
"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch

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


UpperCAmelCase__      :    Union[str, Any]    							= logging.get_logger(__name__)  # pylint: disable=invalid-name



class 	lowerCAmelCase_						(__a  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




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



				@torch.no_grad()
				def __call__(self   ,       SCREAMING_SNAKE_CASE__ = 1   ,       SCREAMING_SNAKE_CASE__ = 1_00   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = True   ,       )					->		List[str]:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__						:       Dict           =   audio_length_in_s * self.unet.config.sample_rate

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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}.'''    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   int(_A    )
											if sample_size % down_scale_factor != 0:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   (
																		    (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."""    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   int(_A    )

											SCREAMING_SNAKE_CASE__						:       Any           =   next(iter(self.unet.parameters()    )    ).dtype
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   (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.'''    )

											SCREAMING_SNAKE_CASE__						:       int           =   randn_tensor(_A   ,       generator=_A   ,       device=self.device   ,       dtype=_A    )

											# set step values
											self.scheduler.set_timesteps(_A   ,       device=audio.device    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.scheduler.timesteps.to(_A    )

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

																		# 2. compute previous image: x_t -> t_t-1
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.scheduler.step(_A   ,       _A   ,       _A    ).prev_sample

											SCREAMING_SNAKE_CASE__						:       Any           =   audio.clamp(-1   ,       1    ).float().cpu().numpy()

											SCREAMING_SNAKE_CASE__						:       List[str]           =   audio[:, :, :original_sample_size]

											if not return_dict:
																		return (audio,)

											return AudioPipelineOutput(audios=_A    )








'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__    					=      {
    '''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''VivitImageProcessor''']


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''VivitModel''',
      '''VivitPreTrainedModel''',
      '''VivitForVideoClassification''',
  ]


if TYPE_CHECKING:
  from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_vivit import VivitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vivit import (
        VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        VivitForVideoClassification,
        VivitModel,
        VivitPreTrainedModel,
    )


else:
  import sys

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