





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,
)


__A							:			List[str]         =			{
    "configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}

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

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Tuple         =			[
     "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:
 __A							:			Optional[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:
 __A							:			Union[str, 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

 __A							:			Optional[Any]         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












from math import pi, sqrt, tan





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> 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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> 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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> 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'				)
    _A     =							(height**2 + (radius_a - radius_a) ** 2) ** 0.5
    return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)





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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> 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'				)
    _A     =							(sidea + sidea + sidea) / 2
    _A     =							sqrt(
        semi_perimeter
        * (semi_perimeter - sidea)
        * (semi_perimeter - sidea)
        * (semi_perimeter - sidea)				)
    return area





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> 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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> 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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> 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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




    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) = }")





