
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  __magic_name__				(					self     :							Any     ):

      """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  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							List[str]=1     ):

      """simple docstring"""
      # 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=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  __magic_name__				(					self     :							Optional[int]			,			lowerCAmelCase_     :							List[Any]     ):

      """simple docstring"""
      TrainingJobAnalytics(lowerCAmelCase_     ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      # create estimator
      _A:  Tuple					=       self.create_estimator()

      # run training
      estimator.fit()

      # result dataframe
      _A:  int					=       TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

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





import random

from .binary_exp_mod import bin_exp_mod






def   lowerCamelCase__							(					a		,		a=10_00      )      ->   Optional[int]:
   if n < 2:
      return False

   if n % 2 == 0:
      return n == 2

   # this means n is odd
   _A:  List[Any]					=       n - 1
   _A:  Dict					=       0
   while d % 2 == 0:
      d /= 2
      exp += 1

   # n - 1=d*(2**exp)
   _A:  List[str]					=       0
   while count < prec:
      _A:  Optional[int]					=       random.randint(2		,		n - 1      )
      _A:  Union[str, Any]					=       bin_exp_mod(a		,		a		,		a      )
      if b != 1:
         _A:  Optional[Any]					=       True
         for _ in range(a      ):
            if b == n - 1:
               _A:  int					=       False
               break
            _A:  Optional[Any]					=       b * b
            b %= n
         if flag:
            return False
         count += 1
   return True


if __name__ == "__main__":
   UpperCAmelCase__		:     Dict     			=      abs(int(input('Enter bound : ').strip()))
   print('Here\'s the list of primes:')
   print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))



