



"""simple docstring"""





from typing import List, Optional, Tuple, Union

import torch

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


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




class 			lowercase__			(      __SCREAMING_SNAKE_CASE    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case						)			->							Tuple:
									super().__init__()
									self.register_modules(unet=__snake_case	,       scheduler=__snake_case						)






							@torch.no_grad()
							def __call__(  self	,       snake_case = 1	,       snake_case = 100	,       snake_case = None	,       snake_case = None	,       snake_case = True	,       )			->							List[str]:

									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(__snake_case						)
									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(__snake_case						)

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

									_UpperCAmelCase							 =		randn_tensor(__snake_case	,       generator=__snake_case	,       device=self.device	,       dtype=__snake_case						)

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

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

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

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

									_UpperCAmelCase							 =		audio[:, :, :original_sample_size]

									if not return_dict:
											return (audio,)

									return AudioPipelineOutput(audios=__snake_case						)







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  							=					{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CLIPSegModel''',
					    '''CLIPSegPreTrainedModel''',
					    '''CLIPSegTextModel''',
					    '''CLIPSegVisionModel''',
					    '''CLIPSegForImageSegmentation''',
					]

if TYPE_CHECKING:
					from .configuration_clipseg import (
					    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPSegConfig,
					    CLIPSegTextConfig,
					    CLIPSegVisionConfig,
					)
					from .processing_clipseg import CLIPSegProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clipseg import (
										    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPSegForImageSegmentation,
										    CLIPSegModel,
										    CLIPSegPreTrainedModel,
										    CLIPSegTextModel,
										    CLIPSegVisionModel,
										)

else:
					import sys

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


