






from typing import List, Optional, Tuple, Union

import torch

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


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





class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	Optional[int]		,     __a				:	Any		,     __a				:	List[str]							)			->  Optional[int]:


												"""simple docstring"""

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



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


												"""simple docstring"""

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

												__lowercase      :							List[str]				=     audio_length_in_s * self.unet.config.sample_rate

												__lowercase      :							str				=     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				=     int(__a							)
												if sample_size % down_scale_factor != 0:
																			__lowercase      :							Optional[Any]				=     (
																			    (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      :							str				=     int(__a							)

												__lowercase      :							List[Any]				=     next(iter(self.unet.parameters()							)							).dtype
												__lowercase      :							Dict				=     (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."							)

												__lowercase      :							Dict				=     randn_tensor(__a		,     generator=__a		,     device=self.device		,     dtype=__a							)

												# set step values
												self.scheduler.set_timesteps(__a		,     device=audio.device							)
												__lowercase      :							Tuple				=     self.scheduler.timesteps.to(__a							)

												for t in self.progress_bar(self.scheduler.timesteps							):
																			# 1. predict noise model_output
																			__lowercase      :							Optional[Any]				=     self.unet(__a		,     __a							).sample

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

												__lowercase      :							Dict				=     audio.clamp(-1		,     1							).float().cpu().numpy()

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

												if not return_dict:
																			return (audio,)

												return AudioPipelineOutput(audios=__a							)







# Copyright 2021 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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCamelCase : int						   =       '''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''
def       snake_case_		(     ):
							__lowercase      :							List[Any]				=     _ask_options(
							    """In which compute environment are you running?"""       ,       ["""This machine""", """AWS (Amazon SageMaker)"""]       ,       _convert_compute_environment       ,       )
							if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
														__lowercase      :							Union[str, Any]				=     get_sagemaker_input()
							else:
														__lowercase      :							str				=     get_cluster_input()
							return config
def       snake_case_		(     lowerCAmelCase_       :						List[str]=None      ):
							if subparsers is not None:
														__lowercase      :							Optional[int]				=     subparsers.add_parser("""config"""       ,       description=lowerCAmelCase_      )
							else:
														__lowercase      :							List[str]				=     argparse.ArgumentParser("""Accelerate config command"""       ,       description=lowerCAmelCase_      )

							parser.add_argument(
							    """--config_file"""       ,       default=lowerCAmelCase_       ,       help=(
							        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
							        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
							        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
							        """with 'huggingface'."""
							    )       ,       )

							if subparsers is not None:
														parser.set_defaults(func=lowerCAmelCase_      )
							return parser
def       snake_case_		(     lowerCAmelCase_       :						Tuple      ):
							__lowercase      :							Union[str, Any]				=     get_user_input()
							if args.config_file is not None:
														__lowercase      :							List[Any]				=     args.config_file
							else:
														if not os.path.isdir(lowerCAmelCase_      ):
																					os.makedirs(lowerCAmelCase_      )
														__lowercase      :							Any				=     default_yaml_config_file

							if config_file.endswith(""".json"""      ):
														config.to_json_file(lowerCAmelCase_      )
							else:
														config.to_yaml_file(lowerCAmelCase_      )
							print(F"accelerate configuration saved at {config_file}"      )
def       snake_case_		(     ):
							__lowercase      :							str				=     config_command_parser()
							__lowercase      :							str				=     parser.parse_args()
							config_command(lowerCAmelCase_      )


if __name__ == "__main__":
	main()