

'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

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


__A    	=   logging.get_logger(__name__)  # pylint: disable=invalid-name




class a_    (    __A							):







   def __init__(self					,   __a					,   __a)			->      Any:
      """simple docstring"""






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







   @torch.no_grad()
   def __call__(self					,   __a = 1					,   __a = 1_0_0					,   __a = None					,   __a = None					,   __a = True					,   )			->      str:
      """simple docstring"""






      if audio_length_in_s is None:
         __snake_case				:      Union[str, Any]     							= self.unet.config.sample_size / self.unet.config.sample_rate

      __snake_case				:      Optional[Any]     							= audio_length_in_s * self.unet.config.sample_rate

      __snake_case				:      int     							= 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}.""")

      __snake_case				:      str     							= int(__a)
      if sample_size % down_scale_factor != 0:
         __snake_case				:      Dict     							= (
             (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.')
      __snake_case				:      Any     							= int(__a)

      __snake_case				:      Union[str, Any]     							= next(iter(self.unet.parameters())).dtype
      __snake_case				:      List[Any]     							= (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.""")

      __snake_case				:      List[str]     							= randn_tensor(__a					,   generator=__a					,   device=self.device					,   dtype=__a)

      # set step values
      self.scheduler.set_timesteps(__a					,   device=audio.device)
      __snake_case				:      Optional[int]     							= self.scheduler.timesteps.to(__a)

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

         # 2. compute previous image: x_t -> t_t-1
         __snake_case				:      Union[str, Any]     							= self.scheduler.step(__a					,   __a					,   __a).prev_sample

      __snake_case				:      Optional[int]     							= audio.clamp(-1					,   1).float().cpu().numpy()

      __snake_case				:      Optional[Any]     							= audio[:, :, :original_sample_size]

      if not return_dict:
         return (audio,)

      return AudioPipelineOutput(audios=__a)







'''simple docstring'''






from typing import TYPE_CHECKING

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


__A    	=   {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   ['''DistilBertTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DistilBertForMaskedLM''',
          '''DistilBertForMultipleChoice''',
          '''DistilBertForQuestionAnswering''',
          '''DistilBertForSequenceClassification''',
          '''DistilBertForTokenClassification''',
          '''DistilBertModel''',
          '''DistilBertPreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFDistilBertForMaskedLM''',
          '''TFDistilBertForMultipleChoice''',
          '''TFDistilBertForQuestionAnswering''',
          '''TFDistilBertForSequenceClassification''',
          '''TFDistilBertForTokenClassification''',
          '''TFDistilBertMainLayer''',
          '''TFDistilBertModel''',
          '''TFDistilBertPreTrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''FlaxDistilBertForMaskedLM''',
          '''FlaxDistilBertForMultipleChoice''',
          '''FlaxDistilBertForQuestionAnswering''',
          '''FlaxDistilBertForSequenceClassification''',
          '''FlaxDistilBertForTokenClassification''',
          '''FlaxDistilBertModel''',
          '''FlaxDistilBertPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)