code: 
  """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__":
      _a	 =							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)
  
      _a , _a	 =							parser.parse_known_args()
  
      # overwrite batch size until we have tf_glue.py
      _a	 =							16
      _a	 =							16
  
      # Set up logging
      _a	 =							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
      _a	 =							TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      _a	 =							AutoTokenizer.from_pretrained(args.model_name_or_path)
  
      # Load dataset
      _a , _a	 =							load_dataset('imdb', split=['train', 'test'])
      _a	 =							train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
      _a	 =							test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500
  
      # Preprocess train dataset
      _a	 =							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'])
  
      _a	 =							{
          x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
          for x in ['input_ids', 'attention_mask']
      }
      _a	 =							tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
          args.per_device_train_batch_size
      )
  
      # Preprocess test dataset
      _a	 =							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'])
  
      _a	 =							{
          x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
          for x in ['input_ids', 'attention_mask']
      }
      _a	 =							tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
          args.per_device_eval_batch_size
      )
  
      # fine optimizer and loss
      _a	 =							tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
      _a	 =							tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
      _a	 =							[tf.keras.metrics.SparseCategoricalAccuracy()]
      model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
  
      _a	 =							time.time()
      _a	 =							model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
      _a	 =							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}""")
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from string import ascii_uppercase
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase = False,	__lowerCamelCase = ascii_uppercase						):
     UpperCAmelCase_      :      List[str]      =					keyword.upper()
     UpperCAmelCase_      :      str      =					plaintext.upper()
     UpperCAmelCase_      :      Tuple      =					set(__lowerCamelCase						)
  
     # create a list of unique characters in the keyword - their order matters
     # it determines how we will map plaintext characters to the ciphertext
     UpperCAmelCase_      :      Tuple      =					[]
     for char in keyword:
        if char in alphabet_set and char not in unique_chars:
           unique_chars.append(__lowerCamelCase						)
      # the number of those unique characters will determine the number of rows
     UpperCAmelCase_      :      Union[str, Any]      =					len(__lowerCamelCase						)
  
     # create a shifted version of the alphabet
     UpperCAmelCase_      :      Tuple      =					unique_chars + [
         char for char in alphabet if char not in unique_chars
     ]
  
     # create a modified alphabet by splitting the shifted alphabet into rows
     UpperCAmelCase_      :      List[str]      =					[
         shifted_alphabet[k : k + num_unique_chars_in_keyword]
         for k in range(0,	26,	__lowerCamelCase						)
     ]
  
     # map the alphabet characters to the modified alphabet characters
     # going 'vertically' through the modified alphabet - consider columns first
     UpperCAmelCase_      :      Tuple      =					{}
     UpperCAmelCase_      :      Dict      =					0
     for column in range(__lowerCamelCase						):
        for row in modified_alphabet:
           # if current row (the last one) is too short, break out of loop
           if len(__lowerCamelCase						) <= column:
              break
  
           # map current letter to letter in modified alphabet
           UpperCAmelCase_      :      Tuple      =					row[column]
           letter_index += 1
  
     if verbose:
        print(__lowerCamelCase						)
     # create the encrypted text by mapping the plaintext to the modified alphabet
     return "".join(mapping[char] if char in mapping else char for char in plaintext						)
  
  
  if __name__ == "__main__":
      # example use
      print(mixed_keyword('college', 'UNIVERSITY'))
  
  
  