




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       a__	( unittest.TestCase				):







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''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       lowerCAmelCase_    (     self  ,		A=1			)  ->   List[str]:



          '''simple docstring'''



          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=A  ,		instance_type=self.instance_type  ,		debugger_hook_config=A  ,		hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}  ,		metric_definitions=self.env.metric_definitions  ,		py_version="py36"  ,		)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   List[Any]:



          '''simple docstring'''



          TrainingJobAnalytics(A			).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				self.create_estimator()

          # run training
          estimator.fit()

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

          # extract kpis
          a     =				list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]			)
          a     =				list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]			)
          # get train time from SageMaker job, this includes starting, preprocessing, stopping
          a     =				(
              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}  ,		A			)







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					int:
     return 1 if digit in (0, 1) else (digit * factorial(digit - 1))

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:
     a     =				0
     a     =				number
     while duplicate > 0:
          a  ,				a     =				divmod(__UpperCamelCase		, 10)
          fact_sum += factorial(__UpperCamelCase)
     return fact_sum == number


if __name__ == "__main__":
   print("Program to check whether a number is a Krisnamurthy Number or not.")
   lowercase__				:      str	   = int(input("Enter number: ").strip())
   print(
       F'{number} is {"" if krishnamurthy(number) else "not "}a Krishnamurthy Number.'
   )

