

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 SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


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







			@torch.no_grad()
			def __call__(     self			,					__UpperCamelCase = 1			,					__UpperCamelCase = 1_00			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = True			,					):

				"""simple docstring"""


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

				snake_case_   		=			audio_length_in_s * self.unet.config.sample_rate

				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}."""				)

				snake_case_   		=			int(__UpperCamelCase				)
				if sample_size % down_scale_factor != 0:
					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.'				)
				snake_case_   		=			int(__UpperCamelCase				)

				snake_case_   		=			next(iter(self.unet.parameters()				)				).dtype
				snake_case_   		=			(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."""				)

				snake_case_   		=			randn_tensor(__UpperCamelCase			,					generator=__UpperCamelCase			,					device=self.device			,					dtype=__UpperCamelCase				)

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

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

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

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

				snake_case_   		=			audio[:, :, :original_sample_size]

				if not return_dict:
					return (audio,)

				return AudioPipelineOutput(audios=__UpperCamelCase				)



from typing import TYPE_CHECKING

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


A									=  {
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  ['Speech2TextTokenizer']

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  ['Speech2TextFeatureExtractor']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFSpeech2TextForConditionalGeneration',
		    'TFSpeech2TextModel',
		    'TFSpeech2TextPreTrainedModel',
		]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Speech2TextForConditionalGeneration',
		    'Speech2TextModel',
		    'Speech2TextPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
		from .processing_speech_to_text import SpeechaTextProcessor

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_speech_to_text import SpeechaTextTokenizer

		try:
				if not is_speech_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_speech_to_text import (
				    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSpeechaTextForConditionalGeneration,
				    TFSpeechaTextModel,
				    TFSpeechaTextPreTrainedModel,
				)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_to_text import (
				    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechaTextForConditionalGeneration,
				    SpeechaTextModel,
				    SpeechaTextPreTrainedModel,
				)

else:
		import sys

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