





"""simple docstring"""




from string import ascii_uppercase





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = False					,   _UpperCAmelCase = ascii_uppercase   ):






  """simple docstring"""





  A_    :			List[Any]									=    keyword.upper()
  A_    :			int									=    plaintext.upper()
  A_    :			List[Any]									=    set(_UpperCAmelCase   )

  # create a list of unique characters in the keyword - their order matters
  # it determines how we will map plaintext characters to the ciphertext
  A_    :			Optional[int]									=    []
  for char in keyword:
    if char in alphabet_set and char not in unique_chars:
      unique_chars.append(_UpperCAmelCase   )
    # the number of those unique characters will determine the number of rows
  A_    :			List[Any]									=    len(_UpperCAmelCase   )

  # create a shifted version of the alphabet
  A_    :			List[str]									=    unique_chars + [
      char for char in alphabet if char not in unique_chars
  ]

  # create a modified alphabet by splitting the shifted alphabet into rows
  A_    :			str									=    [
      shifted_alphabet[k : k + num_unique_chars_in_keyword]
      for k in range(0					,   26					,   _UpperCAmelCase   )
  ]

  # map the alphabet characters to the modified alphabet characters
  # going 'vertically' through the modified alphabet - consider columns first
  A_    :			List[str]									=    {}
  A_    :			List[str]									=    0
  for column in range(_UpperCAmelCase   ):
    for row in modified_alphabet:
      # if current row (the last one) is too short, break out of loop
      if len(_UpperCAmelCase   ) <= column:
        break

      # map current letter to letter in modified alphabet
      A_    :			Tuple									=    row[column]
      letter_index += 1

  if verbose:
    print(_UpperCAmelCase   )
  # 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'))
'''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  : 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  : Dict		       =    parser.parse_known_args()

  # overwrite batch size until we have tf_glue.py
  __snake_case  : Any		       =    16
  __snake_case  : Optional[Any]		       =    16

  # Set up logging
  __snake_case  : Union[str, Any]		       =    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[Any]		       =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  __snake_case  : Optional[Any]		       =    AutoTokenizer.from_pretrained(args.model_name_or_path)

  # Load dataset
  __snake_case       ,       __snake_case  : Optional[int]		       =    load_dataset('imdb', split=['train', 'test'])
  __snake_case  : List[str]		       =    train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
  __snake_case  : List[str]		       =    test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

  # Preprocess train dataset
  __snake_case  : int		       =    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  : str		       =    tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
      args.per_device_train_batch_size
  )

  # Preprocess test dataset
  __snake_case  : Tuple		       =    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  : Optional[int]		       =    {
      x: test_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((test_features, test_dataset['label'])).batch(
      args.per_device_eval_batch_size
  )

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

  __snake_case  : Any		       =    time.time()
  __snake_case  : int		       =    model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
  __snake_case  : Union[str, 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}""")






