


'''simple docstring'''

from math import pi, sqrt, tan





def __snake_case      (   UpperCAmelCase_		:		float	):
 if side_length < 0:
  raise ValueError("surface_area_cube() only accepts non-negative values"	)
 return 6 * side_length**2





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		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 __snake_case      (   UpperCAmelCase_		:		float	):
 if radius < 0:
  raise ValueError("surface_area_sphere() only accepts non-negative values"	)
 return 4 * pi * radius**2





def __snake_case      (   UpperCAmelCase_		:		float	):
 if radius < 0:
  raise ValueError("surface_area_hemisphere() only accepts non-negative values"	)
 return 3 * pi * radius**2





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		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 __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float	):
 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError(
      "surface_area_conical_frustum() only accepts non-negative values"	)
 lowerCamelCase_  				=						(height**2 + (radius_a - radius_a) ** 2) ** 0.5
 return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float	):
 if radius < 0 or height < 0:
  raise ValueError("surface_area_cylinder() only accepts non-negative values"	)
 return 2 * pi * radius * (height + radius)





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		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 __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float	):
 if length < 0 or width < 0:
  raise ValueError("area_rectangle() only accepts non-negative values"	)
 return length * width





def __snake_case      (   UpperCAmelCase_		:		float	):
 if side_length < 0:
  raise ValueError("area_square() only accepts non-negative values"	)
 return side_length**2





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float	):
 if base < 0 or height < 0:
  raise ValueError("area_triangle() only accepts non-negative values"	)
 return (base * height) / 2





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		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"	)
 lowerCamelCase_  				=						(sidea + sidea + sidea) / 2
 lowerCamelCase_  				=						sqrt(
     semi_perimeter
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)	)
 return area





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float	):
 if base < 0 or height < 0:
  raise ValueError("area_parallelogram() only accepts non-negative values"	)
 return base * height





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		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 __snake_case      (   UpperCAmelCase_		:		float	):
 if radius < 0:
  raise ValueError("area_circle() only accepts non-negative values"	)
 return pi * radius**2





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		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 __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		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 __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		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) = }''')







'''simple docstring'''

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf

     from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   for model_result in results.values():
    for batch_size, sequence_length in zip(model_result["bs"]    ,						model_result["ss"]  ):
     lowerCamelCase_  				=						model_result["result"][batch_size][sequence_length]
     self.assertIsNotNone(UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						eager_mode=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sgugger/tiny-distilbert-classification"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						only_pretrain_model=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						eager_mode=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						[config]  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						[config]  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result  )
   self.check_results_dict_not_empty(results.memory_train_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						[config]  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result  )
   self.check_results_dict_not_empty(results.memory_train_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"patrickvonplaten/t5-tiny-random"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						configs=[config]  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"  )  ) == 0    ,						"Cannot do xla on CPU."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						use_xla=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   with tempfile.TemporaryDirectory() as tmp_dir:
    lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
        models=[MODEL_ID]    ,						inference=UpperCamelCase    ,						save_to_csv=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						inference_time_csv_file=os.path.join(UpperCamelCase    ,						"inf_time.csv"  )    ,						inference_memory_csv_file=os.path.join(UpperCamelCase    ,						"inf_mem.csv"  )    ,						env_info_csv_file=os.path.join(UpperCamelCase    ,						"env.csv"  )    ,						multi_process=UpperCamelCase    ,						)
    lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
    benchmark.run()
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"inf_time.csv"  )  ).exists()  )
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"inf_mem.csv"  )  ).exists()  )
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"env.csv"  )  ).exists()  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"

   def _check_summary_is_not_empty(UpperCamelCase  ):
    self.assertTrue(hasattr(UpperCamelCase    ,						"sequential"  )  )
    self.assertTrue(hasattr(UpperCamelCase    ,						"cumulative"  )  )
    self.assertTrue(hasattr(UpperCamelCase    ,						"current"  )  )
    self.assertTrue(hasattr(UpperCamelCase    ,						"total"  )  )

   with tempfile.TemporaryDirectory() as tmp_dir:
    lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
        models=[MODEL_ID]    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						log_filename=os.path.join(UpperCamelCase    ,						"log.txt"  )    ,						log_print=UpperCamelCase    ,						trace_memory_line_by_line=UpperCamelCase    ,						eager_mode=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
    lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
    lowerCamelCase_  				=						benchmark.run()
    _check_summary_is_not_empty(result.inference_summary  )
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"log.txt"  )  ).exists()  )



