team-10/env/Lib/site-packages/diffusers/utils/outputs.py

# Copyright 2025 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.
"""
Generic utilities
"""

from collections import OrderedDict
from dataclasses import fields, is_dataclass
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available, is_torch_version


def is_tensor(x) -> bool:
    """
    Tests if `x` is a `torch.Tensor` or `np.ndarray`.
    """
    if is_torch_available():
        import torch

        if isinstance(x, torch.Tensor):
            return True

    return isinstance(x, np.ndarray)


class BaseOutput(OrderedDict):
    """
    Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a
    tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular
    Python dictionary.

    <Tip warning={true}>

    You can't unpack a [`BaseOutput`] directly. Use the [`~utils.BaseOutput.to_tuple`] method to convert it to a tuple
    first.

    </Tip>
    """

    def __init_subclass__(cls) -> None:
        """Register subclasses as pytree nodes.

        This is necessary to synchronize gradients when using `torch.nn.parallel.DistributedDataParallel` with
        `static_graph=True` with modules that output `ModelOutput` subclasses.
        """
        if is_torch_available():
            import torch.utils._pytree

            if is_torch_version("<", "2.2"):
                torch.utils._pytree._register_pytree_node(
                    cls,
                    torch.utils._pytree._dict_flatten,
                    lambda values, context: cls(**torch.utils._pytree._dict_unflatten(values, context)),
                )
            else:
                torch.utils._pytree.register_pytree_node(
                    cls,
                    torch.utils._pytree._dict_flatten,
                    lambda values, context: cls(**torch.utils._pytree._dict_unflatten(values, context)),
                    serialized_type_name=f"{cls.__module__}.{cls.__name__}",
                )

    def __post_init__(self) -> None:
        class_fields = fields(self)

        # Safety and consistency checks
        if not len(class_fields):
            raise ValueError(f"{self.__class__.__name__} has no fields.")

        first_field = getattr(self, class_fields[0].name)
        other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:])

        if other_fields_are_none and isinstance(first_field, dict):
            for key, value in first_field.items():
                self[key] = value
        else:
            for field in class_fields:
                v = getattr(self, field.name)
                if v is not None:
                    self[field.name] = v

    def __delitem__(self, *args, **kwargs):
        raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")

    def setdefault(self, *args, **kwargs):
        raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")

    def pop(self, *args, **kwargs):
        raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")

    def update(self, *args, **kwargs):
        raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")

    def __getitem__(self, k: Any) -> Any:
        if isinstance(k, str):
            inner_dict = dict(self.items())
            return inner_dict[k]
        else:
            return self.to_tuple()[k]

    def __setattr__(self, name: Any, value: Any) -> None:
        if name in self.keys() and value is not None:
            # Don't call self.__setitem__ to avoid recursion errors
            super().__setitem__(name, value)
        super().__setattr__(name, value)

    def __setitem__(self, key, value):
        # Will raise a KeyException if needed
        super().__setitem__(key, value)
        # Don't call self.__setattr__ to avoid recursion errors
        super().__setattr__(key, value)

    def __reduce__(self):
        if not is_dataclass(self):
            return super().__reduce__()
        callable, _args, *remaining = super().__reduce__()
        args = tuple(getattr(self, field.name) for field in fields(self))
        return callable, args, *remaining

    def to_tuple(self) -> Tuple[Any, ...]:
        """
        Convert self to a tuple containing all the attributes/keys that are not `None`.
        """
        return tuple(self[k] for k in self.keys())