


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

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


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





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def __init__(       self						,    __a						,    __a							):

						'''simple docstring'''





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





	@torch.no_grad()
	def __call__(       self						,    __a = 1						,    __a = 100						,    __a = None						,    __a = None						,    __a = True						,    ):

						'''simple docstring'''





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

						__a      :	int    =  audio_length_in_s * self.unet.config.sample_rate

						__a      :	str    =  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      :	List[str]    =  int(__a							)
						if sample_size % down_scale_factor != 0:
											__a      :	Union[str, Any]    =  (
											    (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      :	Optional[int]    =  int(__a							)

						__a      :	Optional[Any]    =  next(iter(self.unet.parameters()							)							).dtype
						__a      :	Tuple    =  (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."""							)

						__a      :	Dict    =  randn_tensor(__a						,    generator=__a						,    device=self.device						,    dtype=__a							)

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

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

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

						__a      :	Dict    =  audio.clamp(-1						,    1							).float().cpu().numpy()

						__a      :	List[str]    =  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,
)


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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						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:
	__lowercase     :						List[str]	=     [
	    '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:
	__lowercase     :						List[str]	=     [
	    '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

	__lowercase     :						int	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



