



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



  def 	lowerCamelCase__       (						self :					List[str]		)     ->					int:


     '''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=lowerCamelCase_       ,		)
     assert hasattr(self       ,		"""env"""		)



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Any=1		)     ->					Optional[int]:


     '''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=lowerCamelCase_       ,		instance_type=self.instance_type       ,		debugger_hook_config=lowerCamelCase_       ,		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 :					str       ,		lowerCAmelCase :					int		)     ->					Optional[Any]:


     '''simple docstring'''




     TrainingJobAnalytics(lowerCamelCase_		).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''		)





  def 	lowerCamelCase__       (						self :					str		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=self.create_estimator()

     # run training
     estimator.fit()

     # result dataframe
     SCREAMING_SNAKE_CASE_: Optional[int]     			=TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

     # extract kpis
     SCREAMING_SNAKE_CASE_: int     			=list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]		)
     SCREAMING_SNAKE_CASE_: Dict     			=list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]		)
     # get train time from SageMaker job, this includes starting, preprocessing, stopping
     SCREAMING_SNAKE_CASE_: Any     			=(
         Session().describe_training_job(estimator.latest_training_job.name		).get("""TrainingTimeInSeconds"""       ,		99_9999		)
     )

     # 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}       ,		lowerCamelCase_		)








"""simple docstring"""
import string





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=""""""
   for i in sequence:
      SCREAMING_SNAKE_CASE_: List[Any]     			=ord(lowercase )
      if 65 <= extract <= 90:
         output += chr(155 - extract )
      elif 97 <= extract <= 122:
         output += chr(219 - extract )
      else:
         output += i
   return output





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=string.ascii_letters
   SCREAMING_SNAKE_CASE_: Tuple     			=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
   return "".join(
       letters_reversed[letters.index(lowercase )] if c in letters else c for c in sequence )





def      __magic_name__				(							):
   from timeit import timeit

   print("""Running performance benchmarks...""" )
   SCREAMING_SNAKE_CASE_: int     			="""from string import printable ; from __main__ import atbash, atbash_slow"""
   print(f'''> atbash_slow(): {timeit("atbash_slow(printable)"							,			setup=lowercase )} seconds''' )
   print(f'''>      atbash(): {timeit("atbash(printable)"							,			setup=lowercase )} seconds''' )


if __name__ == "__main__":
   for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
      print(f"""{example} encrypted in atbash: {atbash(example)}""")
   benchmark()



