

"""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 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''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=_A    ,)
      assert hasattr(self    ,'env'      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
      # distributed data settings
      _lowerCAmelCase       :   Union[str, Any]  =    {'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=_A    ,instance_count=_A    ,instance_type=self.instance_type    ,debugger_hook_config=_A    ,hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path}    ,metric_definitions=self.env.metric_definitions    ,distribution=_A    ,py_version='py36'    ,)

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      TrainingJobAnalytics(_A      ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""      )







  @parameterized.expand([(2,)]      )
  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    self.create_estimator(_A      )

      # run training
      estimator.fit()

      # result dataframe
      _lowerCAmelCase       :   List[Any]  =    TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

      # extract kpis
      _lowerCAmelCase       :   List[str]  =    list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
      _lowerCAmelCase       :   Union[str, Any]  =    list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']      )
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      _lowerCAmelCase       :   Tuple  =    (
          Session().describe_training_job(estimator.latest_training_job.name      ).get('TrainingTimeInSeconds'    ,99_9999      )
      )

      # 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}    ,_A      )








"""simple docstring"""






from __future__ import annotations

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    create_state_space_tree(_lowerCamelCase ,   [] ,   0 ,   [0 for i in range(len(_lowerCamelCase   )   )]   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   ):

    '''simple docstring'''



    if index == len(_lowerCamelCase   ):
        print(_lowerCamelCase   )
        return

    for i in range(len(_lowerCamelCase   )   ):
        if not index_used[i]:
            current_sequence.append(sequence[i]   )
            _lowerCAmelCase       :   List[str]  =    True
            create_state_space_tree(_lowerCamelCase ,   _lowerCamelCase ,   index + 1 ,   _lowerCamelCase   )
            current_sequence.pop()
            _lowerCAmelCase       :   int  =    False
_lowerCAmelCase						=		[3, 1, 2, 4]
generate_all_permutations(sequence)

_lowerCAmelCase						=		["A", "B", "C"]

generate_all_permutations(sequence_a)





