


"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

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


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







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:Optional[int]       ,  lowercase_			:Optional[Any]       ,  lowercase_			:Tuple )    ->      Optional[Any]:
												super().__init__()
												self.register_modules(unet=lowercase_       ,  scheduler=lowercase_ )






						@torch.no_grad()
						def __call__( self			:int       ,  lowercase_			:int = 1       ,  lowercase_			:int = 1_00       ,  lowercase_			:Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,  lowercase_			:Optional[float] = None       ,  lowercase_			:bool = True       ,  )    ->      Union[AudioPipelineOutput, Tuple]:

												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(lowercase_ )
												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(lowercase_ )

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

												UpperCAmelCase					=			randn_tensor(lowercase_       ,  generator=lowercase_       ,  device=self.device       ,  dtype=lowercase_ )

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

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

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

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

												UpperCAmelCase					=			audio[:, :, :original_sample_size]

												if not return_dict:
																		return (audio,)

												return AudioPipelineOutput(audios=lowercase_ )










"""simple docstring"""


from typing import TYPE_CHECKING

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


snake_case_    					=	{
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """RemBertForCausalLM""",
				    """RemBertForMaskedLM""",
				    """RemBertForMultipleChoice""",
				    """RemBertForQuestionAnswering""",
				    """RemBertForSequenceClassification""",
				    """RemBertForTokenClassification""",
				    """RemBertLayer""",
				    """RemBertModel""",
				    """RemBertPreTrainedModel""",
				    """load_tf_weights_in_rembert""",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFRemBertForCausalLM""",
				    """TFRemBertForMaskedLM""",
				    """TFRemBertForMultipleChoice""",
				    """TFRemBertForQuestionAnswering""",
				    """TFRemBertForSequenceClassification""",
				    """TFRemBertForTokenClassification""",
				    """TFRemBertLayer""",
				    """TFRemBertModel""",
				    """TFRemBertPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert import RemBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert_fast import RemBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rembert import (
								    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RemBertForCausalLM,
								    RemBertForMaskedLM,
								    RemBertForMultipleChoice,
								    RemBertForQuestionAnswering,
								    RemBertForSequenceClassification,
								    RemBertForTokenClassification,
								    RemBertLayer,
								    RemBertModel,
								    RemBertPreTrainedModel,
								    load_tf_weights_in_rembert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rembert import (
								    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRemBertForCausalLM,
								    TFRemBertForMaskedLM,
								    TFRemBertForMultipleChoice,
								    TFRemBertForQuestionAnswering,
								    TFRemBertForSequenceClassification,
								    TFRemBertForTokenClassification,
								    TFRemBertLayer,
								    TFRemBertModel,
								    TFRemBertPreTrainedModel,
								)


else:
				import sys

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





