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.gitattributes

@@ -1258,3 +1258,5 @@ infer_4_47_1/lib/python3.10/site-packages/triton/backends/nvidia/bin/ptxas filte
 falcon/bin/x86_64-conda-linux-gnu-ld filter=lfs diff=lfs merge=lfs -text
 falcon/lib/python3.10/tkinter/__pycache__/__init__.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
 falcon/lib/python3.10/site-packages/huggingface_hub/inference/__pycache__/_client.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+falcon/lib/python3.10/site-packages/pysam.libs/libsasl2-7de4d792.so.3.0.0 filter=lfs diff=lfs merge=lfs -text
+evalkit_tf437/lib/python3.10/site-packages/pydantic_core/__pycache__/core_schema.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text

evalkit_tf437/lib/python3.10/site-packages/pydantic/aliases.py

@@ -0,0 +1,132 @@
+"""Support for alias configurations."""
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, Callable, Literal
+
+from pydantic_core import PydanticUndefined
+
+from ._internal import _internal_dataclass
+
+__all__ = ('AliasGenerator', 'AliasPath', 'AliasChoices')
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true)
+class AliasPath:
+    """Usage docs: https://docs.pydantic.dev/2.9/concepts/alias#aliaspath-and-aliaschoices
+
+    A data class used by `validation_alias` as a convenience to create aliases.
+
+    Attributes:
+        path: A list of string or integer aliases.
+    """
+
+    path: list[int | str]
+
+    def __init__(self, first_arg: str, *args: str | int) -> None:
+        self.path = [first_arg] + list(args)
+
+    def convert_to_aliases(self) -> list[str | int]:
+        """Converts arguments to a list of string or integer aliases.
+
+        Returns:
+            The list of aliases.
+        """
+        return self.path
+
+    def search_dict_for_path(self, d: dict) -> Any:
+        """Searches a dictionary for the path specified by the alias.
+
+        Returns:
+            The value at the specified path, or `PydanticUndefined` if the path is not found.
+        """
+        v = d
+        for k in self.path:
+            if isinstance(v, str):
+                # disallow indexing into a str, like for AliasPath('x', 0) and x='abc'
+                return PydanticUndefined
+            try:
+                v = v[k]
+            except (KeyError, IndexError, TypeError):
+                return PydanticUndefined
+        return v
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true)
+class AliasChoices:
+    """Usage docs: https://docs.pydantic.dev/2.9/concepts/alias#aliaspath-and-aliaschoices
+
+    A data class used by `validation_alias` as a convenience to create aliases.
+
+    Attributes:
+        choices: A list containing a string or `AliasPath`.
+    """
+
+    choices: list[str | AliasPath]
+
+    def __init__(self, first_choice: str | AliasPath, *choices: str | AliasPath) -> None:
+        self.choices = [first_choice] + list(choices)
+
+    def convert_to_aliases(self) -> list[list[str | int]]:
+        """Converts arguments to a list of lists containing string or integer aliases.
+
+        Returns:
+            The list of aliases.
+        """
+        aliases: list[list[str | int]] = []
+        for c in self.choices:
+            if isinstance(c, AliasPath):
+                aliases.append(c.convert_to_aliases())
+            else:
+                aliases.append([c])
+        return aliases
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true)
+class AliasGenerator:
+    """Usage docs: https://docs.pydantic.dev/2.9/concepts/alias#using-an-aliasgenerator
+
+    A data class used by `alias_generator` as a convenience to create various aliases.
+
+    Attributes:
+        alias: A callable that takes a field name and returns an alias for it.
+        validation_alias: A callable that takes a field name and returns a validation alias for it.
+        serialization_alias: A callable that takes a field name and returns a serialization alias for it.
+    """
+
+    alias: Callable[[str], str] | None = None
+    validation_alias: Callable[[str], str | AliasPath | AliasChoices] | None = None
+    serialization_alias: Callable[[str], str] | None = None
+
+    def _generate_alias(
+        self,
+        alias_kind: Literal['alias', 'validation_alias', 'serialization_alias'],
+        allowed_types: tuple[type[str] | type[AliasPath] | type[AliasChoices], ...],
+        field_name: str,
+    ) -> str | AliasPath | AliasChoices | None:
+        """Generate an alias of the specified kind. Returns None if the alias generator is None.
+
+        Raises:
+            TypeError: If the alias generator produces an invalid type.
+        """
+        alias = None
+        if alias_generator := getattr(self, alias_kind):
+            alias = alias_generator(field_name)
+            if alias and not isinstance(alias, allowed_types):
+                raise TypeError(
+                    f'Invalid `{alias_kind}` type. `{alias_kind}` generator must produce one of `{allowed_types}`'
+                )
+        return alias
+
+    def generate_aliases(self, field_name: str) -> tuple[str | None, str | AliasPath | AliasChoices | None, str | None]:
+        """Generate `alias`, `validation_alias`, and `serialization_alias` for a field.
+
+        Returns:
+            A tuple of three aliases - validation, alias, and serialization.
+        """
+        alias = self._generate_alias('alias', (str,), field_name)
+        validation_alias = self._generate_alias('validation_alias', (str, AliasChoices, AliasPath), field_name)
+        serialization_alias = self._generate_alias('serialization_alias', (str,), field_name)
+
+        return alias, validation_alias, serialization_alias  # type: ignore

evalkit_tf437/lib/python3.10/site-packages/pydantic/class_validators.py

@@ -0,0 +1,5 @@
+"""`class_validators` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)

evalkit_tf437/lib/python3.10/site-packages/pydantic/datetime_parse.py

@@ -0,0 +1,5 @@
+"""The `datetime_parse` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)

evalkit_tf437/lib/python3.10/site-packages/pydantic/decorator.py

@@ -0,0 +1,5 @@
+"""The `decorator` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)

evalkit_tf437/lib/python3.10/site-packages/pydantic/functional_serializers.py

@@ -0,0 +1,438 @@
+"""This module contains related classes and functions for serialization."""
+
+from __future__ import annotations
+
+import dataclasses
+from functools import partial, partialmethod
+from typing import TYPE_CHECKING, Any, Callable, TypeVar, overload
+
+from pydantic_core import PydanticUndefined, core_schema
+from pydantic_core.core_schema import SerializationInfo, SerializerFunctionWrapHandler, WhenUsed
+from typing_extensions import Annotated, Literal, TypeAlias
+
+from . import PydanticUndefinedAnnotation
+from ._internal import _decorators, _internal_dataclass
+from .annotated_handlers import GetCoreSchemaHandler
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
+class PlainSerializer:
+    """Plain serializers use a function to modify the output of serialization.
+
+    This is particularly helpful when you want to customize the serialization for annotated types.
+    Consider an input of `list`, which will be serialized into a space-delimited string.
+
+    ```python
+    from typing import List
+
+    from typing_extensions import Annotated
+
+    from pydantic import BaseModel, PlainSerializer
+
+    CustomStr = Annotated[
+        List, PlainSerializer(lambda x: ' '.join(x), return_type=str)
+    ]
+
+    class StudentModel(BaseModel):
+        courses: CustomStr
+
+    student = StudentModel(courses=['Math', 'Chemistry', 'English'])
+    print(student.model_dump())
+    #> {'courses': 'Math Chemistry English'}
+    ```
+
+    Attributes:
+        func: The serializer function.
+        return_type: The return type for the function. If omitted it will be inferred from the type annotation.
+        when_used: Determines when this serializer should be used. Accepts a string with values `'always'`,
+            `'unless-none'`, `'json'`, and `'json-unless-none'`. Defaults to 'always'.
+    """
+
+    func: core_schema.SerializerFunction
+    return_type: Any = PydanticUndefined
+    when_used: WhenUsed = 'always'
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        """Gets the Pydantic core schema.
+
+        Args:
+            source_type: The source type.
+            handler: The `GetCoreSchemaHandler` instance.
+
+        Returns:
+            The Pydantic core schema.
+        """
+        schema = handler(source_type)
+        try:
+            return_type = _decorators.get_function_return_type(
+                self.func, self.return_type, handler._get_types_namespace()
+            )
+        except NameError as e:
+            raise PydanticUndefinedAnnotation.from_name_error(e) from e
+        return_schema = None if return_type is PydanticUndefined else handler.generate_schema(return_type)
+        schema['serialization'] = core_schema.plain_serializer_function_ser_schema(
+            function=self.func,
+            info_arg=_decorators.inspect_annotated_serializer(self.func, 'plain'),
+            return_schema=return_schema,
+            when_used=self.when_used,
+        )
+        return schema
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
+class WrapSerializer:
+    """Wrap serializers receive the raw inputs along with a handler function that applies the standard serialization
+    logic, and can modify the resulting value before returning it as the final output of serialization.
+
+    For example, here's a scenario in which a wrap serializer transforms timezones to UTC **and** utilizes the existing `datetime` serialization logic.
+
+    ```python
+    from datetime import datetime, timezone
+    from typing import Any, Dict
+
+    from typing_extensions import Annotated
+
+    from pydantic import BaseModel, WrapSerializer
+
+    class EventDatetime(BaseModel):
+        start: datetime
+        end: datetime
+
+    def convert_to_utc(value: Any, handler, info) -> Dict[str, datetime]:
+        # Note that `handler` can actually help serialize the `value` for
+        # further custom serialization in case it's a subclass.
+        partial_result = handler(value, info)
+        if info.mode == 'json':
+            return {
+                k: datetime.fromisoformat(v).astimezone(timezone.utc)
+                for k, v in partial_result.items()
+            }
+        return {k: v.astimezone(timezone.utc) for k, v in partial_result.items()}
+
+    UTCEventDatetime = Annotated[EventDatetime, WrapSerializer(convert_to_utc)]
+
+    class EventModel(BaseModel):
+        event_datetime: UTCEventDatetime
+
+    dt = EventDatetime(
+        start='2024-01-01T07:00:00-08:00', end='2024-01-03T20:00:00+06:00'
+    )
+    event = EventModel(event_datetime=dt)
+    print(event.model_dump())
+    '''
+    {
+        'event_datetime': {
+            'start': datetime.datetime(
+                2024, 1, 1, 15, 0, tzinfo=datetime.timezone.utc
+            ),
+            'end': datetime.datetime(
+                2024, 1, 3, 14, 0, tzinfo=datetime.timezone.utc
+            ),
+        }
+    }
+    '''
+
+    print(event.model_dump_json())
+    '''
+    {"event_datetime":{"start":"2024-01-01T15:00:00Z","end":"2024-01-03T14:00:00Z"}}
+    '''
+    ```
+
+    Attributes:
+        func: The serializer function to be wrapped.
+        return_type: The return type for the function. If omitted it will be inferred from the type annotation.
+        when_used: Determines when this serializer should be used. Accepts a string with values `'always'`,
+            `'unless-none'`, `'json'`, and `'json-unless-none'`. Defaults to 'always'.
+    """
+
+    func: core_schema.WrapSerializerFunction
+    return_type: Any = PydanticUndefined
+    when_used: WhenUsed = 'always'
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        """This method is used to get the Pydantic core schema of the class.
+
+        Args:
+            source_type: Source type.
+            handler: Core schema handler.
+
+        Returns:
+            The generated core schema of the class.
+        """
+        schema = handler(source_type)
+        try:
+            return_type = _decorators.get_function_return_type(
+                self.func, self.return_type, handler._get_types_namespace()
+            )
+        except NameError as e:
+            raise PydanticUndefinedAnnotation.from_name_error(e) from e
+        return_schema = None if return_type is PydanticUndefined else handler.generate_schema(return_type)
+        schema['serialization'] = core_schema.wrap_serializer_function_ser_schema(
+            function=self.func,
+            info_arg=_decorators.inspect_annotated_serializer(self.func, 'wrap'),
+            return_schema=return_schema,
+            when_used=self.when_used,
+        )
+        return schema
+
+
+if TYPE_CHECKING:
+    _Partial: TypeAlias = 'partial[Any] | partialmethod[Any]'
+
+    FieldPlainSerializer: TypeAlias = 'core_schema.SerializerFunction | _Partial'
+    """A field serializer method or function in `plain` mode."""
+
+    FieldWrapSerializer: TypeAlias = 'core_schema.WrapSerializerFunction | _Partial'
+    """A field serializer method or function in `wrap` mode."""
+
+    FieldSerializer: TypeAlias = 'FieldPlainSerializer | FieldWrapSerializer'
+    """A field serializer method or function."""
+
+    _FieldPlainSerializerT = TypeVar('_FieldPlainSerializerT', bound=FieldPlainSerializer)
+    _FieldWrapSerializerT = TypeVar('_FieldWrapSerializerT', bound=FieldWrapSerializer)
+
+
+@overload
+def field_serializer(
+    field: str,
+    /,
+    *fields: str,
+    mode: Literal['wrap'],
+    return_type: Any = ...,
+    when_used: WhenUsed = ...,
+    check_fields: bool | None = ...,
+) -> Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT]: ...
+
+
+@overload
+def field_serializer(
+    field: str,
+    /,
+    *fields: str,
+    mode: Literal['plain'] = ...,
+    return_type: Any = ...,
+    when_used: WhenUsed = ...,
+    check_fields: bool | None = ...,
+) -> Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]: ...
+
+
+def field_serializer(
+    *fields: str,
+    mode: Literal['plain', 'wrap'] = 'plain',
+    return_type: Any = PydanticUndefined,
+    when_used: WhenUsed = 'always',
+    check_fields: bool | None = None,
+) -> (
+    Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT]
+    | Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]
+):
+    """Decorator that enables custom field serialization.
+
+    In the below example, a field of type `set` is used to mitigate duplication. A `field_serializer` is used to serialize the data as a sorted list.
+
+    ```python
+    from typing import Set
+
+    from pydantic import BaseModel, field_serializer
+
+    class StudentModel(BaseModel):
+        name: str = 'Jane'
+        courses: Set[str]
+
+        @field_serializer('courses', when_used='json')
+        def serialize_courses_in_order(self, courses: Set[str]):
+            return sorted(courses)
+
+    student = StudentModel(courses={'Math', 'Chemistry', 'English'})
+    print(student.model_dump_json())
+    #> {"name":"Jane","courses":["Chemistry","English","Math"]}
+    ```
+
+    See [Custom serializers](../concepts/serialization.md#custom-serializers) for more information.
+
+    Four signatures are supported:
+
+    - `(self, value: Any, info: FieldSerializationInfo)`
+    - `(self, value: Any, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo)`
+    - `(value: Any, info: SerializationInfo)`
+    - `(value: Any, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)`
+
+    Args:
+        fields: Which field(s) the method should be called on.
+        mode: The serialization mode.
+
+            - `plain` means the function will be called instead of the default serialization logic,
+            - `wrap` means the function will be called with an argument to optionally call the
+               default serialization logic.
+        return_type: Optional return type for the function, if omitted it will be inferred from the type annotation.
+        when_used: Determines the serializer will be used for serialization.
+        check_fields: Whether to check that the fields actually exist on the model.
+
+    Returns:
+        The decorator function.
+    """
+
+    def dec(f: FieldSerializer) -> _decorators.PydanticDescriptorProxy[Any]:
+        dec_info = _decorators.FieldSerializerDecoratorInfo(
+            fields=fields,
+            mode=mode,
+            return_type=return_type,
+            when_used=when_used,
+            check_fields=check_fields,
+        )
+        return _decorators.PydanticDescriptorProxy(f, dec_info)  # pyright: ignore[reportArgumentType]
+
+    return dec  # pyright: ignore[reportReturnType]
+
+
+if TYPE_CHECKING:
+    # The first argument in the following callables represent the `self` type:
+
+    ModelPlainSerializerWithInfo: TypeAlias = Callable[[Any, SerializationInfo], Any]
+    """A model serializer method with the `info` argument, in `plain` mode."""
+
+    ModelPlainSerializerWithoutInfo: TypeAlias = Callable[[Any], Any]
+    """A model serializer method without the `info` argument, in `plain` mode."""
+
+    ModelPlainSerializer: TypeAlias = 'ModelPlainSerializerWithInfo | ModelPlainSerializerWithoutInfo'
+    """A model serializer method in `plain` mode."""
+
+    ModelWrapSerializerWithInfo: TypeAlias = Callable[[Any, SerializerFunctionWrapHandler, SerializationInfo], Any]
+    """A model serializer method with the `info` argument, in `wrap` mode."""
+
+    ModelWrapSerializerWithoutInfo: TypeAlias = Callable[[Any, SerializerFunctionWrapHandler], Any]
+    """A model serializer method without the `info` argument, in `wrap` mode."""
+
+    ModelWrapSerializer: TypeAlias = 'ModelWrapSerializerWithInfo | ModelWrapSerializerWithoutInfo'
+    """A model serializer method in `wrap` mode."""
+
+    ModelSerializer: TypeAlias = 'ModelPlainSerializer | ModelWrapSerializer'
+
+    _ModelPlainSerializerT = TypeVar('_ModelPlainSerializerT', bound=ModelPlainSerializer)
+    _ModelWrapSerializerT = TypeVar('_ModelWrapSerializerT', bound=ModelWrapSerializer)
+
+
+@overload
+def model_serializer(f: _ModelPlainSerializerT, /) -> _ModelPlainSerializerT: ...
+
+
+@overload
+def model_serializer(
+    *, mode: Literal['wrap'], when_used: WhenUsed = 'always', return_type: Any = ...
+) -> Callable[[_ModelWrapSerializerT], _ModelWrapSerializerT]: ...
+
+
+@overload
+def model_serializer(
+    *,
+    mode: Literal['plain'] = ...,
+    when_used: WhenUsed = 'always',
+    return_type: Any = ...,
+) -> Callable[[_ModelPlainSerializerT], _ModelPlainSerializerT]: ...
+
+
+def model_serializer(
+    f: _ModelPlainSerializerT | _ModelWrapSerializerT | None = None,
+    /,
+    *,
+    mode: Literal['plain', 'wrap'] = 'plain',
+    when_used: WhenUsed = 'always',
+    return_type: Any = PydanticUndefined,
+) -> (
+    _ModelPlainSerializerT
+    | Callable[[_ModelWrapSerializerT], _ModelWrapSerializerT]
+    | Callable[[_ModelPlainSerializerT], _ModelPlainSerializerT]
+):
+    """Decorator that enables custom model serialization.
+
+    This is useful when a model need to be serialized in a customized manner, allowing for flexibility beyond just specific fields.
+
+    An example would be to serialize temperature to the same temperature scale, such as degrees Celsius.
+
+    ```python
+    from typing import Literal
+
+    from pydantic import BaseModel, model_serializer
+
+    class TemperatureModel(BaseModel):
+        unit: Literal['C', 'F']
+        value: int
+
+        @model_serializer()
+        def serialize_model(self):
+            if self.unit == 'F':
+                return {'unit': 'C', 'value': int((self.value - 32) / 1.8)}
+            return {'unit': self.unit, 'value': self.value}
+
+    temperature = TemperatureModel(unit='F', value=212)
+    print(temperature.model_dump())
+    #> {'unit': 'C', 'value': 100}
+    ```
+
+    Two signatures are supported for `mode='plain'`, which is the default:
+
+    - `(self)`
+    - `(self, info: SerializationInfo)`
+
+    And two other signatures for `mode='wrap'`:
+
+    - `(self, nxt: SerializerFunctionWrapHandler)`
+    - `(self, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)`
+
+        See [Custom serializers](../concepts/serialization.md#custom-serializers) for more information.
+
+    Args:
+        f: The function to be decorated.
+        mode: The serialization mode.
+
+            - `'plain'` means the function will be called instead of the default serialization logic
+            - `'wrap'` means the function will be called with an argument to optionally call the default
+                serialization logic.
+        when_used: Determines when this serializer should be used.
+        return_type: The return type for the function. If omitted it will be inferred from the type annotation.
+
+    Returns:
+        The decorator function.
+    """
+
+    def dec(f: ModelSerializer) -> _decorators.PydanticDescriptorProxy[Any]:
+        dec_info = _decorators.ModelSerializerDecoratorInfo(mode=mode, return_type=return_type, when_used=when_used)
+        return _decorators.PydanticDescriptorProxy(f, dec_info)
+
+    if f is None:
+        return dec  # pyright: ignore[reportReturnType]
+    else:
+        return dec(f)  # pyright: ignore[reportReturnType]
+
+
+AnyType = TypeVar('AnyType')
+
+
+if TYPE_CHECKING:
+    SerializeAsAny = Annotated[AnyType, ...]  # SerializeAsAny[list[str]] will be treated by type checkers as list[str]
+    """Force serialization to ignore whatever is defined in the schema and instead ask the object
+    itself how it should be serialized.
+    In particular, this means that when model subclasses are serialized, fields present in the subclass
+    but not in the original schema will be included.
+    """
+else:
+
+    @dataclasses.dataclass(**_internal_dataclass.slots_true)
+    class SerializeAsAny:  # noqa: D101
+        def __class_getitem__(cls, item: Any) -> Any:
+            return Annotated[item, SerializeAsAny()]
+
+        def __get_pydantic_core_schema__(
+            self, source_type: Any, handler: GetCoreSchemaHandler
+        ) -> core_schema.CoreSchema:
+            schema = handler(source_type)
+            schema_to_update = schema
+            while schema_to_update['type'] == 'definitions':
+                schema_to_update = schema_to_update.copy()
+                schema_to_update = schema_to_update['schema']
+            schema_to_update['serialization'] = core_schema.wrap_serializer_function_ser_schema(
+                lambda x, h: h(x), schema=core_schema.any_schema()
+            )
+            return schema
+
+        __hash__ = object.__hash__

evalkit_tf437/lib/python3.10/site-packages/pydantic/tools.py

@@ -0,0 +1,5 @@
+"""The `tools` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)

evalkit_tf437/lib/python3.10/site-packages/pydantic/validate_call_decorator.py

@@ -0,0 +1,69 @@
+"""Decorator for validating function calls."""
+
+from __future__ import annotations as _annotations
+
+import functools
+from typing import TYPE_CHECKING, Any, Callable, TypeVar, overload
+
+from ._internal import _typing_extra, _validate_call
+
+__all__ = ('validate_call',)
+
+if TYPE_CHECKING:
+    from .config import ConfigDict
+
+    AnyCallableT = TypeVar('AnyCallableT', bound=Callable[..., Any])
+
+
+@overload
+def validate_call(
+    *, config: ConfigDict | None = None, validate_return: bool = False
+) -> Callable[[AnyCallableT], AnyCallableT]: ...
+
+
+@overload
+def validate_call(func: AnyCallableT, /) -> AnyCallableT: ...
+
+
+def validate_call(
+    func: AnyCallableT | None = None,
+    /,
+    *,
+    config: ConfigDict | None = None,
+    validate_return: bool = False,
+) -> AnyCallableT | Callable[[AnyCallableT], AnyCallableT]:
+    """Usage docs: https://docs.pydantic.dev/2.9/concepts/validation_decorator/
+
+    Returns a decorated wrapper around the function that validates the arguments and, optionally, the return value.
+
+    Usage may be either as a plain decorator `@validate_call` or with arguments `@validate_call(...)`.
+
+    Args:
+        func: The function to be decorated.
+        config: The configuration dictionary.
+        validate_return: Whether to validate the return value.
+
+    Returns:
+        The decorated function.
+    """
+    local_ns = _typing_extra.parent_frame_namespace()
+
+    def validate(function: AnyCallableT) -> AnyCallableT:
+        if isinstance(function, (classmethod, staticmethod)):
+            name = type(function).__name__
+            raise TypeError(f'The `@{name}` decorator should be applied after `@validate_call` (put `@{name}` on top)')
+
+        validate_call_wrapper = _validate_call.ValidateCallWrapper(function, config, validate_return, local_ns)
+
+        @functools.wraps(function)
+        def wrapper_function(*args, **kwargs):
+            return validate_call_wrapper(*args, **kwargs)
+
+        wrapper_function.raw_function = function  # type: ignore
+
+        return wrapper_function  # type: ignore
+
+    if func:
+        return validate(func)
+    else:
+        return validate

evalkit_tf437/lib/python3.10/site-packages/pydantic_core/__pycache__/core_schema.cpython-310.pyc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:efa173282e69e63cd89631f686772e33bc74ad1f486d50700538bedf737419ea
+size 123086

falcon/lib/python3.10/site-packages/anyio-4.8.0.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

falcon/lib/python3.10/site-packages/anyio-4.8.0.dist-info/METADATA

@@ -0,0 +1,104 @@
+Metadata-Version: 2.1
+Name: anyio
+Version: 4.8.0
+Summary: High level compatibility layer for multiple asynchronous event loop implementations
+Author-email: Alex Grönholm <alex.gronholm@nextday.fi>
+License: MIT
+Project-URL: Documentation, https://anyio.readthedocs.io/en/latest/
+Project-URL: Changelog, https://anyio.readthedocs.io/en/stable/versionhistory.html
+Project-URL: Source code, https://github.com/agronholm/anyio
+Project-URL: Issue tracker, https://github.com/agronholm/anyio/issues
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Framework :: AnyIO
+Classifier: Typing :: Typed
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Requires-Python: >=3.9
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: exceptiongroup>=1.0.2; python_version < "3.11"
+Requires-Dist: idna>=2.8
+Requires-Dist: sniffio>=1.1
+Requires-Dist: typing_extensions>=4.5; python_version < "3.13"
+Provides-Extra: trio
+Requires-Dist: trio>=0.26.1; extra == "trio"
+Provides-Extra: test
+Requires-Dist: anyio[trio]; extra == "test"
+Requires-Dist: coverage[toml]>=7; extra == "test"
+Requires-Dist: exceptiongroup>=1.2.0; extra == "test"
+Requires-Dist: hypothesis>=4.0; extra == "test"
+Requires-Dist: psutil>=5.9; extra == "test"
+Requires-Dist: pytest>=7.0; extra == "test"
+Requires-Dist: trustme; extra == "test"
+Requires-Dist: truststore>=0.9.1; python_version >= "3.10" and extra == "test"
+Requires-Dist: uvloop>=0.21; (platform_python_implementation == "CPython" and platform_system != "Windows" and python_version < "3.14") and extra == "test"
+Provides-Extra: doc
+Requires-Dist: packaging; extra == "doc"
+Requires-Dist: Sphinx~=7.4; extra == "doc"
+Requires-Dist: sphinx_rtd_theme; extra == "doc"
+Requires-Dist: sphinx-autodoc-typehints>=1.2.0; extra == "doc"
+
+.. image:: https://github.com/agronholm/anyio/actions/workflows/test.yml/badge.svg
+  :target: https://github.com/agronholm/anyio/actions/workflows/test.yml
+  :alt: Build Status
+.. image:: https://coveralls.io/repos/github/agronholm/anyio/badge.svg?branch=master
+  :target: https://coveralls.io/github/agronholm/anyio?branch=master
+  :alt: Code Coverage
+.. image:: https://readthedocs.org/projects/anyio/badge/?version=latest
+  :target: https://anyio.readthedocs.io/en/latest/?badge=latest
+  :alt: Documentation
+.. image:: https://badges.gitter.im/gitterHQ/gitter.svg
+  :target: https://gitter.im/python-trio/AnyIO
+  :alt: Gitter chat
+
+AnyIO is an asynchronous networking and concurrency library that works on top of either asyncio_ or
+trio_. It implements trio-like `structured concurrency`_ (SC) on top of asyncio and works in harmony
+with the native SC of trio itself.
+
+Applications and libraries written against AnyIO's API will run unmodified on either asyncio_ or
+trio_. AnyIO can also be adopted into a library or application incrementally – bit by bit, no full
+refactoring necessary. It will blend in with the native libraries of your chosen backend.
+
+Documentation
+-------------
+
+View full documentation at: https://anyio.readthedocs.io/
+
+Features
+--------
+
+AnyIO offers the following functionality:
+
+* Task groups (nurseries_ in trio terminology)
+* High-level networking (TCP, UDP and UNIX sockets)
+
+  * `Happy eyeballs`_ algorithm for TCP connections (more robust than that of asyncio on Python
+    3.8)
+  * async/await style UDP sockets (unlike asyncio where you still have to use Transports and
+    Protocols)
+
+* A versatile API for byte streams and object streams
+* Inter-task synchronization and communication (locks, conditions, events, semaphores, object
+  streams)
+* Worker threads
+* Subprocesses
+* Asynchronous file I/O (using worker threads)
+* Signal handling
+
+AnyIO also comes with its own pytest_ plugin which also supports asynchronous fixtures.
+It even works with the popular Hypothesis_ library.
+
+.. _asyncio: https://docs.python.org/3/library/asyncio.html
+.. _trio: https://github.com/python-trio/trio
+.. _structured concurrency: https://en.wikipedia.org/wiki/Structured_concurrency
+.. _nurseries: https://trio.readthedocs.io/en/stable/reference-core.html#nurseries-and-spawning
+.. _Happy eyeballs: https://en.wikipedia.org/wiki/Happy_Eyeballs
+.. _pytest: https://docs.pytest.org/en/latest/
+.. _Hypothesis: https://hypothesis.works/

falcon/lib/python3.10/site-packages/functorch/_src/aot_autograd/__init__.py

@@ -0,0 +1,8 @@
+# This file has moved to under torch/_functorch. It is not public API.
+# If you are not a PyTorch developer and you are relying on the following
+# imports, please file an issue.
+from torch._functorch.aot_autograd import (
+    aot_autograd_decompositions,
+    KNOWN_TYPES,
+    PytreeThunk,
+)

falcon/lib/python3.10/site-packages/functorch/dim/__init__.py

@@ -0,0 +1,179 @@
+import dis
+import inspect
+from typing import Sequence, Union
+
+import torch
+
+import functorch._C
+from functorch._C import dim as _C
+from .tree_map import tree_flatten, tree_map
+from .wrap_type import wrap_type
+
+_C._patch_tensor_class()
+dims, DimList, dimlists = _C.dims, _C.DimList, _C.dimlists
+
+
+class DimensionMismatchError(Exception):
+    pass
+
+
+class DimensionBindError(Exception):
+    pass
+
+
+from . import op_properties
+
+# use dict to avoid writing C++ bindings for set
+pointwise = dict.fromkeys(op_properties.pointwise, True)
+
+use_c = True
+if not use_c:
+    from . import reference
+
+
+class _Tensor:
+    # fast path around slow wrapping/unwrapping logic for simply queries used
+    # by the implementation...
+
+    @property
+    def dims(self):
+        return tuple(d for d in self._levels if isinstance(d, Dim))
+
+    def dim(self):
+        return self.ndim
+
+    if use_c:
+        __torch_function__ = classmethod(_C.__torch_function__)
+        expand = _C._instancemethod(_C.expand)
+    else:
+        __torch_function__ = reference.__torch_function__
+        expand = reference.expand
+
+    index = _C._instancemethod(_C.index)
+
+    def __repr__(self):
+        tensor, levels, ndim = self._tensor, self._levels, self.ndim
+        return f"{tensor}\nwith dims={tuple(l + ndim if isinstance(l, int) else l for l in levels)} sizes={tuple(tensor.size())}"
+
+
+TensorLike = (_Tensor, torch.Tensor)
+
+
+class Dim(_C.Dim, _Tensor):
+    # note that _C.Dim comes before tensor because we want the Dim API for things like size to take precendence.
+    # Tensor defines format, but we want to print Dims with special formatting
+    __format__ = object.__format__
+
+
+class Tensor(_Tensor, _C.Tensor):
+    if not use_c:
+        from_batched = staticmethod(_C.Tensor_from_batched)
+    from_positional = staticmethod(_C.Tensor_from_positional)
+    sum = _C._instancemethod(_C.Tensor_sum)
+
+
+def cat(tensors, dim, new_dim):
+    n = dims()
+    return stack(tensors, n, dim).index([n, dim], new_dim)
+
+
+if use_c:
+    _wrap = _C._wrap
+
+    def _def(name, *args, **kwargs):
+        orig = getattr(torch.Tensor, name)
+        setattr(_Tensor, name, _C._instancemethod(_wrap(orig, *args, **kwargs)))
+
+    t__getitem__ = _C._instancemethod(_C.__getitem__)
+    stack = _C.stack
+    split = _C._instancemethod(_C.split)
+else:
+    _wrap, _def = reference._wrap, reference._def
+    t__getitem__ = reference.t__getitem__
+    stack = reference.stack
+    split = reference.split
+
+# note: there is no python reference
+t__setitem__ = _C._instancemethod(_C.__setitem__)
+# this is patched in the C API because otherwise torch.Tensor will
+# no longer be considered a sequence and things will break
+# torch.Tensor.__getitem__ = t__getitem__
+
+_Tensor.__getitem__ = t__getitem__
+# torch.Tensor.__setitem__ = t__setitem__
+_Tensor.__setitem__ = t__setitem__
+
+torch.Tensor.split = split
+_Tensor.split = split
+torch.Tensor.expand = _C._instancemethod(_C.expand)
+torch.Tensor.index = _C._instancemethod(_C.index)
+wrap_type(use_c, _Tensor, torch.Tensor, _Tensor.__torch_function__)
+del _Tensor.ndim
+
+if use_c:
+    _Tensor.order = _C._instancemethod(_C.order)
+else:
+    _Tensor.order = reference.positional
+
+_def("mean")
+_def("sum")
+_def("all")
+_def("amax")
+_def("amin")
+_def("aminmax")
+_def("any")
+_def("count_nonzero")
+_def("logsumexp")
+_def("nanmean")
+_def("nansum")
+_def("prod")
+_def("std", keepdim_offset=2)
+_def("var", keepdim_offset=2)
+_def("max", single_dim=True)
+_def("min", single_dim=True)
+_def("argmax", single_dim=True)
+_def("argmin", single_dim=True)
+_def("kthvalue", single_dim=True)
+_def("median", single_dim=True)
+_def("nanmedian", single_dim=True)
+_def("mode", single_dim=True)
+_def("sort", reduce=False)
+_def("argsort", reduce=False)
+_def("unbind", single_dim=True)
+_def("chunk", dim_offset=1, reduce=False)
+_def("cummax", single_dim=True, reduce=False)
+_def("cummin", single_dim=True, reduce=False)
+_def("cumprod", single_dim=True, reduce=False)
+_def("cumprod_", single_dim=True, reduce=False)
+_def("cumsum", single_dim=True, reduce=False)
+_def("cumsum_", single_dim=True, reduce=False)
+_def("logcumsumexp", single_dim=True, reduce=False)
+_def("renorm", dim_offset=1, single_dim=True, reduce=False)
+_def("softmax", single_dim=True, reduce=False)
+softmax = _wrap(torch.nn.functional.softmax, single_dim=True, reduce=False)
+
+# stuff to handle in the future, because they require special
+# binding logic for dims
+# cross
+# diag_embed
+# diagonal
+# diagonal_scatter
+# diff
+# nanquantile
+# quantile
+# roll
+# rot90
+# topk (new dimes on output)
+# should these all be subsumed by inplace indexing?
+# index_add_
+# index_add
+# index_copy
+# index_copy_
+# index_fill
+# index_fill_
+# index_select
+# scatter
+# scatter_
+# scatter_add
+# scatter_add_
+# scatter_reduce

falcon/lib/python3.10/site-packages/functorch/dim/batch_tensor.py

@@ -0,0 +1,25 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+from contextlib import contextmanager
+
+from torch._C._functorch import _vmap_add_layers, _vmap_remove_layers
+
+_enabled = False
+
+
+@contextmanager
+def _enable_layers(dims):
+    global _enabled
+    assert not _enabled
+    input = sorted((d._level, d.size) for d in dims if not isinstance(d, int))
+    n = len(input)
+    try:
+        _vmap_add_layers(input)
+        _enabled = True
+        yield
+    finally:
+        _enabled = False
+        _vmap_remove_layers(n)

falcon/lib/python3.10/site-packages/functorch/dim/delayed_mul_tensor.py

@@ -0,0 +1,77 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import torch
+
+from . import _Tensor, Tensor
+from .reference import _dims, _enable_layers, llist, ltuple
+
+
+class DelayedMulTensor(_Tensor):
+    def __init__(self, lhs, rhs):
+        self._lhs, self._rhs = lhs, rhs
+        self._data = None
+        self._levels_data = None
+        self._has_device = lhs._has_device or rhs._has_device
+        self._batchtensor_data = None
+        self._tensor_data = None
+
+    @property
+    def _levels(self):
+        if self._levels_data is None:
+            levels = llist(self._lhs._levels)
+            for l in self._rhs._levels:
+                if l not in levels:
+                    levels.append(l)
+            self._levels_data = ltuple(levels)
+        return self._levels_data
+
+    @property
+    def _batchtensor(self):
+        if self._batchtensor_data is None:
+            with _enable_layers(self._levels):
+                print("bt multiply fallback")
+                self._batchtensor_data = self._lhs._batchtensor * self._rhs._batchtensor
+        return self._batchtensor_data
+
+    @property
+    def _tensor(self):
+        if self._tensor_data is None:
+            self._tensor_data = Tensor.from_batched(
+                self._batchtensor, self._has_device
+            )._tensor
+        return self._tensor_data
+
+    @property
+    def ndim(self):
+        return self._batchtensor.ndim
+
+    @property
+    def dims(self):
+        return ltuple(super().dims)
+
+    def sum(self, dim):
+        dims = _dims(dim, 0, False, False)
+        n = ord("a")
+        all_levels = self._levels
+
+        def to_char(d):
+            return chr(n + all_levels.index(d))
+
+        plhs, levelslhs = self._lhs._tensor, self._lhs._levels
+        prhs, levelsrhs = self._rhs._tensor, self._rhs._levels
+        new_dims = tuple(d for d in self.dims if d not in dims)
+        new_levels = [l for l in self._levels if l not in dims]
+        fmt = "".join(
+            [
+                *(to_char(d) for d in levelslhs),
+                ",",
+                *(to_char(d) for d in levelsrhs),
+                "->",
+                *(to_char(d) for d in new_levels),
+            ]
+        )
+        result_data = torch.einsum(fmt, (plhs, prhs))
+        return Tensor.from_positional(result_data, new_levels, True)

falcon/lib/python3.10/site-packages/functorch/dim/dim.py

@@ -0,0 +1,121 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import dis
+import inspect
+
+from dataclasses import dataclass
+from typing import Union
+
+from . import DimList
+
+_vmap_levels = []
+
+
+@dataclass
+class LevelInfo:
+    level: int
+    alive: bool = True
+
+
+class Dim:
+    def __init__(self, name: str, size: Union[None, int] = None):
+        self.name = name
+        self._size = None
+        self._vmap_level = None
+        if size is not None:
+            self.size = size
+
+    def __del__(self):
+        if self._vmap_level is not None:
+            _vmap_active_levels[self._vmap_stack].alive = False  # noqa: F821
+            while (
+                not _vmap_levels[-1].alive
+                and current_level() == _vmap_levels[-1].level  # noqa: F821
+            ):
+                _vmap_decrement_nesting()  # noqa: F821
+                _vmap_levels.pop()
+
+    @property
+    def size(self):
+        assert self.is_bound
+        return self._size
+
+    @size.setter
+    def size(self, size: int):
+        from . import DimensionBindError
+
+        if self._size is None:
+            self._size = size
+            self._vmap_level = _vmap_increment_nesting(size, "same")  # noqa: F821
+            self._vmap_stack = len(_vmap_levels)
+            _vmap_levels.append(LevelInfo(self._vmap_level))
+
+        elif self._size != size:
+            raise DimensionBindError(
+                f"Dim '{self}' previously bound to a dimension of size {self._size} cannot bind to a dimension of size {size}"
+            )
+
+    @property
+    def is_bound(self):
+        return self._size is not None
+
+    def __repr__(self):
+        return self.name
+
+
+def extract_name(inst):
+    assert inst.opname == "STORE_FAST" or inst.opname == "STORE_NAME"
+    return inst.argval
+
+
+_cache = {}
+
+
+def dims(lists=0):
+    frame = inspect.currentframe()
+    assert frame is not None
+    calling_frame = frame.f_back
+    assert calling_frame is not None
+    code, lasti = calling_frame.f_code, calling_frame.f_lasti
+    key = (code, lasti)
+    if key not in _cache:
+        first = lasti // 2 + 1
+        instructions = list(dis.get_instructions(calling_frame.f_code))
+        unpack = instructions[first]
+
+        if unpack.opname == "STORE_FAST" or unpack.opname == "STORE_NAME":
+            # just a single dim, not a list
+            name = unpack.argval
+            ctor = Dim if lists == 0 else DimList
+            _cache[key] = lambda: ctor(name=name)
+        else:
+            assert unpack.opname == "UNPACK_SEQUENCE"
+            ndims = unpack.argval
+            names = tuple(
+                extract_name(instructions[first + 1 + i]) for i in range(ndims)
+            )
+            first_list = len(names) - lists
+            _cache[key] = lambda: tuple(
+                Dim(n) if i < first_list else DimList(name=n)
+                for i, n in enumerate(names)
+            )
+    return _cache[key]()
+
+
+def _dim_set(positional, arg):
+    def convert(a):
+        if isinstance(a, Dim):
+            return a
+        else:
+            assert isinstance(a, int)
+            return positional[a]
+
+    if arg is None:
+        return positional
+    elif not isinstance(arg, (Dim, int)):
+        return tuple(convert(a) for a in arg)
+    else:
+        return (convert(arg),)

falcon/lib/python3.10/site-packages/functorch/dim/reference.py

@@ -0,0 +1,645 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# reference python implementations for C ops
+import torch
+
+from functorch._C import dim as _C
+from . import op_properties
+from .batch_tensor import _enable_layers
+from .tree_map import tree_flatten, tree_map
+
+DimList = _C.DimList
+import operator
+from functools import reduce
+
+
+# use dict to avoid writing C++ bindings for set
+pointwise = set(op_properties.pointwise)
+
+
+def prod(x):
+    return reduce(operator.mul, x, 1)
+
+
+def _wrap_dim(d, N, keepdim):
+    from . import Dim
+
+    if isinstance(d, Dim):
+        assert not keepdim, "cannot preserve first-class dimensions with keepdim=True"
+        return d
+    elif d >= 0:
+        return d - N
+    else:
+        return d
+
+
+def _dims(d, N, keepdim, single_dim):
+    from . import Dim
+
+    if isinstance(d, (Dim, int)):
+        return ltuple((_wrap_dim(d, N, keepdim),))
+    assert not single_dim, f"expected a single dimension or int but found: {d}"
+    return ltuple(_wrap_dim(x, N, keepdim) for x in d)
+
+
+def _bind_dims_to_size(lhs_size, rhs, lhs_debug):
+    from . import DimensionMismatchError
+
+    not_bound = tuple((i, r) for i, r in enumerate(rhs) if not r.is_bound)
+    if len(not_bound) == 1:
+        idx, d = not_bound[0]
+        rhs_so_far = prod(r.size for r in rhs if r.is_bound)
+        if lhs_size % rhs_so_far != 0:
+            rhs_s = tuple("?" if not r.is_bound else str(r.size) for r in rhs)
+            raise DimensionMismatchError(
+                f"inferred dimension does not evenly fit into larger dimension: {lhs_size} vs {rhs_s}"
+            )
+        new_size = lhs_size // rhs_so_far
+        d.size = new_size
+    elif len(not_bound) > 1:
+        rhs_s = tuple("?" if not r.is_bound else str(r.size) for r in rhs)
+        raise DimensionMismatchError(
+            f"cannot infer the size of two dimensions at once: {rhs} with sizes {rhs_s}"
+        )
+    else:
+        rhs_size = prod(r.size for r in rhs)
+        if lhs_size != rhs_size:
+            raise DimensionMismatchError(
+                f"Dimension sizes to do not match ({lhs_size} != {rhs_size}) when matching {lhs_debug} to {rhs}"
+            )
+
+
+def _tensor_levels(inp):
+    from . import _Tensor
+
+    if isinstance(inp, _Tensor):
+        return inp._tensor, llist(inp._levels), inp._has_device
+    else:
+        return inp, llist(range(-inp.ndim, 0)), True
+
+
+def _match_levels(v, from_levels, to_levels):
+    view = []
+    permute = []
+    requires_view = False
+    size = v.size()
+    for t in to_levels:
+        try:
+            idx = from_levels.index(t)
+            permute.append(idx)
+            view.append(size[idx])
+        except ValueError:
+            view.append(1)
+            requires_view = True
+    if permute != list(range(len(permute))):
+        v = v.permute(*permute)
+    if requires_view:
+        v = v.view(*view)
+    return v
+
+
+# make a single dimension positional but do not permute it,
+# used to do multi-tensor operators where the dim being acted on
+# should not physically move if possible
+def _positional_no_permute(self, dim, expand_dim=False):
+    from . import Tensor
+
+    ptensor, levels = self._tensor, llist(self._levels)
+    try:
+        idx = levels.index(dim)
+    except ValueError:
+        if not expand_dim:
+            raise
+        idx = 0
+        ptensor = ptensor.expand(dim.size, *ptensor.size())
+        levels.insert(0, 0)
+    idx_batched = 0
+    for i in range(idx):
+        if isinstance(levels[i], int):
+            levels[i] -= 1
+            idx_batched += 1
+    levels[idx] = -idx_batched - 1
+    return Tensor.from_positional(ptensor, levels, self._has_device), idx_batched
+
+
+def seq(a, b):
+    from . import Dim
+
+    if isinstance(a, Dim) != isinstance(b, Dim):
+        return False
+    if isinstance(a, Dim):
+        return a is b
+    else:
+        return a == b
+
+
+class isin:
+    def __contains__(self, item):
+        for x in self:
+            if seq(item, x):
+                return True
+        return False
+
+    def index(self, item):
+        for i, x in enumerate(self):
+            if seq(item, x):
+                return i
+        raise ValueError
+
+
+class llist(isin, list):
+    pass
+
+
+class ltuple(isin, tuple):
+    pass
+
+
+empty_dict = {}
+
+
+@classmethod
+def __torch_function__(self, orig, cls, args, kwargs=empty_dict):
+    from . import _Tensor, Tensor, TensorLike
+    from .delayed_mul_tensor import DelayedMulTensor
+
+    if orig is torch.Tensor.__mul__:
+        lhs, rhs = args
+        if (
+            isinstance(lhs, _Tensor)
+            and isinstance(rhs, _Tensor)
+            and lhs.ndim == 0
+            and rhs.ndim == 0
+        ):
+            return DelayedMulTensor(lhs, rhs)
+    all_dims = llist()
+    flat_args, unflatten = tree_flatten((args, kwargs))
+    device_holding_tensor = None
+    for f in flat_args:
+        if isinstance(f, _Tensor):
+            if f._has_device:
+                device_holding_tensor = f._batchtensor
+            for d in f.dims:
+                if d not in all_dims:
+                    all_dims.append(d)
+
+    def unwrap(t):
+        if isinstance(t, _Tensor):
+            r = t._batchtensor
+            if device_holding_tensor is not None and not t._has_device:
+                r = r.to(device=device_holding_tensor.device)
+            return r
+        return t
+
+    if orig in pointwise:
+        result_levels = llist()
+        arg_levels = llist()
+        to_expand = []
+        for i, f in enumerate(flat_args):
+            if isinstance(f, TensorLike):
+                ptensor, levels, _ = _tensor_levels(f)
+                if (
+                    isinstance(f, _Tensor)
+                    and not f._has_device
+                    and device_holding_tensor is not None
+                ):
+                    ptensor = ptensor.to(device=device_holding_tensor.device)
+                flat_args[i] = ptensor
+                for l in levels:
+                    if l not in result_levels:
+                        result_levels.append(l)
+                to_expand.append((i, levels))
+
+        for i, levels in to_expand:
+            flat_args[i] = _match_levels(flat_args[i], levels, result_levels)
+        args, kwargs = unflatten(flat_args)
+        result = orig(*args, **kwargs)
+
+        def wrap(t):
+            if isinstance(t, TensorLike):
+                return Tensor.from_positional(
+                    t, result_levels, device_holding_tensor is not None
+                )
+            return t
+
+        return tree_map(wrap, result)
+    else:
+
+        def wrap(t):
+            if isinstance(t, TensorLike):
+                return Tensor.from_batched(t, device_holding_tensor is not None)
+            return t
+
+        with _enable_layers(all_dims):
+            print(f"batch_tensor for {orig}")
+            args, kwargs = unflatten(unwrap(f) for f in flat_args)
+            result = orig(*args, **kwargs)
+            # print("END", orig)
+            return tree_map(wrap, result)
+
+
+def positional(self, *dims):
+    from . import Dim, DimensionBindError, Tensor
+
+    ptensor, levels = self._tensor, llist(self._levels)
+    flat_dims = llist()
+    view = []
+    needs_view = False
+    ndim = self.ndim
+    for d in dims:
+        if isinstance(d, DimList):
+            flat_dims.extend(d)
+            view.extend(e.size for e in d)
+        elif isinstance(d, Dim):
+            flat_dims.append(d)
+            view.append(d.size)
+        elif isinstance(d, int):
+            d = _wrap_dim(d, ndim, False)
+            flat_dims.append(d)
+            view.append(ptensor.size(d))
+        else:
+            flat_dims.extend(d)
+            view.append(prod(e.size for e in d))
+            needs_view = True
+
+    permute = list(range(len(levels)))
+    nflat = len(flat_dims)
+    for i, d in enumerate(flat_dims):
+        try:
+            idx = levels.index(d)
+        except ValueError as e:
+            raise DimensionBindError(
+                f"tensor of dimensions {self.dims} does not contain dim {d}"
+            ) from e
+        p = permute[idx]
+        del levels[idx]
+        del permute[idx]
+        levels.insert(i, 0)
+        permute.insert(i, p)
+    ptensor = ptensor.permute(*permute)
+    seen = 0
+    for i in range(len(levels) - 1, -1, -1):
+        if isinstance(levels[i], int):
+            seen += 1
+            levels[i] = -seen
+    result = Tensor.from_positional(ptensor, levels, self._has_device)
+    if needs_view:
+        result = result.reshape(*view, *result.size()[len(flat_dims) :])
+    return result
+
+
+def _contains_dim(input):
+    from . import Dim
+
+    for i in input:
+        if isinstance(i, Dim):
+            return True
+
+
+def expand(self, *sizes):
+    if not _contains_dim(sizes):
+        return self.__torch_function__(torch.Tensor.expand, None, (self, *sizes))
+    dims = sizes
+    sizes = [d.size for d in dims] + [-1] * self.ndim
+    self = self.expand(*sizes)
+    return self[dims]
+
+
+_not_present = object()
+
+
+def _getarg(name, offset, args, kwargs, default):
+    if len(args) > offset:
+        return args[offset]
+    return kwargs.get(name, default)
+
+
+def _patcharg(name, offset, args, kwargs, value):
+    if len(args) > offset:
+        args[offset] = value
+    else:
+        kwargs[name] = value
+
+
+def _wrap(
+    orig, dim_offset=0, keepdim_offset=1, dim_name="dim", single_dim=False, reduce=True
+):
+    from . import Dim, Tensor, TensorLike
+
+    def fn(self, *args, **kwargs):
+        dim = _getarg(dim_name, dim_offset, args, kwargs, _not_present)
+        if dim is _not_present or (single_dim and not isinstance(dim, Dim)):
+            with _enable_layers(self.dims):
+                print(f"dim fallback batch_tensor for {orig}")
+                return Tensor.from_batched(
+                    orig(self._batchtensor, *args, **kwargs), self._has_device
+                )
+        keepdim = (
+            _getarg("keepdim", keepdim_offset, args, kwargs, False) if reduce else False
+        )
+        t, levels = self._tensor, llist(self._levels)
+        dims = _dims(dim, self._batchtensor.ndim, keepdim, single_dim)
+        dim_indices = tuple(levels.index(d) for d in dims)
+        if reduce and not keepdim:
+            new_levels = [l for i, l in enumerate(levels) if i not in dim_indices]
+        else:
+            new_levels = levels
+
+        if len(dim_indices) == 1:
+            dim_indices = dim_indices[
+                0
+            ]  # so that dims that really only take a single argument work...
+        args = list(args)
+        _patcharg(dim_name, dim_offset, args, kwargs, dim_indices)
+
+        def wrap(t):
+            if isinstance(t, TensorLike):
+                return Tensor.from_positional(t, new_levels, self._has_device)
+            return t
+
+        with _enable_layers(new_levels):
+            print(f"dim used batch_tensor for {orig}")
+            r = orig(t, *args, **kwargs)
+            return tree_map(wrap, r)
+
+    return fn
+
+
+def _def(name, *args, **kwargs):
+    from . import _Tensor
+
+    orig = getattr(torch.Tensor, name)
+    setattr(_Tensor, name, _wrap(orig, *args, **kwargs))
+
+
+no_slice = slice(None)
+
+_orig_getitem = torch.Tensor.__getitem__
+
+
+class dim_tracker:
+    def __init__(self):
+        self.dims = llist()
+        self.count = []
+
+    def record(self, d):
+        if d not in self.dims:
+            self.dims.append(d)
+            self.count.append(1)
+
+    def __getitem__(self, d):
+        return self.count[self.dims.index(d)]
+
+
+def t__getitem__(self, input):
+    from . import _Tensor, Dim, DimensionBindError, DimList, Tensor, TensorLike
+
+    # * bail to original example if we have a single non-Dim tensor, or a non-tensor
+    # * locate ... or an unbound tensor list, and determine its size, bind dim list
+    #   (remember that None does not count to the total dim count)
+    # * bind simple dims and dim-packs to their sizes, count the number of uses of each dim,
+    #   produce the re-view if needed
+    # * for each single-use dim index, replace with no_slice and mark that it will be added
+    #   (keep track of whether we have to call super)
+    # * call super if needed
+    # * if we have dims to bind, bind them (it will help if we eliminated ... and None before)
+
+    # this handles bool indexing handling, as well as some other simple cases.
+
+    is_simple = (
+        not isinstance(input, Dim)
+        and not isinstance(input, (tuple, list))
+        and
+        # WAR for functorch bug where zero time tensors in getitem are not handled correctly.
+        not (isinstance(input, TensorLike) and input.ndim == 0)
+    )
+
+    if is_simple:
+        if isinstance(self, _Tensor):
+            return _Tensor.__torch_function__(_orig_getitem, None, (self, input))
+        else:
+            return _orig_getitem(self, input)
+
+    # can further optimize this case
+    if not isinstance(input, tuple):
+        input = [input]
+    else:
+        input = list(input)
+
+    dims_indexed = 0
+    expanding_object = None
+    dimlists = []
+    for i, s in enumerate(input):
+        if s is ... or isinstance(s, DimList) and not s.is_bound:
+            if expanding_object is not None:
+                msg = (
+                    "at most one ... or unbound dimension list can exist in indexing list but"
+                    f" found 2 at offsets {i} and {expanding_object}"
+                )
+                raise DimensionBindError(msg)
+            expanding_object = i
+
+        if isinstance(s, DimList):
+            dims_indexed += len(s) if s.is_bound else 0
+            dimlists.append(i)
+        elif s is not None and s is not ...:
+            dims_indexed += 1
+
+    ndim = self.ndim
+    if dims_indexed > ndim:
+        raise IndexError(
+            f"at least {dims_indexed} indices were supplied but the tensor only has {ndim} dimensions."
+        )
+    if expanding_object is not None:
+        expanding_ndims = ndim - dims_indexed
+        obj = input[expanding_object]
+        if obj is ...:
+            input[expanding_object : expanding_object + 1] = [
+                no_slice
+            ] * expanding_ndims
+        else:
+            obj.bind_len(expanding_ndims)
+    # flatten the dimslists into the indexing
+    for i in reversed(dimlists):
+        input[i : i + 1] = input[i]
+    dims_indexed = 0
+    requires_view = False
+    size = self.size()
+    view_sizes = []
+    dims_seen = dim_tracker()
+
+    def add_dims(t):
+        if not isinstance(t, _Tensor):
+            return
+        for d in t.dims:
+            dims_seen.record(d)
+
+    add_dims(self)
+    dim_packs = []
+    for i, idx in enumerate(input):
+        if idx is None:
+            input[i] = no_slice
+            view_sizes.append(1)
+            requires_view = True
+        else:
+            sz = size[dims_indexed]
+            if isinstance(idx, Dim):
+                idx.size = sz
+                dims_seen.record(idx)
+                view_sizes.append(sz)
+            elif isinstance(idx, (tuple, list)) and idx and isinstance(idx[0], Dim):
+                for d in idx:
+                    dims_seen.record(idx)
+                _bind_dims_to_size(sz, idx, f"offset {i}")
+                view_sizes.extend(d.size for d in idx)
+                requires_view = True
+                dim_packs.append(i)
+            else:
+                add_dims(idx)
+                view_sizes.append(sz)
+            dims_indexed += 1
+    if requires_view:
+        self = self.view(*view_sizes)
+    for i in reversed(dim_packs):
+        input[i : i + 1] = input[i]
+
+    # currenty:
+    # input is flat, containing either Dim, or Tensor, or something valid for standard indexing
+    # self may have first-class dims as well.
+
+    # to index:
+    # drop the first class dims from self, they just become direct indices of their positions
+
+    # figure out the dimensions of the indexing tensors: union of all the dims in the tensors in the index.
+    # these dimensions will appear and need to be bound at the first place tensor occures
+
+    if isinstance(self, _Tensor):
+        ptensor_self, levels = self._tensor, list(self._levels)
+        # indices to ptensor rather than self which has first-class dimensions
+        input_it = iter(input)
+        flat_inputs = [next(input_it) if isinstance(l, int) else l for l in levels]
+        has_device = self._has_device
+        to_pad = 0
+    else:
+        ptensor_self, flat_inputs = self, input
+        to_pad = ptensor_self.ndim - len(flat_inputs)
+        has_device = True
+
+    result_levels = []
+    index_levels = []
+    tensor_insert_point = None
+    to_expand = {}
+    requires_getindex = False
+    for i, inp in enumerate(flat_inputs):
+        if isinstance(inp, Dim) and dims_seen[inp] == 1:
+            flat_inputs[i] = no_slice
+            result_levels.append(inp)
+        elif isinstance(inp, TensorLike):
+            requires_getindex = True
+            if tensor_insert_point is None:
+                tensor_insert_point = len(result_levels)
+            ptensor, levels, _ = _tensor_levels(inp)
+            to_expand[i] = levels
+            flat_inputs[i] = ptensor
+            for l in levels:
+                if l not in index_levels:
+                    index_levels.append(l)
+        else:
+            requires_getindex = True
+            result_levels.append(0)
+
+    if tensor_insert_point is not None:
+        result_levels[tensor_insert_point:tensor_insert_point] = index_levels
+
+    for i, levels in to_expand.items():
+        flat_inputs[i] = _match_levels(flat_inputs[i], levels, index_levels)
+
+    if requires_getindex:
+        result = _orig_getitem(ptensor_self, flat_inputs)
+    else:
+        result = ptensor_self
+
+    next_positional = -1
+    if to_pad > 0:
+        result_levels.extend([0] * to_pad)
+    for i, r in enumerate(reversed(result_levels)):
+        if isinstance(r, int):
+            result_levels[-1 - i] = next_positional
+            next_positional -= 1
+
+    return Tensor.from_positional(result, result_levels, has_device)
+
+
+# XXX - dim is optional and can be the outer-most dimension...
+def stack(tensors, new_dim, dim=0, out=None):
+    if isinstance(dim, int):
+        return torch.stack(tensors, dim, out).index(dim, new_dim)
+    index = None
+    if out is not None:
+        out, index = _positional_no_permute(out, dim, expand_dim=True)
+    ptensors = []
+    for t in tensors:
+        pt, pi = _positional_no_permute(t, dim, expand_dim=True)
+        if index is not None and pi != index:
+            pt = pt.move_dim(pi, index)
+        else:
+            index = pi
+        ptensors.append(pt)
+    pr = torch.stack(ptensors, index, out=out)
+    return pr.index((index, index + 1), (new_dim, dim))
+
+
+_orig_split = torch.Tensor.split
+
+
+def split(self, split_size_or_sections, dim=0):
+    from . import _Tensor, Dim
+
+    if isinstance(split_size_or_sections, int) or any(
+        isinstance(t, int) for t in split_size_or_sections
+    ):
+        if isinstance(dim, Dim):
+            raise ValueError(
+                "when dim is specified as a Dim object, split sizes must also be dimensions."
+            )
+        return _orig_split(self, split_size_or_sections, dim=dim)
+
+    if isinstance(dim, Dim):
+        assert isinstance(self, _Tensor), f"Tensor does not have dimension {dim}"
+        self, dim = _positional_no_permute(self, dim)
+
+    size = self.size(dim)
+    total_bound_size = 0
+    unbound = []
+    sizes = []
+    for i, d in enumerate(split_size_or_sections):
+        if d.is_bound:
+            sizes.append(d.size)
+            total_bound_size += d.size
+        else:
+            sizes.append(0)
+            unbound.append(i)
+
+    if unbound:
+        assert (
+            total_bound_size <= size
+        ), f"result dimensions are larger than original: {total_bound_size} vs {size} ({split_size_or_sections})"
+        remaining_size = size - total_bound_size
+        chunk_size = -(-remaining_size // len(unbound))
+        for u in unbound:
+            sz = min(chunk_size, remaining_size)
+            split_size_or_sections[u].size = sz
+            sizes[u] = sz
+            remaining_size -= sz
+    else:
+        assert (
+            total_bound_size == size
+        ), f"result dimensions do not match original: {total_bound_size} vs {size} ({split_size_or_sections})"
+    return tuple(
+        t.index(dim, d)
+        for d, t in zip(split_size_or_sections, _orig_split(self, sizes, dim=dim))
+    )

falcon/lib/python3.10/site-packages/functorch/dim/tree_map.py

@@ -0,0 +1,14 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from functorch._C import dim
+
+tree_flatten = dim.tree_flatten
+
+
+def tree_map(fn, tree):
+    vs, unflatten = tree_flatten(tree)
+    return unflatten(fn(v) for v in vs)

falcon/lib/python3.10/site-packages/functorch/dim/wrap_type.py

@@ -0,0 +1,71 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from types import (
+    BuiltinMethodType,
+    FunctionType,
+    GetSetDescriptorType,
+    MethodDescriptorType,
+    WrapperDescriptorType,
+)
+
+from functorch._C import dim as _C
+
+_wrap_method = _C._wrap_method
+
+FUNC_TYPES = (
+    FunctionType,
+    MethodDescriptorType,
+    BuiltinMethodType,
+    WrapperDescriptorType,
+)
+PROPERTY_TYPES = (GetSetDescriptorType, property)
+
+
+def _py_wrap_method(orig, __torch_function__):
+    def impl(*args, **kwargs):
+        return __torch_function__(orig, None, args, kwargs)
+
+    return impl
+
+
+def wrap_type(use_c, to_patch, pattern, __torch_function__):
+    if use_c:
+        wrap_method = _wrap_method
+    else:
+        wrap_method = _py_wrap_method
+
+    all = {}
+    for t in reversed(pattern.mro()[:-1]):  # skip object
+        all.update(t.__dict__)
+
+    def wrap_attr(orig):
+        return property(wrap_method(orig.__get__, __torch_function__))
+
+    for name, obj in all.items():
+        if name in (
+            "__dict__",
+            "__new__",
+            "__init__",
+            "__repr__",
+            "__weakref__",
+            "__doc__",
+            "__module__",
+            "__dir__",
+        ):
+            continue
+
+        # skip things that have been overloaded
+        # things that come from object like `__eq__` still need to be patched, however.
+        if hasattr(to_patch, name) and getattr(to_patch, name) is not getattr(
+            object, name, None
+        ):
+            continue
+
+        if isinstance(obj, FUNC_TYPES):
+            setattr(to_patch, name, wrap_method(obj, __torch_function__))
+        elif isinstance(obj, PROPERTY_TYPES):
+            setattr(to_patch, name, wrap_attr(obj))