


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE	:Dict  		=     {
    '''configuration_musicgen''': [
        '''MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MusicgenConfig''',
        '''MusicgenDecoderConfig''',
    ],
    '''processing_musicgen''': ['''MusicgenProcessor'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE	:str  		=     [
       '''MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''MusicgenForConditionalGeneration''',
       '''MusicgenForCausalLM''',
       '''MusicgenModel''',
       '''MusicgenPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_musicgen import (
       MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MusicgenConfig,
       MusicgenDecoderConfig,
   )
   from .processing_musicgen import MusicgenProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_musicgen import (
          MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
          MusicgenForCausalLM,
          MusicgenForConditionalGeneration,
          MusicgenModel,
          MusicgenPreTrainedModel,
      )

else:
   import sys

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








from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  def __init__(     self       :       List[str]    ,				_lowerCAmelCase       :       Optional[Any]    ,				_lowerCAmelCase       :       Tuple  )   -> str:




     """simple docstring"""
     super().__init__()
     self.register_modules(unet=_lowerCAmelCase    ,				scheduler=_lowerCAmelCase  )



  @torch.no_grad()
  def __call__(     self       :       Tuple    ,				_lowerCAmelCase       :       int = 1    ,				_lowerCAmelCase       :       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,				_lowerCAmelCase       :       int = 1_0_0_0    ,				_lowerCAmelCase       :       Optional[str] = "pil"    ,				_lowerCAmelCase       :       bool = True    ,				)   -> Union[ImagePipelineOutput, Tuple]:




     """simple docstring"""
     # Sample gaussian noise to begin loop
     if isinstance(self.unet.config.sample_size    ,				_lowerCAmelCase  ):
        snake_case_					    =					(
            batch_size,
            self.unet.config.in_channels,
            self.unet.config.sample_size,
            self.unet.config.sample_size,
        )
     else:
        snake_case_					    =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

     if self.device.type == "mps":
        # randn does not work reproducibly on mps
        snake_case_					    =					randn_tensor(_lowerCAmelCase    ,				generator=_lowerCAmelCase  )
        snake_case_					    =					image.to(self.device  )
     else:
        snake_case_					    =					randn_tensor(_lowerCAmelCase    ,				generator=_lowerCAmelCase    ,				device=self.device  )

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

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

        # 2. compute previous image: x_t -> x_t-1
        snake_case_					    =					self.scheduler.step(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase    ,				generator=_lowerCAmelCase  ).prev_sample

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

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowerCAmelCase  )




