


import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
     snake_case       :					Union[str, Any]        =  argparse.ArgumentParser()

     # Hyperparameters sent by the client are passed as command-line arguments to the script.
     parser.add_argument('''--epochs''', type=int, default=1)
     parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
     parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
     parser.add_argument('''--model_name_or_path''', type=str)
     parser.add_argument('''--learning_rate''', type=str, default=5E-5)
     parser.add_argument('''--do_train''', type=bool, default=True)
     parser.add_argument('''--do_eval''', type=bool, default=True)
     parser.add_argument('''--output_dir''', type=str)

     snake_case       ,	snake_case       :					Optional[Any]        =  parser.parse_known_args()

     # overwrite batch size until we have tf_glue.py
     snake_case       :					Optional[int]        =  16
     snake_case       :					Union[str, Any]        =  16

     # Set up logging
     snake_case       :					Dict        =  logging.getLogger(__name__)

     logging.basicConfig(
         level=logging.getLevelName('''INFO'''),
         handlers=[logging.StreamHandler(sys.stdout)],
         format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
     )

     # Load model and tokenizer
     snake_case       :					Optional[int]        =  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
     snake_case       :					List[Any]        =  AutoTokenizer.from_pretrained(args.model_name_or_path)

     # Load dataset
     snake_case       ,	snake_case       :					List[Any]        =  load_dataset('''imdb''', split=['''train''', '''test'''])
     snake_case       :					List[Any]        =  train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
     snake_case       :					str        =  test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

     # Preprocess train dataset
     snake_case       :					Any        =  train_dataset.map(
         lambda e: tokenizer(e['''text'''], truncation=True, padding='''max_length'''), batched=True
     )
     train_dataset.set_format(type='''tensorflow''', columns=['''input_ids''', '''attention_mask''', '''label'''])

     snake_case       :					Dict        =  {
         x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
         for x in ['''input_ids''', '''attention_mask''']
     }
     snake_case       :					List[str]        =  tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])).batch(
         args.per_device_train_batch_size
     )

     # Preprocess test dataset
     snake_case       :					str        =  test_dataset.map(
         lambda e: tokenizer(e['''text'''], truncation=True, padding='''max_length'''), batched=True
     )
     test_dataset.set_format(type='''tensorflow''', columns=['''input_ids''', '''attention_mask''', '''label'''])

     snake_case       :					Any        =  {
         x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
         for x in ['''input_ids''', '''attention_mask''']
     }
     snake_case       :					Optional[int]        =  tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])).batch(
         args.per_device_eval_batch_size
     )

     # fine optimizer and loss
     snake_case       :					Union[str, Any]        =  tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
     snake_case       :					int        =  tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
     snake_case       :					List[Any]        =  [tf.keras.metrics.SparseCategoricalAccuracy()]
     model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

     snake_case       :					Optional[int]        =  time.time()
     snake_case       :					Union[str, Any]        =  model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
     snake_case       :					Any        =  time.time() - start_train_time

     logger.info('''*** Train ***''')
     logger.info(F"""train_runtime = {end_train_time}""")
     for key, value in train_results.history.items():
          logger.info(F"""  {key} = {value}""")









import os
import sys

from . import rsa_key_generator as rkg

snake_case       :					int        =  1_28
snake_case       :					Any        =  2_56
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE		):




      """simple docstring"""





      a		:Union[str, Any]											= message.encode('''ascii'''		)

      a		:List[Any]											= []
      for block_start in range(0				,						len(UpperCAmelCase_		)				,						UpperCAmelCase_		):
            a		:List[str]											= 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        __lowerCamelCase		(				UpperCAmelCase_		:	list[int]				,						UpperCAmelCase_		:	int				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE		):




      """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(UpperCAmelCase_		) + i < message_length:
                        a		:List[str]											= block_int // (BYTE_SIZE**i)
                        a		:List[str]											= block_int % (BYTE_SIZE**i)
                        block_message.insert(0				,						chr(UpperCAmelCase_		)		)
            message.extend(UpperCAmelCase_		)
      return "".join(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	tuple[int, int]				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE		):




      """simple docstring"""





      a		:Tuple											= []
      a							,       a		:Any											= key

      for block in get_blocks_from_text(UpperCAmelCase_				,						UpperCAmelCase_		):
            encrypted_blocks.append(pow(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)		)
      return encrypted_blocks
def        __lowerCamelCase		(				UpperCAmelCase_		:	list[int]				,						UpperCAmelCase_		:	int				,						UpperCAmelCase_		:	tuple[int, int]				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE				,						):




      """simple docstring"""





      a		:Optional[Any]											= []
      a							,       a		:int											= key
      for block in encrypted_blocks:
            decrypted_blocks.append(pow(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)		)
      return get_text_from_blocks(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	str		):




      """simple docstring"""





      with open(UpperCAmelCase_		) as fo:
            a		:Dict											= fo.read()
      a							,       a							,       a		:Tuple											= content.split(''','''		)
      return (int(UpperCAmelCase_		), int(UpperCAmelCase_		), int(UpperCAmelCase_		))
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	int = DEFAULT_BLOCK_SIZE				,						):




      """simple docstring"""





      a							,       a							,       a		:List[str]											= 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		:int											= [str(UpperCAmelCase_		) for i in encrypt_message(UpperCAmelCase_				,						(n, e)				,						UpperCAmelCase_		)]

      a		:str											= ''','''.join(UpperCAmelCase_		)
      a		:Optional[Any]											= F'''{len(UpperCAmelCase_		)}_{block_size}_{encrypted_content}'''
      with open(UpperCAmelCase_				,						'''w'''		) as fo:
            fo.write(UpperCAmelCase_		)
      return encrypted_content
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	str		):




      """simple docstring"""





      a							,       a							,       a		:List[Any]											= read_key_file(UpperCAmelCase_		)
      with open(UpperCAmelCase_		) as fo:
            a		:int											= fo.read()
      a							,       a							,       a		:List[str]											= content.split('''_'''		)
      a		:List[str]											= int(UpperCAmelCase_		)
      a		:List[str]											= 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		:Dict											= []
      for block in encrypted_message.split(''','''		):
            encrypted_blocks.append(int(UpperCAmelCase_		)		)

      return decrypt_message(UpperCAmelCase_				,						UpperCAmelCase_				,						(n, d)				,						UpperCAmelCase_		)
def        __lowerCamelCase		(				):




      """simple docstring"""





      a		:Union[str, Any]											= '''encrypted_file.txt'''
      a		:List[Any]											= input(R'''Encrypt\Decrypt [e\d]: '''		)

      if response.lower().startswith('''e'''		):
            a		:int											= '''encrypt'''
      elif response.lower().startswith('''d'''		):
            a		:Optional[Any]											= '''decrypt'''

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

            a		:List[str]											= input('''\nEnter message: '''		)
            a		:str											= '''rsa_pubkey.txt'''
            print(F'''Encrypting and writing to {filename}...'''		)
            a		:Optional[int]											= encrypt_and_write_to_file(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)

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

      elif mode == "decrypt":
            a		:str											= '''rsa_privkey.txt'''
            print(F'''Reading from {filename} and decrypting...'''		)
            a		:Optional[int]											= 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()





