code: 
  
  
  
  
  from typing import List, Optional, Tuple, Union
  
  import torch
  
  from ...utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Dict	,      UpperCamelCase       :       str	,      UpperCamelCase       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              self.register_modules(unet=UpperCamelCase	,      scheduler=UpperCamelCase		)
  
  
  
  
       @torch.no_grad()
       def __call__( self       :       Tuple	,      UpperCamelCase       :       int = 1	,      UpperCamelCase       :       Optional[Union[torch.Generator, List[torch.Generator]]] = None	,      UpperCamelCase       :       int = 10_00	,      UpperCamelCase       :       Optional[str] = "pil"	,      UpperCamelCase       :       bool = True	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if isinstance(self.unet.config.sample_size	,      UpperCamelCase		):
                     _lowercase					:				str           =			(
                         batch_size,
                         self.unet.config.in_channels,
                         self.unet.config.sample_size,
                         self.unet.config.sample_size,
                     )
              else:
                     _lowercase					:				Optional[Any]           =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
  
              if self.device.type == "mps":
                     # randn does not work reproducibly on mps
                     _lowercase					:				Optional[Any]           =			randn_tensor(UpperCamelCase	,      generator=UpperCamelCase		)
                     _lowercase					:				Optional[int]           =			image.to(self.device		)
              else:
                     _lowercase					:				Tuple           =			randn_tensor(UpperCamelCase	,      generator=UpperCamelCase	,      device=self.device		)
  
              # set step values
              self.scheduler.set_timesteps(UpperCamelCase		)
  
              for t in self.progress_bar(self.scheduler.timesteps		):
                     # 1. predict noise model_output
                     _lowercase					:				Tuple           =			self.unet(UpperCamelCase	,      UpperCamelCase		).sample
  
                     # 2. compute previous image: x_t -> x_t-1
                     _lowercase					:				Optional[int]           =			self.scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      generator=UpperCamelCase		).prev_sample
  
              _lowercase					:				str           =			(image / 2 + 0.5).clamp(0	,      1		)
              _lowercase					:				List[str]           =			image.cpu().permute(0	,      2	,      3	,      1		).numpy()
              if output_type == "pil":
                     _lowercase					:				Optional[Any]           =			self.numpy_to_pil(UpperCamelCase		)
  
              if not return_dict:
                     return (image,)
  
              return ImagePipelineOutput(images=UpperCamelCase		)
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  UpperCamelCase__				       =			{
      'configuration_wav2vec2_conformer': [
          'WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'Wav2Vec2ConformerConfig',
      ],
  }
  
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     UpperCamelCase__				       =			[
         'WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'Wav2Vec2ConformerForAudioFrameClassification',
         'Wav2Vec2ConformerForCTC',
         'Wav2Vec2ConformerForPreTraining',
         'Wav2Vec2ConformerForSequenceClassification',
         'Wav2Vec2ConformerForXVector',
         'Wav2Vec2ConformerModel',
         'Wav2Vec2ConformerPreTrainedModel',
     ]
  
  if TYPE_CHECKING:
     from .configuration_wavaveca_conformer import (
         WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         WavaVecaConformerConfig,
     )
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_wavaveca_conformer import (
            WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            WavaVecaConformerForAudioFrameClassification,
            WavaVecaConformerForCTC,
            WavaVecaConformerForPreTraining,
            WavaVecaConformerForSequenceClassification,
            WavaVecaConformerForXVector,
            WavaVecaConformerModel,
            WavaVecaConformerPreTrainedModel,
        )
  
  else:
     import sys
  
     UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)