





import random
import sys

from . import cryptomath_module as cryptomath

_SCREAMING_SNAKE_CASE   						=		(
    R""" !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`"""
    R"""abcdefghijklmnopqrstuvwxyz{|}~"""
)


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ):
	if mode == "encrypt":
		if key_a == 1:
			sys.exit(
			    'The affine cipher becomes weak when key '
			    'A is set to 1. Choose different key' )
		if key_b == 0:
			sys.exit(
			    'The affine cipher becomes weak when key '
			    'B is set to 0. Choose different key' )
	if key_a < 0 or key_b < 0 or key_b > len(__a ) - 1:
		sys.exit(
		    'Key A must be greater than 0 and key B must '
		    f"""be between 0 and {len(__a ) - 1}.""" )
	if cryptomath.gcd(__a ,			len(__a ) ) != 1:
		sys.exit(
		    f"""Key A {key_a} and the symbol set size {len(__a )} """
		    'are not relatively prime. Choose a different key.' )


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_    ,snake_case_		:      Dict           =						divmod(__a ,			len(__a ) )
	check_keys(__a ,			__a ,			'encrypt' )
	snake_case_		:      Any           =						''
	for symbol in message:
		if symbol in SYMBOLS:
			snake_case_		:      Optional[Any]           =						SYMBOLS.find(__a )
			cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(__a )]
		else:
			cipher_text += symbol
	return cipher_text


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_    ,snake_case_		:      Any           =						divmod(__a ,			len(__a ) )
	check_keys(__a ,			__a ,			'decrypt' )
	snake_case_		:      List[Any]           =						''
	snake_case_		:      Optional[int]           =						cryptomath.find_mod_inverse(__a ,			len(__a ) )
	for symbol in message:
		if symbol in SYMBOLS:
			snake_case_		:      List[str]           =						SYMBOLS.find(__a )
			plain_text += SYMBOLS[
			    (sym_index - key_b) * mod_inverse_of_key_a % len(__a )
			]
		else:
			plain_text += symbol
	return plain_text


def 				SCREAMING_SNAKE_CASE__    (     ):
	while True:
		snake_case_		:      str           =						random.randint(2 ,			len(__a ) )
		snake_case_		:      int           =						random.randint(2 ,			len(__a ) )
		if cryptomath.gcd(__a ,			len(__a ) ) == 1 and key_b % len(__a ) != 0:
			return key_b * len(__a ) + key_b


def 				SCREAMING_SNAKE_CASE__    (     ):
	snake_case_		:      Tuple           =						input('Enter message: ' ).strip()
	snake_case_		:      Optional[Any]           =						int(input('Enter key [2000 - 9000]: ' ).strip() )
	snake_case_		:      int           =						input('Encrypt/Decrypt [E/D]: ' ).strip().lower()

	if mode.startswith('e' ):
		snake_case_		:      Any           =						'encrypt'
		snake_case_		:      Union[str, Any]           =						encrypt_message(__a ,			__a )
	elif mode.startswith('d' ):
		snake_case_		:      Optional[int]           =						'decrypt'
		snake_case_		:      List[Any]           =						decrypt_message(__a ,			__a )
	print(f"""\n{mode.title()}ed text: \n{translated}""" )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# main()








import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch
class SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						@patch('sys.argv'		, ['fakeprogrampath', 'env']						)
						def UpperCAmelCase_					(   self   :   Tuple						)				->     Dict:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							with CaptureStd() as cs:
								transformers.commands.transformers_cli.main()
							self.assertIn('Python version'		, cs.out						)
							self.assertIn('Platform'		, cs.out						)
							self.assertIn('Using distributed or parallel set-up in script?'		, cs.out						)






						@is_pt_tf_cross_test
						@patch(
						    'sys.argv'		, ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']						)
						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     Optional[Any]:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'		, ignore_errors=_A						)  # cleans potential past runs
							transformers.commands.transformers_cli.main()

							# The original repo has no TF weights -- if they exist, they were created by the CLI
							self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'						)						)






						@require_torch
						@patch('sys.argv'		, ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']						)
						def UpperCAmelCase_					(   self   :   List[Any]						)				->     str:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							# # remove any previously downloaded model to start clean
							shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'		, ignore_errors=_A						)

							# run the command
							transformers.commands.transformers_cli.main()

							# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'						)						)
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'						)						)
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'						)						)






						@require_torch
						@patch(
						    'sys.argv'		, [
						        'fakeprogrampath',
						        'download',
						        'hf-internal-testing/test_dynamic_model_with_tokenizer',
						        '--trust-remote-code',
						        '--cache-dir',
						        '/tmp',
						    ]		, )
						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     str:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							# # remove any previously downloaded model to start clean
							shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'		, ignore_errors=_A						)

							# run the command
							transformers.commands.transformers_cli.main()

							# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'						)						)
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'						)						)
							self.assertTrue(
							    os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'						)						)

