






from typing import List, Optional, Tuple, Union

import torch

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


a_		:						Optional[Any]             =    logging.get_logger(__name__)  # pylint: disable=invalid-name

class 		_snake_case  (  A__					):






			def __init__(       self		,					a		,					a)      ->       Optional[Any]:
						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		,					)      ->       Union[AudioPipelineOutput, Tuple]:

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

						SCREAMING_SNAKE_CASE						= audio_length_in_s * self.unet.config.sample_rate

						SCREAMING_SNAKE_CASE						= 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}.''')

						SCREAMING_SNAKE_CASE						= int(a)
						if sample_size % down_scale_factor != 0:
									SCREAMING_SNAKE_CASE						= (
									    (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.')
						SCREAMING_SNAKE_CASE						= int(a)

						SCREAMING_SNAKE_CASE						= next(iter(self.unet.parameters())).dtype
						SCREAMING_SNAKE_CASE						= (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.''')

						SCREAMING_SNAKE_CASE						= randn_tensor(a		,					generator=a		,					device=self.device		,					dtype=a)

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

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

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

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

						SCREAMING_SNAKE_CASE						= audio[:, :, :original_sample_size]

						if not return_dict:
									return (audio,)

						return AudioPipelineOutput(audios=a)












from typing import TYPE_CHECKING

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


a_		:						int             =    {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[Any]             =    ['SpeechT5Tokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[int]             =    [
				    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SpeechT5ForSpeechToText',
				    'SpeechT5ForSpeechToSpeech',
				    'SpeechT5ForTextToSpeech',
				    'SpeechT5Model',
				    'SpeechT5PreTrainedModel',
				    'SpeechT5HifiGan',
				]

if TYPE_CHECKING:
				from .configuration_speechta import (
				    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
				    SpeechTaConfig,
				    SpeechTaHifiGanConfig,
				)
				from .feature_extraction_speechta import SpeechTaFeatureExtractor
				from .processing_speechta import SpeechTaProcessor

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_speechta import SpeechTaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speechta import (
								    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechTaForSpeechToSpeech,
								    SpeechTaForSpeechToText,
								    SpeechTaForTextToSpeech,
								    SpeechTaHifiGan,
								    SpeechTaModel,
								    SpeechTaPreTrainedModel,
								)

else:
				import sys

				a_		:						Dict             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




