


import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('''SDP_ENABLED''') or is_sagemaker_dp_enabled():
       __lowerCamelCase   	=  True
       __lowerCamelCase   	=  '''p3dn.24xlarge'''
       import smdistributed.dataparallel.tensorflow as sdp
else:
       __lowerCamelCase   	=  False



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase=None			):
     a_     :  int				      =     tqdm(__UpperCamelCase			)
     for i, batch in enumerate(__UpperCamelCase			):
          with tf.GradientTape() as tape:
               a_      ,      a_     :  List[Any]				      =     batch
               a_     :  List[str]				      =     model(__UpperCamelCase			)
               a_     :  Dict				      =     loss(__UpperCamelCase	,	outputs.logits			)

          if SDP_ENABLED:
               a_     :  Dict				      =     sdp.DistributedGradientTape(__UpperCamelCase	,	sparse_as_dense=__UpperCamelCase			)

          a_     :  Optional[Any]				      =     tape.gradient(__UpperCamelCase	,	model.trainable_variables			)
          opt.apply_gradients(zip(__UpperCamelCase	,	model.trainable_variables			)			)

          pbar.set_description(F'''Loss: {loss_value:.4f}'''			)

          if SDP_ENABLED and i == 0:
               sdp.broadcast_variables(model.variables	,	root_rank=0			)
               sdp.broadcast_variables(opt.variables()	,	root_rank=0			)

          if max_steps and i >= max_steps:
               break

     a_     :  List[str]				      =     {"""loss""": loss_value.numpy()}
     return train_results



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     # Load dataset
     a_      ,      a_     :  int				      =     load_dataset("""imdb"""	,	split=["""train""", """test"""]			)

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

     a_     :  Optional[Any]				      =     {
         x: train_dataset[x].to_tensor(default_value=0	,	shape=[None, tokenizer.model_max_length]			)
         for x in ["""input_ids""", """attention_mask"""]
     }
     a_     :  int				      =     tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])			)

     # Preprocess test dataset
     a_     :  Any				      =     test_dataset.map(
         lambda __UpperCamelCase			: tokenizer(e["""text"""]	,	truncation=__UpperCamelCase	,	padding="""max_length"""			)	,	batched=__UpperCamelCase			)
     test_dataset.set_format(type="""tensorflow"""	,	columns=["""input_ids""", """attention_mask""", """label"""]			)

     a_     :  int				      =     {
         x: test_dataset[x].to_tensor(default_value=0	,	shape=[None, tokenizer.model_max_length]			)
         for x in ["""input_ids""", """attention_mask"""]
     }
     a_     :  Optional[int]				      =     tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])			)

     if SDP_ENABLED:
          a_     :  int				      =     tf_train_dataset.shard(sdp.size()	,	sdp.rank()			)
          a_     :  Optional[Any]				      =     tf_test_dataset.shard(sdp.size()	,	sdp.rank()			)
     a_     :  str				      =     tf_train_dataset.batch(__UpperCamelCase	,	drop_remainder=__UpperCamelCase			)
     a_     :  List[str]				      =     tf_test_dataset.batch(__UpperCamelCase	,	drop_remainder=__UpperCamelCase			)

     return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
       __lowerCamelCase   	=  argparse.ArgumentParser()

       # Hyperparameters sent by the client are passed as command-line arguments to the script.
       parser.add_argument('''--epochs''', type=int, default=3)
       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)
       parser.add_argument('''--max_steps''', type=int, default=None)

       # Data, model, and output directories
       parser.add_argument('''--output_data_dir''', type=str, default=os.environ['''SM_OUTPUT_DATA_DIR'''])
       parser.add_argument('''--model_dir''', type=str, default=os.environ['''SM_MODEL_DIR'''])
       parser.add_argument('''--n_gpus''', type=str, default=os.environ['''SM_NUM_GPUS'''])

       __lowerCamelCase	,							__lowerCamelCase   	=  parser.parse_known_args()

       # Set up logging
       __lowerCamelCase   	=  logging.getLogger(__name__)

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

       if SDP_ENABLED:
              sdp.init()

              __lowerCamelCase   	=  tf.config.experimental.list_physical_devices('''GPU''')
              for gpu in gpus:
                     tf.config.experimental.set_memory_growth(gpu, True)
              if gpus:
                     tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], '''GPU''')

    # Load model and tokenizer
       __lowerCamelCase   	=  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
       __lowerCamelCase   	=  AutoTokenizer.from_pretrained(args.model_name_or_path)

       # get datasets
       __lowerCamelCase	,							__lowerCamelCase   	=  get_datasets(
           tokenizer=tokenizer,
           train_batch_size=args.per_device_train_batch_size,
           eval_batch_size=args.per_device_eval_batch_size,
       )

       # fine optimizer and loss
       __lowerCamelCase   	=  tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
       __lowerCamelCase   	=  tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
       __lowerCamelCase   	=  [tf.keras.metrics.SparseCategoricalAccuracy()]
       model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

       # Training
       if args.do_train:
              # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
              __lowerCamelCase   	=  time.time()
              __lowerCamelCase   	=  fit(
                  model,
                  loss,
                  optimizer,
                  tf_train_dataset,
                  args.epochs,
                  args.per_device_train_batch_size,
                  max_steps=args.max_steps,
              )
              __lowerCamelCase   	=  time.time() - start_train_time
              logger.info('''*** Train ***''')
              logger.info(f'''train_runtime = {end_train_time}''')

              __lowerCamelCase   	=  os.path.join(args.output_dir, '''train_results.txt''')

              if not SDP_ENABLED or sdp.rank() == 0:
                     with open(output_eval_file, '''w''') as writer:
                            logger.info('''***** Train results *****''')
                            logger.info(train_results)
                            for key, value in train_results.items():
                                   logger.info(f'''  {key} = {value}''')
                                   writer.write(f'''{key} = {value}\n''')

    # Evaluation
       if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
              __lowerCamelCase   	=  model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
              logger.info('''*** Evaluate ***''')

              __lowerCamelCase   	=  os.path.join(args.output_dir, '''eval_results.txt''')

              with open(output_eval_file, '''w''') as writer:
                     logger.info('''***** Eval results *****''')
                     logger.info(result)
                     for key, value in result.items():
                            logger.info(f'''  {key} = {value}''')
                            writer.write(f'''{key} = {value}\n''')

    # Save result
       if SDP_ENABLED:
              if sdp.rank() == 0:
                     model.save_pretrained(args.output_dir)
                     tokenizer.save_pretrained(args.output_dir)
       else:
              model.save_pretrained(args.output_dir)
              tokenizer.save_pretrained(args.output_dir)









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()



