


from typing import List, Optional, Tuple, Union

import torch

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


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



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self   :						Dict				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Any		)						->       List[Any]:




							"""simple docstring"""


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


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




							"""simple docstring"""


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

							lowercase__					      =		audio_length_in_s * self.unet.config.sample_rate

							lowercase__					      =		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}.'''		)

							lowercase__					      =		int(_UpperCAmelCase		)
							if sample_size % down_scale_factor != 0:
									lowercase__					      =		(
									    (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."""		)
							lowercase__					      =		int(_UpperCAmelCase		)

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

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

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

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

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

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

							lowercase__					      =		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_torch_available, is_vision_available


A	:		Dict    =       {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Tuple    =       ['GLPNFeatureExtractor']
	A	:		List[str]    =       ['GLPNImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Optional[int]    =       [
	    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GLPNForDepthEstimation',
	    'GLPNLayer',
	    'GLPNModel',
	    'GLPNPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_glpn import GLPNFeatureExtractor
		from .image_processing_glpn import GLPNImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_glpn import (
		    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GLPNForDepthEstimation,
		    GLPNLayer,
		    GLPNModel,
		    GLPNPreTrainedModel,
		)


else:
	import sys

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

