


"""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
a__				:							Dict						=     '''src/transformers'''
a__				:							Union[str, Any]						=     '''docs/source/en/tasks'''




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					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.
a__				:							Optional[Any]						=     direct_transformers_import(TRANSFORMERS_PATH)

a__				:							int						=     {
    '''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__				:							Optional[Any]						=     {
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_=False   ):




					'''simple docstring'''


					__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__":
		a__				:							List[Any]						=     argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		a__				:							Optional[Any]						=     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"""







import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs




@pytest.mark.parametrize(
    "kwargs, expected"  ,						[
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 10, "max_num_jobs": 1}, [range(10   )]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(lowerCAmelCase_  ,						i + 1   ) for i in range(10   )]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1   )]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0  ,						4   ), range(4  ,						7   ), range(7  ,						10   )]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0  ,						1   ), range(1  ,						2   ), range(2  ,						3   )]),
    ]  ,						)
def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				_distribute_shards(**lowerCAmelCase_   )
					assert out == expected




@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"  ,						[
        ({"foo": 0}, 10, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]  ,						)
def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				_split_gen_kwargs(lowerCAmelCase_  ,						lowerCAmelCase_   )
					assert out == expected




@pytest.mark.parametrize(
    "gen_kwargs, expected"  ,						[
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]  ,						)
def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					if expected is RuntimeError:
										with pytest.raises(lowerCAmelCase_   ):
															_number_of_shards_in_gen_kwargs(lowerCAmelCase_   )
					else:
										__SCREAMING_SNAKE_CASE						   =				_number_of_shards_in_gen_kwargs(lowerCAmelCase_   )
										assert out == expected
