code: 
  
  
  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
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					get_logger(__name__)
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if version.parse(UpperCamelCase_       ) < version.parse(UpperCamelCase_       ):
            if "dev" in min_version:
                 snake_case					=       (
                     '''This example requires a source install from HuggingFace diffusers (see '''
                     '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
                 )
            else:
                 snake_case					=       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: 
  
  
  import torch
  
  from accelerate import Accelerator, DistributedType
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __enter__( self					):
           if self.enabled:
                snake_case					=       self.model.no_sync()
                self.model_sync_obj.__enter__()
  
           return self
      def __exit__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           if self.enabled:
                # Average all models on exit
                self._sync_and_avg_model_params()
                self.model_sync_obj.__exit__(__snake_case    ,      __snake_case    ,      __snake_case					)
      def __init__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case = True					):
           if accelerator.distributed_type not in [
               DistributedType.NO,
               DistributedType.MULTI_CPU,
               DistributedType.MULTI_GPU,
           ]:
                raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''					)
           snake_case					=       enabled and accelerator.distributed_type != DistributedType.NO
           snake_case					=       0
           if self.enabled:
                snake_case					=       accelerator
                snake_case					=       model
                snake_case					=       local_sgd_steps
      def 	a_    ( self					):
           self.num_steps += 1
           if not self.enabled:
                return
  
           if self.num_steps % self.local_sgd_steps == 0:
                self._sync_and_avg_model_params()
  
  
  
  
      def 	a_    ( self					):
           self.accelerator.wait_for_everyone()
           with self.accelerator.autocast():
                for param in self.model.parameters():
                     snake_case					=       self.accelerator.reduce(param.data    ,      reduction='''mean'''					)
  
  
  
  
  