






'''simple docstring'''

from math import pi, sqrt, tan
def      __lowercase	(_lowercase  ) ->  float:


     """simple docstring"""
     if side_length < 0:
          raise ValueError("""surface_area_cube() only accepts non-negative values"""  )
     return 6 * side_length**2
def      __lowercase	(_lowercase,    _lowercase,    _lowercase  ) ->  float:


     """simple docstring"""
     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      __lowercase	(_lowercase  ) ->  float:


     """simple docstring"""
     if radius < 0:
          raise ValueError("""surface_area_sphere() only accepts non-negative values"""  )
     return 4 * pi * radius**2
def      __lowercase	(_lowercase  ) ->  float:


     """simple docstring"""
     if radius < 0:
          raise ValueError("""surface_area_hemisphere() only accepts non-negative values"""  )
     return 3 * pi * radius**2
def      __lowercase	(_lowercase,    _lowercase  ) ->  float:


     """simple docstring"""
     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      __lowercase	(_lowercase,    _lowercase,    _lowercase  ) ->  float:


     """simple docstring"""
     if radius_a < 0 or radius_a < 0 or height < 0:
          raise ValueError(
              """surface_area_conical_frustum() only accepts non-negative values"""  )
     __lowerCamelCase    :							List[str]					 =					(height**2 + (radius_a - radius_a) ** 2) ** 0.5
     return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def      __lowercase	(_lowercase,    _lowercase  ) ->  float:


     """simple docstring"""
     if radius < 0 or height < 0:
          raise ValueError("""surface_area_cylinder() only accepts non-negative values"""  )
     return 2 * pi * radius * (height + radius)
def      __lowercase	(_lowercase,    _lowercase  ) ->  float:


     """simple docstring"""
     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(__lowercase,    2  ) * torus_radius * tube_radius
def      __lowercase	(_lowercase,    _lowercase  ) ->  float:


     """simple docstring"""
     if length < 0 or width < 0:
          raise ValueError("""area_rectangle() only accepts non-negative values"""  )
     return length * width
def      __lowercase	(_lowercase  ) ->  float:


     """simple docstring"""
     if side_length < 0:
          raise ValueError("""area_square() only accepts non-negative values"""  )
     return side_length**2
def      __lowercase	(_lowercase,    _lowercase  ) ->  float:


     """simple docstring"""
     if base < 0 or height < 0:
          raise ValueError("""area_triangle() only accepts non-negative values"""  )
     return (base * height) / 2
def      __lowercase	(_lowercase,    _lowercase,    _lowercase  ) ->  float:


     """simple docstring"""
     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"""  )
     __lowerCamelCase    :							int					 =					(sidea + sidea + sidea) / 2
     __lowerCamelCase    :							Tuple					 =					sqrt(
         semi_perimeter
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)  )
     return area
def      __lowercase	(_lowercase,    _lowercase  ) ->  float:


     """simple docstring"""
     if base < 0 or height < 0:
          raise ValueError("""area_parallelogram() only accepts non-negative values"""  )
     return base * height
def      __lowercase	(_lowercase,    _lowercase,    _lowercase  ) ->  float:


     """simple docstring"""
     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      __lowercase	(_lowercase  ) ->  float:


     """simple docstring"""
     if radius < 0:
          raise ValueError("""area_circle() only accepts non-negative values"""  )
     return pi * radius**2
def      __lowercase	(_lowercase,    _lowercase  ) ->  float:


     """simple docstring"""
     if radius_x < 0 or radius_y < 0:
          raise ValueError("""area_ellipse() only accepts non-negative values"""  )
     return pi * radius_x * radius_y
def      __lowercase	(_lowercase,    _lowercase  ) ->  float:


     """simple docstring"""
     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      __lowercase	(_lowercase,    _lowercase  ) ->  float:


     """simple docstring"""
     if not isinstance(__lowercase,    __lowercase  ) 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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase			:	Dict								={
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Optional[int]								=[
	    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AlbertForMaskedLM',
	    'AlbertForMultipleChoice',
	    'AlbertForPreTraining',
	    'AlbertForQuestionAnswering',
	    'AlbertForSequenceClassification',
	    'AlbertForTokenClassification',
	    'AlbertModel',
	    'AlbertPreTrainedModel',
	    'load_tf_weights_in_albert',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=[
	    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFAlbertForMaskedLM',
	    'TFAlbertForMultipleChoice',
	    'TFAlbertForPreTraining',
	    'TFAlbertForQuestionAnswering',
	    'TFAlbertForSequenceClassification',
	    'TFAlbertForTokenClassification',
	    'TFAlbertMainLayer',
	    'TFAlbertModel',
	    'TFAlbertPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Any								=[
	    'FlaxAlbertForMaskedLM',
	    'FlaxAlbertForMultipleChoice',
	    'FlaxAlbertForPreTraining',
	    'FlaxAlbertForQuestionAnswering',
	    'FlaxAlbertForSequenceClassification',
	    'FlaxAlbertForTokenClassification',
	    'FlaxAlbertModel',
	    'FlaxAlbertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert import AlbertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert_fast import AlbertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_albert import (
		    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AlbertForMaskedLM,
		    AlbertForMultipleChoice,
		    AlbertForPreTraining,
		    AlbertForQuestionAnswering,
		    AlbertForSequenceClassification,
		    AlbertForTokenClassification,
		    AlbertModel,
		    AlbertPreTrainedModel,
		    load_tf_weights_in_albert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_albert import (
		    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFAlbertForMaskedLM,
		    TFAlbertForMultipleChoice,
		    TFAlbertForPreTraining,
		    TFAlbertForQuestionAnswering,
		    TFAlbertForSequenceClassification,
		    TFAlbertForTokenClassification,
		    TFAlbertMainLayer,
		    TFAlbertModel,
		    TFAlbertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_albert import (
		    FlaxAlbertForMaskedLM,
		    FlaxAlbertForMultipleChoice,
		    FlaxAlbertForPreTraining,
		    FlaxAlbertForQuestionAnswering,
		    FlaxAlbertForSequenceClassification,
		    FlaxAlbertForTokenClassification,
		    FlaxAlbertModel,
		    FlaxAlbertPreTrainedModel,
		)
else:
	import sys

	_UpperCamelCase			:	str								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



