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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			str   )   ->			str:





						"""simple docstring"""






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

						snake_case        =     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.
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 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			Optional[int]:





						"""simple docstring"""






						snake_case        =     TASK_GUIDE_TO_MODELS[task_guide]
						snake_case        =     SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_UpperCamelCase	,     set()   )
						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 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Any=False   )   ->			Optional[int]:





						"""simple docstring"""






						snake_case   ,snake_case   ,snake_case   ,snake_case        =     _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-->'	,     )

						snake_case        =     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__":
					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)



"""simple docstring"""
import os





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str = "matrix.txt"   )   ->			int:





						"""simple docstring"""






						with open(os.path.join(os.path.dirname(_UpperCamelCase   )	,     _UpperCamelCase   )   ) as in_file:
												snake_case        =     in_file.read()

						snake_case        =     [[int(_UpperCamelCase   ) for cell in row.split(','   )] for row in data.strip().splitlines()]
						snake_case        =     [[0 for cell in row] for row in grid]
						snake_case        =     len(grid[0]   )

						snake_case        =     [[0 for i in range(_UpperCamelCase   )] for j in range(_UpperCamelCase   )]
						snake_case        =     grid[0][0]
						for i in range(1	,     _UpperCamelCase   ):
												snake_case        =     grid[0][i] + dp[0][i - 1]
						for i in range(1	,     _UpperCamelCase   ):
												snake_case        =     grid[i][0] + dp[i - 1][0]

						for i in range(1	,     _UpperCamelCase   ):
												for j in range(1	,     _UpperCamelCase   ):
																		snake_case        =     grid[i][j] + min(dp[i - 1][j]	,     dp[i][j - 1]   )

						return dp[-1][-1]


if __name__ == "__main__":
					print(f"""{solution() = }""")


