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evalkit_internvl/lib/python3.10/site-packages/annotated_types/__init__.py

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+import math
+import sys
+import types
+from dataclasses import dataclass
+from datetime import tzinfo
+from typing import TYPE_CHECKING, Any, Callable, Iterator, Optional, SupportsFloat, SupportsIndex, TypeVar, Union
+
+if sys.version_info < (3, 8):
+    from typing_extensions import Protocol, runtime_checkable
+else:
+    from typing import Protocol, runtime_checkable
+
+if sys.version_info < (3, 9):
+    from typing_extensions import Annotated, Literal
+else:
+    from typing import Annotated, Literal
+
+if sys.version_info < (3, 10):
+    EllipsisType = type(Ellipsis)
+    KW_ONLY = {}
+    SLOTS = {}
+else:
+    from types import EllipsisType
+
+    KW_ONLY = {"kw_only": True}
+    SLOTS = {"slots": True}
+
+
+__all__ = (
+    'BaseMetadata',
+    'GroupedMetadata',
+    'Gt',
+    'Ge',
+    'Lt',
+    'Le',
+    'Interval',
+    'MultipleOf',
+    'MinLen',
+    'MaxLen',
+    'Len',
+    'Timezone',
+    'Predicate',
+    'LowerCase',
+    'UpperCase',
+    'IsDigits',
+    'IsFinite',
+    'IsNotFinite',
+    'IsNan',
+    'IsNotNan',
+    'IsInfinite',
+    'IsNotInfinite',
+    'doc',
+    'DocInfo',
+    '__version__',
+)
+
+__version__ = '0.7.0'
+
+
+T = TypeVar('T')
+
+
+# arguments that start with __ are considered
+# positional only
+# see https://peps.python.org/pep-0484/#positional-only-arguments
+
+
+class SupportsGt(Protocol):
+    def __gt__(self: T, __other: T) -> bool:
+        ...
+
+
+class SupportsGe(Protocol):
+    def __ge__(self: T, __other: T) -> bool:
+        ...
+
+
+class SupportsLt(Protocol):
+    def __lt__(self: T, __other: T) -> bool:
+        ...
+
+
+class SupportsLe(Protocol):
+    def __le__(self: T, __other: T) -> bool:
+        ...
+
+
+class SupportsMod(Protocol):
+    def __mod__(self: T, __other: T) -> T:
+        ...
+
+
+class SupportsDiv(Protocol):
+    def __div__(self: T, __other: T) -> T:
+        ...
+
+
+class BaseMetadata:
+    """Base class for all metadata.
+
+    This exists mainly so that implementers
+    can do `isinstance(..., BaseMetadata)` while traversing field annotations.
+    """
+
+    __slots__ = ()
+
+
+@dataclass(frozen=True, **SLOTS)
+class Gt(BaseMetadata):
+    """Gt(gt=x) implies that the value must be greater than x.
+
+    It can be used with any type that supports the ``>`` operator,
+    including numbers, dates and times, strings, sets, and so on.
+    """
+
+    gt: SupportsGt
+
+
+@dataclass(frozen=True, **SLOTS)
+class Ge(BaseMetadata):
+    """Ge(ge=x) implies that the value must be greater than or equal to x.
+
+    It can be used with any type that supports the ``>=`` operator,
+    including numbers, dates and times, strings, sets, and so on.
+    """
+
+    ge: SupportsGe
+
+
+@dataclass(frozen=True, **SLOTS)
+class Lt(BaseMetadata):
+    """Lt(lt=x) implies that the value must be less than x.
+
+    It can be used with any type that supports the ``<`` operator,
+    including numbers, dates and times, strings, sets, and so on.
+    """
+
+    lt: SupportsLt
+
+
+@dataclass(frozen=True, **SLOTS)
+class Le(BaseMetadata):
+    """Le(le=x) implies that the value must be less than or equal to x.
+
+    It can be used with any type that supports the ``<=`` operator,
+    including numbers, dates and times, strings, sets, and so on.
+    """
+
+    le: SupportsLe
+
+
+@runtime_checkable
+class GroupedMetadata(Protocol):
+    """A grouping of multiple objects, like typing.Unpack.
+
+    `GroupedMetadata` on its own is not metadata and has no meaning.
+    All of the constraints and metadata should be fully expressable
+    in terms of the `BaseMetadata`'s returned by `GroupedMetadata.__iter__()`.
+
+    Concrete implementations should override `GroupedMetadata.__iter__()`
+    to add their own metadata.
+    For example:
+
+    >>> @dataclass
+    >>> class Field(GroupedMetadata):
+    >>>     gt: float | None = None
+    >>>     description: str | None = None
+    ...
+    >>>     def __iter__(self) -> Iterable[object]:
+    >>>         if self.gt is not None:
+    >>>             yield Gt(self.gt)
+    >>>         if self.description is not None:
+    >>>             yield Description(self.gt)
+
+    Also see the implementation of `Interval` below for an example.
+
+    Parsers should recognize this and unpack it so that it can be used
+    both with and without unpacking:
+
+    - `Annotated[int, Field(...)]` (parser must unpack Field)
+    - `Annotated[int, *Field(...)]` (PEP-646)
+    """  # noqa: trailing-whitespace
+
+    @property
+    def __is_annotated_types_grouped_metadata__(self) -> Literal[True]:
+        return True
+
+    def __iter__(self) -> Iterator[object]:
+        ...
+
+    if not TYPE_CHECKING:
+        __slots__ = ()  # allow subclasses to use slots
+
+        def __init_subclass__(cls, *args: Any, **kwargs: Any) -> None:
+            # Basic ABC like functionality without the complexity of an ABC
+            super().__init_subclass__(*args, **kwargs)
+            if cls.__iter__ is GroupedMetadata.__iter__:
+                raise TypeError("Can't subclass GroupedMetadata without implementing __iter__")
+
+        def __iter__(self) -> Iterator[object]:  # noqa: F811
+            raise NotImplementedError  # more helpful than "None has no attribute..." type errors
+
+
+@dataclass(frozen=True, **KW_ONLY, **SLOTS)
+class Interval(GroupedMetadata):
+    """Interval can express inclusive or exclusive bounds with a single object.
+
+    It accepts keyword arguments ``gt``, ``ge``, ``lt``, and/or ``le``, which
+    are interpreted the same way as the single-bound constraints.
+    """
+
+    gt: Union[SupportsGt, None] = None
+    ge: Union[SupportsGe, None] = None
+    lt: Union[SupportsLt, None] = None
+    le: Union[SupportsLe, None] = None
+
+    def __iter__(self) -> Iterator[BaseMetadata]:
+        """Unpack an Interval into zero or more single-bounds."""
+        if self.gt is not None:
+            yield Gt(self.gt)
+        if self.ge is not None:
+            yield Ge(self.ge)
+        if self.lt is not None:
+            yield Lt(self.lt)
+        if self.le is not None:
+            yield Le(self.le)
+
+
+@dataclass(frozen=True, **SLOTS)
+class MultipleOf(BaseMetadata):
+    """MultipleOf(multiple_of=x) might be interpreted in two ways:
+
+    1. Python semantics, implying ``value % multiple_of == 0``, or
+    2. JSONschema semantics, where ``int(value / multiple_of) == value / multiple_of``
+
+    We encourage users to be aware of these two common interpretations,
+    and libraries to carefully document which they implement.
+    """
+
+    multiple_of: Union[SupportsDiv, SupportsMod]
+
+
+@dataclass(frozen=True, **SLOTS)
+class MinLen(BaseMetadata):
+    """
+    MinLen() implies minimum inclusive length,
+    e.g. ``len(value) >= min_length``.
+    """
+
+    min_length: Annotated[int, Ge(0)]
+
+
+@dataclass(frozen=True, **SLOTS)
+class MaxLen(BaseMetadata):
+    """
+    MaxLen() implies maximum inclusive length,
+    e.g. ``len(value) <= max_length``.
+    """
+
+    max_length: Annotated[int, Ge(0)]
+
+
+@dataclass(frozen=True, **SLOTS)
+class Len(GroupedMetadata):
+    """
+    Len() implies that ``min_length <= len(value) <= max_length``.
+
+    Upper bound may be omitted or ``None`` to indicate no upper length bound.
+    """
+
+    min_length: Annotated[int, Ge(0)] = 0
+    max_length: Optional[Annotated[int, Ge(0)]] = None
+
+    def __iter__(self) -> Iterator[BaseMetadata]:
+        """Unpack a Len into zone or more single-bounds."""
+        if self.min_length > 0:
+            yield MinLen(self.min_length)
+        if self.max_length is not None:
+            yield MaxLen(self.max_length)
+
+
+@dataclass(frozen=True, **SLOTS)
+class Timezone(BaseMetadata):
+    """Timezone(tz=...) requires a datetime to be aware (or ``tz=None``, naive).
+
+    ``Annotated[datetime, Timezone(None)]`` must be a naive datetime.
+    ``Timezone[...]`` (the ellipsis literal) expresses that the datetime must be
+    tz-aware but any timezone is allowed.
+
+    You may also pass a specific timezone string or tzinfo object such as
+    ``Timezone(timezone.utc)`` or ``Timezone("Africa/Abidjan")`` to express that
+    you only allow a specific timezone, though we note that this is often
+    a symptom of poor design.
+    """
+
+    tz: Union[str, tzinfo, EllipsisType, None]
+
+
+@dataclass(frozen=True, **SLOTS)
+class Unit(BaseMetadata):
+    """Indicates that the value is a physical quantity with the specified unit.
+
+    It is intended for usage with numeric types, where the value represents the
+    magnitude of the quantity. For example, ``distance: Annotated[float, Unit('m')]``
+    or ``speed: Annotated[float, Unit('m/s')]``.
+
+    Interpretation of the unit string is left to the discretion of the consumer.
+    It is suggested to follow conventions established by python libraries that work
+    with physical quantities, such as
+
+    - ``pint`` : <https://pint.readthedocs.io/en/stable/>
+    - ``astropy.units``: <https://docs.astropy.org/en/stable/units/>
+
+    For indicating a quantity with a certain dimensionality but without a specific unit
+    it is recommended to use square brackets, e.g. `Annotated[float, Unit('[time]')]`.
+    Note, however, ``annotated_types`` itself makes no use of the unit string.
+    """
+
+    unit: str
+
+
+@dataclass(frozen=True, **SLOTS)
+class Predicate(BaseMetadata):
+    """``Predicate(func: Callable)`` implies `func(value)` is truthy for valid values.
+
+    Users should prefer statically inspectable metadata, but if you need the full
+    power and flexibility of arbitrary runtime predicates... here it is.
+
+    We provide a few predefined predicates for common string constraints:
+    ``IsLower = Predicate(str.islower)``, ``IsUpper = Predicate(str.isupper)``, and
+    ``IsDigits = Predicate(str.isdigit)``. Users are encouraged to use methods which
+    can be given special handling, and avoid indirection like ``lambda s: s.lower()``.
+
+    Some libraries might have special logic to handle certain predicates, e.g. by
+    checking for `str.isdigit` and using its presence to both call custom logic to
+    enforce digit-only strings, and customise some generated external schema.
+
+    We do not specify what behaviour should be expected for predicates that raise
+    an exception.  For example `Annotated[int, Predicate(str.isdigit)]` might silently
+    skip invalid constraints, or statically raise an error; or it might try calling it
+    and then propagate or discard the resulting exception.
+    """
+
+    func: Callable[[Any], bool]
+
+    def __repr__(self) -> str:
+        if getattr(self.func, "__name__", "<lambda>") == "<lambda>":
+            return f"{self.__class__.__name__}({self.func!r})"
+        if isinstance(self.func, (types.MethodType, types.BuiltinMethodType)) and (
+            namespace := getattr(self.func.__self__, "__name__", None)
+        ):
+            return f"{self.__class__.__name__}({namespace}.{self.func.__name__})"
+        if isinstance(self.func, type(str.isascii)):  # method descriptor
+            return f"{self.__class__.__name__}({self.func.__qualname__})"
+        return f"{self.__class__.__name__}({self.func.__name__})"
+
+
+@dataclass
+class Not:
+    func: Callable[[Any], bool]
+
+    def __call__(self, __v: Any) -> bool:
+        return not self.func(__v)
+
+
+_StrType = TypeVar("_StrType", bound=str)
+
+LowerCase = Annotated[_StrType, Predicate(str.islower)]
+"""
+Return True if the string is a lowercase string, False otherwise.
+
+A string is lowercase if all cased characters in the string are lowercase and there is at least one cased character in the string.
+"""  # noqa: E501
+UpperCase = Annotated[_StrType, Predicate(str.isupper)]
+"""
+Return True if the string is an uppercase string, False otherwise.
+
+A string is uppercase if all cased characters in the string are uppercase and there is at least one cased character in the string.
+"""  # noqa: E501
+IsDigit = Annotated[_StrType, Predicate(str.isdigit)]
+IsDigits = IsDigit  # type: ignore  # plural for backwards compatibility, see #63
+"""
+Return True if the string is a digit string, False otherwise.
+
+A string is a digit string if all characters in the string are digits and there is at least one character in the string.
+"""  # noqa: E501
+IsAscii = Annotated[_StrType, Predicate(str.isascii)]
+"""
+Return True if all characters in the string are ASCII, False otherwise.
+
+ASCII characters have code points in the range U+0000-U+007F. Empty string is ASCII too.
+"""
+
+_NumericType = TypeVar('_NumericType', bound=Union[SupportsFloat, SupportsIndex])
+IsFinite = Annotated[_NumericType, Predicate(math.isfinite)]
+"""Return True if x is neither an infinity nor a NaN, and False otherwise."""
+IsNotFinite = Annotated[_NumericType, Predicate(Not(math.isfinite))]
+"""Return True if x is one of infinity or NaN, and False otherwise"""
+IsNan = Annotated[_NumericType, Predicate(math.isnan)]
+"""Return True if x is a NaN (not a number), and False otherwise."""
+IsNotNan = Annotated[_NumericType, Predicate(Not(math.isnan))]
+"""Return True if x is anything but NaN (not a number), and False otherwise."""
+IsInfinite = Annotated[_NumericType, Predicate(math.isinf)]
+"""Return True if x is a positive or negative infinity, and False otherwise."""
+IsNotInfinite = Annotated[_NumericType, Predicate(Not(math.isinf))]
+"""Return True if x is neither a positive or negative infinity, and False otherwise."""
+
+try:
+    from typing_extensions import DocInfo, doc  # type: ignore [attr-defined]
+except ImportError:
+
+    @dataclass(frozen=True, **SLOTS)
+    class DocInfo:  # type: ignore [no-redef]
+        """ "
+        The return value of doc(), mainly to be used by tools that want to extract the
+        Annotated documentation at runtime.
+        """
+
+        documentation: str
+        """The documentation string passed to doc()."""
+
+    def doc(
+        documentation: str,
+    ) -> DocInfo:
+        """
+        Add documentation to a type annotation inside of Annotated.
+
+        For example:
+
+        >>> def hi(name: Annotated[int, doc("The name of the user")]) -> None: ...
+        """
+        return DocInfo(documentation)

evalkit_internvl/lib/python3.10/site-packages/annotated_types/test_cases.py

@@ -0,0 +1,151 @@
+import math
+import sys
+from datetime import date, datetime, timedelta, timezone
+from decimal import Decimal
+from typing import Any, Dict, Iterable, Iterator, List, NamedTuple, Set, Tuple
+
+if sys.version_info < (3, 9):
+    from typing_extensions import Annotated
+else:
+    from typing import Annotated
+
+import annotated_types as at
+
+
+class Case(NamedTuple):
+    """
+    A test case for `annotated_types`.
+    """
+
+    annotation: Any
+    valid_cases: Iterable[Any]
+    invalid_cases: Iterable[Any]
+
+
+def cases() -> Iterable[Case]:
+    # Gt, Ge, Lt, Le
+    yield Case(Annotated[int, at.Gt(4)], (5, 6, 1000), (4, 0, -1))
+    yield Case(Annotated[float, at.Gt(0.5)], (0.6, 0.7, 0.8, 0.9), (0.5, 0.0, -0.1))
+    yield Case(
+        Annotated[datetime, at.Gt(datetime(2000, 1, 1))],
+        [datetime(2000, 1, 2), datetime(2000, 1, 3)],
+        [datetime(2000, 1, 1), datetime(1999, 12, 31)],
+    )
+    yield Case(
+        Annotated[datetime, at.Gt(date(2000, 1, 1))],
+        [date(2000, 1, 2), date(2000, 1, 3)],
+        [date(2000, 1, 1), date(1999, 12, 31)],
+    )
+    yield Case(
+        Annotated[datetime, at.Gt(Decimal('1.123'))],
+        [Decimal('1.1231'), Decimal('123')],
+        [Decimal('1.123'), Decimal('0')],
+    )
+
+    yield Case(Annotated[int, at.Ge(4)], (4, 5, 6, 1000, 4), (0, -1))
+    yield Case(Annotated[float, at.Ge(0.5)], (0.5, 0.6, 0.7, 0.8, 0.9), (0.4, 0.0, -0.1))
+    yield Case(
+        Annotated[datetime, at.Ge(datetime(2000, 1, 1))],
+        [datetime(2000, 1, 2), datetime(2000, 1, 3)],
+        [datetime(1998, 1, 1), datetime(1999, 12, 31)],
+    )
+
+    yield Case(Annotated[int, at.Lt(4)], (0, -1), (4, 5, 6, 1000, 4))
+    yield Case(Annotated[float, at.Lt(0.5)], (0.4, 0.0, -0.1), (0.5, 0.6, 0.7, 0.8, 0.9))
+    yield Case(
+        Annotated[datetime, at.Lt(datetime(2000, 1, 1))],
+        [datetime(1999, 12, 31), datetime(1999, 12, 31)],
+        [datetime(2000, 1, 2), datetime(2000, 1, 3)],
+    )
+
+    yield Case(Annotated[int, at.Le(4)], (4, 0, -1), (5, 6, 1000))
+    yield Case(Annotated[float, at.Le(0.5)], (0.5, 0.0, -0.1), (0.6, 0.7, 0.8, 0.9))
+    yield Case(
+        Annotated[datetime, at.Le(datetime(2000, 1, 1))],
+        [datetime(2000, 1, 1), datetime(1999, 12, 31)],
+        [datetime(2000, 1, 2), datetime(2000, 1, 3)],
+    )
+
+    # Interval
+    yield Case(Annotated[int, at.Interval(gt=4)], (5, 6, 1000), (4, 0, -1))
+    yield Case(Annotated[int, at.Interval(gt=4, lt=10)], (5, 6), (4, 10, 1000, 0, -1))
+    yield Case(Annotated[float, at.Interval(ge=0.5, le=1)], (0.5, 0.9, 1), (0.49, 1.1))
+    yield Case(
+        Annotated[datetime, at.Interval(gt=datetime(2000, 1, 1), le=datetime(2000, 1, 3))],
+        [datetime(2000, 1, 2), datetime(2000, 1, 3)],
+        [datetime(2000, 1, 1), datetime(2000, 1, 4)],
+    )
+
+    yield Case(Annotated[int, at.MultipleOf(multiple_of=3)], (0, 3, 9), (1, 2, 4))
+    yield Case(Annotated[float, at.MultipleOf(multiple_of=0.5)], (0, 0.5, 1, 1.5), (0.4, 1.1))
+
+    # lengths
+
+    yield Case(Annotated[str, at.MinLen(3)], ('123', '1234', 'x' * 10), ('', '1', '12'))
+    yield Case(Annotated[str, at.Len(3)], ('123', '1234', 'x' * 10), ('', '1', '12'))
+    yield Case(Annotated[List[int], at.MinLen(3)], ([1, 2, 3], [1, 2, 3, 4], [1] * 10), ([], [1], [1, 2]))
+    yield Case(Annotated[List[int], at.Len(3)], ([1, 2, 3], [1, 2, 3, 4], [1] * 10), ([], [1], [1, 2]))
+
+    yield Case(Annotated[str, at.MaxLen(4)], ('', '1234'), ('12345', 'x' * 10))
+    yield Case(Annotated[str, at.Len(0, 4)], ('', '1234'), ('12345', 'x' * 10))
+    yield Case(Annotated[List[str], at.MaxLen(4)], ([], ['a', 'bcdef'], ['a', 'b', 'c']), (['a'] * 5, ['b'] * 10))
+    yield Case(Annotated[List[str], at.Len(0, 4)], ([], ['a', 'bcdef'], ['a', 'b', 'c']), (['a'] * 5, ['b'] * 10))
+
+    yield Case(Annotated[str, at.Len(3, 5)], ('123', '12345'), ('', '1', '12', '123456', 'x' * 10))
+    yield Case(Annotated[str, at.Len(3, 3)], ('123',), ('12', '1234'))
+
+    yield Case(Annotated[Dict[int, int], at.Len(2, 3)], [{1: 1, 2: 2}], [{}, {1: 1}, {1: 1, 2: 2, 3: 3, 4: 4}])
+    yield Case(Annotated[Set[int], at.Len(2, 3)], ({1, 2}, {1, 2, 3}), (set(), {1}, {1, 2, 3, 4}))
+    yield Case(Annotated[Tuple[int, ...], at.Len(2, 3)], ((1, 2), (1, 2, 3)), ((), (1,), (1, 2, 3, 4)))
+
+    # Timezone
+
+    yield Case(
+        Annotated[datetime, at.Timezone(None)], [datetime(2000, 1, 1)], [datetime(2000, 1, 1, tzinfo=timezone.utc)]
+    )
+    yield Case(
+        Annotated[datetime, at.Timezone(...)], [datetime(2000, 1, 1, tzinfo=timezone.utc)], [datetime(2000, 1, 1)]
+    )
+    yield Case(
+        Annotated[datetime, at.Timezone(timezone.utc)],
+        [datetime(2000, 1, 1, tzinfo=timezone.utc)],
+        [datetime(2000, 1, 1), datetime(2000, 1, 1, tzinfo=timezone(timedelta(hours=6)))],
+    )
+    yield Case(
+        Annotated[datetime, at.Timezone('Europe/London')],
+        [datetime(2000, 1, 1, tzinfo=timezone(timedelta(0), name='Europe/London'))],
+        [datetime(2000, 1, 1), datetime(2000, 1, 1, tzinfo=timezone(timedelta(hours=6)))],
+    )
+
+    # Quantity
+
+    yield Case(Annotated[float, at.Unit(unit='m')], (5, 4.2), ('5m', '4.2m'))
+
+    # predicate types
+
+    yield Case(at.LowerCase[str], ['abc', 'foobar'], ['', 'A', 'Boom'])
+    yield Case(at.UpperCase[str], ['ABC', 'DEFO'], ['', 'a', 'abc', 'AbC'])
+    yield Case(at.IsDigit[str], ['123'], ['', 'ab', 'a1b2'])
+    yield Case(at.IsAscii[str], ['123', 'foo bar'], ['£100', '😊', 'whatever 👀'])
+
+    yield Case(Annotated[int, at.Predicate(lambda x: x % 2 == 0)], [0, 2, 4], [1, 3, 5])
+
+    yield Case(at.IsFinite[float], [1.23], [math.nan, math.inf, -math.inf])
+    yield Case(at.IsNotFinite[float], [math.nan, math.inf], [1.23])
+    yield Case(at.IsNan[float], [math.nan], [1.23, math.inf])
+    yield Case(at.IsNotNan[float], [1.23, math.inf], [math.nan])
+    yield Case(at.IsInfinite[float], [math.inf], [math.nan, 1.23])
+    yield Case(at.IsNotInfinite[float], [math.nan, 1.23], [math.inf])
+
+    # check stacked predicates
+    yield Case(at.IsInfinite[Annotated[float, at.Predicate(lambda x: x > 0)]], [math.inf], [-math.inf, 1.23, math.nan])
+
+    # doc
+    yield Case(Annotated[int, at.doc("A number")], [1, 2], [])
+
+    # custom GroupedMetadata
+    class MyCustomGroupedMetadata(at.GroupedMetadata):
+        def __iter__(self) -> Iterator[at.Predicate]:
+            yield at.Predicate(lambda x: float(x).is_integer())
+
+    yield Case(Annotated[float, MyCustomGroupedMetadata()], [0, 2.0], [0.01, 1.5])

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/__init__.py

@@ -0,0 +1,1002 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# ***********
+# `huggingface_hub` init has 2 modes:
+# - Normal usage:
+#       If imported to use it, all modules and functions are lazy-loaded. This means
+#       they exist at top level in module but are imported only the first time they are
+#       used. This way, `from huggingface_hub import something` will import `something`
+#       quickly without the hassle of importing all the features from `huggingface_hub`.
+# - Static check:
+#       If statically analyzed, all modules and functions are loaded normally. This way
+#       static typing check works properly as well as autocomplete in text editors and
+#       IDEs.
+#
+# The static model imports are done inside the `if TYPE_CHECKING:` statement at
+# the bottom of this file. Since module/functions imports are duplicated, it is
+# mandatory to make sure to add them twice when adding one. This is checked in the
+# `make quality` command.
+#
+# To update the static imports, please run the following command and commit the changes.
+# ```
+# # Use script
+# python utils/check_static_imports.py --update-file
+#
+# # Or run style on codebase
+# make style
+# ```
+#
+# ***********
+# Lazy loader vendored from https://github.com/scientific-python/lazy_loader
+import importlib
+import os
+import sys
+from typing import TYPE_CHECKING
+
+
+__version__ = "0.26.2"
+
+# Alphabetical order of definitions is ensured in tests
+# WARNING: any comment added in this dictionary definition will be lost when
+# re-generating the file !
+_SUBMOD_ATTRS = {
+    "_commit_scheduler": [
+        "CommitScheduler",
+    ],
+    "_inference_endpoints": [
+        "InferenceEndpoint",
+        "InferenceEndpointError",
+        "InferenceEndpointStatus",
+        "InferenceEndpointTimeoutError",
+        "InferenceEndpointType",
+    ],
+    "_login": [
+        "auth_list",
+        "auth_switch",
+        "interpreter_login",
+        "login",
+        "logout",
+        "notebook_login",
+    ],
+    "_multi_commits": [
+        "MultiCommitException",
+        "plan_multi_commits",
+    ],
+    "_snapshot_download": [
+        "snapshot_download",
+    ],
+    "_space_api": [
+        "SpaceHardware",
+        "SpaceRuntime",
+        "SpaceStage",
+        "SpaceStorage",
+        "SpaceVariable",
+    ],
+    "_tensorboard_logger": [
+        "HFSummaryWriter",
+    ],
+    "_webhooks_payload": [
+        "WebhookPayload",
+        "WebhookPayloadComment",
+        "WebhookPayloadDiscussion",
+        "WebhookPayloadDiscussionChanges",
+        "WebhookPayloadEvent",
+        "WebhookPayloadMovedTo",
+        "WebhookPayloadRepo",
+        "WebhookPayloadUrl",
+        "WebhookPayloadWebhook",
+    ],
+    "_webhooks_server": [
+        "WebhooksServer",
+        "webhook_endpoint",
+    ],
+    "community": [
+        "Discussion",
+        "DiscussionComment",
+        "DiscussionCommit",
+        "DiscussionEvent",
+        "DiscussionStatusChange",
+        "DiscussionTitleChange",
+        "DiscussionWithDetails",
+    ],
+    "constants": [
+        "CONFIG_NAME",
+        "FLAX_WEIGHTS_NAME",
+        "HUGGINGFACE_CO_URL_HOME",
+        "HUGGINGFACE_CO_URL_TEMPLATE",
+        "PYTORCH_WEIGHTS_NAME",
+        "REPO_TYPE_DATASET",
+        "REPO_TYPE_MODEL",
+        "REPO_TYPE_SPACE",
+        "TF2_WEIGHTS_NAME",
+        "TF_WEIGHTS_NAME",
+    ],
+    "fastai_utils": [
+        "_save_pretrained_fastai",
+        "from_pretrained_fastai",
+        "push_to_hub_fastai",
+    ],
+    "file_download": [
+        "HfFileMetadata",
+        "_CACHED_NO_EXIST",
+        "get_hf_file_metadata",
+        "hf_hub_download",
+        "hf_hub_url",
+        "try_to_load_from_cache",
+    ],
+    "hf_api": [
+        "Collection",
+        "CollectionItem",
+        "CommitInfo",
+        "CommitOperation",
+        "CommitOperationAdd",
+        "CommitOperationCopy",
+        "CommitOperationDelete",
+        "DatasetInfo",
+        "GitCommitInfo",
+        "GitRefInfo",
+        "GitRefs",
+        "HfApi",
+        "ModelInfo",
+        "RepoUrl",
+        "SpaceInfo",
+        "User",
+        "UserLikes",
+        "WebhookInfo",
+        "WebhookWatchedItem",
+        "accept_access_request",
+        "add_collection_item",
+        "add_space_secret",
+        "add_space_variable",
+        "auth_check",
+        "cancel_access_request",
+        "change_discussion_status",
+        "comment_discussion",
+        "create_branch",
+        "create_collection",
+        "create_commit",
+        "create_commits_on_pr",
+        "create_discussion",
+        "create_inference_endpoint",
+        "create_pull_request",
+        "create_repo",
+        "create_tag",
+        "create_webhook",
+        "dataset_info",
+        "delete_branch",
+        "delete_collection",
+        "delete_collection_item",
+        "delete_file",
+        "delete_folder",
+        "delete_inference_endpoint",
+        "delete_repo",
+        "delete_space_secret",
+        "delete_space_storage",
+        "delete_space_variable",
+        "delete_tag",
+        "delete_webhook",
+        "disable_webhook",
+        "duplicate_space",
+        "edit_discussion_comment",
+        "enable_webhook",
+        "file_exists",
+        "get_collection",
+        "get_dataset_tags",
+        "get_discussion_details",
+        "get_full_repo_name",
+        "get_inference_endpoint",
+        "get_model_tags",
+        "get_paths_info",
+        "get_repo_discussions",
+        "get_safetensors_metadata",
+        "get_space_runtime",
+        "get_space_variables",
+        "get_token_permission",
+        "get_user_overview",
+        "get_webhook",
+        "grant_access",
+        "like",
+        "list_accepted_access_requests",
+        "list_collections",
+        "list_datasets",
+        "list_inference_endpoints",
+        "list_liked_repos",
+        "list_metrics",
+        "list_models",
+        "list_organization_members",
+        "list_papers",
+        "list_pending_access_requests",
+        "list_rejected_access_requests",
+        "list_repo_commits",
+        "list_repo_files",
+        "list_repo_likers",
+        "list_repo_refs",
+        "list_repo_tree",
+        "list_spaces",
+        "list_user_followers",
+        "list_user_following",
+        "list_webhooks",
+        "merge_pull_request",
+        "model_info",
+        "move_repo",
+        "paper_info",
+        "parse_safetensors_file_metadata",
+        "pause_inference_endpoint",
+        "pause_space",
+        "preupload_lfs_files",
+        "reject_access_request",
+        "rename_discussion",
+        "repo_exists",
+        "repo_info",
+        "repo_type_and_id_from_hf_id",
+        "request_space_hardware",
+        "request_space_storage",
+        "restart_space",
+        "resume_inference_endpoint",
+        "revision_exists",
+        "run_as_future",
+        "scale_to_zero_inference_endpoint",
+        "set_space_sleep_time",
+        "space_info",
+        "super_squash_history",
+        "unlike",
+        "update_collection_item",
+        "update_collection_metadata",
+        "update_inference_endpoint",
+        "update_repo_settings",
+        "update_repo_visibility",
+        "update_webhook",
+        "upload_file",
+        "upload_folder",
+        "upload_large_folder",
+        "whoami",
+    ],
+    "hf_file_system": [
+        "HfFileSystem",
+        "HfFileSystemFile",
+        "HfFileSystemResolvedPath",
+        "HfFileSystemStreamFile",
+    ],
+    "hub_mixin": [
+        "ModelHubMixin",
+        "PyTorchModelHubMixin",
+    ],
+    "inference._client": [
+        "InferenceClient",
+        "InferenceTimeoutError",
+    ],
+    "inference._generated._async_client": [
+        "AsyncInferenceClient",
+    ],
+    "inference._generated.types": [
+        "AudioClassificationInput",
+        "AudioClassificationOutputElement",
+        "AudioClassificationOutputTransform",
+        "AudioClassificationParameters",
+        "AudioToAudioInput",
+        "AudioToAudioOutputElement",
+        "AutomaticSpeechRecognitionEarlyStoppingEnum",
+        "AutomaticSpeechRecognitionGenerationParameters",
+        "AutomaticSpeechRecognitionInput",
+        "AutomaticSpeechRecognitionOutput",
+        "AutomaticSpeechRecognitionOutputChunk",
+        "AutomaticSpeechRecognitionParameters",
+        "ChatCompletionInput",
+        "ChatCompletionInputFunctionDefinition",
+        "ChatCompletionInputFunctionName",
+        "ChatCompletionInputGrammarType",
+        "ChatCompletionInputMessage",
+        "ChatCompletionInputMessageChunk",
+        "ChatCompletionInputStreamOptions",
+        "ChatCompletionInputToolType",
+        "ChatCompletionInputURL",
+        "ChatCompletionOutput",
+        "ChatCompletionOutputComplete",
+        "ChatCompletionOutputFunctionDefinition",
+        "ChatCompletionOutputLogprob",
+        "ChatCompletionOutputLogprobs",
+        "ChatCompletionOutputMessage",
+        "ChatCompletionOutputToolCall",
+        "ChatCompletionOutputTopLogprob",
+        "ChatCompletionOutputUsage",
+        "ChatCompletionStreamOutput",
+        "ChatCompletionStreamOutputChoice",
+        "ChatCompletionStreamOutputDelta",
+        "ChatCompletionStreamOutputDeltaToolCall",
+        "ChatCompletionStreamOutputFunction",
+        "ChatCompletionStreamOutputLogprob",
+        "ChatCompletionStreamOutputLogprobs",
+        "ChatCompletionStreamOutputTopLogprob",
+        "ChatCompletionStreamOutputUsage",
+        "DepthEstimationInput",
+        "DepthEstimationOutput",
+        "DocumentQuestionAnsweringInput",
+        "DocumentQuestionAnsweringInputData",
+        "DocumentQuestionAnsweringOutputElement",
+        "DocumentQuestionAnsweringParameters",
+        "FeatureExtractionInput",
+        "FillMaskInput",
+        "FillMaskOutputElement",
+        "FillMaskParameters",
+        "ImageClassificationInput",
+        "ImageClassificationOutputElement",
+        "ImageClassificationOutputTransform",
+        "ImageClassificationParameters",
+        "ImageSegmentationInput",
+        "ImageSegmentationOutputElement",
+        "ImageSegmentationParameters",
+        "ImageToImageInput",
+        "ImageToImageOutput",
+        "ImageToImageParameters",
+        "ImageToImageTargetSize",
+        "ImageToTextEarlyStoppingEnum",
+        "ImageToTextGenerationParameters",
+        "ImageToTextInput",
+        "ImageToTextOutput",
+        "ImageToTextParameters",
+        "ObjectDetectionBoundingBox",
+        "ObjectDetectionInput",
+        "ObjectDetectionOutputElement",
+        "ObjectDetectionParameters",
+        "QuestionAnsweringInput",
+        "QuestionAnsweringInputData",
+        "QuestionAnsweringOutputElement",
+        "QuestionAnsweringParameters",
+        "SentenceSimilarityInput",
+        "SentenceSimilarityInputData",
+        "SummarizationInput",
+        "SummarizationOutput",
+        "SummarizationParameters",
+        "TableQuestionAnsweringInput",
+        "TableQuestionAnsweringInputData",
+        "TableQuestionAnsweringOutputElement",
+        "Text2TextGenerationInput",
+        "Text2TextGenerationOutput",
+        "Text2TextGenerationParameters",
+        "TextClassificationInput",
+        "TextClassificationOutputElement",
+        "TextClassificationOutputTransform",
+        "TextClassificationParameters",
+        "TextGenerationInput",
+        "TextGenerationInputGenerateParameters",
+        "TextGenerationInputGrammarType",
+        "TextGenerationOutput",
+        "TextGenerationOutputBestOfSequence",
+        "TextGenerationOutputDetails",
+        "TextGenerationOutputPrefillToken",
+        "TextGenerationOutputToken",
+        "TextGenerationStreamOutput",
+        "TextGenerationStreamOutputStreamDetails",
+        "TextGenerationStreamOutputToken",
+        "TextToAudioEarlyStoppingEnum",
+        "TextToAudioGenerationParameters",
+        "TextToAudioInput",
+        "TextToAudioOutput",
+        "TextToAudioParameters",
+        "TextToImageInput",
+        "TextToImageOutput",
+        "TextToImageParameters",
+        "TextToImageTargetSize",
+        "TextToSpeechEarlyStoppingEnum",
+        "TextToSpeechGenerationParameters",
+        "TextToSpeechInput",
+        "TextToSpeechOutput",
+        "TextToSpeechParameters",
+        "TokenClassificationInput",
+        "TokenClassificationOutputElement",
+        "TokenClassificationParameters",
+        "ToolElement",
+        "TranslationInput",
+        "TranslationOutput",
+        "TranslationParameters",
+        "VideoClassificationInput",
+        "VideoClassificationOutputElement",
+        "VideoClassificationOutputTransform",
+        "VideoClassificationParameters",
+        "VisualQuestionAnsweringInput",
+        "VisualQuestionAnsweringInputData",
+        "VisualQuestionAnsweringOutputElement",
+        "VisualQuestionAnsweringParameters",
+        "ZeroShotClassificationInput",
+        "ZeroShotClassificationInputData",
+        "ZeroShotClassificationOutputElement",
+        "ZeroShotClassificationParameters",
+        "ZeroShotImageClassificationInput",
+        "ZeroShotImageClassificationInputData",
+        "ZeroShotImageClassificationOutputElement",
+        "ZeroShotImageClassificationParameters",
+        "ZeroShotObjectDetectionBoundingBox",
+        "ZeroShotObjectDetectionInput",
+        "ZeroShotObjectDetectionInputData",
+        "ZeroShotObjectDetectionOutputElement",
+    ],
+    "inference_api": [
+        "InferenceApi",
+    ],
+    "keras_mixin": [
+        "KerasModelHubMixin",
+        "from_pretrained_keras",
+        "push_to_hub_keras",
+        "save_pretrained_keras",
+    ],
+    "repocard": [
+        "DatasetCard",
+        "ModelCard",
+        "RepoCard",
+        "SpaceCard",
+        "metadata_eval_result",
+        "metadata_load",
+        "metadata_save",
+        "metadata_update",
+    ],
+    "repocard_data": [
+        "CardData",
+        "DatasetCardData",
+        "EvalResult",
+        "ModelCardData",
+        "SpaceCardData",
+    ],
+    "repository": [
+        "Repository",
+    ],
+    "serialization": [
+        "StateDictSplit",
+        "get_tf_storage_size",
+        "get_torch_storage_id",
+        "get_torch_storage_size",
+        "save_torch_model",
+        "save_torch_state_dict",
+        "split_state_dict_into_shards_factory",
+        "split_tf_state_dict_into_shards",
+        "split_torch_state_dict_into_shards",
+    ],
+    "utils": [
+        "CacheNotFound",
+        "CachedFileInfo",
+        "CachedRepoInfo",
+        "CachedRevisionInfo",
+        "CorruptedCacheException",
+        "DeleteCacheStrategy",
+        "HFCacheInfo",
+        "HfFolder",
+        "cached_assets_path",
+        "configure_http_backend",
+        "dump_environment_info",
+        "get_session",
+        "get_token",
+        "logging",
+        "scan_cache_dir",
+    ],
+}
+
+
+def _attach(package_name, submodules=None, submod_attrs=None):
+    """Attach lazily loaded submodules, functions, or other attributes.
+
+    Typically, modules import submodules and attributes as follows:
+
+    ```py
+    import mysubmodule
+    import anothersubmodule
+
+    from .foo import someattr
+    ```
+
+    The idea is to replace a package's `__getattr__`, `__dir__`, and
+    `__all__`, such that all imports work exactly the way they would
+    with normal imports, except that the import occurs upon first use.
+
+    The typical way to call this function, replacing the above imports, is:
+
+    ```python
+    __getattr__, __dir__, __all__ = lazy.attach(
+        __name__,
+        ['mysubmodule', 'anothersubmodule'],
+        {'foo': ['someattr']}
+    )
+    ```
+    This functionality requires Python 3.7 or higher.
+
+    Args:
+        package_name (`str`):
+            Typically use `__name__`.
+        submodules (`set`):
+            List of submodules to attach.
+        submod_attrs (`dict`):
+            Dictionary of submodule -> list of attributes / functions.
+            These attributes are imported as they are used.
+
+    Returns:
+        __getattr__, __dir__, __all__
+
+    """
+    if submod_attrs is None:
+        submod_attrs = {}
+
+    if submodules is None:
+        submodules = set()
+    else:
+        submodules = set(submodules)
+
+    attr_to_modules = {attr: mod for mod, attrs in submod_attrs.items() for attr in attrs}
+
+    __all__ = list(submodules | attr_to_modules.keys())
+
+    def __getattr__(name):
+        if name in submodules:
+            try:
+                return importlib.import_module(f"{package_name}.{name}")
+            except Exception as e:
+                print(f"Error importing {package_name}.{name}: {e}")
+                raise
+        elif name in attr_to_modules:
+            submod_path = f"{package_name}.{attr_to_modules[name]}"
+            try:
+                submod = importlib.import_module(submod_path)
+            except Exception as e:
+                print(f"Error importing {submod_path}: {e}")
+                raise
+            attr = getattr(submod, name)
+
+            # If the attribute lives in a file (module) with the same
+            # name as the attribute, ensure that the attribute and *not*
+            # the module is accessible on the package.
+            if name == attr_to_modules[name]:
+                pkg = sys.modules[package_name]
+                pkg.__dict__[name] = attr
+
+            return attr
+        else:
+            raise AttributeError(f"No {package_name} attribute {name}")
+
+    def __dir__():
+        return __all__
+
+    return __getattr__, __dir__, list(__all__)
+
+
+__getattr__, __dir__, __all__ = _attach(__name__, submodules=[], submod_attrs=_SUBMOD_ATTRS)
+
+if os.environ.get("EAGER_IMPORT", ""):
+    for attr in __all__:
+        __getattr__(attr)
+
+# WARNING: any content below this statement is generated automatically. Any manual edit
+# will be lost when re-generating this file !
+#
+# To update the static imports, please run the following command and commit the changes.
+# ```
+# # Use script
+# python utils/check_static_imports.py --update-file
+#
+# # Or run style on codebase
+# make style
+# ```
+if TYPE_CHECKING:  # pragma: no cover
+    from ._commit_scheduler import CommitScheduler  # noqa: F401
+    from ._inference_endpoints import (
+        InferenceEndpoint,  # noqa: F401
+        InferenceEndpointError,  # noqa: F401
+        InferenceEndpointStatus,  # noqa: F401
+        InferenceEndpointTimeoutError,  # noqa: F401
+        InferenceEndpointType,  # noqa: F401
+    )
+    from ._login import (
+        auth_list,  # noqa: F401
+        auth_switch,  # noqa: F401
+        interpreter_login,  # noqa: F401
+        login,  # noqa: F401
+        logout,  # noqa: F401
+        notebook_login,  # noqa: F401
+    )
+    from ._multi_commits import (
+        MultiCommitException,  # noqa: F401
+        plan_multi_commits,  # noqa: F401
+    )
+    from ._snapshot_download import snapshot_download  # noqa: F401
+    from ._space_api import (
+        SpaceHardware,  # noqa: F401
+        SpaceRuntime,  # noqa: F401
+        SpaceStage,  # noqa: F401
+        SpaceStorage,  # noqa: F401
+        SpaceVariable,  # noqa: F401
+    )
+    from ._tensorboard_logger import HFSummaryWriter  # noqa: F401
+    from ._webhooks_payload import (
+        WebhookPayload,  # noqa: F401
+        WebhookPayloadComment,  # noqa: F401
+        WebhookPayloadDiscussion,  # noqa: F401
+        WebhookPayloadDiscussionChanges,  # noqa: F401
+        WebhookPayloadEvent,  # noqa: F401
+        WebhookPayloadMovedTo,  # noqa: F401
+        WebhookPayloadRepo,  # noqa: F401
+        WebhookPayloadUrl,  # noqa: F401
+        WebhookPayloadWebhook,  # noqa: F401
+    )
+    from ._webhooks_server import (
+        WebhooksServer,  # noqa: F401
+        webhook_endpoint,  # noqa: F401
+    )
+    from .community import (
+        Discussion,  # noqa: F401
+        DiscussionComment,  # noqa: F401
+        DiscussionCommit,  # noqa: F401
+        DiscussionEvent,  # noqa: F401
+        DiscussionStatusChange,  # noqa: F401
+        DiscussionTitleChange,  # noqa: F401
+        DiscussionWithDetails,  # noqa: F401
+    )
+    from .constants import (
+        CONFIG_NAME,  # noqa: F401
+        FLAX_WEIGHTS_NAME,  # noqa: F401
+        HUGGINGFACE_CO_URL_HOME,  # noqa: F401
+        HUGGINGFACE_CO_URL_TEMPLATE,  # noqa: F401
+        PYTORCH_WEIGHTS_NAME,  # noqa: F401
+        REPO_TYPE_DATASET,  # noqa: F401
+        REPO_TYPE_MODEL,  # noqa: F401
+        REPO_TYPE_SPACE,  # noqa: F401
+        TF2_WEIGHTS_NAME,  # noqa: F401
+        TF_WEIGHTS_NAME,  # noqa: F401
+    )
+    from .fastai_utils import (
+        _save_pretrained_fastai,  # noqa: F401
+        from_pretrained_fastai,  # noqa: F401
+        push_to_hub_fastai,  # noqa: F401
+    )
+    from .file_download import (
+        _CACHED_NO_EXIST,  # noqa: F401
+        HfFileMetadata,  # noqa: F401
+        get_hf_file_metadata,  # noqa: F401
+        hf_hub_download,  # noqa: F401
+        hf_hub_url,  # noqa: F401
+        try_to_load_from_cache,  # noqa: F401
+    )
+    from .hf_api import (
+        Collection,  # noqa: F401
+        CollectionItem,  # noqa: F401
+        CommitInfo,  # noqa: F401
+        CommitOperation,  # noqa: F401
+        CommitOperationAdd,  # noqa: F401
+        CommitOperationCopy,  # noqa: F401
+        CommitOperationDelete,  # noqa: F401
+        DatasetInfo,  # noqa: F401
+        GitCommitInfo,  # noqa: F401
+        GitRefInfo,  # noqa: F401
+        GitRefs,  # noqa: F401
+        HfApi,  # noqa: F401
+        ModelInfo,  # noqa: F401
+        RepoUrl,  # noqa: F401
+        SpaceInfo,  # noqa: F401
+        User,  # noqa: F401
+        UserLikes,  # noqa: F401
+        WebhookInfo,  # noqa: F401
+        WebhookWatchedItem,  # noqa: F401
+        accept_access_request,  # noqa: F401
+        add_collection_item,  # noqa: F401
+        add_space_secret,  # noqa: F401
+        add_space_variable,  # noqa: F401
+        auth_check,  # noqa: F401
+        cancel_access_request,  # noqa: F401
+        change_discussion_status,  # noqa: F401
+        comment_discussion,  # noqa: F401
+        create_branch,  # noqa: F401
+        create_collection,  # noqa: F401
+        create_commit,  # noqa: F401
+        create_commits_on_pr,  # noqa: F401
+        create_discussion,  # noqa: F401
+        create_inference_endpoint,  # noqa: F401
+        create_pull_request,  # noqa: F401
+        create_repo,  # noqa: F401
+        create_tag,  # noqa: F401
+        create_webhook,  # noqa: F401
+        dataset_info,  # noqa: F401
+        delete_branch,  # noqa: F401
+        delete_collection,  # noqa: F401
+        delete_collection_item,  # noqa: F401
+        delete_file,  # noqa: F401
+        delete_folder,  # noqa: F401
+        delete_inference_endpoint,  # noqa: F401
+        delete_repo,  # noqa: F401
+        delete_space_secret,  # noqa: F401
+        delete_space_storage,  # noqa: F401
+        delete_space_variable,  # noqa: F401
+        delete_tag,  # noqa: F401
+        delete_webhook,  # noqa: F401
+        disable_webhook,  # noqa: F401
+        duplicate_space,  # noqa: F401
+        edit_discussion_comment,  # noqa: F401
+        enable_webhook,  # noqa: F401
+        file_exists,  # noqa: F401
+        get_collection,  # noqa: F401
+        get_dataset_tags,  # noqa: F401
+        get_discussion_details,  # noqa: F401
+        get_full_repo_name,  # noqa: F401
+        get_inference_endpoint,  # noqa: F401
+        get_model_tags,  # noqa: F401
+        get_paths_info,  # noqa: F401
+        get_repo_discussions,  # noqa: F401
+        get_safetensors_metadata,  # noqa: F401
+        get_space_runtime,  # noqa: F401
+        get_space_variables,  # noqa: F401
+        get_token_permission,  # noqa: F401
+        get_user_overview,  # noqa: F401
+        get_webhook,  # noqa: F401
+        grant_access,  # noqa: F401
+        like,  # noqa: F401
+        list_accepted_access_requests,  # noqa: F401
+        list_collections,  # noqa: F401
+        list_datasets,  # noqa: F401
+        list_inference_endpoints,  # noqa: F401
+        list_liked_repos,  # noqa: F401
+        list_metrics,  # noqa: F401
+        list_models,  # noqa: F401
+        list_organization_members,  # noqa: F401
+        list_papers,  # noqa: F401
+        list_pending_access_requests,  # noqa: F401
+        list_rejected_access_requests,  # noqa: F401
+        list_repo_commits,  # noqa: F401
+        list_repo_files,  # noqa: F401
+        list_repo_likers,  # noqa: F401
+        list_repo_refs,  # noqa: F401
+        list_repo_tree,  # noqa: F401
+        list_spaces,  # noqa: F401
+        list_user_followers,  # noqa: F401
+        list_user_following,  # noqa: F401
+        list_webhooks,  # noqa: F401
+        merge_pull_request,  # noqa: F401
+        model_info,  # noqa: F401
+        move_repo,  # noqa: F401
+        paper_info,  # noqa: F401
+        parse_safetensors_file_metadata,  # noqa: F401
+        pause_inference_endpoint,  # noqa: F401
+        pause_space,  # noqa: F401
+        preupload_lfs_files,  # noqa: F401
+        reject_access_request,  # noqa: F401
+        rename_discussion,  # noqa: F401
+        repo_exists,  # noqa: F401
+        repo_info,  # noqa: F401
+        repo_type_and_id_from_hf_id,  # noqa: F401
+        request_space_hardware,  # noqa: F401
+        request_space_storage,  # noqa: F401
+        restart_space,  # noqa: F401
+        resume_inference_endpoint,  # noqa: F401
+        revision_exists,  # noqa: F401
+        run_as_future,  # noqa: F401
+        scale_to_zero_inference_endpoint,  # noqa: F401
+        set_space_sleep_time,  # noqa: F401
+        space_info,  # noqa: F401
+        super_squash_history,  # noqa: F401
+        unlike,  # noqa: F401
+        update_collection_item,  # noqa: F401
+        update_collection_metadata,  # noqa: F401
+        update_inference_endpoint,  # noqa: F401
+        update_repo_settings,  # noqa: F401
+        update_repo_visibility,  # noqa: F401
+        update_webhook,  # noqa: F401
+        upload_file,  # noqa: F401
+        upload_folder,  # noqa: F401
+        upload_large_folder,  # noqa: F401
+        whoami,  # noqa: F401
+    )
+    from .hf_file_system import (
+        HfFileSystem,  # noqa: F401
+        HfFileSystemFile,  # noqa: F401
+        HfFileSystemResolvedPath,  # noqa: F401
+        HfFileSystemStreamFile,  # noqa: F401
+    )
+    from .hub_mixin import (
+        ModelHubMixin,  # noqa: F401
+        PyTorchModelHubMixin,  # noqa: F401
+    )
+    from .inference._client import (
+        InferenceClient,  # noqa: F401
+        InferenceTimeoutError,  # noqa: F401
+    )
+    from .inference._generated._async_client import AsyncInferenceClient  # noqa: F401
+    from .inference._generated.types import (
+        AudioClassificationInput,  # noqa: F401
+        AudioClassificationOutputElement,  # noqa: F401
+        AudioClassificationOutputTransform,  # noqa: F401
+        AudioClassificationParameters,  # noqa: F401
+        AudioToAudioInput,  # noqa: F401
+        AudioToAudioOutputElement,  # noqa: F401
+        AutomaticSpeechRecognitionEarlyStoppingEnum,  # noqa: F401
+        AutomaticSpeechRecognitionGenerationParameters,  # noqa: F401
+        AutomaticSpeechRecognitionInput,  # noqa: F401
+        AutomaticSpeechRecognitionOutput,  # noqa: F401
+        AutomaticSpeechRecognitionOutputChunk,  # noqa: F401
+        AutomaticSpeechRecognitionParameters,  # noqa: F401
+        ChatCompletionInput,  # noqa: F401
+        ChatCompletionInputFunctionDefinition,  # noqa: F401
+        ChatCompletionInputFunctionName,  # noqa: F401
+        ChatCompletionInputGrammarType,  # noqa: F401
+        ChatCompletionInputMessage,  # noqa: F401
+        ChatCompletionInputMessageChunk,  # noqa: F401
+        ChatCompletionInputStreamOptions,  # noqa: F401
+        ChatCompletionInputToolType,  # noqa: F401
+        ChatCompletionInputURL,  # noqa: F401
+        ChatCompletionOutput,  # noqa: F401
+        ChatCompletionOutputComplete,  # noqa: F401
+        ChatCompletionOutputFunctionDefinition,  # noqa: F401
+        ChatCompletionOutputLogprob,  # noqa: F401
+        ChatCompletionOutputLogprobs,  # noqa: F401
+        ChatCompletionOutputMessage,  # noqa: F401
+        ChatCompletionOutputToolCall,  # noqa: F401
+        ChatCompletionOutputTopLogprob,  # noqa: F401
+        ChatCompletionOutputUsage,  # noqa: F401
+        ChatCompletionStreamOutput,  # noqa: F401
+        ChatCompletionStreamOutputChoice,  # noqa: F401
+        ChatCompletionStreamOutputDelta,  # noqa: F401
+        ChatCompletionStreamOutputDeltaToolCall,  # noqa: F401
+        ChatCompletionStreamOutputFunction,  # noqa: F401
+        ChatCompletionStreamOutputLogprob,  # noqa: F401
+        ChatCompletionStreamOutputLogprobs,  # noqa: F401
+        ChatCompletionStreamOutputTopLogprob,  # noqa: F401
+        ChatCompletionStreamOutputUsage,  # noqa: F401
+        DepthEstimationInput,  # noqa: F401
+        DepthEstimationOutput,  # noqa: F401
+        DocumentQuestionAnsweringInput,  # noqa: F401
+        DocumentQuestionAnsweringInputData,  # noqa: F401
+        DocumentQuestionAnsweringOutputElement,  # noqa: F401
+        DocumentQuestionAnsweringParameters,  # noqa: F401
+        FeatureExtractionInput,  # noqa: F401
+        FillMaskInput,  # noqa: F401
+        FillMaskOutputElement,  # noqa: F401
+        FillMaskParameters,  # noqa: F401
+        ImageClassificationInput,  # noqa: F401
+        ImageClassificationOutputElement,  # noqa: F401
+        ImageClassificationOutputTransform,  # noqa: F401
+        ImageClassificationParameters,  # noqa: F401
+        ImageSegmentationInput,  # noqa: F401
+        ImageSegmentationOutputElement,  # noqa: F401
+        ImageSegmentationParameters,  # noqa: F401
+        ImageToImageInput,  # noqa: F401
+        ImageToImageOutput,  # noqa: F401
+        ImageToImageParameters,  # noqa: F401
+        ImageToImageTargetSize,  # noqa: F401
+        ImageToTextEarlyStoppingEnum,  # noqa: F401
+        ImageToTextGenerationParameters,  # noqa: F401
+        ImageToTextInput,  # noqa: F401
+        ImageToTextOutput,  # noqa: F401
+        ImageToTextParameters,  # noqa: F401
+        ObjectDetectionBoundingBox,  # noqa: F401
+        ObjectDetectionInput,  # noqa: F401
+        ObjectDetectionOutputElement,  # noqa: F401
+        ObjectDetectionParameters,  # noqa: F401
+        QuestionAnsweringInput,  # noqa: F401
+        QuestionAnsweringInputData,  # noqa: F401
+        QuestionAnsweringOutputElement,  # noqa: F401
+        QuestionAnsweringParameters,  # noqa: F401
+        SentenceSimilarityInput,  # noqa: F401
+        SentenceSimilarityInputData,  # noqa: F401
+        SummarizationInput,  # noqa: F401
+        SummarizationOutput,  # noqa: F401
+        SummarizationParameters,  # noqa: F401
+        TableQuestionAnsweringInput,  # noqa: F401
+        TableQuestionAnsweringInputData,  # noqa: F401
+        TableQuestionAnsweringOutputElement,  # noqa: F401
+        Text2TextGenerationInput,  # noqa: F401
+        Text2TextGenerationOutput,  # noqa: F401
+        Text2TextGenerationParameters,  # noqa: F401
+        TextClassificationInput,  # noqa: F401
+        TextClassificationOutputElement,  # noqa: F401
+        TextClassificationOutputTransform,  # noqa: F401
+        TextClassificationParameters,  # noqa: F401
+        TextGenerationInput,  # noqa: F401
+        TextGenerationInputGenerateParameters,  # noqa: F401
+        TextGenerationInputGrammarType,  # noqa: F401
+        TextGenerationOutput,  # noqa: F401
+        TextGenerationOutputBestOfSequence,  # noqa: F401
+        TextGenerationOutputDetails,  # noqa: F401
+        TextGenerationOutputPrefillToken,  # noqa: F401
+        TextGenerationOutputToken,  # noqa: F401
+        TextGenerationStreamOutput,  # noqa: F401
+        TextGenerationStreamOutputStreamDetails,  # noqa: F401
+        TextGenerationStreamOutputToken,  # noqa: F401
+        TextToAudioEarlyStoppingEnum,  # noqa: F401
+        TextToAudioGenerationParameters,  # noqa: F401
+        TextToAudioInput,  # noqa: F401
+        TextToAudioOutput,  # noqa: F401
+        TextToAudioParameters,  # noqa: F401
+        TextToImageInput,  # noqa: F401
+        TextToImageOutput,  # noqa: F401
+        TextToImageParameters,  # noqa: F401
+        TextToImageTargetSize,  # noqa: F401
+        TextToSpeechEarlyStoppingEnum,  # noqa: F401
+        TextToSpeechGenerationParameters,  # noqa: F401
+        TextToSpeechInput,  # noqa: F401
+        TextToSpeechOutput,  # noqa: F401
+        TextToSpeechParameters,  # noqa: F401
+        TokenClassificationInput,  # noqa: F401
+        TokenClassificationOutputElement,  # noqa: F401
+        TokenClassificationParameters,  # noqa: F401
+        ToolElement,  # noqa: F401
+        TranslationInput,  # noqa: F401
+        TranslationOutput,  # noqa: F401
+        TranslationParameters,  # noqa: F401
+        VideoClassificationInput,  # noqa: F401
+        VideoClassificationOutputElement,  # noqa: F401
+        VideoClassificationOutputTransform,  # noqa: F401
+        VideoClassificationParameters,  # noqa: F401
+        VisualQuestionAnsweringInput,  # noqa: F401
+        VisualQuestionAnsweringInputData,  # noqa: F401
+        VisualQuestionAnsweringOutputElement,  # noqa: F401
+        VisualQuestionAnsweringParameters,  # noqa: F401
+        ZeroShotClassificationInput,  # noqa: F401
+        ZeroShotClassificationInputData,  # noqa: F401
+        ZeroShotClassificationOutputElement,  # noqa: F401
+        ZeroShotClassificationParameters,  # noqa: F401
+        ZeroShotImageClassificationInput,  # noqa: F401
+        ZeroShotImageClassificationInputData,  # noqa: F401
+        ZeroShotImageClassificationOutputElement,  # noqa: F401
+        ZeroShotImageClassificationParameters,  # noqa: F401
+        ZeroShotObjectDetectionBoundingBox,  # noqa: F401
+        ZeroShotObjectDetectionInput,  # noqa: F401
+        ZeroShotObjectDetectionInputData,  # noqa: F401
+        ZeroShotObjectDetectionOutputElement,  # noqa: F401
+    )
+    from .inference_api import InferenceApi  # noqa: F401
+    from .keras_mixin import (
+        KerasModelHubMixin,  # noqa: F401
+        from_pretrained_keras,  # noqa: F401
+        push_to_hub_keras,  # noqa: F401
+        save_pretrained_keras,  # noqa: F401
+    )
+    from .repocard import (
+        DatasetCard,  # noqa: F401
+        ModelCard,  # noqa: F401
+        RepoCard,  # noqa: F401
+        SpaceCard,  # noqa: F401
+        metadata_eval_result,  # noqa: F401
+        metadata_load,  # noqa: F401
+        metadata_save,  # noqa: F401
+        metadata_update,  # noqa: F401
+    )
+    from .repocard_data import (
+        CardData,  # noqa: F401
+        DatasetCardData,  # noqa: F401
+        EvalResult,  # noqa: F401
+        ModelCardData,  # noqa: F401
+        SpaceCardData,  # noqa: F401
+    )
+    from .repository import Repository  # noqa: F401
+    from .serialization import (
+        StateDictSplit,  # noqa: F401
+        get_tf_storage_size,  # noqa: F401
+        get_torch_storage_id,  # noqa: F401
+        get_torch_storage_size,  # noqa: F401
+        save_torch_model,  # noqa: F401
+        save_torch_state_dict,  # noqa: F401
+        split_state_dict_into_shards_factory,  # noqa: F401
+        split_tf_state_dict_into_shards,  # noqa: F401
+        split_torch_state_dict_into_shards,  # noqa: F401
+    )
+    from .utils import (
+        CachedFileInfo,  # noqa: F401
+        CachedRepoInfo,  # noqa: F401
+        CachedRevisionInfo,  # noqa: F401
+        CacheNotFound,  # noqa: F401
+        CorruptedCacheException,  # noqa: F401
+        DeleteCacheStrategy,  # noqa: F401
+        HFCacheInfo,  # noqa: F401
+        HfFolder,  # noqa: F401
+        cached_assets_path,  # noqa: F401
+        configure_http_backend,  # noqa: F401
+        dump_environment_info,  # noqa: F401
+        get_session,  # noqa: F401
+        get_token,  # noqa: F401
+        logging,  # noqa: F401
+        scan_cache_dir,  # noqa: F401
+    )

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/_commit_scheduler.py

@@ -0,0 +1,327 @@
+import atexit
+import logging
+import os
+import time
+from concurrent.futures import Future
+from dataclasses import dataclass
+from io import SEEK_END, SEEK_SET, BytesIO
+from pathlib import Path
+from threading import Lock, Thread
+from typing import Dict, List, Optional, Union
+
+from .hf_api import DEFAULT_IGNORE_PATTERNS, CommitInfo, CommitOperationAdd, HfApi
+from .utils import filter_repo_objects
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass(frozen=True)
+class _FileToUpload:
+    """Temporary dataclass to store info about files to upload. Not meant to be used directly."""
+
+    local_path: Path
+    path_in_repo: str
+    size_limit: int
+    last_modified: float
+
+
+class CommitScheduler:
+    """
+    Scheduler to upload a local folder to the Hub at regular intervals (e.g. push to hub every 5 minutes).
+
+    The scheduler is started when instantiated and run indefinitely. At the end of your script, a last commit is
+    triggered. Checkout the [upload guide](https://huggingface.co/docs/huggingface_hub/guides/upload#scheduled-uploads)
+    to learn more about how to use it.
+
+    Args:
+        repo_id (`str`):
+            The id of the repo to commit to.
+        folder_path (`str` or `Path`):
+            Path to the local folder to upload regularly.
+        every (`int` or `float`, *optional*):
+            The number of minutes between each commit. Defaults to 5 minutes.
+        path_in_repo (`str`, *optional*):
+            Relative path of the directory in the repo, for example: `"checkpoints/"`. Defaults to the root folder
+            of the repository.
+        repo_type (`str`, *optional*):
+            The type of the repo to commit to. Defaults to `model`.
+        revision (`str`, *optional*):
+            The revision of the repo to commit to. Defaults to `main`.
+        private (`bool`, *optional*):
+            Whether to make the repo private. Defaults to `False`. This value is ignored if the repo already exist.
+        token (`str`, *optional*):
+            The token to use to commit to the repo. Defaults to the token saved on the machine.
+        allow_patterns (`List[str]` or `str`, *optional*):
+            If provided, only files matching at least one pattern are uploaded.
+        ignore_patterns (`List[str]` or `str`, *optional*):
+            If provided, files matching any of the patterns are not uploaded.
+        squash_history (`bool`, *optional*):
+            Whether to squash the history of the repo after each commit. Defaults to `False`. Squashing commits is
+            useful to avoid degraded performances on the repo when it grows too large.
+        hf_api (`HfApi`, *optional*):
+            The [`HfApi`] client to use to commit to the Hub. Can be set with custom settings (user agent, token,...).
+
+    Example:
+    ```py
+    >>> from pathlib import Path
+    >>> from huggingface_hub import CommitScheduler
+
+    # Scheduler uploads every 10 minutes
+    >>> csv_path = Path("watched_folder/data.csv")
+    >>> CommitScheduler(repo_id="test_scheduler", repo_type="dataset", folder_path=csv_path.parent, every=10)
+
+    >>> with csv_path.open("a") as f:
+    ...     f.write("first line")
+
+    # Some time later (...)
+    >>> with csv_path.open("a") as f:
+    ...     f.write("second line")
+    ```
+    """
+
+    def __init__(
+        self,
+        *,
+        repo_id: str,
+        folder_path: Union[str, Path],
+        every: Union[int, float] = 5,
+        path_in_repo: Optional[str] = None,
+        repo_type: Optional[str] = None,
+        revision: Optional[str] = None,
+        private: bool = False,
+        token: Optional[str] = None,
+        allow_patterns: Optional[Union[List[str], str]] = None,
+        ignore_patterns: Optional[Union[List[str], str]] = None,
+        squash_history: bool = False,
+        hf_api: Optional["HfApi"] = None,
+    ) -> None:
+        self.api = hf_api or HfApi(token=token)
+
+        # Folder
+        self.folder_path = Path(folder_path).expanduser().resolve()
+        self.path_in_repo = path_in_repo or ""
+        self.allow_patterns = allow_patterns
+
+        if ignore_patterns is None:
+            ignore_patterns = []
+        elif isinstance(ignore_patterns, str):
+            ignore_patterns = [ignore_patterns]
+        self.ignore_patterns = ignore_patterns + DEFAULT_IGNORE_PATTERNS
+
+        if self.folder_path.is_file():
+            raise ValueError(f"'folder_path' must be a directory, not a file: '{self.folder_path}'.")
+        self.folder_path.mkdir(parents=True, exist_ok=True)
+
+        # Repository
+        repo_url = self.api.create_repo(repo_id=repo_id, private=private, repo_type=repo_type, exist_ok=True)
+        self.repo_id = repo_url.repo_id
+        self.repo_type = repo_type
+        self.revision = revision
+        self.token = token
+
+        # Keep track of already uploaded files
+        self.last_uploaded: Dict[Path, float] = {}  # key is local path, value is timestamp
+
+        # Scheduler
+        if not every > 0:
+            raise ValueError(f"'every' must be a positive integer, not '{every}'.")
+        self.lock = Lock()
+        self.every = every
+        self.squash_history = squash_history
+
+        logger.info(f"Scheduled job to push '{self.folder_path}' to '{self.repo_id}' every {self.every} minutes.")
+        self._scheduler_thread = Thread(target=self._run_scheduler, daemon=True)
+        self._scheduler_thread.start()
+        atexit.register(self._push_to_hub)
+
+        self.__stopped = False
+
+    def stop(self) -> None:
+        """Stop the scheduler.
+
+        A stopped scheduler cannot be restarted. Mostly for tests purposes.
+        """
+        self.__stopped = True
+
+    def _run_scheduler(self) -> None:
+        """Dumb thread waiting between each scheduled push to Hub."""
+        while True:
+            self.last_future = self.trigger()
+            time.sleep(self.every * 60)
+            if self.__stopped:
+                break
+
+    def trigger(self) -> Future:
+        """Trigger a `push_to_hub` and return a future.
+
+        This method is automatically called every `every` minutes. You can also call it manually to trigger a commit
+        immediately, without waiting for the next scheduled commit.
+        """
+        return self.api.run_as_future(self._push_to_hub)
+
+    def _push_to_hub(self) -> Optional[CommitInfo]:
+        if self.__stopped:  # If stopped, already scheduled commits are ignored
+            return None
+
+        logger.info("(Background) scheduled commit triggered.")
+        try:
+            value = self.push_to_hub()
+            if self.squash_history:
+                logger.info("(Background) squashing repo history.")
+                self.api.super_squash_history(repo_id=self.repo_id, repo_type=self.repo_type, branch=self.revision)
+            return value
+        except Exception as e:
+            logger.error(f"Error while pushing to Hub: {e}")  # Depending on the setup, error might be silenced
+            raise
+
+    def push_to_hub(self) -> Optional[CommitInfo]:
+        """
+        Push folder to the Hub and return the commit info.
+
+        <Tip warning={true}>
+
+        This method is not meant to be called directly. It is run in the background by the scheduler, respecting a
+        queue mechanism to avoid concurrent commits. Making a direct call to the method might lead to concurrency
+        issues.
+
+        </Tip>
+
+        The default behavior of `push_to_hub` is to assume an append-only folder. It lists all files in the folder and
+        uploads only changed files. If no changes are found, the method returns without committing anything. If you want
+        to change this behavior, you can inherit from [`CommitScheduler`] and override this method. This can be useful
+        for example to compress data together in a single file before committing. For more details and examples, check
+        out our [integration guide](https://huggingface.co/docs/huggingface_hub/main/en/guides/upload#scheduled-uploads).
+        """
+        # Check files to upload (with lock)
+        with self.lock:
+            logger.debug("Listing files to upload for scheduled commit.")
+
+            # List files from folder (taken from `_prepare_upload_folder_additions`)
+            relpath_to_abspath = {
+                path.relative_to(self.folder_path).as_posix(): path
+                for path in sorted(self.folder_path.glob("**/*"))  # sorted to be deterministic
+                if path.is_file()
+            }
+            prefix = f"{self.path_in_repo.strip('/')}/" if self.path_in_repo else ""
+
+            # Filter with pattern + filter out unchanged files + retrieve current file size
+            files_to_upload: List[_FileToUpload] = []
+            for relpath in filter_repo_objects(
+                relpath_to_abspath.keys(), allow_patterns=self.allow_patterns, ignore_patterns=self.ignore_patterns
+            ):
+                local_path = relpath_to_abspath[relpath]
+                stat = local_path.stat()
+                if self.last_uploaded.get(local_path) is None or self.last_uploaded[local_path] != stat.st_mtime:
+                    files_to_upload.append(
+                        _FileToUpload(
+                            local_path=local_path,
+                            path_in_repo=prefix + relpath,
+                            size_limit=stat.st_size,
+                            last_modified=stat.st_mtime,
+                        )
+                    )
+
+        # Return if nothing to upload
+        if len(files_to_upload) == 0:
+            logger.debug("Dropping schedule commit: no changed file to upload.")
+            return None
+
+        # Convert `_FileToUpload` as `CommitOperationAdd` (=> compute file shas + limit to file size)
+        logger.debug("Removing unchanged files since previous scheduled commit.")
+        add_operations = [
+            CommitOperationAdd(
+                # Cap the file to its current size, even if the user append data to it while a scheduled commit is happening
+                path_or_fileobj=PartialFileIO(file_to_upload.local_path, size_limit=file_to_upload.size_limit),
+                path_in_repo=file_to_upload.path_in_repo,
+            )
+            for file_to_upload in files_to_upload
+        ]
+
+        # Upload files (append mode expected - no need for lock)
+        logger.debug("Uploading files for scheduled commit.")
+        commit_info = self.api.create_commit(
+            repo_id=self.repo_id,
+            repo_type=self.repo_type,
+            operations=add_operations,
+            commit_message="Scheduled Commit",
+            revision=self.revision,
+        )
+
+        # Successful commit: keep track of the latest "last_modified" for each file
+        for file in files_to_upload:
+            self.last_uploaded[file.local_path] = file.last_modified
+        return commit_info
+
+
+class PartialFileIO(BytesIO):
+    """A file-like object that reads only the first part of a file.
+
+    Useful to upload a file to the Hub when the user might still be appending data to it. Only the first part of the
+    file is uploaded (i.e. the part that was available when the filesystem was first scanned).
+
+    In practice, only used internally by the CommitScheduler to regularly push a folder to the Hub with minimal
+    disturbance for the user. The object is passed to `CommitOperationAdd`.
+
+    Only supports `read`, `tell` and `seek` methods.
+
+    Args:
+        file_path (`str` or `Path`):
+            Path to the file to read.
+        size_limit (`int`):
+            The maximum number of bytes to read from the file. If the file is larger than this, only the first part
+            will be read (and uploaded).
+    """
+
+    def __init__(self, file_path: Union[str, Path], size_limit: int) -> None:
+        self._file_path = Path(file_path)
+        self._file = self._file_path.open("rb")
+        self._size_limit = min(size_limit, os.fstat(self._file.fileno()).st_size)
+
+    def __del__(self) -> None:
+        self._file.close()
+        return super().__del__()
+
+    def __repr__(self) -> str:
+        return f"<PartialFileIO file_path={self._file_path} size_limit={self._size_limit}>"
+
+    def __len__(self) -> int:
+        return self._size_limit
+
+    def __getattribute__(self, name: str):
+        if name.startswith("_") or name in ("read", "tell", "seek"):  # only 3 public methods supported
+            return super().__getattribute__(name)
+        raise NotImplementedError(f"PartialFileIO does not support '{name}'.")
+
+    def tell(self) -> int:
+        """Return the current file position."""
+        return self._file.tell()
+
+    def seek(self, __offset: int, __whence: int = SEEK_SET) -> int:
+        """Change the stream position to the given offset.
+
+        Behavior is the same as a regular file, except that the position is capped to the size limit.
+        """
+        if __whence == SEEK_END:
+            # SEEK_END => set from the truncated end
+            __offset = len(self) + __offset
+            __whence = SEEK_SET
+
+        pos = self._file.seek(__offset, __whence)
+        if pos > self._size_limit:
+            return self._file.seek(self._size_limit)
+        return pos
+
+    def read(self, __size: Optional[int] = -1) -> bytes:
+        """Read at most `__size` bytes from the file.
+
+        Behavior is the same as a regular file, except that it is capped to the size limit.
+        """
+        current = self._file.tell()
+        if __size is None or __size < 0:
+            # Read until file limit
+            truncated_size = self._size_limit - current
+        else:
+            # Read until file limit or __size
+            truncated_size = min(__size, self._size_limit - current)
+        return self._file.read(truncated_size)

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/_inference_endpoints.py

@@ -0,0 +1,396 @@
+import time
+from dataclasses import dataclass, field
+from datetime import datetime
+from enum import Enum
+from typing import TYPE_CHECKING, Dict, Optional, Union
+
+from huggingface_hub.errors import InferenceEndpointError, InferenceEndpointTimeoutError
+
+from .inference._client import InferenceClient
+from .inference._generated._async_client import AsyncInferenceClient
+from .utils import get_session, logging, parse_datetime
+
+
+if TYPE_CHECKING:
+    from .hf_api import HfApi
+
+
+logger = logging.get_logger(__name__)
+
+
+class InferenceEndpointStatus(str, Enum):
+    PENDING = "pending"
+    INITIALIZING = "initializing"
+    UPDATING = "updating"
+    UPDATE_FAILED = "updateFailed"
+    RUNNING = "running"
+    PAUSED = "paused"
+    FAILED = "failed"
+    SCALED_TO_ZERO = "scaledToZero"
+
+
+class InferenceEndpointType(str, Enum):
+    PUBlIC = "public"
+    PROTECTED = "protected"
+    PRIVATE = "private"
+
+
+@dataclass
+class InferenceEndpoint:
+    """
+    Contains information about a deployed Inference Endpoint.
+
+    Args:
+        name (`str`):
+            The unique name of the Inference Endpoint.
+        namespace (`str`):
+            The namespace where the Inference Endpoint is located.
+        repository (`str`):
+            The name of the model repository deployed on this Inference Endpoint.
+        status ([`InferenceEndpointStatus`]):
+            The current status of the Inference Endpoint.
+        url (`str`, *optional*):
+            The URL of the Inference Endpoint, if available. Only a deployed Inference Endpoint will have a URL.
+        framework (`str`):
+            The machine learning framework used for the model.
+        revision (`str`):
+            The specific model revision deployed on the Inference Endpoint.
+        task (`str`):
+            The task associated with the deployed model.
+        created_at (`datetime.datetime`):
+            The timestamp when the Inference Endpoint was created.
+        updated_at (`datetime.datetime`):
+            The timestamp of the last update of the Inference Endpoint.
+        type ([`InferenceEndpointType`]):
+            The type of the Inference Endpoint (public, protected, private).
+        raw (`Dict`):
+            The raw dictionary data returned from the API.
+        token (`str` or `bool`, *optional*):
+            Authentication token for the Inference Endpoint, if set when requesting the API. Will default to the
+            locally saved token if not provided. Pass `token=False` if you don't want to send your token to the server.
+
+    Example:
+        ```python
+        >>> from huggingface_hub import get_inference_endpoint
+        >>> endpoint = get_inference_endpoint("my-text-to-image")
+        >>> endpoint
+        InferenceEndpoint(name='my-text-to-image', ...)
+
+        # Get status
+        >>> endpoint.status
+        'running'
+        >>> endpoint.url
+        'https://my-text-to-image.region.vendor.endpoints.huggingface.cloud'
+
+        # Run inference
+        >>> endpoint.client.text_to_image(...)
+
+        # Pause endpoint to save $$$
+        >>> endpoint.pause()
+
+        # ...
+        # Resume and wait for deployment
+        >>> endpoint.resume()
+        >>> endpoint.wait()
+        >>> endpoint.client.text_to_image(...)
+        ```
+    """
+
+    # Field in __repr__
+    name: str = field(init=False)
+    namespace: str
+    repository: str = field(init=False)
+    status: InferenceEndpointStatus = field(init=False)
+    url: Optional[str] = field(init=False)
+
+    # Other fields
+    framework: str = field(repr=False, init=False)
+    revision: str = field(repr=False, init=False)
+    task: str = field(repr=False, init=False)
+    created_at: datetime = field(repr=False, init=False)
+    updated_at: datetime = field(repr=False, init=False)
+    type: InferenceEndpointType = field(repr=False, init=False)
+
+    # Raw dict from the API
+    raw: Dict = field(repr=False)
+
+    # Internal fields
+    _token: Union[str, bool, None] = field(repr=False, compare=False)
+    _api: "HfApi" = field(repr=False, compare=False)
+
+    @classmethod
+    def from_raw(
+        cls, raw: Dict, namespace: str, token: Union[str, bool, None] = None, api: Optional["HfApi"] = None
+    ) -> "InferenceEndpoint":
+        """Initialize object from raw dictionary."""
+        if api is None:
+            from .hf_api import HfApi
+
+            api = HfApi()
+        if token is None:
+            token = api.token
+
+        # All other fields are populated in __post_init__
+        return cls(raw=raw, namespace=namespace, _token=token, _api=api)
+
+    def __post_init__(self) -> None:
+        """Populate fields from raw dictionary."""
+        self._populate_from_raw()
+
+    @property
+    def client(self) -> InferenceClient:
+        """Returns a client to make predictions on this Inference Endpoint.
+
+        Returns:
+            [`InferenceClient`]: an inference client pointing to the deployed endpoint.
+
+        Raises:
+            [`InferenceEndpointError`]: If the Inference Endpoint is not yet deployed.
+        """
+        if self.url is None:
+            raise InferenceEndpointError(
+                "Cannot create a client for this Inference Endpoint as it is not yet deployed. "
+                "Please wait for the Inference Endpoint to be deployed using `endpoint.wait()` and try again."
+            )
+        return InferenceClient(model=self.url, token=self._token)
+
+    @property
+    def async_client(self) -> AsyncInferenceClient:
+        """Returns a client to make predictions on this Inference Endpoint.
+
+        Returns:
+            [`AsyncInferenceClient`]: an asyncio-compatible inference client pointing to the deployed endpoint.
+
+        Raises:
+            [`InferenceEndpointError`]: If the Inference Endpoint is not yet deployed.
+        """
+        if self.url is None:
+            raise InferenceEndpointError(
+                "Cannot create a client for this Inference Endpoint as it is not yet deployed. "
+                "Please wait for the Inference Endpoint to be deployed using `endpoint.wait()` and try again."
+            )
+        return AsyncInferenceClient(model=self.url, token=self._token)
+
+    def wait(self, timeout: Optional[int] = None, refresh_every: int = 5) -> "InferenceEndpoint":
+        """Wait for the Inference Endpoint to be deployed.
+
+        Information from the server will be fetched every 1s. If the Inference Endpoint is not deployed after `timeout`
+        seconds, a [`InferenceEndpointTimeoutError`] will be raised. The [`InferenceEndpoint`] will be mutated in place with the latest
+        data.
+
+        Args:
+            timeout (`int`, *optional*):
+                The maximum time to wait for the Inference Endpoint to be deployed, in seconds. If `None`, will wait
+                indefinitely.
+            refresh_every (`int`, *optional*):
+                The time to wait between each fetch of the Inference Endpoint status, in seconds. Defaults to 5s.
+
+        Returns:
+            [`InferenceEndpoint`]: the same Inference Endpoint, mutated in place with the latest data.
+
+        Raises:
+            [`InferenceEndpointError`]
+                If the Inference Endpoint ended up in a failed state.
+            [`InferenceEndpointTimeoutError`]
+                If the Inference Endpoint is not deployed after `timeout` seconds.
+        """
+        if timeout is not None and timeout < 0:
+            raise ValueError("`timeout` cannot be negative.")
+        if refresh_every <= 0:
+            raise ValueError("`refresh_every` must be positive.")
+
+        start = time.time()
+        while True:
+            if self.url is not None:
+                # Means the URL is provisioned => check if the endpoint is reachable
+                response = get_session().get(self.url, headers=self._api._build_hf_headers(token=self._token))
+                if response.status_code == 200:
+                    logger.info("Inference Endpoint is ready to be used.")
+                    return self
+            if self.status == InferenceEndpointStatus.FAILED:
+                raise InferenceEndpointError(
+                    f"Inference Endpoint {self.name} failed to deploy. Please check the logs for more information."
+                )
+            if timeout is not None:
+                if time.time() - start > timeout:
+                    raise InferenceEndpointTimeoutError("Timeout while waiting for Inference Endpoint to be deployed.")
+            logger.info(f"Inference Endpoint is not deployed yet ({self.status}). Waiting {refresh_every}s...")
+            time.sleep(refresh_every)
+            self.fetch()
+
+    def fetch(self) -> "InferenceEndpoint":
+        """Fetch latest information about the Inference Endpoint.
+
+        Returns:
+            [`InferenceEndpoint`]: the same Inference Endpoint, mutated in place with the latest data.
+        """
+        obj = self._api.get_inference_endpoint(name=self.name, namespace=self.namespace, token=self._token)  # type: ignore [arg-type]
+        self.raw = obj.raw
+        self._populate_from_raw()
+        return self
+
+    def update(
+        self,
+        *,
+        # Compute update
+        accelerator: Optional[str] = None,
+        instance_size: Optional[str] = None,
+        instance_type: Optional[str] = None,
+        min_replica: Optional[int] = None,
+        max_replica: Optional[int] = None,
+        scale_to_zero_timeout: Optional[int] = None,
+        # Model update
+        repository: Optional[str] = None,
+        framework: Optional[str] = None,
+        revision: Optional[str] = None,
+        task: Optional[str] = None,
+        custom_image: Optional[Dict] = None,
+        secrets: Optional[Dict[str, str]] = None,
+    ) -> "InferenceEndpoint":
+        """Update the Inference Endpoint.
+
+        This method allows the update of either the compute configuration, the deployed model, or both. All arguments are
+        optional but at least one must be provided.
+
+        This is an alias for [`HfApi.update_inference_endpoint`]. The current object is mutated in place with the
+        latest data from the server.
+
+        Args:
+            accelerator (`str`, *optional*):
+                The hardware accelerator to be used for inference (e.g. `"cpu"`).
+            instance_size (`str`, *optional*):
+                The size or type of the instance to be used for hosting the model (e.g. `"x4"`).
+            instance_type (`str`, *optional*):
+                The cloud instance type where the Inference Endpoint will be deployed (e.g. `"intel-icl"`).
+            min_replica (`int`, *optional*):
+                The minimum number of replicas (instances) to keep running for the Inference Endpoint.
+            max_replica (`int`, *optional*):
+                The maximum number of replicas (instances) to scale to for the Inference Endpoint.
+            scale_to_zero_timeout (`int`, *optional*):
+                The duration in minutes before an inactive endpoint is scaled to zero.
+
+            repository (`str`, *optional*):
+                The name of the model repository associated with the Inference Endpoint (e.g. `"gpt2"`).
+            framework (`str`, *optional*):
+                The machine learning framework used for the model (e.g. `"custom"`).
+            revision (`str`, *optional*):
+                The specific model revision to deploy on the Inference Endpoint (e.g. `"6c0e6080953db56375760c0471a8c5f2929baf11"`).
+            task (`str`, *optional*):
+                The task on which to deploy the model (e.g. `"text-classification"`).
+            custom_image (`Dict`, *optional*):
+                A custom Docker image to use for the Inference Endpoint. This is useful if you want to deploy an
+                Inference Endpoint running on the `text-generation-inference` (TGI) framework (see examples).
+            secrets (`Dict[str, str]`, *optional*):
+                Secret values to inject in the container environment.
+        Returns:
+            [`InferenceEndpoint`]: the same Inference Endpoint, mutated in place with the latest data.
+        """
+        # Make API call
+        obj = self._api.update_inference_endpoint(
+            name=self.name,
+            namespace=self.namespace,
+            accelerator=accelerator,
+            instance_size=instance_size,
+            instance_type=instance_type,
+            min_replica=min_replica,
+            max_replica=max_replica,
+            scale_to_zero_timeout=scale_to_zero_timeout,
+            repository=repository,
+            framework=framework,
+            revision=revision,
+            task=task,
+            custom_image=custom_image,
+            secrets=secrets,
+            token=self._token,  # type: ignore [arg-type]
+        )
+
+        # Mutate current object
+        self.raw = obj.raw
+        self._populate_from_raw()
+        return self
+
+    def pause(self) -> "InferenceEndpoint":
+        """Pause the Inference Endpoint.
+
+        A paused Inference Endpoint will not be charged. It can be resumed at any time using [`InferenceEndpoint.resume`].
+        This is different than scaling the Inference Endpoint to zero with [`InferenceEndpoint.scale_to_zero`], which
+        would be automatically restarted when a request is made to it.
+
+        This is an alias for [`HfApi.pause_inference_endpoint`]. The current object is mutated in place with the
+        latest data from the server.
+
+        Returns:
+            [`InferenceEndpoint`]: the same Inference Endpoint, mutated in place with the latest data.
+        """
+        obj = self._api.pause_inference_endpoint(name=self.name, namespace=self.namespace, token=self._token)  # type: ignore [arg-type]
+        self.raw = obj.raw
+        self._populate_from_raw()
+        return self
+
+    def resume(self, running_ok: bool = True) -> "InferenceEndpoint":
+        """Resume the Inference Endpoint.
+
+        This is an alias for [`HfApi.resume_inference_endpoint`]. The current object is mutated in place with the
+        latest data from the server.
+
+        Args:
+            running_ok (`bool`, *optional*):
+                If `True`, the method will not raise an error if the Inference Endpoint is already running. Defaults to
+                `True`.
+
+        Returns:
+            [`InferenceEndpoint`]: the same Inference Endpoint, mutated in place with the latest data.
+        """
+        obj = self._api.resume_inference_endpoint(
+            name=self.name, namespace=self.namespace, running_ok=running_ok, token=self._token
+        )  # type: ignore [arg-type]
+        self.raw = obj.raw
+        self._populate_from_raw()
+        return self
+
+    def scale_to_zero(self) -> "InferenceEndpoint":
+        """Scale Inference Endpoint to zero.
+
+        An Inference Endpoint scaled to zero will not be charged. It will be resume on the next request to it, with a
+        cold start delay. This is different than pausing the Inference Endpoint with [`InferenceEndpoint.pause`], which
+        would require a manual resume with [`InferenceEndpoint.resume`].
+
+        This is an alias for [`HfApi.scale_to_zero_inference_endpoint`]. The current object is mutated in place with the
+        latest data from the server.
+
+        Returns:
+            [`InferenceEndpoint`]: the same Inference Endpoint, mutated in place with the latest data.
+        """
+        obj = self._api.scale_to_zero_inference_endpoint(name=self.name, namespace=self.namespace, token=self._token)  # type: ignore [arg-type]
+        self.raw = obj.raw
+        self._populate_from_raw()
+        return self
+
+    def delete(self) -> None:
+        """Delete the Inference Endpoint.
+
+        This operation is not reversible. If you don't want to be charged for an Inference Endpoint, it is preferable
+        to pause it with [`InferenceEndpoint.pause`] or scale it to zero with [`InferenceEndpoint.scale_to_zero`].
+
+        This is an alias for [`HfApi.delete_inference_endpoint`].
+        """
+        self._api.delete_inference_endpoint(name=self.name, namespace=self.namespace, token=self._token)  # type: ignore [arg-type]
+
+    def _populate_from_raw(self) -> None:
+        """Populate fields from raw dictionary.
+
+        Called in __post_init__ + each time the Inference Endpoint is updated.
+        """
+        # Repr fields
+        self.name = self.raw["name"]
+        self.repository = self.raw["model"]["repository"]
+        self.status = self.raw["status"]["state"]
+        self.url = self.raw["status"].get("url")
+
+        # Other fields
+        self.framework = self.raw["model"]["framework"]
+        self.revision = self.raw["model"]["revision"]
+        self.task = self.raw["model"]["task"]
+        self.created_at = parse_datetime(self.raw["status"]["createdAt"])
+        self.updated_at = parse_datetime(self.raw["status"]["updatedAt"])
+        self.type = self.raw["type"]

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/_local_folder.py

@@ -0,0 +1,425 @@
+# coding=utf-8
+# Copyright 2024-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities to handle the `../.cache/huggingface` folder in local directories.
+
+First discussed in https://github.com/huggingface/huggingface_hub/issues/1738 to store
+download metadata when downloading files from the hub to a local directory (without
+using the cache).
+
+./.cache/huggingface folder structure:
+[4.0K]  data
+├── [4.0K]  .cache
+│   └── [4.0K]  huggingface
+│       └── [4.0K]  download
+│           ├── [  16]  file.parquet.metadata
+│           ├── [  16]  file.txt.metadata
+│           └── [4.0K]  folder
+│               └── [  16]  file.parquet.metadata
+│
+├── [6.5G]  file.parquet
+├── [1.5K]  file.txt
+└── [4.0K]  folder
+    └── [   16]  file.parquet
+
+
+Download metadata file structure:
+```
+# file.txt.metadata
+11c5a3d5811f50298f278a704980280950aedb10
+a16a55fda99d2f2e7b69cce5cf93ff4ad3049930
+1712656091.123
+
+# file.parquet.metadata
+11c5a3d5811f50298f278a704980280950aedb10
+7c5d3f4b8b76583b422fcb9189ad6c89d5d97a094541ce8932dce3ecabde1421
+1712656091.123
+}
+```
+"""
+
+import logging
+import os
+import time
+from dataclasses import dataclass
+from functools import lru_cache
+from pathlib import Path
+from typing import Optional
+
+from .utils import WeakFileLock
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class LocalDownloadFilePaths:
+    """
+    Paths to the files related to a download process in a local dir.
+
+    Returned by [`get_local_download_paths`].
+
+    Attributes:
+        file_path (`Path`):
+            Path where the file will be saved.
+        lock_path (`Path`):
+            Path to the lock file used to ensure atomicity when reading/writing metadata.
+        metadata_path (`Path`):
+            Path to the metadata file.
+    """
+
+    file_path: Path
+    lock_path: Path
+    metadata_path: Path
+
+    def incomplete_path(self, etag: str) -> Path:
+        """Return the path where a file will be temporarily downloaded before being moved to `file_path`."""
+        return self.metadata_path.with_suffix(f".{etag}.incomplete")
+
+
+@dataclass(frozen=True)
+class LocalUploadFilePaths:
+    """
+    Paths to the files related to an upload process in a local dir.
+
+    Returned by [`get_local_upload_paths`].
+
+    Attributes:
+        path_in_repo (`str`):
+            Path of the file in the repo.
+        file_path (`Path`):
+            Path where the file will be saved.
+        lock_path (`Path`):
+            Path to the lock file used to ensure atomicity when reading/writing metadata.
+        metadata_path (`Path`):
+            Path to the metadata file.
+    """
+
+    path_in_repo: str
+    file_path: Path
+    lock_path: Path
+    metadata_path: Path
+
+
+@dataclass
+class LocalDownloadFileMetadata:
+    """
+    Metadata about a file in the local directory related to a download process.
+
+    Attributes:
+        filename (`str`):
+            Path of the file in the repo.
+        commit_hash (`str`):
+            Commit hash of the file in the repo.
+        etag (`str`):
+            ETag of the file in the repo. Used to check if the file has changed.
+            For LFS files, this is the sha256 of the file. For regular files, it corresponds to the git hash.
+        timestamp (`int`):
+            Unix timestamp of when the metadata was saved i.e. when the metadata was accurate.
+    """
+
+    filename: str
+    commit_hash: str
+    etag: str
+    timestamp: float
+
+
+@dataclass
+class LocalUploadFileMetadata:
+    """
+    Metadata about a file in the local directory related to an upload process.
+    """
+
+    size: int
+
+    # Default values correspond to "we don't know yet"
+    timestamp: Optional[float] = None
+    should_ignore: Optional[bool] = None
+    sha256: Optional[str] = None
+    upload_mode: Optional[str] = None
+    is_uploaded: bool = False
+    is_committed: bool = False
+
+    def save(self, paths: LocalUploadFilePaths) -> None:
+        """Save the metadata to disk."""
+        with WeakFileLock(paths.lock_path):
+            with paths.metadata_path.open("w") as f:
+                new_timestamp = time.time()
+                f.write(str(new_timestamp) + "\n")
+
+                f.write(str(self.size))  # never None
+                f.write("\n")
+
+                if self.should_ignore is not None:
+                    f.write(str(int(self.should_ignore)))
+                f.write("\n")
+
+                if self.sha256 is not None:
+                    f.write(self.sha256)
+                f.write("\n")
+
+                if self.upload_mode is not None:
+                    f.write(self.upload_mode)
+                f.write("\n")
+
+                f.write(str(int(self.is_uploaded)) + "\n")
+                f.write(str(int(self.is_committed)) + "\n")
+
+            self.timestamp = new_timestamp
+
+
+@lru_cache(maxsize=128)  # ensure singleton
+def get_local_download_paths(local_dir: Path, filename: str) -> LocalDownloadFilePaths:
+    """Compute paths to the files related to a download process.
+
+    Folders containing the paths are all guaranteed to exist.
+
+    Args:
+        local_dir (`Path`):
+            Path to the local directory in which files are downloaded.
+        filename (`str`):
+            Path of the file in the repo.
+
+    Return:
+        [`LocalDownloadFilePaths`]: the paths to the files (file_path, lock_path, metadata_path, incomplete_path).
+    """
+    # filename is the path in the Hub repository (separated by '/')
+    # make sure to have a cross platform transcription
+    sanitized_filename = os.path.join(*filename.split("/"))
+    if os.name == "nt":
+        if sanitized_filename.startswith("..\\") or "\\..\\" in sanitized_filename:
+            raise ValueError(
+                f"Invalid filename: cannot handle filename '{sanitized_filename}' on Windows. Please ask the repository"
+                " owner to rename this file."
+            )
+    file_path = local_dir / sanitized_filename
+    metadata_path = _huggingface_dir(local_dir) / "download" / f"{sanitized_filename}.metadata"
+    lock_path = metadata_path.with_suffix(".lock")
+
+    # Some Windows versions do not allow for paths longer than 255 characters.
+    # In this case, we must specify it as an extended path by using the "\\?\" prefix
+    if os.name == "nt":
+        if not str(local_dir).startswith("\\\\?\\") and len(os.path.abspath(lock_path)) > 255:
+            file_path = Path("\\\\?\\" + os.path.abspath(file_path))
+            lock_path = Path("\\\\?\\" + os.path.abspath(lock_path))
+            metadata_path = Path("\\\\?\\" + os.path.abspath(metadata_path))
+
+    file_path.parent.mkdir(parents=True, exist_ok=True)
+    metadata_path.parent.mkdir(parents=True, exist_ok=True)
+    return LocalDownloadFilePaths(file_path=file_path, lock_path=lock_path, metadata_path=metadata_path)
+
+
+@lru_cache(maxsize=128)  # ensure singleton
+def get_local_upload_paths(local_dir: Path, filename: str) -> LocalUploadFilePaths:
+    """Compute paths to the files related to an upload process.
+
+    Folders containing the paths are all guaranteed to exist.
+
+    Args:
+        local_dir (`Path`):
+            Path to the local directory that is uploaded.
+        filename (`str`):
+            Path of the file in the repo.
+
+    Return:
+        [`LocalUploadFilePaths`]: the paths to the files (file_path, lock_path, metadata_path).
+    """
+    # filename is the path in the Hub repository (separated by '/')
+    # make sure to have a cross platform transcription
+    sanitized_filename = os.path.join(*filename.split("/"))
+    if os.name == "nt":
+        if sanitized_filename.startswith("..\\") or "\\..\\" in sanitized_filename:
+            raise ValueError(
+                f"Invalid filename: cannot handle filename '{sanitized_filename}' on Windows. Please ask the repository"
+                " owner to rename this file."
+            )
+    file_path = local_dir / sanitized_filename
+    metadata_path = _huggingface_dir(local_dir) / "upload" / f"{sanitized_filename}.metadata"
+    lock_path = metadata_path.with_suffix(".lock")
+
+    # Some Windows versions do not allow for paths longer than 255 characters.
+    # In this case, we must specify it as an extended path by using the "\\?\" prefix
+    if os.name == "nt":
+        if not str(local_dir).startswith("\\\\?\\") and len(os.path.abspath(lock_path)) > 255:
+            file_path = Path("\\\\?\\" + os.path.abspath(file_path))
+            lock_path = Path("\\\\?\\" + os.path.abspath(lock_path))
+            metadata_path = Path("\\\\?\\" + os.path.abspath(metadata_path))
+
+    file_path.parent.mkdir(parents=True, exist_ok=True)
+    metadata_path.parent.mkdir(parents=True, exist_ok=True)
+    return LocalUploadFilePaths(
+        path_in_repo=filename, file_path=file_path, lock_path=lock_path, metadata_path=metadata_path
+    )
+
+
+def read_download_metadata(local_dir: Path, filename: str) -> Optional[LocalDownloadFileMetadata]:
+    """Read metadata about a file in the local directory related to a download process.
+
+    Args:
+        local_dir (`Path`):
+            Path to the local directory in which files are downloaded.
+        filename (`str`):
+            Path of the file in the repo.
+
+    Return:
+        `[LocalDownloadFileMetadata]` or `None`: the metadata if it exists, `None` otherwise.
+    """
+    paths = get_local_download_paths(local_dir, filename)
+    with WeakFileLock(paths.lock_path):
+        if paths.metadata_path.exists():
+            try:
+                with paths.metadata_path.open() as f:
+                    commit_hash = f.readline().strip()
+                    etag = f.readline().strip()
+                    timestamp = float(f.readline().strip())
+                    metadata = LocalDownloadFileMetadata(
+                        filename=filename,
+                        commit_hash=commit_hash,
+                        etag=etag,
+                        timestamp=timestamp,
+                    )
+            except Exception as e:
+                # remove the metadata file if it is corrupted / not the right format
+                logger.warning(
+                    f"Invalid metadata file {paths.metadata_path}: {e}. Removing it from disk and continue."
+                )
+                try:
+                    paths.metadata_path.unlink()
+                except Exception as e:
+                    logger.warning(f"Could not remove corrupted metadata file {paths.metadata_path}: {e}")
+
+            try:
+                # check if the file exists and hasn't been modified since the metadata was saved
+                stat = paths.file_path.stat()
+                if (
+                    stat.st_mtime - 1 <= metadata.timestamp
+                ):  # allow 1s difference as stat.st_mtime might not be precise
+                    return metadata
+                logger.info(f"Ignored metadata for '{filename}' (outdated). Will re-compute hash.")
+            except FileNotFoundError:
+                # file does not exist => metadata is outdated
+                return None
+    return None
+
+
+def read_upload_metadata(local_dir: Path, filename: str) -> LocalUploadFileMetadata:
+    """Read metadata about a file in the local directory related to an upload process.
+
+    TODO: factorize logic with `read_download_metadata`.
+
+    Args:
+        local_dir (`Path`):
+            Path to the local directory in which files are downloaded.
+        filename (`str`):
+            Path of the file in the repo.
+
+    Return:
+        `[LocalUploadFileMetadata]` or `None`: the metadata if it exists, `None` otherwise.
+    """
+    paths = get_local_upload_paths(local_dir, filename)
+    with WeakFileLock(paths.lock_path):
+        if paths.metadata_path.exists():
+            try:
+                with paths.metadata_path.open() as f:
+                    timestamp = float(f.readline().strip())
+
+                    size = int(f.readline().strip())  # never None
+
+                    _should_ignore = f.readline().strip()
+                    should_ignore = None if _should_ignore == "" else bool(int(_should_ignore))
+
+                    _sha256 = f.readline().strip()
+                    sha256 = None if _sha256 == "" else _sha256
+
+                    _upload_mode = f.readline().strip()
+                    upload_mode = None if _upload_mode == "" else _upload_mode
+                    if upload_mode not in (None, "regular", "lfs"):
+                        raise ValueError(f"Invalid upload mode in metadata {paths.path_in_repo}: {upload_mode}")
+
+                    is_uploaded = bool(int(f.readline().strip()))
+                    is_committed = bool(int(f.readline().strip()))
+
+                    metadata = LocalUploadFileMetadata(
+                        timestamp=timestamp,
+                        size=size,
+                        should_ignore=should_ignore,
+                        sha256=sha256,
+                        upload_mode=upload_mode,
+                        is_uploaded=is_uploaded,
+                        is_committed=is_committed,
+                    )
+            except Exception as e:
+                # remove the metadata file if it is corrupted / not the right format
+                logger.warning(
+                    f"Invalid metadata file {paths.metadata_path}: {e}. Removing it from disk and continue."
+                )
+                try:
+                    paths.metadata_path.unlink()
+                except Exception as e:
+                    logger.warning(f"Could not remove corrupted metadata file {paths.metadata_path}: {e}")
+
+            # TODO: can we do better?
+            if (
+                metadata.timestamp is not None
+                and metadata.is_uploaded  # file was uploaded
+                and not metadata.is_committed  # but not committed
+                and time.time() - metadata.timestamp > 20 * 3600  # and it's been more than 20 hours
+            ):  # => we consider it as garbage-collected by S3
+                metadata.is_uploaded = False
+
+            # check if the file exists and hasn't been modified since the metadata was saved
+            try:
+                if metadata.timestamp is not None and paths.file_path.stat().st_mtime <= metadata.timestamp:
+                    return metadata
+                logger.info(f"Ignored metadata for '{filename}' (outdated). Will re-compute hash.")
+            except FileNotFoundError:
+                # file does not exist => metadata is outdated
+                pass
+
+    # empty metadata => we don't know anything expect its size
+    return LocalUploadFileMetadata(size=paths.file_path.stat().st_size)
+
+
+def write_download_metadata(local_dir: Path, filename: str, commit_hash: str, etag: str) -> None:
+    """Write metadata about a file in the local directory related to a download process.
+
+    Args:
+        local_dir (`Path`):
+            Path to the local directory in which files are downloaded.
+    """
+    paths = get_local_download_paths(local_dir, filename)
+    with WeakFileLock(paths.lock_path):
+        with paths.metadata_path.open("w") as f:
+            f.write(f"{commit_hash}\n{etag}\n{time.time()}\n")
+
+
+@lru_cache()
+def _huggingface_dir(local_dir: Path) -> Path:
+    """Return the path to the `.cache/huggingface` directory in a local directory."""
+    # Wrap in lru_cache to avoid overwriting the .gitignore file if called multiple times
+    path = local_dir / ".cache" / "huggingface"
+    path.mkdir(exist_ok=True, parents=True)
+
+    # Create a .gitignore file in the .cache/huggingface directory if it doesn't exist
+    # Should be thread-safe enough like this.
+    gitignore = path / ".gitignore"
+    gitignore_lock = path / ".gitignore.lock"
+    if not gitignore.exists():
+        try:
+            with WeakFileLock(gitignore_lock):
+                gitignore.write_text("*")
+            gitignore_lock.unlink()
+        except OSError:  # FileNotFoundError, PermissionError, etc.
+            pass
+    return path

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/_login.py

@@ -0,0 +1,536 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains methods to log in to the Hub."""
+
+import os
+import subprocess
+from functools import partial
+from getpass import getpass
+from pathlib import Path
+from typing import Optional
+
+from . import constants
+from .commands._cli_utils import ANSI
+from .utils import (
+    capture_output,
+    get_token,
+    is_google_colab,
+    is_notebook,
+    list_credential_helpers,
+    logging,
+    run_subprocess,
+    set_git_credential,
+    unset_git_credential,
+)
+from .utils._auth import (
+    _get_token_by_name,
+    _get_token_from_environment,
+    _get_token_from_file,
+    _get_token_from_google_colab,
+    _save_stored_tokens,
+    _save_token,
+    get_stored_tokens,
+)
+
+
+logger = logging.get_logger(__name__)
+
+_HF_LOGO_ASCII = """
+    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|
+    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|
+    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|
+    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|
+    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|
+"""
+
+
+def login(
+    token: Optional[str] = None,
+    add_to_git_credential: bool = False,
+    new_session: bool = True,
+    write_permission: bool = False,
+) -> None:
+    """Login the machine to access the Hub.
+
+    The `token` is persisted in cache and set as a git credential. Once done, the machine
+    is logged in and the access token will be available across all `huggingface_hub`
+    components. If `token` is not provided, it will be prompted to the user either with
+    a widget (in a notebook) or via the terminal.
+
+    To log in from outside of a script, one can also use `huggingface-cli login` which is
+    a cli command that wraps [`login`].
+
+    <Tip>
+
+    [`login`] is a drop-in replacement method for [`notebook_login`] as it wraps and
+    extends its capabilities.
+
+    </Tip>
+
+    <Tip>
+
+    When the token is not passed, [`login`] will automatically detect if the script runs
+    in a notebook or not. However, this detection might not be accurate due to the
+    variety of notebooks that exists nowadays. If that is the case, you can always force
+    the UI by using [`notebook_login`] or [`interpreter_login`].
+
+    </Tip>
+
+    Args:
+        token (`str`, *optional*):
+            User access token to generate from https://huggingface.co/settings/token.
+        add_to_git_credential (`bool`, defaults to `False`):
+            If `True`, token will be set as git credential. If no git credential helper
+            is configured, a warning will be displayed to the user. If `token` is `None`,
+            the value of `add_to_git_credential` is ignored and will be prompted again
+            to the end user.
+        new_session (`bool`, defaults to `True`):
+            If `True`, will request a token even if one is already saved on the machine.
+        write_permission (`bool`, defaults to `False`):
+            If `True`, requires a token with write permission.
+    Raises:
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If an organization token is passed. Only personal account tokens are valid
+            to log in.
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If token is invalid.
+        [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+            If running in a notebook but `ipywidgets` is not installed.
+    """
+    if token is not None:
+        if not add_to_git_credential:
+            logger.info(
+                "The token has not been saved to the git credentials helper. Pass "
+                "`add_to_git_credential=True` in this function directly or "
+                "`--add-to-git-credential` if using via `huggingface-cli` if "
+                "you want to set the git credential as well."
+            )
+        _login(token, add_to_git_credential=add_to_git_credential, write_permission=write_permission)
+    elif is_notebook():
+        notebook_login(new_session=new_session, write_permission=write_permission)
+    else:
+        interpreter_login(new_session=new_session, write_permission=write_permission)
+
+
+def logout(token_name: Optional[str] = None) -> None:
+    """Logout the machine from the Hub.
+
+    Token is deleted from the machine and removed from git credential.
+
+    Args:
+        token_name (`str`, *optional*):
+            Name of the access token to logout from. If `None`, will logout from all saved access tokens.
+    Raises:
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError):
+            If the access token name is not found.
+    """
+    if get_token() is None and not get_stored_tokens():  # No active token and no saved access tokens
+        logger.warning("Not logged in!")
+        return
+    if not token_name:
+        # Delete all saved access tokens and token
+        for file_path in (constants.HF_TOKEN_PATH, constants.HF_STORED_TOKENS_PATH):
+            try:
+                Path(file_path).unlink()
+            except FileNotFoundError:
+                pass
+        logger.info("Successfully logged out from all access tokens.")
+    else:
+        _logout_from_token(token_name)
+        logger.info(f"Successfully logged out from access token: {token_name}.")
+
+    unset_git_credential()
+
+    # Check if still logged in
+    if _get_token_from_google_colab() is not None:
+        raise EnvironmentError(
+            "You are automatically logged in using a Google Colab secret.\n"
+            "To log out, you must unset the `HF_TOKEN` secret in your Colab settings."
+        )
+    if _get_token_from_environment() is not None:
+        raise EnvironmentError(
+            "Token has been deleted from your machine but you are still logged in.\n"
+            "To log out, you must clear out both `HF_TOKEN` and `HUGGING_FACE_HUB_TOKEN` environment variables."
+        )
+
+
+def auth_switch(token_name: str, add_to_git_credential: bool = False) -> None:
+    """Switch to a different access token.
+
+    Args:
+        token_name (`str`):
+            Name of the access token to switch to.
+        add_to_git_credential (`bool`, defaults to `False`):
+            If `True`, token will be set as git credential. If no git credential helper
+            is configured, a warning will be displayed to the user. If `token` is `None`,
+            the value of `add_to_git_credential` is ignored and will be prompted again
+            to the end user.
+
+    Raises:
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError):
+            If the access token name is not found.
+    """
+    token = _get_token_by_name(token_name)
+    if not token:
+        raise ValueError(f"Access token {token_name} not found in {constants.HF_STORED_TOKENS_PATH}")
+    # Write token to HF_TOKEN_PATH
+    _set_active_token(token_name, add_to_git_credential)
+    logger.info(f"The current active token is: {token_name}")
+    token_from_environment = _get_token_from_environment()
+    if token_from_environment is not None and token_from_environment != token:
+        logger.warning(
+            "The environment variable `HF_TOKEN` is set and will override the access token you've just switched to."
+        )
+
+
+def auth_list() -> None:
+    """List all stored access tokens."""
+    tokens = get_stored_tokens()
+
+    if not tokens:
+        logger.info("No access tokens found.")
+        return
+    # Find current token
+    current_token = get_token()
+    current_token_name = None
+    for token_name in tokens:
+        if tokens.get(token_name) == current_token:
+            current_token_name = token_name
+    # Print header
+    max_offset = max(len("token"), max(len(token) for token in tokens)) + 2
+    print(f"  {{:<{max_offset}}}| {{:<15}}".format("name", "token"))
+    print("-" * (max_offset + 2) + "|" + "-" * 15)
+
+    # Print saved access tokens
+    for token_name in tokens:
+        token = tokens.get(token_name, "<not set>")
+        masked_token = f"{token[:3]}****{token[-4:]}" if token != "<not set>" else token
+        is_current = "*" if token == current_token else " "
+
+        print(f"{is_current} {{:<{max_offset}}}| {{:<15}}".format(token_name, masked_token))
+
+    if _get_token_from_environment():
+        logger.warning(
+            "\nNote: Environment variable `HF_TOKEN` is set and is the current active token independently from the stored tokens listed above."
+        )
+    elif current_token_name is None:
+        logger.warning(
+            "\nNote: No active token is set and no environment variable `HF_TOKEN` is found. Use `huggingface-cli login` to log in."
+        )
+
+
+###
+# Interpreter-based login (text)
+###
+
+
+def interpreter_login(new_session: bool = True, write_permission: bool = False) -> None:
+    """
+    Displays a prompt to log in to the HF website and store the token.
+
+    This is equivalent to [`login`] without passing a token when not run in a notebook.
+    [`interpreter_login`] is useful if you want to force the use of the terminal prompt
+    instead of a notebook widget.
+
+    For more details, see [`login`].
+
+    Args:
+        new_session (`bool`, defaults to `True`):
+            If `True`, will request a token even if one is already saved on the machine.
+        write_permission (`bool`, defaults to `False`):
+            If `True`, requires a token with write permission.
+
+    """
+    if not new_session and _current_token_okay(write_permission=write_permission):
+        logger.info("User is already logged in.")
+        return
+
+    from .commands.delete_cache import _ask_for_confirmation_no_tui
+
+    print(_HF_LOGO_ASCII)
+    if get_token() is not None:
+        logger.info(
+            "    A token is already saved on your machine. Run `huggingface-cli"
+            " whoami` to get more information or `huggingface-cli logout` if you want"
+            " to log out."
+        )
+        logger.info("    Setting a new token will erase the existing one.")
+
+    logger.info(
+        "    To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens ."
+    )
+    if os.name == "nt":
+        logger.info("Token can be pasted using 'Right-Click'.")
+    token = getpass("Enter your token (input will not be visible): ")
+    add_to_git_credential = _ask_for_confirmation_no_tui("Add token as git credential?")
+
+    _login(
+        token=token,
+        add_to_git_credential=add_to_git_credential,
+        write_permission=write_permission,
+    )
+
+
+###
+# Notebook-based login (widget)
+###
+
+NOTEBOOK_LOGIN_PASSWORD_HTML = """<center> <img
+src=https://huggingface.co/front/assets/huggingface_logo-noborder.svg
+alt='Hugging Face'> <br> Immediately click login after typing your password or
+it might be stored in plain text in this notebook file. </center>"""
+
+
+NOTEBOOK_LOGIN_TOKEN_HTML_START = """<center> <img
+src=https://huggingface.co/front/assets/huggingface_logo-noborder.svg
+alt='Hugging Face'> <br> Copy a token from <a
+href="https://huggingface.co/settings/tokens" target="_blank">your Hugging Face
+tokens page</a> and paste it below. <br> Immediately click login after copying
+your token or it might be stored in plain text in this notebook file. </center>"""
+
+
+NOTEBOOK_LOGIN_TOKEN_HTML_END = """
+<b>Pro Tip:</b> If you don't already have one, you can create a dedicated
+'notebooks' token with 'write' access, that you can then easily reuse for all
+notebooks. </center>"""
+
+
+def notebook_login(new_session: bool = True, write_permission: bool = False) -> None:
+    """
+    Displays a widget to log in to the HF website and store the token.
+
+    This is equivalent to [`login`] without passing a token when run in a notebook.
+    [`notebook_login`] is useful if you want to force the use of the notebook widget
+    instead of a prompt in the terminal.
+
+    For more details, see [`login`].
+
+    Args:
+        new_session (`bool`, defaults to `True`):
+            If `True`, will request a token even if one is already saved on the machine.
+        write_permission (`bool`, defaults to `False`):
+            If `True`, requires a token with write permission.
+    """
+    try:
+        import ipywidgets.widgets as widgets  # type: ignore
+        from IPython.display import display  # type: ignore
+    except ImportError:
+        raise ImportError(
+            "The `notebook_login` function can only be used in a notebook (Jupyter or"
+            " Colab) and you need the `ipywidgets` module: `pip install ipywidgets`."
+        )
+    if not new_session and _current_token_okay(write_permission=write_permission):
+        logger.info("User is already logged in.")
+        return
+
+    box_layout = widgets.Layout(display="flex", flex_flow="column", align_items="center", width="50%")
+
+    token_widget = widgets.Password(description="Token:")
+    git_checkbox_widget = widgets.Checkbox(value=True, description="Add token as git credential?")
+    token_finish_button = widgets.Button(description="Login")
+
+    login_token_widget = widgets.VBox(
+        [
+            widgets.HTML(NOTEBOOK_LOGIN_TOKEN_HTML_START),
+            token_widget,
+            git_checkbox_widget,
+            token_finish_button,
+            widgets.HTML(NOTEBOOK_LOGIN_TOKEN_HTML_END),
+        ],
+        layout=box_layout,
+    )
+    display(login_token_widget)
+
+    # On click events
+    def login_token_event(t, write_permission: bool = False):
+        """
+        Event handler for the login button.
+
+        Args:
+            write_permission (`bool`, defaults to `False`):
+                If `True`, requires a token with write permission.
+        """
+        token = token_widget.value
+        add_to_git_credential = git_checkbox_widget.value
+        # Erase token and clear value to make sure it's not saved in the notebook.
+        token_widget.value = ""
+        # Hide inputs
+        login_token_widget.children = [widgets.Label("Connecting...")]
+        try:
+            with capture_output() as captured:
+                _login(token, add_to_git_credential=add_to_git_credential, write_permission=write_permission)
+            message = captured.getvalue()
+        except Exception as error:
+            message = str(error)
+        # Print result (success message or error)
+        login_token_widget.children = [widgets.Label(line) for line in message.split("\n") if line.strip()]
+
+    token_finish_button.on_click(partial(login_token_event, write_permission=write_permission))
+
+
+###
+# Login private helpers
+###
+
+
+def _login(
+    token: str,
+    add_to_git_credential: bool,
+    write_permission: bool = False,
+) -> None:
+    from .hf_api import whoami  # avoid circular import
+
+    if token.startswith("api_org"):
+        raise ValueError("You must use your personal account token, not an organization token.")
+
+    token_info = whoami(token)
+    permission = token_info["auth"]["accessToken"]["role"]
+    if write_permission and permission != "write":
+        raise ValueError(
+            "Token is valid but is 'read-only' and a 'write' token is required.\nPlease provide a new token with"
+            " correct permission."
+        )
+    logger.info(f"Token is valid (permission: {permission}).")
+
+    token_name = token_info["auth"]["accessToken"]["displayName"]
+    # Store token locally
+    _save_token(token=token, token_name=token_name)
+    # Set active token
+    _set_active_token(token_name=token_name, add_to_git_credential=add_to_git_credential)
+    logger.info("Login successful.")
+    if _get_token_from_environment():
+        logger.warning(
+            "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured."
+        )
+    else:
+        logger.info(f"The current active token is: `{token_name}`")
+
+
+def _logout_from_token(token_name: str) -> None:
+    """Logout from a specific access token.
+
+    Args:
+        token_name (`str`):
+            The name of the access token to logout from.
+    Raises:
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError):
+            If the access token name is not found.
+    """
+    stored_tokens = get_stored_tokens()
+    # If there is no access tokens saved or the access token name is not found, do nothing
+    if not stored_tokens or token_name not in stored_tokens:
+        return
+
+    token = stored_tokens.pop(token_name)
+    _save_stored_tokens(stored_tokens)
+
+    if token == _get_token_from_file():
+        logger.warning(f"Active token '{token_name}' has been deleted.")
+        Path(constants.HF_TOKEN_PATH).unlink(missing_ok=True)
+
+
+def _set_active_token(
+    token_name: str,
+    add_to_git_credential: bool,
+) -> None:
+    """Set the active access token.
+
+    Args:
+        token_name (`str`):
+            The name of the token to set as active.
+    """
+    token = _get_token_by_name(token_name)
+    if not token:
+        raise ValueError(f"Token {token_name} not found in {constants.HF_STORED_TOKENS_PATH}")
+    if add_to_git_credential:
+        if _is_git_credential_helper_configured():
+            set_git_credential(token)
+            logger.info(
+                "Your token has been saved in your configured git credential helpers"
+                + f" ({','.join(list_credential_helpers())})."
+            )
+        else:
+            logger.warning("Token has not been saved to git credential helper.")
+    # Write token to HF_TOKEN_PATH
+    path = Path(constants.HF_TOKEN_PATH)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(token)
+    logger.info(f"Your token has been saved to {constants.HF_TOKEN_PATH}")
+
+
+def _current_token_okay(write_permission: bool = False):
+    """Check if the current token is valid.
+
+    Args:
+        write_permission (`bool`, defaults to `False`):
+            If `True`, requires a token with write permission.
+
+    Returns:
+        `bool`: `True` if the current token is valid, `False` otherwise.
+    """
+    from .hf_api import get_token_permission  # avoid circular import
+
+    permission = get_token_permission()
+    if permission is None or (write_permission and permission != "write"):
+        return False
+    return True
+
+
+def _is_git_credential_helper_configured() -> bool:
+    """Check if a git credential helper is configured.
+
+    Warns user if not the case (except for Google Colab where "store" is set by default
+    by `huggingface_hub`).
+    """
+    helpers = list_credential_helpers()
+    if len(helpers) > 0:
+        return True  # Do not warn: at least 1 helper is set
+
+    # Only in Google Colab to avoid the warning message
+    # See https://github.com/huggingface/huggingface_hub/issues/1043#issuecomment-1247010710
+    if is_google_colab():
+        _set_store_as_git_credential_helper_globally()
+        return True  # Do not warn: "store" is used by default in Google Colab
+
+    # Otherwise, warn user
+    print(
+        ANSI.red(
+            "Cannot authenticate through git-credential as no helper is defined on your"
+            " machine.\nYou might have to re-authenticate when pushing to the Hugging"
+            " Face Hub.\nRun the following command in your terminal in case you want to"
+            " set the 'store' credential helper as default.\n\ngit config --global"
+            " credential.helper store\n\nRead"
+            " https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more"
+            " details."
+        )
+    )
+    return False
+
+
+def _set_store_as_git_credential_helper_globally() -> None:
+    """Set globally the credential.helper to `store`.
+
+    To be used only in Google Colab as we assume the user doesn't care about the git
+    credential config. It is the only particular case where we don't want to display the
+    warning message in [`notebook_login()`].
+
+    Related:
+    - https://github.com/huggingface/huggingface_hub/issues/1043
+    - https://github.com/huggingface/huggingface_hub/issues/1051
+    - https://git-scm.com/docs/git-credential-store
+    """
+    try:
+        run_subprocess("git config --global credential.helper store")
+    except subprocess.CalledProcessError as exc:
+        raise EnvironmentError(exc.stderr)

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/_multi_commits.py

@@ -0,0 +1,306 @@
+# coding=utf-8
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities to multi-commits (i.e. push changes iteratively on a PR)."""
+
+import re
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Iterable, List, Optional, Set, Tuple, Union
+
+from ._commit_api import CommitOperationAdd, CommitOperationDelete
+from .community import DiscussionWithDetails
+from .utils import experimental
+from .utils._cache_manager import _format_size
+from .utils.insecure_hashlib import sha256
+
+
+if TYPE_CHECKING:
+    from .hf_api import HfApi
+
+
+class MultiCommitException(Exception):
+    """Base exception for any exception happening while doing a multi-commit."""
+
+
+MULTI_COMMIT_PR_DESCRIPTION_TEMPLATE = """
+## {commit_message}
+
+{commit_description}
+
+**Multi commit ID:** {multi_commit_id}
+
+Scheduled commits:
+
+{multi_commit_strategy}
+
+_This is a PR opened using the `huggingface_hub` library in the context of a multi-commit. PR can be commented as a usual PR. However, please be aware that manually updating the PR description, changing the PR status, or pushing new commits, is not recommended as it might corrupt the commit process. Learn more about multi-commits [in this guide](https://huggingface.co/docs/huggingface_hub/main/guides/upload)._
+"""
+
+MULTI_COMMIT_PR_COMPLETION_COMMENT_TEMPLATE = """
+Multi-commit is now completed! You can ping the repo owner to review the changes. This PR can now be commented or modified without risking to corrupt it.
+
+_This is a comment posted using the `huggingface_hub` library in the context of a multi-commit. Learn more about multi-commits [in this guide](https://huggingface.co/docs/huggingface_hub/main/guides/upload)._
+"""
+
+MULTI_COMMIT_PR_CLOSING_COMMENT_TEMPLATE = """
+`create_pr=False` has been passed so PR is automatically merged.
+
+_This is a comment posted using the `huggingface_hub` library in the context of a multi-commit. Learn more about multi-commits [in this guide](https://huggingface.co/docs/huggingface_hub/main/guides/upload)._
+"""
+
+MULTI_COMMIT_PR_CLOSE_COMMENT_FAILURE_NO_CHANGES_TEMPLATE = """
+Cannot merge Pull Requests as no changes are associated. This PR will be closed automatically.
+
+_This is a comment posted using the `huggingface_hub` library in the context of a multi-commit. Learn more about multi-commits [in this guide](https://huggingface.co/docs/huggingface_hub/main/guides/upload)._
+"""
+
+MULTI_COMMIT_PR_CLOSE_COMMENT_FAILURE_BAD_REQUEST_TEMPLATE = """
+An error occurred while trying to merge the Pull Request: `{error_message}`.
+
+_This is a comment posted using the `huggingface_hub` library in the context of a multi-commit. Learn more about multi-commits [in this guide](https://huggingface.co/docs/huggingface_hub/main/guides/upload)._
+"""
+
+
+STEP_ID_REGEX = re.compile(r"- \[(?P<completed>[ |x])\].*(?P<step_id>[a-fA-F0-9]{64})", flags=re.MULTILINE)
+
+
+@experimental
+def plan_multi_commits(
+    operations: Iterable[Union[CommitOperationAdd, CommitOperationDelete]],
+    max_operations_per_commit: int = 50,
+    max_upload_size_per_commit: int = 2 * 1024 * 1024 * 1024,
+) -> Tuple[List[List[CommitOperationAdd]], List[List[CommitOperationDelete]]]:
+    """Split a list of operations in a list of commits to perform.
+
+    Implementation follows a sub-optimal (yet simple) algorithm:
+    1. Delete operations are grouped together by commits of maximum `max_operations_per_commits` operations.
+    2. All additions exceeding `max_upload_size_per_commit` are committed 1 by 1.
+    3. All remaining additions are grouped together and split each time the `max_operations_per_commit` or the
+       `max_upload_size_per_commit` limit is reached.
+
+    We do not try to optimize the splitting to get the lowest number of commits as this is a NP-hard problem (see
+    [bin packing problem](https://en.wikipedia.org/wiki/Bin_packing_problem)). For our use case, it is not problematic
+    to use a sub-optimal solution so we favored an easy-to-explain implementation.
+
+    Args:
+        operations (`List` of [`~hf_api.CommitOperation`]):
+            The list of operations to split into commits.
+        max_operations_per_commit (`int`):
+            Maximum number of operations in a single commit. Defaults to 50.
+        max_upload_size_per_commit (`int`):
+            Maximum size to upload (in bytes) in a single commit. Defaults to 2GB. Files bigger than this limit are
+            uploaded, 1 per commit.
+
+    Returns:
+        `Tuple[List[List[CommitOperationAdd]], List[List[CommitOperationDelete]]]`: a tuple. First item is a list of
+        lists of [`CommitOperationAdd`] representing the addition commits to push. The second item is a list of lists
+        of [`CommitOperationDelete`] representing the deletion commits.
+
+    <Tip warning={true}>
+
+    `plan_multi_commits` is experimental. Its API and behavior is subject to change in the future without prior notice.
+
+    </Tip>
+
+    Example:
+    ```python
+    >>> from huggingface_hub import HfApi, plan_multi_commits
+    >>> addition_commits, deletion_commits = plan_multi_commits(
+    ...     operations=[
+    ...          CommitOperationAdd(...),
+    ...          CommitOperationAdd(...),
+    ...          CommitOperationDelete(...),
+    ...          CommitOperationDelete(...),
+    ...          CommitOperationAdd(...),
+    ...     ],
+    ... )
+    >>> HfApi().create_commits_on_pr(
+    ...     repo_id="my-cool-model",
+    ...     addition_commits=addition_commits,
+    ...     deletion_commits=deletion_commits,
+    ...     (...)
+    ...     verbose=True,
+    ... )
+    ```
+
+    <Tip warning={true}>
+
+    The initial order of the operations is not guaranteed! All deletions will be performed before additions. If you are
+    not updating multiple times the same file, you are fine.
+
+    </Tip>
+    """
+    addition_commits: List[List[CommitOperationAdd]] = []
+    deletion_commits: List[List[CommitOperationDelete]] = []
+
+    additions: List[CommitOperationAdd] = []
+    additions_size = 0
+    deletions: List[CommitOperationDelete] = []
+    for op in operations:
+        if isinstance(op, CommitOperationDelete):
+            # Group delete operations together
+            deletions.append(op)
+            if len(deletions) >= max_operations_per_commit:
+                deletion_commits.append(deletions)
+                deletions = []
+
+        elif op.upload_info.size >= max_upload_size_per_commit:
+            # Upload huge files 1 by 1
+            addition_commits.append([op])
+
+        elif additions_size + op.upload_info.size < max_upload_size_per_commit:
+            # Group other additions and split if size limit is reached (either max_nb_files or max_upload_size)
+            additions.append(op)
+            additions_size += op.upload_info.size
+
+        else:
+            addition_commits.append(additions)
+            additions = [op]
+            additions_size = op.upload_info.size
+
+        if len(additions) >= max_operations_per_commit:
+            addition_commits.append(additions)
+            additions = []
+            additions_size = 0
+
+    if len(additions) > 0:
+        addition_commits.append(additions)
+    if len(deletions) > 0:
+        deletion_commits.append(deletions)
+
+    return addition_commits, deletion_commits
+
+
+@dataclass
+class MultiCommitStep:
+    """Dataclass containing a list of CommitOperation to commit at once.
+
+    A [`MultiCommitStep`] is one atomic part of a [`MultiCommitStrategy`]. Each step is identified by its own
+    deterministic ID based on the list of commit operations (hexadecimal sha256). ID is persistent between re-runs if
+    the list of commits is kept the same.
+    """
+
+    operations: List[Union[CommitOperationAdd, CommitOperationDelete]]
+
+    id: str = field(init=False)
+    completed: bool = False
+
+    def __post_init__(self) -> None:
+        if len(self.operations) == 0:
+            raise ValueError("A MultiCommitStep must have at least 1 commit operation, got 0.")
+
+        # Generate commit id
+        sha = sha256()
+        for op in self.operations:
+            if isinstance(op, CommitOperationAdd):
+                sha.update(b"ADD")
+                sha.update(op.path_in_repo.encode())
+                sha.update(op.upload_info.sha256)
+            elif isinstance(op, CommitOperationDelete):
+                sha.update(b"DELETE")
+                sha.update(op.path_in_repo.encode())
+                sha.update(str(op.is_folder).encode())
+            else:
+                NotImplementedError()
+        self.id = sha.hexdigest()
+
+    def __str__(self) -> str:
+        """Format a step for PR description.
+
+        Formatting can be changed in the future as long as it is single line, starts with `- [ ]`/`- [x]` and contains
+        `self.id`. Must be able to match `STEP_ID_REGEX`.
+        """
+        additions = [op for op in self.operations if isinstance(op, CommitOperationAdd)]
+        file_deletions = [op for op in self.operations if isinstance(op, CommitOperationDelete) and not op.is_folder]
+        folder_deletions = [op for op in self.operations if isinstance(op, CommitOperationDelete) and op.is_folder]
+        if len(additions) > 0:
+            return (
+                f"- [{'x' if self.completed else ' '}] Upload {len(additions)} file(s) "
+                f"totalling {_format_size(sum(add.upload_info.size for add in additions))}"
+                f" ({self.id})"
+            )
+        else:
+            return (
+                f"- [{'x' if self.completed else ' '}] Delete {len(file_deletions)} file(s) and"
+                f" {len(folder_deletions)} folder(s) ({self.id})"
+            )
+
+
+@dataclass
+class MultiCommitStrategy:
+    """Dataclass containing a list of [`MultiCommitStep`] to commit iteratively.
+
+    A strategy is identified by its own deterministic ID based on the list of its steps (hexadecimal sha256). ID is
+    persistent between re-runs if the list of commits is kept the same.
+    """
+
+    addition_commits: List[MultiCommitStep]
+    deletion_commits: List[MultiCommitStep]
+
+    id: str = field(init=False)
+    all_steps: Set[str] = field(init=False)
+
+    def __post_init__(self) -> None:
+        self.all_steps = {step.id for step in self.addition_commits + self.deletion_commits}
+        if len(self.all_steps) < len(self.addition_commits) + len(self.deletion_commits):
+            raise ValueError("Got duplicate commits in MultiCommitStrategy. All commits must be unique.")
+
+        if len(self.all_steps) == 0:
+            raise ValueError("A MultiCommitStrategy must have at least 1 commit, got 0.")
+
+        # Generate strategy id
+        sha = sha256()
+        for step in self.addition_commits + self.deletion_commits:
+            sha.update("new step".encode())
+            sha.update(step.id.encode())
+        self.id = sha.hexdigest()
+
+
+def multi_commit_create_pull_request(
+    api: "HfApi",
+    repo_id: str,
+    commit_message: str,
+    commit_description: Optional[str],
+    strategy: MultiCommitStrategy,
+    repo_type: Optional[str],
+    token: Union[str, bool, None] = None,
+) -> DiscussionWithDetails:
+    return api.create_pull_request(
+        repo_id=repo_id,
+        title=f"[WIP] {commit_message} (multi-commit {strategy.id})",
+        description=multi_commit_generate_comment(
+            commit_message=commit_message, commit_description=commit_description, strategy=strategy
+        ),
+        token=token,
+        repo_type=repo_type,
+    )
+
+
+def multi_commit_generate_comment(
+    commit_message: str,
+    commit_description: Optional[str],
+    strategy: MultiCommitStrategy,
+) -> str:
+    return MULTI_COMMIT_PR_DESCRIPTION_TEMPLATE.format(
+        commit_message=commit_message,
+        commit_description=commit_description or "",
+        multi_commit_id=strategy.id,
+        multi_commit_strategy="\n".join(
+            str(commit) for commit in strategy.deletion_commits + strategy.addition_commits
+        ),
+    )
+
+
+def multi_commit_parse_pr_description(description: str) -> Set[str]:
+    return {match[1] for match in STEP_ID_REGEX.findall(description)}

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/_snapshot_download.py

@@ -0,0 +1,304 @@
+import os
+from pathlib import Path
+from typing import Dict, List, Literal, Optional, Union
+
+import requests
+from tqdm.auto import tqdm as base_tqdm
+from tqdm.contrib.concurrent import thread_map
+
+from . import constants
+from .errors import GatedRepoError, LocalEntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
+from .file_download import REGEX_COMMIT_HASH, hf_hub_download, repo_folder_name
+from .hf_api import DatasetInfo, HfApi, ModelInfo, SpaceInfo
+from .utils import OfflineModeIsEnabled, filter_repo_objects, logging, validate_hf_hub_args
+from .utils import tqdm as hf_tqdm
+
+
+logger = logging.get_logger(__name__)
+
+
+@validate_hf_hub_args
+def snapshot_download(
+    repo_id: str,
+    *,
+    repo_type: Optional[str] = None,
+    revision: Optional[str] = None,
+    cache_dir: Union[str, Path, None] = None,
+    local_dir: Union[str, Path, None] = None,
+    library_name: Optional[str] = None,
+    library_version: Optional[str] = None,
+    user_agent: Optional[Union[Dict, str]] = None,
+    proxies: Optional[Dict] = None,
+    etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+    force_download: bool = False,
+    token: Optional[Union[bool, str]] = None,
+    local_files_only: bool = False,
+    allow_patterns: Optional[Union[List[str], str]] = None,
+    ignore_patterns: Optional[Union[List[str], str]] = None,
+    max_workers: int = 8,
+    tqdm_class: Optional[base_tqdm] = None,
+    headers: Optional[Dict[str, str]] = None,
+    endpoint: Optional[str] = None,
+    # Deprecated args
+    local_dir_use_symlinks: Union[bool, Literal["auto"]] = "auto",
+    resume_download: Optional[bool] = None,
+) -> str:
+    """Download repo files.
+
+    Download a whole snapshot of a repo's files at the specified revision. This is useful when you want all files from
+    a repo, because you don't know which ones you will need a priori. All files are nested inside a folder in order
+    to keep their actual filename relative to that folder. You can also filter which files to download using
+    `allow_patterns` and `ignore_patterns`.
+
+    If `local_dir` is provided, the file structure from the repo will be replicated in this location. When using this
+    option, the `cache_dir` will not be used and a `.cache/huggingface/` folder will be created at the root of `local_dir`
+    to store some metadata related to the downloaded files. While this mechanism is not as robust as the main
+    cache-system, it's optimized for regularly pulling the latest version of a repository.
+
+    An alternative would be to clone the repo but this requires git and git-lfs to be installed and properly
+    configured. It is also not possible to filter which files to download when cloning a repository using git.
+
+    Args:
+        repo_id (`str`):
+            A user or an organization name and a repo name separated by a `/`.
+        repo_type (`str`, *optional*):
+            Set to `"dataset"` or `"space"` if downloading from a dataset or space,
+            `None` or `"model"` if downloading from a model. Default is `None`.
+        revision (`str`, *optional*):
+            An optional Git revision id which can be a branch name, a tag, or a
+            commit hash.
+        cache_dir (`str`, `Path`, *optional*):
+            Path to the folder where cached files are stored.
+        local_dir (`str` or `Path`, *optional*):
+            If provided, the downloaded files will be placed under this directory.
+        library_name (`str`, *optional*):
+            The name of the library to which the object corresponds.
+        library_version (`str`, *optional*):
+            The version of the library.
+        user_agent (`str`, `dict`, *optional*):
+            The user-agent info in the form of a dictionary or a string.
+        proxies (`dict`, *optional*):
+            Dictionary mapping protocol to the URL of the proxy passed to
+            `requests.request`.
+        etag_timeout (`float`, *optional*, defaults to `10`):
+            When fetching ETag, how many seconds to wait for the server to send
+            data before giving up which is passed to `requests.request`.
+        force_download (`bool`, *optional*, defaults to `False`):
+            Whether the file should be downloaded even if it already exists in the local cache.
+        token (`str`, `bool`, *optional*):
+            A token to be used for the download.
+                - If `True`, the token is read from the HuggingFace config
+                  folder.
+                - If a string, it's used as the authentication token.
+        headers (`dict`, *optional*):
+            Additional headers to include in the request. Those headers take precedence over the others.
+        local_files_only (`bool`, *optional*, defaults to `False`):
+            If `True`, avoid downloading the file and return the path to the
+            local cached file if it exists.
+        allow_patterns (`List[str]` or `str`, *optional*):
+            If provided, only files matching at least one pattern are downloaded.
+        ignore_patterns (`List[str]` or `str`, *optional*):
+            If provided, files matching any of the patterns are not downloaded.
+        max_workers (`int`, *optional*):
+            Number of concurrent threads to download files (1 thread = 1 file download).
+            Defaults to 8.
+        tqdm_class (`tqdm`, *optional*):
+            If provided, overwrites the default behavior for the progress bar. Passed
+            argument must inherit from `tqdm.auto.tqdm` or at least mimic its behavior.
+            Note that the `tqdm_class` is not passed to each individual download.
+            Defaults to the custom HF progress bar that can be disabled by setting
+            `HF_HUB_DISABLE_PROGRESS_BARS` environment variable.
+
+    Returns:
+        `str`: folder path of the repo snapshot.
+
+    Raises:
+        [`~utils.RepositoryNotFoundError`]
+            If the repository to download from cannot be found. This may be because it doesn't exist,
+            or because it is set to `private` and you do not have access.
+        [`~utils.RevisionNotFoundError`]
+            If the revision to download from cannot be found.
+        [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
+            If `token=True` and the token cannot be found.
+        [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError) if
+            ETag cannot be determined.
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            if some parameter value is invalid.
+    """
+    if cache_dir is None:
+        cache_dir = constants.HF_HUB_CACHE
+    if revision is None:
+        revision = constants.DEFAULT_REVISION
+    if isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    if repo_type is None:
+        repo_type = "model"
+    if repo_type not in constants.REPO_TYPES:
+        raise ValueError(f"Invalid repo type: {repo_type}. Accepted repo types are: {str(constants.REPO_TYPES)}")
+
+    storage_folder = os.path.join(cache_dir, repo_folder_name(repo_id=repo_id, repo_type=repo_type))
+
+    repo_info: Union[ModelInfo, DatasetInfo, SpaceInfo, None] = None
+    api_call_error: Optional[Exception] = None
+    if not local_files_only:
+        # try/except logic to handle different errors => taken from `hf_hub_download`
+        try:
+            # if we have internet connection we want to list files to download
+            api = HfApi(
+                library_name=library_name,
+                library_version=library_version,
+                user_agent=user_agent,
+                endpoint=endpoint,
+                headers=headers,
+            )
+            repo_info = api.repo_info(repo_id=repo_id, repo_type=repo_type, revision=revision, token=token)
+        except (requests.exceptions.SSLError, requests.exceptions.ProxyError):
+            # Actually raise for those subclasses of ConnectionError
+            raise
+        except (
+            requests.exceptions.ConnectionError,
+            requests.exceptions.Timeout,
+            OfflineModeIsEnabled,
+        ) as error:
+            # Internet connection is down
+            # => will try to use local files only
+            api_call_error = error
+            pass
+        except RevisionNotFoundError:
+            # The repo was found but the revision doesn't exist on the Hub (never existed or got deleted)
+            raise
+        except requests.HTTPError as error:
+            # Multiple reasons for an http error:
+            # - Repository is private and invalid/missing token sent
+            # - Repository is gated and invalid/missing token sent
+            # - Hub is down (error 500 or 504)
+            # => let's switch to 'local_files_only=True' to check if the files are already cached.
+            #    (if it's not the case, the error will be re-raised)
+            api_call_error = error
+            pass
+
+    # At this stage, if `repo_info` is None it means either:
+    # - internet connection is down
+    # - internet connection is deactivated (local_files_only=True or HF_HUB_OFFLINE=True)
+    # - repo is private/gated and invalid/missing token sent
+    # - Hub is down
+    # => let's look if we can find the appropriate folder in the cache:
+    #    - if the specified revision is a commit hash, look inside "snapshots".
+    #    - f the specified revision is a branch or tag, look inside "refs".
+    # => if local_dir is not None, we will return the path to the local folder if it exists.
+    if repo_info is None:
+        # Try to get which commit hash corresponds to the specified revision
+        commit_hash = None
+        if REGEX_COMMIT_HASH.match(revision):
+            commit_hash = revision
+        else:
+            ref_path = os.path.join(storage_folder, "refs", revision)
+            if os.path.exists(ref_path):
+                # retrieve commit_hash from refs file
+                with open(ref_path) as f:
+                    commit_hash = f.read()
+
+        # Try to locate snapshot folder for this commit hash
+        if commit_hash is not None:
+            snapshot_folder = os.path.join(storage_folder, "snapshots", commit_hash)
+            if os.path.exists(snapshot_folder):
+                # Snapshot folder exists => let's return it
+                # (but we can't check if all the files are actually there)
+                return snapshot_folder
+        # If local_dir is not None, return it if it exists and is not empty
+        if local_dir is not None:
+            local_dir = Path(local_dir)
+            if local_dir.is_dir() and any(local_dir.iterdir()):
+                logger.warning(
+                    f"Returning existing local_dir `{local_dir}` as remote repo cannot be accessed in `snapshot_download` ({api_call_error})."
+                )
+                return str(local_dir.resolve())
+        # If we couldn't find the appropriate folder on disk, raise an error.
+        if local_files_only:
+            raise LocalEntryNotFoundError(
+                "Cannot find an appropriate cached snapshot folder for the specified revision on the local disk and "
+                "outgoing traffic has been disabled. To enable repo look-ups and downloads online, pass "
+                "'local_files_only=False' as input."
+            )
+        elif isinstance(api_call_error, OfflineModeIsEnabled):
+            raise LocalEntryNotFoundError(
+                "Cannot find an appropriate cached snapshot folder for the specified revision on the local disk and "
+                "outgoing traffic has been disabled. To enable repo look-ups and downloads online, set "
+                "'HF_HUB_OFFLINE=0' as environment variable."
+            ) from api_call_error
+        elif isinstance(api_call_error, RepositoryNotFoundError) or isinstance(api_call_error, GatedRepoError):
+            # Repo not found => let's raise the actual error
+            raise api_call_error
+        else:
+            # Otherwise: most likely a connection issue or Hub downtime => let's warn the user
+            raise LocalEntryNotFoundError(
+                "An error happened while trying to locate the files on the Hub and we cannot find the appropriate"
+                " snapshot folder for the specified revision on the local disk. Please check your internet connection"
+                " and try again."
+            ) from api_call_error
+
+    # At this stage, internet connection is up and running
+    # => let's download the files!
+    assert repo_info.sha is not None, "Repo info returned from server must have a revision sha."
+    assert repo_info.siblings is not None, "Repo info returned from server must have a siblings list."
+    filtered_repo_files = list(
+        filter_repo_objects(
+            items=[f.rfilename for f in repo_info.siblings],
+            allow_patterns=allow_patterns,
+            ignore_patterns=ignore_patterns,
+        )
+    )
+    commit_hash = repo_info.sha
+    snapshot_folder = os.path.join(storage_folder, "snapshots", commit_hash)
+    # if passed revision is not identical to commit_hash
+    # then revision has to be a branch name or tag name.
+    # In that case store a ref.
+    if revision != commit_hash:
+        ref_path = os.path.join(storage_folder, "refs", revision)
+        os.makedirs(os.path.dirname(ref_path), exist_ok=True)
+        with open(ref_path, "w") as f:
+            f.write(commit_hash)
+
+    # we pass the commit_hash to hf_hub_download
+    # so no network call happens if we already
+    # have the file locally.
+    def _inner_hf_hub_download(repo_file: str):
+        return hf_hub_download(
+            repo_id,
+            filename=repo_file,
+            repo_type=repo_type,
+            revision=commit_hash,
+            endpoint=endpoint,
+            cache_dir=cache_dir,
+            local_dir=local_dir,
+            local_dir_use_symlinks=local_dir_use_symlinks,
+            library_name=library_name,
+            library_version=library_version,
+            user_agent=user_agent,
+            proxies=proxies,
+            etag_timeout=etag_timeout,
+            resume_download=resume_download,
+            force_download=force_download,
+            token=token,
+            headers=headers,
+        )
+
+    if constants.HF_HUB_ENABLE_HF_TRANSFER:
+        # when using hf_transfer we don't want extra parallelism
+        # from the one hf_transfer provides
+        for file in filtered_repo_files:
+            _inner_hf_hub_download(file)
+    else:
+        thread_map(
+            _inner_hf_hub_download,
+            filtered_repo_files,
+            desc=f"Fetching {len(filtered_repo_files)} files",
+            max_workers=max_workers,
+            # User can use its own tqdm class or the default one from `huggingface_hub.utils`
+            tqdm_class=tqdm_class or hf_tqdm,
+        )
+
+    if local_dir is not None:
+        return str(os.path.realpath(local_dir))
+    return snapshot_folder

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/_space_api.py

@@ -0,0 +1,160 @@
+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from dataclasses import dataclass
+from datetime import datetime
+from enum import Enum
+from typing import Dict, Optional
+
+from huggingface_hub.utils import parse_datetime
+
+
+class SpaceStage(str, Enum):
+    """
+    Enumeration of possible stage of a Space on the Hub.
+
+    Value can be compared to a string:
+    ```py
+    assert SpaceStage.BUILDING == "BUILDING"
+    ```
+
+    Taken from https://github.com/huggingface/moon-landing/blob/main/server/repo_types/SpaceInfo.ts#L61 (private url).
+    """
+
+    # Copied from moon-landing > server > repo_types > SpaceInfo.ts (private repo)
+    NO_APP_FILE = "NO_APP_FILE"
+    CONFIG_ERROR = "CONFIG_ERROR"
+    BUILDING = "BUILDING"
+    BUILD_ERROR = "BUILD_ERROR"
+    RUNNING = "RUNNING"
+    RUNNING_BUILDING = "RUNNING_BUILDING"
+    RUNTIME_ERROR = "RUNTIME_ERROR"
+    DELETING = "DELETING"
+    STOPPED = "STOPPED"
+    PAUSED = "PAUSED"
+
+
+class SpaceHardware(str, Enum):
+    """
+    Enumeration of hardwares available to run your Space on the Hub.
+
+    Value can be compared to a string:
+    ```py
+    assert SpaceHardware.CPU_BASIC == "cpu-basic"
+    ```
+
+    Taken from https://github.com/huggingface/moon-landing/blob/main/server/repo_types/SpaceInfo.ts#L73 (private url).
+    """
+
+    CPU_BASIC = "cpu-basic"
+    CPU_UPGRADE = "cpu-upgrade"
+    T4_SMALL = "t4-small"
+    T4_MEDIUM = "t4-medium"
+    L4X1 = "l4x1"
+    L4X4 = "l4x4"
+    ZERO_A10G = "zero-a10g"
+    A10G_SMALL = "a10g-small"
+    A10G_LARGE = "a10g-large"
+    A10G_LARGEX2 = "a10g-largex2"
+    A10G_LARGEX4 = "a10g-largex4"
+    A100_LARGE = "a100-large"
+    V5E_1X1 = "v5e-1x1"
+    V5E_2X2 = "v5e-2x2"
+    V5E_2X4 = "v5e-2x4"
+
+
+class SpaceStorage(str, Enum):
+    """
+    Enumeration of persistent storage available for your Space on the Hub.
+
+    Value can be compared to a string:
+    ```py
+    assert SpaceStorage.SMALL == "small"
+    ```
+
+    Taken from https://github.com/huggingface/moon-landing/blob/main/server/repo_types/SpaceHardwareFlavor.ts#L24 (private url).
+    """
+
+    SMALL = "small"
+    MEDIUM = "medium"
+    LARGE = "large"
+
+
+@dataclass
+class SpaceRuntime:
+    """
+    Contains information about the current runtime of a Space.
+
+    Args:
+        stage (`str`):
+            Current stage of the space. Example: RUNNING.
+        hardware (`str` or `None`):
+            Current hardware of the space. Example: "cpu-basic". Can be `None` if Space
+            is `BUILDING` for the first time.
+        requested_hardware (`str` or `None`):
+            Requested hardware. Can be different than `hardware` especially if the request
+            has just been made. Example: "t4-medium". Can be `None` if no hardware has
+            been requested yet.
+        sleep_time (`int` or `None`):
+            Number of seconds the Space will be kept alive after the last request. By default (if value is `None`), the
+            Space will never go to sleep if it's running on an upgraded hardware, while it will go to sleep after 48
+            hours on a free 'cpu-basic' hardware. For more details, see https://huggingface.co/docs/hub/spaces-gpus#sleep-time.
+        raw (`dict`):
+            Raw response from the server. Contains more information about the Space
+            runtime like number of replicas, number of cpu, memory size,...
+    """
+
+    stage: SpaceStage
+    hardware: Optional[SpaceHardware]
+    requested_hardware: Optional[SpaceHardware]
+    sleep_time: Optional[int]
+    storage: Optional[SpaceStorage]
+    raw: Dict
+
+    def __init__(self, data: Dict) -> None:
+        self.stage = data["stage"]
+        self.hardware = data.get("hardware", {}).get("current")
+        self.requested_hardware = data.get("hardware", {}).get("requested")
+        self.sleep_time = data.get("gcTimeout")
+        self.storage = data.get("storage")
+        self.raw = data
+
+
+@dataclass
+class SpaceVariable:
+    """
+    Contains information about the current variables of a Space.
+
+    Args:
+        key (`str`):
+            Variable key. Example: `"MODEL_REPO_ID"`
+        value (`str`):
+            Variable value. Example: `"the_model_repo_id"`.
+        description (`str` or None):
+            Description of the variable. Example: `"Model Repo ID of the implemented model"`.
+        updatedAt (`datetime` or None):
+            datetime of the last update of the variable (if the variable has been updated at least once).
+    """
+
+    key: str
+    value: str
+    description: Optional[str]
+    updated_at: Optional[datetime]
+
+    def __init__(self, key: str, values: Dict) -> None:
+        self.key = key
+        self.value = values["value"]
+        self.description = values.get("description")
+        updated_at = values.get("updatedAt")
+        self.updated_at = parse_datetime(updated_at) if updated_at is not None else None

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/_upload_large_folder.py

@@ -0,0 +1,621 @@
+# coding=utf-8
+# Copyright 2024-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import enum
+import logging
+import os
+import queue
+import shutil
+import sys
+import threading
+import time
+import traceback
+from datetime import datetime
+from pathlib import Path
+from threading import Lock
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+
+from . import constants
+from ._commit_api import CommitOperationAdd, UploadInfo, _fetch_upload_modes
+from ._local_folder import LocalUploadFileMetadata, LocalUploadFilePaths, get_local_upload_paths, read_upload_metadata
+from .constants import DEFAULT_REVISION, REPO_TYPES
+from .utils import DEFAULT_IGNORE_PATTERNS, filter_repo_objects, tqdm
+from .utils._cache_manager import _format_size
+from .utils.sha import sha_fileobj
+
+
+if TYPE_CHECKING:
+    from .hf_api import HfApi
+
+logger = logging.getLogger(__name__)
+
+WAITING_TIME_IF_NO_TASKS = 10  # seconds
+MAX_NB_REGULAR_FILES_PER_COMMIT = 75
+MAX_NB_LFS_FILES_PER_COMMIT = 150
+
+
+def upload_large_folder_internal(
+    api: "HfApi",
+    repo_id: str,
+    folder_path: Union[str, Path],
+    *,
+    repo_type: str,  # Repo type is required!
+    revision: Optional[str] = None,
+    private: bool = False,
+    allow_patterns: Optional[Union[List[str], str]] = None,
+    ignore_patterns: Optional[Union[List[str], str]] = None,
+    num_workers: Optional[int] = None,
+    print_report: bool = True,
+    print_report_every: int = 60,
+):
+    """Upload a large folder to the Hub in the most resilient way possible.
+
+    See [`HfApi.upload_large_folder`] for the full documentation.
+    """
+    # 1. Check args and setup
+    if repo_type is None:
+        raise ValueError(
+            "For large uploads, `repo_type` is explicitly required. Please set it to `model`, `dataset` or `space`."
+            " If you are using the CLI, pass it as `--repo-type=model`."
+        )
+    if repo_type not in REPO_TYPES:
+        raise ValueError(f"Invalid repo type, must be one of {REPO_TYPES}")
+    if revision is None:
+        revision = DEFAULT_REVISION
+
+    folder_path = Path(folder_path).expanduser().resolve()
+    if not folder_path.is_dir():
+        raise ValueError(f"Provided path: '{folder_path}' is not a directory")
+
+    if ignore_patterns is None:
+        ignore_patterns = []
+    elif isinstance(ignore_patterns, str):
+        ignore_patterns = [ignore_patterns]
+    ignore_patterns += DEFAULT_IGNORE_PATTERNS
+
+    if num_workers is None:
+        nb_cores = os.cpu_count() or 1
+        num_workers = max(nb_cores - 2, 2)  # Use all but 2 cores, or at least 2 cores
+
+    # 2. Create repo if missing
+    repo_url = api.create_repo(repo_id=repo_id, repo_type=repo_type, private=private, exist_ok=True)
+    logger.info(f"Repo created: {repo_url}")
+    repo_id = repo_url.repo_id
+
+    # 3. List files to upload
+    filtered_paths_list = filter_repo_objects(
+        (path.relative_to(folder_path).as_posix() for path in folder_path.glob("**/*") if path.is_file()),
+        allow_patterns=allow_patterns,
+        ignore_patterns=ignore_patterns,
+    )
+    paths_list = [get_local_upload_paths(folder_path, relpath) for relpath in filtered_paths_list]
+    logger.info(f"Found {len(paths_list)} candidate files to upload")
+
+    # Read metadata for each file
+    items = [
+        (paths, read_upload_metadata(folder_path, paths.path_in_repo))
+        for paths in tqdm(paths_list, desc="Recovering from metadata files")
+    ]
+
+    # 4. Start workers
+    status = LargeUploadStatus(items)
+    threads = [
+        threading.Thread(
+            target=_worker_job,
+            kwargs={
+                "status": status,
+                "api": api,
+                "repo_id": repo_id,
+                "repo_type": repo_type,
+                "revision": revision,
+            },
+        )
+        for _ in range(num_workers)
+    ]
+
+    for thread in threads:
+        thread.start()
+
+    # 5. Print regular reports
+    if print_report:
+        print("\n\n" + status.current_report())
+    last_report_ts = time.time()
+    while True:
+        time.sleep(1)
+        if time.time() - last_report_ts >= print_report_every:
+            if print_report:
+                _print_overwrite(status.current_report())
+            last_report_ts = time.time()
+        if status.is_done():
+            logging.info("Is done: exiting main loop")
+            break
+
+    for thread in threads:
+        thread.join()
+
+    logger.info(status.current_report())
+    logging.info("Upload is complete!")
+
+
+####################
+# Logic to manage workers and synchronize tasks
+####################
+
+
+class WorkerJob(enum.Enum):
+    SHA256 = enum.auto()
+    GET_UPLOAD_MODE = enum.auto()
+    PREUPLOAD_LFS = enum.auto()
+    COMMIT = enum.auto()
+    WAIT = enum.auto()  # if no tasks are available but we don't want to exit
+
+
+JOB_ITEM_T = Tuple[LocalUploadFilePaths, LocalUploadFileMetadata]
+
+
+class LargeUploadStatus:
+    """Contains information, queues and tasks for a large upload process."""
+
+    def __init__(self, items: List[JOB_ITEM_T]):
+        self.items = items
+        self.queue_sha256: "queue.Queue[JOB_ITEM_T]" = queue.Queue()
+        self.queue_get_upload_mode: "queue.Queue[JOB_ITEM_T]" = queue.Queue()
+        self.queue_preupload_lfs: "queue.Queue[JOB_ITEM_T]" = queue.Queue()
+        self.queue_commit: "queue.Queue[JOB_ITEM_T]" = queue.Queue()
+        self.lock = Lock()
+
+        self.nb_workers_sha256: int = 0
+        self.nb_workers_get_upload_mode: int = 0
+        self.nb_workers_preupload_lfs: int = 0
+        self.nb_workers_commit: int = 0
+        self.nb_workers_waiting: int = 0
+        self.last_commit_attempt: Optional[float] = None
+
+        self._started_at = datetime.now()
+
+        # Setup queues
+        for item in self.items:
+            paths, metadata = item
+            if metadata.sha256 is None:
+                self.queue_sha256.put(item)
+            elif metadata.upload_mode is None:
+                self.queue_get_upload_mode.put(item)
+            elif metadata.upload_mode == "lfs" and not metadata.is_uploaded:
+                self.queue_preupload_lfs.put(item)
+            elif not metadata.is_committed:
+                self.queue_commit.put(item)
+            else:
+                logger.debug(f"Skipping file {paths.path_in_repo} (already uploaded and committed)")
+
+    def current_report(self) -> str:
+        """Generate a report of the current status of the large upload."""
+        nb_hashed = 0
+        size_hashed = 0
+        nb_preuploaded = 0
+        nb_lfs = 0
+        nb_lfs_unsure = 0
+        size_preuploaded = 0
+        nb_committed = 0
+        size_committed = 0
+        total_size = 0
+        ignored_files = 0
+        total_files = 0
+
+        with self.lock:
+            for _, metadata in self.items:
+                if metadata.should_ignore:
+                    ignored_files += 1
+                    continue
+                total_size += metadata.size
+                total_files += 1
+                if metadata.sha256 is not None:
+                    nb_hashed += 1
+                    size_hashed += metadata.size
+                if metadata.upload_mode == "lfs":
+                    nb_lfs += 1
+                if metadata.upload_mode is None:
+                    nb_lfs_unsure += 1
+                if metadata.is_uploaded:
+                    nb_preuploaded += 1
+                    size_preuploaded += metadata.size
+                if metadata.is_committed:
+                    nb_committed += 1
+                    size_committed += metadata.size
+            total_size_str = _format_size(total_size)
+
+            now = datetime.now()
+            now_str = now.strftime("%Y-%m-%d %H:%M:%S")
+            elapsed = now - self._started_at
+            elapsed_str = str(elapsed).split(".")[0]  # remove milliseconds
+
+            message = "\n" + "-" * 10
+            message += f" {now_str} ({elapsed_str}) "
+            message += "-" * 10 + "\n"
+
+            message += "Files:   "
+            message += f"hashed {nb_hashed}/{total_files} ({_format_size(size_hashed)}/{total_size_str}) | "
+            message += f"pre-uploaded: {nb_preuploaded}/{nb_lfs} ({_format_size(size_preuploaded)}/{total_size_str})"
+            if nb_lfs_unsure > 0:
+                message += f" (+{nb_lfs_unsure} unsure)"
+            message += f" | committed: {nb_committed}/{total_files} ({_format_size(size_committed)}/{total_size_str})"
+            message += f" | ignored: {ignored_files}\n"
+
+            message += "Workers: "
+            message += f"hashing: {self.nb_workers_sha256} | "
+            message += f"get upload mode: {self.nb_workers_get_upload_mode} | "
+            message += f"pre-uploading: {self.nb_workers_preupload_lfs} | "
+            message += f"committing: {self.nb_workers_commit} | "
+            message += f"waiting: {self.nb_workers_waiting}\n"
+            message += "-" * 51
+
+            return message
+
+    def is_done(self) -> bool:
+        with self.lock:
+            return all(metadata.is_committed or metadata.should_ignore for _, metadata in self.items)
+
+
+def _worker_job(
+    status: LargeUploadStatus,
+    api: "HfApi",
+    repo_id: str,
+    repo_type: str,
+    revision: str,
+):
+    """
+    Main process for a worker. The worker will perform tasks based on the priority list until all files are uploaded
+    and committed. If no tasks are available, the worker will wait for 10 seconds before checking again.
+
+    If a task fails for any reason, the item(s) are put back in the queue for another worker to pick up.
+
+    Read `upload_large_folder` docstring for more information on how tasks are prioritized.
+    """
+    while True:
+        next_job: Optional[Tuple[WorkerJob, List[JOB_ITEM_T]]] = None
+
+        # Determine next task
+        next_job = _determine_next_job(status)
+        if next_job is None:
+            return
+        job, items = next_job
+
+        # Perform task
+        if job == WorkerJob.SHA256:
+            item = items[0]  # single item
+            try:
+                _compute_sha256(item)
+                status.queue_get_upload_mode.put(item)
+            except KeyboardInterrupt:
+                raise
+            except Exception as e:
+                logger.error(f"Failed to compute sha256: {e}")
+                traceback.format_exc()
+                status.queue_sha256.put(item)
+
+            with status.lock:
+                status.nb_workers_sha256 -= 1
+
+        elif job == WorkerJob.GET_UPLOAD_MODE:
+            try:
+                _get_upload_mode(items, api=api, repo_id=repo_id, repo_type=repo_type, revision=revision)
+            except KeyboardInterrupt:
+                raise
+            except Exception as e:
+                logger.error(f"Failed to get upload mode: {e}")
+                traceback.format_exc()
+
+            # Items are either:
+            # - dropped (if should_ignore)
+            # - put in LFS queue (if LFS)
+            # - put in commit queue (if regular)
+            # - or put back (if error occurred).
+            for item in items:
+                _, metadata = item
+                if metadata.should_ignore:
+                    continue
+                if metadata.upload_mode == "lfs":
+                    status.queue_preupload_lfs.put(item)
+                elif metadata.upload_mode == "regular":
+                    status.queue_commit.put(item)
+                else:
+                    status.queue_get_upload_mode.put(item)
+
+            with status.lock:
+                status.nb_workers_get_upload_mode -= 1
+
+        elif job == WorkerJob.PREUPLOAD_LFS:
+            item = items[0]  # single item
+            try:
+                _preupload_lfs(item, api=api, repo_id=repo_id, repo_type=repo_type, revision=revision)
+                status.queue_commit.put(item)
+            except KeyboardInterrupt:
+                raise
+            except Exception as e:
+                logger.error(f"Failed to preupload LFS: {e}")
+                traceback.format_exc()
+                status.queue_preupload_lfs.put(item)
+
+            with status.lock:
+                status.nb_workers_preupload_lfs -= 1
+
+        elif job == WorkerJob.COMMIT:
+            try:
+                _commit(items, api=api, repo_id=repo_id, repo_type=repo_type, revision=revision)
+            except KeyboardInterrupt:
+                raise
+            except Exception as e:
+                logger.error(f"Failed to commit: {e}")
+                traceback.format_exc()
+                for item in items:
+                    status.queue_commit.put(item)
+            with status.lock:
+                status.last_commit_attempt = time.time()
+                status.nb_workers_commit -= 1
+
+        elif job == WorkerJob.WAIT:
+            time.sleep(WAITING_TIME_IF_NO_TASKS)
+            with status.lock:
+                status.nb_workers_waiting -= 1
+
+
+def _determine_next_job(status: LargeUploadStatus) -> Optional[Tuple[WorkerJob, List[JOB_ITEM_T]]]:
+    with status.lock:
+        # 1. Commit if more than 5 minutes since last commit attempt (and at least 1 file)
+        if (
+            status.nb_workers_commit == 0
+            and status.queue_commit.qsize() > 0
+            and status.last_commit_attempt is not None
+            and time.time() - status.last_commit_attempt > 5 * 60
+        ):
+            status.nb_workers_commit += 1
+            logger.debug("Job: commit (more than 5 minutes since last commit attempt)")
+            return (WorkerJob.COMMIT, _get_items_to_commit(status.queue_commit))
+
+        # 2. Commit if at least 100 files are ready to commit
+        elif status.nb_workers_commit == 0 and status.queue_commit.qsize() >= 150:
+            status.nb_workers_commit += 1
+            logger.debug("Job: commit (>100 files ready)")
+            return (WorkerJob.COMMIT, _get_items_to_commit(status.queue_commit))
+
+        # 3. Get upload mode if at least 10 files
+        elif status.queue_get_upload_mode.qsize() >= 10:
+            status.nb_workers_get_upload_mode += 1
+            logger.debug("Job: get upload mode (>10 files ready)")
+            return (WorkerJob.GET_UPLOAD_MODE, _get_n(status.queue_get_upload_mode, 50))
+
+        # 4. Preupload LFS file if at least 1 file and no worker is preuploading LFS
+        elif status.queue_preupload_lfs.qsize() > 0 and status.nb_workers_preupload_lfs == 0:
+            status.nb_workers_preupload_lfs += 1
+            logger.debug("Job: preupload LFS (no other worker preuploading LFS)")
+            return (WorkerJob.PREUPLOAD_LFS, _get_one(status.queue_preupload_lfs))
+
+        # 5. Compute sha256 if at least 1 file and no worker is computing sha256
+        elif status.queue_sha256.qsize() > 0 and status.nb_workers_sha256 == 0:
+            status.nb_workers_sha256 += 1
+            logger.debug("Job: sha256 (no other worker computing sha256)")
+            return (WorkerJob.SHA256, _get_one(status.queue_sha256))
+
+        # 6. Get upload mode if at least 1 file and no worker is getting upload mode
+        elif status.queue_get_upload_mode.qsize() > 0 and status.nb_workers_get_upload_mode == 0:
+            status.nb_workers_get_upload_mode += 1
+            logger.debug("Job: get upload mode (no other worker getting upload mode)")
+            return (WorkerJob.GET_UPLOAD_MODE, _get_n(status.queue_get_upload_mode, 50))
+
+        # 7. Preupload LFS file if at least 1 file
+        #    Skip if hf_transfer is enabled and there is already a worker preuploading LFS
+        elif status.queue_preupload_lfs.qsize() > 0 and (
+            status.nb_workers_preupload_lfs == 0 or not constants.HF_HUB_ENABLE_HF_TRANSFER
+        ):
+            status.nb_workers_preupload_lfs += 1
+            logger.debug("Job: preupload LFS")
+            return (WorkerJob.PREUPLOAD_LFS, _get_one(status.queue_preupload_lfs))
+
+        # 8. Compute sha256 if at least 1 file
+        elif status.queue_sha256.qsize() > 0:
+            status.nb_workers_sha256 += 1
+            logger.debug("Job: sha256")
+            return (WorkerJob.SHA256, _get_one(status.queue_sha256))
+
+        # 9. Get upload mode if at least 1 file
+        elif status.queue_get_upload_mode.qsize() > 0:
+            status.nb_workers_get_upload_mode += 1
+            logger.debug("Job: get upload mode")
+            return (WorkerJob.GET_UPLOAD_MODE, _get_n(status.queue_get_upload_mode, 50))
+
+        # 10. Commit if at least 1 file and 1 min since last commit attempt
+        elif (
+            status.nb_workers_commit == 0
+            and status.queue_commit.qsize() > 0
+            and status.last_commit_attempt is not None
+            and time.time() - status.last_commit_attempt > 1 * 60
+        ):
+            status.nb_workers_commit += 1
+            logger.debug("Job: commit (1 min since last commit attempt)")
+            return (WorkerJob.COMMIT, _get_items_to_commit(status.queue_commit))
+
+        # 11. Commit if at least 1 file all other queues are empty and all workers are waiting
+        #     e.g. when it's the last commit
+        elif (
+            status.nb_workers_commit == 0
+            and status.queue_commit.qsize() > 0
+            and status.queue_sha256.qsize() == 0
+            and status.queue_get_upload_mode.qsize() == 0
+            and status.queue_preupload_lfs.qsize() == 0
+            and status.nb_workers_sha256 == 0
+            and status.nb_workers_get_upload_mode == 0
+            and status.nb_workers_preupload_lfs == 0
+        ):
+            status.nb_workers_commit += 1
+            logger.debug("Job: commit")
+            return (WorkerJob.COMMIT, _get_items_to_commit(status.queue_commit))
+
+        # 12. If all queues are empty, exit
+        elif all(metadata.is_committed or metadata.should_ignore for _, metadata in status.items):
+            logger.info("All files have been processed! Exiting worker.")
+            return None
+
+        # 13. If no task is available, wait
+        else:
+            status.nb_workers_waiting += 1
+            logger.debug(f"No task available, waiting... ({WAITING_TIME_IF_NO_TASKS}s)")
+            return (WorkerJob.WAIT, [])
+
+
+####################
+# Atomic jobs (sha256, get_upload_mode, preupload_lfs, commit)
+####################
+
+
+def _compute_sha256(item: JOB_ITEM_T) -> None:
+    """Compute sha256 of a file and save it in metadata."""
+    paths, metadata = item
+    if metadata.sha256 is None:
+        with paths.file_path.open("rb") as f:
+            metadata.sha256 = sha_fileobj(f).hex()
+    metadata.save(paths)
+
+
+def _get_upload_mode(items: List[JOB_ITEM_T], api: "HfApi", repo_id: str, repo_type: str, revision: str) -> None:
+    """Get upload mode for each file and update metadata.
+
+    Also receive info if the file should be ignored.
+    """
+    additions = [_build_hacky_operation(item) for item in items]
+    _fetch_upload_modes(
+        additions=additions,
+        repo_type=repo_type,
+        repo_id=repo_id,
+        headers=api._build_hf_headers(),
+        revision=revision,
+    )
+    for item, addition in zip(items, additions):
+        paths, metadata = item
+        metadata.upload_mode = addition._upload_mode
+        metadata.should_ignore = addition._should_ignore
+        metadata.save(paths)
+
+
+def _preupload_lfs(item: JOB_ITEM_T, api: "HfApi", repo_id: str, repo_type: str, revision: str) -> None:
+    """Preupload LFS file and update metadata."""
+    paths, metadata = item
+    addition = _build_hacky_operation(item)
+    api.preupload_lfs_files(
+        repo_id=repo_id,
+        repo_type=repo_type,
+        revision=revision,
+        additions=[addition],
+    )
+
+    metadata.is_uploaded = True
+    metadata.save(paths)
+
+
+def _commit(items: List[JOB_ITEM_T], api: "HfApi", repo_id: str, repo_type: str, revision: str) -> None:
+    """Commit files to the repo."""
+    additions = [_build_hacky_operation(item) for item in items]
+    api.create_commit(
+        repo_id=repo_id,
+        repo_type=repo_type,
+        revision=revision,
+        operations=additions,
+        commit_message="Add files using upload-large-folder tool",
+    )
+    for paths, metadata in items:
+        metadata.is_committed = True
+        metadata.save(paths)
+
+
+####################
+# Hacks with CommitOperationAdd to bypass checks/sha256 calculation
+####################
+
+
+class HackyCommitOperationAdd(CommitOperationAdd):
+    def __post_init__(self) -> None:
+        if isinstance(self.path_or_fileobj, Path):
+            self.path_or_fileobj = str(self.path_or_fileobj)
+
+
+def _build_hacky_operation(item: JOB_ITEM_T) -> HackyCommitOperationAdd:
+    paths, metadata = item
+    operation = HackyCommitOperationAdd(path_in_repo=paths.path_in_repo, path_or_fileobj=paths.file_path)
+    with paths.file_path.open("rb") as file:
+        sample = file.peek(512)[:512]
+    if metadata.sha256 is None:
+        raise ValueError("sha256 must have been computed by now!")
+    operation.upload_info = UploadInfo(sha256=bytes.fromhex(metadata.sha256), size=metadata.size, sample=sample)
+    return operation
+
+
+####################
+# Misc helpers
+####################
+
+
+def _get_one(queue: "queue.Queue[JOB_ITEM_T]") -> List[JOB_ITEM_T]:
+    return [queue.get()]
+
+
+def _get_n(queue: "queue.Queue[JOB_ITEM_T]", n: int) -> List[JOB_ITEM_T]:
+    return [queue.get() for _ in range(min(queue.qsize(), n))]
+
+
+def _get_items_to_commit(queue: "queue.Queue[JOB_ITEM_T]") -> List[JOB_ITEM_T]:
+    """Special case for commit job: the number of items to commit depends on the type of files."""
+    # Can take at most 50 regular files and/or 100 LFS files in a single commit
+    items: List[JOB_ITEM_T] = []
+    nb_lfs, nb_regular = 0, 0
+    while True:
+        # If empty queue => commit everything
+        if queue.qsize() == 0:
+            return items
+
+        # If we have enough items => commit them
+        if nb_lfs >= MAX_NB_LFS_FILES_PER_COMMIT or nb_regular >= MAX_NB_REGULAR_FILES_PER_COMMIT:
+            return items
+
+        # Else, get a new item and increase counter
+        item = queue.get()
+        items.append(item)
+        _, metadata = item
+        if metadata.upload_mode == "lfs":
+            nb_lfs += 1
+        else:
+            nb_regular += 1
+
+
+def _print_overwrite(report: str) -> None:
+    """Print a report, overwriting the previous lines.
+
+    Since tqdm in using `sys.stderr` to (re-)write progress bars, we need to use `sys.stdout`
+    to print the report.
+
+    Note: works well only if no other process is writing to `sys.stdout`!
+    """
+    report += "\n"
+    # Get terminal width
+    terminal_width = shutil.get_terminal_size().columns
+
+    # Count number of lines that should be cleared
+    nb_lines = sum(len(line) // terminal_width + 1 for line in report.splitlines())
+
+    # Clear previous lines based on the number of lines in the report
+    for _ in range(nb_lines):
+        sys.stdout.write("\r\033[K")  # Clear line
+        sys.stdout.write("\033[F")  # Move cursor up one line
+
+    # Print the new report, filling remaining space with whitespace
+    sys.stdout.write(report)
+    sys.stdout.write(" " * (terminal_width - len(report.splitlines()[-1])))
+    sys.stdout.flush()

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/_webhooks_payload.py

@@ -0,0 +1,137 @@
+# coding=utf-8
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains data structures to parse the webhooks payload."""
+
+from typing import List, Literal, Optional
+
+from .utils import is_pydantic_available
+
+
+if is_pydantic_available():
+    from pydantic import BaseModel
+else:
+    # Define a dummy BaseModel to avoid import errors when pydantic is not installed
+    # Import error will be raised when trying to use the class
+
+    class BaseModel:  # type: ignore [no-redef]
+        def __init__(self, *args, **kwargs) -> None:
+            raise ImportError(
+                "You must have `pydantic` installed to use `WebhookPayload`. This is an optional dependency that"
+                " should be installed separately. Please run `pip install --upgrade pydantic` and retry."
+            )
+
+
+# This is an adaptation of the ReportV3 interface implemented in moon-landing. V0, V1 and V2 have been ignored as they
+# are not in used anymore. To keep in sync when format is updated in
+# https://github.com/huggingface/moon-landing/blob/main/server/lib/HFWebhooks.ts (internal link).
+
+
+WebhookEvent_T = Literal[
+    "create",
+    "delete",
+    "move",
+    "update",
+]
+RepoChangeEvent_T = Literal[
+    "add",
+    "move",
+    "remove",
+    "update",
+]
+RepoType_T = Literal[
+    "dataset",
+    "model",
+    "space",
+]
+DiscussionStatus_T = Literal[
+    "closed",
+    "draft",
+    "open",
+    "merged",
+]
+SupportedWebhookVersion = Literal[3]
+
+
+class ObjectId(BaseModel):
+    id: str
+
+
+class WebhookPayloadUrl(BaseModel):
+    web: str
+    api: Optional[str] = None
+
+
+class WebhookPayloadMovedTo(BaseModel):
+    name: str
+    owner: ObjectId
+
+
+class WebhookPayloadWebhook(ObjectId):
+    version: SupportedWebhookVersion
+
+
+class WebhookPayloadEvent(BaseModel):
+    action: WebhookEvent_T
+    scope: str
+
+
+class WebhookPayloadDiscussionChanges(BaseModel):
+    base: str
+    mergeCommitId: Optional[str] = None
+
+
+class WebhookPayloadComment(ObjectId):
+    author: ObjectId
+    hidden: bool
+    content: Optional[str] = None
+    url: WebhookPayloadUrl
+
+
+class WebhookPayloadDiscussion(ObjectId):
+    num: int
+    author: ObjectId
+    url: WebhookPayloadUrl
+    title: str
+    isPullRequest: bool
+    status: DiscussionStatus_T
+    changes: Optional[WebhookPayloadDiscussionChanges] = None
+    pinned: Optional[bool] = None
+
+
+class WebhookPayloadRepo(ObjectId):
+    owner: ObjectId
+    head_sha: Optional[str] = None
+    name: str
+    private: bool
+    subdomain: Optional[str] = None
+    tags: Optional[List[str]] = None
+    type: Literal["dataset", "model", "space"]
+    url: WebhookPayloadUrl
+
+
+class WebhookPayloadUpdatedRef(BaseModel):
+    ref: str
+    oldSha: Optional[str] = None
+    newSha: Optional[str] = None
+
+
+class WebhookPayload(BaseModel):
+    event: WebhookPayloadEvent
+    repo: WebhookPayloadRepo
+    discussion: Optional[WebhookPayloadDiscussion] = None
+    comment: Optional[WebhookPayloadComment] = None
+    webhook: WebhookPayloadWebhook
+    movedTo: Optional[WebhookPayloadMovedTo] = None
+    updatedRefs: Optional[List[WebhookPayloadUpdatedRef]] = None

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/_webhooks_server.py

@@ -0,0 +1,386 @@
+# coding=utf-8
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains `WebhooksServer` and `webhook_endpoint` to create a webhook server easily."""
+
+import atexit
+import inspect
+import os
+from functools import wraps
+from typing import TYPE_CHECKING, Any, Callable, Dict, Optional
+
+from .utils import experimental, is_fastapi_available, is_gradio_available
+
+
+if TYPE_CHECKING:
+    import gradio as gr
+    from fastapi import Request
+
+if is_fastapi_available():
+    from fastapi import FastAPI, Request
+    from fastapi.responses import JSONResponse
+else:
+    # Will fail at runtime if FastAPI is not available
+    FastAPI = Request = JSONResponse = None  # type: ignore [misc, assignment]
+
+
+_global_app: Optional["WebhooksServer"] = None
+_is_local = os.environ.get("SPACE_ID") is None
+
+
+@experimental
+class WebhooksServer:
+    """
+    The [`WebhooksServer`] class lets you create an instance of a Gradio app that can receive Huggingface webhooks.
+    These webhooks can be registered using the [`~WebhooksServer.add_webhook`] decorator. Webhook endpoints are added to
+    the app as a POST endpoint to the FastAPI router. Once all the webhooks are registered, the `launch` method has to be
+    called to start the app.
+
+    It is recommended to accept [`WebhookPayload`] as the first argument of the webhook function. It is a Pydantic
+    model that contains all the information about the webhook event. The data will be parsed automatically for you.
+
+    Check out the [webhooks guide](../guides/webhooks_server) for a step-by-step tutorial on how to setup your
+    WebhooksServer and deploy it on a Space.
+
+    <Tip warning={true}>
+
+    `WebhooksServer` is experimental. Its API is subject to change in the future.
+
+    </Tip>
+
+    <Tip warning={true}>
+
+    You must have `gradio` installed to use `WebhooksServer` (`pip install --upgrade gradio`).
+
+    </Tip>
+
+    Args:
+        ui (`gradio.Blocks`, optional):
+            A Gradio UI instance to be used as the Space landing page. If `None`, a UI displaying instructions
+            about the configured webhooks is created.
+        webhook_secret (`str`, optional):
+            A secret key to verify incoming webhook requests. You can set this value to any secret you want as long as
+            you also configure it in your [webhooks settings panel](https://huggingface.co/settings/webhooks). You
+            can also set this value as the `WEBHOOK_SECRET` environment variable. If no secret is provided, the
+            webhook endpoints are opened without any security.
+
+    Example:
+
+        ```python
+        import gradio as gr
+        from huggingface_hub import WebhooksServer, WebhookPayload
+
+        with gr.Blocks() as ui:
+            ...
+
+        app = WebhooksServer(ui=ui, webhook_secret="my_secret_key")
+
+        @app.add_webhook("/say_hello")
+        async def hello(payload: WebhookPayload):
+            return {"message": "hello"}
+
+        app.launch()
+        ```
+    """
+
+    def __new__(cls, *args, **kwargs) -> "WebhooksServer":
+        if not is_gradio_available():
+            raise ImportError(
+                "You must have `gradio` installed to use `WebhooksServer`. Please run `pip install --upgrade gradio`"
+                " first."
+            )
+        if not is_fastapi_available():
+            raise ImportError(
+                "You must have `fastapi` installed to use `WebhooksServer`. Please run `pip install --upgrade fastapi`"
+                " first."
+            )
+        return super().__new__(cls)
+
+    def __init__(
+        self,
+        ui: Optional["gr.Blocks"] = None,
+        webhook_secret: Optional[str] = None,
+    ) -> None:
+        self._ui = ui
+
+        self.webhook_secret = webhook_secret or os.getenv("WEBHOOK_SECRET")
+        self.registered_webhooks: Dict[str, Callable] = {}
+        _warn_on_empty_secret(self.webhook_secret)
+
+    def add_webhook(self, path: Optional[str] = None) -> Callable:
+        """
+        Decorator to add a webhook to the [`WebhooksServer`] server.
+
+        Args:
+            path (`str`, optional):
+                The URL path to register the webhook function. If not provided, the function name will be used as the
+                path. In any case, all webhooks are registered under `/webhooks`.
+
+        Raises:
+            ValueError: If the provided path is already registered as a webhook.
+
+        Example:
+            ```python
+            from huggingface_hub import WebhooksServer, WebhookPayload
+
+            app = WebhooksServer()
+
+            @app.add_webhook
+            async def trigger_training(payload: WebhookPayload):
+                if payload.repo.type == "dataset" and payload.event.action == "update":
+                    # Trigger a training job if a dataset is updated
+                    ...
+
+            app.launch()
+        ```
+        """
+        # Usage: directly as decorator. Example: `@app.add_webhook`
+        if callable(path):
+            # If path is a function, it means it was used as a decorator without arguments
+            return self.add_webhook()(path)
+
+        # Usage: provide a path. Example: `@app.add_webhook(...)`
+        @wraps(FastAPI.post)
+        def _inner_post(*args, **kwargs):
+            func = args[0]
+            abs_path = f"/webhooks/{(path or func.__name__).strip('/')}"
+            if abs_path in self.registered_webhooks:
+                raise ValueError(f"Webhook {abs_path} already exists.")
+            self.registered_webhooks[abs_path] = func
+
+        return _inner_post
+
+    def launch(self, prevent_thread_lock: bool = False, **launch_kwargs: Any) -> None:
+        """Launch the Gradio app and register webhooks to the underlying FastAPI server.
+
+        Input parameters are forwarded to Gradio when launching the app.
+        """
+        ui = self._ui or self._get_default_ui()
+
+        # Start Gradio App
+        #   - as non-blocking so that webhooks can be added afterwards
+        #   - as shared if launch locally (to debug webhooks)
+        launch_kwargs.setdefault("share", _is_local)
+        self.fastapi_app, _, _ = ui.launch(prevent_thread_lock=True, **launch_kwargs)
+
+        # Register webhooks to FastAPI app
+        for path, func in self.registered_webhooks.items():
+            # Add secret check if required
+            if self.webhook_secret is not None:
+                func = _wrap_webhook_to_check_secret(func, webhook_secret=self.webhook_secret)
+
+            # Add route to FastAPI app
+            self.fastapi_app.post(path)(func)
+
+        # Print instructions and block main thread
+        space_host = os.environ.get("SPACE_HOST")
+        url = "https://" + space_host if space_host is not None else (ui.share_url or ui.local_url)
+        url = url.strip("/")
+        message = "\nWebhooks are correctly setup and ready to use:"
+        message += "\n" + "\n".join(f"  - POST {url}{webhook}" for webhook in self.registered_webhooks)
+        message += "\nGo to https://huggingface.co/settings/webhooks to setup your webhooks."
+        print(message)
+
+        if not prevent_thread_lock:
+            ui.block_thread()
+
+    def _get_default_ui(self) -> "gr.Blocks":
+        """Default UI if not provided (lists webhooks and provides basic instructions)."""
+        import gradio as gr
+
+        with gr.Blocks() as ui:
+            gr.Markdown("# This is an app to process 🤗 Webhooks")
+            gr.Markdown(
+                "Webhooks are a foundation for MLOps-related features. They allow you to listen for new changes on"
+                " specific repos or to all repos belonging to particular set of users/organizations (not just your"
+                " repos, but any repo). Check out this [guide](https://huggingface.co/docs/hub/webhooks) to get to"
+                " know more about webhooks on the Huggingface Hub."
+            )
+            gr.Markdown(
+                f"{len(self.registered_webhooks)} webhook(s) are registered:"
+                + "\n\n"
+                + "\n ".join(
+                    f"- [{webhook_path}]({_get_webhook_doc_url(webhook.__name__, webhook_path)})"
+                    for webhook_path, webhook in self.registered_webhooks.items()
+                )
+            )
+            gr.Markdown(
+                "Go to https://huggingface.co/settings/webhooks to setup your webhooks."
+                + "\nYou app is running locally. Please look at the logs to check the full URL you need to set."
+                if _is_local
+                else (
+                    "\nThis app is running on a Space. You can find the corresponding URL in the options menu"
+                    " (top-right) > 'Embed the Space'. The URL looks like 'https://{username}-{repo_name}.hf.space'."
+                )
+            )
+        return ui
+
+
+@experimental
+def webhook_endpoint(path: Optional[str] = None) -> Callable:
+    """Decorator to start a [`WebhooksServer`] and register the decorated function as a webhook endpoint.
+
+    This is a helper to get started quickly. If you need more flexibility (custom landing page or webhook secret),
+    you can use [`WebhooksServer`] directly. You can register multiple webhook endpoints (to the same server) by using
+    this decorator multiple times.
+
+    Check out the [webhooks guide](../guides/webhooks_server) for a step-by-step tutorial on how to setup your
+    server and deploy it on a Space.
+
+    <Tip warning={true}>
+
+    `webhook_endpoint` is experimental. Its API is subject to change in the future.
+
+    </Tip>
+
+    <Tip warning={true}>
+
+    You must have `gradio` installed to use `webhook_endpoint` (`pip install --upgrade gradio`).
+
+    </Tip>
+
+    Args:
+        path (`str`, optional):
+            The URL path to register the webhook function. If not provided, the function name will be used as the path.
+            In any case, all webhooks are registered under `/webhooks`.
+
+    Examples:
+        The default usage is to register a function as a webhook endpoint. The function name will be used as the path.
+        The server will be started automatically at exit (i.e. at the end of the script).
+
+        ```python
+        from huggingface_hub import webhook_endpoint, WebhookPayload
+
+        @webhook_endpoint
+        async def trigger_training(payload: WebhookPayload):
+            if payload.repo.type == "dataset" and payload.event.action == "update":
+                # Trigger a training job if a dataset is updated
+                ...
+
+        # Server is automatically started at the end of the script.
+        ```
+
+        Advanced usage: register a function as a webhook endpoint and start the server manually. This is useful if you
+        are running it in a notebook.
+
+        ```python
+        from huggingface_hub import webhook_endpoint, WebhookPayload
+
+        @webhook_endpoint
+        async def trigger_training(payload: WebhookPayload):
+            if payload.repo.type == "dataset" and payload.event.action == "update":
+                # Trigger a training job if a dataset is updated
+                ...
+
+        # Start the server manually
+        trigger_training.launch()
+        ```
+    """
+    if callable(path):
+        # If path is a function, it means it was used as a decorator without arguments
+        return webhook_endpoint()(path)
+
+    @wraps(WebhooksServer.add_webhook)
+    def _inner(func: Callable) -> Callable:
+        app = _get_global_app()
+        app.add_webhook(path)(func)
+        if len(app.registered_webhooks) == 1:
+            # Register `app.launch` to run at exit (only once)
+            atexit.register(app.launch)
+
+        @wraps(app.launch)
+        def _launch_now():
+            # Run the app directly (without waiting atexit)
+            atexit.unregister(app.launch)
+            app.launch()
+
+        func.launch = _launch_now  # type: ignore
+        return func
+
+    return _inner
+
+
+def _get_global_app() -> WebhooksServer:
+    global _global_app
+    if _global_app is None:
+        _global_app = WebhooksServer()
+    return _global_app
+
+
+def _warn_on_empty_secret(webhook_secret: Optional[str]) -> None:
+    if webhook_secret is None:
+        print("Webhook secret is not defined. This means your webhook endpoints will be open to everyone.")
+        print(
+            "To add a secret, set `WEBHOOK_SECRET` as environment variable or pass it at initialization: "
+            "\n\t`app = WebhooksServer(webhook_secret='my_secret', ...)`"
+        )
+        print(
+            "For more details about webhook secrets, please refer to"
+            " https://huggingface.co/docs/hub/webhooks#webhook-secret."
+        )
+    else:
+        print("Webhook secret is correctly defined.")
+
+
+def _get_webhook_doc_url(webhook_name: str, webhook_path: str) -> str:
+    """Returns the anchor to a given webhook in the docs (experimental)"""
+    return "/docs#/default/" + webhook_name + webhook_path.replace("/", "_") + "_post"
+
+
+def _wrap_webhook_to_check_secret(func: Callable, webhook_secret: str) -> Callable:
+    """Wraps a webhook function to check the webhook secret before calling the function.
+
+    This is a hacky way to add the `request` parameter to the function signature. Since FastAPI based itself on route
+    parameters to inject the values to the function, we need to hack the function signature to retrieve the `Request`
+    object (and hence the headers). A far cleaner solution would be to use a middleware. However, since
+    `fastapi==0.90.1`, a middleware cannot be added once the app has started. And since the FastAPI app is started by
+    Gradio internals (and not by us), we cannot add a middleware.
+
+    This method is called only when a secret has been defined by the user. If a request is sent without the
+    "x-webhook-secret", the function will return a 401 error (unauthorized). If the header is sent but is incorrect,
+    the function will return a 403 error (forbidden).
+
+    Inspired by https://stackoverflow.com/a/33112180.
+    """
+    initial_sig = inspect.signature(func)
+
+    @wraps(func)
+    async def _protected_func(request: Request, **kwargs):
+        request_secret = request.headers.get("x-webhook-secret")
+        if request_secret is None:
+            return JSONResponse({"error": "x-webhook-secret header not set."}, status_code=401)
+        if request_secret != webhook_secret:
+            return JSONResponse({"error": "Invalid webhook secret."}, status_code=403)
+
+        # Inject `request` in kwargs if required
+        if "request" in initial_sig.parameters:
+            kwargs["request"] = request
+
+        # Handle both sync and async routes
+        if inspect.iscoroutinefunction(func):
+            return await func(**kwargs)
+        else:
+            return func(**kwargs)
+
+    # Update signature to include request
+    if "request" not in initial_sig.parameters:
+        _protected_func.__signature__ = initial_sig.replace(  # type: ignore
+            parameters=(
+                inspect.Parameter(name="request", kind=inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=Request),
+            )
+            + tuple(initial_sig.parameters.values())
+        )
+
+    # Return protected route
+    return _protected_func

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/community.py

@@ -0,0 +1,355 @@
+"""
+Data structures to interact with Discussions and Pull Requests on the Hub.
+
+See [the Discussions and Pull Requests guide](https://huggingface.co/docs/hub/repositories-pull-requests-discussions)
+for more information on Pull Requests, Discussions, and the community tab.
+"""
+
+from dataclasses import dataclass
+from datetime import datetime
+from typing import List, Literal, Optional, Union
+
+from . import constants
+from .utils import parse_datetime
+
+
+DiscussionStatus = Literal["open", "closed", "merged", "draft"]
+
+
+@dataclass
+class Discussion:
+    """
+    A Discussion or Pull Request on the Hub.
+
+    This dataclass is not intended to be instantiated directly.
+
+    Attributes:
+        title (`str`):
+            The title of the Discussion / Pull Request
+        status (`str`):
+            The status of the Discussion / Pull Request.
+            It must be one of:
+                * `"open"`
+                * `"closed"`
+                * `"merged"` (only for Pull Requests )
+                * `"draft"` (only for Pull Requests )
+        num (`int`):
+            The number of the Discussion / Pull Request.
+        repo_id (`str`):
+            The id (`"{namespace}/{repo_name}"`) of the repo on which
+            the Discussion / Pull Request was open.
+        repo_type (`str`):
+            The type of the repo on which the Discussion / Pull Request was open.
+            Possible values are: `"model"`, `"dataset"`, `"space"`.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+        is_pull_request (`bool`):
+            Whether or not this is a Pull Request.
+        created_at (`datetime`):
+            The `datetime` of creation of the Discussion / Pull Request.
+        endpoint (`str`):
+            Endpoint of the Hub. Default is https://huggingface.co.
+        git_reference (`str`, *optional*):
+            (property) Git reference to which changes can be pushed if this is a Pull Request, `None` otherwise.
+        url (`str`):
+            (property) URL of the discussion on the Hub.
+    """
+
+    title: str
+    status: DiscussionStatus
+    num: int
+    repo_id: str
+    repo_type: str
+    author: str
+    is_pull_request: bool
+    created_at: datetime
+    endpoint: str
+
+    @property
+    def git_reference(self) -> Optional[str]:
+        """
+        If this is a Pull Request , returns the git reference to which changes can be pushed.
+        Returns `None` otherwise.
+        """
+        if self.is_pull_request:
+            return f"refs/pr/{self.num}"
+        return None
+
+    @property
+    def url(self) -> str:
+        """Returns the URL of the discussion on the Hub."""
+        if self.repo_type is None or self.repo_type == constants.REPO_TYPE_MODEL:
+            return f"{self.endpoint}/{self.repo_id}/discussions/{self.num}"
+        return f"{self.endpoint}/{self.repo_type}s/{self.repo_id}/discussions/{self.num}"
+
+
+@dataclass
+class DiscussionWithDetails(Discussion):
+    """
+    Subclass of [`Discussion`].
+
+    Attributes:
+        title (`str`):
+            The title of the Discussion / Pull Request
+        status (`str`):
+            The status of the Discussion / Pull Request.
+            It can be one of:
+                * `"open"`
+                * `"closed"`
+                * `"merged"` (only for Pull Requests )
+                * `"draft"` (only for Pull Requests )
+        num (`int`):
+            The number of the Discussion / Pull Request.
+        repo_id (`str`):
+            The id (`"{namespace}/{repo_name}"`) of the repo on which
+            the Discussion / Pull Request was open.
+        repo_type (`str`):
+            The type of the repo on which the Discussion / Pull Request was open.
+            Possible values are: `"model"`, `"dataset"`, `"space"`.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+        is_pull_request (`bool`):
+            Whether or not this is a Pull Request.
+        created_at (`datetime`):
+            The `datetime` of creation of the Discussion / Pull Request.
+        events (`list` of [`DiscussionEvent`])
+            The list of [`DiscussionEvents`] in this Discussion or Pull Request.
+        conflicting_files (`Union[List[str], bool, None]`, *optional*):
+            A list of conflicting files if this is a Pull Request.
+            `None` if `self.is_pull_request` is `False`.
+            `True` if there are conflicting files but the list can't be retrieved.
+        target_branch (`str`, *optional*):
+            The branch into which changes are to be merged if this is a
+            Pull Request . `None`  if `self.is_pull_request` is `False`.
+        merge_commit_oid (`str`, *optional*):
+            If this is a merged Pull Request , this is set to the OID / SHA of
+            the merge commit, `None` otherwise.
+        diff (`str`, *optional*):
+            The git diff if this is a Pull Request , `None` otherwise.
+        endpoint (`str`):
+            Endpoint of the Hub. Default is https://huggingface.co.
+        git_reference (`str`, *optional*):
+            (property) Git reference to which changes can be pushed if this is a Pull Request, `None` otherwise.
+        url (`str`):
+            (property) URL of the discussion on the Hub.
+    """
+
+    events: List["DiscussionEvent"]
+    conflicting_files: Union[List[str], bool, None]
+    target_branch: Optional[str]
+    merge_commit_oid: Optional[str]
+    diff: Optional[str]
+
+
+@dataclass
+class DiscussionEvent:
+    """
+    An event in a Discussion or Pull Request.
+
+    Use concrete classes:
+        * [`DiscussionComment`]
+        * [`DiscussionStatusChange`]
+        * [`DiscussionCommit`]
+        * [`DiscussionTitleChange`]
+
+    Attributes:
+        id (`str`):
+            The ID of the event. An hexadecimal string.
+        type (`str`):
+            The type of the event.
+        created_at (`datetime`):
+            A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+            object holding the creation timestamp for the event.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+    """
+
+    id: str
+    type: str
+    created_at: datetime
+    author: str
+
+    _event: dict
+    """Stores the original event data, in case we need to access it later."""
+
+
+@dataclass
+class DiscussionComment(DiscussionEvent):
+    """A comment in a Discussion / Pull Request.
+
+    Subclass of [`DiscussionEvent`].
+
+
+    Attributes:
+        id (`str`):
+            The ID of the event. An hexadecimal string.
+        type (`str`):
+            The type of the event.
+        created_at (`datetime`):
+            A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+            object holding the creation timestamp for the event.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+        content (`str`):
+            The raw markdown content of the comment. Mentions, links and images are not rendered.
+        edited (`bool`):
+            Whether or not this comment has been edited.
+        hidden (`bool`):
+            Whether or not this comment has been hidden.
+    """
+
+    content: str
+    edited: bool
+    hidden: bool
+
+    @property
+    def rendered(self) -> str:
+        """The rendered comment, as a HTML string"""
+        return self._event["data"]["latest"]["html"]
+
+    @property
+    def last_edited_at(self) -> datetime:
+        """The last edit time, as a `datetime` object."""
+        return parse_datetime(self._event["data"]["latest"]["updatedAt"])
+
+    @property
+    def last_edited_by(self) -> str:
+        """The last edit time, as a `datetime` object."""
+        return self._event["data"]["latest"].get("author", {}).get("name", "deleted")
+
+    @property
+    def edit_history(self) -> List[dict]:
+        """The edit history of the comment"""
+        return self._event["data"]["history"]
+
+    @property
+    def number_of_edits(self) -> int:
+        return len(self.edit_history)
+
+
+@dataclass
+class DiscussionStatusChange(DiscussionEvent):
+    """A change of status in a Discussion / Pull Request.
+
+    Subclass of [`DiscussionEvent`].
+
+    Attributes:
+        id (`str`):
+            The ID of the event. An hexadecimal string.
+        type (`str`):
+            The type of the event.
+        created_at (`datetime`):
+            A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+            object holding the creation timestamp for the event.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+        new_status (`str`):
+            The status of the Discussion / Pull Request after the change.
+            It can be one of:
+                * `"open"`
+                * `"closed"`
+                * `"merged"` (only for Pull Requests )
+    """
+
+    new_status: str
+
+
+@dataclass
+class DiscussionCommit(DiscussionEvent):
+    """A commit in a Pull Request.
+
+    Subclass of [`DiscussionEvent`].
+
+    Attributes:
+        id (`str`):
+            The ID of the event. An hexadecimal string.
+        type (`str`):
+            The type of the event.
+        created_at (`datetime`):
+            A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+            object holding the creation timestamp for the event.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+        summary (`str`):
+            The summary of the commit.
+        oid (`str`):
+            The OID / SHA of the commit, as a hexadecimal string.
+    """
+
+    summary: str
+    oid: str
+
+
+@dataclass
+class DiscussionTitleChange(DiscussionEvent):
+    """A rename event in a Discussion / Pull Request.
+
+    Subclass of [`DiscussionEvent`].
+
+    Attributes:
+        id (`str`):
+            The ID of the event. An hexadecimal string.
+        type (`str`):
+            The type of the event.
+        created_at (`datetime`):
+            A [`datetime`](https://docs.python.org/3/library/datetime.html?highlight=datetime#datetime.datetime)
+            object holding the creation timestamp for the event.
+        author (`str`):
+            The username of the Discussion / Pull Request author.
+            Can be `"deleted"` if the user has been deleted since.
+        old_title (`str`):
+            The previous title for the Discussion / Pull Request.
+        new_title (`str`):
+            The new title.
+    """
+
+    old_title: str
+    new_title: str
+
+
+def deserialize_event(event: dict) -> DiscussionEvent:
+    """Instantiates a [`DiscussionEvent`] from a dict"""
+    event_id: str = event["id"]
+    event_type: str = event["type"]
+    created_at = parse_datetime(event["createdAt"])
+
+    common_args = dict(
+        id=event_id,
+        type=event_type,
+        created_at=created_at,
+        author=event.get("author", {}).get("name", "deleted"),
+        _event=event,
+    )
+
+    if event_type == "comment":
+        return DiscussionComment(
+            **common_args,
+            edited=event["data"]["edited"],
+            hidden=event["data"]["hidden"],
+            content=event["data"]["latest"]["raw"],
+        )
+    if event_type == "status-change":
+        return DiscussionStatusChange(
+            **common_args,
+            new_status=event["data"]["status"],
+        )
+    if event_type == "commit":
+        return DiscussionCommit(
+            **common_args,
+            summary=event["data"]["subject"],
+            oid=event["data"]["oid"],
+        )
+    if event_type == "title-change":
+        return DiscussionTitleChange(
+            **common_args,
+            old_title=event["data"]["from"],
+            new_title=event["data"]["to"],
+        )
+
+    return DiscussionEvent(**common_args)

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/constants.py

@@ -0,0 +1,225 @@
+import os
+import re
+import typing
+from typing import Literal, Optional, Tuple
+
+
+# Possible values for env variables
+
+
+ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"}
+ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"})
+
+
+def _is_true(value: Optional[str]) -> bool:
+    if value is None:
+        return False
+    return value.upper() in ENV_VARS_TRUE_VALUES
+
+
+def _as_int(value: Optional[str]) -> Optional[int]:
+    if value is None:
+        return None
+    return int(value)
+
+
+# Constants for file downloads
+
+PYTORCH_WEIGHTS_NAME = "pytorch_model.bin"
+TF2_WEIGHTS_NAME = "tf_model.h5"
+TF_WEIGHTS_NAME = "model.ckpt"
+FLAX_WEIGHTS_NAME = "flax_model.msgpack"
+CONFIG_NAME = "config.json"
+REPOCARD_NAME = "README.md"
+DEFAULT_ETAG_TIMEOUT = 10
+DEFAULT_DOWNLOAD_TIMEOUT = 10
+DEFAULT_REQUEST_TIMEOUT = 10
+DOWNLOAD_CHUNK_SIZE = 10 * 1024 * 1024
+HF_TRANSFER_CONCURRENCY = 100
+
+# Constants for serialization
+
+PYTORCH_WEIGHTS_FILE_PATTERN = "pytorch_model{suffix}.bin"  # Unsafe pickle: use safetensors instead
+SAFETENSORS_WEIGHTS_FILE_PATTERN = "model{suffix}.safetensors"
+TF2_WEIGHTS_FILE_PATTERN = "tf_model{suffix}.h5"
+
+# Constants for safetensors repos
+
+SAFETENSORS_SINGLE_FILE = "model.safetensors"
+SAFETENSORS_INDEX_FILE = "model.safetensors.index.json"
+SAFETENSORS_MAX_HEADER_LENGTH = 25_000_000
+
+# Timeout of aquiring file lock and logging the attempt
+FILELOCK_LOG_EVERY_SECONDS = 10
+
+# Git-related constants
+
+DEFAULT_REVISION = "main"
+REGEX_COMMIT_OID = re.compile(r"[A-Fa-f0-9]{5,40}")
+
+HUGGINGFACE_CO_URL_HOME = "https://huggingface.co/"
+
+_staging_mode = _is_true(os.environ.get("HUGGINGFACE_CO_STAGING"))
+
+_HF_DEFAULT_ENDPOINT = "https://huggingface.co"
+_HF_DEFAULT_STAGING_ENDPOINT = "https://hub-ci.huggingface.co"
+ENDPOINT = os.getenv("HF_ENDPOINT") or (_HF_DEFAULT_STAGING_ENDPOINT if _staging_mode else _HF_DEFAULT_ENDPOINT)
+
+HUGGINGFACE_CO_URL_TEMPLATE = ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
+HUGGINGFACE_HEADER_X_REPO_COMMIT = "X-Repo-Commit"
+HUGGINGFACE_HEADER_X_LINKED_ETAG = "X-Linked-Etag"
+HUGGINGFACE_HEADER_X_LINKED_SIZE = "X-Linked-Size"
+
+INFERENCE_ENDPOINT = os.environ.get("HF_INFERENCE_ENDPOINT", "https://api-inference.huggingface.co")
+
+# See https://huggingface.co/docs/inference-endpoints/index
+INFERENCE_ENDPOINTS_ENDPOINT = "https://api.endpoints.huggingface.cloud/v2"
+
+
+REPO_ID_SEPARATOR = "--"
+# ^ this substring is not allowed in repo_ids on hf.co
+# and is the canonical one we use for serialization of repo ids elsewhere.
+
+
+REPO_TYPE_DATASET = "dataset"
+REPO_TYPE_SPACE = "space"
+REPO_TYPE_MODEL = "model"
+REPO_TYPES = [None, REPO_TYPE_MODEL, REPO_TYPE_DATASET, REPO_TYPE_SPACE]
+SPACES_SDK_TYPES = ["gradio", "streamlit", "docker", "static"]
+
+REPO_TYPES_URL_PREFIXES = {
+    REPO_TYPE_DATASET: "datasets/",
+    REPO_TYPE_SPACE: "spaces/",
+}
+REPO_TYPES_MAPPING = {
+    "datasets": REPO_TYPE_DATASET,
+    "spaces": REPO_TYPE_SPACE,
+    "models": REPO_TYPE_MODEL,
+}
+
+DiscussionTypeFilter = Literal["all", "discussion", "pull_request"]
+DISCUSSION_TYPES: Tuple[DiscussionTypeFilter, ...] = typing.get_args(DiscussionTypeFilter)
+DiscussionStatusFilter = Literal["all", "open", "closed"]
+DISCUSSION_STATUS: Tuple[DiscussionTypeFilter, ...] = typing.get_args(DiscussionStatusFilter)
+
+# Webhook subscription types
+WEBHOOK_DOMAIN_T = Literal["repo", "discussions"]
+
+# default cache
+default_home = os.path.join(os.path.expanduser("~"), ".cache")
+HF_HOME = os.path.expanduser(
+    os.getenv(
+        "HF_HOME",
+        os.path.join(os.getenv("XDG_CACHE_HOME", default_home), "huggingface"),
+    )
+)
+hf_cache_home = HF_HOME  # for backward compatibility. TODO: remove this in 1.0.0
+
+default_cache_path = os.path.join(HF_HOME, "hub")
+default_assets_cache_path = os.path.join(HF_HOME, "assets")
+
+# Legacy env variables
+HUGGINGFACE_HUB_CACHE = os.getenv("HUGGINGFACE_HUB_CACHE", default_cache_path)
+HUGGINGFACE_ASSETS_CACHE = os.getenv("HUGGINGFACE_ASSETS_CACHE", default_assets_cache_path)
+
+# New env variables
+HF_HUB_CACHE = os.getenv("HF_HUB_CACHE", HUGGINGFACE_HUB_CACHE)
+HF_ASSETS_CACHE = os.getenv("HF_ASSETS_CACHE", HUGGINGFACE_ASSETS_CACHE)
+
+HF_HUB_OFFLINE = _is_true(os.environ.get("HF_HUB_OFFLINE") or os.environ.get("TRANSFORMERS_OFFLINE"))
+
+# Opt-out from telemetry requests
+HF_HUB_DISABLE_TELEMETRY = (
+    _is_true(os.environ.get("HF_HUB_DISABLE_TELEMETRY"))  # HF-specific env variable
+    or _is_true(os.environ.get("DISABLE_TELEMETRY"))
+    or _is_true(os.environ.get("DO_NOT_TRACK"))  # https://consoledonottrack.com/
+)
+
+# In the past, token was stored in a hardcoded location
+# `_OLD_HF_TOKEN_PATH` is deprecated and will be removed "at some point".
+# See https://github.com/huggingface/huggingface_hub/issues/1232
+_OLD_HF_TOKEN_PATH = os.path.expanduser("~/.huggingface/token")
+HF_TOKEN_PATH = os.environ.get("HF_TOKEN_PATH", os.path.join(HF_HOME, "token"))
+HF_STORED_TOKENS_PATH = os.path.join(os.path.dirname(HF_TOKEN_PATH), "stored_tokens")
+
+if _staging_mode:
+    # In staging mode, we use a different cache to ensure we don't mix up production and staging data or tokens
+    _staging_home = os.path.join(os.path.expanduser("~"), ".cache", "huggingface_staging")
+    HUGGINGFACE_HUB_CACHE = os.path.join(_staging_home, "hub")
+    _OLD_HF_TOKEN_PATH = os.path.join(_staging_home, "_old_token")
+    HF_TOKEN_PATH = os.path.join(_staging_home, "token")
+
+# Here, `True` will disable progress bars globally without possibility of enabling it
+# programmatically. `False` will enable them without possibility of disabling them.
+# If environment variable is not set (None), then the user is free to enable/disable
+# them programmatically.
+# TL;DR: env variable has priority over code
+__HF_HUB_DISABLE_PROGRESS_BARS = os.environ.get("HF_HUB_DISABLE_PROGRESS_BARS")
+HF_HUB_DISABLE_PROGRESS_BARS: Optional[bool] = (
+    _is_true(__HF_HUB_DISABLE_PROGRESS_BARS) if __HF_HUB_DISABLE_PROGRESS_BARS is not None else None
+)
+
+# Disable warning on machines that do not support symlinks (e.g. Windows non-developer)
+HF_HUB_DISABLE_SYMLINKS_WARNING: bool = _is_true(os.environ.get("HF_HUB_DISABLE_SYMLINKS_WARNING"))
+
+# Disable warning when using experimental features
+HF_HUB_DISABLE_EXPERIMENTAL_WARNING: bool = _is_true(os.environ.get("HF_HUB_DISABLE_EXPERIMENTAL_WARNING"))
+
+# Disable sending the cached token by default is all HTTP requests to the Hub
+HF_HUB_DISABLE_IMPLICIT_TOKEN: bool = _is_true(os.environ.get("HF_HUB_DISABLE_IMPLICIT_TOKEN"))
+
+# Enable fast-download using external dependency "hf_transfer"
+# See:
+# - https://pypi.org/project/hf-transfer/
+# - https://github.com/huggingface/hf_transfer (private)
+HF_HUB_ENABLE_HF_TRANSFER: bool = _is_true(os.environ.get("HF_HUB_ENABLE_HF_TRANSFER"))
+
+
+# UNUSED
+# We don't use symlinks in local dir anymore.
+HF_HUB_LOCAL_DIR_AUTO_SYMLINK_THRESHOLD: int = (
+    _as_int(os.environ.get("HF_HUB_LOCAL_DIR_AUTO_SYMLINK_THRESHOLD")) or 5 * 1024 * 1024
+)
+
+# Used to override the etag timeout on a system level
+HF_HUB_ETAG_TIMEOUT: int = _as_int(os.environ.get("HF_HUB_ETAG_TIMEOUT")) or DEFAULT_ETAG_TIMEOUT
+
+# Used to override the get request timeout on a system level
+HF_HUB_DOWNLOAD_TIMEOUT: int = _as_int(os.environ.get("HF_HUB_DOWNLOAD_TIMEOUT")) or DEFAULT_DOWNLOAD_TIMEOUT
+
+# List frameworks that are handled by the InferenceAPI service. Useful to scan endpoints and check which models are
+# deployed and running. Since 95% of the models are using the top 4 frameworks listed below, we scan only those by
+# default. We still keep the full list of supported frameworks in case we want to scan all of them.
+MAIN_INFERENCE_API_FRAMEWORKS = [
+    "diffusers",
+    "sentence-transformers",
+    "text-generation-inference",
+    "transformers",
+]
+
+ALL_INFERENCE_API_FRAMEWORKS = MAIN_INFERENCE_API_FRAMEWORKS + [
+    "adapter-transformers",
+    "allennlp",
+    "asteroid",
+    "bertopic",
+    "doctr",
+    "espnet",
+    "fairseq",
+    "fastai",
+    "fasttext",
+    "flair",
+    "k2",
+    "keras",
+    "mindspore",
+    "nemo",
+    "open_clip",
+    "paddlenlp",
+    "peft",
+    "pyannote-audio",
+    "sklearn",
+    "spacy",
+    "span-marker",
+    "speechbrain",
+    "stanza",
+    "timm",
+]

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/errors.py

@@ -0,0 +1,310 @@
+"""Contains all custom errors."""
+
+from pathlib import Path
+from typing import Optional, Union
+
+from requests import HTTPError, Response
+
+
+# CACHE ERRORS
+
+
+class CacheNotFound(Exception):
+    """Exception thrown when the Huggingface cache is not found."""
+
+    cache_dir: Union[str, Path]
+
+    def __init__(self, msg: str, cache_dir: Union[str, Path], *args, **kwargs):
+        super().__init__(msg, *args, **kwargs)
+        self.cache_dir = cache_dir
+
+
+class CorruptedCacheException(Exception):
+    """Exception for any unexpected structure in the Huggingface cache-system."""
+
+
+# HEADERS ERRORS
+
+
+class LocalTokenNotFoundError(EnvironmentError):
+    """Raised if local token is required but not found."""
+
+
+# HTTP ERRORS
+
+
+class OfflineModeIsEnabled(ConnectionError):
+    """Raised when a request is made but `HF_HUB_OFFLINE=1` is set as environment variable."""
+
+
+class HfHubHTTPError(HTTPError):
+    """
+    HTTPError to inherit from for any custom HTTP Error raised in HF Hub.
+
+    Any HTTPError is converted at least into a `HfHubHTTPError`. If some information is
+    sent back by the server, it will be added to the error message.
+
+    Added details:
+    - Request id from "X-Request-Id" header if exists. If not, fallback to "X-Amzn-Trace-Id" header if exists.
+    - Server error message from the header "X-Error-Message".
+    - Server error message if we can found one in the response body.
+
+    Example:
+    ```py
+        import requests
+        from huggingface_hub.utils import get_session, hf_raise_for_status, HfHubHTTPError
+
+        response = get_session().post(...)
+        try:
+            hf_raise_for_status(response)
+        except HfHubHTTPError as e:
+            print(str(e)) # formatted message
+            e.request_id, e.server_message # details returned by server
+
+            # Complete the error message with additional information once it's raised
+            e.append_to_message("\n`create_commit` expects the repository to exist.")
+            raise
+    ```
+    """
+
+    def __init__(self, message: str, response: Optional[Response] = None, *, server_message: Optional[str] = None):
+        self.request_id = (
+            response.headers.get("x-request-id") or response.headers.get("X-Amzn-Trace-Id")
+            if response is not None
+            else None
+        )
+        self.server_message = server_message
+
+        super().__init__(
+            message,
+            response=response,  # type: ignore [arg-type]
+            request=response.request if response is not None else None,  # type: ignore [arg-type]
+        )
+
+    def append_to_message(self, additional_message: str) -> None:
+        """Append additional information to the `HfHubHTTPError` initial message."""
+        self.args = (self.args[0] + additional_message,) + self.args[1:]
+
+
+# INFERENCE CLIENT ERRORS
+
+
+class InferenceTimeoutError(HTTPError, TimeoutError):
+    """Error raised when a model is unavailable or the request times out."""
+
+
+# INFERENCE ENDPOINT ERRORS
+
+
+class InferenceEndpointError(Exception):
+    """Generic exception when dealing with Inference Endpoints."""
+
+
+class InferenceEndpointTimeoutError(InferenceEndpointError, TimeoutError):
+    """Exception for timeouts while waiting for Inference Endpoint."""
+
+
+# SAFETENSORS ERRORS
+
+
+class SafetensorsParsingError(Exception):
+    """Raised when failing to parse a safetensors file metadata.
+
+    This can be the case if the file is not a safetensors file or does not respect the specification.
+    """
+
+
+class NotASafetensorsRepoError(Exception):
+    """Raised when a repo is not a Safetensors repo i.e. doesn't have either a `model.safetensors` or a
+    `model.safetensors.index.json` file.
+    """
+
+
+# TEXT GENERATION ERRORS
+
+
+class TextGenerationError(HTTPError):
+    """Generic error raised if text-generation went wrong."""
+
+
+# Text Generation Inference Errors
+class ValidationError(TextGenerationError):
+    """Server-side validation error."""
+
+
+class GenerationError(TextGenerationError):
+    pass
+
+
+class OverloadedError(TextGenerationError):
+    pass
+
+
+class IncompleteGenerationError(TextGenerationError):
+    pass
+
+
+class UnknownError(TextGenerationError):
+    pass
+
+
+# VALIDATION ERRORS
+
+
+class HFValidationError(ValueError):
+    """Generic exception thrown by `huggingface_hub` validators.
+
+    Inherits from [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError).
+    """
+
+
+# FILE METADATA ERRORS
+
+
+class FileMetadataError(OSError):
+    """Error triggered when the metadata of a file on the Hub cannot be retrieved (missing ETag or commit_hash).
+
+    Inherits from `OSError` for backward compatibility.
+    """
+
+
+# REPOSITORY ERRORS
+
+
+class RepositoryNotFoundError(HfHubHTTPError):
+    """
+    Raised when trying to access a hf.co URL with an invalid repository name, or
+    with a private repo name the user does not have access to.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import model_info
+    >>> model_info("<non_existent_repository>")
+    (...)
+    huggingface_hub.utils._errors.RepositoryNotFoundError: 401 Client Error. (Request ID: PvMw_VjBMjVdMz53WKIzP)
+
+    Repository Not Found for url: https://huggingface.co/api/models/%3Cnon_existent_repository%3E.
+    Please make sure you specified the correct `repo_id` and `repo_type`.
+    If the repo is private, make sure you are authenticated.
+    Invalid username or password.
+    ```
+    """
+
+
+class GatedRepoError(RepositoryNotFoundError):
+    """
+    Raised when trying to access a gated repository for which the user is not on the
+    authorized list.
+
+    Note: derives from `RepositoryNotFoundError` to ensure backward compatibility.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import model_info
+    >>> model_info("<gated_repository>")
+    (...)
+    huggingface_hub.utils._errors.GatedRepoError: 403 Client Error. (Request ID: ViT1Bf7O_026LGSQuVqfa)
+
+    Cannot access gated repo for url https://huggingface.co/api/models/ardent-figment/gated-model.
+    Access to model ardent-figment/gated-model is restricted and you are not in the authorized list.
+    Visit https://huggingface.co/ardent-figment/gated-model to ask for access.
+    ```
+    """
+
+
+class DisabledRepoError(HfHubHTTPError):
+    """
+    Raised when trying to access a repository that has been disabled by its author.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import dataset_info
+    >>> dataset_info("laion/laion-art")
+    (...)
+    huggingface_hub.utils._errors.DisabledRepoError: 403 Client Error. (Request ID: Root=1-659fc3fa-3031673e0f92c71a2260dbe2;bc6f4dfb-b30a-4862-af0a-5cfe827610d8)
+
+    Cannot access repository for url https://huggingface.co/api/datasets/laion/laion-art.
+    Access to this resource is disabled.
+    ```
+    """
+
+
+# REVISION ERROR
+
+
+class RevisionNotFoundError(HfHubHTTPError):
+    """
+    Raised when trying to access a hf.co URL with a valid repository but an invalid
+    revision.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import hf_hub_download
+    >>> hf_hub_download('bert-base-cased', 'config.json', revision='<non-existent-revision>')
+    (...)
+    huggingface_hub.utils._errors.RevisionNotFoundError: 404 Client Error. (Request ID: Mwhe_c3Kt650GcdKEFomX)
+
+    Revision Not Found for url: https://huggingface.co/bert-base-cased/resolve/%3Cnon-existent-revision%3E/config.json.
+    ```
+    """
+
+
+# ENTRY ERRORS
+class EntryNotFoundError(HfHubHTTPError):
+    """
+    Raised when trying to access a hf.co URL with a valid repository and revision
+    but an invalid filename.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import hf_hub_download
+    >>> hf_hub_download('bert-base-cased', '<non-existent-file>')
+    (...)
+    huggingface_hub.utils._errors.EntryNotFoundError: 404 Client Error. (Request ID: 53pNl6M0MxsnG5Sw8JA6x)
+
+    Entry Not Found for url: https://huggingface.co/bert-base-cased/resolve/main/%3Cnon-existent-file%3E.
+    ```
+    """
+
+
+class LocalEntryNotFoundError(EntryNotFoundError, FileNotFoundError, ValueError):
+    """
+    Raised when trying to access a file or snapshot that is not on the disk when network is
+    disabled or unavailable (connection issue). The entry may exist on the Hub.
+
+    Note: `ValueError` type is to ensure backward compatibility.
+    Note: `LocalEntryNotFoundError` derives from `HTTPError` because of `EntryNotFoundError`
+          even when it is not a network issue.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import hf_hub_download
+    >>> hf_hub_download('bert-base-cased', '<non-cached-file>',  local_files_only=True)
+    (...)
+    huggingface_hub.utils._errors.LocalEntryNotFoundError: Cannot find the requested files in the disk cache and outgoing traffic has been disabled. To enable hf.co look-ups and downloads online, set 'local_files_only' to False.
+    ```
+    """
+
+    def __init__(self, message: str):
+        super().__init__(message, response=None)
+
+
+# REQUEST ERROR
+class BadRequestError(HfHubHTTPError, ValueError):
+    """
+    Raised by `hf_raise_for_status` when the server returns a HTTP 400 error.
+
+    Example:
+
+    ```py
+    >>> resp = requests.post("hf.co/api/check", ...)
+    >>> hf_raise_for_status(resp, endpoint_name="check")
+    huggingface_hub.utils._errors.BadRequestError: Bad request for check endpoint: {details} (Request ID: XXX)
+    ```
+    """

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/fastai_utils.py

@@ -0,0 +1,424 @@
+import json
+import os
+from pathlib import Path
+from pickle import DEFAULT_PROTOCOL, PicklingError
+from typing import Any, Dict, List, Optional, Union
+
+from packaging import version
+
+from huggingface_hub import constants, snapshot_download
+from huggingface_hub.hf_api import HfApi
+from huggingface_hub.utils import (
+    SoftTemporaryDirectory,
+    get_fastai_version,
+    get_fastcore_version,
+    get_python_version,
+)
+
+from .utils import logging, validate_hf_hub_args
+from .utils._runtime import _PY_VERSION  # noqa: F401 # for backward compatibility...
+
+
+logger = logging.get_logger(__name__)
+
+
+def _check_fastai_fastcore_versions(
+    fastai_min_version: str = "2.4",
+    fastcore_min_version: str = "1.3.27",
+):
+    """
+    Checks that the installed fastai and fastcore versions are compatible for pickle serialization.
+
+    Args:
+        fastai_min_version (`str`, *optional*):
+            The minimum fastai version supported.
+        fastcore_min_version (`str`, *optional*):
+            The minimum fastcore version supported.
+
+    <Tip>
+    Raises the following error:
+
+        - [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+          if the fastai or fastcore libraries are not available or are of an invalid version.
+
+    </Tip>
+    """
+
+    if (get_fastcore_version() or get_fastai_version()) == "N/A":
+        raise ImportError(
+            f"fastai>={fastai_min_version} and fastcore>={fastcore_min_version} are"
+            f" required. Currently using fastai=={get_fastai_version()} and"
+            f" fastcore=={get_fastcore_version()}."
+        )
+
+    current_fastai_version = version.Version(get_fastai_version())
+    current_fastcore_version = version.Version(get_fastcore_version())
+
+    if current_fastai_version < version.Version(fastai_min_version):
+        raise ImportError(
+            "`push_to_hub_fastai` and `from_pretrained_fastai` require a"
+            f" fastai>={fastai_min_version} version, but you are using fastai version"
+            f" {get_fastai_version()} which is incompatible. Upgrade with `pip install"
+            " fastai==2.5.6`."
+        )
+
+    if current_fastcore_version < version.Version(fastcore_min_version):
+        raise ImportError(
+            "`push_to_hub_fastai` and `from_pretrained_fastai` require a"
+            f" fastcore>={fastcore_min_version} version, but you are using fastcore"
+            f" version {get_fastcore_version()} which is incompatible. Upgrade with"
+            " `pip install fastcore==1.3.27`."
+        )
+
+
+def _check_fastai_fastcore_pyproject_versions(
+    storage_folder: str,
+    fastai_min_version: str = "2.4",
+    fastcore_min_version: str = "1.3.27",
+):
+    """
+    Checks that the `pyproject.toml` file in the directory `storage_folder` has fastai and fastcore versions
+    that are compatible with `from_pretrained_fastai` and `push_to_hub_fastai`. If `pyproject.toml` does not exist
+    or does not contain versions for fastai and fastcore, then it logs a warning.
+
+    Args:
+        storage_folder (`str`):
+            Folder to look for the `pyproject.toml` file.
+        fastai_min_version (`str`, *optional*):
+            The minimum fastai version supported.
+        fastcore_min_version (`str`, *optional*):
+            The minimum fastcore version supported.
+
+    <Tip>
+    Raises the following errors:
+
+        - [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+          if the `toml` module is not installed.
+        - [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
+          if the `pyproject.toml` indicates a lower than minimum supported version of fastai or fastcore.
+
+    </Tip>
+    """
+
+    try:
+        import toml
+    except ModuleNotFoundError:
+        raise ImportError(
+            "`push_to_hub_fastai` and `from_pretrained_fastai` require the toml module."
+            " Install it with `pip install toml`."
+        )
+
+    # Checks that a `pyproject.toml`, with `build-system` and `requires` sections, exists in the repository. If so, get a list of required packages.
+    if not os.path.isfile(f"{storage_folder}/pyproject.toml"):
+        logger.warning(
+            "There is no `pyproject.toml` in the repository that contains the fastai"
+            " `Learner`. The `pyproject.toml` would allow us to verify that your fastai"
+            " and fastcore versions are compatible with those of the model you want to"
+            " load."
+        )
+        return
+    pyproject_toml = toml.load(f"{storage_folder}/pyproject.toml")
+
+    if "build-system" not in pyproject_toml.keys():
+        logger.warning(
+            "There is no `build-system` section in the pyproject.toml of the repository"
+            " that contains the fastai `Learner`. The `build-system` would allow us to"
+            " verify that your fastai and fastcore versions are compatible with those"
+            " of the model you want to load."
+        )
+        return
+    build_system_toml = pyproject_toml["build-system"]
+
+    if "requires" not in build_system_toml.keys():
+        logger.warning(
+            "There is no `requires` section in the pyproject.toml of the repository"
+            " that contains the fastai `Learner`. The `requires` would allow us to"
+            " verify that your fastai and fastcore versions are compatible with those"
+            " of the model you want to load."
+        )
+        return
+    package_versions = build_system_toml["requires"]
+
+    # Extracts contains fastai and fastcore versions from `pyproject.toml` if available.
+    # If the package is specified but not the version (e.g. "fastai" instead of "fastai=2.4"), the default versions are the highest.
+    fastai_packages = [pck for pck in package_versions if pck.startswith("fastai")]
+    if len(fastai_packages) == 0:
+        logger.warning("The repository does not have a fastai version specified in the `pyproject.toml`.")
+    # fastai_version is an empty string if not specified
+    else:
+        fastai_version = str(fastai_packages[0]).partition("=")[2]
+        if fastai_version != "" and version.Version(fastai_version) < version.Version(fastai_min_version):
+            raise ImportError(
+                "`from_pretrained_fastai` requires"
+                f" fastai>={fastai_min_version} version but the model to load uses"
+                f" {fastai_version} which is incompatible."
+            )
+
+    fastcore_packages = [pck for pck in package_versions if pck.startswith("fastcore")]
+    if len(fastcore_packages) == 0:
+        logger.warning("The repository does not have a fastcore version specified in the `pyproject.toml`.")
+    # fastcore_version is an empty string if not specified
+    else:
+        fastcore_version = str(fastcore_packages[0]).partition("=")[2]
+        if fastcore_version != "" and version.Version(fastcore_version) < version.Version(fastcore_min_version):
+            raise ImportError(
+                "`from_pretrained_fastai` requires"
+                f" fastcore>={fastcore_min_version} version, but you are using fastcore"
+                f" version {fastcore_version} which is incompatible."
+            )
+
+
+README_TEMPLATE = """---
+tags:
+- fastai
+---
+
+# Amazing!
+
+🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
+
+# Some next steps
+1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
+
+2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
+
+3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
+
+Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
+
+
+---
+
+
+# Model card
+
+## Model description
+More information needed
+
+## Intended uses & limitations
+More information needed
+
+## Training and evaluation data
+More information needed
+"""
+
+PYPROJECT_TEMPLATE = f"""[build-system]
+requires = ["setuptools>=40.8.0", "wheel", "python={get_python_version()}", "fastai={get_fastai_version()}", "fastcore={get_fastcore_version()}"]
+build-backend = "setuptools.build_meta:__legacy__"
+"""
+
+
+def _create_model_card(repo_dir: Path):
+    """
+    Creates a model card for the repository.
+
+    Args:
+        repo_dir (`Path`):
+            Directory where model card is created.
+    """
+    readme_path = repo_dir / "README.md"
+
+    if not readme_path.exists():
+        with readme_path.open("w", encoding="utf-8") as f:
+            f.write(README_TEMPLATE)
+
+
+def _create_model_pyproject(repo_dir: Path):
+    """
+    Creates a `pyproject.toml` for the repository.
+
+    Args:
+        repo_dir (`Path`):
+            Directory where `pyproject.toml` is created.
+    """
+    pyproject_path = repo_dir / "pyproject.toml"
+
+    if not pyproject_path.exists():
+        with pyproject_path.open("w", encoding="utf-8") as f:
+            f.write(PYPROJECT_TEMPLATE)
+
+
+def _save_pretrained_fastai(
+    learner,
+    save_directory: Union[str, Path],
+    config: Optional[Dict[str, Any]] = None,
+):
+    """
+    Saves a fastai learner to `save_directory` in pickle format using the default pickle protocol for the version of python used.
+
+    Args:
+        learner (`Learner`):
+            The `fastai.Learner` you'd like to save.
+        save_directory (`str` or `Path`):
+            Specific directory in which you want to save the fastai learner.
+        config (`dict`, *optional*):
+            Configuration object. Will be uploaded as a .json file. Example: 'https://huggingface.co/espejelomar/fastai-pet-breeds-classification/blob/main/config.json'.
+
+    <Tip>
+
+    Raises the following error:
+
+        - [`RuntimeError`](https://docs.python.org/3/library/exceptions.html#RuntimeError)
+          if the config file provided is not a dictionary.
+
+    </Tip>
+    """
+    _check_fastai_fastcore_versions()
+
+    os.makedirs(save_directory, exist_ok=True)
+
+    # if the user provides config then we update it with the fastai and fastcore versions in CONFIG_TEMPLATE.
+    if config is not None:
+        if not isinstance(config, dict):
+            raise RuntimeError(f"Provided config should be a dict. Got: '{type(config)}'")
+        path = os.path.join(save_directory, constants.CONFIG_NAME)
+        with open(path, "w") as f:
+            json.dump(config, f)
+
+    _create_model_card(Path(save_directory))
+    _create_model_pyproject(Path(save_directory))
+
+    # learner.export saves the model in `self.path`.
+    learner.path = Path(save_directory)
+    os.makedirs(save_directory, exist_ok=True)
+    try:
+        learner.export(
+            fname="model.pkl",
+            pickle_protocol=DEFAULT_PROTOCOL,
+        )
+    except PicklingError:
+        raise PicklingError(
+            "You are using a lambda function, i.e., an anonymous function. `pickle`"
+            " cannot pickle function objects and requires that all functions have"
+            " names. One possible solution is to name the function."
+        )
+
+
+@validate_hf_hub_args
+def from_pretrained_fastai(
+    repo_id: str,
+    revision: Optional[str] = None,
+):
+    """
+    Load pretrained fastai model from the Hub or from a local directory.
+
+    Args:
+        repo_id (`str`):
+            The location where the pickled fastai.Learner is. It can be either of the two:
+                - Hosted on the Hugging Face Hub. E.g.: 'espejelomar/fatai-pet-breeds-classification' or 'distilgpt2'.
+                  You can add a `revision` by appending `@` at the end of `repo_id`. E.g.: `dbmdz/bert-base-german-cased@main`.
+                  Revision is the specific model version to use. Since we use a git-based system for storing models and other
+                  artifacts on the Hugging Face Hub, it can be a branch name, a tag name, or a commit id.
+                - Hosted locally. `repo_id` would be a directory containing the pickle and a pyproject.toml
+                  indicating the fastai and fastcore versions used to build the `fastai.Learner`. E.g.: `./my_model_directory/`.
+        revision (`str`, *optional*):
+            Revision at which the repo's files are downloaded. See documentation of `snapshot_download`.
+
+    Returns:
+        The `fastai.Learner` model in the `repo_id` repo.
+    """
+    _check_fastai_fastcore_versions()
+
+    # Load the `repo_id` repo.
+    # `snapshot_download` returns the folder where the model was stored.
+    # `cache_dir` will be the default '/root/.cache/huggingface/hub'
+    if not os.path.isdir(repo_id):
+        storage_folder = snapshot_download(
+            repo_id=repo_id,
+            revision=revision,
+            library_name="fastai",
+            library_version=get_fastai_version(),
+        )
+    else:
+        storage_folder = repo_id
+
+    _check_fastai_fastcore_pyproject_versions(storage_folder)
+
+    from fastai.learner import load_learner  # type: ignore
+
+    return load_learner(os.path.join(storage_folder, "model.pkl"))
+
+
+@validate_hf_hub_args
+def push_to_hub_fastai(
+    learner,
+    *,
+    repo_id: str,
+    commit_message: str = "Push FastAI model using huggingface_hub.",
+    private: bool = False,
+    token: Optional[str] = None,
+    config: Optional[dict] = None,
+    branch: Optional[str] = None,
+    create_pr: Optional[bool] = None,
+    allow_patterns: Optional[Union[List[str], str]] = None,
+    ignore_patterns: Optional[Union[List[str], str]] = None,
+    delete_patterns: Optional[Union[List[str], str]] = None,
+    api_endpoint: Optional[str] = None,
+):
+    """
+    Upload learner checkpoint files to the Hub.
+
+    Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use
+    `delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more
+    details.
+
+    Args:
+        learner (`Learner`):
+            The `fastai.Learner' you'd like to push to the Hub.
+        repo_id (`str`):
+            The repository id for your model in Hub in the format of "namespace/repo_name". The namespace can be your individual account or an organization to which you have write access (for example, 'stanfordnlp/stanza-de').
+        commit_message (`str`, *optional*):
+            Message to commit while pushing. Will default to :obj:`"add model"`.
+        private (`bool`, *optional*, defaults to `False`):
+            Whether or not the repository created should be private.
+        token (`str`, *optional*):
+            The Hugging Face account token to use as HTTP bearer authorization for remote files. If :obj:`None`, the token will be asked by a prompt.
+        config (`dict`, *optional*):
+            Configuration object to be saved alongside the model weights.
+        branch (`str`, *optional*):
+            The git branch on which to push the model. This defaults to
+            the default branch as specified in your repository, which
+            defaults to `"main"`.
+        create_pr (`boolean`, *optional*):
+            Whether or not to create a Pull Request from `branch` with that commit.
+            Defaults to `False`.
+        api_endpoint (`str`, *optional*):
+            The API endpoint to use when pushing the model to the hub.
+        allow_patterns (`List[str]` or `str`, *optional*):
+            If provided, only files matching at least one pattern are pushed.
+        ignore_patterns (`List[str]` or `str`, *optional*):
+            If provided, files matching any of the patterns are not pushed.
+        delete_patterns (`List[str]` or `str`, *optional*):
+            If provided, remote files matching any of the patterns will be deleted from the repo.
+
+    Returns:
+        The url of the commit of your model in the given repository.
+
+    <Tip>
+
+    Raises the following error:
+
+        - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+          if the user is not log on to the Hugging Face Hub.
+
+    </Tip>
+    """
+    _check_fastai_fastcore_versions()
+    api = HfApi(endpoint=api_endpoint)
+    repo_id = api.create_repo(repo_id=repo_id, token=token, private=private, exist_ok=True).repo_id
+
+    # Push the files to the repo in a single commit
+    with SoftTemporaryDirectory() as tmp:
+        saved_path = Path(tmp) / repo_id
+        _save_pretrained_fastai(learner, saved_path, config=config)
+        return api.upload_folder(
+            repo_id=repo_id,
+            token=token,
+            folder_path=saved_path,
+            commit_message=commit_message,
+            revision=branch,
+            create_pr=create_pr,
+            allow_patterns=allow_patterns,
+            ignore_patterns=ignore_patterns,
+            delete_patterns=delete_patterns,
+        )

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/file_download.py

@@ -0,0 +1,1624 @@
+import contextlib
+import copy
+import errno
+import inspect
+import os
+import re
+import shutil
+import stat
+import time
+import uuid
+import warnings
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, BinaryIO, Dict, Literal, NoReturn, Optional, Tuple, Union
+from urllib.parse import quote, urlparse
+
+import requests
+
+from . import (
+    __version__,  # noqa: F401 # for backward compatibility
+    constants,
+)
+from ._local_folder import get_local_download_paths, read_download_metadata, write_download_metadata
+from .constants import (
+    HUGGINGFACE_CO_URL_TEMPLATE,  # noqa: F401 # for backward compatibility
+    HUGGINGFACE_HUB_CACHE,  # noqa: F401 # for backward compatibility
+)
+from .errors import (
+    EntryNotFoundError,
+    FileMetadataError,
+    GatedRepoError,
+    LocalEntryNotFoundError,
+    RepositoryNotFoundError,
+    RevisionNotFoundError,
+)
+from .utils import (
+    OfflineModeIsEnabled,
+    SoftTemporaryDirectory,
+    WeakFileLock,
+    build_hf_headers,
+    get_fastai_version,  # noqa: F401 # for backward compatibility
+    get_fastcore_version,  # noqa: F401 # for backward compatibility
+    get_graphviz_version,  # noqa: F401 # for backward compatibility
+    get_jinja_version,  # noqa: F401 # for backward compatibility
+    get_pydot_version,  # noqa: F401 # for backward compatibility
+    get_session,
+    get_tf_version,  # noqa: F401 # for backward compatibility
+    get_torch_version,  # noqa: F401 # for backward compatibility
+    hf_raise_for_status,
+    is_fastai_available,  # noqa: F401 # for backward compatibility
+    is_fastcore_available,  # noqa: F401 # for backward compatibility
+    is_graphviz_available,  # noqa: F401 # for backward compatibility
+    is_jinja_available,  # noqa: F401 # for backward compatibility
+    is_pydot_available,  # noqa: F401 # for backward compatibility
+    is_tf_available,  # noqa: F401 # for backward compatibility
+    is_torch_available,  # noqa: F401 # for backward compatibility
+    logging,
+    reset_sessions,
+    tqdm,
+    validate_hf_hub_args,
+)
+from .utils._runtime import _PY_VERSION  # noqa: F401 # for backward compatibility
+from .utils._typing import HTTP_METHOD_T
+from .utils.sha import sha_fileobj
+
+
+logger = logging.get_logger(__name__)
+
+# Return value when trying to load a file from cache but the file does not exist in the distant repo.
+_CACHED_NO_EXIST = object()
+_CACHED_NO_EXIST_T = Any
+
+# Regex to get filename from a "Content-Disposition" header for CDN-served files
+HEADER_FILENAME_PATTERN = re.compile(r'filename="(?P<filename>.*?)";')
+
+# Regex to check if the revision IS directly a commit_hash
+REGEX_COMMIT_HASH = re.compile(r"^[0-9a-f]{40}$")
+
+# Regex to check if the file etag IS a valid sha256
+REGEX_SHA256 = re.compile(r"^[0-9a-f]{64}$")
+
+_are_symlinks_supported_in_dir: Dict[str, bool] = {}
+
+
+def are_symlinks_supported(cache_dir: Union[str, Path, None] = None) -> bool:
+    """Return whether the symlinks are supported on the machine.
+
+    Since symlinks support can change depending on the mounted disk, we need to check
+    on the precise cache folder. By default, the default HF cache directory is checked.
+
+    Args:
+        cache_dir (`str`, `Path`, *optional*):
+            Path to the folder where cached files are stored.
+
+    Returns: [bool] Whether symlinks are supported in the directory.
+    """
+    # Defaults to HF cache
+    if cache_dir is None:
+        cache_dir = constants.HF_HUB_CACHE
+    cache_dir = str(Path(cache_dir).expanduser().resolve())  # make it unique
+
+    # Check symlink compatibility only once (per cache directory) at first time use
+    if cache_dir not in _are_symlinks_supported_in_dir:
+        _are_symlinks_supported_in_dir[cache_dir] = True
+
+        os.makedirs(cache_dir, exist_ok=True)
+        with SoftTemporaryDirectory(dir=cache_dir) as tmpdir:
+            src_path = Path(tmpdir) / "dummy_file_src"
+            src_path.touch()
+            dst_path = Path(tmpdir) / "dummy_file_dst"
+
+            # Relative source path as in `_create_symlink``
+            relative_src = os.path.relpath(src_path, start=os.path.dirname(dst_path))
+            try:
+                os.symlink(relative_src, dst_path)
+            except OSError:
+                # Likely running on Windows
+                _are_symlinks_supported_in_dir[cache_dir] = False
+
+                if not constants.HF_HUB_DISABLE_SYMLINKS_WARNING:
+                    message = (
+                        "`huggingface_hub` cache-system uses symlinks by default to"
+                        " efficiently store duplicated files but your machine does not"
+                        f" support them in {cache_dir}. Caching files will still work"
+                        " but in a degraded version that might require more space on"
+                        " your disk. This warning can be disabled by setting the"
+                        " `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For"
+                        " more details, see"
+                        " https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations."
+                    )
+                    if os.name == "nt":
+                        message += (
+                            "\nTo support symlinks on Windows, you either need to"
+                            " activate Developer Mode or to run Python as an"
+                            " administrator. In order to activate developer mode,"
+                            " see this article:"
+                            " https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development"
+                        )
+                    warnings.warn(message)
+
+    return _are_symlinks_supported_in_dir[cache_dir]
+
+
+@dataclass(frozen=True)
+class HfFileMetadata:
+    """Data structure containing information about a file versioned on the Hub.
+
+    Returned by [`get_hf_file_metadata`] based on a URL.
+
+    Args:
+        commit_hash (`str`, *optional*):
+            The commit_hash related to the file.
+        etag (`str`, *optional*):
+            Etag of the file on the server.
+        location (`str`):
+            Location where to download the file. Can be a Hub url or not (CDN).
+        size (`size`):
+            Size of the file. In case of an LFS file, contains the size of the actual
+            LFS file, not the pointer.
+    """
+
+    commit_hash: Optional[str]
+    etag: Optional[str]
+    location: str
+    size: Optional[int]
+
+
+@validate_hf_hub_args
+def hf_hub_url(
+    repo_id: str,
+    filename: str,
+    *,
+    subfolder: Optional[str] = None,
+    repo_type: Optional[str] = None,
+    revision: Optional[str] = None,
+    endpoint: Optional[str] = None,
+) -> str:
+    """Construct the URL of a file from the given information.
+
+    The resolved address can either be a huggingface.co-hosted url, or a link to
+    Cloudfront (a Content Delivery Network, or CDN) for large files which are
+    more than a few MBs.
+
+    Args:
+        repo_id (`str`):
+            A namespace (user or an organization) name and a repo name separated
+            by a `/`.
+        filename (`str`):
+            The name of the file in the repo.
+        subfolder (`str`, *optional*):
+            An optional value corresponding to a folder inside the repo.
+        repo_type (`str`, *optional*):
+            Set to `"dataset"` or `"space"` if downloading from a dataset or space,
+            `None` or `"model"` if downloading from a model. Default is `None`.
+        revision (`str`, *optional*):
+            An optional Git revision id which can be a branch name, a tag, or a
+            commit hash.
+
+    Example:
+
+    ```python
+    >>> from huggingface_hub import hf_hub_url
+
+    >>> hf_hub_url(
+    ...     repo_id="julien-c/EsperBERTo-small", filename="pytorch_model.bin"
+    ... )
+    'https://huggingface.co/julien-c/EsperBERTo-small/resolve/main/pytorch_model.bin'
+    ```
+
+    <Tip>
+
+    Notes:
+
+        Cloudfront is replicated over the globe so downloads are way faster for
+        the end user (and it also lowers our bandwidth costs).
+
+        Cloudfront aggressively caches files by default (default TTL is 24
+        hours), however this is not an issue here because we implement a
+        git-based versioning system on huggingface.co, which means that we store
+        the files on S3/Cloudfront in a content-addressable way (i.e., the file
+        name is its hash). Using content-addressable filenames means cache can't
+        ever be stale.
+
+        In terms of client-side caching from this library, we base our caching
+        on the objects' entity tag (`ETag`), which is an identifier of a
+        specific version of a resource [1]_. An object's ETag is: its git-sha1
+        if stored in git, or its sha256 if stored in git-lfs.
+
+    </Tip>
+
+    References:
+
+    -  [1] https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/ETag
+    """
+    if subfolder == "":
+        subfolder = None
+    if subfolder is not None:
+        filename = f"{subfolder}/{filename}"
+
+    if repo_type not in constants.REPO_TYPES:
+        raise ValueError("Invalid repo type")
+
+    if repo_type in constants.REPO_TYPES_URL_PREFIXES:
+        repo_id = constants.REPO_TYPES_URL_PREFIXES[repo_type] + repo_id
+
+    if revision is None:
+        revision = constants.DEFAULT_REVISION
+    url = HUGGINGFACE_CO_URL_TEMPLATE.format(
+        repo_id=repo_id, revision=quote(revision, safe=""), filename=quote(filename)
+    )
+    # Update endpoint if provided
+    if endpoint is not None and url.startswith(constants.ENDPOINT):
+        url = endpoint + url[len(constants.ENDPOINT) :]
+    return url
+
+
+def _request_wrapper(
+    method: HTTP_METHOD_T, url: str, *, follow_relative_redirects: bool = False, **params
+) -> requests.Response:
+    """Wrapper around requests methods to follow relative redirects if `follow_relative_redirects=True` even when
+    `allow_redirection=False`.
+
+    Args:
+        method (`str`):
+            HTTP method, such as 'GET' or 'HEAD'.
+        url (`str`):
+            The URL of the resource to fetch.
+        follow_relative_redirects (`bool`, *optional*, defaults to `False`)
+            If True, relative redirection (redirection to the same site) will be resolved even when `allow_redirection`
+            kwarg is set to False. Useful when we want to follow a redirection to a renamed repository without
+            following redirection to a CDN.
+        **params (`dict`, *optional*):
+            Params to pass to `requests.request`.
+    """
+    # Recursively follow relative redirects
+    if follow_relative_redirects:
+        response = _request_wrapper(
+            method=method,
+            url=url,
+            follow_relative_redirects=False,
+            **params,
+        )
+
+        # If redirection, we redirect only relative paths.
+        # This is useful in case of a renamed repository.
+        if 300 <= response.status_code <= 399:
+            parsed_target = urlparse(response.headers["Location"])
+            if parsed_target.netloc == "":
+                # This means it is a relative 'location' headers, as allowed by RFC 7231.
+                # (e.g. '/path/to/resource' instead of 'http://domain.tld/path/to/resource')
+                # We want to follow this relative redirect !
+                #
+                # Highly inspired by `resolve_redirects` from requests library.
+                # See https://github.com/psf/requests/blob/main/requests/sessions.py#L159
+                next_url = urlparse(url)._replace(path=parsed_target.path).geturl()
+                return _request_wrapper(method=method, url=next_url, follow_relative_redirects=True, **params)
+        return response
+
+    # Perform request and return if status_code is not in the retry list.
+    response = get_session().request(method=method, url=url, **params)
+    hf_raise_for_status(response)
+    return response
+
+
+def http_get(
+    url: str,
+    temp_file: BinaryIO,
+    *,
+    proxies: Optional[Dict] = None,
+    resume_size: float = 0,
+    headers: Optional[Dict[str, str]] = None,
+    expected_size: Optional[int] = None,
+    displayed_filename: Optional[str] = None,
+    _nb_retries: int = 5,
+    _tqdm_bar: Optional[tqdm] = None,
+) -> None:
+    """
+    Download a remote file. Do not gobble up errors, and will return errors tailored to the Hugging Face Hub.
+
+    If ConnectionError (SSLError) or ReadTimeout happen while streaming data from the server, it is most likely a
+    transient error (network outage?). We log a warning message and try to resume the download a few times before
+    giving up. The method gives up after 5 attempts if no new data has being received from the server.
+
+    Args:
+        url (`str`):
+            The URL of the file to download.
+        temp_file (`BinaryIO`):
+            The file-like object where to save the file.
+        proxies (`dict`, *optional*):
+            Dictionary mapping protocol to the URL of the proxy passed to `requests.request`.
+        resume_size (`float`, *optional*):
+            The number of bytes already downloaded. If set to 0 (default), the whole file is download. If set to a
+            positive number, the download will resume at the given position.
+        headers (`dict`, *optional*):
+            Dictionary of HTTP Headers to send with the request.
+        expected_size (`int`, *optional*):
+            The expected size of the file to download. If set, the download will raise an error if the size of the
+            received content is different from the expected one.
+        displayed_filename (`str`, *optional*):
+            The filename of the file that is being downloaded. Value is used only to display a nice progress bar. If
+            not set, the filename is guessed from the URL or the `Content-Disposition` header.
+    """
+    if expected_size is not None and resume_size == expected_size:
+        # If the file is already fully downloaded, we don't need to download it again.
+        return
+
+    hf_transfer = None
+    if constants.HF_HUB_ENABLE_HF_TRANSFER:
+        if resume_size != 0:
+            warnings.warn("'hf_transfer' does not support `resume_size`: falling back to regular download method")
+        elif proxies is not None:
+            warnings.warn("'hf_transfer' does not support `proxies`: falling back to regular download method")
+        else:
+            try:
+                import hf_transfer  # type: ignore[no-redef]
+            except ImportError:
+                raise ValueError(
+                    "Fast download using 'hf_transfer' is enabled"
+                    " (HF_HUB_ENABLE_HF_TRANSFER=1) but 'hf_transfer' package is not"
+                    " available in your environment. Try `pip install hf_transfer`."
+                )
+
+    initial_headers = headers
+    headers = copy.deepcopy(headers) or {}
+    if resume_size > 0:
+        headers["Range"] = "bytes=%d-" % (resume_size,)
+
+    r = _request_wrapper(
+        method="GET", url=url, stream=True, proxies=proxies, headers=headers, timeout=constants.HF_HUB_DOWNLOAD_TIMEOUT
+    )
+    hf_raise_for_status(r)
+    content_length = r.headers.get("Content-Length")
+
+    # NOTE: 'total' is the total number of bytes to download, not the number of bytes in the file.
+    #       If the file is compressed, the number of bytes in the saved file will be higher than 'total'.
+    total = resume_size + int(content_length) if content_length is not None else None
+
+    if displayed_filename is None:
+        displayed_filename = url
+        content_disposition = r.headers.get("Content-Disposition")
+        if content_disposition is not None:
+            match = HEADER_FILENAME_PATTERN.search(content_disposition)
+            if match is not None:
+                # Means file is on CDN
+                displayed_filename = match.groupdict()["filename"]
+
+    # Truncate filename if too long to display
+    if len(displayed_filename) > 40:
+        displayed_filename = f"(…){displayed_filename[-40:]}"
+
+    consistency_error_message = (
+        f"Consistency check failed: file should be of size {expected_size} but has size"
+        f" {{actual_size}} ({displayed_filename}).\nWe are sorry for the inconvenience. Please retry"
+        " with `force_download=True`.\nIf the issue persists, please let us know by opening an issue "
+        "on https://github.com/huggingface/huggingface_hub."
+    )
+
+    # Stream file to buffer
+    progress_cm: tqdm = (
+        tqdm(  # type: ignore[assignment]
+            unit="B",
+            unit_scale=True,
+            total=total,
+            initial=resume_size,
+            desc=displayed_filename,
+            disable=True if (logger.getEffectiveLevel() == logging.NOTSET) else None,
+            # ^ set `disable=None` rather than `disable=False` by default to disable progress bar when no TTY attached
+            # see https://github.com/huggingface/huggingface_hub/pull/2000
+            name="huggingface_hub.http_get",
+        )
+        if _tqdm_bar is None
+        else contextlib.nullcontext(_tqdm_bar)
+        # ^ `contextlib.nullcontext` mimics a context manager that does nothing
+        #   Makes it easier to use the same code path for both cases but in the later
+        #   case, the progress bar is not closed when exiting the context manager.
+    )
+
+    with progress_cm as progress:
+        if hf_transfer and total is not None and total > 5 * constants.DOWNLOAD_CHUNK_SIZE:
+            supports_callback = "callback" in inspect.signature(hf_transfer.download).parameters
+            if not supports_callback:
+                warnings.warn(
+                    "You are using an outdated version of `hf_transfer`. "
+                    "Consider upgrading to latest version to enable progress bars "
+                    "using `pip install -U hf_transfer`."
+                )
+            try:
+                hf_transfer.download(
+                    url=url,
+                    filename=temp_file.name,
+                    max_files=constants.HF_TRANSFER_CONCURRENCY,
+                    chunk_size=constants.DOWNLOAD_CHUNK_SIZE,
+                    headers=headers,
+                    parallel_failures=3,
+                    max_retries=5,
+                    **({"callback": progress.update} if supports_callback else {}),
+                )
+            except Exception as e:
+                raise RuntimeError(
+                    "An error occurred while downloading using `hf_transfer`. Consider"
+                    " disabling HF_HUB_ENABLE_HF_TRANSFER for better error handling."
+                ) from e
+            if not supports_callback:
+                progress.update(total)
+            if expected_size is not None and expected_size != os.path.getsize(temp_file.name):
+                raise EnvironmentError(
+                    consistency_error_message.format(
+                        actual_size=os.path.getsize(temp_file.name),
+                    )
+                )
+            return
+        new_resume_size = resume_size
+        try:
+            for chunk in r.iter_content(chunk_size=constants.DOWNLOAD_CHUNK_SIZE):
+                if chunk:  # filter out keep-alive new chunks
+                    progress.update(len(chunk))
+                    temp_file.write(chunk)
+                    new_resume_size += len(chunk)
+                    # Some data has been downloaded from the server so we reset the number of retries.
+                    _nb_retries = 5
+        except (requests.ConnectionError, requests.ReadTimeout) as e:
+            # If ConnectionError (SSLError) or ReadTimeout happen while streaming data from the server, it is most likely
+            # a transient error (network outage?). We log a warning message and try to resume the download a few times
+            # before giving up. Tre retry mechanism is basic but should be enough in most cases.
+            if _nb_retries <= 0:
+                logger.warning("Error while downloading from %s: %s\nMax retries exceeded.", url, str(e))
+                raise
+            logger.warning("Error while downloading from %s: %s\nTrying to resume download...", url, str(e))
+            time.sleep(1)
+            reset_sessions()  # In case of SSLError it's best to reset the shared requests.Session objects
+            return http_get(
+                url=url,
+                temp_file=temp_file,
+                proxies=proxies,
+                resume_size=new_resume_size,
+                headers=initial_headers,
+                expected_size=expected_size,
+                _nb_retries=_nb_retries - 1,
+                _tqdm_bar=_tqdm_bar,
+            )
+
+    if expected_size is not None and expected_size != temp_file.tell():
+        raise EnvironmentError(
+            consistency_error_message.format(
+                actual_size=temp_file.tell(),
+            )
+        )
+
+
+def _normalize_etag(etag: Optional[str]) -> Optional[str]:
+    """Normalize ETag HTTP header, so it can be used to create nice filepaths.
+
+    The HTTP spec allows two forms of ETag:
+      ETag: W/"<etag_value>"
+      ETag: "<etag_value>"
+
+    For now, we only expect the second form from the server, but we want to be future-proof so we support both. For
+    more context, see `TestNormalizeEtag` tests and https://github.com/huggingface/huggingface_hub/pull/1428.
+
+    Args:
+        etag (`str`, *optional*): HTTP header
+
+    Returns:
+        `str` or `None`: string that can be used as a nice directory name.
+        Returns `None` if input is None.
+    """
+    if etag is None:
+        return None
+    return etag.lstrip("W/").strip('"')
+
+
+def _create_relative_symlink(src: str, dst: str, new_blob: bool = False) -> None:
+    """Alias method used in `transformers` conversion script."""
+    return _create_symlink(src=src, dst=dst, new_blob=new_blob)
+
+
+def _create_symlink(src: str, dst: str, new_blob: bool = False) -> None:
+    """Create a symbolic link named dst pointing to src.
+
+    By default, it will try to create a symlink using a relative path. Relative paths have 2 advantages:
+    - If the cache_folder is moved (example: back-up on a shared drive), relative paths within the cache folder will
+      not break.
+    - Relative paths seems to be better handled on Windows. Issue was reported 3 times in less than a week when
+      changing from relative to absolute paths. See https://github.com/huggingface/huggingface_hub/issues/1398,
+      https://github.com/huggingface/diffusers/issues/2729 and https://github.com/huggingface/transformers/pull/22228.
+      NOTE: The issue with absolute paths doesn't happen on admin mode.
+    When creating a symlink from the cache to a local folder, it is possible that a relative path cannot be created.
+    This happens when paths are not on the same volume. In that case, we use absolute paths.
+
+
+    The result layout looks something like
+        └── [ 128]  snapshots
+            ├── [ 128]  2439f60ef33a0d46d85da5001d52aeda5b00ce9f
+            │   ├── [  52]  README.md -> ../../../blobs/d7edf6bd2a681fb0175f7735299831ee1b22b812
+            │   └── [  76]  pytorch_model.bin -> ../../../blobs/403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd
+
+    If symlinks cannot be created on this platform (most likely to be Windows), the workaround is to avoid symlinks by
+    having the actual file in `dst`. If it is a new file (`new_blob=True`), we move it to `dst`. If it is not a new file
+    (`new_blob=False`), we don't know if the blob file is already referenced elsewhere. To avoid breaking existing
+    cache, the file is duplicated on the disk.
+
+    In case symlinks are not supported, a warning message is displayed to the user once when loading `huggingface_hub`.
+    The warning message can be disabled with the `DISABLE_SYMLINKS_WARNING` environment variable.
+    """
+    try:
+        os.remove(dst)
+    except OSError:
+        pass
+
+    abs_src = os.path.abspath(os.path.expanduser(src))
+    abs_dst = os.path.abspath(os.path.expanduser(dst))
+    abs_dst_folder = os.path.dirname(abs_dst)
+
+    # Use relative_dst in priority
+    try:
+        relative_src = os.path.relpath(abs_src, abs_dst_folder)
+    except ValueError:
+        # Raised on Windows if src and dst are not on the same volume. This is the case when creating a symlink to a
+        # local_dir instead of within the cache directory.
+        # See https://docs.python.org/3/library/os.path.html#os.path.relpath
+        relative_src = None
+
+    try:
+        commonpath = os.path.commonpath([abs_src, abs_dst])
+        _support_symlinks = are_symlinks_supported(commonpath)
+    except ValueError:
+        # Raised if src and dst are not on the same volume. Symlinks will still work on Linux/Macos.
+        # See https://docs.python.org/3/library/os.path.html#os.path.commonpath
+        _support_symlinks = os.name != "nt"
+    except PermissionError:
+        # Permission error means src and dst are not in the same volume (e.g. destination path has been provided
+        # by the user via `local_dir`. Let's test symlink support there)
+        _support_symlinks = are_symlinks_supported(abs_dst_folder)
+    except OSError as e:
+        # OS error (errno=30) means that the commonpath is readonly on Linux/MacOS.
+        if e.errno == errno.EROFS:
+            _support_symlinks = are_symlinks_supported(abs_dst_folder)
+        else:
+            raise
+
+    # Symlinks are supported => let's create a symlink.
+    if _support_symlinks:
+        src_rel_or_abs = relative_src or abs_src
+        logger.debug(f"Creating pointer from {src_rel_or_abs} to {abs_dst}")
+        try:
+            os.symlink(src_rel_or_abs, abs_dst)
+            return
+        except FileExistsError:
+            if os.path.islink(abs_dst) and os.path.realpath(abs_dst) == os.path.realpath(abs_src):
+                # `abs_dst` already exists and is a symlink to the `abs_src` blob. It is most likely that the file has
+                # been cached twice concurrently (exactly between `os.remove` and `os.symlink`). Do nothing.
+                return
+            else:
+                # Very unlikely to happen. Means a file `dst` has been created exactly between `os.remove` and
+                # `os.symlink` and is not a symlink to the `abs_src` blob file. Raise exception.
+                raise
+        except PermissionError:
+            # Permission error means src and dst are not in the same volume (e.g. download to local dir) and symlink
+            # is supported on both volumes but not between them. Let's just make a hard copy in that case.
+            pass
+
+    # Symlinks are not supported => let's move or copy the file.
+    if new_blob:
+        logger.info(f"Symlink not supported. Moving file from {abs_src} to {abs_dst}")
+        shutil.move(abs_src, abs_dst, copy_function=_copy_no_matter_what)
+    else:
+        logger.info(f"Symlink not supported. Copying file from {abs_src} to {abs_dst}")
+        shutil.copyfile(abs_src, abs_dst)
+
+
+def _cache_commit_hash_for_specific_revision(storage_folder: str, revision: str, commit_hash: str) -> None:
+    """Cache reference between a revision (tag, branch or truncated commit hash) and the corresponding commit hash.
+
+    Does nothing if `revision` is already a proper `commit_hash` or reference is already cached.
+    """
+    if revision != commit_hash:
+        ref_path = Path(storage_folder) / "refs" / revision
+        ref_path.parent.mkdir(parents=True, exist_ok=True)
+        if not ref_path.exists() or commit_hash != ref_path.read_text():
+            # Update ref only if has been updated. Could cause useless error in case
+            # repo is already cached and user doesn't have write access to cache folder.
+            # See https://github.com/huggingface/huggingface_hub/issues/1216.
+            ref_path.write_text(commit_hash)
+
+
+@validate_hf_hub_args
+def repo_folder_name(*, repo_id: str, repo_type: str) -> str:
+    """Return a serialized version of a hf.co repo name and type, safe for disk storage
+    as a single non-nested folder.
+
+    Example: models--julien-c--EsperBERTo-small
+    """
+    # remove all `/` occurrences to correctly convert repo to directory name
+    parts = [f"{repo_type}s", *repo_id.split("/")]
+    return constants.REPO_ID_SEPARATOR.join(parts)
+
+
+def _check_disk_space(expected_size: int, target_dir: Union[str, Path]) -> None:
+    """Check disk usage and log a warning if there is not enough disk space to download the file.
+
+    Args:
+        expected_size (`int`):
+            The expected size of the file in bytes.
+        target_dir (`str`):
+            The directory where the file will be stored after downloading.
+    """
+
+    target_dir = Path(target_dir)  # format as `Path`
+    for path in [target_dir] + list(target_dir.parents):  # first check target_dir, then each parents one by one
+        try:
+            target_dir_free = shutil.disk_usage(path).free
+            if target_dir_free < expected_size:
+                warnings.warn(
+                    "Not enough free disk space to download the file. "
+                    f"The expected file size is: {expected_size / 1e6:.2f} MB. "
+                    f"The target location {target_dir} only has {target_dir_free / 1e6:.2f} MB free disk space."
+                )
+            return
+        except OSError:  # raise on anything: file does not exist or space disk cannot be checked
+            pass
+
+
+@validate_hf_hub_args
+def hf_hub_download(
+    repo_id: str,
+    filename: str,
+    *,
+    subfolder: Optional[str] = None,
+    repo_type: Optional[str] = None,
+    revision: Optional[str] = None,
+    library_name: Optional[str] = None,
+    library_version: Optional[str] = None,
+    cache_dir: Union[str, Path, None] = None,
+    local_dir: Union[str, Path, None] = None,
+    user_agent: Union[Dict, str, None] = None,
+    force_download: bool = False,
+    proxies: Optional[Dict] = None,
+    etag_timeout: float = constants.DEFAULT_ETAG_TIMEOUT,
+    token: Union[bool, str, None] = None,
+    local_files_only: bool = False,
+    headers: Optional[Dict[str, str]] = None,
+    endpoint: Optional[str] = None,
+    resume_download: Optional[bool] = None,
+    force_filename: Optional[str] = None,
+    local_dir_use_symlinks: Union[bool, Literal["auto"]] = "auto",
+) -> str:
+    """Download a given file if it's not already present in the local cache.
+
+    The new cache file layout looks like this:
+    - The cache directory contains one subfolder per repo_id (namespaced by repo type)
+    - inside each repo folder:
+        - refs is a list of the latest known revision => commit_hash pairs
+        - blobs contains the actual file blobs (identified by their git-sha or sha256, depending on
+          whether they're LFS files or not)
+        - snapshots contains one subfolder per commit, each "commit" contains the subset of the files
+          that have been resolved at that particular commit. Each filename is a symlink to the blob
+          at that particular commit.
+
+    ```
+    [  96]  .
+    └── [ 160]  models--julien-c--EsperBERTo-small
+        ├── [ 160]  blobs
+        │   ├── [321M]  403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd
+        │   ├── [ 398]  7cb18dc9bafbfcf74629a4b760af1b160957a83e
+        │   └── [1.4K]  d7edf6bd2a681fb0175f7735299831ee1b22b812
+        ├── [  96]  refs
+        │   └── [  40]  main
+        └── [ 128]  snapshots
+            ├── [ 128]  2439f60ef33a0d46d85da5001d52aeda5b00ce9f
+            │   ├── [  52]  README.md -> ../../blobs/d7edf6bd2a681fb0175f7735299831ee1b22b812
+            │   └── [  76]  pytorch_model.bin -> ../../blobs/403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd
+            └── [ 128]  bbc77c8132af1cc5cf678da3f1ddf2de43606d48
+                ├── [  52]  README.md -> ../../blobs/7cb18dc9bafbfcf74629a4b760af1b160957a83e
+                └── [  76]  pytorch_model.bin -> ../../blobs/403450e234d65943a7dcf7e05a771ce3c92faa84dd07db4ac20f592037a1e4bd
+    ```
+
+    If `local_dir` is provided, the file structure from the repo will be replicated in this location. When using this
+    option, the `cache_dir` will not be used and a `.cache/huggingface/` folder will be created at the root of `local_dir`
+    to store some metadata related to the downloaded files. While this mechanism is not as robust as the main
+    cache-system, it's optimized for regularly pulling the latest version of a repository.
+
+    Args:
+        repo_id (`str`):
+            A user or an organization name and a repo name separated by a `/`.
+        filename (`str`):
+            The name of the file in the repo.
+        subfolder (`str`, *optional*):
+            An optional value corresponding to a folder inside the model repo.
+        repo_type (`str`, *optional*):
+            Set to `"dataset"` or `"space"` if downloading from a dataset or space,
+            `None` or `"model"` if downloading from a model. Default is `None`.
+        revision (`str`, *optional*):
+            An optional Git revision id which can be a branch name, a tag, or a
+            commit hash.
+        library_name (`str`, *optional*):
+            The name of the library to which the object corresponds.
+        library_version (`str`, *optional*):
+            The version of the library.
+        cache_dir (`str`, `Path`, *optional*):
+            Path to the folder where cached files are stored.
+        local_dir (`str` or `Path`, *optional*):
+            If provided, the downloaded file will be placed under this directory.
+        user_agent (`dict`, `str`, *optional*):
+            The user-agent info in the form of a dictionary or a string.
+        force_download (`bool`, *optional*, defaults to `False`):
+            Whether the file should be downloaded even if it already exists in
+            the local cache.
+        proxies (`dict`, *optional*):
+            Dictionary mapping protocol to the URL of the proxy passed to
+            `requests.request`.
+        etag_timeout (`float`, *optional*, defaults to `10`):
+            When fetching ETag, how many seconds to wait for the server to send
+            data before giving up which is passed to `requests.request`.
+        token (`str`, `bool`, *optional*):
+            A token to be used for the download.
+                - If `True`, the token is read from the HuggingFace config
+                  folder.
+                - If a string, it's used as the authentication token.
+        local_files_only (`bool`, *optional*, defaults to `False`):
+            If `True`, avoid downloading the file and return the path to the
+            local cached file if it exists.
+        headers (`dict`, *optional*):
+            Additional headers to be sent with the request.
+
+    Returns:
+        `str`: Local path of file or if networking is off, last version of file cached on disk.
+
+    Raises:
+        [`~utils.RepositoryNotFoundError`]
+            If the repository to download from cannot be found. This may be because it doesn't exist,
+            or because it is set to `private` and you do not have access.
+        [`~utils.RevisionNotFoundError`]
+            If the revision to download from cannot be found.
+        [`~utils.EntryNotFoundError`]
+            If the file to download cannot be found.
+        [`~utils.LocalEntryNotFoundError`]
+            If network is disabled or unavailable and file is not found in cache.
+        [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
+            If `token=True` but the token cannot be found.
+        [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError)
+            If ETag cannot be determined.
+        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
+            If some parameter value is invalid.
+
+    """
+    if constants.HF_HUB_ETAG_TIMEOUT != constants.DEFAULT_ETAG_TIMEOUT:
+        # Respect environment variable above user value
+        etag_timeout = constants.HF_HUB_ETAG_TIMEOUT
+
+    if force_filename is not None:
+        warnings.warn(
+            "The `force_filename` parameter is deprecated as a new caching system, "
+            "which keeps the filenames as they are on the Hub, is now in place.",
+            FutureWarning,
+        )
+    if resume_download is not None:
+        warnings.warn(
+            "`resume_download` is deprecated and will be removed in version 1.0.0. "
+            "Downloads always resume when possible. "
+            "If you want to force a new download, use `force_download=True`.",
+            FutureWarning,
+        )
+
+    if cache_dir is None:
+        cache_dir = constants.HF_HUB_CACHE
+    if revision is None:
+        revision = constants.DEFAULT_REVISION
+    if isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+    if isinstance(local_dir, Path):
+        local_dir = str(local_dir)
+
+    if subfolder == "":
+        subfolder = None
+    if subfolder is not None:
+        # This is used to create a URL, and not a local path, hence the forward slash.
+        filename = f"{subfolder}/{filename}"
+
+    if repo_type is None:
+        repo_type = "model"
+    if repo_type not in constants.REPO_TYPES:
+        raise ValueError(f"Invalid repo type: {repo_type}. Accepted repo types are: {str(constants.REPO_TYPES)}")
+
+    headers = build_hf_headers(
+        token=token,
+        library_name=library_name,
+        library_version=library_version,
+        user_agent=user_agent,
+        headers=headers,
+    )
+
+    if local_dir is not None:
+        if local_dir_use_symlinks != "auto":
+            warnings.warn(
+                "`local_dir_use_symlinks` parameter is deprecated and will be ignored. "
+                "The process to download files to a local folder has been updated and do "
+                "not rely on symlinks anymore. You only need to pass a destination folder "
+                "as`local_dir`.\n"
+                "For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder."
+            )
+
+        return _hf_hub_download_to_local_dir(
+            # Destination
+            local_dir=local_dir,
+            # File info
+            repo_id=repo_id,
+            repo_type=repo_type,
+            filename=filename,
+            revision=revision,
+            # HTTP info
+            endpoint=endpoint,
+            etag_timeout=etag_timeout,
+            headers=headers,
+            proxies=proxies,
+            token=token,
+            # Additional options
+            cache_dir=cache_dir,
+            force_download=force_download,
+            local_files_only=local_files_only,
+        )
+    else:
+        return _hf_hub_download_to_cache_dir(
+            # Destination
+            cache_dir=cache_dir,
+            # File info
+            repo_id=repo_id,
+            filename=filename,
+            repo_type=repo_type,
+            revision=revision,
+            # HTTP info
+            endpoint=endpoint,
+            etag_timeout=etag_timeout,
+            headers=headers,
+            proxies=proxies,
+            token=token,
+            # Additional options
+            local_files_only=local_files_only,
+            force_download=force_download,
+        )
+
+
+def _hf_hub_download_to_cache_dir(
+    *,
+    # Destination
+    cache_dir: str,
+    # File info
+    repo_id: str,
+    filename: str,
+    repo_type: str,
+    revision: str,
+    # HTTP info
+    endpoint: Optional[str],
+    etag_timeout: float,
+    headers: Dict[str, str],
+    proxies: Optional[Dict],
+    token: Optional[Union[bool, str]],
+    # Additional options
+    local_files_only: bool,
+    force_download: bool,
+) -> str:
+    """Download a given file to a cache folder, if not already present.
+
+    Method should not be called directly. Please use `hf_hub_download` instead.
+    """
+    locks_dir = os.path.join(cache_dir, ".locks")
+    storage_folder = os.path.join(cache_dir, repo_folder_name(repo_id=repo_id, repo_type=repo_type))
+
+    # cross platform transcription of filename, to be used as a local file path.
+    relative_filename = os.path.join(*filename.split("/"))
+    if os.name == "nt":
+        if relative_filename.startswith("..\\") or "\\..\\" in relative_filename:
+            raise ValueError(
+                f"Invalid filename: cannot handle filename '{relative_filename}' on Windows. Please ask the repository"
+                " owner to rename this file."
+            )
+
+    # if user provides a commit_hash and they already have the file on disk, shortcut everything.
+    if REGEX_COMMIT_HASH.match(revision):
+        pointer_path = _get_pointer_path(storage_folder, revision, relative_filename)
+        if os.path.exists(pointer_path) and not force_download:
+            return pointer_path
+
+    # Try to get metadata (etag, commit_hash, url, size) from the server.
+    # If we can't, a HEAD request error is returned.
+    (url_to_download, etag, commit_hash, expected_size, head_call_error) = _get_metadata_or_catch_error(
+        repo_id=repo_id,
+        filename=filename,
+        repo_type=repo_type,
+        revision=revision,
+        endpoint=endpoint,
+        proxies=proxies,
+        etag_timeout=etag_timeout,
+        headers=headers,
+        token=token,
+        local_files_only=local_files_only,
+        storage_folder=storage_folder,
+        relative_filename=relative_filename,
+    )
+
+    # etag can be None for several reasons:
+    # 1. we passed local_files_only.
+    # 2. we don't have a connection
+    # 3. Hub is down (HTTP 500, 503, 504)
+    # 4. repo is not found -for example private or gated- and invalid/missing token sent
+    # 5. Hub is blocked by a firewall or proxy is not set correctly.
+    # => Try to get the last downloaded one from the specified revision.
+    #
+    # If the specified revision is a commit hash, look inside "snapshots".
+    # If the specified revision is a branch or tag, look inside "refs".
+    if head_call_error is not None:
+        # Couldn't make a HEAD call => let's try to find a local file
+        if not force_download:
+            commit_hash = None
+            if REGEX_COMMIT_HASH.match(revision):
+                commit_hash = revision
+            else:
+                ref_path = os.path.join(storage_folder, "refs", revision)
+                if os.path.isfile(ref_path):
+                    with open(ref_path) as f:
+                        commit_hash = f.read()
+
+            # Return pointer file if exists
+            if commit_hash is not None:
+                pointer_path = _get_pointer_path(storage_folder, commit_hash, relative_filename)
+                if os.path.exists(pointer_path) and not force_download:
+                    return pointer_path
+
+        # Otherwise, raise appropriate error
+        _raise_on_head_call_error(head_call_error, force_download, local_files_only)
+
+    # From now on, etag, commit_hash, url and size are not None.
+    assert etag is not None, "etag must have been retrieved from server"
+    assert commit_hash is not None, "commit_hash must have been retrieved from server"
+    assert url_to_download is not None, "file location must have been retrieved from server"
+    assert expected_size is not None, "expected_size must have been retrieved from server"
+    blob_path = os.path.join(storage_folder, "blobs", etag)
+    pointer_path = _get_pointer_path(storage_folder, commit_hash, relative_filename)
+
+    os.makedirs(os.path.dirname(blob_path), exist_ok=True)
+    os.makedirs(os.path.dirname(pointer_path), exist_ok=True)
+
+    # if passed revision is not identical to commit_hash
+    # then revision has to be a branch name or tag name.
+    # In that case store a ref.
+    _cache_commit_hash_for_specific_revision(storage_folder, revision, commit_hash)
+
+    # If file already exists, return it (except if force_download=True)
+    if not force_download:
+        if os.path.exists(pointer_path):
+            return pointer_path
+
+        if os.path.exists(blob_path):
+            # we have the blob already, but not the pointer
+            _create_symlink(blob_path, pointer_path, new_blob=False)
+            return pointer_path
+
+    # Prevent parallel downloads of the same file with a lock.
+    # etag could be duplicated across repos,
+    lock_path = os.path.join(locks_dir, repo_folder_name(repo_id=repo_id, repo_type=repo_type), f"{etag}.lock")
+
+    # Some Windows versions do not allow for paths longer than 255 characters.
+    # In this case, we must specify it as an extended path by using the "\\?\" prefix.
+    if os.name == "nt" and len(os.path.abspath(lock_path)) > 255:
+        lock_path = "\\\\?\\" + os.path.abspath(lock_path)
+
+    if os.name == "nt" and len(os.path.abspath(blob_path)) > 255:
+        blob_path = "\\\\?\\" + os.path.abspath(blob_path)
+
+    Path(lock_path).parent.mkdir(parents=True, exist_ok=True)
+    with WeakFileLock(lock_path):
+        _download_to_tmp_and_move(
+            incomplete_path=Path(blob_path + ".incomplete"),
+            destination_path=Path(blob_path),
+            url_to_download=url_to_download,
+            proxies=proxies,
+            headers=headers,
+            expected_size=expected_size,
+            filename=filename,
+            force_download=force_download,
+        )
+        if not os.path.exists(pointer_path):
+            _create_symlink(blob_path, pointer_path, new_blob=True)
+
+    return pointer_path
+
+
+def _hf_hub_download_to_local_dir(
+    *,
+    # Destination
+    local_dir: Union[str, Path],
+    # File info
+    repo_id: str,
+    repo_type: str,
+    filename: str,
+    revision: str,
+    # HTTP info
+    endpoint: Optional[str],
+    etag_timeout: float,
+    headers: Dict[str, str],
+    proxies: Optional[Dict],
+    token: Union[bool, str, None],
+    # Additional options
+    cache_dir: str,
+    force_download: bool,
+    local_files_only: bool,
+) -> str:
+    """Download a given file to a local folder, if not already present.
+
+    Method should not be called directly. Please use `hf_hub_download` instead.
+    """
+    # Some Windows versions do not allow for paths longer than 255 characters.
+    # In this case, we must specify it as an extended path by using the "\\?\" prefix.
+    if os.name == "nt" and len(os.path.abspath(local_dir)) > 255:
+        local_dir = "\\\\?\\" + os.path.abspath(local_dir)
+    local_dir = Path(local_dir)
+    paths = get_local_download_paths(local_dir=local_dir, filename=filename)
+    local_metadata = read_download_metadata(local_dir=local_dir, filename=filename)
+
+    # Local file exists + metadata exists + commit_hash matches => return file
+    if (
+        not force_download
+        and REGEX_COMMIT_HASH.match(revision)
+        and paths.file_path.is_file()
+        and local_metadata is not None
+        and local_metadata.commit_hash == revision
+    ):
+        return str(paths.file_path)
+
+    # Local file doesn't exist or commit_hash doesn't match => we need the etag
+    (url_to_download, etag, commit_hash, expected_size, head_call_error) = _get_metadata_or_catch_error(
+        repo_id=repo_id,
+        filename=filename,
+        repo_type=repo_type,
+        revision=revision,
+        endpoint=endpoint,
+        proxies=proxies,
+        etag_timeout=etag_timeout,
+        headers=headers,
+        token=token,
+        local_files_only=local_files_only,
+    )
+
+    if head_call_error is not None:
+        # No HEAD call but local file exists => default to local file
+        if not force_download and paths.file_path.is_file():
+            logger.warning(
+                f"Couldn't access the Hub to check for update but local file already exists. Defaulting to existing file. (error: {head_call_error})"
+            )
+            return str(paths.file_path)
+        # Otherwise => raise
+        _raise_on_head_call_error(head_call_error, force_download, local_files_only)
+
+    # From now on, etag, commit_hash, url and size are not None.
+    assert etag is not None, "etag must have been retrieved from server"
+    assert commit_hash is not None, "commit_hash must have been retrieved from server"
+    assert url_to_download is not None, "file location must have been retrieved from server"
+    assert expected_size is not None, "expected_size must have been retrieved from server"
+
+    # Local file exists => check if it's up-to-date
+    if not force_download and paths.file_path.is_file():
+        # etag matches => update metadata and return file
+        if local_metadata is not None and local_metadata.etag == etag:
+            write_download_metadata(local_dir=local_dir, filename=filename, commit_hash=commit_hash, etag=etag)
+            return str(paths.file_path)
+
+        # metadata is outdated + etag is a sha256
+        # => means it's an LFS file (large)
+        # => let's compute local hash and compare
+        # => if match, update metadata and return file
+        if local_metadata is None and REGEX_SHA256.match(etag) is not None:
+            with open(paths.file_path, "rb") as f:
+                file_hash = sha_fileobj(f).hex()
+            if file_hash == etag:
+                write_download_metadata(local_dir=local_dir, filename=filename, commit_hash=commit_hash, etag=etag)
+                return str(paths.file_path)
+
+    # Local file doesn't exist or etag isn't a match => retrieve file from remote (or cache)
+
+    # If we are lucky enough, the file is already in the cache => copy it
+    if not force_download:
+        cached_path = try_to_load_from_cache(
+            repo_id=repo_id,
+            filename=filename,
+            cache_dir=cache_dir,
+            revision=commit_hash,
+            repo_type=repo_type,
+        )
+        if isinstance(cached_path, str):
+            with WeakFileLock(paths.lock_path):
+                paths.file_path.parent.mkdir(parents=True, exist_ok=True)
+                shutil.copyfile(cached_path, paths.file_path)
+            write_download_metadata(local_dir=local_dir, filename=filename, commit_hash=commit_hash, etag=etag)
+            return str(paths.file_path)
+
+    # Otherwise, let's download the file!
+    with WeakFileLock(paths.lock_path):
+        paths.file_path.unlink(missing_ok=True)  # delete outdated file first
+        _download_to_tmp_and_move(
+            incomplete_path=paths.incomplete_path(etag),
+            destination_path=paths.file_path,
+            url_to_download=url_to_download,
+            proxies=proxies,
+            headers=headers,
+            expected_size=expected_size,
+            filename=filename,
+            force_download=force_download,
+        )
+
+    write_download_metadata(local_dir=local_dir, filename=filename, commit_hash=commit_hash, etag=etag)
+    return str(paths.file_path)
+
+
+@validate_hf_hub_args
+def try_to_load_from_cache(
+    repo_id: str,
+    filename: str,
+    cache_dir: Union[str, Path, None] = None,
+    revision: Optional[str] = None,
+    repo_type: Optional[str] = None,
+) -> Union[str, _CACHED_NO_EXIST_T, None]:
+    """
+    Explores the cache to return the latest cached file for a given revision if found.
+
+    This function will not raise any exception if the file in not cached.
+
+    Args:
+        cache_dir (`str` or `os.PathLike`):
+            The folder where the cached files lie.
+        repo_id (`str`):
+            The ID of the repo on huggingface.co.
+        filename (`str`):
+            The filename to look for inside `repo_id`.
+        revision (`str`, *optional*):
+            The specific model version to use. Will default to `"main"` if it's not provided and no `commit_hash` is
+            provided either.
+        repo_type (`str`, *optional*):
+            The type of the repository. Will default to `"model"`.
+
+    Returns:
+        `Optional[str]` or `_CACHED_NO_EXIST`:
+            Will return `None` if the file was not cached. Otherwise:
+            - The exact path to the cached file if it's found in the cache
+            - A special value `_CACHED_NO_EXIST` if the file does not exist at the given commit hash and this fact was
+              cached.
+
+    Example:
+
+    ```python
+    from huggingface_hub import try_to_load_from_cache, _CACHED_NO_EXIST
+
+    filepath = try_to_load_from_cache()
+    if isinstance(filepath, str):
+        # file exists and is cached
+        ...
+    elif filepath is _CACHED_NO_EXIST:
+        # non-existence of file is cached
+        ...
+    else:
+        # file is not cached
+        ...
+    ```
+    """
+    if revision is None:
+        revision = "main"
+    if repo_type is None:
+        repo_type = "model"
+    if repo_type not in constants.REPO_TYPES:
+        raise ValueError(f"Invalid repo type: {repo_type}. Accepted repo types are: {str(constants.REPO_TYPES)}")
+    if cache_dir is None:
+        cache_dir = constants.HF_HUB_CACHE
+
+    object_id = repo_id.replace("/", "--")
+    repo_cache = os.path.join(cache_dir, f"{repo_type}s--{object_id}")
+    if not os.path.isdir(repo_cache):
+        # No cache for this model
+        return None
+
+    refs_dir = os.path.join(repo_cache, "refs")
+    snapshots_dir = os.path.join(repo_cache, "snapshots")
+    no_exist_dir = os.path.join(repo_cache, ".no_exist")
+
+    # Resolve refs (for instance to convert main to the associated commit sha)
+    if os.path.isdir(refs_dir):
+        revision_file = os.path.join(refs_dir, revision)
+        if os.path.isfile(revision_file):
+            with open(revision_file) as f:
+                revision = f.read()
+
+    # Check if file is cached as "no_exist"
+    if os.path.isfile(os.path.join(no_exist_dir, revision, filename)):
+        return _CACHED_NO_EXIST
+
+    # Check if revision folder exists
+    if not os.path.exists(snapshots_dir):
+        return None
+    cached_shas = os.listdir(snapshots_dir)
+    if revision not in cached_shas:
+        # No cache for this revision and we won't try to return a random revision
+        return None
+
+    # Check if file exists in cache
+    cached_file = os.path.join(snapshots_dir, revision, filename)
+    return cached_file if os.path.isfile(cached_file) else None
+
+
+@validate_hf_hub_args
+def get_hf_file_metadata(
+    url: str,
+    token: Union[bool, str, None] = None,
+    proxies: Optional[Dict] = None,
+    timeout: Optional[float] = constants.DEFAULT_REQUEST_TIMEOUT,
+    library_name: Optional[str] = None,
+    library_version: Optional[str] = None,
+    user_agent: Union[Dict, str, None] = None,
+    headers: Optional[Dict[str, str]] = None,
+) -> HfFileMetadata:
+    """Fetch metadata of a file versioned on the Hub for a given url.
+
+    Args:
+        url (`str`):
+            File url, for example returned by [`hf_hub_url`].
+        token (`str` or `bool`, *optional*):
+            A token to be used for the download.
+                - If `True`, the token is read from the HuggingFace config
+                  folder.
+                - If `False` or `None`, no token is provided.
+                - If a string, it's used as the authentication token.
+        proxies (`dict`, *optional*):
+            Dictionary mapping protocol to the URL of the proxy passed to
+            `requests.request`.
+        timeout (`float`, *optional*, defaults to 10):
+            How many seconds to wait for the server to send metadata before giving up.
+        library_name (`str`, *optional*):
+            The name of the library to which the object corresponds.
+        library_version (`str`, *optional*):
+            The version of the library.
+        user_agent (`dict`, `str`, *optional*):
+            The user-agent info in the form of a dictionary or a string.
+        headers (`dict`, *optional*):
+            Additional headers to be sent with the request.
+
+    Returns:
+        A [`HfFileMetadata`] object containing metadata such as location, etag, size and
+        commit_hash.
+    """
+    headers = build_hf_headers(
+        token=token,
+        library_name=library_name,
+        library_version=library_version,
+        user_agent=user_agent,
+        headers=headers,
+    )
+    headers["Accept-Encoding"] = "identity"  # prevent any compression => we want to know the real size of the file
+
+    # Retrieve metadata
+    r = _request_wrapper(
+        method="HEAD",
+        url=url,
+        headers=headers,
+        allow_redirects=False,
+        follow_relative_redirects=True,
+        proxies=proxies,
+        timeout=timeout,
+    )
+    hf_raise_for_status(r)
+
+    # Return
+    return HfFileMetadata(
+        commit_hash=r.headers.get(constants.HUGGINGFACE_HEADER_X_REPO_COMMIT),
+        # We favor a custom header indicating the etag of the linked resource, and
+        # we fallback to the regular etag header.
+        etag=_normalize_etag(r.headers.get(constants.HUGGINGFACE_HEADER_X_LINKED_ETAG) or r.headers.get("ETag")),
+        # Either from response headers (if redirected) or defaults to request url
+        # Do not use directly `url`, as `_request_wrapper` might have followed relative
+        # redirects.
+        location=r.headers.get("Location") or r.request.url,  # type: ignore
+        size=_int_or_none(
+            r.headers.get(constants.HUGGINGFACE_HEADER_X_LINKED_SIZE) or r.headers.get("Content-Length")
+        ),
+    )
+
+
+def _get_metadata_or_catch_error(
+    *,
+    repo_id: str,
+    filename: str,
+    repo_type: str,
+    revision: str,
+    endpoint: Optional[str],
+    proxies: Optional[Dict],
+    etag_timeout: Optional[float],
+    headers: Dict[str, str],  # mutated inplace!
+    token: Union[bool, str, None],
+    local_files_only: bool,
+    relative_filename: Optional[str] = None,  # only used to store `.no_exists` in cache
+    storage_folder: Optional[str] = None,  # only used to store `.no_exists` in cache
+) -> Union[
+    # Either an exception is caught and returned
+    Tuple[None, None, None, None, Exception],
+    # Or the metadata is returned as
+    # `(url_to_download, etag, commit_hash, expected_size, None)`
+    Tuple[str, str, str, int, None],
+]:
+    """Get metadata for a file on the Hub, safely handling network issues.
+
+    Returns either the etag, commit_hash and expected size of the file, or the error
+    raised while fetching the metadata.
+
+    NOTE: This function mutates `headers` inplace! It removes the `authorization` header
+          if the file is a LFS blob and the domain of the url is different from the
+          domain of the location (typically an S3 bucket).
+    """
+    if local_files_only:
+        return (
+            None,
+            None,
+            None,
+            None,
+            OfflineModeIsEnabled(
+                f"Cannot access file since 'local_files_only=True' as been set. (repo_id: {repo_id}, repo_type: {repo_type}, revision: {revision}, filename: {filename})"
+            ),
+        )
+
+    url = hf_hub_url(repo_id, filename, repo_type=repo_type, revision=revision, endpoint=endpoint)
+    url_to_download: str = url
+    etag: Optional[str] = None
+    commit_hash: Optional[str] = None
+    expected_size: Optional[int] = None
+    head_error_call: Optional[Exception] = None
+
+    # Try to get metadata from the server.
+    # Do not raise yet if the file is not found or not accessible.
+    if not local_files_only:
+        try:
+            try:
+                metadata = get_hf_file_metadata(
+                    url=url, proxies=proxies, timeout=etag_timeout, headers=headers, token=token
+                )
+            except EntryNotFoundError as http_error:
+                if storage_folder is not None and relative_filename is not None:
+                    # Cache the non-existence of the file
+                    commit_hash = http_error.response.headers.get(constants.HUGGINGFACE_HEADER_X_REPO_COMMIT)
+                    if commit_hash is not None:
+                        no_exist_file_path = Path(storage_folder) / ".no_exist" / commit_hash / relative_filename
+                        try:
+                            no_exist_file_path.parent.mkdir(parents=True, exist_ok=True)
+                            no_exist_file_path.touch()
+                        except OSError as e:
+                            logger.error(
+                                f"Could not cache non-existence of file. Will ignore error and continue. Error: {e}"
+                            )
+                        _cache_commit_hash_for_specific_revision(storage_folder, revision, commit_hash)
+                raise
+
+            # Commit hash must exist
+            commit_hash = metadata.commit_hash
+            if commit_hash is None:
+                raise FileMetadataError(
+                    "Distant resource does not seem to be on huggingface.co. It is possible that a configuration issue"
+                    " prevents you from downloading resources from https://huggingface.co. Please check your firewall"
+                    " and proxy settings and make sure your SSL certificates are updated."
+                )
+
+            # Etag must exist
+            # If we don't have any of those, raise an error.
+            etag = metadata.etag
+            if etag is None:
+                raise FileMetadataError(
+                    "Distant resource does not have an ETag, we won't be able to reliably ensure reproducibility."
+                )
+
+            # Size must exist
+            expected_size = metadata.size
+            if expected_size is None:
+                raise FileMetadataError("Distant resource does not have a Content-Length.")
+
+            # In case of a redirect, save an extra redirect on the request.get call,
+            # and ensure we download the exact atomic version even if it changed
+            # between the HEAD and the GET (unlikely, but hey).
+            #
+            # If url domain is different => we are downloading from a CDN => url is signed => don't send auth
+            # If url domain is the same => redirect due to repo rename AND downloading a regular file => keep auth
+            if url != metadata.location:
+                url_to_download = metadata.location
+                if urlparse(url).netloc != urlparse(metadata.location).netloc:
+                    # Remove authorization header when downloading a LFS blob
+                    headers.pop("authorization", None)
+        except (requests.exceptions.SSLError, requests.exceptions.ProxyError):
+            # Actually raise for those subclasses of ConnectionError
+            raise
+        except (
+            requests.exceptions.ConnectionError,
+            requests.exceptions.Timeout,
+            OfflineModeIsEnabled,
+        ) as error:
+            # Otherwise, our Internet connection is down.
+            # etag is None
+            head_error_call = error
+        except (RevisionNotFoundError, EntryNotFoundError):
+            # The repo was found but the revision or entry doesn't exist on the Hub (never existed or got deleted)
+            raise
+        except requests.HTTPError as error:
+            # Multiple reasons for an http error:
+            # - Repository is private and invalid/missing token sent
+            # - Repository is gated and invalid/missing token sent
+            # - Hub is down (error 500 or 504)
+            # => let's switch to 'local_files_only=True' to check if the files are already cached.
+            #    (if it's not the case, the error will be re-raised)
+            head_error_call = error
+        except FileMetadataError as error:
+            # Multiple reasons for a FileMetadataError:
+            # - Wrong network configuration (proxy, firewall, SSL certificates)
+            # - Inconsistency on the Hub
+            # => let's switch to 'local_files_only=True' to check if the files are already cached.
+            #    (if it's not the case, the error will be re-raised)
+            head_error_call = error
+
+    if not (local_files_only or etag is not None or head_error_call is not None):
+        raise RuntimeError("etag is empty due to uncovered problems")
+
+    return (url_to_download, etag, commit_hash, expected_size, head_error_call)  # type: ignore [return-value]
+
+
+def _raise_on_head_call_error(head_call_error: Exception, force_download: bool, local_files_only: bool) -> NoReturn:
+    """Raise an appropriate error when the HEAD call failed and we cannot locate a local file."""
+
+    # No head call => we cannot force download.
+    if force_download:
+        if local_files_only:
+            raise ValueError("Cannot pass 'force_download=True' and 'local_files_only=True' at the same time.")
+        elif isinstance(head_call_error, OfflineModeIsEnabled):
+            raise ValueError("Cannot pass 'force_download=True' when offline mode is enabled.") from head_call_error
+        else:
+            raise ValueError("Force download failed due to the above error.") from head_call_error
+
+    # No head call + couldn't find an appropriate file on disk => raise an error.
+    if local_files_only:
+        raise LocalEntryNotFoundError(
+            "Cannot find the requested files in the disk cache and outgoing traffic has been disabled. To enable"
+            " hf.co look-ups and downloads online, set 'local_files_only' to False."
+        )
+    elif isinstance(head_call_error, RepositoryNotFoundError) or isinstance(head_call_error, GatedRepoError):
+        # Repo not found or gated => let's raise the actual error
+        raise head_call_error
+    else:
+        # Otherwise: most likely a connection issue or Hub downtime => let's warn the user
+        raise LocalEntryNotFoundError(
+            "An error happened while trying to locate the file on the Hub and we cannot find the requested files"
+            " in the local cache. Please check your connection and try again or make sure your Internet connection"
+            " is on."
+        ) from head_call_error
+
+
+def _download_to_tmp_and_move(
+    incomplete_path: Path,
+    destination_path: Path,
+    url_to_download: str,
+    proxies: Optional[Dict],
+    headers: Dict[str, str],
+    expected_size: Optional[int],
+    filename: str,
+    force_download: bool,
+) -> None:
+    """Download content from a URL to a destination path.
+
+    Internal logic:
+    - return early if file is already downloaded
+    - resume download if possible (from incomplete file)
+    - do not resume download if `force_download=True` or `HF_HUB_ENABLE_HF_TRANSFER=True`
+    - check disk space before downloading
+    - download content to a temporary file
+    - set correct permissions on temporary file
+    - move the temporary file to the destination path
+
+    Both `incomplete_path` and `destination_path` must be on the same volume to avoid a local copy.
+    """
+    if destination_path.exists() and not force_download:
+        # Do nothing if already exists (except if force_download=True)
+        return
+
+    if incomplete_path.exists() and (force_download or (constants.HF_HUB_ENABLE_HF_TRANSFER and not proxies)):
+        # By default, we will try to resume the download if possible.
+        # However, if the user has set `force_download=True` or if `hf_transfer` is enabled, then we should
+        # not resume the download => delete the incomplete file.
+        message = f"Removing incomplete file '{incomplete_path}'"
+        if force_download:
+            message += " (force_download=True)"
+        elif constants.HF_HUB_ENABLE_HF_TRANSFER and not proxies:
+            message += " (hf_transfer=True)"
+        logger.info(message)
+        incomplete_path.unlink(missing_ok=True)
+
+    with incomplete_path.open("ab") as f:
+        resume_size = f.tell()
+        message = f"Downloading '{filename}' to '{incomplete_path}'"
+        if resume_size > 0 and expected_size is not None:
+            message += f" (resume from {resume_size}/{expected_size})"
+        logger.info(message)
+
+        if expected_size is not None:  # might be None if HTTP header not set correctly
+            # Check disk space in both tmp and destination path
+            _check_disk_space(expected_size, incomplete_path.parent)
+            _check_disk_space(expected_size, destination_path.parent)
+
+        http_get(
+            url_to_download,
+            f,
+            proxies=proxies,
+            resume_size=resume_size,
+            headers=headers,
+            expected_size=expected_size,
+        )
+
+    logger.info(f"Download complete. Moving file to {destination_path}")
+    _chmod_and_move(incomplete_path, destination_path)
+
+
+def _int_or_none(value: Optional[str]) -> Optional[int]:
+    try:
+        return int(value)  # type: ignore
+    except (TypeError, ValueError):
+        return None
+
+
+def _chmod_and_move(src: Path, dst: Path) -> None:
+    """Set correct permission before moving a blob from tmp directory to cache dir.
+
+    Do not take into account the `umask` from the process as there is no convenient way
+    to get it that is thread-safe.
+
+    See:
+    - About umask: https://docs.python.org/3/library/os.html#os.umask
+    - Thread-safety: https://stackoverflow.com/a/70343066
+    - About solution: https://github.com/huggingface/huggingface_hub/pull/1220#issuecomment-1326211591
+    - Fix issue: https://github.com/huggingface/huggingface_hub/issues/1141
+    - Fix issue: https://github.com/huggingface/huggingface_hub/issues/1215
+    """
+    # Get umask by creating a temporary file in the cached repo folder.
+    tmp_file = dst.parent.parent / f"tmp_{uuid.uuid4()}"
+    try:
+        tmp_file.touch()
+        cache_dir_mode = Path(tmp_file).stat().st_mode
+        os.chmod(str(src), stat.S_IMODE(cache_dir_mode))
+    except OSError as e:
+        logger.warning(
+            f"Could not set the permissions on the file '{src}'. "
+            f"Error: {e}.\nContinuing without setting permissions."
+        )
+    finally:
+        try:
+            tmp_file.unlink()
+        except OSError:
+            # fails if `tmp_file.touch()` failed => do nothing
+            # See https://github.com/huggingface/huggingface_hub/issues/2359
+            pass
+
+    shutil.move(str(src), str(dst), copy_function=_copy_no_matter_what)
+
+
+def _copy_no_matter_what(src: str, dst: str) -> None:
+    """Copy file from src to dst.
+
+    If `shutil.copy2` fails, fallback to `shutil.copyfile`.
+    """
+    try:
+        # Copy file with metadata and permission
+        # Can fail e.g. if dst is an S3 mount
+        shutil.copy2(src, dst)
+    except OSError:
+        # Copy only file content
+        shutil.copyfile(src, dst)
+
+
+def _get_pointer_path(storage_folder: str, revision: str, relative_filename: str) -> str:
+    # Using `os.path.abspath` instead of `Path.resolve()` to avoid resolving symlinks
+    snapshot_path = os.path.join(storage_folder, "snapshots")
+    pointer_path = os.path.join(snapshot_path, revision, relative_filename)
+    if Path(os.path.abspath(snapshot_path)) not in Path(os.path.abspath(pointer_path)).parents:
+        raise ValueError(
+            "Invalid pointer path: cannot create pointer path in snapshot folder if"
+            f" `storage_folder='{storage_folder}'`, `revision='{revision}'` and"
+            f" `relative_filename='{relative_filename}'`."
+        )
+    return pointer_path

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_common.py

@@ -0,0 +1,478 @@
+# coding=utf-8
+# Copyright 2023-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains utilities used by both the sync and async inference clients."""
+
+import base64
+import io
+import json
+import logging
+from contextlib import contextmanager
+from dataclasses import dataclass
+from pathlib import Path
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    AsyncIterable,
+    BinaryIO,
+    ContextManager,
+    Dict,
+    Generator,
+    Iterable,
+    List,
+    Literal,
+    NoReturn,
+    Optional,
+    Union,
+    overload,
+)
+
+from requests import HTTPError
+
+from huggingface_hub.errors import (
+    GenerationError,
+    IncompleteGenerationError,
+    OverloadedError,
+    TextGenerationError,
+    UnknownError,
+    ValidationError,
+)
+
+from ..constants import ENDPOINT
+from ..utils import (
+    build_hf_headers,
+    get_session,
+    hf_raise_for_status,
+    is_aiohttp_available,
+    is_numpy_available,
+    is_pillow_available,
+)
+from ._generated.types import ChatCompletionStreamOutput, TextGenerationStreamOutput
+
+
+if TYPE_CHECKING:
+    from aiohttp import ClientResponse, ClientSession
+    from PIL.Image import Image
+
+# TYPES
+UrlT = str
+PathT = Union[str, Path]
+BinaryT = Union[bytes, BinaryIO]
+ContentT = Union[BinaryT, PathT, UrlT]
+
+# Use to set a Accept: image/png header
+TASKS_EXPECTING_IMAGES = {"text-to-image", "image-to-image"}
+
+logger = logging.getLogger(__name__)
+
+
+# Add dataclass for ModelStatus. We use this dataclass in get_model_status function.
+@dataclass
+class ModelStatus:
+    """
+    This Dataclass represents the the model status in the Hugging Face Inference API.
+
+    Args:
+        loaded (`bool`):
+            If the model is currently loaded into Hugging Face's InferenceAPI. Models
+            are loaded on-demand, leading to the user's first request taking longer.
+            If a model is loaded, you can be assured that it is in a healthy state.
+        state (`str`):
+            The current state of the model. This can be 'Loaded', 'Loadable', 'TooBig'.
+            If a model's state is 'Loadable', it's not too big and has a supported
+            backend. Loadable models are automatically loaded when the user first
+            requests inference on the endpoint. This means it is transparent for the
+            user to load a model, except that the first call takes longer to complete.
+        compute_type (`Dict`):
+            Information about the compute resource the model is using or will use, such as 'gpu' type and number of
+            replicas.
+        framework (`str`):
+            The name of the framework that the model was built with, such as 'transformers'
+            or 'text-generation-inference'.
+    """
+
+    loaded: bool
+    state: str
+    compute_type: Dict
+    framework: str
+
+
+## IMPORT UTILS
+
+
+def _import_aiohttp():
+    # Make sure `aiohttp` is installed on the machine.
+    if not is_aiohttp_available():
+        raise ImportError("Please install aiohttp to use `AsyncInferenceClient` (`pip install aiohttp`).")
+    import aiohttp
+
+    return aiohttp
+
+
+def _import_numpy():
+    """Make sure `numpy` is installed on the machine."""
+    if not is_numpy_available():
+        raise ImportError("Please install numpy to use deal with embeddings (`pip install numpy`).")
+    import numpy
+
+    return numpy
+
+
+def _import_pil_image():
+    """Make sure `PIL` is installed on the machine."""
+    if not is_pillow_available():
+        raise ImportError(
+            "Please install Pillow to use deal with images (`pip install Pillow`). If you don't want the image to be"
+            " post-processed, use `client.post(...)` and get the raw response from the server."
+        )
+    from PIL import Image
+
+    return Image
+
+
+## RECOMMENDED MODELS
+
+# Will be globally fetched only once (see '_fetch_recommended_models')
+_RECOMMENDED_MODELS: Optional[Dict[str, Optional[str]]] = None
+
+
+def _fetch_recommended_models() -> Dict[str, Optional[str]]:
+    global _RECOMMENDED_MODELS
+    if _RECOMMENDED_MODELS is None:
+        response = get_session().get(f"{ENDPOINT}/api/tasks", headers=build_hf_headers())
+        hf_raise_for_status(response)
+        _RECOMMENDED_MODELS = {
+            task: _first_or_none(details["widgetModels"]) for task, details in response.json().items()
+        }
+    return _RECOMMENDED_MODELS
+
+
+def _first_or_none(items: List[Any]) -> Optional[Any]:
+    try:
+        return items[0] or None
+    except IndexError:
+        return None
+
+
+## ENCODING / DECODING UTILS
+
+
+@overload
+def _open_as_binary(
+    content: ContentT,
+) -> ContextManager[BinaryT]: ...  # means "if input is not None, output is not None"
+
+
+@overload
+def _open_as_binary(
+    content: Literal[None],
+) -> ContextManager[Literal[None]]: ...  # means "if input is None, output is None"
+
+
+@contextmanager  # type: ignore
+def _open_as_binary(content: Optional[ContentT]) -> Generator[Optional[BinaryT], None, None]:
+    """Open `content` as a binary file, either from a URL, a local path, or raw bytes.
+
+    Do nothing if `content` is None,
+
+    TODO: handle a PIL.Image as input
+    TODO: handle base64 as input
+    """
+    # If content is a string => must be either a URL or a path
+    if isinstance(content, str):
+        if content.startswith("https://") or content.startswith("http://"):
+            logger.debug(f"Downloading content from {content}")
+            yield get_session().get(content).content  # TODO: retrieve as stream and pipe to post request ?
+            return
+        content = Path(content)
+        if not content.exists():
+            raise FileNotFoundError(
+                f"File not found at {content}. If `data` is a string, it must either be a URL or a path to a local"
+                " file. To pass raw content, please encode it as bytes first."
+            )
+
+    # If content is a Path => open it
+    if isinstance(content, Path):
+        logger.debug(f"Opening content from {content}")
+        with content.open("rb") as f:
+            yield f
+    else:
+        # Otherwise: already a file-like object or None
+        yield content
+
+
+def _b64_encode(content: ContentT) -> str:
+    """Encode a raw file (image, audio) into base64. Can be byes, an opened file, a path or a URL."""
+    with _open_as_binary(content) as data:
+        data_as_bytes = data if isinstance(data, bytes) else data.read()
+        return base64.b64encode(data_as_bytes).decode()
+
+
+def _b64_to_image(encoded_image: str) -> "Image":
+    """Parse a base64-encoded string into a PIL Image."""
+    Image = _import_pil_image()
+    return Image.open(io.BytesIO(base64.b64decode(encoded_image)))
+
+
+def _bytes_to_list(content: bytes) -> List:
+    """Parse bytes from a Response object into a Python list.
+
+    Expects the response body to be JSON-encoded data.
+
+    NOTE: This is exactly the same implementation as `_bytes_to_dict` and will not complain if the returned data is a
+    dictionary. The only advantage of having both is to help the user (and mypy) understand what kind of data to expect.
+    """
+    return json.loads(content.decode())
+
+
+def _bytes_to_dict(content: bytes) -> Dict:
+    """Parse bytes from a Response object into a Python dictionary.
+
+    Expects the response body to be JSON-encoded data.
+
+    NOTE: This is exactly the same implementation as `_bytes_to_list` and will not complain if the returned data is a
+    list. The only advantage of having both is to help the user (and mypy) understand what kind of data to expect.
+    """
+    return json.loads(content.decode())
+
+
+def _bytes_to_image(content: bytes) -> "Image":
+    """Parse bytes from a Response object into a PIL Image.
+
+    Expects the response body to be raw bytes. To deal with b64 encoded images, use `_b64_to_image` instead.
+    """
+    Image = _import_pil_image()
+    return Image.open(io.BytesIO(content))
+
+
+## PAYLOAD UTILS
+
+
+def _prepare_payload(
+    inputs: Union[str, Dict[str, Any], ContentT],
+    parameters: Optional[Dict[str, Any]],
+    expect_binary: bool = False,
+) -> Dict[str, Any]:
+    """
+    Used in `InferenceClient` and `AsyncInferenceClient` to prepare the payload for an API request, handling various input types and parameters.
+    `expect_binary` is set to `True` when the inputs are a binary object or a local path or URL. This is the case for image and audio inputs.
+    """
+    if parameters is None:
+        parameters = {}
+    parameters = {k: v for k, v in parameters.items() if v is not None}
+    has_parameters = len(parameters) > 0
+
+    is_binary = isinstance(inputs, (bytes, Path))
+    # If expect_binary is True, inputs must be a binary object or a local path or a URL.
+    if expect_binary and not is_binary and not isinstance(inputs, str):
+        raise ValueError(f"Expected binary inputs or a local path or a URL. Got {inputs}")  # type: ignore
+    # Send inputs as raw content when no parameters are provided
+    if expect_binary and not has_parameters:
+        return {"data": inputs}
+    # If expect_binary is False, inputs must not be a binary object.
+    if not expect_binary and is_binary:
+        raise ValueError(f"Unexpected binary inputs. Got {inputs}")  # type: ignore
+
+    json: Dict[str, Any] = {}
+    # If inputs is a bytes-like object, encode it to base64
+    if expect_binary:
+        json["inputs"] = _b64_encode(inputs)  # type: ignore
+    # Otherwise (string, dict, list) send it as is
+    else:
+        json["inputs"] = inputs
+    # Add parameters to the json payload if any
+    if has_parameters:
+        json["parameters"] = parameters
+    return {"json": json}
+
+
+## STREAMING UTILS
+
+
+def _stream_text_generation_response(
+    bytes_output_as_lines: Iterable[bytes], details: bool
+) -> Union[Iterable[str], Iterable[TextGenerationStreamOutput]]:
+    """Used in `InferenceClient.text_generation`."""
+    # Parse ServerSentEvents
+    for byte_payload in bytes_output_as_lines:
+        try:
+            output = _format_text_generation_stream_output(byte_payload, details)
+        except StopIteration:
+            break
+        if output is not None:
+            yield output
+
+
+async def _async_stream_text_generation_response(
+    bytes_output_as_lines: AsyncIterable[bytes], details: bool
+) -> Union[AsyncIterable[str], AsyncIterable[TextGenerationStreamOutput]]:
+    """Used in `AsyncInferenceClient.text_generation`."""
+    # Parse ServerSentEvents
+    async for byte_payload in bytes_output_as_lines:
+        try:
+            output = _format_text_generation_stream_output(byte_payload, details)
+        except StopIteration:
+            break
+        if output is not None:
+            yield output
+
+
+def _format_text_generation_stream_output(
+    byte_payload: bytes, details: bool
+) -> Optional[Union[str, TextGenerationStreamOutput]]:
+    if not byte_payload.startswith(b"data:"):
+        return None  # empty line
+
+    if byte_payload.strip() == b"data: [DONE]":
+        raise StopIteration("[DONE] signal received.")
+
+    # Decode payload
+    payload = byte_payload.decode("utf-8")
+    json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
+
+    # Either an error as being returned
+    if json_payload.get("error") is not None:
+        raise _parse_text_generation_error(json_payload["error"], json_payload.get("error_type"))
+
+    # Or parse token payload
+    output = TextGenerationStreamOutput.parse_obj_as_instance(json_payload)
+    return output.token.text if not details else output
+
+
+def _stream_chat_completion_response(
+    bytes_lines: Iterable[bytes],
+) -> Iterable[ChatCompletionStreamOutput]:
+    """Used in `InferenceClient.chat_completion` if model is served with TGI."""
+    for item in bytes_lines:
+        try:
+            output = _format_chat_completion_stream_output(item)
+        except StopIteration:
+            break
+        if output is not None:
+            yield output
+
+
+async def _async_stream_chat_completion_response(
+    bytes_lines: AsyncIterable[bytes],
+) -> AsyncIterable[ChatCompletionStreamOutput]:
+    """Used in `AsyncInferenceClient.chat_completion`."""
+    async for item in bytes_lines:
+        try:
+            output = _format_chat_completion_stream_output(item)
+        except StopIteration:
+            break
+        if output is not None:
+            yield output
+
+
+def _format_chat_completion_stream_output(
+    byte_payload: bytes,
+) -> Optional[ChatCompletionStreamOutput]:
+    if not byte_payload.startswith(b"data:"):
+        return None  # empty line
+
+    if byte_payload.strip() == b"data: [DONE]":
+        raise StopIteration("[DONE] signal received.")
+
+    # Decode payload
+    payload = byte_payload.decode("utf-8")
+    json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
+
+    # Either an error as being returned
+    if json_payload.get("error") is not None:
+        raise _parse_text_generation_error(json_payload["error"], json_payload.get("error_type"))
+
+    # Or parse token payload
+    return ChatCompletionStreamOutput.parse_obj_as_instance(json_payload)
+
+
+async def _async_yield_from(client: "ClientSession", response: "ClientResponse") -> AsyncIterable[bytes]:
+    async for byte_payload in response.content:
+        yield byte_payload.strip()
+    await client.close()
+
+
+# "TGI servers" are servers running with the `text-generation-inference` backend.
+# This backend is the go-to solution to run large language models at scale. However,
+# for some smaller models (e.g. "gpt2") the default `transformers` + `api-inference`
+# solution is still in use.
+#
+# Both approaches have very similar APIs, but not exactly the same. What we do first in
+# the `text_generation` method is to assume the model is served via TGI. If we realize
+# it's not the case (i.e. we receive an HTTP 400 Bad Request), we fallback to the
+# default API with a warning message. When that's the case, We remember the unsupported
+# attributes for this model in the `_UNSUPPORTED_TEXT_GENERATION_KWARGS` global variable.
+#
+# In addition, TGI servers have a built-in API route for chat-completion, which is not
+# available on the default API. We use this route to provide a more consistent behavior
+# when available.
+#
+# For more details, see https://github.com/huggingface/text-generation-inference and
+# https://huggingface.co/docs/api-inference/detailed_parameters#text-generation-task.
+
+_UNSUPPORTED_TEXT_GENERATION_KWARGS: Dict[Optional[str], List[str]] = {}
+
+
+def _set_unsupported_text_generation_kwargs(model: Optional[str], unsupported_kwargs: List[str]) -> None:
+    _UNSUPPORTED_TEXT_GENERATION_KWARGS.setdefault(model, []).extend(unsupported_kwargs)
+
+
+def _get_unsupported_text_generation_kwargs(model: Optional[str]) -> List[str]:
+    return _UNSUPPORTED_TEXT_GENERATION_KWARGS.get(model, [])
+
+
+# TEXT GENERATION ERRORS
+# ----------------------
+# Text-generation errors are parsed separately to handle as much as possible the errors returned by the text generation
+# inference project (https://github.com/huggingface/text-generation-inference).
+# ----------------------
+
+
+def raise_text_generation_error(http_error: HTTPError) -> NoReturn:
+    """
+    Try to parse text-generation-inference error message and raise HTTPError in any case.
+
+    Args:
+        error (`HTTPError`):
+            The HTTPError that have been raised.
+    """
+    # Try to parse a Text Generation Inference error
+
+    try:
+        # Hacky way to retrieve payload in case of aiohttp error
+        payload = getattr(http_error, "response_error_payload", None) or http_error.response.json()
+        error = payload.get("error")
+        error_type = payload.get("error_type")
+    except Exception:  # no payload
+        raise http_error
+
+    # If error_type => more information than `hf_raise_for_status`
+    if error_type is not None:
+        exception = _parse_text_generation_error(error, error_type)
+        raise exception from http_error
+
+    # Otherwise, fallback to default error
+    raise http_error
+
+
+def _parse_text_generation_error(error: Optional[str], error_type: Optional[str]) -> TextGenerationError:
+    if error_type == "generation":
+        return GenerationError(error)  # type: ignore
+    if error_type == "incomplete_generation":
+        return IncompleteGenerationError(error)  # type: ignore
+    if error_type == "overloaded":
+        return OverloadedError(error)  # type: ignore
+    if error_type == "validation":
+        return ValidationError(error)  # type: ignore
+    return UnknownError(error)  # type: ignore

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/__init__.py

@@ -0,0 +1,165 @@
+# This file is auto-generated by `utils/generate_inference_types.py`.
+# Do not modify it manually.
+#
+# ruff: noqa: F401
+
+from .audio_classification import (
+    AudioClassificationInput,
+    AudioClassificationOutputElement,
+    AudioClassificationOutputTransform,
+    AudioClassificationParameters,
+)
+from .audio_to_audio import AudioToAudioInput, AudioToAudioOutputElement
+from .automatic_speech_recognition import (
+    AutomaticSpeechRecognitionEarlyStoppingEnum,
+    AutomaticSpeechRecognitionGenerationParameters,
+    AutomaticSpeechRecognitionInput,
+    AutomaticSpeechRecognitionOutput,
+    AutomaticSpeechRecognitionOutputChunk,
+    AutomaticSpeechRecognitionParameters,
+)
+from .base import BaseInferenceType
+from .chat_completion import (
+    ChatCompletionInput,
+    ChatCompletionInputFunctionDefinition,
+    ChatCompletionInputFunctionName,
+    ChatCompletionInputGrammarType,
+    ChatCompletionInputMessage,
+    ChatCompletionInputMessageChunk,
+    ChatCompletionInputStreamOptions,
+    ChatCompletionInputToolType,
+    ChatCompletionInputURL,
+    ChatCompletionOutput,
+    ChatCompletionOutputComplete,
+    ChatCompletionOutputFunctionDefinition,
+    ChatCompletionOutputLogprob,
+    ChatCompletionOutputLogprobs,
+    ChatCompletionOutputMessage,
+    ChatCompletionOutputToolCall,
+    ChatCompletionOutputTopLogprob,
+    ChatCompletionOutputUsage,
+    ChatCompletionStreamOutput,
+    ChatCompletionStreamOutputChoice,
+    ChatCompletionStreamOutputDelta,
+    ChatCompletionStreamOutputDeltaToolCall,
+    ChatCompletionStreamOutputFunction,
+    ChatCompletionStreamOutputLogprob,
+    ChatCompletionStreamOutputLogprobs,
+    ChatCompletionStreamOutputTopLogprob,
+    ChatCompletionStreamOutputUsage,
+    ToolElement,
+)
+from .depth_estimation import DepthEstimationInput, DepthEstimationOutput
+from .document_question_answering import (
+    DocumentQuestionAnsweringInput,
+    DocumentQuestionAnsweringInputData,
+    DocumentQuestionAnsweringOutputElement,
+    DocumentQuestionAnsweringParameters,
+)
+from .feature_extraction import FeatureExtractionInput
+from .fill_mask import FillMaskInput, FillMaskOutputElement, FillMaskParameters
+from .image_classification import (
+    ImageClassificationInput,
+    ImageClassificationOutputElement,
+    ImageClassificationOutputTransform,
+    ImageClassificationParameters,
+)
+from .image_segmentation import ImageSegmentationInput, ImageSegmentationOutputElement, ImageSegmentationParameters
+from .image_to_image import ImageToImageInput, ImageToImageOutput, ImageToImageParameters, ImageToImageTargetSize
+from .image_to_text import (
+    ImageToTextEarlyStoppingEnum,
+    ImageToTextGenerationParameters,
+    ImageToTextInput,
+    ImageToTextOutput,
+    ImageToTextParameters,
+)
+from .object_detection import (
+    ObjectDetectionBoundingBox,
+    ObjectDetectionInput,
+    ObjectDetectionOutputElement,
+    ObjectDetectionParameters,
+)
+from .question_answering import (
+    QuestionAnsweringInput,
+    QuestionAnsweringInputData,
+    QuestionAnsweringOutputElement,
+    QuestionAnsweringParameters,
+)
+from .sentence_similarity import SentenceSimilarityInput, SentenceSimilarityInputData
+from .summarization import SummarizationInput, SummarizationOutput, SummarizationParameters
+from .table_question_answering import (
+    TableQuestionAnsweringInput,
+    TableQuestionAnsweringInputData,
+    TableQuestionAnsweringOutputElement,
+)
+from .text2text_generation import Text2TextGenerationInput, Text2TextGenerationOutput, Text2TextGenerationParameters
+from .text_classification import (
+    TextClassificationInput,
+    TextClassificationOutputElement,
+    TextClassificationOutputTransform,
+    TextClassificationParameters,
+)
+from .text_generation import (
+    TextGenerationInput,
+    TextGenerationInputGenerateParameters,
+    TextGenerationInputGrammarType,
+    TextGenerationOutput,
+    TextGenerationOutputBestOfSequence,
+    TextGenerationOutputDetails,
+    TextGenerationOutputPrefillToken,
+    TextGenerationOutputToken,
+    TextGenerationStreamOutput,
+    TextGenerationStreamOutputStreamDetails,
+    TextGenerationStreamOutputToken,
+)
+from .text_to_audio import (
+    TextToAudioEarlyStoppingEnum,
+    TextToAudioGenerationParameters,
+    TextToAudioInput,
+    TextToAudioOutput,
+    TextToAudioParameters,
+)
+from .text_to_image import TextToImageInput, TextToImageOutput, TextToImageParameters, TextToImageTargetSize
+from .text_to_speech import (
+    TextToSpeechEarlyStoppingEnum,
+    TextToSpeechGenerationParameters,
+    TextToSpeechInput,
+    TextToSpeechOutput,
+    TextToSpeechParameters,
+)
+from .token_classification import (
+    TokenClassificationInput,
+    TokenClassificationOutputElement,
+    TokenClassificationParameters,
+)
+from .translation import TranslationInput, TranslationOutput, TranslationParameters
+from .video_classification import (
+    VideoClassificationInput,
+    VideoClassificationOutputElement,
+    VideoClassificationOutputTransform,
+    VideoClassificationParameters,
+)
+from .visual_question_answering import (
+    VisualQuestionAnsweringInput,
+    VisualQuestionAnsweringInputData,
+    VisualQuestionAnsweringOutputElement,
+    VisualQuestionAnsweringParameters,
+)
+from .zero_shot_classification import (
+    ZeroShotClassificationInput,
+    ZeroShotClassificationInputData,
+    ZeroShotClassificationOutputElement,
+    ZeroShotClassificationParameters,
+)
+from .zero_shot_image_classification import (
+    ZeroShotImageClassificationInput,
+    ZeroShotImageClassificationInputData,
+    ZeroShotImageClassificationOutputElement,
+    ZeroShotImageClassificationParameters,
+)
+from .zero_shot_object_detection import (
+    ZeroShotObjectDetectionBoundingBox,
+    ZeroShotObjectDetectionInput,
+    ZeroShotObjectDetectionInputData,
+    ZeroShotObjectDetectionOutputElement,
+)

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/audio_classification.py

@@ -0,0 +1,46 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Literal, Optional
+
+from .base import BaseInferenceType
+
+
+AudioClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass
+class AudioClassificationParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Audio Classification
+    """
+
+    function_to_apply: Optional["AudioClassificationOutputTransform"] = None
+    """The function to apply to the output."""
+    top_k: Optional[int] = None
+    """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass
+class AudioClassificationInput(BaseInferenceType):
+    """Inputs for Audio Classification inference"""
+
+    inputs: str
+    """The input audio data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the audio data as a raw bytes payload.
+    """
+    parameters: Optional[AudioClassificationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class AudioClassificationOutputElement(BaseInferenceType):
+    """Outputs for Audio Classification inference"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/audio_to_audio.py

@@ -0,0 +1,31 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class AudioToAudioInput(BaseInferenceType):
+    """Inputs for Audio to Audio inference"""
+
+    inputs: Any
+    """The input audio data"""
+
+
+@dataclass
+class AudioToAudioOutputElement(BaseInferenceType):
+    """Outputs of inference for the Audio To Audio task
+    A generated audio file with its label.
+    """
+
+    blob: Any
+    """The generated audio file."""
+    content_type: str
+    """The content type of audio file."""
+    label: str
+    """The label of the audio file."""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/base.py

@@ -0,0 +1,140 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains a base class for all inference types."""
+
+import inspect
+import json
+from dataclasses import asdict, dataclass
+from typing import Any, Dict, List, Type, TypeVar, Union, get_args
+
+
+T = TypeVar("T", bound="BaseInferenceType")
+
+
+@dataclass
+class BaseInferenceType(dict):
+    """Base class for all inference types.
+
+    Object is a dataclass and a dict for backward compatibility but plan is to remove the dict part in the future.
+
+    Handle parsing from dict, list and json strings in a permissive way to ensure future-compatibility (e.g. all fields
+    are made optional, and non-expected fields are added as dict attributes).
+    """
+
+    @classmethod
+    def parse_obj_as_list(cls: Type[T], data: Union[bytes, str, List, Dict]) -> List[T]:
+        """Alias to parse server response and return a single instance.
+
+        See `parse_obj` for more details.
+        """
+        output = cls.parse_obj(data)
+        if not isinstance(output, list):
+            raise ValueError(f"Invalid input data for {cls}. Expected a list, but got {type(output)}.")
+        return output
+
+    @classmethod
+    def parse_obj_as_instance(cls: Type[T], data: Union[bytes, str, List, Dict]) -> T:
+        """Alias to parse server response and return a single instance.
+
+        See `parse_obj` for more details.
+        """
+        output = cls.parse_obj(data)
+        if isinstance(output, list):
+            raise ValueError(f"Invalid input data for {cls}. Expected a single instance, but got a list.")
+        return output
+
+    @classmethod
+    def parse_obj(cls: Type[T], data: Union[bytes, str, List, Dict]) -> Union[List[T], T]:
+        """Parse server response as a dataclass or list of dataclasses.
+
+        To enable future-compatibility, we want to handle cases where the server return more fields than expected.
+        In such cases, we don't want to raise an error but still create the dataclass object. Remaining fields are
+        added as dict attributes.
+        """
+        # Parse server response (from bytes)
+        if isinstance(data, bytes):
+            data = data.decode()
+        if isinstance(data, str):
+            data = json.loads(data)
+
+        # If a list, parse each item individually
+        if isinstance(data, List):
+            return [cls.parse_obj(d) for d in data]  # type: ignore [misc]
+
+        # At this point, we expect a dict
+        if not isinstance(data, dict):
+            raise ValueError(f"Invalid data type: {type(data)}")
+
+        init_values = {}
+        other_values = {}
+        for key, value in data.items():
+            key = normalize_key(key)
+            if key in cls.__dataclass_fields__ and cls.__dataclass_fields__[key].init:
+                if isinstance(value, dict) or isinstance(value, list):
+                    field_type = cls.__dataclass_fields__[key].type
+
+                    # if `field_type` is a `BaseInferenceType`, parse it
+                    if inspect.isclass(field_type) and issubclass(field_type, BaseInferenceType):
+                        value = field_type.parse_obj(value)
+
+                    # otherwise, recursively parse nested dataclasses (if possible)
+                    # `get_args` returns handle Union and Optional for us
+                    else:
+                        expected_types = get_args(field_type)
+                        for expected_type in expected_types:
+                            if getattr(expected_type, "_name", None) == "List":
+                                expected_type = get_args(expected_type)[
+                                    0
+                                ]  # assume same type for all items in the list
+                            if inspect.isclass(expected_type) and issubclass(expected_type, BaseInferenceType):
+                                value = expected_type.parse_obj(value)
+                                break
+                init_values[key] = value
+            else:
+                other_values[key] = value
+
+        # Make all missing fields default to None
+        # => ensure that dataclass initialization will never fail even if the server does not return all fields.
+        for key in cls.__dataclass_fields__:
+            if key not in init_values:
+                init_values[key] = None
+
+        # Initialize dataclass with expected values
+        item = cls(**init_values)
+
+        # Add remaining fields as dict attributes
+        item.update(other_values)
+        return item
+
+    def __post_init__(self):
+        self.update(asdict(self))
+
+    def __setitem__(self, __key: Any, __value: Any) -> None:
+        # Hacky way to keep dataclass values in sync when dict is updated
+        super().__setitem__(__key, __value)
+        if __key in self.__dataclass_fields__ and getattr(self, __key, None) != __value:
+            self.__setattr__(__key, __value)
+        return
+
+    def __setattr__(self, __name: str, __value: Any) -> None:
+        # Hacky way to keep dict values is sync when dataclass is updated
+        super().__setattr__(__name, __value)
+        if self.get(__name) != __value:
+            self[__name] = __value
+        return
+
+
+def normalize_key(key: str) -> str:
+    # e.g "content-type" -> "content_type", "Accept" -> "accept"
+    return key.replace("-", "_").replace(" ", "_").lower()

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/document_question_answering.py

@@ -0,0 +1,85 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, List, Optional, Union
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class DocumentQuestionAnsweringInputData(BaseInferenceType):
+    """One (document, question) pair to answer"""
+
+    image: Any
+    """The image on which the question is asked"""
+    question: str
+    """A question to ask of the document"""
+
+
+@dataclass
+class DocumentQuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Document Question Answering
+    """
+
+    doc_stride: Optional[int] = None
+    """If the words in the document are too long to fit with the question for the model, it will
+    be split in several chunks with some overlap. This argument controls the size of that
+    overlap.
+    """
+    handle_impossible_answer: Optional[bool] = None
+    """Whether to accept impossible as an answer"""
+    lang: Optional[str] = None
+    """Language to use while running OCR. Defaults to english."""
+    max_answer_len: Optional[int] = None
+    """The maximum length of predicted answers (e.g., only answers with a shorter length are
+    considered).
+    """
+    max_question_len: Optional[int] = None
+    """The maximum length of the question after tokenization. It will be truncated if needed."""
+    max_seq_len: Optional[int] = None
+    """The maximum length of the total sentence (context + question) in tokens of each chunk
+    passed to the model. The context will be split in several chunks (using doc_stride as
+    overlap) if needed.
+    """
+    top_k: Optional[int] = None
+    """The number of answers to return (will be chosen by order of likelihood). Can return less
+    than top_k answers if there are not enough options available within the context.
+    """
+    word_boxes: Optional[List[Union[List[float], str]]] = None
+    """A list of words and bounding boxes (normalized 0->1000). If provided, the inference will
+    skip the OCR step and use the provided bounding boxes instead.
+    """
+
+
+@dataclass
+class DocumentQuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Document Question Answering inference"""
+
+    inputs: DocumentQuestionAnsweringInputData
+    """One (document, question) pair to answer"""
+    parameters: Optional[DocumentQuestionAnsweringParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class DocumentQuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Document Question Answering task"""
+
+    answer: str
+    """The answer to the question."""
+    end: int
+    """The end word index of the answer (in the OCR’d version of the input or provided word
+    boxes).
+    """
+    score: float
+    """The probability associated to the answer."""
+    start: int
+    """The start word index of the answer (in the OCR’d version of the input or provided word
+    boxes).
+    """
+    words: List[int]
+    """The index of each word/box pair that is in the answer"""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/feature_extraction.py

@@ -0,0 +1,37 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Literal, Optional
+
+from .base import BaseInferenceType
+
+
+FeatureExtractionInputTruncationDirection = Literal["Left", "Right"]
+
+
+@dataclass
+class FeatureExtractionInput(BaseInferenceType):
+    """Feature Extraction Input.
+    Auto-generated from TEI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tei-import.ts.
+    """
+
+    inputs: str
+    """The text to embed."""
+    normalize: Optional[bool] = None
+    prompt_name: Optional[str] = None
+    """The name of the prompt that should be used by for encoding. If not set, no prompt
+    will be applied.
+    Must be a key in the `Sentence Transformers` configuration `prompts` dictionary.
+    For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ",
+    ...},
+    then the sentence "What is the capital of France?" will be encoded as
+    "query: What is the capital of France?" because the prompt text will be prepended before
+    any text to encode.
+    """
+    truncate: Optional[bool] = None
+    truncation_direction: Optional["FeatureExtractionInputTruncationDirection"] = None

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_classification.py

@@ -0,0 +1,46 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Literal, Optional
+
+from .base import BaseInferenceType
+
+
+ImageClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass
+class ImageClassificationParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Image Classification
+    """
+
+    function_to_apply: Optional["ImageClassificationOutputTransform"] = None
+    """The function to apply to the output."""
+    top_k: Optional[int] = None
+    """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass
+class ImageClassificationInput(BaseInferenceType):
+    """Inputs for Image Classification inference"""
+
+    inputs: str
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload.
+    """
+    parameters: Optional[ImageClassificationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class ImageClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Image Classification task"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_segmentation.py

@@ -0,0 +1,54 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Literal, Optional
+
+from .base import BaseInferenceType
+
+
+ImageSegmentationSubtask = Literal["instance", "panoptic", "semantic"]
+
+
+@dataclass
+class ImageSegmentationParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Image Segmentation
+    """
+
+    mask_threshold: Optional[float] = None
+    """Threshold to use when turning the predicted masks into binary values."""
+    overlap_mask_area_threshold: Optional[float] = None
+    """Mask overlap threshold to eliminate small, disconnected segments."""
+    subtask: Optional["ImageSegmentationSubtask"] = None
+    """Segmentation task to be performed, depending on model capabilities."""
+    threshold: Optional[float] = None
+    """Probability threshold to filter out predicted masks."""
+
+
+@dataclass
+class ImageSegmentationInput(BaseInferenceType):
+    """Inputs for Image Segmentation inference"""
+
+    inputs: str
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload.
+    """
+    parameters: Optional[ImageSegmentationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class ImageSegmentationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Image Segmentation task
+    A predicted mask / segment
+    """
+
+    label: str
+    """The label of the predicted segment."""
+    mask: str
+    """The corresponding mask as a black-and-white image (base64-encoded)."""
+    score: Optional[float] = None
+    """The score or confidence degree the model has."""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_to_image.py

@@ -0,0 +1,57 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, List, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class ImageToImageTargetSize(BaseInferenceType):
+    """The size in pixel of the output image."""
+
+    height: int
+    width: int
+
+
+@dataclass
+class ImageToImageParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Image To Image
+    """
+
+    guidance_scale: Optional[float] = None
+    """For diffusion models. A higher guidance scale value encourages the model to generate
+    images closely linked to the text prompt at the expense of lower image quality.
+    """
+    negative_prompt: Optional[List[str]] = None
+    """One or several prompt to guide what NOT to include in image generation."""
+    num_inference_steps: Optional[int] = None
+    """For diffusion models. The number of denoising steps. More denoising steps usually lead to
+    a higher quality image at the expense of slower inference.
+    """
+    target_size: Optional[ImageToImageTargetSize] = None
+    """The size in pixel of the output image."""
+
+
+@dataclass
+class ImageToImageInput(BaseInferenceType):
+    """Inputs for Image To Image inference"""
+
+    inputs: str
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload.
+    """
+    parameters: Optional[ImageToImageParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class ImageToImageOutput(BaseInferenceType):
+    """Outputs of inference for the Image To Image task"""
+
+    image: Any
+    """The output image returned as raw bytes in the payload."""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/summarization.py

@@ -0,0 +1,44 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Dict, Literal, Optional
+
+from .base import BaseInferenceType
+
+
+SummarizationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"]
+
+
+@dataclass
+class SummarizationParameters(BaseInferenceType):
+    """Additional inference parameters.
+    Additional inference parameters for summarization.
+    """
+
+    clean_up_tokenization_spaces: Optional[bool] = None
+    """Whether to clean up the potential extra spaces in the text output."""
+    generate_parameters: Optional[Dict[str, Any]] = None
+    """Additional parametrization of the text generation algorithm."""
+    truncation: Optional["SummarizationTruncationStrategy"] = None
+    """The truncation strategy to use."""
+
+
+@dataclass
+class SummarizationInput(BaseInferenceType):
+    """Inputs for Summarization inference"""
+
+    inputs: str
+    """The input text to summarize."""
+    parameters: Optional[SummarizationParameters] = None
+    """Additional inference parameters."""
+
+
+@dataclass
+class SummarizationOutput(BaseInferenceType):
+    """Outputs of inference for the Summarization task"""
+
+    summary_text: str
+    """The summarized text."""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/table_question_answering.py

@@ -0,0 +1,45 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class TableQuestionAnsweringInputData(BaseInferenceType):
+    """One (table, question) pair to answer"""
+
+    question: str
+    """The question to be answered about the table"""
+    table: Dict[str, List[str]]
+    """The table to serve as context for the questions"""
+
+
+@dataclass
+class TableQuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Table Question Answering inference"""
+
+    inputs: TableQuestionAnsweringInputData
+    """One (table, question) pair to answer"""
+    parameters: Optional[Dict[str, Any]] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class TableQuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Table Question Answering task"""
+
+    answer: str
+    """The answer of the question given the table. If there is an aggregator, the answer will be
+    preceded by `AGGREGATOR >`.
+    """
+    cells: List[str]
+    """List of strings made up of the answer cell values."""
+    coordinates: List[List[int]]
+    """Coordinates of the cells of the answers."""
+    aggregator: Optional[str] = None
+    """If the model has an aggregator, this returns the aggregator."""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text2text_generation.py

@@ -0,0 +1,45 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Dict, Literal, Optional
+
+from .base import BaseInferenceType
+
+
+Text2TextGenerationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"]
+
+
+@dataclass
+class Text2TextGenerationParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Text2text Generation
+    """
+
+    clean_up_tokenization_spaces: Optional[bool] = None
+    """Whether to clean up the potential extra spaces in the text output."""
+    generate_parameters: Optional[Dict[str, Any]] = None
+    """Additional parametrization of the text generation algorithm"""
+    truncation: Optional["Text2TextGenerationTruncationStrategy"] = None
+    """The truncation strategy to use"""
+
+
+@dataclass
+class Text2TextGenerationInput(BaseInferenceType):
+    """Inputs for Text2text Generation inference"""
+
+    inputs: str
+    """The input text data"""
+    parameters: Optional[Text2TextGenerationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class Text2TextGenerationOutput(BaseInferenceType):
+    """Outputs of inference for the Text2text Generation task"""
+
+    generated_text: Any
+    text2_text_generation_output_generated_text: Optional[str] = None
+    """The generated text."""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_classification.py

@@ -0,0 +1,48 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Literal, Optional
+
+from .base import BaseInferenceType
+
+
+TextClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass
+class TextClassificationParameters(BaseInferenceType):
+    """
+    Additional inference parameters for Text Classification.
+    """
+
+    function_to_apply: Optional["TextClassificationOutputTransform"] = None
+    """
+    The function to apply to the output.
+    """
+    top_k: Optional[int] = None
+    """
+    When specified, limits the output to the top K most probable classes.
+    """
+
+
+@dataclass
+class TextClassificationInput(BaseInferenceType):
+    """Inputs for Text Classification inference"""
+
+    inputs: str
+    """The text to classify"""
+    parameters: Optional[TextClassificationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class TextClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Text Classification task"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_generation.py

@@ -0,0 +1,169 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, List, Literal, Optional
+
+from .base import BaseInferenceType
+
+
+TypeEnum = Literal["json", "regex"]
+
+
+@dataclass
+class TextGenerationInputGrammarType(BaseInferenceType):
+    type: "TypeEnum"
+    value: Any
+    """A string that represents a [JSON Schema](https://json-schema.org/).
+    JSON Schema is a declarative language that allows to annotate JSON documents
+    with types and descriptions.
+    """
+
+
+@dataclass
+class TextGenerationInputGenerateParameters(BaseInferenceType):
+    adapter_id: Optional[str] = None
+    """Lora adapter id"""
+    best_of: Optional[int] = None
+    """Generate best_of sequences and return the one if the highest token logprobs."""
+    decoder_input_details: Optional[bool] = None
+    """Whether to return decoder input token logprobs and ids."""
+    details: Optional[bool] = None
+    """Whether to return generation details."""
+    do_sample: Optional[bool] = None
+    """Activate logits sampling."""
+    frequency_penalty: Optional[float] = None
+    """The parameter for frequency penalty. 1.0 means no penalty
+    Penalize new tokens based on their existing frequency in the text so far,
+    decreasing the model's likelihood to repeat the same line verbatim.
+    """
+    grammar: Optional[TextGenerationInputGrammarType] = None
+    max_new_tokens: Optional[int] = None
+    """Maximum number of tokens to generate."""
+    repetition_penalty: Optional[float] = None
+    """The parameter for repetition penalty. 1.0 means no penalty.
+    See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+    """
+    return_full_text: Optional[bool] = None
+    """Whether to prepend the prompt to the generated text"""
+    seed: Optional[int] = None
+    """Random sampling seed."""
+    stop: Optional[List[str]] = None
+    """Stop generating tokens if a member of `stop` is generated."""
+    temperature: Optional[float] = None
+    """The value used to module the logits distribution."""
+    top_k: Optional[int] = None
+    """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+    top_n_tokens: Optional[int] = None
+    """The number of highest probability vocabulary tokens to keep for top-n-filtering."""
+    top_p: Optional[float] = None
+    """Top-p value for nucleus sampling."""
+    truncate: Optional[int] = None
+    """Truncate inputs tokens to the given size."""
+    typical_p: Optional[float] = None
+    """Typical Decoding mass
+    See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666)
+    for more information.
+    """
+    watermark: Optional[bool] = None
+    """Watermarking with [A Watermark for Large Language
+    Models](https://arxiv.org/abs/2301.10226).
+    """
+
+
+@dataclass
+class TextGenerationInput(BaseInferenceType):
+    """Text Generation Input.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    inputs: str
+    parameters: Optional[TextGenerationInputGenerateParameters] = None
+    stream: Optional[bool] = None
+
+
+TextGenerationOutputFinishReason = Literal["length", "eos_token", "stop_sequence"]
+
+
+@dataclass
+class TextGenerationOutputPrefillToken(BaseInferenceType):
+    id: int
+    logprob: float
+    text: str
+
+
+@dataclass
+class TextGenerationOutputToken(BaseInferenceType):
+    id: int
+    logprob: float
+    special: bool
+    text: str
+
+
+@dataclass
+class TextGenerationOutputBestOfSequence(BaseInferenceType):
+    finish_reason: "TextGenerationOutputFinishReason"
+    generated_text: str
+    generated_tokens: int
+    prefill: List[TextGenerationOutputPrefillToken]
+    tokens: List[TextGenerationOutputToken]
+    seed: Optional[int] = None
+    top_tokens: Optional[List[List[TextGenerationOutputToken]]] = None
+
+
+@dataclass
+class TextGenerationOutputDetails(BaseInferenceType):
+    finish_reason: "TextGenerationOutputFinishReason"
+    generated_tokens: int
+    prefill: List[TextGenerationOutputPrefillToken]
+    tokens: List[TextGenerationOutputToken]
+    best_of_sequences: Optional[List[TextGenerationOutputBestOfSequence]] = None
+    seed: Optional[int] = None
+    top_tokens: Optional[List[List[TextGenerationOutputToken]]] = None
+
+
+@dataclass
+class TextGenerationOutput(BaseInferenceType):
+    """Text Generation Output.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    generated_text: str
+    details: Optional[TextGenerationOutputDetails] = None
+
+
+@dataclass
+class TextGenerationStreamOutputStreamDetails(BaseInferenceType):
+    finish_reason: "TextGenerationOutputFinishReason"
+    generated_tokens: int
+    input_length: int
+    seed: Optional[int] = None
+
+
+@dataclass
+class TextGenerationStreamOutputToken(BaseInferenceType):
+    id: int
+    logprob: float
+    special: bool
+    text: str
+
+
+@dataclass
+class TextGenerationStreamOutput(BaseInferenceType):
+    """Text Generation Stream Output.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    index: int
+    token: TextGenerationStreamOutputToken
+    details: Optional[TextGenerationStreamOutputStreamDetails] = None
+    generated_text: Optional[str] = None
+    top_tokens: Optional[List[TextGenerationStreamOutputToken]] = None

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_audio.py

@@ -0,0 +1,105 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Literal, Optional, Union
+
+from .base import BaseInferenceType
+
+
+TextToAudioEarlyStoppingEnum = Literal["never"]
+
+
+@dataclass
+class TextToAudioGenerationParameters(BaseInferenceType):
+    """Parametrization of the text generation process
+    Ad-hoc parametrization of the text generation process
+    """
+
+    do_sample: Optional[bool] = None
+    """Whether to use sampling instead of greedy decoding when generating new tokens."""
+    early_stopping: Optional[Union[bool, "TextToAudioEarlyStoppingEnum"]] = None
+    """Controls the stopping condition for beam-based methods."""
+    epsilon_cutoff: Optional[float] = None
+    """If set to float strictly between 0 and 1, only tokens with a conditional probability
+    greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
+    3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
+    Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
+    """
+    eta_cutoff: Optional[float] = None
+    """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
+    float strictly between 0 and 1, a token is only considered if it is greater than either
+    eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
+    term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
+    the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
+    See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
+    for more details.
+    """
+    max_length: Optional[int] = None
+    """The maximum length (in tokens) of the generated text, including the input."""
+    max_new_tokens: Optional[int] = None
+    """The maximum number of tokens to generate. Takes precedence over maxLength."""
+    min_length: Optional[int] = None
+    """The minimum length (in tokens) of the generated text, including the input."""
+    min_new_tokens: Optional[int] = None
+    """The minimum number of tokens to generate. Takes precedence over maxLength."""
+    num_beam_groups: Optional[int] = None
+    """Number of groups to divide num_beams into in order to ensure diversity among different
+    groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
+    """
+    num_beams: Optional[int] = None
+    """Number of beams to use for beam search."""
+    penalty_alpha: Optional[float] = None
+    """The value balances the model confidence and the degeneration penalty in contrastive
+    search decoding.
+    """
+    temperature: Optional[float] = None
+    """The value used to modulate the next token probabilities."""
+    top_k: Optional[int] = None
+    """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+    top_p: Optional[float] = None
+    """If set to float < 1, only the smallest set of most probable tokens with probabilities
+    that add up to top_p or higher are kept for generation.
+    """
+    typical_p: Optional[float] = None
+    """Local typicality measures how similar the conditional probability of predicting a target
+    token next is to the expected conditional probability of predicting a random token next,
+    given the partial text already generated. If set to float < 1, the smallest set of the
+    most locally typical tokens with probabilities that add up to typical_p or higher are
+    kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
+    """
+    use_cache: Optional[bool] = None
+    """Whether the model should use the past last key/values attentions to speed up decoding"""
+
+
+@dataclass
+class TextToAudioParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Text To Audio
+    """
+
+    generate: Optional[TextToAudioGenerationParameters] = None
+    """Parametrization of the text generation process"""
+
+
+@dataclass
+class TextToAudioInput(BaseInferenceType):
+    """Inputs for Text To Audio inference"""
+
+    inputs: str
+    """The input text data"""
+    parameters: Optional[TextToAudioParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class TextToAudioOutput(BaseInferenceType):
+    """Outputs of inference for the Text To Audio task"""
+
+    audio: Any
+    """The generated audio waveform."""
+    sampling_rate: Any
+    text_to_audio_output_sampling_rate: Optional[float] = None
+    """The sampling rate of the generated audio waveform."""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_speech.py

@@ -0,0 +1,107 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Literal, Optional, Union
+
+from .base import BaseInferenceType
+
+
+TextToSpeechEarlyStoppingEnum = Literal["never"]
+
+
+@dataclass
+class TextToSpeechGenerationParameters(BaseInferenceType):
+    """Parametrization of the text generation process
+    Ad-hoc parametrization of the text generation process
+    """
+
+    do_sample: Optional[bool] = None
+    """Whether to use sampling instead of greedy decoding when generating new tokens."""
+    early_stopping: Optional[Union[bool, "TextToSpeechEarlyStoppingEnum"]] = None
+    """Controls the stopping condition for beam-based methods."""
+    epsilon_cutoff: Optional[float] = None
+    """If set to float strictly between 0 and 1, only tokens with a conditional probability
+    greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
+    3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
+    Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
+    """
+    eta_cutoff: Optional[float] = None
+    """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
+    float strictly between 0 and 1, a token is only considered if it is greater than either
+    eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
+    term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
+    the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
+    See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
+    for more details.
+    """
+    max_length: Optional[int] = None
+    """The maximum length (in tokens) of the generated text, including the input."""
+    max_new_tokens: Optional[int] = None
+    """The maximum number of tokens to generate. Takes precedence over maxLength."""
+    min_length: Optional[int] = None
+    """The minimum length (in tokens) of the generated text, including the input."""
+    min_new_tokens: Optional[int] = None
+    """The minimum number of tokens to generate. Takes precedence over maxLength."""
+    num_beam_groups: Optional[int] = None
+    """Number of groups to divide num_beams into in order to ensure diversity among different
+    groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
+    """
+    num_beams: Optional[int] = None
+    """Number of beams to use for beam search."""
+    penalty_alpha: Optional[float] = None
+    """The value balances the model confidence and the degeneration penalty in contrastive
+    search decoding.
+    """
+    temperature: Optional[float] = None
+    """The value used to modulate the next token probabilities."""
+    top_k: Optional[int] = None
+    """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+    top_p: Optional[float] = None
+    """If set to float < 1, only the smallest set of most probable tokens with probabilities
+    that add up to top_p or higher are kept for generation.
+    """
+    typical_p: Optional[float] = None
+    """Local typicality measures how similar the conditional probability of predicting a target
+    token next is to the expected conditional probability of predicting a random token next,
+    given the partial text already generated. If set to float < 1, the smallest set of the
+    most locally typical tokens with probabilities that add up to typical_p or higher are
+    kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
+    """
+    use_cache: Optional[bool] = None
+    """Whether the model should use the past last key/values attentions to speed up decoding"""
+
+
+@dataclass
+class TextToSpeechParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Text To Speech
+    """
+
+    generate: Optional[TextToSpeechGenerationParameters] = None
+    """Parametrization of the text generation process"""
+
+
+@dataclass
+class TextToSpeechInput(BaseInferenceType):
+    """Inputs for Text To Speech inference"""
+
+    inputs: str
+    """The input text data"""
+    parameters: Optional[TextToSpeechParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class TextToSpeechOutput(BaseInferenceType):
+    """Outputs for Text to Speech inference
+    Outputs of inference for the Text To Audio task
+    """
+
+    audio: Any
+    """The generated audio waveform."""
+    sampling_rate: Any
+    text_to_speech_output_sampling_rate: Optional[float] = None
+    """The sampling rate of the generated audio waveform."""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/video_classification.py

@@ -0,0 +1,47 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Literal, Optional
+
+from .base import BaseInferenceType
+
+
+VideoClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass
+class VideoClassificationParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Video Classification
+    """
+
+    frame_sampling_rate: Optional[int] = None
+    """The sampling rate used to select frames from the video."""
+    function_to_apply: Optional["VideoClassificationOutputTransform"] = None
+    num_frames: Optional[int] = None
+    """The number of sampled frames to consider for classification."""
+    top_k: Optional[int] = None
+    """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass
+class VideoClassificationInput(BaseInferenceType):
+    """Inputs for Video Classification inference"""
+
+    inputs: Any
+    """The input video data"""
+    parameters: Optional[VideoClassificationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class VideoClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Video Classification task"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/zero_shot_classification.py

@@ -0,0 +1,56 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import List, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class ZeroShotClassificationInputData(BaseInferenceType):
+    """The input text data, with candidate labels"""
+
+    candidate_labels: List[str]
+    """The set of possible class labels to classify the text into."""
+    text: str
+    """The text to classify"""
+
+
+@dataclass
+class ZeroShotClassificationParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Zero Shot Classification
+    """
+
+    hypothesis_template: Optional[str] = None
+    """The sentence used in conjunction with candidateLabels to attempt the text classification
+    by replacing the placeholder with the candidate labels.
+    """
+    multi_label: Optional[bool] = None
+    """Whether multiple candidate labels can be true. If false, the scores are normalized such
+    that the sum of the label likelihoods for each sequence is 1. If true, the labels are
+    considered independent and probabilities are normalized for each candidate.
+    """
+
+
+@dataclass
+class ZeroShotClassificationInput(BaseInferenceType):
+    """Inputs for Zero Shot Classification inference"""
+
+    inputs: ZeroShotClassificationInputData
+    """The input text data, with candidate labels"""
+    parameters: Optional[ZeroShotClassificationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class ZeroShotClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Zero Shot Classification task"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/zero_shot_object_detection.py

@@ -0,0 +1,55 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class ZeroShotObjectDetectionInputData(BaseInferenceType):
+    """The input image data, with candidate labels"""
+
+    candidate_labels: List[str]
+    """The candidate labels for this image"""
+    image: Any
+    """The image data to generate bounding boxes from"""
+
+
+@dataclass
+class ZeroShotObjectDetectionInput(BaseInferenceType):
+    """Inputs for Zero Shot Object Detection inference"""
+
+    inputs: ZeroShotObjectDetectionInputData
+    """The input image data, with candidate labels"""
+    parameters: Optional[Dict[str, Any]] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class ZeroShotObjectDetectionBoundingBox(BaseInferenceType):
+    """The predicted bounding box. Coordinates are relative to the top left corner of the input
+    image.
+    """
+
+    xmax: int
+    xmin: int
+    ymax: int
+    ymin: int
+
+
+@dataclass
+class ZeroShotObjectDetectionOutputElement(BaseInferenceType):
+    """Outputs of inference for the Zero Shot Object Detection task"""
+
+    box: ZeroShotObjectDetectionBoundingBox
+    """The predicted bounding box. Coordinates are relative to the top left corner of the input
+    image.
+    """
+    label: str
+    """A candidate label"""
+    score: float
+    """The associated score / probability"""

evalkit_internvl/lib/python3.10/site-packages/huggingface_hub/inference_api.py

@@ -0,0 +1,217 @@
+import io
+from typing import Any, Dict, List, Optional, Union
+
+from . import constants
+from .hf_api import HfApi
+from .utils import build_hf_headers, get_session, is_pillow_available, logging, validate_hf_hub_args
+from .utils._deprecation import _deprecate_method
+
+
+logger = logging.get_logger(__name__)
+
+
+ALL_TASKS = [
+    # NLP
+    "text-classification",
+    "token-classification",
+    "table-question-answering",
+    "question-answering",
+    "zero-shot-classification",
+    "translation",
+    "summarization",
+    "conversational",
+    "feature-extraction",
+    "text-generation",
+    "text2text-generation",
+    "fill-mask",
+    "sentence-similarity",
+    # Audio
+    "text-to-speech",
+    "automatic-speech-recognition",
+    "audio-to-audio",
+    "audio-classification",
+    "voice-activity-detection",
+    # Computer vision
+    "image-classification",
+    "object-detection",
+    "image-segmentation",
+    "text-to-image",
+    "image-to-image",
+    # Others
+    "tabular-classification",
+    "tabular-regression",
+]
+
+
+class InferenceApi:
+    """Client to configure requests and make calls to the HuggingFace Inference API.
+
+    Example:
+
+    ```python
+    >>> from huggingface_hub.inference_api import InferenceApi
+
+    >>> # Mask-fill example
+    >>> inference = InferenceApi("bert-base-uncased")
+    >>> inference(inputs="The goal of life is [MASK].")
+    [{'sequence': 'the goal of life is life.', 'score': 0.10933292657136917, 'token': 2166, 'token_str': 'life'}]
+
+    >>> # Question Answering example
+    >>> inference = InferenceApi("deepset/roberta-base-squad2")
+    >>> inputs = {
+    ...     "question": "What's my name?",
+    ...     "context": "My name is Clara and I live in Berkeley.",
+    ... }
+    >>> inference(inputs)
+    {'score': 0.9326569437980652, 'start': 11, 'end': 16, 'answer': 'Clara'}
+
+    >>> # Zero-shot example
+    >>> inference = InferenceApi("typeform/distilbert-base-uncased-mnli")
+    >>> inputs = "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!"
+    >>> params = {"candidate_labels": ["refund", "legal", "faq"]}
+    >>> inference(inputs, params)
+    {'sequence': 'Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!', 'labels': ['refund', 'faq', 'legal'], 'scores': [0.9378499388694763, 0.04914155602455139, 0.013008488342165947]}
+
+    >>> # Overriding configured task
+    >>> inference = InferenceApi("bert-base-uncased", task="feature-extraction")
+
+    >>> # Text-to-image
+    >>> inference = InferenceApi("stabilityai/stable-diffusion-2-1")
+    >>> inference("cat")
+    <PIL.PngImagePlugin.PngImageFile image (...)>
+
+    >>> # Return as raw response to parse the output yourself
+    >>> inference = InferenceApi("mio/amadeus")
+    >>> response = inference("hello world", raw_response=True)
+    >>> response.headers
+    {"Content-Type": "audio/flac", ...}
+    >>> response.content # raw bytes from server
+    b'(...)'
+    ```
+    """
+
+    @validate_hf_hub_args
+    @_deprecate_method(
+        version="1.0",
+        message=(
+            "`InferenceApi` client is deprecated in favor of the more feature-complete `InferenceClient`. Check out"
+            " this guide to learn how to convert your script to use it:"
+            " https://huggingface.co/docs/huggingface_hub/guides/inference#legacy-inferenceapi-client."
+        ),
+    )
+    def __init__(
+        self,
+        repo_id: str,
+        task: Optional[str] = None,
+        token: Optional[str] = None,
+        gpu: bool = False,
+    ):
+        """Inits headers and API call information.
+
+        Args:
+            repo_id (``str``):
+                Id of repository (e.g. `user/bert-base-uncased`).
+            task (``str``, `optional`, defaults ``None``):
+                Whether to force a task instead of using task specified in the
+                repository.
+            token (`str`, `optional`):
+                The API token to use as HTTP bearer authorization. This is not
+                the authentication token. You can find the token in
+                https://huggingface.co/settings/token. Alternatively, you can
+                find both your organizations and personal API tokens using
+                `HfApi().whoami(token)`.
+            gpu (`bool`, `optional`, defaults `False`):
+                Whether to use GPU instead of CPU for inference(requires Startup
+                plan at least).
+        """
+        self.options = {"wait_for_model": True, "use_gpu": gpu}
+        self.headers = build_hf_headers(token=token)
+
+        # Configure task
+        model_info = HfApi(token=token).model_info(repo_id=repo_id)
+        if not model_info.pipeline_tag and not task:
+            raise ValueError(
+                "Task not specified in the repository. Please add it to the model card"
+                " using pipeline_tag"
+                " (https://huggingface.co/docs#how-is-a-models-type-of-inference-api-and-widget-determined)"
+            )
+
+        if task and task != model_info.pipeline_tag:
+            if task not in ALL_TASKS:
+                raise ValueError(f"Invalid task {task}. Make sure it's valid.")
+
+            logger.warning(
+                "You're using a different task than the one specified in the"
+                " repository. Be sure to know what you're doing :)"
+            )
+            self.task = task
+        else:
+            assert model_info.pipeline_tag is not None, "Pipeline tag cannot be None"
+            self.task = model_info.pipeline_tag
+
+        self.api_url = f"{constants.INFERENCE_ENDPOINT}/pipeline/{self.task}/{repo_id}"
+
+    def __repr__(self):
+        # Do not add headers to repr to avoid leaking token.
+        return f"InferenceAPI(api_url='{self.api_url}', task='{self.task}', options={self.options})"
+
+    def __call__(
+        self,
+        inputs: Optional[Union[str, Dict, List[str], List[List[str]]]] = None,
+        params: Optional[Dict] = None,
+        data: Optional[bytes] = None,
+        raw_response: bool = False,
+    ) -> Any:
+        """Make a call to the Inference API.
+
+        Args:
+            inputs (`str` or `Dict` or `List[str]` or `List[List[str]]`, *optional*):
+                Inputs for the prediction.
+            params (`Dict`, *optional*):
+                Additional parameters for the models. Will be sent as `parameters` in the
+                payload.
+            data (`bytes`, *optional*):
+                Bytes content of the request. In this case, leave `inputs` and `params` empty.
+            raw_response (`bool`, defaults to `False`):
+                If `True`, the raw `Response` object is returned. You can parse its content
+                as preferred. By default, the content is parsed into a more practical format
+                (json dictionary or PIL Image for example).
+        """
+        # Build payload
+        payload: Dict[str, Any] = {
+            "options": self.options,
+        }
+        if inputs:
+            payload["inputs"] = inputs
+        if params:
+            payload["parameters"] = params
+
+        # Make API call
+        response = get_session().post(self.api_url, headers=self.headers, json=payload, data=data)
+
+        # Let the user handle the response
+        if raw_response:
+            return response
+
+        # By default, parse the response for the user.
+        content_type = response.headers.get("Content-Type") or ""
+        if content_type.startswith("image"):
+            if not is_pillow_available():
+                raise ImportError(
+                    f"Task '{self.task}' returned as image but Pillow is not installed."
+                    " Please install it (`pip install Pillow`) or pass"
+                    " `raw_response=True` to get the raw `Response` object and parse"
+                    " the image by yourself."
+                )
+
+            from PIL import Image
+
+            return Image.open(io.BytesIO(response.content))
+        elif content_type == "application/json":
+            return response.json()
+        else:
+            raise NotImplementedError(
+                f"{content_type} output type is not implemented yet. You can pass"
+                " `raw_response=True` to get the raw `Response` object and parse the"
+                " output by yourself."
+            )