"""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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 6_50, "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": 6_00, "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": 6_00, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ]       )
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									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     :			Optional[int]					,lowerCamelCase__     :			Dict    ):
									UpperCAmelCase__    =							f'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
									# distributed data settings
									UpperCAmelCase__    =							{'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     :			Any					,lowerCamelCase__     :			Dict    ):
									TrainingJobAnalytics(lowerCamelCase__    ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''    )







					@parameterized.expand([(2,)]    )
					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			Optional[int]    ):
									# create estimator
									UpperCAmelCase__    =							self.create_estimator(lowerCamelCase__    )

									# run training
									estimator.fit()

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

									# extract kpis
									UpperCAmelCase__    =							list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']    )
									UpperCAmelCase__    =							list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']    )
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									UpperCAmelCase__    =							(
									    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}					,lowerCamelCase__    )




"""simple docstring"""






from __future__ import annotations

from math import ceil, floor, sqrt



def 						a_		(      lowerCamelCase = 2_0_0_0_0_0_0 ):
				UpperCAmelCase__    =							[0]
				UpperCAmelCase__    =							42

				for idx in range(1      ,    ceil(sqrt(target * 2 ) * 1.1 ) ):
								triangle_numbers.append(triangle_numbers[-1] + idx )

				# we want this to be as close as possible to target
				UpperCAmelCase__    =							0
				# the area corresponding to the grid that gives the product closest to target
				UpperCAmelCase__    =							0
				# an estimate of b, using the quadratic formula
				UpperCAmelCase__    =							42
				# the largest integer less than b_estimate
				UpperCAmelCase__    =							42
				# the largest integer less than b_estimate
				UpperCAmelCase__    =							42
				# the triangle number corresponding to b_floor
				UpperCAmelCase__    =							42
				# the triangle number corresponding to b_ceil
				UpperCAmelCase__    =							42

				for idx_a, triangle_a in enumerate(triangle_numbers[1:]      ,    1 ):
								UpperCAmelCase__    =							(-1 + sqrt(1 + 8 * target / triangle_a )) / 2
								UpperCAmelCase__    =							floor(lowerCamelCase )
								UpperCAmelCase__    =							ceil(lowerCamelCase )
								UpperCAmelCase__    =							triangle_numbers[b_floor]
								UpperCAmelCase__    =							triangle_numbers[b_ceil]

								if abs(target - triangle_b_first_guess * triangle_a ) < abs(
								    target - best_product ):
												UpperCAmelCase__    =							triangle_b_first_guess * triangle_a
												UpperCAmelCase__    =							idx_a * b_floor

								if abs(target - triangle_b_second_guess * triangle_a ) < abs(
								    target - best_product ):
												UpperCAmelCase__    =							triangle_b_second_guess * triangle_a
												UpperCAmelCase__    =							idx_a * b_ceil

				return area


if __name__ == "__main__":
						print(F"""{solution() = }""")



