
"""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        _UpperCAmelCase(       lowerCamelCase      ):



		def __init__(   self							,						__a							,						__a)			->			str:






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




		@torch.no_grad()
		def __call__(   self							,						__a = 1							,						__a = 1_00							,						__a = None							,						__a = None							,						__a = True							,						)			->			Union[AudioPipelineOutput, Tuple]:






							'''simple docstring'''

							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)












'''simple docstring'''






from typing import TYPE_CHECKING

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


A_			      =		{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaPreLayerNormForCausalLM",
          "RobertaPreLayerNormForMaskedLM",
          "RobertaPreLayerNormForMultipleChoice",
          "RobertaPreLayerNormForQuestionAnswering",
          "RobertaPreLayerNormForSequenceClassification",
          "RobertaPreLayerNormForTokenClassification",
          "RobertaPreLayerNormModel",
          "RobertaPreLayerNormPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaPreLayerNormForCausalLM",
          "TFRobertaPreLayerNormForMaskedLM",
          "TFRobertaPreLayerNormForMultipleChoice",
          "TFRobertaPreLayerNormForQuestionAnswering",
          "TFRobertaPreLayerNormForSequenceClassification",
          "TFRobertaPreLayerNormForTokenClassification",
          "TFRobertaPreLayerNormMainLayer",
          "TFRobertaPreLayerNormModel",
          "TFRobertaPreLayerNormPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "FlaxRobertaPreLayerNormForCausalLM",
          "FlaxRobertaPreLayerNormForMaskedLM",
          "FlaxRobertaPreLayerNormForMultipleChoice",
          "FlaxRobertaPreLayerNormForQuestionAnswering",
          "FlaxRobertaPreLayerNormForSequenceClassification",
          "FlaxRobertaPreLayerNormForTokenClassification",
          "FlaxRobertaPreLayerNormModel",
          "FlaxRobertaPreLayerNormPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta_prelayernorm import (
          ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
          RobertaPreLayerNormConfig,
          RobertaPreLayerNormOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta_prelayernorm import (
                ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaPreLayerNormForCausalLM,
                RobertaPreLayerNormForMaskedLM,
                RobertaPreLayerNormForMultipleChoice,
                RobertaPreLayerNormForQuestionAnswering,
                RobertaPreLayerNormForSequenceClassification,
                RobertaPreLayerNormForTokenClassification,
                RobertaPreLayerNormModel,
                RobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta_prelayernorm import (
                TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaPreLayerNormForCausalLM,
                TFRobertaPreLayerNormForMaskedLM,
                TFRobertaPreLayerNormForMultipleChoice,
                TFRobertaPreLayerNormForQuestionAnswering,
                TFRobertaPreLayerNormForSequenceClassification,
                TFRobertaPreLayerNormForTokenClassification,
                TFRobertaPreLayerNormMainLayer,
                TFRobertaPreLayerNormModel,
                TFRobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta_prelayernorm import (
                FlaxRobertaPreLayerNormForCausalLM,
                FlaxRobertaPreLayerNormForMaskedLM,
                FlaxRobertaPreLayerNormForMultipleChoice,
                FlaxRobertaPreLayerNormForQuestionAnswering,
                FlaxRobertaPreLayerNormForSequenceClassification,
                FlaxRobertaPreLayerNormForTokenClassification,
                FlaxRobertaPreLayerNormModel,
                FlaxRobertaPreLayerNormPreTrainedModel,
            )

else:
      import sys

      A_			      =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
