
'''simple docstring'''







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_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]			)
class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self      ):
								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=UpperCamelCase					,						)
								assert hasattr(self					,						"env"      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								# configuration for running training on smdistributed Model Parallel
								_SCREAMING_SNAKE_CASE				= {
								    "enabled": True,
								    "processes_per_host": 8,
								}
								_SCREAMING_SNAKE_CASE				= {
								    "enabled": True,
								    "parameters": {
								        "microbatches": 4,
								        "placement_strategy": "spread",
								        "pipeline": "interleaved",
								        "optimize": "speed",
								        "partitions": 4,
								        "ddp": True,
								    },
								}

								_SCREAMING_SNAKE_CASE				= {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

								_SCREAMING_SNAKE_CASE				= "trainer" if self.script == "run_glue.py" else "smtrainer"
								# 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=F'{self.env.base_job_name}-{instance_count}-smp-{name_extension}'					,						instance_count=UpperCamelCase					,						instance_type=self.instance_type					,						debugger_hook_config=UpperCamelCase					,						hyperparameters={
								        **self.env.hyperparameters,
								        "model_name_or_path": self.model_name_or_path,
								        "max_steps": 500,
								    }					,						metric_definitions=self.env.metric_definitions					,						distribution=UpperCamelCase					,						py_version="py36"					,						)




		def     lowercase       (				self					,						UpperCamelCase      ):
								TrainingJobAnalytics(UpperCamelCase      ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'      )




		@parameterized.expand([(1,)]      )
		def     lowercase       (				self					,						UpperCamelCase      ):
								# create estimator
								_SCREAMING_SNAKE_CASE				= self.create_estimator(UpperCamelCase      )

								# run training
								estimator.fit()

								# result dataframe
								_SCREAMING_SNAKE_CASE				= TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

								# extract kpis
								_SCREAMING_SNAKE_CASE				= list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]      )
								_SCREAMING_SNAKE_CASE				= list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]      )
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								_SCREAMING_SNAKE_CASE				= (
								    Session().describe_training_job(estimator.latest_training_job.name      ).get("TrainingTimeInSeconds"					,						999_999      )
								)

								# 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}					,						UpperCamelCase      )

'''simple docstring'''







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):

						# A local function to see if a dot lands in the circle.
						def is_in_circle(_SCREAMING_SNAKE_CASE		:   float	,			_SCREAMING_SNAKE_CASE		:   float  ) -> bool:
												_SCREAMING_SNAKE_CASE				= sqrt((x**2) + (y**2)  )
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						_SCREAMING_SNAKE_CASE				= mean(
						    int(is_in_circle(uniform(-1.0	,			1.0  )	,			uniform(-1.0	,			1.0  )  )  )
						    for _ in range(_SCREAMING_SNAKE_CASE  )  )
						# The ratio of the area for circle to square is pi/4.
						_SCREAMING_SNAKE_CASE				= proportion * 4
						print(F'The estimated value of pi is {pi_estimate}'  )
						print(F'The numpy value of pi is {pi}'  )
						print(F'The total error is {abs(pi - pi_estimate  )}'  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Callable[[float], float]	,			_SCREAMING_SNAKE_CASE		:   float = 0.0	,			_SCREAMING_SNAKE_CASE		:   float = 1.0	,			):
						return mean(
						    function_to_integrate(uniform(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )  ) for _ in range(_SCREAMING_SNAKE_CASE  )  ) * (max_value - min_value)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   float = 0.0	,			_SCREAMING_SNAKE_CASE		:   float = 1.0  ):

						def identity_function(_SCREAMING_SNAKE_CASE		:   float  ) -> float:
												return x

						_SCREAMING_SNAKE_CASE				= area_under_curve_estimator(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= (max_value * max_value - min_value * min_value) / 2

						print("******************"  )
						print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'  )
						print(F'Estimated value is {estimated_value}'  )
						print(F'Expected value is {expected_value}'  )
						print(F'Total error is {abs(estimated_value - expected_value  )}'  )
						print("******************"  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):

						def function_to_integrate(_SCREAMING_SNAKE_CASE		:   float  ) -> float:
												return sqrt(4.0 - x * x  )

						_SCREAMING_SNAKE_CASE				= area_under_curve_estimator(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			0.0	,			2.0  )

						print("******************"  )
						print("Estimating pi using area_under_curve_estimator"  )
						print(F'Estimated value is {estimated_value}'  )
						print(F'Expected value is {pi}'  )
						print(F'Total error is {abs(estimated_value - pi  )}'  )
						print("******************"  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()