

from math import pi, sqrt, tan
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float				)							->				float:





				'''simple docstring'''






				if side_length < 0:
								raise ValueError('surface_area_cube() only accepts non-negative values'				)
				return 6 * side_length**2
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float				)							->				float:





				'''simple docstring'''






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





				'''simple docstring'''






				if radius < 0:
								raise ValueError('surface_area_hemisphere() only accepts non-negative values'				)
				return 3 * pi * radius**2
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     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(SCREAMING_SNAKE_CASE__   ,      2				) * torus_radius * tube_radius
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     float				)							->				float:





				'''simple docstring'''






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





				'''simple docstring'''






				if side_length < 0:
								raise ValueError('area_square() only accepts non-negative values'				)
				return side_length**2
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     float				)							->				float:





				'''simple docstring'''






				if base < 0 or height < 0:
								raise ValueError('area_triangle() only accepts non-negative values'				)
				return (base * height) / 2
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     float				)							->				float:





				'''simple docstring'''






				if base < 0 or height < 0:
								raise ValueError('area_parallelogram() only accepts non-negative values'				)
				return base * height
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float				)							->				float:





				'''simple docstring'''






				if radius < 0:
								raise ValueError('area_circle() only accepts non-negative values'				)
				return pi * radius**2
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     float   ,      SCREAMING_SNAKE_CASE__      :     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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     float				)							->				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) = }""")




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
     from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[Any],lowercase_			:	str		)->   List[Any]:





        '''simple docstring'''







        for model_result in results.values():
            for batch_size, sequence_length in zip(model_result['bs'],model_result['ss']		):
                A__   				=       model_result['result'][batch_size][sequence_length]
                self.assertIsNotNone(lowercase_		)


    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       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			:	Dict		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sgugger/tiny-distilbert-classification'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,only_pretrain_model=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       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			:	List[Any]		)->   Any:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,torchscript=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    @unittest.skipIf(torch_device == 'cpu','Cant do half precision'		)
    def 						snake_case__					(				self			:	Any		)->   Dict:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,fpaa=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       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			:	Any		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        # set architectures equal to `None`
        A__   				=       None
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       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			:	Union[str, Any]		)->   int:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    @unittest.skipIf(torch_device == 'cpu','Can\'t do half precision'		)
    def 						snake_case__					(				self			:	List[Any]		)->   Dict:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],fpaa=lowercase_,multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       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			:	int		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       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			:	List[Any]		)->   Any:





        '''simple docstring'''







        A__   				=       'sshleifer/tinier_bart'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       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			:	List[str]		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       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			:	int		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       'sshleifer/tinier_bart'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       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			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        with tempfile.TemporaryDirectory() as tmp_dir:
            A__   				=       PyTorchBenchmarkArguments(
                models=[MODEL_ID],training=lowercase_,inference=lowercase_,save_to_csv=lowercase_,sequence_lengths=[8],batch_sizes=[1],inference_time_csv_file=os.path.join(lowercase_,'inf_time.csv'		),train_memory_csv_file=os.path.join(lowercase_,'train_mem.csv'		),inference_memory_csv_file=os.path.join(lowercase_,'inf_mem.csv'		),train_time_csv_file=os.path.join(lowercase_,'train_time.csv'		),env_info_csv_file=os.path.join(lowercase_,'env.csv'		),multi_process=lowercase_,)
            A__   				=       PyTorchBenchmark(lowercase_		)
            benchmark.run()
            self.assertTrue(Path(os.path.join(lowercase_,'inf_time.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'train_time.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'inf_mem.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'train_mem.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'env.csv'		)		).exists()		)


    def 						snake_case__					(				self			:	Tuple		)->   str:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'

        def _check_summary_is_not_empty(lowercase_			:	Optional[Any]		):
            self.assertTrue(hasattr(lowercase_,'sequential'		)		)
            self.assertTrue(hasattr(lowercase_,'cumulative'		)		)
            self.assertTrue(hasattr(lowercase_,'current'		)		)
            self.assertTrue(hasattr(lowercase_,'total'		)		)

        with tempfile.TemporaryDirectory() as tmp_dir:
            A__   				=       PyTorchBenchmarkArguments(
                models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],log_filename=os.path.join(lowercase_,'log.txt'		),log_print=lowercase_,trace_memory_line_by_line=lowercase_,multi_process=lowercase_,)
            A__   				=       PyTorchBenchmark(lowercase_		)
            A__   				=       benchmark.run()
            _check_summary_is_not_empty(result.inference_summary		)
            _check_summary_is_not_empty(result.train_summary		)
            self.assertTrue(Path(os.path.join(lowercase_,'log.txt'		)		).exists()		)

