
+        except PydanticOmit:  # noqa: PERF203
+            pass
+        else:
+            items.append(v)
+
+    if info.mode_is_json():
+        return items
+    else:
+        return mapped_origin(items)

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_signature.py

@@ -0,0 +1,189 @@
+from __future__ import annotations
+
+import dataclasses
+from inspect import Parameter, Signature
+from typing import TYPE_CHECKING, Any, Callable
+
+from pydantic_core import PydanticUndefined
+
+from ._typing_extra import signature_no_eval
+from ._utils import is_valid_identifier
+
+if TYPE_CHECKING:
+    from ..config import ExtraValues
+    from ..fields import FieldInfo
+
+
+# Copied over from stdlib dataclasses
+class _HAS_DEFAULT_FACTORY_CLASS:
+    def __repr__(self):
+        return '<factory>'
+
+
+_HAS_DEFAULT_FACTORY = _HAS_DEFAULT_FACTORY_CLASS()
+
+
+def _field_name_for_signature(field_name: str, field_info: FieldInfo) -> str:
+    """Extract the correct name to use for the field when generating a signature.
+
+    Assuming the field has a valid alias, this will return the alias. Otherwise, it will return the field name.
+    First priority is given to the alias, then the validation_alias, then the field name.
+
+    Args:
+        field_name: The name of the field
+        field_info: The corresponding FieldInfo object.
+
+    Returns:
+        The correct name to use when generating a signature.
+    """
+    if isinstance(field_info.alias, str) and is_valid_identifier(field_info.alias):
+        return field_info.alias
+    if isinstance(field_info.validation_alias, str) and is_valid_identifier(field_info.validation_alias):
+        return field_info.validation_alias
+
+    return field_name
+
+
+def _process_param_defaults(param: Parameter) -> Parameter:
+    """Modify the signature for a parameter in a dataclass where the default value is a FieldInfo instance.
+
+    Args:
+        param (Parameter): The parameter
+
+    Returns:
+        Parameter: The custom processed parameter
+    """
+    from ..fields import FieldInfo
+
+    param_default = param.default
+    if isinstance(param_default, FieldInfo):
+        annotation = param.annotation
+        # Replace the annotation if appropriate
+        # inspect does "clever" things to show annotations as strings because we have
+        # `from __future__ import annotations` in main, we don't want that
+        if annotation == 'Any':
+            annotation = Any
+
+        # Replace the field default
+        default = param_default.default
+        if default is PydanticUndefined:
+            if param_default.default_factory is None:
+                default = Signature.empty
+            else:
+                # this is used by dataclasses to indicate a factory exists:
+                default = dataclasses._HAS_DEFAULT_FACTORY  # type: ignore
+        return param.replace(
+            annotation=annotation, name=_field_name_for_signature(param.name, param_default), default=default
+        )
+    return param
+
+
+def _generate_signature_parameters(  # noqa: C901 (ignore complexity, could use a refactor)
+    init: Callable[..., None],
+    fields: dict[str, FieldInfo],
+    validate_by_name: bool,
+    extra: ExtraValues | None,
+) -> dict[str, Parameter]:
+    """Generate a mapping of parameter names to Parameter objects for a pydantic BaseModel or dataclass."""
+    from itertools import islice
+
+    present_params = signature_no_eval(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
+        # inspect does "clever" things to show annotations as strings because we have
+        # `from __future__ import annotations` in main, we don't want that
+        if fields.get(param.name):
+            # exclude params with init=False
+            if getattr(fields[param.name], 'init', True) is False:
+                continue
+            param = param.replace(name=_field_name_for_signature(param.name, fields[param.name]))
+        if param.annotation == 'Any':
+            param = param.replace(annotation=Any)
+        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 = validate_by_name
+        for field_name, field in fields.items():
+            # when alias is a str it should be used for signature generation
+            param_name = _field_name_for_signature(field_name, field)
+
+            if field_name in merged_params or param_name in merged_params:
+                continue
+
+            if not is_valid_identifier(param_name):
+                if allow_names:
+                    param_name = field_name
+                else:
+                    use_var_kw = True
+                    continue
+
+            if field.is_required():
+                default = Parameter.empty
+            elif field.default_factory is not None:
+                # Mimics stdlib dataclasses:
+                default = _HAS_DEFAULT_FACTORY
+            else:
+                default = field.default
+            merged_params[param_name] = Parameter(
+                param_name,
+                Parameter.KEYWORD_ONLY,
+                annotation=field.rebuild_annotation(),
+                default=default,
+            )
+
+    if 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 = [
+            ('self', Parameter.POSITIONAL_ONLY),
+            ('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 merged_params
+
+
+def generate_pydantic_signature(
+    init: Callable[..., None],
+    fields: dict[str, FieldInfo],
+    validate_by_name: bool,
+    extra: ExtraValues | None,
+    is_dataclass: bool = False,
+) -> Signature:
+    """Generate signature for a pydantic BaseModel or dataclass.
+
+    Args:
+        init: The class init.
+        fields: The model fields.
+        validate_by_name: The `validate_by_name` value of the config.
+        extra: The `extra` value of the config.
+        is_dataclass: Whether the model is a dataclass.
+
+    Returns:
+        The dataclass/BaseModel subclass signature.
+    """
+    merged_params = _generate_signature_parameters(init, fields, validate_by_name, extra)
+
+    if is_dataclass:
+        merged_params = {k: _process_param_defaults(v) for k, v in merged_params.items()}
+
+    return Signature(parameters=list(merged_params.values()), return_annotation=None)

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_typing_extra.py

@@ -0,0 +1,785 @@
+"""Logic for interacting with type annotations, mostly extensions, shims and hacks to wrap Python's typing module."""
+
+from __future__ import annotations
+
+import collections.abc
+import re
+import sys
+import types
+import typing
+from functools import partial
+from inspect import Signature, signature
+from typing import TYPE_CHECKING, Any, Callable, cast
+
+import typing_extensions
+from typing_extensions import deprecated, get_args, get_origin
+from typing_inspection import typing_objects
+from typing_inspection.introspection import is_union_origin
+
+from pydantic.version import version_short
+
+from ._namespace_utils import GlobalsNamespace, MappingNamespace, NsResolver, get_module_ns_of
+
+if sys.version_info < (3, 10):
+    NoneType = type(None)
+    EllipsisType = type(Ellipsis)
+else:
+    from types import EllipsisType as EllipsisType
+    from types import NoneType as NoneType
+
+if sys.version_info >= (3, 14):
+    import annotationlib
+
+if TYPE_CHECKING:
+    from pydantic import BaseModel
+    from pydantic.fields import FieldInfo
+
+# As per https://typing-extensions.readthedocs.io/en/latest/#runtime-use-of-types,
+# always check for both `typing` and `typing_extensions` variants of a typing construct.
+# (this is implemented differently than the suggested approach in the `typing_extensions`
+# docs for performance).
+
+
+_t_annotated = typing.Annotated
+_te_annotated = typing_extensions.Annotated
+
+
+def is_annotated(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Annotated` special form.
+
+    ```python {test="skip" lint="skip"}
+    is_annotated(Annotated[int, ...])
+    #> True
+    ```
+    """
+    origin = get_origin(tp)
+    return origin is _t_annotated or origin is _te_annotated
+
+
+def annotated_type(tp: Any, /) -> Any | None:
+    """Return the type of the `Annotated` special form, or `None`."""
+    return tp.__origin__ if typing_objects.is_annotated(get_origin(tp)) else None
+
+
+def unpack_type(tp: Any, /) -> Any | None:
+    """Return the type wrapped by the `Unpack` special form, or `None`."""
+    return get_args(tp)[0] if typing_objects.is_unpack(get_origin(tp)) else None
+
+
+def is_hashable(tp: Any, /) -> bool:
+    """Return whether the provided argument is the `Hashable` class.
+
+    ```python {test="skip" lint="skip"}
+    is_hashable(Hashable)
+    #> True
+    ```
+    """
+    # `get_origin` is documented as normalizing any typing-module aliases to `collections` classes,
+    # hence the second check:
+    return tp is collections.abc.Hashable or get_origin(tp) is collections.abc.Hashable
+
+
+def is_callable(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Callable`, parametrized or not.
+
+    ```python {test="skip" lint="skip"}
+    is_callable(Callable[[int], str])
+    #> True
+    is_callable(typing.Callable)
+    #> True
+    is_callable(collections.abc.Callable)
+    #> True
+    ```
+    """
+    # `get_origin` is documented as normalizing any typing-module aliases to `collections` classes,
+    # hence the second check:
+    return tp is collections.abc.Callable or get_origin(tp) is collections.abc.Callable
+
+
+_classvar_re = re.compile(r'((\w+\.)?Annotated\[)?(\w+\.)?ClassVar\[')
+
+
+def is_classvar_annotation(tp: Any, /) -> bool:
+    """Return whether the provided argument represents a class variable annotation.
+
+    Although not explicitly stated by the typing specification, `ClassVar` can be used
+    inside `Annotated` and as such, this function checks for this specific scenario.
+
+    Because this function is used to detect class variables before evaluating forward references
+    (or because evaluation failed), we also implement a naive regex match implementation. This is
+    required because class variables are inspected before fields are collected, so we try to be
+    as accurate as possible.
+    """
+    if typing_objects.is_classvar(tp):
+        return True
+
+    origin = get_origin(tp)
+
+    if typing_objects.is_classvar(origin):
+        return True
+
+    if typing_objects.is_annotated(origin):
+        annotated_type = tp.__origin__
+        if typing_objects.is_classvar(annotated_type) or typing_objects.is_classvar(get_origin(annotated_type)):
+            return True
+
+    str_ann: str | None = None
+    if isinstance(tp, typing.ForwardRef):
+        str_ann = tp.__forward_arg__
+    if isinstance(tp, str):
+        str_ann = tp
+
+    if str_ann is not None and _classvar_re.match(str_ann):
+        # stdlib dataclasses do something similar, although a bit more advanced
+        # (see `dataclass._is_type`).
+        return True
+
+    return False
+
+
+_t_final = typing.Final
+_te_final = typing_extensions.Final
+
+
+# TODO implement `is_finalvar_annotation` as Final can be wrapped with other special forms:
+def is_finalvar(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Final` special form, parametrized or not.
+
+    ```python {test="skip" lint="skip"}
+    is_finalvar(Final[int])
+    #> True
+    is_finalvar(Final)
+    #> True
+    """
+    # Final is not necessarily parametrized:
+    if tp is _t_final or tp is _te_final:
+        return True
+    origin = get_origin(tp)
+    return origin is _t_final or origin is _te_final
+
+
+_NONE_TYPES: tuple[Any, ...] = (None, NoneType, typing.Literal[None], typing_extensions.Literal[None])
+
+
+def is_none_type(tp: Any, /) -> bool:
+    """Return whether the argument represents the `None` type as part of an annotation.
+
+    ```python {test="skip" lint="skip"}
+    is_none_type(None)
+    #> True
+    is_none_type(NoneType)
+    #> True
+    is_none_type(Literal[None])
+    #> True
+    is_none_type(type[None])
+    #> False
+    """
+    return tp in _NONE_TYPES
+
+
+def is_namedtuple(tp: Any, /) -> bool:
+    """Return whether the provided argument is a named tuple class.
+
+    The class can be created using `typing.NamedTuple` or `collections.namedtuple`.
+    Parametrized generic classes are *not* assumed to be named tuples.
+    """
+    from ._utils import lenient_issubclass  # circ. import
+
+    return lenient_issubclass(tp, tuple) and hasattr(tp, '_fields')
+
+
+# TODO In 2.12, delete this export. It is currently defined only to not break
+# pydantic-settings which relies on it:
+origin_is_union = is_union_origin
+
+
+def is_generic_alias(tp: Any, /) -> bool:
+    return isinstance(tp, (types.GenericAlias, typing._GenericAlias))  # pyright: ignore[reportAttributeAccessIssue]
+
+
+# TODO: Ideally, we should avoid relying on the private `typing` constructs:
+
+if sys.version_info < (3, 10):
+    WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias, types.GenericAlias)  # pyright: ignore[reportAttributeAccessIssue]
+else:
+    WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias, types.GenericAlias, types.UnionType)  # pyright: ignore[reportAttributeAccessIssue]
+
+
+# Similarly, we shouldn't rely on this `_Final` class, which is even more private than `_GenericAlias`:
+typing_base: Any = typing._Final  # pyright: ignore[reportAttributeAccessIssue]
+
+
+### Annotation evaluations functions:
+
+
+def parent_frame_namespace(*, parent_depth: int = 2, force: bool = False) -> dict[str, Any] | None:
+    """Fetch the local namespace of the parent frame where this function is called.
+
+    Using this function is mostly useful to resolve forward annotations pointing to members defined in a local namespace,
+    such as assignments inside a function. Using the standard library tools, it is currently not possible to resolve
+    such annotations:
+
+    ```python {lint="skip" test="skip"}
+    from typing import get_type_hints
+
+    def func() -> None:
+        Alias = int
+
+        class C:
+            a: 'Alias'
+
+        # Raises a `NameError: 'Alias' is not defined`
+        get_type_hints(C)
+    ```
+
+    Pydantic uses this function when a Pydantic model is being defined to fetch the parent frame locals. However,
+    this only allows us to fetch the parent frame namespace and not other parents (e.g. a model defined in a function,
+    itself defined in another function). Inspecting the next outer frames (using `f_back`) is not reliable enough
+    (see https://discuss.python.org/t/20659).
+
+    Because this function is mostly used to better resolve forward annotations, nothing is returned if the parent frame's
+    code object is defined at the module level. In this case, the locals of the frame will be the same as the module
+    globals where the class is defined (see `_namespace_utils.get_module_ns_of`). However, if you still want to fetch
+    the module globals (e.g. when rebuilding a model, where the frame where the rebuild call is performed might contain
+    members that you want to use for forward annotations evaluation), you can use the `force` parameter.
+
+    Args:
+        parent_depth: The depth at which to get the frame. Defaults to 2, meaning the parent frame where this function
+            is called will be used.
+        force: Whether to always return the frame locals, even if the frame's code object is defined at the module level.
+
+    Returns:
+        The locals of the namespace, or `None` if it was skipped as per the described logic.
+    """
+    frame = sys._getframe(parent_depth)
+
+    if frame.f_code.co_name.startswith('<generic parameters of'):
+        # As `parent_frame_namespace` is mostly called in `ModelMetaclass.__new__`,
+        # the parent frame can be the annotation scope if the PEP 695 generic syntax is used.
+        # (see https://docs.python.org/3/reference/executionmodel.html#annotation-scopes,
+        # https://docs.python.org/3/reference/compound_stmts.html#generic-classes).
+        # In this case, the code name is set to `<generic parameters of MyClass>`,
+        # and we need to skip this frame as it is irrelevant.
+        frame = cast(types.FrameType, frame.f_back)  # guaranteed to not be `None`
+
+    # note, we don't copy frame.f_locals here (or during the last return call), because we don't expect the namespace to be
+    # modified down the line if this becomes a problem, we could implement some sort of frozen mapping structure to enforce this.
+    if force:
+        return frame.f_locals
+
+    # If either of the following conditions are true, the class is defined at the top module level.
+    # To better understand why we need both of these checks, see
+    # https://github.com/pydantic/pydantic/pull/10113#discussion_r1714981531.
+    if frame.f_back is None or frame.f_code.co_name == '<module>':
+        return None
+
+    return frame.f_locals
+
+
+def _type_convert(arg: Any) -> Any:
+    """Convert `None` to `NoneType` and strings to `ForwardRef` instances.
+
+    This is a backport of the private `typing._type_convert` function. When
+    evaluating a type, `ForwardRef._evaluate` ends up being called, and is
+    responsible for making this conversion. However, we still have to apply
+    it for the first argument passed to our type evaluation functions, similarly
+    to the `typing.get_type_hints` function.
+    """
+    if arg is None:
+        return NoneType
+    if isinstance(arg, str):
+        # Like `typing.get_type_hints`, assume the arg can be in any context,
+        # hence the proper `is_argument` and `is_class` args:
+        return _make_forward_ref(arg, is_argument=False, is_class=True)
+    return arg
+
+
+def safe_get_annotations(obj: Any) -> dict[str, Any]:
+    """Get the annotations for the provided object, accounting for potential deferred forward references.
+
+    Starting with Python 3.14, accessing the `__annotations__` attribute might raise a `NameError` if
+    a referenced symbol isn't defined yet. In this case, we return the annotation in the *forward ref*
+    format.
+    """
+    if sys.version_info >= (3, 14):
+        return annotationlib.get_annotations(obj, format=annotationlib.Format.FORWARDREF)
+    else:
+        # TODO just do getattr(obj, '__annotations__', {}) when dropping support for Python 3.9:
+        if isinstance(obj, type):
+            return obj.__dict__.get('__annotations__', {})
+        else:
+            return getattr(obj, '__annotations__', {})
+
+
+def get_model_type_hints(
+    model_class: type[BaseModel],
+    *,
+    ns_resolver: NsResolver | None = None,
+) -> dict[str, tuple[Any, bool]]:
+    """Collect annotations from a Pydantic model class, including those from parent classes.
+
+    Args:
+        model_class: The Pydantic model class to inspect.
+        ns_resolver: A namespace resolver instance to use. Defaults to an empty instance.
+
+    Returns:
+        A dictionary mapping annotation names to a two-tuple: the first element is the evaluated
+        type or the original annotation if a `NameError` occurred, the second element is a boolean
+        indicating if whether the evaluation succeeded.
+    """
+    hints: dict[str, Any] | dict[str, tuple[Any, bool]] = {}
+    ns_resolver = ns_resolver or NsResolver()
+
+    for base in reversed(model_class.__mro__[:-1]):
+        # For Python 3.14, we could also use `Format.VALUE` and pass the globals/locals
+        # from the ns_resolver, but we want to be able to know which specific field failed
+        # to evaluate:
+        ann = safe_get_annotations(base)
+
+        if not ann:
+            continue
+
+        with ns_resolver.push(base):
+            base_model_fields: dict[str, FieldInfo] | None = base.__dict__.get('__pydantic_fields__')
+
+            for name, value in ann.items():
+                if name.startswith('_'):
+                    globalns, localns = ns_resolver.types_namespace
+
+                    # For private attributes, we only need the annotation to detect the `ClassVar` special form.
+                    # For this reason, we still try to evaluate it, but we also catch any possible exception (on
+                    # top of the `NameError`s caught in `try_eval_type`) that could happen so that users are free
+                    # to use any kind of forward annotation for private fields (e.g. circular imports, new typing
+                    # syntax, etc).
+                    try:
+                        hints[name] = try_eval_type(value, globalns, localns)
+                    except Exception:
+                        hints[name] = (value, False)
+                else:
+                    if base_model_fields is not None and name in base_model_fields:
+                        # Avoid unnecessarily evaluating annotations from parent models, as we'll end up
+                        # copying the `FieldInfo` instance from it anyway if we need to.
+                        # We use the `annotation` attribute here, but in reality could put anything here,
+                        # As we are guaranteed to not make use of it:
+                        hints[name] = (base_model_fields[name].annotation, True)
+                    else:
+                        globalns, localns = ns_resolver.types_namespace
+
+                        hints[name] = try_eval_type(value, globalns, localns)
+
+    return hints
+
+
+def get_cls_type_hints(
+    obj: type[Any],
+    *,
+    ns_resolver: NsResolver | None = None,
+) -> dict[str, Any]:
+    """Collect annotations from a class, including those from parent classes.
+
+    Args:
+        obj: The class to inspect.
+        ns_resolver: A namespace resolver instance to use. Defaults to an empty instance.
+    """
+    hints: dict[str, Any] = {}
+    ns_resolver = ns_resolver or NsResolver()
+
+    for base in reversed(obj.__mro__):
+        # For Python 3.14, we could also use `Format.VALUE` and pass the globals/locals
+        # from the ns_resolver, but we want to be able to know which specific field failed
+        # to evaluate:
+        ann = safe_get_annotations(base)
+
+        if not ann:
+            continue
+
+        with ns_resolver.push(base):
+            globalns, localns = ns_resolver.types_namespace
+            for name, value in ann.items():
+                hints[name] = eval_type(value, globalns, localns)
+    return hints
+
+
+def try_eval_type(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> tuple[Any, bool]:
+    """Try evaluating the annotation using the provided namespaces.
+
+    Args:
+        value: The value to evaluate. If `None`, it will be replaced by `type[None]`. If an instance
+            of `str`, it will be converted to a `ForwardRef`.
+        localns: The global namespace to use during annotation evaluation.
+        globalns: The local namespace to use during annotation evaluation.
+
+    Returns:
+        A two-tuple containing the possibly evaluated type and a boolean indicating
+            whether the evaluation succeeded or not.
+    """
+    value = _type_convert(value)
+
+    try:
+        return eval_type_backport(value, globalns, localns), True
+    except NameError:
+        return value, False
+
+
+def eval_type(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> Any:
+    """Evaluate the annotation using the provided namespaces.
+
+    Args:
+        value: The value to evaluate. If `None`, it will be replaced by `type[None]`. If an instance
+            of `str`, it will be converted to a `ForwardRef`.
+        localns: The global namespace to use during annotation evaluation.
+        globalns: The local namespace to use during annotation evaluation.
+    """
+    value = _type_convert(value)
+    return eval_type_backport(value, globalns, localns)
+
+
+@deprecated(
+    '`eval_type_lenient` is deprecated, use `try_eval_type` instead.',
+    category=None,
+)
+def eval_type_lenient(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> Any:
+    ev, _ = try_eval_type(value, globalns, localns)
+    return ev
+
+
+def eval_type_backport(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+    type_params: tuple[Any, ...] | None = None,
+) -> Any:
+    """An enhanced version of `typing._eval_type` which will fall back to using the `eval_type_backport`
+    package if it's installed to let older Python versions use newer typing constructs.
+
+    Specifically, this transforms `X | Y` into `typing.Union[X, Y]` and `list[X]` into `typing.List[X]`
+    (as well as all the types made generic in PEP 585) if the original syntax is not supported in the
+    current Python version.
+
+    This function will also display a helpful error if the value passed fails to evaluate.
+    """
+    try:
+        return _eval_type_backport(value, globalns, localns, type_params)
+    except TypeError as e:
+        if 'Unable to evaluate type annotation' in str(e):
+            raise
+
+        # If it is a `TypeError` and value isn't a `ForwardRef`, it would have failed during annotation definition.
+        # Thus we assert here for type checking purposes:
+        assert isinstance(value, typing.ForwardRef)
+
+        message = f'Unable to evaluate type annotation {value.__forward_arg__!r}.'
+        if sys.version_info >= (3, 11):
+            e.add_note(message)
+            raise
+        else:
+            raise TypeError(message) from e
+    except RecursionError as e:
+        # TODO ideally recursion errors should be checked in `eval_type` above, but `eval_type_backport`
+        # is used directly in some places.
+        message = (
+            "If you made use of an implicit recursive type alias (e.g. `MyType = list['MyType']), "
+            'consider using PEP 695 type aliases instead. For more details, refer to the documentation: '
+            f'https://docs.pydantic.dev/{version_short()}/concepts/types/#named-recursive-types'
+        )
+        if sys.version_info >= (3, 11):
+            e.add_note(message)
+            raise
+        else:
+            raise RecursionError(f'{e.args[0]}\n{message}')
+
+
+def _eval_type_backport(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+    type_params: tuple[Any, ...] | None = None,
+) -> Any:
+    try:
+        return _eval_type(value, globalns, localns, type_params)
+    except TypeError as e:
+        if not (isinstance(value, typing.ForwardRef) and is_backport_fixable_error(e)):
+            raise
+
+        try:
+            from eval_type_backport import eval_type_backport
+        except ImportError:
+            raise TypeError(
+                f'Unable to evaluate type annotation {value.__forward_arg__!r}. If you are making use '
+                'of the new typing syntax (unions using `|` since Python 3.10 or builtins subscripting '
+                'since Python 3.9), you should either replace the use of new syntax with the existing '
+                '`typing` constructs or install the `eval_type_backport` package.'
+            ) from e
+
+        return eval_type_backport(
+            value,
+            globalns,
+            localns,  # pyright: ignore[reportArgumentType], waiting on a new `eval_type_backport` release.
+            try_default=False,
+        )
+
+
+def _eval_type(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+    type_params: tuple[Any, ...] | None = None,
+) -> Any:
+    if sys.version_info >= (3, 14):
+        # Starting in 3.14, `_eval_type()` does *not* apply `_type_convert()`
+        # anymore. This means the `None` -> `type(None)` conversion does not apply:
+        evaluated = typing._eval_type(  # type: ignore
+            value,
+            globalns,
+            localns,
+            type_params=type_params,
+            # This is relevant when evaluating types from `TypedDict` classes, where string annotations
+            # are automatically converted to `ForwardRef` instances with a module set. In this case,
+            # Our `globalns` is irrelevant and we need to indicate `typing._eval_type()` that it should
+            # infer it from the `ForwardRef.__forward_module__` attribute instead (`typing.get_type_hints()`
+            # does the same). Note that this would probably be unnecessary if we properly iterated over the
+            # `__orig_bases__` for TypedDicts in `get_cls_type_hints()`:
+            prefer_fwd_module=True,
+        )
+        if evaluated is None:
+            evaluated = type(None)
+        return evaluated
+    elif sys.version_info >= (3, 13):
+        return typing._eval_type(  # type: ignore
+            value, globalns, localns, type_params=type_params
+        )
+    else:
+        return typing._eval_type(  # type: ignore
+            value, globalns, localns
+        )
+
+
+def is_backport_fixable_error(e: TypeError) -> bool:
+    msg = str(e)
+
+    return sys.version_info < (3, 10) and msg.startswith('unsupported operand type(s) for |: ')
+
+
+def signature_no_eval(f: Callable[..., Any]) -> Signature:
+    """Get the signature of a callable without evaluating any annotations."""
+    if sys.version_info >= (3, 14):
+        from annotationlib import Format
+
+        return signature(f, annotation_format=Format.FORWARDREF)
+    else:
+        return signature(f)
+
+
+def get_function_type_hints(
+    function: Callable[..., Any],
+    *,
+    include_keys: set[str] | None = None,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> dict[str, Any]:
+    """Return type hints for a function.
+
+    This is similar to the `typing.get_type_hints` function, with a few differences:
+    - Support `functools.partial` by using the underlying `func` attribute.
+    - Do not wrap type annotation of a parameter with `Optional` if it has a default value of `None`
+      (related bug: https://github.com/python/cpython/issues/90353, only fixed in 3.11+).
+    """
+    if isinstance(function, partial):
+        annotations = safe_get_annotations(function.func)
+    else:
+        annotations = safe_get_annotations(function)
+
+    if globalns is None:
+        globalns = get_module_ns_of(function)
+    type_params: tuple[Any, ...] | None = None
+    if localns is None:
+        # If localns was specified, it is assumed to already contain type params. This is because
+        # Pydantic has more advanced logic to do so (see `_namespace_utils.ns_for_function`).
+        type_params = getattr(function, '__type_params__', ())
+
+    type_hints = {}
+    for name, value in annotations.items():
+        if include_keys is not None and name not in include_keys:
+            continue
+        if value is None:
+            value = NoneType
+        elif isinstance(value, str):
+            value = _make_forward_ref(value)
+
+        type_hints[name] = eval_type_backport(value, globalns, localns, type_params)
+
+    return type_hints
+
+
+# TODO use typing.ForwardRef directly when we stop supporting 3.9:
+if sys.version_info < (3, 9, 8) or (3, 10) <= sys.version_info < (3, 10, 1):
+
+    def _make_forward_ref(
+        arg: Any,
+        is_argument: bool = True,
+        *,
+        is_class: bool = False,
+    ) -> typing.ForwardRef:
+        """Wrapper for ForwardRef that accounts for the `is_class` argument missing in older versions.
+        The `module` argument is omitted as it breaks <3.9.8, =3.10.0 and isn't used in the calls below.
+
+        See https://github.com/python/cpython/pull/28560 for some background.
+        The backport happened on 3.9.8, see:
+        https://github.com/pydantic/pydantic/discussions/6244#discussioncomment-6275458,
+        and on 3.10.1 for the 3.10 branch, see:
+        https://github.com/pydantic/pydantic/issues/6912
+
+        Implemented as EAFP with memory.
+        """
+        return typing.ForwardRef(arg, is_argument)  # pyright: ignore[reportCallIssue]
+
+else:
+    _make_forward_ref = typing.ForwardRef  # pyright: ignore[reportAssignmentType]
+
+
+if sys.version_info >= (3, 10):
+    get_type_hints = typing.get_type_hints
+
+else:
+    """
+    For older versions of python, we have a custom implementation of `get_type_hints` which is a close as possible to
+    the implementation in CPython 3.10.8.
+    """
+
+    @typing.no_type_check
+    def get_type_hints(  # noqa: C901
+        obj: Any,
+        globalns: dict[str, Any] | None = None,
+        localns: dict[str, Any] | None = None,
+        include_extras: bool = False,
+    ) -> dict[str, Any]:  # pragma: no cover
+        """Taken verbatim from python 3.10.8 unchanged, except:
+        * type annotations of the function definition above.
+        * prefixing `typing.` where appropriate
+        * Use `_make_forward_ref` instead of `typing.ForwardRef` to handle the `is_class` argument.
+
+        https://github.com/python/cpython/blob/aaaf5174241496afca7ce4d4584570190ff972fe/Lib/typing.py#L1773-L1875
+
+        DO NOT CHANGE THIS METHOD UNLESS ABSOLUTELY NECESSARY.
+        ======================================================
+
+        Return type hints for an object.
+
+        This is often the same as obj.__annotations__, but it handles
+        forward references encoded as string literals, adds Optional[t] if a
+        default value equal to None is set and recursively replaces all
+        'Annotated[T, ...]' with 'T' (unless 'include_extras=True').
+
+        The argument may be a module, class, method, or function. The annotations
+        are returned as a dictionary. For classes, annotations include also
+        inherited members.
+
+        TypeError is raised if the argument is not of a type that can contain
+        annotations, and an empty dictionary is returned if no annotations are
+        present.
+
+        BEWARE -- the behavior of globalns and localns is counterintuitive
+        (unless you are familiar with how eval() and exec() work).  The
+        search order is locals first, then globals.
+
+        - If no dict arguments are passed, an attempt is made to use the
+          globals from obj (or the respective module's globals for classes),
+          and these are also used as the locals.  If the object does not appear
+          to have globals, an empty dictionary is used.  For classes, the search
+          order is globals first then locals.
+
+        - If one dict argument is passed, it is used for both globals and
+          locals.
+
+        - If two dict arguments are passed, they specify globals and
+          locals, respectively.
+        """
+        if getattr(obj, '__no_type_check__', None):
+            return {}
+        # Classes require a special treatment.
+        if isinstance(obj, type):
+            hints = {}
+            for base in reversed(obj.__mro__):
+                if globalns is None:
+                    base_globals = getattr(sys.modules.get(base.__module__, None), '__dict__', {})
+                else:
+                    base_globals = globalns
+                ann = base.__dict__.get('__annotations__', {})
+                if isinstance(ann, types.GetSetDescriptorType):
+                    ann = {}
+                base_locals = dict(vars(base)) if localns is None else localns
+                if localns is None and globalns is None:
+                    # This is surprising, but required.  Before Python 3.10,
+                    # get_type_hints only evaluated the globalns of
+                    # a class.  To maintain backwards compatibility, we reverse
+                    # the globalns and localns order so that eval() looks into
+                    # *base_globals* first rather than *base_locals*.
+                    # This only affects ForwardRefs.
+                    base_globals, base_locals = base_locals, base_globals
+                for name, value in ann.items():
+                    if value is None:
+                        value = type(None)
+                    if isinstance(value, str):
+                        value = _make_forward_ref(value, is_argument=False, is_class=True)
+
+                    value = eval_type_backport(value, base_globals, base_locals)
+                    hints[name] = value
+            if not include_extras and hasattr(typing, '_strip_annotations'):
+                return {
+                    k: typing._strip_annotations(t)  # type: ignore
+                    for k, t in hints.items()
+                }
+            else:
+                return hints
+
+        if globalns is None:
+            if isinstance(obj, types.ModuleType):
+                globalns = obj.__dict__
+            else:
+                nsobj = obj
+                # Find globalns for the unwrapped object.
+                while hasattr(nsobj, '__wrapped__'):
+                    nsobj = nsobj.__wrapped__
+                globalns = getattr(nsobj, '__globals__', {})
+            if localns is None:
+                localns = globalns
+        elif localns is None:
+            localns = globalns
+        hints = getattr(obj, '__annotations__', None)
+        if hints is None:
+            # Return empty annotations for something that _could_ have them.
+            if isinstance(obj, typing._allowed_types):  # type: ignore
+                return {}
+            else:
+                raise TypeError(f'{obj!r} is not a module, class, method, or function.')
+        defaults = typing._get_defaults(obj)  # type: ignore
+        hints = dict(hints)
+        for name, value in hints.items():
+            if value is None:
+                value = type(None)
+            if isinstance(value, str):
+                # class-level forward refs were handled above, this must be either
+                # a module-level annotation or a function argument annotation
+
+                value = _make_forward_ref(
+                    value,
+                    is_argument=not isinstance(obj, types.ModuleType),
+                    is_class=False,
+                )
+            value = eval_type_backport(value, globalns, localns)
+            if name in defaults and defaults[name] is None:
+                value = typing.Optional[value]
+            hints[name] = value
+        return hints if include_extras else {k: typing._strip_annotations(t) for k, t in hints.items()}  # type: ignore

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_utils.py

@@ -0,0 +1,446 @@
+"""Bucket of reusable internal utilities.
+
+This should be reduced as much as possible with functions only used in one place, moved to that place.
+"""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import keyword
+import sys
+import warnings
+import weakref
+from collections import OrderedDict, defaultdict, deque
+from collections.abc import Callable, Iterable, Mapping
+from collections.abc import Set as AbstractSet
+from copy import deepcopy
+from functools import cached_property
+from inspect import Parameter
+from itertools import zip_longest
+from types import BuiltinFunctionType, CodeType, FunctionType, GeneratorType, LambdaType, ModuleType
+from typing import TYPE_CHECKING, Any, Generic, TypeVar, overload
+
+from pydantic_core import MISSING, PydanticUndefined
+from typing_extensions import TypeAlias, TypeGuard, deprecated
+
+from pydantic import PydanticDeprecatedSince211
+
+from . import _repr, _typing_extra
+from ._import_utils import import_cached_base_model
+
+if TYPE_CHECKING:
+    # TODO remove type error comments when we drop support for Python 3.9
+    MappingIntStrAny: TypeAlias = Mapping[int, Any] | Mapping[str, Any]  # pyright: ignore[reportGeneralTypeIssues]
+    AbstractSetIntStr: TypeAlias = AbstractSet[int] | AbstractSet[str]  # pyright: ignore[reportGeneralTypeIssues]
+    from ..main import BaseModel
+
+
+# these are types that are returned unchanged by deepcopy
+IMMUTABLE_NON_COLLECTIONS_TYPES: set[type[Any]] = {
+    int,
+    float,
+    complex,
+    str,
+    bool,
+    bytes,
+    type,
+    _typing_extra.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 can_be_positional(param: Parameter) -> bool:
+    """Return whether the parameter accepts a positional argument.
+
+    ```python {test="skip" lint="skip"}
+    def func(a, /, b, *, c):
+        pass
+
+    params = inspect.signature(func).parameters
+    can_be_positional(params['a'])
+    #> True
+    can_be_positional(params['b'])
+    #> True
+    can_be_positional(params['c'])
+    #> False
+    ```
+    """
+    return param.kind in (Parameter.POSITIONAL_ONLY, Parameter.POSITIONAL_OR_KEYWORD)
+
+
+def sequence_like(v: Any) -> bool:
+    return isinstance(v, (list, tuple, set, frozenset, GeneratorType, deque))
+
+
+def lenient_isinstance(o: Any, class_or_tuple: type[Any] | tuple[type[Any], ...] | None) -> bool:  # pragma: no cover
+    try:
+        return isinstance(o, class_or_tuple)  # type: ignore[arg-type]
+    except TypeError:
+        return False
+
+
+def lenient_issubclass(cls: Any, class_or_tuple: Any) -> bool:  # pragma: no cover
+    try:
+        return isinstance(cls, type) and issubclass(cls, class_or_tuple)
+    except TypeError:
+        if isinstance(cls, _typing_extra.WithArgsTypes):
+            return False
+        raise  # pragma: no cover
+
+
+def is_model_class(cls: Any) -> TypeGuard[type[BaseModel]]:
+    """Returns true if cls is a _proper_ subclass of BaseModel, and provides proper type-checking,
+    unlike raw calls to lenient_issubclass.
+    """
+    BaseModel = import_cached_base_model()
+
+    return lenient_issubclass(cls, BaseModel) and cls is not BaseModel
+
+
+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})
+
+
+T = TypeVar('T')
+
+
+def unique_list(
+    input_list: 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. model 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 ValueItems(_repr.Representation):
+    """Class for more convenient calculation of excluded or included fields on values."""
+
+    __slots__ = ('_items', '_type')
+
+    def __init__(self, value: Any, items: AbstractSetIntStr | MappingIntStrAny) -> None:
+        items = self._coerce_items(items)
+
+        if isinstance(value, (list, tuple)):
+            items = self._normalize_indexes(items, len(value))  # type: ignore
+
+        self._items: MappingIntStrAny = items  # type: ignore
+
+    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: int | str) -> AbstractSetIntStr | MappingIntStrAny | None:
+        """: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)  # type: ignore
+        return item if not self.is_true(item) else None
+
+    def _normalize_indexes(self, items: MappingIntStrAny, v_length: int) -> dict[int | str, Any]:
+        """: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: dict[int | str, Any] = {}
+        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: dict[int | str, Any] = {}
+        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: AbstractSetIntStr | MappingIntStrAny) -> MappingIntStrAny:
+        if isinstance(items, Mapping):
+            pass
+        elif isinstance(items, AbstractSet):
+            items = dict.fromkeys(items, ...)  # type: ignore
+        else:
+            class_name = getattr(items, '__class__', '???')
+            raise TypeError(f'Unexpected type of exclude value {class_name}')
+        return items  # type: ignore
+
+    @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) -> _repr.ReprArgs:
+        return [(None, self._items)]
+
+
+if TYPE_CHECKING:
+
+    def LazyClassAttribute(name: str, get_value: Callable[[], T]) -> T: ...
+
+else:
+
+    class LazyClassAttribute:
+        """A descriptor exposing an attribute only accessible on a class (hidden from instances).
+
+        The attribute is lazily computed and cached during the first access.
+        """
+
+        def __init__(self, name: str, get_value: Callable[[], Any]) -> None:
+            self.name = name
+            self.get_value = get_value
+
+        @cached_property
+        def value(self) -> Any:
+            return self.get_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')
+
+
+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.
+    """
+    if obj is MISSING or obj is PydanticUndefined:
+        return obj  # pyright: ignore[reportReturnType]
+    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()  # tuple doesn't have copy method  # type: ignore
+    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
+
+
+_SENTINEL = 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=_SENTINEL):
+        if left_item is not right_item:
+            return False
+    return True
+
+
+def get_first_not_none(a: Any, b: Any) -> Any:
+    """Return the first argument if it is not `None`, otherwise return the second argument."""
+    return a if a is not None else b
+
+
+@dataclasses.dataclass(frozen=True)
+class SafeGetItemProxy:
+    """Wrapper redirecting `__getitem__` to `get` with a sentinel value as default
+
+    This makes is safe to use in `operator.itemgetter` when some keys may be missing
+    """
+
+    # Define __slots__manually for performances
+    # @dataclasses.dataclass() only support slots=True in python>=3.10
+    __slots__ = ('wrapped',)
+
+    wrapped: Mapping[str, Any]
+
+    def __getitem__(self, key: str, /) -> Any:
+        return self.wrapped.get(key, _SENTINEL)
+
+    # required to pass the object to operator.itemgetter() instances due to a quirk of typeshed
+    # https://github.com/python/mypy/issues/13713
+    # https://github.com/python/typeshed/pull/8785
+    # Since this is typing-only, hide it in a typing.TYPE_CHECKING block
+    if TYPE_CHECKING:
+
+        def __contains__(self, key: str, /) -> bool:
+            return self.wrapped.__contains__(key)
+
+
+_ModelT = TypeVar('_ModelT', bound='BaseModel')
+_RT = TypeVar('_RT')
+
+
+class deprecated_instance_property(Generic[_ModelT, _RT]):
+    """A decorator exposing the decorated class method as a property, with a warning on instance access.
+
+    This decorator takes a class method defined on the `BaseModel` class and transforms it into
+    an attribute. The attribute can be accessed on both the class and instances of the class. If accessed
+    via an instance, a deprecation warning is emitted stating that instance access will be removed in V3.
+    """
+
+    def __init__(self, fget: Callable[[type[_ModelT]], _RT], /) -> None:
+        # Note: fget should be a classmethod:
+        self.fget = fget
+
+    @overload
+    def __get__(self, instance: None, objtype: type[_ModelT]) -> _RT: ...
+    @overload
+    @deprecated(
+        'Accessing this attribute on the instance is deprecated, and will be removed in Pydantic V3. '
+        'Instead, you should access this attribute from the model class.',
+        category=None,
+    )
+    def __get__(self, instance: _ModelT, objtype: type[_ModelT]) -> _RT: ...
+    def __get__(self, instance: _ModelT | None, objtype: type[_ModelT]) -> _RT:
+        if instance is not None:
+            # fmt: off
+            attr_name = (
+                self.fget.__name__
+                if sys.version_info >= (3, 10)
+                else self.fget.__func__.__name__  # pyright: ignore[reportFunctionMemberAccess]
+            )
+            # fmt: on
+            warnings.warn(
+                f'Accessing the {attr_name!r} attribute on the instance is deprecated. '
+                'Instead, you should access this attribute from the model class.',
+                category=PydanticDeprecatedSince211,
+                stacklevel=2,
+            )
+        return self.fget.__get__(instance, objtype)()

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_validate_call.py

@@ -0,0 +1,141 @@
+from __future__ import annotations as _annotations
+
+import functools
+import inspect
+from collections.abc import Awaitable
+from functools import partial
+from typing import Any, Callable
+
+import pydantic_core
+
+from ..config import ConfigDict
+from ..plugin._schema_validator import create_schema_validator
+from ._config import ConfigWrapper
+from ._generate_schema import GenerateSchema, ValidateCallSupportedTypes
+from ._namespace_utils import MappingNamespace, NsResolver, ns_for_function
+from ._typing_extra import signature_no_eval
+
+
+def extract_function_name(func: ValidateCallSupportedTypes) -> str:
+    """Extract the name of a `ValidateCallSupportedTypes` object."""
+    return f'partial({func.func.__name__})' if isinstance(func, functools.partial) else func.__name__
+
+
+def extract_function_qualname(func: ValidateCallSupportedTypes) -> str:
+    """Extract the qualname of a `ValidateCallSupportedTypes` object."""
+    return f'partial({func.func.__qualname__})' if isinstance(func, functools.partial) else func.__qualname__
+
+
+def update_wrapper_attributes(wrapped: ValidateCallSupportedTypes, wrapper: Callable[..., Any]):
+    """Update the `wrapper` function with the attributes of the `wrapped` function. Return the updated function."""
+    if inspect.iscoroutinefunction(wrapped):
+
+        @functools.wraps(wrapped)
+        async def wrapper_function(*args, **kwargs):  # type: ignore
+            return await wrapper(*args, **kwargs)
+    else:
+
+        @functools.wraps(wrapped)
+        def wrapper_function(*args, **kwargs):
+            return wrapper(*args, **kwargs)
+
+    # We need to manually update this because `partial` object has no `__name__` and `__qualname__`.
+    wrapper_function.__name__ = extract_function_name(wrapped)
+    wrapper_function.__qualname__ = extract_function_qualname(wrapped)
+    wrapper_function.raw_function = wrapped  # type: ignore
+
+    return wrapper_function
+
+
+class ValidateCallWrapper:
+    """This is a wrapper around a function that validates the arguments passed to it, and optionally the return value."""
+
+    __slots__ = (
+        'function',
+        'validate_return',
+        'schema_type',
+        'module',
+        'qualname',
+        'ns_resolver',
+        'config_wrapper',
+        '__pydantic_complete__',
+        '__pydantic_validator__',
+        '__return_pydantic_validator__',
+    )
+
+    def __init__(
+        self,
+        function: ValidateCallSupportedTypes,
+        config: ConfigDict | None,
+        validate_return: bool,
+        parent_namespace: MappingNamespace | None,
+    ) -> None:
+        self.function = function
+        self.validate_return = validate_return
+        if isinstance(function, partial):
+            self.schema_type = function.func
+            self.module = function.func.__module__
+        else:
+            self.schema_type = function
+            self.module = function.__module__
+        self.qualname = extract_function_qualname(function)
+
+        self.ns_resolver = NsResolver(
+            namespaces_tuple=ns_for_function(self.schema_type, parent_namespace=parent_namespace)
+        )
+        self.config_wrapper = ConfigWrapper(config)
+        if not self.config_wrapper.defer_build:
+            self._create_validators()
+        else:
+            self.__pydantic_complete__ = False
+
+    def _create_validators(self) -> None:
+        gen_schema = GenerateSchema(self.config_wrapper, self.ns_resolver)
+        schema = gen_schema.clean_schema(gen_schema.generate_schema(self.function))
+        core_config = self.config_wrapper.core_config(title=self.qualname)
+
+        self.__pydantic_validator__ = create_schema_validator(
+            schema,
+            self.schema_type,
+            self.module,
+            self.qualname,
+            'validate_call',
+            core_config,
+            self.config_wrapper.plugin_settings,
+        )
+        if self.validate_return:
+            signature = signature_no_eval(self.function)
+            return_type = signature.return_annotation if signature.return_annotation is not signature.empty else Any
+            gen_schema = GenerateSchema(self.config_wrapper, self.ns_resolver)
+            schema = gen_schema.clean_schema(gen_schema.generate_schema(return_type))
+            validator = create_schema_validator(
+                schema,
+                self.schema_type,
+                self.module,
+                self.qualname,
+                'validate_call',
+                core_config,
+                self.config_wrapper.plugin_settings,
+            )
+            if inspect.iscoroutinefunction(self.function):
+
+                async def return_val_wrapper(aw: Awaitable[Any]) -> None:
+                    return validator.validate_python(await aw)
+
+                self.__return_pydantic_validator__ = return_val_wrapper
+            else:
+                self.__return_pydantic_validator__ = validator.validate_python
+        else:
+            self.__return_pydantic_validator__ = None
+
+        self.__pydantic_complete__ = True
+
+    def __call__(self, *args: Any, **kwargs: Any) -> Any:
+        if not self.__pydantic_complete__:
+            self._create_validators()
+
+        res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))
+        if self.__return_pydantic_validator__:
+            return self.__return_pydantic_validator__(res)
+        else:
+            return res

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/_internal/_validators.py

@@ -0,0 +1,534 @@
+"""Validator functions for standard library types.
+
+Import of this module is deferred since it contains imports of many standard library modules.
+"""
+
+from __future__ import annotations as _annotations
+
+import collections.abc
+import math
+import re
+import typing
+from collections.abc import Sequence
+from decimal import Decimal
+from fractions import Fraction
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from typing import Any, Callable, TypeVar, Union, cast
+from zoneinfo import ZoneInfo, ZoneInfoNotFoundError
+
+import typing_extensions
+from pydantic_core import PydanticCustomError, PydanticKnownError, core_schema
+from typing_extensions import get_args, get_origin
+from typing_inspection import typing_objects
+
+from pydantic._internal._import_utils import import_cached_field_info
+from pydantic.errors import PydanticSchemaGenerationError
+
+
+def sequence_validator(
+    input_value: Sequence[Any],
+    /,
+    validator: core_schema.ValidatorFunctionWrapHandler,
+) -> Sequence[Any]:
+    """Validator for `Sequence` types, isinstance(v, Sequence) has already been called."""
+    value_type = type(input_value)
+
+    # We don't accept any plain string as a sequence
+    # Relevant issue: https://github.com/pydantic/pydantic/issues/5595
+    if issubclass(value_type, (str, bytes)):
+        raise PydanticCustomError(
+            'sequence_str',
+            "'{type_name}' instances are not allowed as a Sequence value",
+            {'type_name': value_type.__name__},
+        )
+
+    # TODO: refactor sequence validation to validate with either a list or a tuple
+    # schema, depending on the type of the value.
+    # Additionally, we should be able to remove one of either this validator or the
+    # SequenceValidator in _std_types_schema.py (preferably this one, while porting over some logic).
+    # Effectively, a refactor for sequence validation is needed.
+    if value_type is tuple:
+        input_value = list(input_value)
+
+    v_list = validator(input_value)
+
+    # the rest of the logic is just re-creating the original type from `v_list`
+    if value_type is list:
+        return v_list
+    elif issubclass(value_type, range):
+        # return the list as we probably can't re-create the range
+        return v_list
+    elif value_type is tuple:
+        return tuple(v_list)
+    else:
+        # best guess at how to re-create the original type, more custom construction logic might be required
+        return value_type(v_list)  # type: ignore[call-arg]
+
+
+def import_string(value: Any) -> Any:
+    if isinstance(value, str):
+        try:
+            return _import_string_logic(value)
+        except ImportError as e:
+            raise PydanticCustomError('import_error', 'Invalid python path: {error}', {'error': str(e)}) from e
+    else:
+        # otherwise we just return the value and let the next validator do the rest of the work
+        return value
+
+
+def _import_string_logic(dotted_path: str) -> Any:
+    """Inspired by uvicorn — dotted paths should include a colon before the final item if that item is not a module.
+    (This is necessary to distinguish between a submodule and an attribute when there is a conflict.).
+
+    If the dotted path does not include a colon and the final item is not a valid module, importing as an attribute
+    rather than a submodule will be attempted automatically.
+
+    So, for example, the following values of `dotted_path` result in the following returned values:
+    * 'collections': <module 'collections'>
+    * 'collections.abc': <module 'collections.abc'>
+    * 'collections.abc:Mapping': <class 'collections.abc.Mapping'>
+    * `collections.abc.Mapping`: <class 'collections.abc.Mapping'> (though this is a bit slower than the previous line)
+
+    An error will be raised under any of the following scenarios:
+    * `dotted_path` contains more than one colon (e.g., 'collections:abc:Mapping')
+    * the substring of `dotted_path` before the colon is not a valid module in the environment (e.g., '123:Mapping')
+    * the substring of `dotted_path` after the colon is not an attribute of the module (e.g., 'collections:abc123')
+    """
+    from importlib import import_module
+
+    components = dotted_path.strip().split(':')
+    if len(components) > 2:
+        raise ImportError(f"Import strings should have at most one ':'; received {dotted_path!r}")
+    attribute = None
+    if len(components) == 2:
+        attribute = components[1]
+    module_path = components[0]
+    if not module_path:
+        raise ImportError(f'Import strings should have a nonempty module name; received {dotted_path!r}')
+
+    try:
+        module = import_module(module_path)
+    except ModuleNotFoundError:
+        if attribute is None and '.' in module_path:
+            # Try interpreting the final dotted segment as an attribute, not a submodule
+            maybe_module_path, maybe_attribute = module_path.rsplit('.', 1)
+
+            try:
+                return _import_string_logic(f'{maybe_module_path}:{maybe_attribute}')
+            except ImportError:
+                pass
+        raise
+
+    if attribute is not None:
+        try:
+            return getattr(module, attribute)
+        except AttributeError as e:
+            raise ImportError(f'cannot import name {attribute!r} from {module_path!r}') from e
+    else:
+        return module
+
+
+def pattern_either_validator(input_value: Any, /) -> re.Pattern[Any]:
+    if isinstance(input_value, re.Pattern):
+        return input_value
+    elif isinstance(input_value, (str, bytes)):
+        # todo strict mode
+        return compile_pattern(input_value)  # type: ignore
+    else:
+        raise PydanticCustomError('pattern_type', 'Input should be a valid pattern')
+
+
+def pattern_str_validator(input_value: Any, /) -> re.Pattern[str]:
+    if isinstance(input_value, re.Pattern):
+        if isinstance(input_value.pattern, str):
+            return input_value
+        else:
+            raise PydanticCustomError('pattern_str_type', 'Input should be a string pattern')
+    elif isinstance(input_value, str):
+        return compile_pattern(input_value)
+    elif isinstance(input_value, bytes):
+        raise PydanticCustomError('pattern_str_type', 'Input should be a string pattern')
+    else:
+        raise PydanticCustomError('pattern_type', 'Input should be a valid pattern')
+
+
+def pattern_bytes_validator(input_value: Any, /) -> re.Pattern[bytes]:
+    if isinstance(input_value, re.Pattern):
+        if isinstance(input_value.pattern, bytes):
+            return input_value
+        else:
+            raise PydanticCustomError('pattern_bytes_type', 'Input should be a bytes pattern')
+    elif isinstance(input_value, bytes):
+        return compile_pattern(input_value)
+    elif isinstance(input_value, str):
+        raise PydanticCustomError('pattern_bytes_type', 'Input should be a bytes pattern')
+    else:
+        raise PydanticCustomError('pattern_type', 'Input should be a valid pattern')
+
+
+PatternType = TypeVar('PatternType', str, bytes)
+
+
+def compile_pattern(pattern: PatternType) -> re.Pattern[PatternType]:
+    try:
+        return re.compile(pattern)
+    except re.error:
+        raise PydanticCustomError('pattern_regex', 'Input should be a valid regular expression')
+
+
+def ip_v4_address_validator(input_value: Any, /) -> IPv4Address:
+    if isinstance(input_value, IPv4Address):
+        return input_value
+
+    try:
+        return IPv4Address(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v4_address', 'Input is not a valid IPv4 address')
+
+
+def ip_v6_address_validator(input_value: Any, /) -> IPv6Address:
+    if isinstance(input_value, IPv6Address):
+        return input_value
+
+    try:
+        return IPv6Address(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v6_address', 'Input is not a valid IPv6 address')
+
+
+def ip_v4_network_validator(input_value: Any, /) -> IPv4Network:
+    """Assume IPv4Network initialised with a default `strict` argument.
+
+    See more:
+    https://docs.python.org/library/ipaddress.html#ipaddress.IPv4Network
+    """
+    if isinstance(input_value, IPv4Network):
+        return input_value
+
+    try:
+        return IPv4Network(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v4_network', 'Input is not a valid IPv4 network')
+
+
+def ip_v6_network_validator(input_value: Any, /) -> IPv6Network:
+    """Assume IPv6Network initialised with a default `strict` argument.
+
+    See more:
+    https://docs.python.org/library/ipaddress.html#ipaddress.IPv6Network
+    """
+    if isinstance(input_value, IPv6Network):
+        return input_value
+
+    try:
+        return IPv6Network(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v6_network', 'Input is not a valid IPv6 network')
+
+
+def ip_v4_interface_validator(input_value: Any, /) -> IPv4Interface:
+    if isinstance(input_value, IPv4Interface):
+        return input_value
+
+    try:
+        return IPv4Interface(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v4_interface', 'Input is not a valid IPv4 interface')
+
+
+def ip_v6_interface_validator(input_value: Any, /) -> IPv6Interface:
+    if isinstance(input_value, IPv6Interface):
+        return input_value
+
+    try:
+        return IPv6Interface(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v6_interface', 'Input is not a valid IPv6 interface')
+
+
+def fraction_validator(input_value: Any, /) -> Fraction:
+    if isinstance(input_value, Fraction):
+        return input_value
+
+    try:
+        return Fraction(input_value)
+    except ValueError:
+        raise PydanticCustomError('fraction_parsing', 'Input is not a valid fraction')
+
+
+def forbid_inf_nan_check(x: Any) -> Any:
+    if not math.isfinite(x):
+        raise PydanticKnownError('finite_number')
+    return x
+
+
+def _safe_repr(v: Any) -> int | float | str:
+    """The context argument for `PydanticKnownError` requires a number or str type, so we do a simple repr() coercion for types like timedelta.
+
+    See tests/test_types.py::test_annotated_metadata_any_order for some context.
+    """
+    if isinstance(v, (int, float, str)):
+        return v
+    return repr(v)
+
+
+def greater_than_validator(x: Any, gt: Any) -> Any:
+    try:
+        if not (x > gt):
+            raise PydanticKnownError('greater_than', {'gt': _safe_repr(gt)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'gt' to supplied value {x}")
+
+
+def greater_than_or_equal_validator(x: Any, ge: Any) -> Any:
+    try:
+        if not (x >= ge):
+            raise PydanticKnownError('greater_than_equal', {'ge': _safe_repr(ge)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'ge' to supplied value {x}")
+
+
+def less_than_validator(x: Any, lt: Any) -> Any:
+    try:
+        if not (x < lt):
+            raise PydanticKnownError('less_than', {'lt': _safe_repr(lt)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'lt' to supplied value {x}")
+
+
+def less_than_or_equal_validator(x: Any, le: Any) -> Any:
+    try:
+        if not (x <= le):
+            raise PydanticKnownError('less_than_equal', {'le': _safe_repr(le)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'le' to supplied value {x}")
+
+
+def multiple_of_validator(x: Any, multiple_of: Any) -> Any:
+    try:
+        if x % multiple_of:
+            raise PydanticKnownError('multiple_of', {'multiple_of': _safe_repr(multiple_of)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'multiple_of' to supplied value {x}")
+
+
+def min_length_validator(x: Any, min_length: Any) -> Any:
+    try:
+        if not (len(x) >= min_length):
+            raise PydanticKnownError(
+                'too_short', {'field_type': 'Value', 'min_length': min_length, 'actual_length': len(x)}
+            )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'min_length' to supplied value {x}")
+
+
+def max_length_validator(x: Any, max_length: Any) -> Any:
+    try:
+        if len(x) > max_length:
+            raise PydanticKnownError(
+                'too_long',
+                {'field_type': 'Value', 'max_length': max_length, 'actual_length': len(x)},
+            )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'max_length' to supplied value {x}")
+
+
+def _extract_decimal_digits_info(decimal: Decimal) -> tuple[int, int]:
+    """Compute the total number of digits and decimal places for a given [`Decimal`][decimal.Decimal] instance.
+
+    This function handles both normalized and non-normalized Decimal instances.
+    Example: Decimal('1.230') -> 4 digits, 3 decimal places
+
+    Args:
+        decimal (Decimal): The decimal number to analyze.
+
+    Returns:
+        tuple[int, int]: A tuple containing the number of decimal places and total digits.
+
+    Though this could be divided into two separate functions, the logic is easier to follow if we couple the computation
+    of the number of decimals and digits together.
+    """
+    try:
+        decimal_tuple = decimal.as_tuple()
+
+        assert isinstance(decimal_tuple.exponent, int)
+
+        exponent = decimal_tuple.exponent
+        num_digits = len(decimal_tuple.digits)
+
+        if exponent >= 0:
+            # A positive exponent adds that many trailing zeros
+            # Ex: digit_tuple=(1, 2, 3), exponent=2 -> 12300 -> 0 decimal places, 5 digits
+            num_digits += exponent
+            decimal_places = 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 the digits in digit_tuple and then
+            # add abs(exponent) - len(digit_tuple) leading zeros after the decimal point.
+            # Ex: digit_tuple=(1, 2, 3), exponent=-2 -> 1.23 -> 2 decimal places, 3 digits
+            # Ex: digit_tuple=(1, 2, 3), exponent=-4 -> 0.0123 -> 4 decimal places, 4 digits
+            decimal_places = abs(exponent)
+            num_digits = max(num_digits, decimal_places)
+
+        return decimal_places, num_digits
+    except (AssertionError, AttributeError):
+        raise TypeError(f'Unable to extract decimal digits info from supplied value {decimal}')
+
+
+def max_digits_validator(x: Any, max_digits: Any) -> Any:
+    try:
+        _, num_digits = _extract_decimal_digits_info(x)
+        _, normalized_num_digits = _extract_decimal_digits_info(x.normalize())
+        if (num_digits > max_digits) and (normalized_num_digits > max_digits):
+            raise PydanticKnownError(
+                'decimal_max_digits',
+                {'max_digits': max_digits},
+            )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'max_digits' to supplied value {x}")
+
+
+def decimal_places_validator(x: Any, decimal_places: Any) -> Any:
+    try:
+        decimal_places_, _ = _extract_decimal_digits_info(x)
+        if decimal_places_ > decimal_places:
+            normalized_decimal_places, _ = _extract_decimal_digits_info(x.normalize())
+            if normalized_decimal_places > decimal_places:
+                raise PydanticKnownError(
+                    'decimal_max_places',
+                    {'decimal_places': decimal_places},
+                )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'decimal_places' to supplied value {x}")
+
+
+def deque_validator(input_value: Any, handler: core_schema.ValidatorFunctionWrapHandler) -> collections.deque[Any]:
+    return collections.deque(handler(input_value), maxlen=getattr(input_value, 'maxlen', None))
+
+
+def defaultdict_validator(
+    input_value: Any, handler: core_schema.ValidatorFunctionWrapHandler, default_default_factory: Callable[[], Any]
+) -> collections.defaultdict[Any, Any]:
+    if isinstance(input_value, collections.defaultdict):
+        default_factory = input_value.default_factory
+        return collections.defaultdict(default_factory, handler(input_value))
+    else:
+        return collections.defaultdict(default_default_factory, handler(input_value))
+
+
+def get_defaultdict_default_default_factory(values_source_type: Any) -> Callable[[], Any]:
+    FieldInfo = import_cached_field_info()
+
+    values_type_origin = get_origin(values_source_type)
+
+    def infer_default() -> Callable[[], Any]:
+        allowed_default_types: dict[Any, Any] = {
+            tuple: tuple,
+            collections.abc.Sequence: tuple,
+            collections.abc.MutableSequence: list,
+            list: list,
+            typing.Sequence: list,
+            set: set,
+            typing.MutableSet: set,
+            collections.abc.MutableSet: set,
+            collections.abc.Set: frozenset,
+            typing.MutableMapping: dict,
+            typing.Mapping: dict,
+            collections.abc.Mapping: dict,
+            collections.abc.MutableMapping: dict,
+            float: float,
+            int: int,
+            str: str,
+            bool: bool,
+        }
+        values_type = values_type_origin or values_source_type
+        instructions = 'set using `DefaultDict[..., Annotated[..., Field(default_factory=...)]]`'
+        if typing_objects.is_typevar(values_type):
+
+            def type_var_default_factory() -> None:
+                raise RuntimeError(
+                    'Generic defaultdict cannot be used without a concrete value type or an'
+                    ' explicit default factory, ' + instructions
+                )
+
+            return type_var_default_factory
+        elif values_type not in allowed_default_types:
+            # a somewhat subjective set of types that have reasonable default values
+            allowed_msg = ', '.join([t.__name__ for t in set(allowed_default_types.values())])
+            raise PydanticSchemaGenerationError(
+                f'Unable to infer a default factory for keys of type {values_source_type}.'
+                f' Only {allowed_msg} are supported, other types require an explicit default factory'
+                ' ' + instructions
+            )
+        return allowed_default_types[values_type]
+
+    # Assume Annotated[..., Field(...)]
+    if typing_objects.is_annotated(values_type_origin):
+        field_info = next((v for v in get_args(values_source_type) if isinstance(v, FieldInfo)), None)
+    else:
+        field_info = None
+    if field_info and field_info.default_factory:
+        # Assume the default factory does not take any argument:
+        default_default_factory = cast(Callable[[], Any], field_info.default_factory)
+    else:
+        default_default_factory = infer_default()
+    return default_default_factory
+
+
+def validate_str_is_valid_iana_tz(value: Any, /) -> ZoneInfo:
+    if isinstance(value, ZoneInfo):
+        return value
+    try:
+        return ZoneInfo(value)
+    except (ZoneInfoNotFoundError, ValueError, TypeError):
+        raise PydanticCustomError('zoneinfo_str', 'invalid timezone: {value}', {'value': value})
+
+
+NUMERIC_VALIDATOR_LOOKUP: dict[str, Callable] = {
+    'gt': greater_than_validator,
+    'ge': greater_than_or_equal_validator,
+    'lt': less_than_validator,
+    'le': less_than_or_equal_validator,
+    'multiple_of': multiple_of_validator,
+    'min_length': min_length_validator,
+    'max_length': max_length_validator,
+    'max_digits': max_digits_validator,
+    'decimal_places': decimal_places_validator,
+}
+
+IpType = Union[IPv4Address, IPv6Address, IPv4Network, IPv6Network, IPv4Interface, IPv6Interface]
+
+IP_VALIDATOR_LOOKUP: dict[type[IpType], Callable] = {
+    IPv4Address: ip_v4_address_validator,
+    IPv6Address: ip_v6_address_validator,
+    IPv4Network: ip_v4_network_validator,
+    IPv6Network: ip_v6_network_validator,
+    IPv4Interface: ip_v4_interface_validator,
+    IPv6Interface: ip_v6_interface_validator,
+}
+
+MAPPING_ORIGIN_MAP: dict[Any, Any] = {
+    typing.DefaultDict: collections.defaultdict,  # noqa: UP006
+    collections.defaultdict: collections.defaultdict,
+    typing.OrderedDict: collections.OrderedDict,  # noqa: UP006
+    collections.OrderedDict: collections.OrderedDict,
+    typing_extensions.OrderedDict: collections.OrderedDict,
+    typing.Counter: collections.Counter,
+    collections.Counter: collections.Counter,
+    # this doesn't handle subclasses of these
+    typing.Mapping: dict,
+    typing.MutableMapping: dict,
+    # parametrized typing.{Mutable}Mapping creates one of these
+    collections.abc.Mapping: dict,
+    collections.abc.MutableMapping: dict,
+}

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/deprecated/class_validators.py

@@ -0,0 +1,256 @@
+"""Old `@validator` and `@root_validator` function validators from V1."""
+
+from __future__ import annotations as _annotations
+
+from functools import partial, partialmethod
+from types import FunctionType
+from typing import TYPE_CHECKING, Any, Callable, Literal, TypeVar, Union, overload
+from warnings import warn
+
+from typing_extensions import Protocol, TypeAlias, deprecated
+
+from .._internal import _decorators, _decorators_v1
+from ..errors import PydanticUserError
+from ..warnings import PydanticDeprecatedSince20
+
+_ALLOW_REUSE_WARNING_MESSAGE = '`allow_reuse` is deprecated and will be ignored; it should no longer be necessary'
+
+
+if TYPE_CHECKING:
+
+    class _OnlyValueValidatorClsMethod(Protocol):
+        def __call__(self, __cls: Any, __value: Any) -> Any: ...
+
+    class _V1ValidatorWithValuesClsMethod(Protocol):
+        def __call__(self, __cls: Any, __value: Any, values: dict[str, Any]) -> Any: ...
+
+    class _V1ValidatorWithValuesKwOnlyClsMethod(Protocol):
+        def __call__(self, __cls: Any, __value: Any, *, values: dict[str, Any]) -> Any: ...
+
+    class _V1ValidatorWithKwargsClsMethod(Protocol):
+        def __call__(self, __cls: Any, **kwargs: Any) -> Any: ...
+
+    class _V1ValidatorWithValuesAndKwargsClsMethod(Protocol):
+        def __call__(self, __cls: Any, values: dict[str, Any], **kwargs: Any) -> Any: ...
+
+    class _V1RootValidatorClsMethod(Protocol):
+        def __call__(
+            self, __cls: Any, __values: _decorators_v1.RootValidatorValues
+        ) -> _decorators_v1.RootValidatorValues: ...
+
+    V1Validator = Union[
+        _OnlyValueValidatorClsMethod,
+        _V1ValidatorWithValuesClsMethod,
+        _V1ValidatorWithValuesKwOnlyClsMethod,
+        _V1ValidatorWithKwargsClsMethod,
+        _V1ValidatorWithValuesAndKwargsClsMethod,
+        _decorators_v1.V1ValidatorWithValues,
+        _decorators_v1.V1ValidatorWithValuesKwOnly,
+        _decorators_v1.V1ValidatorWithKwargs,
+        _decorators_v1.V1ValidatorWithValuesAndKwargs,
+    ]
+
+    V1RootValidator = Union[
+        _V1RootValidatorClsMethod,
+        _decorators_v1.V1RootValidatorFunction,
+    ]
+
+    _PartialClsOrStaticMethod: TypeAlias = Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any]]
+
+    # Allow both a V1 (assumed pre=False) or V2 (assumed mode='after') validator
+    # We lie to type checkers and say we return the same thing we get
+    # but in reality we return a proxy object that _mostly_ behaves like the wrapped thing
+    _V1ValidatorType = TypeVar('_V1ValidatorType', V1Validator, _PartialClsOrStaticMethod)
+    _V1RootValidatorFunctionType = TypeVar(
+        '_V1RootValidatorFunctionType',
+        _decorators_v1.V1RootValidatorFunction,
+        _V1RootValidatorClsMethod,
+        _PartialClsOrStaticMethod,
+    )
+else:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+
+@deprecated(
+    'Pydantic V1 style `@validator` validators are deprecated.'
+    ' You should migrate to Pydantic V2 style `@field_validator` validators,'
+    ' see the migration guide for more details',
+    category=None,
+)
+def validator(
+    __field: str,
+    *fields: str,
+    pre: bool = False,
+    each_item: bool = False,
+    always: bool = False,
+    check_fields: bool | None = None,
+    allow_reuse: bool = False,
+) -> Callable[[_V1ValidatorType], _V1ValidatorType]:
+    """Decorate methods on the class indicating that they should be used to validate fields.
+
+    Args:
+        __field (str): The first field the validator should be called on; this is separate
+            from `fields` to ensure an error is raised if you don't pass at least one.
+        *fields (str): Additional field(s) the validator should be called on.
+        pre (bool, optional): Whether this validator should be called before the standard
+            validators (else after). Defaults to False.
+        each_item (bool, optional): For complex objects (sets, lists etc.) whether to validate
+            individual elements rather than the whole object. Defaults to False.
+        always (bool, optional): Whether this method and other validators should be called even if
+            the value is missing. Defaults to False.
+        check_fields (bool | None, optional): Whether to check that the fields actually exist on the model.
+            Defaults to None.
+        allow_reuse (bool, optional): Whether to track and raise an error if another validator refers to
+            the decorated function. Defaults to False.
+
+    Returns:
+        Callable: A decorator that can be used to decorate a
+            function to be used as a validator.
+    """
+    warn(
+        'Pydantic V1 style `@validator` validators are deprecated.'
+        ' You should migrate to Pydantic V2 style `@field_validator` validators,'
+        ' see the migration guide for more details',
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    if allow_reuse is True:  # pragma: no cover
+        warn(_ALLOW_REUSE_WARNING_MESSAGE, DeprecationWarning, stacklevel=2)
+    fields = __field, *fields
+    if isinstance(fields[0], FunctionType):
+        raise PydanticUserError(
+            '`@validator` should be used with fields and keyword arguments, not bare. '
+            "E.g. usage should be `@validator('<field_name>', ...)`",
+            code='decorator-missing-arguments',
+        )
+    elif not all(isinstance(field, str) for field in fields):
+        raise PydanticUserError(
+            '`@validator` fields should be passed as separate string args. '
+            "E.g. usage should be `@validator('<field_name_1>', '<field_name_2>', ...)`",
+            code='decorator-invalid-fields',
+        )
+
+    mode: Literal['before', 'after'] = 'before' if pre is True else 'after'
+
+    def dec(f: Any) -> _decorators.PydanticDescriptorProxy[Any]:
+        if _decorators.is_instance_method_from_sig(f):
+            raise PydanticUserError(
+                '`@validator` cannot be applied to instance methods', code='validator-instance-method'
+            )
+        # auto apply the @classmethod decorator
+        f = _decorators.ensure_classmethod_based_on_signature(f)
+        wrap = _decorators_v1.make_generic_v1_field_validator
+        validator_wrapper_info = _decorators.ValidatorDecoratorInfo(
+            fields=fields,
+            mode=mode,
+            each_item=each_item,
+            always=always,
+            check_fields=check_fields,
+        )
+        return _decorators.PydanticDescriptorProxy(f, validator_wrapper_info, shim=wrap)
+
+    return dec  # type: ignore[return-value]
+
+
+@overload
+def root_validator(
+    *,
+    # if you don't specify `pre` the default is `pre=False`
+    # which means you need to specify `skip_on_failure=True`
+    skip_on_failure: Literal[True],
+    allow_reuse: bool = ...,
+) -> Callable[
+    [_V1RootValidatorFunctionType],
+    _V1RootValidatorFunctionType,
+]: ...
+
+
+@overload
+def root_validator(
+    *,
+    # if you specify `pre=True` then you don't need to specify
+    # `skip_on_failure`, in fact it is not allowed as an argument!
+    pre: Literal[True],
+    allow_reuse: bool = ...,
+) -> Callable[
+    [_V1RootValidatorFunctionType],
+    _V1RootValidatorFunctionType,
+]: ...
+
+
+@overload
+def root_validator(
+    *,
+    # if you explicitly specify `pre=False` then you
+    # MUST specify `skip_on_failure=True`
+    pre: Literal[False],
+    skip_on_failure: Literal[True],
+    allow_reuse: bool = ...,
+) -> Callable[
+    [_V1RootValidatorFunctionType],
+    _V1RootValidatorFunctionType,
+]: ...
+
+
+@deprecated(
+    'Pydantic V1 style `@root_validator` validators are deprecated.'
+    ' You should migrate to Pydantic V2 style `@model_validator` validators,'
+    ' see the migration guide for more details',
+    category=None,
+)
+def root_validator(
+    *__args,
+    pre: bool = False,
+    skip_on_failure: bool = False,
+    allow_reuse: bool = False,
+) -> Any:
+    """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.
+
+    Args:
+        pre (bool, optional): Whether this validator should be called before the standard
+            validators (else after). Defaults to False.
+        skip_on_failure (bool, optional): Whether to stop validation and return as soon as a
+            failure is encountered. Defaults to False.
+        allow_reuse (bool, optional): Whether to track and raise an error if another validator
+            refers to the decorated function. Defaults to False.
+
+    Returns:
+        Any: A decorator that can be used to decorate a function to be used as a root_validator.
+    """
+    warn(
+        'Pydantic V1 style `@root_validator` validators are deprecated.'
+        ' You should migrate to Pydantic V2 style `@model_validator` validators,'
+        ' see the migration guide for more details',
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    if __args:
+        # Ensure a nice error is raised if someone attempts to use the bare decorator
+        return root_validator()(*__args)  # type: ignore
+
+    if allow_reuse is True:  # pragma: no cover
+        warn(_ALLOW_REUSE_WARNING_MESSAGE, DeprecationWarning, stacklevel=2)
+    mode: Literal['before', 'after'] = 'before' if pre is True else 'after'
+    if pre is False and skip_on_failure is not True:
+        raise PydanticUserError(
+            'If you use `@root_validator` with pre=False (the default) you MUST specify `skip_on_failure=True`.'
+            ' Note that `@root_validator` is deprecated and should be replaced with `@model_validator`.',
+            code='root-validator-pre-skip',
+        )
+
+    wrap = partial(_decorators_v1.make_v1_generic_root_validator, pre=pre)
+
+    def dec(f: Callable[..., Any] | classmethod[Any, Any, Any] | staticmethod[Any, Any]) -> Any:
+        if _decorators.is_instance_method_from_sig(f):
+            raise TypeError('`@root_validator` cannot be applied to instance methods')
+        # auto apply the @classmethod decorator
+        res = _decorators.ensure_classmethod_based_on_signature(f)
+        dec_info = _decorators.RootValidatorDecoratorInfo(mode=mode)
+        return _decorators.PydanticDescriptorProxy(res, dec_info, shim=wrap)
+
+    return dec

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/deprecated/config.py

@@ -0,0 +1,72 @@
+from __future__ import annotations as _annotations
+
+import warnings
+from typing import TYPE_CHECKING, Any, Literal
+
+from typing_extensions import deprecated
+
+from .._internal import _config
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__all__ = 'BaseConfig', 'Extra'
+
+
+class _ConfigMetaclass(type):
+    def __getattr__(self, item: str) -> Any:
+        try:
+            obj = _config.config_defaults[item]
+            warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning)
+            return obj
+        except KeyError as exc:
+            raise AttributeError(f"type object '{self.__name__}' has no attribute {exc}") from exc
+
+
+@deprecated('BaseConfig is deprecated. Use the `pydantic.ConfigDict` instead.', category=PydanticDeprecatedSince20)
+class BaseConfig(metaclass=_ConfigMetaclass):
+    """This class is only retained for backwards compatibility.
+
+    !!! Warning "Deprecated"
+        BaseConfig is deprecated. Use the [`pydantic.ConfigDict`][pydantic.ConfigDict] instead.
+    """
+
+    def __getattr__(self, item: str) -> Any:
+        try:
+            obj = super().__getattribute__(item)
+            warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning)
+            return obj
+        except AttributeError as exc:
+            try:
+                return getattr(type(self), item)
+            except AttributeError:
+                # re-raising changes the displayed text to reflect that `self` is not a type
+                raise AttributeError(str(exc)) from exc
+
+    def __init_subclass__(cls, **kwargs: Any) -> None:
+        warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning)
+        return super().__init_subclass__(**kwargs)
+
+
+class _ExtraMeta(type):
+    def __getattribute__(self, __name: str) -> Any:
+        # The @deprecated decorator accesses other attributes, so we only emit a warning for the expected ones
+        if __name in {'allow', 'ignore', 'forbid'}:
+            warnings.warn(
+                "`pydantic.config.Extra` is deprecated, use literal values instead (e.g. `extra='allow'`)",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+        return super().__getattribute__(__name)
+
+
+@deprecated(
+    "Extra is deprecated. Use literal values instead (e.g. `extra='allow'`)", category=PydanticDeprecatedSince20
+)
+class Extra(metaclass=_ExtraMeta):
+    allow: Literal['allow'] = 'allow'
+    ignore: Literal['ignore'] = 'ignore'
+    forbid: Literal['forbid'] = 'forbid'

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/deprecated/copy_internals.py

@@ -0,0 +1,224 @@
+from __future__ import annotations as _annotations
+
+import typing
+from copy import deepcopy
+from enum import Enum
+from typing import Any
+
+import typing_extensions
+
+from .._internal import (
+    _model_construction,
+    _typing_extra,
+    _utils,
+)
+
+if typing.TYPE_CHECKING:
+    from .. import BaseModel
+    from .._internal._utils import AbstractSetIntStr, MappingIntStrAny
+
+    AnyClassMethod = classmethod[Any, Any, Any]
+    TupleGenerator = typing.Generator[tuple[str, Any], None, None]
+    Model = typing.TypeVar('Model', bound='BaseModel')
+    # should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope
+    IncEx: typing_extensions.TypeAlias = 'set[int] | set[str] | dict[int, Any] | dict[str, Any] | None'
+
+_object_setattr = _model_construction.object_setattr
+
+
+def _iter(
+    self: BaseModel,
+    to_dict: bool = False,
+    by_alias: bool = False,
+    include: AbstractSetIntStr | MappingIntStrAny | None = None,
+    exclude: AbstractSetIntStr | MappingIntStrAny | None = 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:
+        exclude = _utils.ValueItems.merge(
+            {k: v.exclude for k, v in self.__pydantic_fields__.items() if v.exclude is not None}, exclude
+        )
+
+    if include is not None:
+        include = _utils.ValueItems.merge(dict.fromkeys(self.__pydantic_fields__, True), include, intersect=True)
+
+    allowed_keys = _calculate_keys(self, 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()
+        if self.__pydantic_extra__:
+            yield from self.__pydantic_extra__.items()
+        return
+
+    value_exclude = _utils.ValueItems(self, exclude) if exclude is not None else None
+    value_include = _utils.ValueItems(self, include) if include is not None else None
+
+    if self.__pydantic_extra__ is None:
+        items = self.__dict__.items()
+    else:
+        items = list(self.__dict__.items()) + list(self.__pydantic_extra__.items())
+
+    for field_key, v in 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:
+            try:
+                field = self.__pydantic_fields__[field_key]
+            except KeyError:
+                pass
+            else:
+                if not field.is_required() and field.default == v:
+                    continue
+
+        if by_alias and field_key in self.__pydantic_fields__:
+            dict_key = self.__pydantic_fields__[field_key].alias or field_key
+        else:
+            dict_key = field_key
+
+        if to_dict or value_include or value_exclude:
+            v = _get_value(
+                type(self),
+                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 _copy_and_set_values(
+    self: Model,
+    values: dict[str, Any],
+    fields_set: set[str],
+    extra: dict[str, Any] | None = None,
+    private: dict[str, Any] | None = None,
+    *,
+    deep: bool,  # UP006
+) -> Model:
+    if deep:
+        # chances of having empty dict here are quite low for using smart_deepcopy
+        values = deepcopy(values)
+        extra = deepcopy(extra)
+        private = deepcopy(private)
+
+    cls = self.__class__
+    m = cls.__new__(cls)
+    _object_setattr(m, '__dict__', values)
+    _object_setattr(m, '__pydantic_extra__', extra)
+    _object_setattr(m, '__pydantic_fields_set__', fields_set)
+    _object_setattr(m, '__pydantic_private__', private)
+
+    return m
+
+
+@typing.no_type_check
+def _get_value(
+    cls: type[BaseModel],
+    v: Any,
+    to_dict: bool,
+    by_alias: bool,
+    include: AbstractSetIntStr | MappingIntStrAny | None,
+    exclude: AbstractSetIntStr | MappingIntStrAny | None,
+    exclude_unset: bool,
+    exclude_defaults: bool,
+    exclude_none: bool,
+) -> Any:
+    from .. import BaseModel
+
+    if isinstance(v, BaseModel):
+        if to_dict:
+            return v.model_dump(
+                by_alias=by_alias,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                include=include,  # type: ignore
+                exclude=exclude,  # type: ignore
+                exclude_none=exclude_none,
+            )
+        else:
+            return v.copy(include=include, exclude=exclude)
+
+    value_exclude = _utils.ValueItems(v, exclude) if exclude else None
+    value_include = _utils.ValueItems(v, include) if include else None
+
+    if isinstance(v, dict):
+        return {
+            k_: _get_value(
+                cls,
+                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 _utils.sequence_like(v):
+        seq_args = (
+            _get_value(
+                cls,
+                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 _typing_extra.is_namedtuple(v.__class__) else v.__class__(seq_args)
+
+    elif isinstance(v, Enum) and getattr(cls.model_config, 'use_enum_values', False):
+        return v.value
+
+    else:
+        return v
+
+
+def _calculate_keys(
+    self: BaseModel,
+    include: MappingIntStrAny | None,
+    exclude: MappingIntStrAny | None,
+    exclude_unset: bool,
+    update: dict[str, Any] | None = None,  # noqa UP006
+) -> typing.AbstractSet[str] | None:
+    if include is None and exclude is None and exclude_unset is False:
+        return None
+
+    keys: typing.AbstractSet[str]
+    if exclude_unset:
+        keys = self.__pydantic_fields_set__.copy()
+    else:
+        keys = set(self.__dict__.keys())
+        keys = keys | (self.__pydantic_extra__ or {}).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 _utils.ValueItems.is_true(v)}
+
+    return keys

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/deprecated/decorator.py

@@ -0,0 +1,284 @@
+import warnings
+from collections.abc import Mapping
+from functools import wraps
+from typing import TYPE_CHECKING, Any, Callable, Optional, TypeVar, Union, overload
+
+from typing_extensions import deprecated
+
+from .._internal import _config, _typing_extra
+from ..alias_generators import to_pascal
+from ..errors import PydanticUserError
+from ..functional_validators import field_validator
+from ..main import BaseModel, create_model
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__all__ = ('validate_arguments',)
+
+if TYPE_CHECKING:
+    AnyCallable = Callable[..., Any]
+
+    AnyCallableT = TypeVar('AnyCallableT', bound=AnyCallable)
+    ConfigType = Union[None, type[Any], dict[str, Any]]
+
+
+@overload
+def validate_arguments(
+    func: None = None, *, config: 'ConfigType' = None
+) -> Callable[['AnyCallableT'], 'AnyCallableT']: ...
+
+
+@overload
+def validate_arguments(func: 'AnyCallableT') -> 'AnyCallableT': ...
+
+
+@deprecated(
+    'The `validate_arguments` method is deprecated; use `validate_call` instead.',
+    category=None,
+)
+def validate_arguments(func: Optional['AnyCallableT'] = None, *, config: 'ConfigType' = None) -> Any:
+    """Decorator to validate the arguments passed to a function."""
+    warnings.warn(
+        'The `validate_arguments` method is deprecated; use `validate_call` instead.',
+        PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+
+    def validate(_func: 'AnyCallable') -> 'AnyCallable':
+        vd = ValidatedFunction(_func, config)
+
+        @wraps(_func)
+        def wrapper_function(*args: Any, **kwargs: Any) -> Any:
+            return vd.call(*args, **kwargs)
+
+        wrapper_function.vd = vd  # type: ignore
+        wrapper_function.validate = vd.init_model_instance  # type: ignore
+        wrapper_function.raw_function = vd.raw_function  # type: ignore
+        wrapper_function.model = vd.model  # type: ignore
+        return wrapper_function
+
+    if func:
+        return validate(func)
+    else:
+        return validate
+
+
+ALT_V_ARGS = 'v__args'
+ALT_V_KWARGS = 'v__kwargs'
+V_POSITIONAL_ONLY_NAME = 'v__positional_only'
+V_DUPLICATE_KWARGS = 'v__duplicate_kwargs'
+
+
+class ValidatedFunction:
+    def __init__(self, function: 'AnyCallable', config: 'ConfigType'):
+        from inspect import Parameter, signature
+
+        parameters: Mapping[str, Parameter] = signature(function).parameters
+
+        if parameters.keys() & {ALT_V_ARGS, ALT_V_KWARGS, V_POSITIONAL_ONLY_NAME, V_DUPLICATE_KWARGS}:
+            raise PydanticUserError(
+                f'"{ALT_V_ARGS}", "{ALT_V_KWARGS}", "{V_POSITIONAL_ONLY_NAME}" and "{V_DUPLICATE_KWARGS}" '
+                f'are not permitted as argument names when using the "{validate_arguments.__name__}" decorator',
+                code=None,
+            )
+
+        self.raw_function = function
+        self.arg_mapping: dict[int, str] = {}
+        self.positional_only_args: set[str] = set()
+        self.v_args_name = 'args'
+        self.v_kwargs_name = 'kwargs'
+
+        type_hints = _typing_extra.get_type_hints(function, include_extras=True)
+        takes_args = False
+        takes_kwargs = False
+        fields: dict[str, tuple[Any, Any]] = {}
+        for i, (name, p) in enumerate(parameters.items()):
+            if p.annotation is p.empty:
+                annotation = Any
+            else:
+                annotation = type_hints[name]
+
+            default = ... if p.default is p.empty else p.default
+            if p.kind == Parameter.POSITIONAL_ONLY:
+                self.arg_mapping[i] = name
+                fields[name] = annotation, default
+                fields[V_POSITIONAL_ONLY_NAME] = list[str], None
+                self.positional_only_args.add(name)
+            elif p.kind == Parameter.POSITIONAL_OR_KEYWORD:
+                self.arg_mapping[i] = name
+                fields[name] = annotation, default
+                fields[V_DUPLICATE_KWARGS] = list[str], None
+            elif p.kind == Parameter.KEYWORD_ONLY:
+                fields[name] = annotation, default
+            elif p.kind == Parameter.VAR_POSITIONAL:
+                self.v_args_name = name
+                fields[name] = tuple[annotation, ...], None
+                takes_args = True
+            else:
+                assert p.kind == Parameter.VAR_KEYWORD, p.kind
+                self.v_kwargs_name = name
+                fields[name] = dict[str, annotation], None
+                takes_kwargs = True
+
+        # these checks avoid a clash between "args" and a field with that name
+        if not takes_args and self.v_args_name in fields:
+            self.v_args_name = ALT_V_ARGS
+
+        # same with "kwargs"
+        if not takes_kwargs and self.v_kwargs_name in fields:
+            self.v_kwargs_name = ALT_V_KWARGS
+
+        if not takes_args:
+            # we add the field so validation below can raise the correct exception
+            fields[self.v_args_name] = list[Any], None
+
+        if not takes_kwargs:
+            # same with kwargs
+            fields[self.v_kwargs_name] = dict[Any, Any], None
+
+        self.create_model(fields, takes_args, takes_kwargs, config)
+
+    def init_model_instance(self, *args: Any, **kwargs: Any) -> BaseModel:
+        values = self.build_values(args, kwargs)
+        return self.model(**values)
+
+    def call(self, *args: Any, **kwargs: Any) -> Any:
+        m = self.init_model_instance(*args, **kwargs)
+        return self.execute(m)
+
+    def build_values(self, args: tuple[Any, ...], kwargs: dict[str, Any]) -> dict[str, Any]:
+        values: dict[str, Any] = {}
+        if args:
+            arg_iter = enumerate(args)
+            while True:
+                try:
+                    i, a = next(arg_iter)
+                except StopIteration:
+                    break
+                arg_name = self.arg_mapping.get(i)
+                if arg_name is not None:
+                    values[arg_name] = a
+                else:
+                    values[self.v_args_name] = [a] + [a for _, a in arg_iter]
+                    break
+
+        var_kwargs: dict[str, Any] = {}
+        wrong_positional_args = []
+        duplicate_kwargs = []
+        fields_alias = [
+            field.alias
+            for name, field in self.model.__pydantic_fields__.items()
+            if name not in (self.v_args_name, self.v_kwargs_name)
+        ]
+        non_var_fields = set(self.model.__pydantic_fields__) - {self.v_args_name, self.v_kwargs_name}
+        for k, v in kwargs.items():
+            if k in non_var_fields or k in fields_alias:
+                if k in self.positional_only_args:
+                    wrong_positional_args.append(k)
+                if k in values:
+                    duplicate_kwargs.append(k)
+                values[k] = v
+            else:
+                var_kwargs[k] = v
+
+        if var_kwargs:
+            values[self.v_kwargs_name] = var_kwargs
+        if wrong_positional_args:
+            values[V_POSITIONAL_ONLY_NAME] = wrong_positional_args
+        if duplicate_kwargs:
+            values[V_DUPLICATE_KWARGS] = duplicate_kwargs
+        return values
+
+    def execute(self, m: BaseModel) -> Any:
+        d = {
+            k: v
+            for k, v in m.__dict__.items()
+            if k in m.__pydantic_fields_set__ or m.__pydantic_fields__[k].default_factory
+        }
+        var_kwargs = d.pop(self.v_kwargs_name, {})
+
+        if self.v_args_name in d:
+            args_: list[Any] = []
+            in_kwargs = False
+            kwargs = {}
+            for name, value in d.items():
+                if in_kwargs:
+                    kwargs[name] = value
+                elif name == self.v_args_name:
+                    args_ += value
+                    in_kwargs = True
+                else:
+                    args_.append(value)
+            return self.raw_function(*args_, **kwargs, **var_kwargs)
+        elif self.positional_only_args:
+            args_ = []
+            kwargs = {}
+            for name, value in d.items():
+                if name in self.positional_only_args:
+                    args_.append(value)
+                else:
+                    kwargs[name] = value
+            return self.raw_function(*args_, **kwargs, **var_kwargs)
+        else:
+            return self.raw_function(**d, **var_kwargs)
+
+    def create_model(self, fields: dict[str, Any], takes_args: bool, takes_kwargs: bool, config: 'ConfigType') -> None:
+        pos_args = len(self.arg_mapping)
+
+        config_wrapper = _config.ConfigWrapper(config)
+
+        if config_wrapper.alias_generator:
+            raise PydanticUserError(
+                'Setting the "alias_generator" property on custom Config for '
+                '@validate_arguments is not yet supported, please remove.',
+                code=None,
+            )
+        if config_wrapper.extra is None:
+            config_wrapper.config_dict['extra'] = 'forbid'
+
+        class DecoratorBaseModel(BaseModel):
+            @field_validator(self.v_args_name, check_fields=False)
+            @classmethod
+            def check_args(cls, v: Optional[list[Any]]) -> Optional[list[Any]]:
+                if takes_args or v is None:
+                    return v
+
+                raise TypeError(f'{pos_args} positional arguments expected but {pos_args + len(v)} given')
+
+            @field_validator(self.v_kwargs_name, check_fields=False)
+            @classmethod
+            def check_kwargs(cls, v: Optional[dict[str, Any]]) -> Optional[dict[str, Any]]:
+                if takes_kwargs or v is None:
+                    return v
+
+                plural = '' if len(v) == 1 else 's'
+                keys = ', '.join(map(repr, v.keys()))
+                raise TypeError(f'unexpected keyword argument{plural}: {keys}')
+
+            @field_validator(V_POSITIONAL_ONLY_NAME, check_fields=False)
+            @classmethod
+            def check_positional_only(cls, v: Optional[list[str]]) -> None:
+                if v is None:
+                    return
+
+                plural = '' if len(v) == 1 else 's'
+                keys = ', '.join(map(repr, v))
+                raise TypeError(f'positional-only argument{plural} passed as keyword argument{plural}: {keys}')
+
+            @field_validator(V_DUPLICATE_KWARGS, check_fields=False)
+            @classmethod
+            def check_duplicate_kwargs(cls, v: Optional[list[str]]) -> None:
+                if v is None:
+                    return
+
+                plural = '' if len(v) == 1 else 's'
+                keys = ', '.join(map(repr, v))
+                raise TypeError(f'multiple values for argument{plural}: {keys}')
+
+            model_config = config_wrapper.config_dict
+
+        self.model = create_model(to_pascal(self.raw_function.__name__), __base__=DecoratorBaseModel, **fields)

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/deprecated/json.py

@@ -0,0 +1,141 @@
+import datetime
+import warnings
+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 TYPE_CHECKING, Any, Callable, Union
+from uuid import UUID
+
+from typing_extensions import deprecated
+
+from .._internal._import_utils import import_cached_base_model
+from ..color import Color
+from ..networks import NameEmail
+from ..types import SecretBytes, SecretStr
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__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
+    """
+    exponent = dec_value.as_tuple().exponent
+    if isinstance(exponent, int) and 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,
+}
+
+
+@deprecated(
+    '`pydantic_encoder` is deprecated, use `pydantic_core.to_jsonable_python` instead.',
+    category=None,
+)
+def pydantic_encoder(obj: Any) -> Any:
+    warnings.warn(
+        '`pydantic_encoder` is deprecated, use `pydantic_core.to_jsonable_python` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    from dataclasses import asdict, is_dataclass
+
+    BaseModel = import_cached_base_model()
+
+    if isinstance(obj, BaseModel):
+        return obj.model_dump()
+    elif is_dataclass(obj):
+        return asdict(obj)  # type: ignore
+
+    # 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")
+
+
+# TODO: Add a suggested migration path once there is a way to use custom encoders
+@deprecated(
+    '`custom_pydantic_encoder` is deprecated, use `BaseModel.model_dump` instead.',
+    category=None,
+)
+def custom_pydantic_encoder(type_encoders: dict[Any, Callable[[type[Any]], Any]], obj: Any) -> Any:
+    warnings.warn(
+        '`custom_pydantic_encoder` is deprecated, use `BaseModel.model_dump` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    # 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)
+
+
+@deprecated('`timedelta_isoformat` is deprecated.', category=None)
+def timedelta_isoformat(td: datetime.timedelta) -> str:
+    """ISO 8601 encoding for Python timedelta object."""
+    warnings.warn('`timedelta_isoformat` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2)
+    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'

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/deprecated/parse.py

@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+import json
+import pickle
+import warnings
+from enum import Enum
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Callable
+
+from typing_extensions import deprecated
+
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+
+class Protocol(str, Enum):
+    json = 'json'
+    pickle = 'pickle'
+
+
+@deprecated('`load_str_bytes` is deprecated.', category=None)
+def load_str_bytes(
+    b: str | bytes,
+    *,
+    content_type: str | None = None,
+    encoding: str = 'utf8',
+    proto: Protocol | None = None,
+    allow_pickle: bool = False,
+    json_loads: Callable[[str], Any] = json.loads,
+) -> Any:
+    warnings.warn('`load_str_bytes` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2)
+    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)  # type: ignore
+    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()  # type: ignore
+        return pickle.loads(bb)
+    else:
+        raise TypeError(f'Unknown protocol: {proto}')
+
+
+@deprecated('`load_file` is deprecated.', category=None)
+def load_file(
+    path: str | Path,
+    *,
+    content_type: str | None = None,
+    encoding: str = 'utf8',
+    proto: Protocol | None = None,
+    allow_pickle: bool = False,
+    json_loads: Callable[[str], Any] = json.loads,
+) -> Any:
+    warnings.warn('`load_file` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2)
+    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
+    )

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/deprecated/tools.py

@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+import json
+import warnings
+from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union
+
+from typing_extensions import deprecated
+
+from ..json_schema import DEFAULT_REF_TEMPLATE, GenerateJsonSchema
+from ..type_adapter import TypeAdapter
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__all__ = 'parse_obj_as', 'schema_of', 'schema_json_of'
+
+NameFactory = Union[str, Callable[[type[Any]], str]]
+
+
+T = TypeVar('T')
+
+
+@deprecated(
+    '`parse_obj_as` is deprecated. Use `pydantic.TypeAdapter.validate_python` instead.',
+    category=None,
+)
+def parse_obj_as(type_: type[T], obj: Any, type_name: NameFactory | None = None) -> T:
+    warnings.warn(
+        '`parse_obj_as` is deprecated. Use `pydantic.TypeAdapter.validate_python` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    if type_name is not None:  # pragma: no cover
+        warnings.warn(
+            'The type_name parameter is deprecated. parse_obj_as no longer creates temporary models',
+            DeprecationWarning,
+            stacklevel=2,
+        )
+    return TypeAdapter(type_).validate_python(obj)
+
+
+@deprecated(
+    '`schema_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
+    category=None,
+)
+def schema_of(
+    type_: Any,
+    *,
+    title: NameFactory | None = None,
+    by_alias: bool = True,
+    ref_template: str = DEFAULT_REF_TEMPLATE,
+    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+) -> dict[str, Any]:
+    """Generate a JSON schema (as dict) for the passed model or dynamically generated one."""
+    warnings.warn(
+        '`schema_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    res = TypeAdapter(type_).json_schema(
+        by_alias=by_alias,
+        schema_generator=schema_generator,
+        ref_template=ref_template,
+    )
+    if title is not None:
+        if isinstance(title, str):
+            res['title'] = title
+        else:
+            warnings.warn(
+                'Passing a callable for the `title` parameter is deprecated and no longer supported',
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            res['title'] = title(type_)
+    return res
+
+
+@deprecated(
+    '`schema_json_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
+    category=None,
+)
+def schema_json_of(
+    type_: Any,
+    *,
+    title: NameFactory | None = None,
+    by_alias: bool = True,
+    ref_template: str = DEFAULT_REF_TEMPLATE,
+    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+    **dumps_kwargs: Any,
+) -> str:
+    """Generate a JSON schema (as JSON) for the passed model or dynamically generated one."""
+    warnings.warn(
+        '`schema_json_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    return json.dumps(
+        schema_of(type_, title=title, by_alias=by_alias, ref_template=ref_template, schema_generator=schema_generator),
+        **dumps_kwargs,
+    )

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/experimental/__init__.py

@@ -0,0 +1 @@
+"""The "experimental" module of pydantic contains potential new features that are subject to change."""

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/experimental/arguments_schema.py

@@ -0,0 +1,44 @@
+"""Experimental module exposing a function to generate a core schema that validates callable arguments."""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import Any, Literal
+
+from pydantic_core import CoreSchema
+
+from pydantic import ConfigDict
+from pydantic._internal import _config, _generate_schema, _namespace_utils
+
+
+def generate_arguments_schema(
+    func: Callable[..., Any],
+    schema_type: Literal['arguments', 'arguments-v3'] = 'arguments-v3',
+    parameters_callback: Callable[[int, str, Any], Literal['skip'] | None] | None = None,
+    config: ConfigDict | None = None,
+) -> CoreSchema:
+    """Generate the schema for the arguments of a function.
+
+    Args:
+        func: The function to generate the schema for.
+        schema_type: The type of schema to generate.
+        parameters_callback: A callable that will be invoked for each parameter. The callback
+            should take three required arguments: the index, the name and the type annotation
+            (or [`Parameter.empty`][inspect.Parameter.empty] if not annotated) of the parameter.
+            The callback can optionally return `'skip'`, so that the parameter gets excluded
+            from the resulting schema.
+        config: The configuration to use.
+
+    Returns:
+        The generated schema.
+    """
+    generate_schema = _generate_schema.GenerateSchema(
+        _config.ConfigWrapper(config),
+        ns_resolver=_namespace_utils.NsResolver(namespaces_tuple=_namespace_utils.ns_for_function(func)),
+    )
+
+    if schema_type == 'arguments':
+        schema = generate_schema._arguments_schema(func, parameters_callback)  # pyright: ignore[reportArgumentType]
+    else:
+        schema = generate_schema._arguments_v3_schema(func, parameters_callback)  # pyright: ignore[reportArgumentType]
+    return generate_schema.clean_schema(schema)

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/experimental/missing_sentinel.py

@@ -0,0 +1,5 @@
+"""Experimental module exposing a function a `MISSING` sentinel."""
+
+from pydantic_core import MISSING
+
+__all__ = ('MISSING',)

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/experimental/pipeline.py

@@ -0,0 +1,663 @@
+"""Experimental pipeline API functionality. Be careful with this API, it's subject to change."""
+
+from __future__ import annotations
+
+import datetime
+import operator
+import re
+import sys
+from collections import deque
+from collections.abc import Container
+from dataclasses import dataclass
+from decimal import Decimal
+from functools import cached_property, partial
+from re import Pattern
+from typing import TYPE_CHECKING, Annotated, Any, Callable, Generic, Protocol, TypeVar, Union, overload
+
+import annotated_types
+
+if TYPE_CHECKING:
+    from pydantic import GetCoreSchemaHandler
+
+from pydantic_core import PydanticCustomError
+from pydantic_core import core_schema as cs
+
+from pydantic import Strict
+from pydantic._internal._internal_dataclass import slots_true as _slots_true
+
+if sys.version_info < (3, 10):
+    EllipsisType = type(Ellipsis)
+else:
+    from types import EllipsisType
+
+__all__ = ['validate_as', 'validate_as_deferred', 'transform']
+
+_slots_frozen = {**_slots_true, 'frozen': True}
+
+
+@dataclass(**_slots_frozen)
+class _ValidateAs:
+    tp: type[Any]
+    strict: bool = False
+
+
+@dataclass
+class _ValidateAsDefer:
+    func: Callable[[], type[Any]]
+
+    @cached_property
+    def tp(self) -> type[Any]:
+        return self.func()
+
+
+@dataclass(**_slots_frozen)
+class _Transform:
+    func: Callable[[Any], Any]
+
+
+@dataclass(**_slots_frozen)
+class _PipelineOr:
+    left: _Pipeline[Any, Any]
+    right: _Pipeline[Any, Any]
+
+
+@dataclass(**_slots_frozen)
+class _PipelineAnd:
+    left: _Pipeline[Any, Any]
+    right: _Pipeline[Any, Any]
+
+
+@dataclass(**_slots_frozen)
+class _Eq:
+    value: Any
+
+
+@dataclass(**_slots_frozen)
+class _NotEq:
+    value: Any
+
+
+@dataclass(**_slots_frozen)
+class _In:
+    values: Container[Any]
+
+
+@dataclass(**_slots_frozen)
+class _NotIn:
+    values: Container[Any]
+
+
+_ConstraintAnnotation = Union[
+    annotated_types.Le,
+    annotated_types.Ge,
+    annotated_types.Lt,
+    annotated_types.Gt,
+    annotated_types.Len,
+    annotated_types.MultipleOf,
+    annotated_types.Timezone,
+    annotated_types.Interval,
+    annotated_types.Predicate,
+    # common predicates not included in annotated_types
+    _Eq,
+    _NotEq,
+    _In,
+    _NotIn,
+    # regular expressions
+    Pattern[str],
+]
+
+
+@dataclass(**_slots_frozen)
+class _Constraint:
+    constraint: _ConstraintAnnotation
+
+
+_Step = Union[_ValidateAs, _ValidateAsDefer, _Transform, _PipelineOr, _PipelineAnd, _Constraint]
+
+_InT = TypeVar('_InT')
+_OutT = TypeVar('_OutT')
+_NewOutT = TypeVar('_NewOutT')
+
+
+class _FieldTypeMarker:
+    pass
+
+
+# TODO: ultimately, make this public, see https://github.com/pydantic/pydantic/pull/9459#discussion_r1628197626
+# Also, make this frozen eventually, but that doesn't work right now because of the generic base
+# Which attempts to modify __orig_base__ and such.
+# We could go with a manual freeze, but that seems overkill for now.
+@dataclass(**_slots_true)
+class _Pipeline(Generic[_InT, _OutT]):
+    """Abstract representation of a chain of validation, transformation, and parsing steps."""
+
+    _steps: tuple[_Step, ...]
+
+    def transform(
+        self,
+        func: Callable[[_OutT], _NewOutT],
+    ) -> _Pipeline[_InT, _NewOutT]:
+        """Transform the output of the previous step.
+
+        If used as the first step in a pipeline, the type of the field is used.
+        That is, the transformation is applied to after the value is parsed to the field's type.
+        """
+        return _Pipeline[_InT, _NewOutT](self._steps + (_Transform(func),))
+
+    @overload
+    def validate_as(self, tp: type[_NewOutT], *, strict: bool = False) -> _Pipeline[_InT, _NewOutT]: ...
+
+    @overload
+    def validate_as(
+        self,
+        tp: ellipsis,  # noqa: F821  # TODO: use `_typing_extra.EllipsisType` when we drop Py3.9
+        *,
+        strict: bool = False,
+    ) -> _Pipeline[_InT, Any]: ...
+
+    # TODO PEP 747: use TypeForm to properly type Annotated aliases (e.g. NewPath, FilePath).
+    # This fallback accepts any type expression but loses generic type inference.
+    @overload
+    def validate_as(self, tp: Any, *, strict: bool = ...) -> _Pipeline[_InT, Any]: ...
+
+    def validate_as(self, tp: type[_NewOutT] | EllipsisType | Any, *, strict: bool = False) -> _Pipeline[_InT, Any]:  # type: ignore
+        """Validate / parse the input into a new type.
+
+        If no type is provided, the type of the field is used.
+
+        Types are parsed in Pydantic's `lax` mode by default,
+        but you can enable `strict` mode by passing `strict=True`.
+        """
+        if isinstance(tp, EllipsisType):
+            return _Pipeline[_InT, Any](self._steps + (_ValidateAs(_FieldTypeMarker, strict=strict),))
+        return _Pipeline[_InT, _NewOutT](self._steps + (_ValidateAs(tp, strict=strict),))
+
+    def validate_as_deferred(self, func: Callable[[], type[_NewOutT]]) -> _Pipeline[_InT, _NewOutT]:
+        """Parse the input into a new type, deferring resolution of the type until the current class
+        is fully defined.
+
+        This is useful when you need to reference the class in it's own type annotations.
+        """
+        return _Pipeline[_InT, _NewOutT](self._steps + (_ValidateAsDefer(func),))
+
+    # constraints
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutGe], constraint: annotated_types.Ge) -> _Pipeline[_InT, _NewOutGe]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutGt], constraint: annotated_types.Gt) -> _Pipeline[_InT, _NewOutGt]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutLe], constraint: annotated_types.Le) -> _Pipeline[_InT, _NewOutLe]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutLt], constraint: annotated_types.Lt) -> _Pipeline[_InT, _NewOutLt]: ...
+
+    @overload
+    def constrain(
+        self: _Pipeline[_InT, _NewOutLen], constraint: annotated_types.Len
+    ) -> _Pipeline[_InT, _NewOutLen]: ...
+
+    @overload
+    def constrain(
+        self: _Pipeline[_InT, _NewOutT], constraint: annotated_types.MultipleOf
+    ) -> _Pipeline[_InT, _NewOutT]: ...
+
+    @overload
+    def constrain(
+        self: _Pipeline[_InT, _NewOutDatetime], constraint: annotated_types.Timezone
+    ) -> _Pipeline[_InT, _NewOutDatetime]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: annotated_types.Predicate) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(
+        self: _Pipeline[_InT, _NewOutInterval], constraint: annotated_types.Interval
+    ) -> _Pipeline[_InT, _NewOutInterval]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: _Eq) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: _NotEq) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: _In) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: _NotIn) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutT], constraint: Pattern[str]) -> _Pipeline[_InT, _NewOutT]: ...
+
+    def constrain(self, constraint: _ConstraintAnnotation) -> Any:
+        """Constrain a value to meet a certain condition.
+
+        We support most conditions from `annotated_types`, as well as regular expressions.
+
+        Most of the time you'll be calling a shortcut method like `gt`, `lt`, `len`, etc
+        so you don't need to call this directly.
+        """
+        return _Pipeline[_InT, _OutT](self._steps + (_Constraint(constraint),))
+
+    def predicate(self: _Pipeline[_InT, _NewOutT], func: Callable[[_NewOutT], bool]) -> _Pipeline[_InT, _NewOutT]:
+        """Constrain a value to meet a certain predicate."""
+        return self.constrain(annotated_types.Predicate(func))
+
+    def gt(self: _Pipeline[_InT, _NewOutGt], gt: _NewOutGt) -> _Pipeline[_InT, _NewOutGt]:
+        """Constrain a value to be greater than a certain value."""
+        return self.constrain(annotated_types.Gt(gt))
+
+    def lt(self: _Pipeline[_InT, _NewOutLt], lt: _NewOutLt) -> _Pipeline[_InT, _NewOutLt]:
+        """Constrain a value to be less than a certain value."""
+        return self.constrain(annotated_types.Lt(lt))
+
+    def ge(self: _Pipeline[_InT, _NewOutGe], ge: _NewOutGe) -> _Pipeline[_InT, _NewOutGe]:
+        """Constrain a value to be greater than or equal to a certain value."""
+        return self.constrain(annotated_types.Ge(ge))
+
+    def le(self: _Pipeline[_InT, _NewOutLe], le: _NewOutLe) -> _Pipeline[_InT, _NewOutLe]:
+        """Constrain a value to be less than or equal to a certain value."""
+        return self.constrain(annotated_types.Le(le))
+
+    def len(self: _Pipeline[_InT, _NewOutLen], min_len: int, max_len: int | None = None) -> _Pipeline[_InT, _NewOutLen]:
+        """Constrain a value to have a certain length."""
+        return self.constrain(annotated_types.Len(min_len, max_len))
+
+    @overload
+    def multiple_of(self: _Pipeline[_InT, _NewOutDiv], multiple_of: _NewOutDiv) -> _Pipeline[_InT, _NewOutDiv]: ...
+
+    @overload
+    def multiple_of(self: _Pipeline[_InT, _NewOutMod], multiple_of: _NewOutMod) -> _Pipeline[_InT, _NewOutMod]: ...
+
+    def multiple_of(self: _Pipeline[_InT, Any], multiple_of: Any) -> _Pipeline[_InT, Any]:
+        """Constrain a value to be a multiple of a certain number."""
+        return self.constrain(annotated_types.MultipleOf(multiple_of))
+
+    def eq(self: _Pipeline[_InT, _OutT], value: _OutT) -> _Pipeline[_InT, _OutT]:
+        """Constrain a value to be equal to a certain value."""
+        return self.constrain(_Eq(value))
+
+    def not_eq(self: _Pipeline[_InT, _OutT], value: _OutT) -> _Pipeline[_InT, _OutT]:
+        """Constrain a value to not be equal to a certain value."""
+        return self.constrain(_NotEq(value))
+
+    def in_(self: _Pipeline[_InT, _OutT], values: Container[_OutT]) -> _Pipeline[_InT, _OutT]:
+        """Constrain a value to be in a certain set."""
+        return self.constrain(_In(values))
+
+    def not_in(self: _Pipeline[_InT, _OutT], values: Container[_OutT]) -> _Pipeline[_InT, _OutT]:
+        """Constrain a value to not be in a certain set."""
+        return self.constrain(_NotIn(values))
+
+    # timezone methods
+    def datetime_tz_naive(self: _Pipeline[_InT, datetime.datetime]) -> _Pipeline[_InT, datetime.datetime]:
+        return self.constrain(annotated_types.Timezone(None))
+
+    def datetime_tz_aware(self: _Pipeline[_InT, datetime.datetime]) -> _Pipeline[_InT, datetime.datetime]:
+        return self.constrain(annotated_types.Timezone(...))
+
+    def datetime_tz(
+        self: _Pipeline[_InT, datetime.datetime], tz: datetime.tzinfo
+    ) -> _Pipeline[_InT, datetime.datetime]:
+        return self.constrain(annotated_types.Timezone(tz))  # type: ignore
+
+    def datetime_with_tz(
+        self: _Pipeline[_InT, datetime.datetime], tz: datetime.tzinfo | None
+    ) -> _Pipeline[_InT, datetime.datetime]:
+        return self.transform(partial(datetime.datetime.replace, tzinfo=tz))
+
+    # string methods
+    def str_lower(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]:
+        return self.transform(str.lower)
+
+    def str_upper(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]:
+        return self.transform(str.upper)
+
+    def str_title(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]:
+        return self.transform(str.title)
+
+    def str_strip(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]:
+        return self.transform(str.strip)
+
+    def str_pattern(self: _Pipeline[_InT, str], pattern: str) -> _Pipeline[_InT, str]:
+        return self.constrain(re.compile(pattern))
+
+    def str_contains(self: _Pipeline[_InT, str], substring: str) -> _Pipeline[_InT, str]:
+        return self.predicate(lambda v: substring in v)
+
+    def str_starts_with(self: _Pipeline[_InT, str], prefix: str) -> _Pipeline[_InT, str]:
+        return self.predicate(lambda v: v.startswith(prefix))
+
+    def str_ends_with(self: _Pipeline[_InT, str], suffix: str) -> _Pipeline[_InT, str]:
+        return self.predicate(lambda v: v.endswith(suffix))
+
+    # operators
+    def otherwise(self, other: _Pipeline[_OtherIn, _OtherOut]) -> _Pipeline[_InT | _OtherIn, _OutT | _OtherOut]:
+        """Combine two validation chains, returning the result of the first chain if it succeeds, and the second chain if it fails."""
+        return _Pipeline((_PipelineOr(self, other),))
+
+    __or__ = otherwise
+
+    def then(self, other: _Pipeline[_OutT, _OtherOut]) -> _Pipeline[_InT, _OtherOut]:
+        """Pipe the result of one validation chain into another."""
+        return _Pipeline((_PipelineAnd(self, other),))
+
+    __and__ = then
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> cs.CoreSchema:
+        queue = deque(self._steps)
+
+        s = None
+
+        while queue:
+            step = queue.popleft()
+            s = _apply_step(step, s, handler, source_type)
+
+        s = s or cs.any_schema()
+        return s
+
+    def __supports_type__(self, _: _OutT) -> bool:
+        raise NotImplementedError
+
+
+validate_as = _Pipeline[Any, Any](()).validate_as
+validate_as_deferred = _Pipeline[Any, Any](()).validate_as_deferred
+transform = _Pipeline[Any, Any]((_ValidateAs(_FieldTypeMarker),)).transform
+
+
+def _check_func(
+    func: Callable[[Any], bool], predicate_err: str | Callable[[], str], s: cs.CoreSchema | None
+) -> cs.CoreSchema:
+    def handler(v: Any) -> Any:
+        if func(v):
+            return v
+        raise ValueError(f'Expected {predicate_err if isinstance(predicate_err, str) else predicate_err()}')
+
+    if s is None:
+        return cs.no_info_plain_validator_function(handler)
+    else:
+        return cs.no_info_after_validator_function(handler, s)
+
+
+def _apply_step(step: _Step, s: cs.CoreSchema | None, handler: GetCoreSchemaHandler, source_type: Any) -> cs.CoreSchema:
+    if isinstance(step, _ValidateAs):
+        s = _apply_parse(s, step.tp, step.strict, handler, source_type)
+    elif isinstance(step, _ValidateAsDefer):
+        s = _apply_parse(s, step.tp, False, handler, source_type)
+    elif isinstance(step, _Transform):
+        s = _apply_transform(s, step.func, handler)
+    elif isinstance(step, _Constraint):
+        s = _apply_constraint(s, step.constraint)
+    elif isinstance(step, _PipelineOr):
+        s = cs.union_schema([handler(step.left), handler(step.right)])
+    else:
+        assert isinstance(step, _PipelineAnd)
+        s = cs.chain_schema([handler(step.left), handler(step.right)])
+    return s
+
+
+def _apply_parse(
+    s: cs.CoreSchema | None,
+    tp: type[Any],
+    strict: bool,
+    handler: GetCoreSchemaHandler,
+    source_type: Any,
+) -> cs.CoreSchema:
+    if tp is _FieldTypeMarker:
+        return cs.chain_schema([s, handler(source_type)]) if s else handler(source_type)
+
+    if strict:
+        tp = Annotated[tp, Strict()]  # type: ignore
+
+    if s and s['type'] == 'any':
+        return handler(tp)
+    else:
+        return cs.chain_schema([s, handler(tp)]) if s else handler(tp)
+
+
+def _apply_transform(
+    s: cs.CoreSchema | None, func: Callable[[Any], Any], handler: GetCoreSchemaHandler
+) -> cs.CoreSchema:
+    if s is None:
+        return cs.no_info_plain_validator_function(func)
+
+    if s['type'] == 'str':
+        if func is str.strip:
+            s = s.copy()
+            s['strip_whitespace'] = True
+            return s
+        elif func is str.lower:
+            s = s.copy()
+            s['to_lower'] = True
+            return s
+        elif func is str.upper:
+            s = s.copy()
+            s['to_upper'] = True
+            return s
+
+    return cs.no_info_after_validator_function(func, s)
+
+
+def _apply_constraint(  # noqa: C901
+    s: cs.CoreSchema | None, constraint: _ConstraintAnnotation
+) -> cs.CoreSchema:
+    """Apply a single constraint to a schema."""
+    if isinstance(constraint, annotated_types.Gt):
+        gt = constraint.gt
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(gt, int):
+                s['gt'] = gt
+            elif s['type'] == 'float' and isinstance(gt, float):
+                s['gt'] = gt
+            elif s['type'] == 'decimal' and isinstance(gt, Decimal):
+                s['gt'] = gt
+        else:
+
+            def check_gt(v: Any) -> bool:
+                return v > gt
+
+            s = _check_func(check_gt, f'> {gt}', s)
+    elif isinstance(constraint, annotated_types.Ge):
+        ge = constraint.ge
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(ge, int):
+                s['ge'] = ge
+            elif s['type'] == 'float' and isinstance(ge, float):
+                s['ge'] = ge
+            elif s['type'] == 'decimal' and isinstance(ge, Decimal):
+                s['ge'] = ge
+
+        def check_ge(v: Any) -> bool:
+            return v >= ge
+
+        s = _check_func(check_ge, f'>= {ge}', s)
+    elif isinstance(constraint, annotated_types.Lt):
+        lt = constraint.lt
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(lt, int):
+                s['lt'] = lt
+            elif s['type'] == 'float' and isinstance(lt, float):
+                s['lt'] = lt
+            elif s['type'] == 'decimal' and isinstance(lt, Decimal):
+                s['lt'] = lt
+
+        def check_lt(v: Any) -> bool:
+            return v < lt
+
+        s = _check_func(check_lt, f'< {lt}', s)
+    elif isinstance(constraint, annotated_types.Le):
+        le = constraint.le
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(le, int):
+                s['le'] = le
+            elif s['type'] == 'float' and isinstance(le, float):
+                s['le'] = le
+            elif s['type'] == 'decimal' and isinstance(le, Decimal):
+                s['le'] = le
+
+        def check_le(v: Any) -> bool:
+            return v <= le
+
+        s = _check_func(check_le, f'<= {le}', s)
+    elif isinstance(constraint, annotated_types.Len):
+        min_len = constraint.min_length
+        max_len = constraint.max_length
+
+        if s and s['type'] in {'str', 'list', 'tuple', 'set', 'frozenset', 'dict'}:
+            assert (
+                s['type'] == 'str'
+                or s['type'] == 'list'
+                or s['type'] == 'tuple'
+                or s['type'] == 'set'
+                or s['type'] == 'dict'
+                or s['type'] == 'frozenset'
+            )
+            s = s.copy()
+            if min_len != 0:
+                s['min_length'] = min_len
+            if max_len is not None:
+                s['max_length'] = max_len
+
+        def check_len(v: Any) -> bool:
+            if max_len is not None:
+                return (min_len <= len(v)) and (len(v) <= max_len)
+            return min_len <= len(v)
+
+        s = _check_func(check_len, f'length >= {min_len} and length <= {max_len}', s)
+    elif isinstance(constraint, annotated_types.MultipleOf):
+        multiple_of = constraint.multiple_of
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(multiple_of, int):
+                s['multiple_of'] = multiple_of
+            elif s['type'] == 'float' and isinstance(multiple_of, float):
+                s['multiple_of'] = multiple_of
+            elif s['type'] == 'decimal' and isinstance(multiple_of, Decimal):
+                s['multiple_of'] = multiple_of
+
+        def check_multiple_of(v: Any) -> bool:
+            return v % multiple_of == 0
+
+        s = _check_func(check_multiple_of, f'% {multiple_of} == 0', s)
+    elif isinstance(constraint, annotated_types.Timezone):
+        tz = constraint.tz
+
+        if tz is ...:
+            if s and s['type'] == 'datetime':
+                s = s.copy()
+                s['tz_constraint'] = 'aware'
+            else:
+
+                def check_tz_aware(v: object) -> bool:
+                    assert isinstance(v, datetime.datetime)
+                    return v.tzinfo is not None
+
+                s = _check_func(check_tz_aware, 'timezone aware', s)
+        elif tz is None:
+            if s and s['type'] == 'datetime':
+                s = s.copy()
+                s['tz_constraint'] = 'naive'
+            else:
+
+                def check_tz_naive(v: object) -> bool:
+                    assert isinstance(v, datetime.datetime)
+                    return v.tzinfo is None
+
+                s = _check_func(check_tz_naive, 'timezone naive', s)
+        else:
+            raise NotImplementedError('Constraining to a specific timezone is not yet supported')
+    elif isinstance(constraint, annotated_types.Interval):
+        if constraint.ge:
+            s = _apply_constraint(s, annotated_types.Ge(constraint.ge))
+        if constraint.gt:
+            s = _apply_constraint(s, annotated_types.Gt(constraint.gt))
+        if constraint.le:
+            s = _apply_constraint(s, annotated_types.Le(constraint.le))
+        if constraint.lt:
+            s = _apply_constraint(s, annotated_types.Lt(constraint.lt))
+        assert s is not None
+    elif isinstance(constraint, annotated_types.Predicate):
+        func = constraint.func
+        # Same logic as in `_known_annotated_metadata.apply_known_metadata()`:
+        predicate_name = f'{func.__qualname__!r} ' if hasattr(func, '__qualname__') else ''
+
+        def predicate_func(v: Any) -> Any:
+            if not func(v):
+                raise PydanticCustomError(
+                    'predicate_failed',
+                    f'Predicate {predicate_name}failed',  # pyright: ignore[reportArgumentType]
+                )
+            return v
+
+        if s is None:
+            s = cs.no_info_plain_validator_function(predicate_func)
+        else:
+            s = cs.no_info_after_validator_function(predicate_func, s)
+    elif isinstance(constraint, _NotEq):
+        value = constraint.value
+
+        def check_not_eq(v: Any) -> bool:
+            return operator.__ne__(v, value)
+
+        s = _check_func(check_not_eq, f'!= {value}', s)
+    elif isinstance(constraint, _Eq):
+        value = constraint.value
+
+        def check_eq(v: Any) -> bool:
+            return operator.__eq__(v, value)
+
+        s = _check_func(check_eq, f'== {value}', s)
+    elif isinstance(constraint, _In):
+        values = constraint.values
+
+        def check_in(v: Any) -> bool:
+            return operator.__contains__(values, v)
+
+        s = _check_func(check_in, f'in {values}', s)
+    elif isinstance(constraint, _NotIn):
+        values = constraint.values
+
+        def check_not_in(v: Any) -> bool:
+            return operator.__not__(operator.__contains__(values, v))
+
+        s = _check_func(check_not_in, f'not in {values}', s)
+    else:
+        assert isinstance(constraint, Pattern)
+        if s and s['type'] == 'str':
+            s = s.copy()
+            s['pattern'] = constraint.pattern
+        else:
+
+            def check_pattern(v: object) -> bool:
+                assert isinstance(v, str)
+                return constraint.match(v) is not None
+
+            s = _check_func(check_pattern, f'~ {constraint.pattern}', s)
+    return s
+
+
+class _SupportsRange(annotated_types.SupportsLe, annotated_types.SupportsGe, Protocol):
+    pass
+
+
+class _SupportsLen(Protocol):
+    def __len__(self) -> int: ...
+
+
+_NewOutGt = TypeVar('_NewOutGt', bound=annotated_types.SupportsGt)
+_NewOutGe = TypeVar('_NewOutGe', bound=annotated_types.SupportsGe)
+_NewOutLt = TypeVar('_NewOutLt', bound=annotated_types.SupportsLt)
+_NewOutLe = TypeVar('_NewOutLe', bound=annotated_types.SupportsLe)
+_NewOutLen = TypeVar('_NewOutLen', bound=_SupportsLen)
+_NewOutDiv = TypeVar('_NewOutDiv', bound=annotated_types.SupportsDiv)
+_NewOutMod = TypeVar('_NewOutMod', bound=annotated_types.SupportsMod)
+_NewOutDatetime = TypeVar('_NewOutDatetime', bound=datetime.datetime)
+_NewOutInterval = TypeVar('_NewOutInterval', bound=_SupportsRange)
+_OtherIn = TypeVar('_OtherIn')
+_OtherOut = TypeVar('_OtherOut')

micromamba_root/envs/pytorch_env/Lib/site-packages/pydantic/plugin/__init__.py

@@ -0,0 +1,193 @@
+"""!!! abstract "Usage Documentation"
+    [Build a Plugin](../concepts/plugins.md#build-a-plugin)
+
+Plugin interface for Pydantic plugins, and related types.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Callable, Literal, NamedTuple
+
+from pydantic_core import CoreConfig, CoreSchema, ValidationError
+from typing_extensions import Protocol, TypeAlias
+
+from pydantic.config import ExtraValues
+
+__all__ = (
+    'PydanticPluginProtocol',
+    'BaseValidateHandlerProtocol',
+    'ValidatePythonHandlerProtocol',
+    'ValidateJsonHandlerProtocol',
+    'ValidateStringsHandlerProtocol',
+    'NewSchemaReturns',
+    'SchemaTypePath',
+    'SchemaKind',
+)
+
+NewSchemaReturns: TypeAlias = 'tuple[ValidatePythonHandlerProtocol | None, ValidateJsonHandlerProtocol | None, ValidateStringsHandlerProtocol | None]'
+
+
+class SchemaTypePath(NamedTuple):
+    """Path defining where `schema_type` was defined, or where `TypeAdapter` was called."""
+
+    module: str
+    name: str
+
+
+SchemaKind: TypeAlias = Literal['BaseModel', 'TypeAdapter', 'dataclass', 'create_model', 'validate_call']
+
+
+class PydanticPluginProtocol(Protocol):
+    """Protocol defining the interface for Pydantic plugins."""
+
+    def new_schema_validator(
+        self,
+        schema: CoreSchema,
+        schema_type: Any,
+        schema_type_path: SchemaTypePath,
+        schema_kind: SchemaKind,
+        config: CoreConfig | None,
+        plugin_settings: dict[str, object],
+    ) -> tuple[
+        ValidatePythonHandlerProtocol | None, ValidateJsonHandlerProtocol | None, ValidateStringsHandlerProtocol | None
+    ]:
+        """This method is called for each plugin every time a new [`SchemaValidator`][pydantic_core.SchemaValidator]
+        is created.
+
+        It should return an event handler for each of the three validation methods, or `None` if the plugin does not
+        implement that method.
+
+        Args:
+            schema: The schema to validate against.
+            schema_type: The original type which the schema was created from, e.g. the model class.
+            schema_type_path: Path defining where `schema_type` was defined, or where `TypeAdapter` was called.
+            schema_kind: The kind of schema to validate against.
+            config: The config to use for validation.
+            plugin_settings: Any plugin settings.
+
+        Returns:
+            A tuple of optional event handlers for each of the three validation methods -
+                `validate_python`, `validate_json`, `validate_strings`.
+        """
+        raise NotImplementedError('Pydantic plugins should implement `new_schema_validator`.')
+
+
+class BaseValidateHandlerProtocol(Protocol):
+    """Base class for plugin callbacks protocols.
+
+    You shouldn't implement this protocol directly, instead use one of the subclasses with adds the correctly
+    typed `on_error` method.
+    """
+
+    on_enter: Callable[..., None]
+    """`on_enter` is changed to be more specific on all subclasses"""
+
+    def on_success(self, result: Any) -> None:
+        """Callback to be notified of successful validation.
+
+        Args:
+            result: The result of the validation.
+        """
+        return
+
+    def on_error(self, error: ValidationError) -> None:
+        """Callback to be notified of validation errors.
+
+        Args:
+            error: The validation error.
+        """
+        return
+
+    def on_exception(self, exception: Exception) -> None:
+        """Callback to be notified of validation exceptions.
+
+        Args:
+            exception: The exception raised during validation.
+        """
+        return
+
+
+class ValidatePythonHandlerProtocol(BaseValidateHandlerProtocol, Protocol):
+    """Event handler for `SchemaValidator.validate_python`."""
+
+    def on_enter(
+        self,
+        input: Any,
+        *,
+        strict: bool | None = None,
+        extra: ExtraValues | None = None,
+        from_attributes: bool | None = None,
+        context: Any | None = None,
+        self_instance: Any | None = None,
+        by_alias: bool | None = None,
+        by_name: bool | None = None,
+    ) -> None:
+        """Callback to be notified of validation start, and create an instance of the event handler.
+
+        Args:
+            input: The input to be validated.
+            strict: Whether to validate the object in strict mode.
+            extra: Whether to ignore, allow, or forbid extra data during model validation.
+            from_attributes: Whether to validate objects as inputs by extracting attributes.
+            context: The context to use for validation, this is passed to functional validators.
+            self_instance: An instance of a model to set attributes on from validation, this is used when running
+                validation from the `__init__` method of a model.
+            by_alias: Whether to use the field's alias to match the input data to an attribute.
+            by_name: Whether to use the field's name to match the input data to an attribute.
+        """
+
+
+class ValidateJsonHandlerProtocol(BaseValidateHandlerProtocol, Protocol):
+    """Event handler for `SchemaValidator.validate_json`."""
+
+    def on_enter(
+        self,
+        input: str | bytes | bytearray,
+        *,
+        strict: bool | None = None,
+        extra: ExtraValues | None = None,
+        context: Any | None = None,
+        self_instance: Any | None = None,
+        by_alias: bool | None = None,
+        by_name: bool | None = None,
+    ) -> None:
+        """Callback to be notified of validation start, and create an instance of the event handler.
+
+        Args:
+            input: The JSON data to be validated.
+            strict: Whether to validate the object in strict mode.
+            extra: Whether to ignore, allow, or forbid extra data during model validation.
+            context: The context to use for validation, this is passed to functional validators.
+            self_instance: An instance of a model to set attributes on from validation, this is used when running
+                validation from the `__init__` method of a model.
+            by_alias: Whether to use the field's alias to match the input data to an attribute.
+            by_name: Whether to use the field's name to match the input data to an attribute.
+        """
+
+
+StringInput: TypeAlias = 'dict[str, StringInput]'
+
+
+class ValidateStringsHandlerProtocol(BaseValidateHandlerProtocol, Protocol):
+    """Event handler for `SchemaValidator.validate_strings`."""
+
+    def on_enter(
+        self,
+        input: StringInput,
+        *,
+        strict: bool | None = None,
+        extra: ExtraValues | None = None,
+        context: Any | None = None,
+        by_alias: bool | None = None,
+        by_name: bool | None = None,
+    ) -> None:
+        """Callback to be notified of validation start, and create an instance of the event handler.
+
+        Args:
+            input: The string data to be validated.
+            strict: Whether to validate the object in strict mode.
+            extra: Whether to ignore, allow, or forbid extra data during model validation.
+            context: The context to use for validation, this is passed to functional validators.
+            by_alias: Whether to use the field's alias to match the input data to an attribute.
+            by_name: Whether to use the field's name to match the input data to an attribute.
+        """