






from typing import List, Optional, Tuple, Union

import torch

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


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


class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Optional[Any]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



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





     @torch.no_grad()
     def __call__(					self							:      Any			,						UpperCamelCase_							:      int = 1			,						UpperCamelCase_							:      int = 100			,						UpperCamelCase_							:      Optional[Union[torch.Generator, List[torch.Generator]]] = None			,						UpperCamelCase_							:      Optional[float] = None			,						UpperCamelCase_							:      bool = True			,						):


         """simple docstring"""



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

         __A       =						audio_length_in_s * self.unet.config.sample_rate

         __A       =						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}."					)

         __A       =						int(UpperCamelCase_					)
         if sample_size % down_scale_factor != 0:
             __A       =						(
                 (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."""					)
         __A       =						int(UpperCamelCase_					)

         __A       =						next(iter(self.unet.parameters()					)					).dtype
         __A       =						(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."					)

         __A       =						randn_tensor(UpperCamelCase_			,						generator=UpperCamelCase_			,						device=self.device			,						dtype=UpperCamelCase_					)

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

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

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

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

         __A       =						audio[:, :, :original_sample_size]

         if not return_dict:
             return (audio,)

         return AudioPipelineOutput(audios=UpperCamelCase_					)












from typing import TYPE_CHECKING

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


__a   :							Optional[int]						= {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Tuple						= ["XLNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							List[str]						= ["XLNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Any						= [
      "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "XLNetForMultipleChoice",
      "XLNetForQuestionAnswering",
      "XLNetForQuestionAnsweringSimple",
      "XLNetForSequenceClassification",
      "XLNetForTokenClassification",
      "XLNetLMHeadModel",
      "XLNetModel",
      "XLNetPreTrainedModel",
      "load_tf_weights_in_xlnet",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFXLNetForMultipleChoice",
      "TFXLNetForQuestionAnsweringSimple",
      "TFXLNetForSequenceClassification",
      "TFXLNetForTokenClassification",
      "TFXLNetLMHeadModel",
      "TFXLNetMainLayer",
      "TFXLNetModel",
      "TFXLNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  __a   :							str						= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




