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

import pytest
from parameterized import 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.g4dn.xlarge",
            "results": {"train_runtime": 6_5_0, "eval_accuracy": 0.6, "eval_loss": 0.9},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 6_0_0, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]      )
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Union[str, Any]:
									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=_snake_case   , )
									assert hasattr(self   , "env"		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				List[str]=1		)							->       Dict:
									# 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}-single"""   , instance_count=_snake_case   , instance_type=self.instance_type   , debugger_hook_config=_snake_case   , hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}   , metric_definitions=self.env.metric_definitions   , py_version="py36"   , )



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				int		)							->       Union[str, Any]:
									TrainingJobAnalytics(_snake_case		).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Any:
									# create estimator
									SCREAMING_SNAKE_CASE__		  =				self.create_estimator()

									# 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"   , 999999		)
									)

									# 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}   , _snake_case		)





"""simple docstring"""






import sys




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]

				for chain_length in range(2  ,					__UpperCAmelCase       ):
								for a in range(1  ,					n - chain_length + 1       ):
												SCREAMING_SNAKE_CASE__		  =				a + chain_length - 1

												SCREAMING_SNAKE_CASE__		  =				sys.maxsize
												for c in range(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				(
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				SCREAMING_SNAKE_CASE__		  =				cost
																				SCREAMING_SNAKE_CASE__		  =				c
				return matrix, sol




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				if i == j:
								print("A" + str(__UpperCAmelCase       )  ,					end=" "       )
				else:
								print("("  ,					end=" "       )
								print_optiomal_solution(__UpperCAmelCase  ,					__UpperCAmelCase  ,					optimal_solution[i][j]       )
								print_optiomal_solution(__UpperCAmelCase  ,					optimal_solution[i][j] + 1  ,					__UpperCAmelCase       )
								print(")"  ,					end=" "       )




def 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				[30, 35, 15, 5, 10, 20, 25]
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				matrix_chain_order(__UpperCAmelCase       )

				print("No. of Operation required: " + str(matrix[1][n - 1]       )       )
				print_optiomal_solution(__UpperCAmelCase  ,					1  ,					n - 1       )


if __name__ == "__main__":
						main()




