"""simple docstring"""


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
lowerCAmelCase_:					List[str]     =					"src/transformers"
lowerCAmelCase_:					Any     =					"docs/source/en/tasks"







def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     with open(A    ,				"r"    ,				encoding="utf-8"    ,				newline="\n"      ) as f:
          lowercase__				 =							f.readlines()
     # Find the start prompt.
     lowercase__				 =							0
     while not lines[start_index].startswith(A      ):
          start_index += 1
     start_index += 1

     lowercase__				 =							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.
lowerCAmelCase_:					Dict     =					direct_transformers_import(TRANSFORMERS_PATH)

lowerCAmelCase_:					Dict     =					{
    "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`).
lowerCAmelCase_:					Union[str, Any]     =					{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							TASK_GUIDE_TO_MODELS[task_guide]
     lowercase__				 =							SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(A    ,				set()      )
     lowercase__				 =							{
         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      ):






     '''simple docstring'''



     lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							_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-->"    ,				)

     lowercase__				 =							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__":
 lowerCAmelCase_:					Tuple     =					argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 lowerCAmelCase_:					Dict     =					parser.parse_args()

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





"""simple docstring"""


from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .notes_encoder import SpectrogramNotesEncoder
 from .continous_encoder import SpectrogramContEncoder
 from .pipeline_spectrogram_diffusion import (
     SpectrogramContEncoder,
     SpectrogramDiffusionPipeline,
     TaFilmDecoder,
 )

try:
 if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
 from .midi_utils import MidiProcessor




