



'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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


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

class      __magic_name__						(   _UpperCamelCase       ):







							def __init__(							self :	int     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Tuple		):
									super().__init__()
									self.register_modules(unet=_UpperCAmelCase     ,scheduler=_UpperCAmelCase		)







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

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

									_a					:				Any				=							audio_length_in_s * self.unet.config.sample_rate

									_a					:				Tuple				=							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					:				Tuple				=							int(_UpperCAmelCase		)
									if sample_size % down_scale_factor != 0:
											_a					:				Tuple				=							(
											    (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					:				List[Any]				=							int(_UpperCAmelCase		)

									_a					:				int				=							next(iter(self.unet.parameters()		)		).dtype
									_a					:				int				=							(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					:				Union[str, Any]				=							randn_tensor(_UpperCAmelCase     ,generator=_UpperCAmelCase     ,device=self.device     ,dtype=_UpperCAmelCase		)

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

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

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

									_a					:				List[str]				=							audio.clamp(-1     ,1		).float().cpu().numpy()

									_a					:				Union[str, Any]				=							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_tf_available, is_torch_available


__lowerCAmelCase						      =       {
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecAudioForAudioFrameClassification''',
							    '''Data2VecAudioForCTC''',
							    '''Data2VecAudioForSequenceClassification''',
							    '''Data2VecAudioForXVector''',
							    '''Data2VecAudioModel''',
							    '''Data2VecAudioPreTrainedModel''',
							]
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecTextForCausalLM''',
							    '''Data2VecTextForMaskedLM''',
							    '''Data2VecTextForMultipleChoice''',
							    '''Data2VecTextForQuestionAnswering''',
							    '''Data2VecTextForSequenceClassification''',
							    '''Data2VecTextForTokenClassification''',
							    '''Data2VecTextModel''',
							    '''Data2VecTextPreTrainedModel''',
							]
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecVisionForImageClassification''',
							    '''Data2VecVisionForMaskedImageModeling''',
							    '''Data2VecVisionForSemanticSegmentation''',
							    '''Data2VecVisionModel''',
							    '''Data2VecVisionPreTrainedModel''',
							]

if is_tf_available():
							__lowerCAmelCase						      =       [
							    '''TFData2VecVisionForImageClassification''',
							    '''TFData2VecVisionForSemanticSegmentation''',
							    '''TFData2VecVisionModel''',
							    '''TFData2VecVisionPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
							from .configuration_dataavec_text import (
							    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DataaVecTextConfig,
							    DataaVecTextOnnxConfig,
							)
							from .configuration_dataavec_vision import (
							    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DataaVecVisionConfig,
							    DataaVecVisionOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_dataavec_audio import (
														    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecAudioForAudioFrameClassification,
														    DataaVecAudioForCTC,
														    DataaVecAudioForSequenceClassification,
														    DataaVecAudioForXVector,
														    DataaVecAudioModel,
														    DataaVecAudioPreTrainedModel,
														)
														from .modeling_dataavec_text import (
														    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecTextForCausalLM,
														    DataaVecTextForMaskedLM,
														    DataaVecTextForMultipleChoice,
														    DataaVecTextForQuestionAnswering,
														    DataaVecTextForSequenceClassification,
														    DataaVecTextForTokenClassification,
														    DataaVecTextModel,
														    DataaVecTextPreTrainedModel,
														)
														from .modeling_dataavec_vision import (
														    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecVisionForImageClassification,
														    DataaVecVisionForMaskedImageModeling,
														    DataaVecVisionForSemanticSegmentation,
														    DataaVecVisionModel,
														    DataaVecVisionPreTrainedModel,
														)
							if is_tf_available():
														from .modeling_tf_dataavec_vision import (
														    TFDataaVecVisionForImageClassification,
														    TFDataaVecVisionForSemanticSegmentation,
														    TFDataaVecVisionModel,
														    TFDataaVecVisionPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase						      =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




