
"""simple docstring"""




import argparse
from collections import defaultdict

import yaml


_A     :			int  						=			"docs/source/en/_toctree.yml"


def     __magic_name__      ( __snake_case							:			List[str]    )					->							Optional[Any]:
       lowercase       :							List[str]									=				defaultdict(UpperCamelCase__    )
       lowercase       :							Optional[Any]									=				[]
       lowercase       :							str									=				[]
       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__    )

       lowercase       :							str									=				new_doc_list
       lowercase       :							List[str]									=				[key for key, value in counts.items() if value > 1]

       lowercase       :							Optional[int]									=				[]
       for duplicate_key in duplicates:
              lowercase       :							Optional[Any]									=				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]    )
       lowercase       :							Tuple									=				sorted(UpperCamelCase__	,					key=lambda __snake_case    : 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     __magic_name__      ( __snake_case							:			Optional[Any]=False    )					->							Optional[int]:
       with open(UpperCamelCase__	,					encoding="utf-8"    ) as f:
              lowercase       :							Union[str, Any]									=				yaml.safe_load(f.read()    )

       # Get to the API doc
       lowercase       :							Optional[Any]									=				0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       lowercase       :							List[Any]									=				content[api_idx]["sections"]

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

       lowercase       :							Union[str, Any]									=				api_doc[scheduler_idx]["sections"]
       lowercase       :							str									=				clean_doc_toc(UpperCamelCase__    )

       lowercase       :							int									=				False
       if new_scheduler_doc != scheduler_doc:
              lowercase       :							Union[str, Any]									=				True
              if overwrite:
                     lowercase       :							List[Any]									=				new_scheduler_doc

       if diff:
              if overwrite:
                     lowercase       :							List[str]									=				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     __magic_name__      ( __snake_case							:			Optional[int]=False    )					->							List[str]:
       with open(UpperCamelCase__	,					encoding="utf-8"    ) as f:
              lowercase       :							List[Any]									=				yaml.safe_load(f.read()    )

       # Get to the API doc
       lowercase       :							Optional[int]									=				0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       lowercase       :							Tuple									=				content[api_idx]["sections"]

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

       lowercase       :							List[Any]									=				False
       lowercase       :							Tuple									=				api_doc[pipeline_idx]["sections"]
       lowercase       :							int									=				[]

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

       # sort overall pipeline doc
       lowercase       :							int									=				clean_doc_toc(UpperCamelCase__    )

       if new_pipeline_docs != pipeline_docs:
              lowercase       :							Union[str, Any]									=				True
              if overwrite:
                     lowercase       :							Union[str, Any]									=				new_pipeline_docs

       if diff:
              if overwrite:
                     lowercase       :							Tuple									=				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     :			Tuple  						=			argparse.ArgumentParser()
   parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
   _A     :			Tuple  						=			parser.parse_args()

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



'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

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


__lowerCAmelCase						:							List[Any]    							=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class     A ( UpperCAmelCase ):





							def __init__(   self					:				Dict  ,		__a					:				List[Any]  ,		__a					:				Optional[Any]						)				->						Dict:
								super().__init__()
								self.register_modules(unet=__a  ,		scheduler=__a						)






							@torch.no_grad()
							def __call__(   self					:				Tuple  ,		__a					:				int = 1  ,		__a					:				int = 1_0_0  ,		__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:
									__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(__a						)
								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(__a						)

								__UpperCAmelCase      					=      next(iter(self.unet.parameters()						)						).dtype
								__UpperCAmelCase      					=      (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."""						)

								__UpperCAmelCase      					=      randn_tensor(__a  ,		generator=__a  ,		device=self.device  ,		dtype=__a						)

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

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

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

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

								__UpperCAmelCase      					=      audio[:, :, :original_sample_size]

								if not return_dict:
									return (audio,)

								return AudioPipelineOutput(audios=__a						)
