






"""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_model_parallelism.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]	)
class 						snake_case  (  unittest.TestCase	):





    def     lowerCamelCase__				(							self     : 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=A    ,		)
      assert hasattr(self    ,		'env'      )





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      str    =       {
          'enabled': True,
          'processes_per_host': 8,
      }
      a					:      Tuple    =       {
          'enabled': True,
          'parameters': {
              'microbatches': 4,
              'placement_strategy': 'spread',
              'pipeline': 'interleaved',
              'optimize': 'speed',
              'partitions': 4,
              'ddp': True,
          },
      }

      a					:      Optional[Any]    =       {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

      a					:      Optional[int]    =       'trainer' if self.script == 'run_glue.py' else 'smtrainer'
      # 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}-{instance_count}-smp-{name_extension}'''    ,		instance_count=A    ,		instance_type=self.instance_type    ,		debugger_hook_config=A    ,		hyperparameters={
              **self.env.hyperparameters,
              'model_name_or_path': self.model_name_or_path,
              'max_steps': 5_0_0,
          }    ,		metric_definitions=self.env.metric_definitions    ,		distribution=A    ,		py_version='py36'    ,		)





    def     lowerCamelCase__				(							self     : Any    ,		A     : List[Any]      ):


      '''simple docstring'''




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





    @parameterized.expand([(1,)]      )
    def     lowerCamelCase__				(							self     : Dict    ,		A     : int      ):


      '''simple docstring'''




      a					:      List[str]    =       self.create_estimator(A      )

      # run training
      estimator.fit()

      # result dataframe
      a					:      Optional[Any]    =       TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

      # extract kpis
      a					:      Union[str, Any]    =       list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
      a					:      List[str]    =       list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']      )
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      a					:      List[str]    =       (
          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}    ,		A      )












"""simple docstring"""


def       snake_case  (A_				:str     ,      A_				:bool = False				):







  '''simple docstring'''

  if not isinstance(A_     ,      A_				):
    a					:      Union[str, Any]    =       f'''Expected string as input, found {type(A_				)}'''
    raise ValueError(A_				)
  if not isinstance(A_     ,      A_				):
    a					:      Optional[int]    =       f'''Expected boolean as use_pascal parameter, found {type(A_				)}'''
    raise ValueError(A_				)

  a					:      Tuple    =       input_str.split('_'				)

  a					:      Dict    =       0 if use_pascal else 1

  a					:      int    =       words[start_index:]

  a					:      int    =       [word[0].upper() + word[1:] for word in words_to_capitalize]

  a					:      List[str]    =       '' if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
   from doctest import testmod

   testmod()




