"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

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


lowerCAmelCase_:					Optional[int]     =					logging.get_logger(__name__)  # pylint: disable=invalid-name






class    a__    (   _a	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



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


   @torch.no_grad()
   def __call__(     self,				_UpperCAmelCase = 1,				_UpperCAmelCase = 100,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = True,				):
        '''simple docstring'''



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

        lowercase__				 =							audio_length_in_s * self.unet.config.sample_rate

        lowercase__				 =							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}.'''  )

        lowercase__				 =							int(_UpperCAmelCase  )
        if sample_size % down_scale_factor != 0:
             lowercase__				 =							(
                 (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."  )
        lowercase__				 =							int(_UpperCAmelCase  )

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

        lowercase__				 =							randn_tensor(_UpperCAmelCase,				generator=_UpperCAmelCase,				device=self.device,				dtype=_UpperCAmelCase  )

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

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

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

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

        lowercase__				 =							audio[:, :, :original_sample_size]

        if not return_dict:
             return (audio,)

        return AudioPipelineOutput(audios=_UpperCAmelCase  )





"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase_:					Union[str, Any]     =					{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Union[str, Any]     =					["DistilBertTokenizerFast"]

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

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Tuple     =					[
     "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:
 lowerCAmelCase_:					Optional[Any]     =					[
     "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

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




