'''simple docstring'''






from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   def __init__(			self			: Any,   UpperCamelCase__			: Optional[Any],   UpperCamelCase__			: int    )     -> int:
        super().__init__()
        self.register_modules(unet=UpperCamelCase__,   scheduler=UpperCamelCase__    )

   @torch.no_grad()
   def __call__(			self			: Dict,   UpperCamelCase__			: int = 1,   UpperCamelCase__			: Optional[torch.Generator] = None,   UpperCamelCase__			: int = 50,   UpperCamelCase__			: Optional[str] = "pil",   UpperCamelCase__			: bool = True,   **UpperCamelCase__			: Union[str, Any],   )     -> Union[ImagePipelineOutput, Tuple]:
        _A								=     torch.randn(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),   generator=UpperCamelCase__,   )
        _A								=     image.to(self.device    )

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

        for t in self.progress_bar(self.scheduler.timesteps    ):
             # 1. predict noise model_output
             _A								=     self.unet(UpperCamelCase__,   UpperCamelCase__    ).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(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__    ).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(UpperCamelCase__    )

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

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




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_UpperCAmelCase						:    Optional[Any]     					=					{
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    Dict     					=					[
       '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''UniSpeechSatForAudioFrameClassification''',
       '''UniSpeechSatForCTC''',
       '''UniSpeechSatForPreTraining''',
       '''UniSpeechSatForSequenceClassification''',
       '''UniSpeechSatForXVector''',
       '''UniSpeechSatModel''',
       '''UniSpeechSatPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech_sat import (
          UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechSatForAudioFrameClassification,
          UniSpeechSatForCTC,
          UniSpeechSatForPreTraining,
          UniSpeechSatForSequenceClassification,
          UniSpeechSatForXVector,
          UniSpeechSatModel,
          UniSpeechSatPreTrainedModel,
      )

else:
   import sys

   _UpperCAmelCase						:    int     					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



