
"""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}""")




"""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'))


