


from math import pi, sqrt, tan
def 							_UpperCAmelCase				(UpperCamelCase__     :  float						):
       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						):
       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						):
       if radius < 0:
              raise ValueError("surface_area_sphere() only accepts non-negative values"						)
       return 4 * pi * radius**2
def 							_UpperCAmelCase				(UpperCamelCase__     :  float						):
       if radius < 0:
              raise ValueError("surface_area_hemisphere() only accepts non-negative values"						)
       return 3 * pi * radius**2
def 							_UpperCAmelCase				(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 							_UpperCAmelCase				(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"						)
       _A      :       Optional[int]				    = (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						):
       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						):
       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						):
       if length < 0 or width < 0:
              raise ValueError("area_rectangle() only accepts non-negative values"						)
       return length * width
def 							_UpperCAmelCase				(UpperCamelCase__     :  float						):
       if side_length < 0:
              raise ValueError("area_square() only accepts non-negative values"						)
       return side_length**2
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       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						):
       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      :       int				    = (sidea + sidea + sidea) / 2
       _A      :       Any				    = sqrt(
           semi_perimeter
           * (semi_perimeter - sidea)
           * (semi_perimeter - sidea)
           * (semi_perimeter - sidea)						)
       return area
def 							_UpperCAmelCase				(UpperCamelCase__     :  float				,							UpperCamelCase__     :  float						):
       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						):
       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						):
       if radius < 0:
              raise ValueError("area_circle() only accepts non-negative values"						)
       return pi * radius**2
def 							_UpperCAmelCase				(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 							_UpperCAmelCase				(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 							_UpperCAmelCase				(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) = }")




import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
    from sagemaker import Session, TrainingJobAnalytics
    from sagemaker.huggingface import HuggingFace







@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"     ,    "False")) is not True     ,    reason="Skipping test because should only be run when releasing minor transformers version"     ,    )
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ])
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				str:
            if self.framework == "pytorch":
                   subprocess.run(
                       F"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split() ,						encoding="utf-8" ,						check=__lowerCamelCase ,						)
            assert hasattr(self ,						"env")


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Tuple:
            _A      :       Dict				    = F"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
            # distributed data settings
            _A      :       Optional[Any]				    = {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

            # creates estimator
            return HuggingFace(
                entry_point=self.script ,						source_dir=self.env.test_path ,						role=self.env.role ,						image_uri=self.env.image_uri ,						base_job_name=__lowerCamelCase ,						instance_count=__lowerCamelCase ,						instance_type=self.instance_type ,						debugger_hook_config=__lowerCamelCase ,						hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path} ,						metric_definitions=self.env.metric_definitions ,						distribution=__lowerCamelCase ,						py_version="py36" ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Optional[Any]:
            TrainingJobAnalytics(__lowerCamelCase).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv")


     @parameterized.expand([(2,)])
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Any:
            # create estimator
            _A      :       Union[str, Any]				    = self.create_estimator(__lowerCamelCase)

            # run training
            estimator.fit()

            # result dataframe
            _A      :       Optional[Any]				    = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

            # extract kpis
            _A      :       List[Any]				    = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
            _A      :       Dict				    = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
            # get train time from SageMaker job, this includes starting, preprocessing, stopping
            _A      :       Optional[Any]				    = (
                Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds" ,						9_9_9_9_9_9)
            )

            # assert kpis
            assert train_runtime <= self.results["train_runtime"]
            assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
            assert all(t <= self.results["eval_loss"] for t in eval_loss)

            # dump tests result into json file to share in PR
            with open(F"{estimator.latest_training_job.name}.json" ,						"w") as outfile:
                   json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss} ,						__lowerCamelCase)
