

"""simple docstring"""




from math import pi, sqrt, tan
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 if side_length < 0:
  raise ValueError("""surface_area_cube() only accepts non-negative values"""   )
 return 6 * side_length**2
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   ):
 '''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    (       __UpperCamelCase   ):
 '''simple docstring'''





 if radius < 0:
  raise ValueError("""surface_area_sphere() only accepts non-negative values"""   )
 return 4 * pi * radius**2
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 if radius < 0:
  raise ValueError("""surface_area_hemisphere() only accepts non-negative values"""   )
 return 3 * pi * radius**2
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''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    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError(
      """surface_area_conical_frustum() only accepts non-negative values"""   )
 UpperCAmelCase__       :	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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''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    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''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(__UpperCamelCase				,     2   ) * torus_radius * tube_radius
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 if length < 0 or width < 0:
  raise ValueError("""area_rectangle() only accepts non-negative values"""   )
 return length * width
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 if side_length < 0:
  raise ValueError("""area_square() only accepts non-negative values"""   )
 return side_length**2
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 if base < 0 or height < 0:
  raise ValueError("""area_triangle() only accepts non-negative values"""   )
 return (base * height) / 2
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   ):
 '''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"""   )
 UpperCAmelCase__       :	List[str]        =       (sidea + sidea + sidea) / 2
 UpperCAmelCase__       :	Tuple        =       sqrt(
     semi_perimeter
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)   )
 return area
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 if base < 0 or height < 0:
  raise ValueError("""area_parallelogram() only accepts non-negative values"""   )
 return base * height
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   ):
 '''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    (       __UpperCamelCase   ):
 '''simple docstring'''





 if radius < 0:
  raise ValueError("""area_circle() only accepts non-negative values"""   )
 return pi * radius**2
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''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    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''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    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 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) = }")













'''simple docstring'''



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import torch
class UpperCAmelCase_       (     TensorFormatter[Mapping, '''torch.Tensor''', Mapping]					):




      """simple docstring"""




      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		str=None  ,   **UpperCAmelCase      :		List[Any]		)	->	int:


       '''simple docstring'''

       super().__init__(features=UpperCAmelCase		)
       lowercase       : List[Any]						=torch_tensor_kwargs
       import torch  # noqa import torch at initialization






      def 					A__   (			self      :		Union[str, Any]  ,   UpperCAmelCase      :		Dict		)	->	List[str]:


       '''simple docstring'''

       import torch

       if isinstance(UpperCAmelCase  ,   UpperCAmelCase		) and column:
        if all(
            isinstance(UpperCAmelCase  ,   torch.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype
            for x in column		):
         return torch.stack(UpperCAmelCase		)
       return column






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		Optional[int]		)	->	Optional[Any]:


       '''simple docstring'''

       import torch

       if isinstance(UpperCAmelCase  ,   (str, bytes, type(UpperCAmelCase		))		):
        return value
       elif isinstance(UpperCAmelCase  ,   (np.character, np.ndarray)		) and np.issubdtype(value.dtype  ,   np.character		):
        return value.tolist()

       lowercase       : Any						={}

       if isinstance(UpperCAmelCase  ,   (np.number, np.ndarray)		) and np.issubdtype(value.dtype  ,   np.integer		):
        lowercase       : Tuple						={'''dtype''': torch.intaa}
       elif isinstance(UpperCAmelCase  ,   (np.number, np.ndarray)		) and np.issubdtype(value.dtype  ,   np.floating		):
        lowercase       : int						={'''dtype''': torch.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
        import PIL.Image

        if isinstance(UpperCAmelCase  ,   PIL.Image.Image		):
         lowercase       : List[Any]						=np.asarray(UpperCAmelCase		)
       return torch.tensor(UpperCAmelCase  ,   **{**default_dtype, **self.torch_tensor_kwargs}		)






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Dict		)	->	Tuple:


       '''simple docstring'''

       import torch

       # support for torch, tf, jax etc.
       if hasattr(UpperCAmelCase  ,   '''__array__'''		) and not isinstance(UpperCAmelCase  ,   torch.Tensor		):
        lowercase       : List[Any]						=data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(UpperCAmelCase  ,   np.ndarray		):
        if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
         return self._consolidate([self.recursive_tensorize(UpperCAmelCase		) for substruct in data_struct]		)
       elif isinstance(UpperCAmelCase  ,   (list, tuple)		):
        return self._consolidate([self.recursive_tensorize(UpperCAmelCase		) for substruct in data_struct]		)
       return self._tensorize(UpperCAmelCase		)






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		dict		)	->	Any:


       '''simple docstring'''

       return map_nested(self._recursive_tensorize  ,   UpperCAmelCase  ,   map_list=UpperCAmelCase		)






      def 					A__   (			self      :		Union[str, Any]  ,   UpperCAmelCase      :		pa.Table		)	->	Mapping:


       '''simple docstring'''

       lowercase       : List[str]						=self.numpy_arrow_extractor().extract_row(UpperCAmelCase		)
       lowercase       : Any						=self.python_features_decoder.decode_row(UpperCAmelCase		)
       return self.recursive_tensorize(UpperCAmelCase		)






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		pa.Table		)	->	"torch.Tensor":


       '''simple docstring'''

       lowercase       : int						=self.numpy_arrow_extractor().extract_column(UpperCAmelCase		)
       lowercase       : Tuple						=self.python_features_decoder.decode_column(UpperCAmelCase  ,   pa_table.column_names[0]		)
       lowercase       : Optional[Any]						=self.recursive_tensorize(UpperCAmelCase		)
       lowercase       : Any						=self._consolidate(UpperCAmelCase		)
       return column






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		pa.Table		)	->	Mapping:


       '''simple docstring'''

       lowercase       : Tuple						=self.numpy_arrow_extractor().extract_batch(UpperCAmelCase		)
       lowercase       : List[str]						=self.python_features_decoder.decode_batch(UpperCAmelCase		)
       lowercase       : Dict						=self.recursive_tensorize(UpperCAmelCase		)
       for column_name in batch:
        lowercase       : str						=self._consolidate(batch[column_name]		)
       return batch



