

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_50, '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_00, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ] )
class  A						(							unittest.TestCase ):







    """simple docstring"""





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





        '''simple docstring'''







        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=lowercase_,)
        assert hasattr(self,'env'		)


    def 						snake_case__     (      self :       int,lowercase_ :       Optional[Any]=1		)->    Optional[int]:





        '''simple docstring'''







        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=lowercase_,instance_type=self.instance_type,debugger_hook_config=lowercase_,hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path},metric_definitions=self.env.metric_definitions,py_version='py36',)


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





        '''simple docstring'''







        TrainingJobAnalytics(lowercase_		).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'		)


    def 						snake_case__     (      self :       Any		)->    Optional[int]:





        '''simple docstring'''







        A__          =   self.create_estimator()

        # run training
        estimator.fit()

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

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




class  A						:







    """simple docstring"""





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





        '''simple docstring'''







        A__          =   name
        A__          =   value
        A__          =   weight


    def __repr__(      self :       int		)->    Tuple:





        '''simple docstring'''







        return F'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'


    def 						snake_case__     (      self :       Any		)->    str:





        '''simple docstring'''







        return self.value


    def 						snake_case__     (      self :       Any		)->    Tuple:





        '''simple docstring'''







        return self.name


    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        return self.weight


    def 						snake_case__     (      self :       Union[str, Any]		)->    Optional[Any]:





        '''simple docstring'''







        return self.value / self.weight




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      List[Any]:





    '''simple docstring'''






    A__          =   []
    for i in range(len(SCREAMING_SNAKE_CASE__      )      ):
        menu.append(Things(name[i]    ,			value[i]    ,			weight[i]      )      )
    return menu
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	int      )							->      Any:





    '''simple docstring'''






    A__          =   sorted(SCREAMING_SNAKE_CASE__    ,			key=SCREAMING_SNAKE_CASE__    ,			reverse=SCREAMING_SNAKE_CASE__      )
    A__          =   []
    A__      ,						A__          =   0.0, 0.0
    for i in range(len(SCREAMING_SNAKE_CASE__      )      ):
        if (total_cost + items_copy[i].get_weight()) <= max_cost:
            result.append(items_copy[i]      )
            total_cost += items_copy[i].get_weight()
            total_value += items_copy[i].get_value()
    return (result, total_value)
def 						_snake_case(    )							->      Any:





    '''simple docstring'''








if __name__ == "__main__":
     import doctest

     doctest.testmod()

