"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

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


_a  :  Optional[int]=				logging.get_logger(__name__)  # pylint: disable=invalid-name

class 	UpperCamelCase    (					lowercase		):



				def __init__(self       :   List[str]			,						_A       :   Optional[int]			,						_A       :   Tuple)					->			Any:
					super().__init__()
					self.register_modules(unet=_A			,						scheduler=_A)






				@torch.no_grad()
				def __call__(self       :   Union[str, Any]			,						_A       :   int = 1			,						_A       :   int = 1_00			,						_A       :   Optional[Union[torch.Generator, List[torch.Generator]]] = None			,						_A       :   Optional[float] = None			,						_A       :   bool = True			,						)					->			Union[AudioPipelineOutput, Tuple]:

					if audio_length_in_s is None:
						__snake_case    :							Tuple   	=				self.unet.config.sample_size / self.unet.config.sample_rate

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

					__snake_case    :							List[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}.")

					__snake_case    :							Optional[Any]   	=				int(_A)
					if sample_size % down_scale_factor != 0:
						__snake_case    :							Dict   	=				(
						    (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.')
					__snake_case    :							str   	=				int(_A)

					__snake_case    :							Any   	=				next(iter(self.unet.parameters())).dtype
					__snake_case    :							List[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.")

					__snake_case    :							Any   	=				randn_tensor(_A			,						generator=_A			,						device=self.device			,						dtype=_A)

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

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

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

					__snake_case    :							Tuple   	=				audio.clamp(-1			,						1).float().cpu().numpy()

					__snake_case    :							List[Any]   	=				audio[:, :, :original_sample_size]

					if not return_dict:
						return (audio,)

					return AudioPipelineOutput(audios=_A)


"""simple docstring"""







import argparse
from collections import defaultdict

import yaml


_a  :  Tuple=				"docs/source/en/_toctree.yml"
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[Any]						)							->    Dict:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				defaultdict(UpperCAmelCase_						)
	__snake_case    :							List[str]   	=				[]
	__snake_case    :							Tuple   	=				[]
	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(UpperCAmelCase_						)

	__snake_case    :							Any   	=				new_doc_list
	__snake_case    :							Dict   	=				[key for key, value in counts.items() if value > 1]

	__snake_case    :							Optional[int]   	=				[]
	for duplicate_key in duplicates:
		__snake_case    :							Tuple   	=				list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}						)
		if len(UpperCAmelCase_						) > 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]						)
	__snake_case    :							List[Any]   	=				sorted(UpperCAmelCase_    ,							key=lambda UpperCAmelCase_						: s["title"].lower()						)

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

	overview_doc.extend(UpperCAmelCase_						)

	# Sort
	return overview_doc
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[int]=False						)							->    str:

	'''simple docstring'''





	with open(UpperCAmelCase_    ,							encoding='utf-8'						) as f:
		__snake_case    :							List[Any]   	=				yaml.safe_load(f.read()						)

	# Get to the API doc
	__snake_case    :							str   	=				0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	__snake_case    :							Union[str, Any]   	=				content[api_idx]['sections']

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

	__snake_case    :							Optional[Any]   	=				api_doc[scheduler_idx]['sections']
	__snake_case    :							Tuple   	=				clean_doc_toc(UpperCAmelCase_						)

	__snake_case    :							Tuple   	=				False
	if new_scheduler_doc != scheduler_doc:
		__snake_case    :							Optional[int]   	=				True
		if overwrite:
			__snake_case    :							Tuple   	=				new_scheduler_doc

	if diff:
		if overwrite:
			__snake_case    :							Optional[int]   	=				api_doc
			with open(UpperCAmelCase_    ,							'w'    ,							encoding='utf-8'						) as f:
				f.write(yaml.dump(UpperCAmelCase_    ,							allow_unicode=UpperCAmelCase_						)						)
		else:
			raise ValueError(
			    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Any=False						)							->    Union[str, Any]:

	'''simple docstring'''





	with open(UpperCAmelCase_    ,							encoding='utf-8'						) as f:
		__snake_case    :							Dict   	=				yaml.safe_load(f.read()						)

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

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

	__snake_case    :							Optional[int]   	=				False
	__snake_case    :							Optional[Any]   	=				api_doc[pipeline_idx]['sections']
	__snake_case    :							Tuple   	=				[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			__snake_case    :							List[str]   	=				pipeline_doc['section']
			__snake_case    :							Optional[int]   	=				clean_doc_toc(UpperCAmelCase_						)
			if overwrite:
				__snake_case    :							List[Any]   	=				new_sub_pipeline_doc
		new_pipeline_docs.append(UpperCAmelCase_						)

	# sort overall pipeline doc
	__snake_case    :							List[Any]   	=				clean_doc_toc(UpperCAmelCase_						)

	if new_pipeline_docs != pipeline_docs:
		__snake_case    :							Dict   	=				True
		if overwrite:
			__snake_case    :							Tuple   	=				new_pipeline_docs

	if diff:
		if overwrite:
			__snake_case    :							Union[str, Any]   	=				api_doc
			with open(UpperCAmelCase_    ,							'w'    ,							encoding='utf-8'						) as f:
				f.write(yaml.dump(UpperCAmelCase_    ,							allow_unicode=UpperCAmelCase_						)						)
		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__":
	_a  :  Optional[int]=				argparse.ArgumentParser()
	parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
	_a  :  List[str]=				parser.parse_args()

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





