


'''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_5_0, '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_0_0, '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_0_0, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ] )
class      lowerCamelCase_  (      unittest.TestCase ):






      def lowercase_				(   self							:       List[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=_A		,		)
          assert hasattr(self		,		'''env'''					)






      def lowercase_				(   self							:       List[Any]		,		_A							:       Optional[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[Any]						=							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=_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 lowercase_				(   self							:       Optional[int]		,		_A							:       Any					):


          '''simple docstring'''


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






      @parameterized.expand([(2,)]					)
      def lowercase_				(   self							:       Optional[int]		,		_A							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[Any]						=							self.create_estimator(_A					)

          # run training
          estimator.fit()

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

          # extract kpis
          UpperCAmelCase__     :						Dict						=							list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']					)
          UpperCAmelCase__     :						Optional[Any]						=							list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']					)
          # get train time from SageMaker job, this includes starting, preprocessing, stopping
          UpperCAmelCase__     :						Any						=							(
              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"""


def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		int				   =			0

						# if input_string is "aba" than new_input_string become "a|b|a"
						UpperCamelCase				:		str				   =			""""""
						UpperCamelCase				:		Dict				   =			""""""

						# append each character + "|" in new_string for range(0, length-1)
						for i in input_string[: len(SCREAMING_SNAKE_CASE     ) - 1]:
												new_input_string += i + "|"
						# append last character
						new_input_string += input_string[-1]

						# we will store the starting and ending of previous furthest ending palindromic
						# substring
						UpperCamelCase   ,						UpperCamelCase				:		int				   =			0, 0

						# length[i] shows the length of palindromic substring with center i
						UpperCamelCase				:		List[Any]				   =			[1 for i in range(len(SCREAMING_SNAKE_CASE     )     )]

						# for each character in new_string find corresponding palindromic string
						UpperCamelCase				:		Optional[Any]				   =			0
						for j in range(len(SCREAMING_SNAKE_CASE     )     ):
												UpperCamelCase				:		Union[str, Any]				   =			1 if j > r else min(length[l + r - j] // 2							,     r - j + 1     )
												while (
												    j - k >= 0
												    and j + k < len(SCREAMING_SNAKE_CASE     )
												    and new_input_string[k + j] == new_input_string[j - k]
												):
																		k += 1

												UpperCamelCase				:		int				   =			2 * k - 1

												# does this string is ending after the previously explored end (that is r) ?
												# if yes the update the new r to the last index of this
												if j + k - 1 > r:
																		UpperCamelCase				:		int				   =			j - k + 1  # noqa: E741
																		UpperCamelCase				:		List[Any]				   =			j + k - 1

												# update max_length and start position
												if max_length < length[j]:
																		UpperCamelCase				:		List[str]				   =			length[j]
																		UpperCamelCase				:		List[str]				   =			j

    # create that string
						UpperCamelCase				:		Optional[Any]				   =			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
						for i in s:
												if i != "|":
																		output_string += i

						return output_string


if __name__ == "__main__":
		import doctest

		doctest.testmod()



