



'''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      __magic_name__						(   unittest.TestCase       ):







							def 							__lowercase      (							self :	List[str]		):
									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 							__lowercase      (							self :	int     ,_UpperCAmelCase :	Optional[int]=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 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	Optional[int]		):
									TrainingJobAnalytics(_UpperCAmelCase		).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""		)







							def 							__lowercase      (							self :	Optional[Any]		):
									# create estimator
									_a					:				List[Any]				=							self.create_estimator()

									# run training
									estimator.fit()

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

									# extract kpis
									_a					:				Optional[int]				=							list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']		)
									_a					:				Dict				=							list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']		)
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									_a					:				Tuple				=							(
									    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}     ,_UpperCAmelCase		)









'''simple docstring'''

import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

__lowerCAmelCase						      =       3



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				int:
		print('Generating primitive root of p'  )
		while True:
				_a					:				List[Any]				=							random.randrange(3						,      lowerCAmelCase_  )
				if pow(lowerCAmelCase_						,      2						,      lowerCAmelCase_  ) == 1:
						continue
				if pow(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  ) == 1:
						continue
				return g



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				tuple[tuple[int, int, int, int], tuple[int, int]]:
		print('Generating prime p...'  )
		_a					:				int				=							rabin_miller.generate_large_prime(lowerCAmelCase_  )  # select large prime number.
		_a					:				List[str]				=							primitive_root(lowerCAmelCase_  )  # one primitive root on modulo p.
		_a					:				Any				=							random.randrange(3						,      lowerCAmelCase_  )  # private_key -> have to be greater than 2 for safety.
		_a					:				List[Any]				=							cryptomath.find_mod_inverse(pow(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )						,      lowerCAmelCase_  )

		_a					:				Tuple				=							(key_size, e_a, e_a, p)
		_a					:				str				=							(key_size, d)

		return public_key, private_key



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				None:
		if os.path.exists(f"""{name}_pubkey.txt"""  ) or os.path.exists(f"""{name}_privkey.txt"""  ):
				print('\nWARNING:'  )
				print(
				    f"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
				    'Use a different name or delete these files and re-run this program.'  )
				sys.exit()

		_a	, _a					:				Dict				=							generate_key(lowerCAmelCase_  )
		print(f"""\nWriting public key to file {name}_pubkey.txt..."""  )
		with open(f"""{name}_pubkey.txt"""						,      'w'  ) as fo:
				fo.write(f"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""  )

		print(f"""Writing private key to file {name}_privkey.txt..."""  )
		with open(f"""{name}_privkey.txt"""						,      'w'  ) as fo:
				fo.write(f"""{private_key[0]},{private_key[1]}"""  )



def 					__lowerCamelCase					(				)      ->				None:
		print('Making key files...'  )
		make_key_files('elgamal'						,      2048  )
		print('Key files generation successful'  )


if __name__ == "__main__":
							main()




