






"""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''': 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  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self ):
          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     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	F'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
          # distributed data settings
          UpperCAmelCase          =	{"""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     _UpperCamelCase							(			self     ,A ):
          TrainingJobAnalytics(A ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv''' )






    @parameterized.expand([(2,)] )
    def     _UpperCamelCase							(			self     ,A ):
          # create estimator
          UpperCAmelCase          =	self.create_estimator(A )

          # run training
          estimator.fit()

          # result dataframe
          UpperCAmelCase          =	TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()

          # extract kpis
          UpperCAmelCase          =	list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
          UpperCAmelCase          =	list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
          # get train time from SageMaker job, this includes starting, preprocessing, stopping
          UpperCAmelCase          =	(
              Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds"""     ,999_999 )
          )

          # 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 _a			(		_snake_case				,	_snake_case = None				,	_snake_case = None							):







      """simple docstring"""






      if start is None:
            UpperCAmelCase          =	0

      if end is None:
            UpperCAmelCase          =	len(_snake_case							) - 1

      if start >= end:
            return

      UpperCAmelCase          =	(start + end) // 2

      slowsort(_snake_case				,	_snake_case				,	_snake_case							)
      slowsort(_snake_case				,	mid + 1				,	_snake_case							)

      if sequence[end] < sequence[mid]:
            UpperCAmelCase      , UpperCAmelCase          =	sequence[mid], sequence[end]

      slowsort(_snake_case				,	_snake_case				,	end - 1							)


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

  testmod()



