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







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if side_length < 0:
    raise ValueError('surface_area_cube() only accepts non-negative values'   )
  return 6 * side_length**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0:
    raise ValueError('surface_area_sphere() only accepts non-negative values'   )
  return 4 * pi * radius**2







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0:
    raise ValueError('surface_area_hemisphere() only accepts non-negative values'   )
  return 3 * pi * radius**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius_a < 0 or radius_a < 0 or height < 0:
    raise ValueError(
        'surface_area_conical_frustum() only accepts non-negative values'   )
  a__:     List[Any]										=	(height**2 + (radius_a - radius_a) ** 2) ** 0.5
  return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0 or height < 0:
    raise ValueError('surface_area_cylinder() only accepts non-negative values'   )
  return 2 * pi * radius * (height + radius)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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(_SCREAMING_SNAKE_CASE		,     2   ) * torus_radius * tube_radius







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if length < 0 or width < 0:
    raise ValueError('area_rectangle() only accepts non-negative values'   )
  return length * width







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if side_length < 0:
    raise ValueError('area_square() only accepts non-negative values'   )
  return side_length**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if base < 0 or height < 0:
    raise ValueError('area_triangle() only accepts non-negative values'   )
  return (base * height) / 2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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'   )
  a__:     int										=	(sidea + sidea + sidea) / 2
  a__:     Tuple										=	sqrt(
      semi_perimeter
      * (semi_perimeter - sidea)
      * (semi_perimeter - sidea)
      * (semi_perimeter - sidea)   )
  return area







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if base < 0 or height < 0:
    raise ValueError('area_parallelogram() only accepts non-negative values'   )
  return base * height







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0:
    raise ValueError('area_circle() only accepts non-negative values'   )
  return pi * radius**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) 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(10, 20) = }")
 print(f"Square: {area_square(10) = }")
 print(f"Triangle: {area_triangle(10, 10) = }")
 print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
 print(f"Parallelogram: {area_parallelogram(10, 20) = }")
 print(f"Rhombus: {area_rhombus(10, 20) = }")
 print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
 print(f"Circle: {area_circle(20) = }")
 print(f"Ellipse: {area_ellipse(10, 20) = }")
 print('\nSurface Areas of various geometric shapes: \n')
 print(f"Cube: {surface_area_cube(20) = }")
 print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
 print(f"Sphere: {surface_area_sphere(20) = }")
 print(f"Hemisphere: {surface_area_hemisphere(20) = }")
 print(f"Cone: {surface_area_cone(10, 20) = }")
 print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
 print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
 print(f"Torus: {surface_area_torus(20, 10) = }")
 print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
 print(f"Square: {area_reg_polygon(4, 10) = }")
 print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")



"""simple docstring"""
import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__					       =	get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__					       =	get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__					       =	'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		CamembertTokenizer
      a__        =		CamembertTokenizerFast
      a__        =		True
      a__        =		True



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





        '''simple docstring'''





        super().setUp()

        # We have a SentencePiece fixture for testing
        a__:     Tuple										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Optional[Any]										=	'<pad>'
        a__:     List[Any]										=	1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase)    ,			lowercase)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase)    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     str										=	list(self.get_tokenizer().get_vocab().keys())

        self.assertEqual(vocab_keys[0]    ,			'<s>NOTUSED')
        self.assertEqual(vocab_keys[1]    ,			'<pad>')
        self.assertEqual(vocab_keys[-1]    ,			'<mask>')
        self.assertEqual(len(lowercase)    ,			10_04)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        self.assertEqual(self.get_tokenizer().vocab_size    ,			10_05)



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





        '''simple docstring'''





        a__:     Optional[Any]										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)
        a__:     List[Any]										=	CamembertTokenizerFast.from_pretrained(self.tmpdirname)

        a__:     Dict										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[int]										=	tokenizer.encode(lowercase)
        a__:     Any										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Optional[Any]										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        # <unk> tokens are not the same for `rust` than for `slow`.
        # Because spm gives back raw token instead of `unk` in EncodeAsPieces
        # tokens = tokenizer.tokenize(sequence)
        a__:     Tuple										=	tokenizer.convert_ids_to_tokens(lowercase)
        a__:     Tuple										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        if not self.test_rust_tokenizer:
          return

        a__:     Dict										=	self.get_tokenizer()
        a__:     str										=	self.get_rust_tokenizer()

        a__:     int										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[Any]										=	tokenizer.tokenize(lowercase)
        a__:     List[Any]										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     str										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Tuple										=	self.get_rust_tokenizer()
        a__:     Union[str, Any]										=	tokenizer.encode(lowercase)
        a__:     List[Any]										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	{'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # camembert is a french model. So we also use french texts.
        a__:     int										=	[
            'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
            'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
            'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
            'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
            'telles que la traduction et la synthèse de texte.',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=lowercase    ,			model_name='camembert-base'    ,			revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'    ,			sequences=lowercase    ,			)


