





import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase      :       List[str]									=  """src/transformers"""
lowercase      :       Optional[int]									=  """docs/source/en/tasks"""



def A_	(		A__		,      A__		,      A__				)  ->		Tuple:
       with open(A__		,      'r'		,      encoding='utf-8'		,      newline='\n'				) as f:
              a__						:	Any =				f.readlines()
       # Find the start prompt.
       a__						:	str =				0
       while not lines[start_index].startswith(A__				):
              start_index += 1
       start_index += 1

       a__						:	int =				start_index
       while not lines[end_index].startswith(A__				):
              end_index += 1
       end_index -= 1

       while len(lines[start_index]				) <= 1:
              start_index += 1
       while len(lines[end_index]				) <= 1:
              end_index -= 1
       end_index += 1
       return "".join(lines[start_index:end_index]				), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
lowercase      :       Tuple									=  direct_transformers_import(TRANSFORMERS_PATH)

lowercase      :       Optional[Any]									=  {
    """asr.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    """audio_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    """language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    """image_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    """masked_language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    """multiple_choice.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    """object_detection.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    """question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    """semantic_segmentation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    """sequence_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    """summarization.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    """token_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    """translation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    """video_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    """document_question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    """monocular_depth_estimation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
lowercase      :       Optional[Any]									=  {
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}



def A_	(		A__				)  ->		Optional[int]:
       a__						:	Optional[Any] =				TASK_GUIDE_TO_MODELS[task_guide]
       a__						:	int =				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(A__		,      set()				)
       a__						:	Optional[Any] =				{
           code: name
           for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
           if (code in model_maping_names or code in special_model_types)
       }
       return ", ".join([F'[{name}](../model_doc/{code})' for code, name in model_names.items()]				) + "\n"



def A_	(		A__		,      A__=False				)  ->		Optional[int]:
       a__ ,							a__ ,							a__ ,							a__						:	Dict =				_find_text_in_file(
           filename=os.path.join(A__		,      A__				)		,      start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'		,      end_prompt='<!--End of the generated tip-->'		,      )

       a__						:	List[Any] =				get_model_list_for_task(A__				)

       if current_list != new_list:
              if overwrite:
                     with open(os.path.join(A__		,      A__				)		,      'w'		,      encoding='utf-8'		,      newline='\n'				) as f:
                            f.writelines(lines[:start_index] + [new_list] + lines[end_index:]				)
              else:
                     raise ValueError(
                         F'The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'
                         ' to fix this.'				)


if __name__ == "__main__":
    lowercase      :       Dict									=  argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    lowercase      :       str									=  parser.parse_args()

    for task_guide in TASK_GUIDE_TO_MODELS.keys():
        check_model_list_for_task(task_guide, args.fix_and_overwrite)









from typing import List, Optional, Tuple, Union

import torch

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


lowercase      :       Any									=  logging.get_logger(__name__)  # pylint: disable=invalid-name





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase)			->						Optional[Any]:







          '''simple docstring'''

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

   @torch.no_grad()
   def __call__(					self					,  lowercase = 1					,  lowercase = 100					,  lowercase = None					,  lowercase = None					,  lowercase = True					,  )			->						Union[AudioPipelineOutput, Tuple]:







          '''simple docstring'''

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

          a__						:	int =				audio_length_in_s * self.unet.config.sample_rate

          a__						:	Union[str, 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}.')

          a__						:	str =				int(lowercase)
          if sample_size % down_scale_factor != 0:
                 a__						:	List[str] =				(
                     (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.')
          a__						:	List[Any] =				int(lowercase)

          a__						:	int =				next(iter(self.unet.parameters())).dtype
          a__						:	Tuple =				(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.')

          a__						:	Optional[Any] =				randn_tensor(lowercase					,  generator=lowercase					,  device=self.device					,  dtype=lowercase)

          # set step values
          self.scheduler.set_timesteps(lowercase					,  device=audio.device)
          a__						:	Union[str, Any] =				self.scheduler.timesteps.to(lowercase)

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

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

          a__						:	str =				audio.clamp(-1					,  1).float().cpu().numpy()

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

          if not return_dict:
                 return (audio,)

          return AudioPipelineOutput(audios=lowercase)


