




'''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__":
    UpperCAmelCase_       				=      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=1_6)
    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)

    UpperCAmelCase_				,		UpperCAmelCase_       				=      parser.parse_known_args()

    # overwrite batch size until we have tf_glue.py
    UpperCAmelCase_       				=      1_6
    UpperCAmelCase_       				=      1_6

    # Set up logging
    UpperCAmelCase_       				=      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
    UpperCAmelCase_       				=      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
    UpperCAmelCase_       				=      AutoTokenizer.from_pretrained(args.model_name_or_path)

    # Load dataset
    UpperCAmelCase_				,		UpperCAmelCase_       				=      load_dataset('imdb', split=['train', 'test'])
    UpperCAmelCase_       				=      train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
    UpperCAmelCase_       				=      test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

    # Preprocess train dataset
    UpperCAmelCase_       				=      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'])

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

    # Preprocess test dataset
    UpperCAmelCase_       				=      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'])

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

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

    UpperCAmelCase_       				=      time.time()
    UpperCAmelCase_       				=      model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
    UpperCAmelCase_       				=      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}")






'''simple docstring'''




from __future__ import annotations

import random
import string

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	int						,		_UpperCAmelCase    :	str | None = None ):






   """simple docstring"""






   UpperCAmelCase__										=    passcode or self.__passcode_creator()
   UpperCAmelCase__										=    self.__make_key_list()
   UpperCAmelCase__										=    self.__make_shift_key()


  def __str__(						self    :	int ):






   """simple docstring"""






   return "".join(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	list[int] ):






   """simple docstring"""






   for i in range(1						,		len(_UpperCAmelCase )						,		2 ):
    iterlist[i] *= -1
   return iterlist


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    string.ascii_letters + string.digits
   UpperCAmelCase__										=    [random.choice(_UpperCAmelCase ) for _ in range(random.randint(10						,		20 ) )]
   return password


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    (
       string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
   )

   UpperCAmelCase__										=    []

   # creates points known as breakpoints to break the key_list_options at those
   # points and pivot each substring
   UpperCAmelCase__										=    sorted(set(self.__passcode ) )
   UpperCAmelCase__										=    []

   # algorithm for creating a new shuffled list, keys_l, out of key_list_options
   for i in key_list_options:
    temp_list.extend(_UpperCAmelCase )

    # checking breakpoints at which to pivot temporary sublist and add it into
    # keys_l
    if i in breakpoints or i == key_list_options[-1]:
     keys_l.extend(temp_list[::-1] )
     temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
   return keys_l


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    sum(self.__neg_pos([ord(_UpperCAmelCase ) for x in self.__passcode] ) )
   return num if num > 0 else len(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

   # decoding shift like Caesar cipher algorithm implementing negative shift or
   # reverse shift or left shift
   for i in encoded_message:
    UpperCAmelCase__										=    self.__key_list.index(_UpperCAmelCase )
    decoded_message += self.__key_list[
        (position - self.__shift_key) % -len(self.__key_list )
    ]

   return decoded_message



  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

   # encoding shift like Caesar cipher algorithm implementing positive shift or
   # forward shift or right shift
   for i in plaintext:
    UpperCAmelCase__										=    self.__key_list.index(_UpperCAmelCase )
    encoded_message += self.__key_list[
        (position + self.__shift_key) % len(self.__key_list )
    ]

   return encoded_message

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str = "Hello, this is a modified Caesar cipher"       ):
 '''simple docstring'''





 UpperCAmelCase__										=    ShuffledShiftCipher()
 return cipa.decrypt(cipa.encrypt(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
