





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        snake_case_		(			_A):







		def __init__(		self			,     __lowercase			,     __lowercase   )				->   List[str]:
				super().__init__()
				self.register_modules(unet=__lowercase			,     scheduler=__lowercase   )







		@torch.no_grad()
		def __call__(		self			,     __lowercase = 1			,     __lowercase = 1_0_0			,     __lowercase = None			,     __lowercase = None			,     __lowercase = True			,     )				->   Union[AudioPipelineOutput, Tuple]:

				if audio_length_in_s is None:
						lowerCamelCase   : List[Any]								=self.unet.config.sample_size / self.unet.config.sample_rate

				lowerCamelCase   : List[Any]								=audio_length_in_s * self.unet.config.sample_rate

				lowerCamelCase   : int								=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}."   )

				lowerCamelCase   : int								=int(__lowercase   )
				if sample_size % down_scale_factor != 0:
						lowerCamelCase   : 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.'''   )
				lowerCamelCase   : Union[str, Any]								=int(__lowercase   )

				lowerCamelCase   : Optional[int]								=next(iter(self.unet.parameters()   )   ).dtype
				lowerCamelCase   : str								=(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."   )

				lowerCamelCase   : List[str]								=randn_tensor(__lowercase			,     generator=__lowercase			,     device=self.device			,     dtype=__lowercase   )

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

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

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

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

				lowerCamelCase   : Optional[Any]								=audio[:, :, :original_sample_size]

				if not return_dict:
						return (audio,)

				return AudioPipelineOutput(audios=__lowercase   )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case_							=			{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwinForImageClassification''',
				    '''SwinForMaskedImageModeling''',
				    '''SwinModel''',
				    '''SwinPreTrainedModel''',
				    '''SwinBackbone''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFSwinForImageClassification''',
				    '''TFSwinForMaskedImageModeling''',
				    '''TFSwinModel''',
				    '''TFSwinPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swin import (
								    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinBackbone,
								    SwinForImageClassification,
								    SwinForMaskedImageModeling,
								    SwinModel,
								    SwinPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_swin import (
								    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSwinForImageClassification,
								    TFSwinForMaskedImageModeling,
								    TFSwinModel,
								    TFSwinPreTrainedModel,
								)

else:
				import sys

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

