code: 
  '''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
  
  
  _UpperCAmelCase   :      Union[str, Any] 						=  get_logger(__name__)
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      Union[str, Any]:
  		if version.parse(UpperCamelCase	) < version.parse(UpperCamelCase	):
  				if "dev" in min_version:
  						_UpperCamelCase       : int    =      (
  						    '''This example requires a source install from HuggingFace diffusers (see '''
  						    '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
  						)
  				else:
  						_UpperCamelCase       : Optional[int]    =      f'''This example requires a minimum version of {min_version},'''
  				error_message += f''' but the version found is {__version__}.\n'''
  				raise ImportError(UpperCamelCase	)
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  _UpperCAmelCase   :      Optional[int] 						=  """
  # Transformers installation
  ! pip install transformers datasets evaluate
  # To install from source instead of the last release, comment the command above and uncomment the following one.
  # ! pip install git+https://github.com/huggingface/transformers.git
  """
  
  _UpperCAmelCase   :      Union[str, Any] 						=  [{"""type""": """code""", """content""": INSTALL_CONTENT}]
  _UpperCAmelCase   :      Union[str, Any] 						=  {
      """{processor_class}""": """FakeProcessorClass""",
      """{model_class}""": """FakeModelClass""",
      """{object_class}""": """FakeObjectClass""",
  }
  
  
  
  
  
  