
from math import pi, sqrt, tan




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ):







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




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    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        __UpperCamelCase					(    lowerCAmelCase__							:    float  ):







 if radius < 0:
  raise ValueError('''surface_area_sphere() only accepts non-negative values'''  )
 return 4 * pi * radius**2




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ):







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




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    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        __UpperCamelCase					(    lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    float  ):







 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError(
      '''surface_area_conical_frustum() only accepts non-negative values'''  )
 __a			:  Optional[int]		    =  (height**2 + (radius_a - radius_a) ** 2) ** 0.5
 return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    float  ):







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




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    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        __UpperCamelCase					(    lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    float  ):







 if length < 0 or width < 0:
  raise ValueError('''area_rectangle() only accepts non-negative values'''  )
 return length * width




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ):







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




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    float  ):







 if base < 0 or height < 0:
  raise ValueError('''area_triangle() only accepts non-negative values'''  )
 return (base * height) / 2




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    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			:  str		    =  (sidea + sidea + sidea) / 2
 __a			:  Dict		    =  sqrt(
     semi_perimeter
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)  )
 return area




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    float  ):







 if base < 0 or height < 0:
  raise ValueError('''area_parallelogram() only accepts non-negative values'''  )
 return base * height




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    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        __UpperCamelCase					(    lowerCAmelCase__							:    float  ):







 if radius < 0:
  raise ValueError('''area_circle() only accepts non-negative values'''  )
 return pi * radius**2




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    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        __UpperCamelCase					(    lowerCAmelCase__							:    float  ,       lowerCAmelCase__							:    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        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    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(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) = }""")







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase__          ={
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Data2VecAudioForAudioFrameClassification',
          'Data2VecAudioForCTC',
          'Data2VecAudioForSequenceClassification',
          'Data2VecAudioForXVector',
          'Data2VecAudioModel',
          'Data2VecAudioPreTrainedModel',
      ]
      lowercase__          =[
          'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Data2VecTextForCausalLM',
          'Data2VecTextForMaskedLM',
          'Data2VecTextForMultipleChoice',
          'Data2VecTextForQuestionAnswering',
          'Data2VecTextForSequenceClassification',
          'Data2VecTextForTokenClassification',
          'Data2VecTextModel',
          'Data2VecTextPreTrainedModel',
      ]
      lowercase__          =[
          'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Data2VecVisionForImageClassification',
          'Data2VecVisionForMaskedImageModeling',
          'Data2VecVisionForSemanticSegmentation',
          'Data2VecVisionModel',
          'Data2VecVisionPreTrainedModel',
      ]

if is_tf_available():
      lowercase__          =[
          'TFData2VecVisionForImageClassification',
          'TFData2VecVisionForSemanticSegmentation',
          'TFData2VecVisionModel',
          'TFData2VecVisionPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
      from .configuration_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecTextConfig,
          DataaVecTextOnnxConfig,
      )
      from .configuration_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecVisionConfig,
          DataaVecVisionOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dataavec_audio import (
                DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecAudioForAudioFrameClassification,
                DataaVecAudioForCTC,
                DataaVecAudioForSequenceClassification,
                DataaVecAudioForXVector,
                DataaVecAudioModel,
                DataaVecAudioPreTrainedModel,
            )
            from .modeling_dataavec_text import (
                DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecTextForCausalLM,
                DataaVecTextForMaskedLM,
                DataaVecTextForMultipleChoice,
                DataaVecTextForQuestionAnswering,
                DataaVecTextForSequenceClassification,
                DataaVecTextForTokenClassification,
                DataaVecTextModel,
                DataaVecTextPreTrainedModel,
            )
            from .modeling_dataavec_vision import (
                DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecVisionForImageClassification,
                DataaVecVisionForMaskedImageModeling,
                DataaVecVisionForSemanticSegmentation,
                DataaVecVisionModel,
                DataaVecVisionPreTrainedModel,
            )
      if is_tf_available():
            from .modeling_tf_dataavec_vision import (
                TFDataaVecVisionForImageClassification,
                TFDataaVecVisionForSemanticSegmentation,
                TFDataaVecVisionModel,
                TFDataaVecVisionPreTrainedModel,
            )

else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
