



"""simple docstring"""







import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.g4dn.xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]  )
class 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        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=SCREAMING_SNAKE_CASE_   ,      )
        assert hasattr(self   ,      'env'	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=1	):
        # 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}-single"   ,      instance_count=SCREAMING_SNAKE_CASE_   ,      instance_type=self.instance_type   ,      debugger_hook_config=SCREAMING_SNAKE_CASE_   ,      hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}   ,      metric_definitions=self.env.metric_definitions   ,      py_version='py36'   ,      )



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]	):
        TrainingJobAnalytics(SCREAMING_SNAKE_CASE_	).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv"	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        # create estimator
        lowerCAmelCase_       :    Union[str, Any]								=			self.create_estimator()

        # run training
        estimator.fit()

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

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










"""simple docstring"""







import re

def     UpperCamelCase_   (   lowerCAmelCase__				:    str						)						->       bool:

  """simple docstring"""




  lowerCAmelCase_       :    str								=			re.compile(
      R'^(?:0|94|\+94|0{2}94)' R'7(0|1|2|4|5|6|7|8)' R'(-| |)' R'\d{7}$'						)

  return bool(re.search(lowerCAmelCase__   ,       lowerCAmelCase__						)						)


if __name__ == "__main__":
     lowercase__		:    Optional[int]				   =     """0094702343221"""

     print(is_sri_lankan_phone_number(phone))





