





"""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 lowerCamelCase__	(     unittest.TestCase					):



   def  _UpperCAmelCase						(							self						)			->     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=__lowerCamelCase   , )
      assert hasattr(self   , """env"""						)



   def  _UpperCAmelCase						(							self   , snake_case						)			->     int:







      """simple docstring"""
      # configuration for running training on smdistributed Model Parallel
      lowercase     :      Optional[int]	=							{
          """enabled""": True,
          """processes_per_host""": 8,
      }
      lowercase     :      Optional[int]	=							{
          """enabled""": True,
          """parameters""": {
              """microbatches""": 4,
              """placement_strategy""": """spread""",
              """pipeline""": """interleaved""",
              """optimize""": """speed""",
              """partitions""": 4,
              """ddp""": True,
          },
      }

      lowercase     :      Optional[Any]	=							{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

      lowercase     :      Tuple	=							"""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=__lowerCamelCase   , instance_type=self.instance_type   , debugger_hook_config=__lowerCamelCase   , 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=__lowerCamelCase   , py_version="""py36"""   , )



   def  _UpperCAmelCase						(							self   , snake_case						)			->     Tuple:







      """simple docstring"""
      TrainingJobAnalytics(__lowerCamelCase						).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''						)





   @parameterized.expand([(1,)]						)
   def  _UpperCAmelCase						(							self   , snake_case						)			->     Any:







      """simple docstring"""
      # create estimator
      lowercase     :      Optional[Any]	=							self.create_estimator(__lowerCamelCase						)

      # run training
      estimator.fit()

      # result dataframe
      lowercase     :      Any	=							TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

      # extract kpis
      lowercase     :      str	=							list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]						)
      lowercase     :      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
      lowercase     :      Optional[Any]	=							(
          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}   , __lowerCamelCase						)





import os





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   os.path.join(os.path.dirname(A__       )			,  "num.txt"       )
    with open(A__       ) as file_hand:
        return str(sum(int(A__       ) for line in file_hand       )       )[:10]


if __name__ == "__main__":
    print(solution())