



'''simple docstring'''

import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
__lowerCAmelCase						      =       logging.getLogger()



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Union[str, Any]:
		_a					:				str				=							'\n'.join(_A  )
		Path(_A  ).open('w'  ).writelines(_A  )


__lowerCAmelCase						      =       'patrickvonplaten/t5-tiny-random'
__lowerCAmelCase						      =       'sshleifer/bart-tiny-random'
__lowerCAmelCase						      =       'sshleifer/tiny-mbart'

__lowerCAmelCase						      =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

class      __magic_name__						(   lowerCAmelCase__       ):







							def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	Any		):
									_a					:				List[Any]				=							Path(self.get_auto_remove_tmp_dir()		) / 'utest_input.source'
									_a					:				str				=							input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()
									_a					:				Union[str, Any]				=							[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
									_dump_articles(_SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE		)

									_a					:				str				=							str(Path(self.get_auto_remove_tmp_dir()		) / 'scores.json'		)
									_a					:				Tuple				=							'translation_en_to_de' if model == T5_TINY else 'summarization'
									_a					:				Optional[int]				=							F"""\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            """.split()

									with patch.object(_SCREAMING_SNAKE_CASE     ,'argv'     ,_SCREAMING_SNAKE_CASE		):
											run_generate()
											assert Path(_SCREAMING_SNAKE_CASE		).exists()
											# os.remove(Path(output_file_name))







							def 							__lowercase      (							self :	Tuple		):
									self.run_eval_tester(_SCREAMING_SNAKE_CASE		)







							@parameterized.expand([BART_TINY, MBART_TINY]		)
							@slow
							def 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	Dict		):
									self.run_eval_tester(_SCREAMING_SNAKE_CASE		)







							@parameterized.expand([T5_TINY, MBART_TINY]		)
							@slow
							def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	str		):
									_a					:				List[str]				=							Path(self.get_auto_remove_tmp_dir()		) / 'utest_input.source'
									_a					:				int				=							input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()

									_a					:				str				=							{
									    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
									    'de': [
									        'Maschinelles Lernen ist großartig, oder?',
									        'Ich esse gerne Bananen',
									        'Morgen ist wieder ein toller Tag!',
									    ],
									}

									_a					:				Dict				=							Path(self.get_auto_remove_tmp_dir()		)
									_a					:				str				=							str(tmp_dir / 'scores.json'		)
									_a					:				List[Any]				=							str(tmp_dir / 'val.target'		)
									_dump_articles(_SCREAMING_SNAKE_CASE     ,text['en']		)
									_dump_articles(_SCREAMING_SNAKE_CASE     ,text['de']		)
									_a					:				Tuple				=							'translation_en_to_de' if model == T5_TINY else 'summarization'
									_a					:				Optional[int]				=							F"""\n            run_eval_search.py\n            {model}\n            {str(_SCREAMING_SNAKE_CASE		)}\n            {str(_SCREAMING_SNAKE_CASE		)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            """.split()
									testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']		)

									with patch.object(_SCREAMING_SNAKE_CASE     ,'argv'     ,_SCREAMING_SNAKE_CASE		):
											with CaptureStdout() as cs:
													run_search()
											_a					:				Optional[Any]				=							[' num_beams | length_penalty', model, 'Best score args']
											_a					:				str				=							['Info']
											if "translation" in task:
													expected_strings.append('bleu'		)
											else:
													expected_strings.extend(_SCREAMING_SNAKE_CASE		)
											for w in expected_strings:
													assert w in cs.out
											for w in un_expected_strings:
													assert w not in cs.out
											assert Path(_SCREAMING_SNAKE_CASE		).exists()
											os.remove(Path(_SCREAMING_SNAKE_CASE		)		)









'''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()




