





import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment

def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple  ,						_UpperCAmelCase							:Any=()  ,						_UpperCAmelCase							:List[Any]=None  ,						_UpperCAmelCase							:Tuple="no"  ,						_UpperCAmelCase							:str="29500"				)	->				Tuple:
     '''simple docstring'''


     A_          =       False
     A_          =       False
     if any(key.startswith('''KAGGLE'''				) for key in os.environ.keys()				):
          A_          =       True
     elif "IPython" in sys.modules:
          A_          =       '''google.colab''' in str(sys.modules['''IPython'''].get_ipython()				)

     try:
          A_          =       PrecisionType(mixed_precision.lower()				)
     except ValueError:
          raise ValueError(
              f'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'				)

     if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME'''  ,						_UpperCAmelCase				) is not None):
          # TPU launch
          import torch_xla.distributed.xla_multiprocessing as xmp

          if len(AcceleratorState._shared_state				) > 0:
               raise ValueError(
                   '''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
                   '''your training function. Restart your notebook and make sure no cells initializes an '''
                   '''`Accelerator`.'''				)
          if num_processes is None:
               A_          =       8

          A_          =       PrepareForLaunch(_UpperCAmelCase  ,						distributed_type='''TPU'''				)
          print(f'Launching a training on {num_processes} TPU cores.'				)
          xmp.spawn(_UpperCAmelCase  ,						args=_UpperCAmelCase  ,						nprocs=_UpperCAmelCase  ,						start_method='''fork'''				)
     elif in_colab:
          # No need for a distributed launch otherwise as it's either CPU or one GPU.
          if torch.cuda.is_available():
               print('''Launching training on one GPU.'''				)
          else:
               print('''Launching training on one CPU.'''				)
          function(*_UpperCAmelCase				)
     else:
          if num_processes is None:
               raise ValueError(
                   '''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'''				)

          if num_processes > 1:
               # Multi-GPU launch
               from torch.multiprocessing import start_processes
               from torch.multiprocessing.spawn import ProcessRaisedException

               if len(AcceleratorState._shared_state				) > 0:
                    raise ValueError(
                        '''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
                        '''inside your training function. Restart your notebook and make sure no cells initializes an '''
                        '''`Accelerator`.'''				)

               if torch.cuda.is_initialized():
                    raise ValueError(
                        '''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
                        '''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
                        '''function.'''				)

               # torch.distributed will expect a few environment variable to be here. We set the ones common to each
               # process here (the other ones will be set be the launcher).
               with patch_environment(
                   world_size=_UpperCAmelCase  ,						master_addr='''127.0.01'''  ,						master_port=_UpperCAmelCase  ,						mixed_precision=_UpperCAmelCase				):
                    A_          =       PrepareForLaunch(_UpperCAmelCase  ,						distributed_type='''MULTI_GPU'''				)
                    print(f'Launching training on {num_processes} GPUs.'				)
                    try:
                         start_processes(_UpperCAmelCase  ,						args=_UpperCAmelCase  ,						nprocs=_UpperCAmelCase  ,						start_method='''fork'''				)
                    except ProcessRaisedException as e:
                         if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                              raise RuntimeError(
                                  '''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
                                  '''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
                                  '''Please review your imports and test them when running the `notebook_launcher()` to identify '''
                                  '''which one is problematic.'''				) from e

          else:
               # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
               if is_mps_available():
                    A_          =       '''1'''
                    print('''Launching training on MPS.'''				)
               elif torch.cuda.is_available():
                    print('''Launching training on one GPU.'''				)
               else:
                    print('''Launching training on CPU.'''				)
               function(*_UpperCAmelCase				)


def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]  ,						_UpperCAmelCase							:int=()  ,						_UpperCAmelCase							:Optional[Any]=2				)	->				Optional[int]:
     '''simple docstring'''


     from torch.multiprocessing import start_processes

     with tempfile.NamedTemporaryFile() as tmp_file:
          # torch.distributed will expect a few environment variable to be here. We set the ones common to each
          # process here (the other ones will be set be the launcher).
          with patch_environment(
              world_size=_UpperCAmelCase  ,						master_addr='''127.0.01'''  ,						master_port='''29500'''  ,						accelerate_mixed_precision='''no'''  ,						accelerate_debug_rdv_file=tmp_file.name  ,						accelerate_use_cpu='''yes'''  ,						):
               A_          =       PrepareForLaunch(_UpperCAmelCase  ,						debug=_UpperCAmelCase				)
               start_processes(_UpperCAmelCase  ,						args=_UpperCAmelCase  ,						nprocs=_UpperCAmelCase  ,						start_method='''fork'''				)










from math import pi, sqrt, tan

def      UpperCAmelCase_    (    _UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				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      UpperCAmelCase_    (    _UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				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      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				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      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				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      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				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(_UpperCAmelCase  ,						2				) * torus_radius * tube_radius

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				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      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				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      UpperCAmelCase_    (    _UpperCAmelCase							:float				)	->				float:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				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      UpperCAmelCase_    (    _UpperCAmelCase							:float  ,						_UpperCAmelCase							:float				)	->				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      UpperCAmelCase_    (    _UpperCAmelCase							:int  ,						_UpperCAmelCase							:float				)	->				float:
     '''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) = }''')



