




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__ :   Tuple						=							'''docs/source/en/tasks'''





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







			"""simple docstring"""





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

			SCREAMING_SNAKE_CASE_   : 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__ :   Dict						=							direct_transformers_import(TRANSFORMERS_PATH)

UpperCamelCase__ :   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`).
UpperCamelCase__ :   List[str]						=							{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}





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







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      TASK_GUIDE_TO_MODELS[task_guide]
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowerCamelCase_  ,							set()  )
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {
			    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_ :     Tuple  ,							lowerCamelCase_ :     Union[str, Any]=False  )       ->  Union[str, Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[int]					  =      _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_   : Optional[int]					  =      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__ :   Optional[int]						=							argparse.ArgumentParser()
						parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
						UpperCamelCase__ :   Optional[int]						=							parser.parse_args()

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









from __future__ import annotations

from scipy.special import comb  # type: ignore







class 				lowerCAmelCase_       :






				def __init__(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      list_of_points
							# Degree determines the flexibility of the curve.
							# Degree = 1 will produce a straight line.
							SCREAMING_SNAKE_CASE_   : Tuple					  =      len(snake_case__ ) - 1






				def 		snake_case	(			self      ,snake_case__ ):
							assert 0 <= t <= 1, "Time t must be between 0 and 1."
							SCREAMING_SNAKE_CASE_   : list[float]					  =      []
							for i in range(len(self.list_of_points ) ):
										# basis function for each i
										output_values.append(
										    comb(self.degree      ,snake_case__ ) * ((1 - t) ** (self.degree - i)) * (t**i) )
							# the basis must sum up to 1 for it to produce a valid Bezier curve.
							assert round(sum(snake_case__ )      ,5 ) == 1
							return output_values






				def 		snake_case	(			self      ,snake_case__ ):
							assert 0 <= t <= 1, "Time t must be between 0 and 1."

							SCREAMING_SNAKE_CASE_   : int					  =      self.basis_function(snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      0.0
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      0.0
							for i in range(len(self.list_of_points ) ):
										# For all points, sum up the product of i-th basis function and i-th point.
										x += basis_function[i] * self.list_of_points[i][0]
										y += basis_function[i] * self.list_of_points[i][1]
							return (x, y)






				def 		snake_case	(			self      ,snake_case__ = 0.01 ):
							from matplotlib import pyplot as plt  # type: ignore

							SCREAMING_SNAKE_CASE_   : list[float]					  =      []  # x coordinates of points to plot
							SCREAMING_SNAKE_CASE_   : list[float]					  =      []  # y coordinates of points to plot

							SCREAMING_SNAKE_CASE_   : List[str]					  =      0.0
							while t <= 1:
										SCREAMING_SNAKE_CASE_   : int					  =      self.bezier_curve_function(snake_case__ )
										to_plot_x.append(value[0] )
										to_plot_y.append(value[1] )
										t += step_size

							SCREAMING_SNAKE_CASE_   : Dict					  =      [i[0] for i in self.list_of_points]
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      [i[1] for i in self.list_of_points]

							plt.plot(
							    snake_case__      ,snake_case__      ,color='blue'      ,label='Curve of Degree ' + str(self.degree )      ,)
							plt.scatter(snake_case__      ,snake_case__      ,color='red'      ,label='Control Points' )
							plt.legend()
							plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
						BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
						BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3



