




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
__SCREAMING_SNAKE_CASE 						="""src/transformers"""
__SCREAMING_SNAKE_CASE 						="""docs/source/en/tasks"""

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

							SCREAMING_SNAKE_CASE_   	=    start_index
							while not lines[end_index].startswith(_lowerCAmelCase     ):
														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.
__SCREAMING_SNAKE_CASE 						=direct_transformers_import(TRANSFORMERS_PATH)

__SCREAMING_SNAKE_CASE 						={
    """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`).
__SCREAMING_SNAKE_CASE 						={
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}

def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    TASK_GUIDE_TO_MODELS[task_guide]
							SCREAMING_SNAKE_CASE_   	=    SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowerCAmelCase  ,    set()     )
							SCREAMING_SNAKE_CASE_   	=    {
							    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   (_lowerCAmelCase  ,    _lowerCAmelCase=False     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    _find_text_in_file(
							    filename=os.path.join(_lowerCAmelCase  ,    _lowerCAmelCase     )  ,    start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''  ,    end_prompt='''<!--End of the generated tip-->'''  ,    )

							SCREAMING_SNAKE_CASE_   	=    get_model_list_for_task(_lowerCAmelCase     )

							if current_list != new_list:
														if overwrite:
																					with open(os.path.join(_lowerCAmelCase  ,    _lowerCAmelCase     )  ,    '''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__":
			__SCREAMING_SNAKE_CASE 						=argparse.ArgumentParser()
			parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
			__SCREAMING_SNAKE_CASE 						=parser.parse_args()

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








import requests

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    {'''Content-Type''': '''application/json'''}
							SCREAMING_SNAKE_CASE_   	=    requests.post(_lowerCAmelCase  ,    json={'''text''': message_body}  ,    headers=_lowerCAmelCase     )
							if response.status_code != 2_0_0:
														SCREAMING_SNAKE_CASE_   	=    (
														    '''Request to slack returned an error '''
														    F"{response.status_code}, the response is:\n{response.text}"
														)
														raise ValueError(_lowerCAmelCase     )


if __name__ == "__main__":
			# Set the slack url to the one provided by Slack when you create the webhook at
			# https://my.slack.com/services/new/incoming-webhook/
			send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")


