


from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class   lowerCamelCase						:

       def __init__(      self						,    a__       ):






        '''simple docstring'''






        snake_case :			Any							=   data
        snake_case :			Node | None							=   None



       def __str__(      self       ):






        '''simple docstring'''






        return f'''{self.data}'''







class   lowerCamelCase						:

       def __init__(      self       ):






        '''simple docstring'''






        snake_case :			Node | None							=   None
        snake_case :			Node | None							=   None

       def __iter__(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   self.front
        while node:
         yield node.data
         snake_case :			str							=   node.next

       def __len__(      self       ):






        '''simple docstring'''






        return len(tuple(iter(self       )       )       )

       def __str__(      self       ):






        '''simple docstring'''






        return " <- ".join(str(a__       ) for item in self       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return len(self       ) == 0

       def 					UpperCAmelCase					(      self						,    a__       ):






        '''simple docstring'''






        snake_case :			Union[str, Any]							=   Node(a__       )
        if self.is_empty():
         snake_case :			Optional[Any]							=   node
        else:
         assert isinstance(self.rear						,    a__       )
         snake_case :			List[str]							=   node
         snake_case :			str							=   node

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        if self.is_empty():
         raise IndexError('''dequeue from empty queue'''       )
        assert isinstance(self.front						,    a__       )
        snake_case :			List[str]							=   self.front
        snake_case :			List[str]							=   self.front.next
        if self.front is None:
         snake_case :			Tuple							=   None
        return node.data



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   None


if __name__ == "__main__":
  from doctest import testmod

  testmod()














"""simple docstring"""



import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
						from .testing_utils import (
						    floats_tensor,
						    load_hf_numpy,
						    load_image,
						    load_numpy,
						    load_pt,
						    nightly,
						    parse_flag_from_env,
						    print_tensor_test,
						    require_torch_a,
						    require_torch_gpu,
						    skip_mps,
						    slow,
						    torch_all_close,
						    torch_device,
						)
						from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A_     =				get_logger(__name__)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Dict   )		->Union[str, Any]:
						if version.parse(UpperCAmelCase__   ) < version.parse(UpperCAmelCase__   ):
												if "dev" in min_version:
																		A__    :	Optional[int]			=	(
																		    """This example requires a source install from HuggingFace diffusers (see """
																		    """`https://huggingface.co/docs/diffusers/installation#install-from-source`),"""
																		)
												else:
																		A__    :	Union[str, Any]			=	f'This example requires a minimum version of {min_version},'
												error_message += f' but the version found is {__version__}.\n'
												raise ImportError(UpperCAmelCase__   )






