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_      =      "src/transformers"
A_      =      "docs/source/en/tasks"






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







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

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

A_      =      {
    "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_      =      {
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		TASK_GUIDE_TO_MODELS[task_guide]
			lowercase							=		SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(SCREAMING_SNAKE_CASE_,		set()       )
			lowercase							=		{
			    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    (       UpperCAmelCase,		UpperCAmelCase=False       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		_find_text_in_file(
			    filename=os.path.join(SCREAMING_SNAKE_CASE_,		SCREAMING_SNAKE_CASE_       ),		start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->''',		end_prompt='''<!--End of the generated tip-->''',		)

			lowercase							=		get_model_list_for_task(SCREAMING_SNAKE_CASE_       )

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

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






import cmath
import math






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       complex:



			"""simple docstring"""







			lowercase							=		math.radians(UpperCAmelCase       )
			lowercase							=		math.radians(UpperCAmelCase       )

			# Convert voltage and current to rectangular form
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )

			# Calculate apparent power
			return voltage_rect * current_rect


if __name__ == "__main__":
						import doctest

						doctest.testmod()





