
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
_UpperCAmelCase					: Any      =       """
Human: <<task>>

Assistant: """


_UpperCAmelCase					: List[str]      =       """huggingface-tools/default-prompts"""
_UpperCAmelCase					: str      =       {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}


def       A       (					lowercase    ,       lowercase    ,       lowercase="run" )   ->  Dict:
		'''simple docstring'''

		if prompt_or_repo_id is None:
				UpperCamelCase		=     DEFAULT_PROMPTS_REPO

		# prompt is considered a repo ID when it does not contain any kind of space
		if re.search('\\s'    ,       A__ ) is not None:
				return prompt_or_repo_id

		UpperCamelCase		=     cached_file(
		    A__    ,       PROMPT_FILES[mode]    ,       repo_type='dataset'    ,       user_agent={'agent': agent_name} )
		with open(A__    ,       'r'    ,       encoding='utf-8' ) as f:
				return f.read()







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)


