





"""simple docstring"""



import os
import sys

from . import rsa_key_generator as rkg

a_				=    128
a_				=    256







def 	__lowercase  (     snake_case_						:						str       ,snake_case_						:						int = DEFAULT_BLOCK_SIZE		)     ->list[int]:



				'''simple docstring'''







				__A					:		List[str]						=  message.encode('''ascii'''		)

				__A					:		Dict						=  []
				for block_start in range(0       ,len(snake_case_		)       ,snake_case_		):
								__A					:		Tuple						=  0
								for i in range(snake_case_       ,min(block_start + block_size       ,len(snake_case_		)		)		):
												block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
								block_ints.append(snake_case_		)
				return block_ints







def 	__lowercase  (     snake_case_						:						list[int]       ,snake_case_						:						int       ,snake_case_						:						int = DEFAULT_BLOCK_SIZE		)     ->str:



				'''simple docstring'''







				__A					:		list[str]						=  []
				for block_int in block_ints:
								__A					:		list[str]						=  []
								for i in range(block_size - 1       ,-1       ,-1		):
												if len(snake_case_		) + i < message_length:
																__A					:		List[Any]						=  block_int // (BYTE_SIZE**i)
																__A					:		Optional[int]						=  block_int % (BYTE_SIZE**i)
																block_message.insert(0       ,chr(snake_case_		)		)
								message.extend(snake_case_		)
				return "".join(snake_case_		)







def 	__lowercase  (     snake_case_						:						str       ,snake_case_						:						tuple[int, int]       ,snake_case_						:						int = DEFAULT_BLOCK_SIZE		)     ->list[int]:



				'''simple docstring'''







				__A					:		List[Any]						=  []
				__A					:		Optional[Any]						=  key

				for block in get_blocks_from_text(snake_case_       ,snake_case_		):
								encrypted_blocks.append(pow(snake_case_       ,snake_case_       ,snake_case_		)		)
				return encrypted_blocks







def 	__lowercase  (     snake_case_						:						list[int]       ,snake_case_						:						int       ,snake_case_						:						tuple[int, int]       ,snake_case_						:						int = DEFAULT_BLOCK_SIZE       ,)     ->str:



				'''simple docstring'''







				__A					:		Any						=  []
				__A					:		Any						=  key
				for block in encrypted_blocks:
								decrypted_blocks.append(pow(snake_case_       ,snake_case_       ,snake_case_		)		)
				return get_text_from_blocks(snake_case_       ,snake_case_       ,snake_case_		)







def 	__lowercase  (     snake_case_						:						str		)     ->tuple[int, int, int]:



				'''simple docstring'''







				with open(snake_case_		) as fo:
								__A					:		Dict						=  fo.read()
				__A					:		str						=  content.split(''','''		)
				return (int(snake_case_		), int(snake_case_		), int(snake_case_		))







def 	__lowercase  (     snake_case_						:						str       ,snake_case_						:						str       ,snake_case_						:						str       ,snake_case_						:						int = DEFAULT_BLOCK_SIZE       ,)     ->str:



				'''simple docstring'''







				__A					:		str						=  read_key_file(snake_case_		)
				if key_size < block_size * 8:
								sys.exit(
								    '''ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '''
								    '''requires the block size to be equal to or greater than the key size. '''
								    '''Either decrease the block size or use different keys.'''.format(
								        block_size * 8       ,snake_case_		)		)

				__A					:		Tuple						=  [str(snake_case_		) for i in encrypt_message(snake_case_       ,(n, e)       ,snake_case_		)]

				__A					:		Tuple						=  ''','''.join(snake_case_		)
				__A					:		int						=  F"""{len(snake_case_		)}_{block_size}_{encrypted_content}"""
				with open(snake_case_       ,'''w'''		) as fo:
								fo.write(snake_case_		)
				return encrypted_content







def 	__lowercase  (     snake_case_						:						str       ,snake_case_						:						str		)     ->str:



				'''simple docstring'''







				__A					:		Tuple						=  read_key_file(snake_case_		)
				with open(snake_case_		) as fo:
								__A					:		List[str]						=  fo.read()
				__A					:		int						=  content.split('''_'''		)
				__A					:		Optional[Any]						=  int(snake_case_		)
				__A					:		Any						=  int(snake_case_		)

				if key_size < block_size * 8:
								sys.exit(
								    '''ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '''
								    '''requires the block size to be equal to or greater than the key size. '''
								    '''Did you specify the correct key file and encrypted file?'''.format(
								        block_size * 8       ,snake_case_		)		)

				__A					:		List[Any]						=  []
				for block in encrypted_message.split(''','''		):
								encrypted_blocks.append(int(snake_case_		)		)

				return decrypt_message(snake_case_       ,snake_case_       ,(n, d)       ,snake_case_		)







def 	__lowercase  (     )     ->None:



				'''simple docstring'''







				__A					:		str						=  '''encrypted_file.txt'''
				__A					:		List[Any]						=  input(r'''Encrypt\Decrypt [e\d]: '''		)

				if response.lower().startswith('''e'''		):
								__A					:		Union[str, Any]						=  '''encrypt'''
				elif response.lower().startswith('''d'''		):
								__A					:		List[Any]						=  '''decrypt'''

				if mode == "encrypt":
								if not os.path.exists('''rsa_pubkey.txt'''		):
												rkg.make_key_files('''rsa'''       ,1024		)

								__A					:		Optional[Any]						=  input('''\nEnter message: '''		)
								__A					:		Tuple						=  '''rsa_pubkey.txt'''
								print(F"""Encrypting and writing to {filename}..."""		)
								__A					:		Union[str, Any]						=  encrypt_and_write_to_file(snake_case_       ,snake_case_       ,snake_case_		)

								print('''\nEncrypted text:'''		)
								print(snake_case_		)

				elif mode == "decrypt":
								__A					:		Any						=  '''rsa_privkey.txt'''
								print(F"""Reading from {filename} and decrypting..."""		)
								__A					:		int						=  read_from_file_and_decrypt(snake_case_       ,snake_case_		)
								print('''writing decryption to rsa_decryption.txt...'''		)
								with open('''rsa_decryption.txt'''       ,'''w'''		) as dec:
												dec.write(snake_case_		)

								print('''\nDecryption:'''		)
								print(snake_case_		)


if __name__ == "__main__":
							main()











"""simple docstring"""



import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin







def 	__lowercase  (     snake_case_						:						Dict=2       ,snake_case_						:						str=True       ,snake_case_						:						Tuple=False		)     ->Any:



    '''simple docstring'''







    __A					:		List[str]						=  Accelerator(step_scheduler_with_optimizer=snake_case_       ,split_batches=snake_case_		)
    __A					:		Dict						=  torch.nn.Linear(2       ,4		)
    __A					:		Tuple						=  torch.optim.AdamW(model.parameters()       ,lr=1.0		)
    __A					:		int						=  torch.optim.lr_scheduler.OneCycleLR(snake_case_       ,max_lr=0.01       ,steps_per_epoch=2       ,epochs=1		)
    __A				, __A				, __A					:		Optional[int]						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

    # Optimizer has stepped
    scheduler.step()
    if step_scheduler_with_optimizer or (num_processes == 1):
        assert (
            scheduler.scheduler.last_epoch == num_processes
        ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
    else:
        assert (
            scheduler.scheduler.last_epoch != num_processes
        ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""







def 	__lowercase  (     snake_case_						:						str=2       ,snake_case_						:						Dict=True       ,snake_case_						:						Tuple=False		)     ->List[Any]:



    '''simple docstring'''







    __A					:		int						=  Accelerator(step_scheduler_with_optimizer=snake_case_       ,split_batches=snake_case_		)
    __A					:		Any						=  torch.nn.Linear(2       ,4		)
    __A					:		Optional[int]						=  torch.optim.AdamW(model.parameters()       ,lr=1.0		)
    __A					:		Tuple						=  torch.optim.lr_scheduler.LambdaLR(snake_case_       ,lr_lambda=lambda snake_case_		: 1 - n / 10		)
    __A				, __A				, __A					:		Tuple						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

    # Optimizer has stepped
    __A					:		List[Any]						=  False
    scheduler.step()
    __A					:		List[Any]						=  1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

    # Optimizer has not stepped
    __A					:		List[str]						=  True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        __A					:		List[Any]						=  1 - 2 / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""







def 	__lowercase  (     snake_case_						:						int = 2		)     ->str:



    '''simple docstring'''







    from transformers import get_linear_schedule_with_warmup

    __A					:		Any						=  [1, 2, 4]
    for num_steps in steps:
        __A					:		Tuple						=  GradientAccumulationPlugin(num_steps=snake_case_       ,adjust_scheduler=num_steps > 1		)
        __A					:		Optional[Any]						=  Accelerator(gradient_accumulation_plugin=snake_case_		)
        __A					:		Optional[int]						=  torch.nn.Linear(2       ,4		)
        __A					:		Any						=  torch.optim.AdamW(model.parameters()       ,lr=10.0		)
        __A					:		Dict						=  get_linear_schedule_with_warmup(optimizer=snake_case_       ,num_warmup_steps=0       ,num_training_steps=20		)

        __A				, __A				, __A					:		Union[str, Any]						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

        for i in range(10 * num_steps		):
            with accelerator.accumulate(snake_case_		):
                optimizer.step()
                scheduler.step()

            if i == (10 * num_steps - 2):
                assert (
                    scheduler.get_last_lr()[0] != 0
                ), F"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
        assert (
            scheduler.get_last_lr()[0] == 0
        ), F"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
        GradientState._reset_state()






@require_cpu
class 	__snake_case      (	unittest.TestCase  ):





 """simple docstring"""


 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)      ,		num_processes=1						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)      ,		num_processes=1						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		step_scheduler_with_optimizer=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		step_scheduler_with_optimizer=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		step_scheduler_with_optimizer=__lowerCamelCase						)						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		step_scheduler_with_optimizer=__lowerCamelCase						)						)







 @require_huggingface_suite
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)						)
     debug_launcher(__lowerCamelCase						)




