


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": 6_5_0, "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": 6_0_0, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]	)
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 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=_UpperCAmelCase		,				)
       assert hasattr(self		,				'''env''' )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase=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=_UpperCAmelCase		,				instance_type=self.instance_type		,				debugger_hook_config=_UpperCAmelCase		,				hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}		,				metric_definitions=self.env.metric_definitions		,				py_version='''py36'''		,				)



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       TrainingJobAnalytics(_UpperCAmelCase ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv''' )





 def 						UpperCamelCase__					(     self ):
       # create estimator
       snake_case_	=				self.create_estimator()

       # run training
       estimator.fit()

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

       # extract kpis
       snake_case_	=				list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''] )
       snake_case_	=				list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''] )
       # get train time from SageMaker job, this includes starting, preprocessing, stopping
       snake_case_	=				(
           Session().describe_training_job(estimator.latest_training_job.name ).get('''TrainingTimeInSeconds'''		,				99_99_99 )
       )

       # 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}		,				_UpperCAmelCase )



def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
            snake_case_	=				f'''Input value of [number={number}] must be an integer'''
            raise TypeError(SCREAMING_SNAKE_CASE )

      if number < 1:
            snake_case_	=				f'''Input value of [number={number}] must be > 0'''
            raise ValueError(SCREAMING_SNAKE_CASE )

      snake_case_	=				1

      for i in range(1 , SCREAMING_SNAKE_CASE ):
            current_number *= 4 * i - 2
            current_number //= i + 1

      return current_number


if __name__ == "__main__":
   import doctest

   doctest.testmod()