



'''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
UpperCamelCase__:   List[Any]								=			"src/transformers"
UpperCamelCase__:   Optional[int]								=			"docs/source/en/tasks"


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

							UpperCAmelCase :       Union[str, Any] 							=		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.
UpperCamelCase__:   List[Any]								=			direct_transformers_import(TRANSFORMERS_PATH)

UpperCamelCase__:   Union[str, Any]								=			{
    "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`).
UpperCamelCase__:   Any								=			{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}


def  snake_case_     (					_lowerCAmelCase  :						Any  )      ->   Dict:
							UpperCAmelCase :       Optional[Any] 							=		TASK_GUIDE_TO_MODELS[task_guide]
							UpperCAmelCase :       List[str] 							=		SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowerCAmelCase   ,      set()  )
							UpperCAmelCase :       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_     (					_lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Optional[Any]=False  )      ->   Dict:
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		_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-->'''   ,      )

							UpperCAmelCase :       Union[str, Any] 							=		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__":
						UpperCamelCase__:   Any								=			argparse.ArgumentParser()
						parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
						UpperCamelCase__:   str								=			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'''






from math import isclose, sqrt


def  snake_case_     (					_lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float  )      ->   tuple[float, float, float]:
							UpperCAmelCase :       Optional[int] 							=		point_y / 4 / point_x
							UpperCAmelCase :       str 							=		2 * normal_gradient / (1 + normal_gradient * normal_gradient)
							UpperCAmelCase :       Any 							=		(1 - normal_gradient * normal_gradient) / (
							    1 + normal_gradient * normal_gradient
							)
							UpperCAmelCase :       Union[str, Any] 							=		(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

							# to find the next point, solve the simultaeneous equations:
							# y^2 + 4x^2 = 100
							# y - b = m * (x - a)
							# ==> A x^2 + B x + C = 0
							UpperCAmelCase :       Union[str, Any] 							=		outgoing_gradient**2 + 4
							UpperCAmelCase :       Dict 							=		2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
							UpperCAmelCase :       List[Any] 							=		(point_y - outgoing_gradient * point_x) ** 2 - 100

							UpperCAmelCase :       List[str] 							=		(
							    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)
							UpperCAmelCase :       Optional[int] 							=		(
							    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)

							# two solutions, one of which is our input point
							UpperCAmelCase :       Optional[Any] 							=		x_minus if isclose(_lowerCAmelCase   ,      _lowerCAmelCase  ) else x_plus
							UpperCAmelCase :       Union[str, Any] 							=		point_y + outgoing_gradient * (next_x - point_x)

							return next_x, next_y, outgoing_gradient


def  snake_case_     (					_lowerCAmelCase  :						float = 1.4   ,      _lowerCAmelCase  :						float = -9.6  )      ->   int:
							UpperCAmelCase :       int 							=		0
							UpperCAmelCase :       float 							=		first_x_coord
							UpperCAmelCase :       float 							=		first_y_coord
							UpperCAmelCase :       float 							=		(1_0.1 - point_y) / (0.0 - point_x)

							while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       str 							=		next_point(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														num_reflections += 1

							return num_reflections


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





