






"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

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


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







class  lowerCamelCase__ (	snake_case			):






    def __init__(			self     ,A     ,A ):
          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     ,):

          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(A )
          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(A )

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

          UpperCAmelCase          =	randn_tensor(A     ,generator=A     ,device=self.device     ,dtype=A )

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

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

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

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

          UpperCAmelCase          =	audio[:, :, :original_sample_size]

          if not return_dict:
                return (audio,)

          return AudioPipelineOutput(audios=A )











"""simple docstring"""







from typing import TYPE_CHECKING

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


_UpperCamelCase					      =  {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""GLPNFeatureExtractor"""]
  _UpperCamelCase					      =  ["""GLPNImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """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

  _UpperCamelCase					      =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



