






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': 650, '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': 600, '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': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]   )
class     a    ( unittest.TestCase   ):



      """simple docstring"""


      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      List[str]:
        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 	UpperCAmelCase      (      self					:			int		,      __lowercase					:			List[str]					)							->      Union[str, Any]:
        __UpperCAmelCase							:	int           =  f"""{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}"""
        # distributed data settings
        __UpperCAmelCase							:	int           =  {"""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 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			Union[str, Any]					)							->      Union[str, Any]:
        TrainingJobAnalytics(__lowercase					).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""					)




      @parameterized.expand([(2,)]					)
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			List[str]					)							->      int:
        # create estimator
        __UpperCAmelCase							:	List[Any]           =  self.create_estimator(__lowercase					)

        # run training
        estimator.fit()

        # result dataframe
        __UpperCAmelCase							:	Union[str, Any]           =  TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

        # extract kpis
        __UpperCAmelCase							:	str           =  list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]					)
        __UpperCAmelCase							:	Optional[int]           =  list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]					)
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        __UpperCAmelCase							:	List[Any]           =  (
            Session().describe_training_job(estimator.latest_training_job.name					).get("""TrainingTimeInSeconds"""		,      999999					)
        )

        # 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					)










def 				lowerCamelCase__      (    __lowerCamelCase			:     Tuple  ,		__lowerCamelCase			:     Union[str, Any]		):
  __UpperCAmelCase							:	Tuple           =  [1]
  for i in range(2  ,		__lowerCamelCase		):
    factorials.append(factorials[-1] * i		)
  assert 0 <= k < factorials[-1] * n, "k out of bounds"

  __UpperCAmelCase							:	Optional[Any]           =  []
  __UpperCAmelCase							:	str           =  list(range(__lowerCamelCase		)		)

  # Find permutation
  while factorials:
    __UpperCAmelCase							:	Any           =  factorials.pop()
    __UpperCAmelCase      , __UpperCAmelCase							:	Optional[Any]           =  divmod(__lowerCamelCase  ,		__lowerCamelCase		)
    permutation.append(elements[number]		)
    elements.remove(elements[number]		)
  permutation.append(elements[0]		)

  return permutation


if __name__ == "__main__":
       import doctest

       doctest.testmod()


