






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
_a    :   Tuple        =			"""src/transformers"""
_a    :   int        =			"""docs/source/en/tasks"""


def 				snake_case__							(							UpperCAmelCase							:  List[str]    ,			UpperCAmelCase							:  int    ,			UpperCAmelCase							:  List[str]       ):
    with open(UpperCAmelCase    ,			"r"    ,			encoding="utf-8"    ,			newline="\n"       ) as f:
        lowerCAmelCase__   :Optional[int]      							=				f.readlines()
    # Find the start prompt.
    lowerCAmelCase__   :Dict      							=				0
    while not lines[start_index].startswith(UpperCAmelCase       ):
        start_index += 1
    start_index += 1

    lowerCAmelCase__   :Union[str, Any]      							=				start_index
    while not lines[end_index].startswith(UpperCAmelCase       ):
        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.
_a    :   Dict        =			direct_transformers_import(TRANSFORMERS_PATH)

_a    :   List[str]        =			{
    """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`).
_a    :   Tuple        =			{
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}


def 				snake_case__							(							UpperCAmelCase							:  int       ):
    lowerCAmelCase__   :Dict      							=				TASK_GUIDE_TO_MODELS[task_guide]
    lowerCAmelCase__   :Dict      							=				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase    ,			set()       )
    lowerCAmelCase__   :Optional[int]      							=				{
        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 				snake_case__							(							UpperCAmelCase							:  Optional[int]    ,			UpperCAmelCase							:  Any=False       ):
    lowerCAmelCase__   :Any      							=				_find_text_in_file(
        filename=os.path.join(UpperCAmelCase    ,			UpperCAmelCase       )    ,			start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"    ,			end_prompt="<!--End of the generated tip-->"    ,			)

    lowerCAmelCase__   :Optional[int]      							=				get_model_list_for_task(UpperCAmelCase       )

    if current_list != new_list:
        if overwrite:
            with open(os.path.join(UpperCAmelCase    ,			UpperCAmelCase       )    ,			"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__":
  _a    :   Tuple        =			argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  _a    :   Union[str, Any]        =			parser.parse_args()

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











from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info


def 				snake_case__							(							UpperCAmelCase							:  Dict       ):
    return {key.lstrip("-"       ): value for key, value in zip(unknown_args[::2]    ,			unknown_args[1::2]       )}


def 				snake_case__							(							):
    lowerCAmelCase__   :str      							=				ArgumentParser(
        "HuggingFace Datasets CLI tool"    ,			usage="datasets-cli <command> [<args>]"    ,			allow_abbrev=UpperCAmelCase       )
    lowerCAmelCase__   :Any      							=				parser.add_subparsers(help="datasets-cli command helpers"       )
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(UpperCAmelCase       )
    EnvironmentCommand.register_subcommand(UpperCAmelCase       )
    TestCommand.register_subcommand(UpperCAmelCase       )
    RunBeamCommand.register_subcommand(UpperCAmelCase       )
    DummyDataCommand.register_subcommand(UpperCAmelCase       )

    # Parse args
    lowerCAmelCase__							,lowerCAmelCase__   :int      							=				parser.parse_known_args()
    if not hasattr(UpperCAmelCase    ,			"func"       ):
        parser.print_help()
        exit(1       )
    lowerCAmelCase__   :Optional[int]      							=				parse_unknown_args(UpperCAmelCase       )

    # Run
    lowerCAmelCase__   :str      							=				args.func(UpperCAmelCase    ,			**UpperCAmelCase       )
    service.run()


if __name__ == "__main__":
 main()



