




import os
import sys

from . import rsa_key_generator as rkg

lowercase__      :			Dict           =					128
lowercase__      :			List[str]           =					256

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase = DEFAULT_BLOCK_SIZE)			->     list[int]:
     a      =      message.encode("ascii")

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

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase = DEFAULT_BLOCK_SIZE)			->     str:
     a      =      []
     for block_int in block_ints:
          a      =      []
          for i in range(block_size - 1		,					-1		,					-1):
               if len(__UpperCamelCase) + i < message_length:
                    a      =      block_int // (BYTE_SIZE**i)
                    a      =      block_int % (BYTE_SIZE**i)
                    block_message.insert(0		,					chr(__UpperCamelCase))
          message.extend(__UpperCamelCase)
     return "".join(__UpperCamelCase)

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase = DEFAULT_BLOCK_SIZE)			->     list[int]:
     a      =      []
     a  ,      a      =      key

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

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase = DEFAULT_BLOCK_SIZE		,					)			->     str:
     a      =      []
     a  ,      a      =      key
     for block in encrypted_blocks:
          decrypted_blocks.append(pow(__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase))
     return get_text_from_blocks(__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase)

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     tuple[int, int, int]:
     with open(__UpperCamelCase) as fo:
          a      =      fo.read()
     a  ,      a  ,      a      =      content.split(",")
     return (int(__UpperCamelCase), int(__UpperCamelCase), int(__UpperCamelCase))

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase = DEFAULT_BLOCK_SIZE		,					)			->     str:
     a  ,      a  ,      a      =      read_key_file(__UpperCamelCase)
     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		,					__UpperCamelCase))

     a      =      [str(__UpperCamelCase) for i in encrypt_message(__UpperCamelCase		,					(n, e)		,					__UpperCamelCase)]

     a      =      ",".join(__UpperCamelCase)
     a      =      f'''{len(__UpperCamelCase)}_{block_size}_{encrypted_content}'''
     with open(__UpperCamelCase		,					"w") as fo:
          fo.write(__UpperCamelCase)
     return encrypted_content

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     str:
     a  ,      a  ,      a      =      read_key_file(__UpperCamelCase)
     with open(__UpperCamelCase) as fo:
          a      =      fo.read()
     a  ,      a  ,      a      =      content.split("_")
     a      =      int(__UpperCamelCase)
     a      =      int(__UpperCamelCase)

     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		,					__UpperCamelCase))

     a      =      []
     for block in encrypted_message.split(","):
          encrypted_blocks.append(int(__UpperCamelCase))

     return decrypt_message(__UpperCamelCase		,					__UpperCamelCase		,					(n, d)		,					__UpperCamelCase)

def 					SCREAMING_SNAKE_CASE							(   )			->     None:
     a      =      "encrypted_file.txt"
     a      =      input(r"Encrypt\Decrypt [e\d]: ")

     if response.lower().startswith("e"):
          a      =      "encrypt"
     elif response.lower().startswith("d"):
          a      =      "decrypt"

     if mode == "encrypt":
          if not os.path.exists("rsa_pubkey.txt"):
               rkg.make_key_files("rsa"		,					10_24)

          a      =      input("\nEnter message: ")
          a      =      "rsa_pubkey.txt"
          print(f'''Encrypting and writing to {filename}...''')
          a      =      encrypt_and_write_to_file(__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase)

          print("\nEncrypted text:")
          print(__UpperCamelCase)

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

          print("\nDecryption:")
          print(__UpperCamelCase)


if __name__ == "__main__":
   main()







import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     int:
     a      =      filter(lambda __UpperCamelCase: p.requires_grad		,					model.parameters())
     a      =      sum([np.prod(p.size()) for p in model_parameters])
     return params


lowercase__      :			Optional[int]           =					logging.getLogger(__name__)




class 			a__       (					pl.Callback      ):







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    str:



          '''simple docstring'''



          a      =      {F'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
          pl_module.logger.log_metrics(A )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A  ,		A=True )  ->    None:



          '''simple docstring'''



          logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
          a      =      trainer.callback_metrics
          trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
          # Log results
          a      =      Path(pl_module.hparams.output_dir )
          if type_path == "test":
               a      =      od / "test_results.txt"
               a      =      od / "test_generations.txt"
          else:
               # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
               # If people want this it will be easy enough to add back.
               a      =      od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
               a      =      od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
               results_file.parent.mkdir(exist_ok=A )
               generations_file.parent.mkdir(exist_ok=A )
          with open(A  ,		"a+" ) as writer:
               for key in sorted(A ):
                    if key in ["log", "progress_bar", "preds"]:
                         continue
                    a      =      metrics[key]
                    if isinstance(A  ,		torch.Tensor ):
                         a      =      val.item()
                    a      =      F'''{key}: {val:.6f}\n'''
                    writer.write(A )

          if not save_generations:
               return

          if "preds" in metrics:
               a      =      "\n".join(metrics["preds"] )
               generations_file.open("w+" ).write(A )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Any:



          '''simple docstring'''



          try:
               a      =      pl_module.model.model.num_parameters()
          except AttributeError:
               a      =      pl_module.model.num_parameters()

          a      =      count_trainable_parameters(A )
          # mp stands for million parameters
          trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6} )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Optional[int]:



          '''simple docstring'''



          save_json(pl_module.metrics  ,		pl_module.metrics_save_path )
          return self._write_logs(A  ,		A  ,		"test" )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Dict:



          '''simple docstring'''



          save_json(pl_module.metrics  ,		pl_module.metrics_save_path )
          # Uncommenting this will save val generations
          # return self._write_logs(trainer, pl_module, "valid")







def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase=1		,					__UpperCamelCase=False)			->     Optional[int]:
     if metric == "rouge2":
          a      =      "{val_avg_rouge2:.4f}-{step_count}"
     elif metric == "bleu":
          a      =      "{val_avg_bleu:.4f}-{step_count}"
     elif metric == "loss":
          a      =      "{val_avg_loss:.4f}-{step_count}"
     else:
          raise NotImplementedError(
              f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
              " this function.")

     a      =      ModelCheckpoint(
         dirpath=__UpperCamelCase		,					filename=__UpperCamelCase		,					monitor=f'''val_{metric}'''		,					mode="min" if "loss" in metric else "max"		,					save_top_k=__UpperCamelCase		,					)
     return checkpoint_callback





def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     Union[str, Any]:
     return EarlyStopping(
         monitor=f'''val_{metric}'''		,					mode="min" if "loss" in metric else "max"		,					patience=__UpperCamelCase		,					verbose=__UpperCamelCase		,					)

