'''simple docstring'''


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__	:     Tuple      =    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__	:     List[str]      =    parser.parse_known_args()

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

 # Set up logging
 snake_case__	:     Optional[int]      =    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__	:     int      =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
 snake_case__	:     str      =    AutoTokenizer.from_pretrained(args.model_name_or_path)

 # Load dataset
 snake_case__     , snake_case__	:     List[str]      =    load_dataset('''imdb''', split=['''train''', '''test'''])
 snake_case__	:     Union[str, Any]      =    train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
 snake_case__	:     Tuple      =    test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

 # Preprocess train dataset
 snake_case__	:     Tuple      =    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__	:     Tuple      =    {
     x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
     for x in ['''input_ids''', '''attention_mask''']
 }
 snake_case__	:     Dict      =    tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])).batch(
     args.per_device_train_batch_size
 )

 # Preprocess test dataset
 snake_case__	:     List[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__	:     str      =    {
     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__	:     Tuple      =    tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
 snake_case__	:     Tuple      =    tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 snake_case__	:     List[str]      =    [tf.keras.metrics.SparseCategoricalAccuracy()]
 model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

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

UpperCAmelCase_    =128
UpperCAmelCase_    =256



def    UpperCAmelCase							(  _snake_case    ,			_snake_case = DEFAULT_BLOCK_SIZE				):
							lowerCAmelCase =		message.encode('''ascii'''				)

							lowerCAmelCase =		[]
							for block_start in range(0    ,			len(_snake_case				)    ,			_snake_case				):
														lowerCAmelCase =		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    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case = DEFAULT_BLOCK_SIZE				):
							lowerCAmelCase =		[]
							for block_int in block_ints:
														lowerCAmelCase =		[]
														for i in range(block_size - 1    ,			-1    ,			-1				):
																					if len(_snake_case				) + i < message_length:
																												lowerCAmelCase =		block_int // (BYTE_SIZE**i)
																												lowerCAmelCase =		block_int % (BYTE_SIZE**i)
																												block_message.insert(0    ,			chr(_snake_case				)				)
														message.extend(_snake_case				)
							return "".join(_snake_case				)



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case = DEFAULT_BLOCK_SIZE				):
							lowerCAmelCase =		[]
							lowerCAmelCase ,  lowerCAmelCase =		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    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = DEFAULT_BLOCK_SIZE    ,			):
							lowerCAmelCase =		[]
							lowerCAmelCase ,  lowerCAmelCase =		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    UpperCAmelCase							(  _snake_case				):
							with open(_snake_case				) as fo:
														lowerCAmelCase =		fo.read()
							lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		content.split(''','''				)
							return (int(_snake_case				), int(_snake_case				), int(_snake_case				))



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = DEFAULT_BLOCK_SIZE    ,			):
							lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		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				)				)

							lowerCAmelCase =		[str(_snake_case				) for i in encrypt_message(_snake_case    ,			(n, e)    ,			_snake_case				)]

							lowerCAmelCase =		''','''.join(_snake_case				)
							lowerCAmelCase =		F"""{len(_snake_case				)}_{block_size}_{encrypted_content}"""
							with open(_snake_case    ,			'''w'''				) as fo:
														fo.write(_snake_case				)
							return encrypted_content



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		read_key_file(_snake_case				)
							with open(_snake_case				) as fo:
														lowerCAmelCase =		fo.read()
							lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		content.split('''_'''				)
							lowerCAmelCase =		int(_snake_case				)
							lowerCAmelCase =		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				)				)

							lowerCAmelCase =		[]
							for block in encrypted_message.split(''','''				):
														encrypted_blocks.append(int(_snake_case				)				)

							return decrypt_message(_snake_case    ,			_snake_case    ,			(n, d)    ,			_snake_case				)



def    UpperCAmelCase							(  ):
							lowerCAmelCase =		'''encrypted_file.txt'''
							lowerCAmelCase =		input(R'''Encrypt\Decrypt [e\d]: '''				)

							if response.lower().startswith('''e'''				):
														lowerCAmelCase =		'''encrypt'''
							elif response.lower().startswith('''d'''				):
														lowerCAmelCase =		'''decrypt'''

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

														lowerCAmelCase =		input('''\nEnter message: '''				)
														lowerCAmelCase =		'''rsa_pubkey.txt'''
														print(F"""Encrypting and writing to {filename}..."""				)
														lowerCAmelCase =		encrypt_and_write_to_file(_snake_case    ,			_snake_case    ,			_snake_case				)

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

							elif mode == "decrypt":
														lowerCAmelCase =		'''rsa_privkey.txt'''
														print(F"""Reading from {filename} and decrypting..."""				)
														lowerCAmelCase =		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()



