






'''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      A     (   __lowercase    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       super().__init__()
       self.register_modules(unet=SCREAMING_SNAKE_CASE_      ,  scheduler=SCREAMING_SNAKE_CASE_			)





 @torch.no_grad()
 def __call__(				self      ,  __lowerCAmelCase = 1      ,  __lowerCAmelCase = 1_00      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = True      ,  ):







       if audio_length_in_s is None:
             UpperCamelCase_					: Optional[Any]            =				self.unet.config.sample_size / self.unet.config.sample_rate

       UpperCamelCase_					: Optional[int]            =				audio_length_in_s * self.unet.config.sample_rate

       UpperCamelCase_					: 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}."			)

       UpperCamelCase_					: Any            =				int(SCREAMING_SNAKE_CASE_			)
       if sample_size % down_scale_factor != 0:
             UpperCamelCase_					: 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."""			)
       UpperCamelCase_					: Optional[int]            =				int(SCREAMING_SNAKE_CASE_			)

       UpperCamelCase_					: List[Any]            =				next(iter(self.unet.parameters()			)			).dtype
       UpperCamelCase_					: Tuple            =				(batch_size, self.unet.config.in_channels, sample_size)
       if isinstance(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_			) and len(SCREAMING_SNAKE_CASE_			) != batch_size:
             raise ValueError(
                 F"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_			)}, but requested an effective batch"
                 F" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

       UpperCamelCase_					: Optional[Any]            =				randn_tensor(SCREAMING_SNAKE_CASE_      ,  generator=SCREAMING_SNAKE_CASE_      ,  device=self.device      ,  dtype=SCREAMING_SNAKE_CASE_			)

       # set step values
       self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_      ,  device=audio.device			)
       UpperCamelCase_					: Dict            =				self.scheduler.timesteps.to(SCREAMING_SNAKE_CASE_			)

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

             # 2. compute previous image: x_t -> t_t-1
             UpperCamelCase_					: Optional[int]            =				self.scheduler.step(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_			).prev_sample

       UpperCamelCase_					: List[str]            =				audio.clamp(-1      ,  1			).float().cpu().numpy()

       UpperCamelCase_					: Optional[int]            =				audio[:, :, :original_sample_size]

       if not return_dict:
             return (audio,)

       return AudioPipelineOutput(audios=SCREAMING_SNAKE_CASE_			)








"""simple docstring"""




# 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.test_utils import execute_subprocess_async



def 							_lowerCamelCase			(			__a=None     ):
		if subparsers is not None:
				SCREAMING_SNAKE_CASE_    			=			subparsers.add_parser('''test'''     )
		else:
				SCREAMING_SNAKE_CASE_    			=			argparse.ArgumentParser('''Accelerate test command'''     )

		parser.add_argument(
		    '''--config_file''',				default=__a,				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=__a     )
		return parser



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			os.path.sep.join(__file__.split(os.path.sep     )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']     )

		if args.config_file is None:
				SCREAMING_SNAKE_CASE_    			=			script_name
		else:
				SCREAMING_SNAKE_CASE_    			=			F'--config_file={args.config_file} {script_name}'

		SCREAMING_SNAKE_CASE_    			=			['''accelerate-launch'''] + test_args.split()
		SCREAMING_SNAKE_CASE_    			=			execute_subprocess_async(__a,				env=os.environ.copy()     )
		if result.returncode == 0:
				print('''Test is a success! You are ready for your distributed training!'''     )



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			test_command_parser()
		SCREAMING_SNAKE_CASE_    			=			parser.parse_args()
		test_command(__a     )


if __name__ == "__main__":
						main()