

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  A						(							unittest.TestCase ):







    """simple docstring"""





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





        '''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 :       Tuple,lowercase_ :       Optional[int]		)->    Dict:





        '''simple docstring'''







        A__          =   F'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
        # distributed data settings
        A__          =   {'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=lowercase_,instance_count=lowercase_,instance_type=self.instance_type,debugger_hook_config=lowercase_,hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path},metric_definitions=self.env.metric_definitions,distribution=lowercase_,py_version='py36',)


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





        '''simple docstring'''







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


    @parameterized.expand([(2,)]		)
    def 						snake_case__     (      self :       Dict,lowercase_ :       Optional[Any]		)->    int:





        '''simple docstring'''







        A__          =   self.create_estimator(lowercase_		)

        # 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_		)









"""simple docstring"""


from __future__ import annotations


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     int,       _lowerCamelCase :     List[Any]=None  ):

     '''simple docstring'''
     __A									= data
     __A									= None






   def __repr__(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= []
     __A									= self
     while temp:
       string_rep.append(f'{temp.data}'  )
       __A									= temp.next
     return "->".join(_lowerCamelCase  )





def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if not elements_list:
    raise Exception('''The Elements List is empty''' )

  __A									= __A									= Node(elements_list[0] )
  for i in range(1		,       len(__UpperCamelCase ) ):
    __A									= Node(elements_list[i] )
    __A									= current.next
  return head



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if head_node is not None and isinstance(__UpperCamelCase		,       __UpperCamelCase ):
    print_reverse(head_node.next )
    print(head_node.data )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  from doctest import testmod

  testmod()

  __A									= make_linked_list([1_4, 5_2, 1_4, 1_2, 4_3] )
  print('''Linked List:''' )
  print(__UpperCamelCase )
  print('''Elements in Reverse:''' )
  print_reverse(__UpperCamelCase )


if __name__ == "__main__":
       main()




