



"""simple docstring"""
from math import pi, sqrt, tan

def       _snake_case						(						UpperCamelCase						:		float						):
 if side_length < 0:
  raise ValueError("""surface_area_cube() only accepts non-negative values"""						)
 return 6 * side_length**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if length < 0 or breadth < 0 or height < 0:
  raise ValueError("""surface_area_cuboid() only accepts non-negative values"""						)
 return 2 * ((length * breadth) + (breadth * height) + (length * height))

def       _snake_case						(						UpperCamelCase						:		float						):
 if radius < 0:
  raise ValueError("""surface_area_sphere() only accepts non-negative values"""						)
 return 4 * pi * radius**2

def       _snake_case						(						UpperCamelCase						:		float						):
 if radius < 0:
  raise ValueError("""surface_area_hemisphere() only accepts non-negative values"""						)
 return 3 * pi * radius**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius < 0 or height < 0:
  raise ValueError("""surface_area_cone() only accepts non-negative values"""						)
 return pi * radius * (radius + (height**2 + radius**2) ** 0.5)

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError(
      """surface_area_conical_frustum() only accepts non-negative values"""						)
 UpperCAmelCase	:					Tuple	=	(height**2 + (radius_a - radius_a) ** 2) ** 0.5
 return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius < 0 or height < 0:
  raise ValueError("""surface_area_cylinder() only accepts non-negative values"""						)
 return 2 * pi * radius * (height + radius)

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if torus_radius < 0 or tube_radius < 0:
  raise ValueError("""surface_area_torus() only accepts non-negative values"""						)
 if torus_radius < tube_radius:
  raise ValueError(
      """surface_area_torus() does not support spindle or self intersecting tori"""						)
 return 4 * pow(UpperCamelCase			,		2						) * torus_radius * tube_radius

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if length < 0 or width < 0:
  raise ValueError("""area_rectangle() only accepts non-negative values"""						)
 return length * width

def       _snake_case						(						UpperCamelCase						:		float						):
 if side_length < 0:
  raise ValueError("""area_square() only accepts non-negative values"""						)
 return side_length**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if base < 0 or height < 0:
  raise ValueError("""area_triangle() only accepts non-negative values"""						)
 return (base * height) / 2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if sidea < 0 or sidea < 0 or sidea < 0:
  raise ValueError("""area_triangle_three_sides() only accepts non-negative values"""						)
 elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
  raise ValueError("""Given three sides do not form a triangle"""						)
 UpperCAmelCase	:					Union[str, Any]	=	(sidea + sidea + sidea) / 2
 UpperCAmelCase	:					Union[str, Any]	=	sqrt(
     semi_perimeter
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)						)
 return area

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if base < 0 or height < 0:
  raise ValueError("""area_parallelogram() only accepts non-negative values"""						)
 return base * height

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if basea < 0 or basea < 0 or height < 0:
  raise ValueError("""area_trapezium() only accepts non-negative values"""						)
 return 1 / 2 * (basea + basea) * height

def       _snake_case						(						UpperCamelCase						:		float						):
 if radius < 0:
  raise ValueError("""area_circle() only accepts non-negative values"""						)
 return pi * radius**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius_x < 0 or radius_y < 0:
  raise ValueError("""area_ellipse() only accepts non-negative values"""						)
 return pi * radius_x * radius_y

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if diagonal_a < 0 or diagonal_a < 0:
  raise ValueError("""area_rhombus() only accepts non-negative values"""						)
 return 1 / 2 * diagonal_a * diagonal_a

def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		float						):
 if not isinstance(UpperCamelCase			,		UpperCamelCase						) or sides < 3:
  raise ValueError(
      """area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"""						)
 elif length < 0:
  raise ValueError(
      """area_reg_polygon() only accepts non-negative values as \
length of a side"""						)
 return (sides * length**2) / (4 * tan(pi / sides						))
 return (sides * length**2) / (4 * tan(pi / sides						))


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

      print("[DEMO] Areas of various geometric shapes: \n")
      print(f"""Rectangle: {area_rectangle(1_0, 2_0) = }""")
      print(f"""Square: {area_square(1_0) = }""")
      print(f"""Triangle: {area_triangle(1_0, 1_0) = }""")
      print(f"""Triangle: {area_triangle_three_sides(5, 1_2, 1_3) = }""")
      print(f"""Parallelogram: {area_parallelogram(1_0, 2_0) = }""")
      print(f"""Rhombus: {area_rhombus(1_0, 2_0) = }""")
      print(f"""Trapezium: {area_trapezium(1_0, 2_0, 3_0) = }""")
      print(f"""Circle: {area_circle(2_0) = }""")
      print(f"""Ellipse: {area_ellipse(1_0, 2_0) = }""")
      print("\nSurface Areas of various geometric shapes: \n")
      print(f"""Cube: {surface_area_cube(2_0) = }""")
      print(f"""Cuboid: {surface_area_cuboid(1_0, 2_0, 3_0) = }""")
      print(f"""Sphere: {surface_area_sphere(2_0) = }""")
      print(f"""Hemisphere: {surface_area_hemisphere(2_0) = }""")
      print(f"""Cone: {surface_area_cone(1_0, 2_0) = }""")
      print(f"""Conical Frustum: {surface_area_conical_frustum(1_0, 2_0, 3_0) = }""")
      print(f"""Cylinder: {surface_area_cylinder(1_0, 2_0) = }""")
      print(f"""Torus: {surface_area_torus(2_0, 1_0) = }""")
      print(f"""Equilateral Triangle: {area_reg_polygon(3, 1_0) = }""")
      print(f"""Square: {area_reg_polygon(4, 1_0) = }""")
      print(f"""Reqular Pentagon: {area_reg_polygon(5, 1_0) = }""")





"""simple docstring"""
import datasets

from .evaluate import evaluate


A:   Optional[Any]								=		"\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

A:   Optional[int]								=		"\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

A:   int								=		"\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,			_KWARGS_DESCRIPTION )
class       SCREAMING_SNAKE_CASE__  (					datasets.Metric ):

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    return datasets.MetricInfo(
        description=_DESCRIPTION      ,     citation=_CITATION      ,     inputs_description=_KWARGS_DESCRIPTION      ,     features=datasets.Features(
            {
                """predictions""": {
                    """id""": datasets.Value("""string"""						),
                    """prediction_text""": datasets.features.Sequence(datasets.Value("""string"""						)						),
                },
                """references""": {
                    """id""": datasets.Value("""string"""						),
                    """answers""": datasets.features.Sequence(
                        {
                            """text""": datasets.Value("""string"""						),
                            """answer_start""": datasets.Value("""int32"""						),
                        }						),
                },
            }						)      ,     codebase_urls=["""https://www.atticusprojectai.org/cuad"""]      ,     reference_urls=["""https://www.atticusprojectai.org/cuad"""]      ,     )




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
    UpperCAmelCase	:					Tuple	=	[
        {
            """paragraphs""": [
                {
                    """qas""": [
                        {
                            """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                            """id""": ref["""id"""],
                        }
                        for ref in references
                    ]
                }
            ]
        }
    ]
    UpperCAmelCase	:					Optional[Any]	=	evaluate(dataset=_SCREAMING_SNAKE_CASE      ,     predictions=_SCREAMING_SNAKE_CASE						)
    return score
