


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__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


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







			@torch.no_grad()
			def __call__(				self   ,    snake_case = 1   ,    snake_case = 100   ,    snake_case = None   ,    snake_case = None   ,    snake_case = True   ,    ):

						'''simple docstring'''


						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(lowercase__  )
						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(lowercase__  )

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

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

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

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

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

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

						UpperCamelCase__       =							audio[:, :, :original_sample_size]

						if not return_dict:
									return (audio,)

						return AudioPipelineOutput(audios=lowercase__  )



import argparse
from collections import defaultdict

import yaml


_lowercase:	List[Any]       			=		'''docs/source/en/_toctree.yml'''






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							defaultdict(snake_case  )
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							[]
							for doc in doc_list:
														if "local" in doc:
																					counts[doc["local"]] += 1

														if doc["title"].lower() == "overview":
																					overview_doc.append({'local': doc['local'], 'title': doc['title']}  )
														else:
																					new_doc_list.append(snake_case  )

							_lowerCAmelCase							=							new_doc_list
							_lowerCAmelCase							=							[key for key, value in counts.items() if value > 1]

							_lowerCAmelCase							=							[]
							for duplicate_key in duplicates:
														_lowerCAmelCase							=							list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}  )
														if len(snake_case  ) > 1:
																					raise ValueError(
																					    F'{duplicate_key} is present several times in the documentation table of content at '
																					    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
																					    'others.'  )
														# Only add this once
														new_doc.append({'local': duplicate_key, 'title': titles[0]}  )

							# Add none duplicate-keys
							new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]  )
							_lowerCAmelCase							=							sorted(snake_case     , key=lambda snake_case  : s["title"].lower()  )

							# "overview" gets special treatment and is always first
							if len(snake_case  ) > 1:
														raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'  )

							overview_doc.extend(snake_case  )

							# Sort
							return overview_doc






def _lowerCamelCase  (		snake_case=False  ):
							with open(snake_case     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase							=							0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase							=							content[api_idx]['sections']

							# Then to the model doc
							_lowerCAmelCase							=							0
							while api_doc[scheduler_idx]["title"] != "Schedulers":
														scheduler_idx += 1

							_lowerCAmelCase							=							api_doc[scheduler_idx]['sections']
							_lowerCAmelCase							=							clean_doc_toc(snake_case  )

							_lowerCAmelCase							=							False
							if new_scheduler_doc != scheduler_doc:
														_lowerCAmelCase							=							True
														if overwrite:
																					_lowerCAmelCase							=							new_scheduler_doc

							if diff:
														if overwrite:
																					_lowerCAmelCase							=							api_doc
																					with open(snake_case     , 'w'     , encoding='utf-8'  ) as f:
																												f.write(yaml.dump(snake_case     , allow_unicode=snake_case  )  )
														else:
																					raise ValueError(
																					    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'  )






def _lowerCamelCase  (		snake_case=False  ):
							with open(snake_case     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase							=							0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase							=							content[api_idx]['sections']

							# Then to the model doc
							_lowerCAmelCase							=							0
							while api_doc[pipeline_idx]["title"] != "Pipelines":
														pipeline_idx += 1

							_lowerCAmelCase							=							False
							_lowerCAmelCase							=							api_doc[pipeline_idx]['sections']
							_lowerCAmelCase							=							[]

							# sort sub pipeline docs
							for pipeline_doc in pipeline_docs:
														if "section" in pipeline_doc:
																					_lowerCAmelCase							=							pipeline_doc['section']
																					_lowerCAmelCase							=							clean_doc_toc(snake_case  )
																					if overwrite:
																												_lowerCAmelCase							=							new_sub_pipeline_doc
														new_pipeline_docs.append(snake_case  )

							# sort overall pipeline doc
							_lowerCAmelCase							=							clean_doc_toc(snake_case  )

							if new_pipeline_docs != pipeline_docs:
														_lowerCAmelCase							=							True
														if overwrite:
																					_lowerCAmelCase							=							new_pipeline_docs

							if diff:
														if overwrite:
																					_lowerCAmelCase							=							api_doc
																					with open(snake_case     , 'w'     , encoding='utf-8'  ) as f:
																												f.write(yaml.dump(snake_case     , allow_unicode=snake_case  )  )
														else:
																					raise ValueError(
																					    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'  )


if __name__ == "__main__":
				_lowercase:	Tuple       			=		argparse.ArgumentParser()
				parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
				_lowercase:	Optional[Any]       			=		parser.parse_args()

				check_scheduler_doc(args.fix_and_overwrite)
				check_pipeline_doc(args.fix_and_overwrite)






