
"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A     :			Dict  						=			{"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			List[Any]  						=			[
       "VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ViTMSNModel",
       "ViTMSNForImageClassification",
       "ViTMSNPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_msn import (
          VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMSNForImageClassification,
          ViTMSNModel,
          ViTMSNPreTrainedModel,
      )

else:
   import sys

   _A     :			Optional[Any]  						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__lowerCAmelCase						:							List[Any]    							=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class     A ( UpperCAmelCase ):





							def __init__(   self					:				Dict  ,		__a					:				List[Any]  ,		__a					:				Optional[Any]						)				->						Dict:
								super().__init__()
								self.register_modules(unet=__a  ,		scheduler=__a						)






							@torch.no_grad()
							def __call__(   self					:				Tuple  ,		__a					:				int = 1  ,		__a					:				int = 1_0_0  ,		__a					:				Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,		__a					:				Optional[float] = None  ,		__a					:				bool = True  ,		)				->						Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
									__UpperCAmelCase      					=      self.unet.config.sample_size / self.unet.config.sample_rate

								__UpperCAmelCase      					=      audio_length_in_s * self.unet.config.sample_rate

								__UpperCAmelCase      					=      2 ** len(self.unet.up_blocks						)
								if sample_size < 3 * down_scale_factor:
									raise ValueError(
									    f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
									    f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""						)

								__UpperCAmelCase      					=      int(__a						)
								if sample_size % down_scale_factor != 0:
									__UpperCAmelCase      					=      (
									    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
									) * down_scale_factor
									logger.info(
									    f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
									    f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
									    ''' process.'''						)
								__UpperCAmelCase      					=      int(__a						)

								__UpperCAmelCase      					=      next(iter(self.unet.parameters()						)						).dtype
								__UpperCAmelCase      					=      (batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(__a  ,		__a						) and len(__a						) != batch_size:
									raise ValueError(
									    f"""You have passed a list of generators of length {len(__a						)}, but requested an effective batch"""
									    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""						)

								__UpperCAmelCase      					=      randn_tensor(__a  ,		generator=__a  ,		device=self.device  ,		dtype=__a						)

								# set step values
								self.scheduler.set_timesteps(__a  ,		device=audio.device						)
								__UpperCAmelCase      					=      self.scheduler.timesteps.to(__a						)

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

									# 2. compute previous image: x_t -> t_t-1
									__UpperCAmelCase      					=      self.scheduler.step(__a  ,		__a  ,		__a						).prev_sample

								__UpperCAmelCase      					=      audio.clamp(-1  ,		1						).float().cpu().numpy()

								__UpperCAmelCase      					=      audio[:, :, :original_sample_size]

								if not return_dict:
									return (audio,)

								return AudioPipelineOutput(audios=__a						)
