"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__magic_name__    				=					{
    "configuration_wav2vec2_conformer": [
        "WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Wav2Vec2ConformerConfig",
    ],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					[
         "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

     __magic_name__    				=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def __init__(   self		:      Dict					,   _snake_case		:      List[str]					,   _snake_case		:      Any				)				->			Optional[Any]:




    '''simple docstring'''






    super().__init__()
    self.register_modules(unet=_snake_case					,   scheduler=_snake_case				)



 @torch.no_grad()
 def __call__(   self		:      int					,   _snake_case		:      int = 1					,   _snake_case		:      Optional[torch.Generator] = None					,   _snake_case		:      int = 50					,   _snake_case		:      Optional[str] = "pil"					,   _snake_case		:      bool = True					,   **_snake_case		:      str					,   )				->			Union[ImagePipelineOutput, Tuple]:




    '''simple docstring'''






    a__     				=		torch.randn(
        (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,   generator=_snake_case					,   )
    a__     				=		image.to(self.device				)

    # set step values
    self.scheduler.set_timesteps(_snake_case				)

    for t in self.progress_bar(self.scheduler.timesteps				):
       # 1. predict noise model_output
       a__     				=		self.unet(_snake_case					,   _snake_case				).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       a__     				=		self.scheduler.step(_snake_case					,   _snake_case					,   _snake_case				).prev_sample

    a__     				=		(image / 2 + 0.5).clamp(0					,   1				)
    a__     				=		image.cpu().permute(0					,   2					,   3					,   1				).numpy()
    if output_type == "pil":
       a__     				=		self.numpy_to_pil(_snake_case				)

    if not return_dict:
       return (image,), "This is a local test"

    return ImagePipelineOutput(images=_snake_case				), "This is a local test"


