




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__    :							Union[str, 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)

   UpperCAmelCase__		,  UpperCAmelCase__    :							Union[str, Any]       		=     parser.parse_known_args()

   # overwrite batch size until we have tf_glue.py
   UpperCAmelCase__    :							int       		=     16
   UpperCAmelCase__    :							str       		=     16

   # Set up logging
   UpperCAmelCase__    :							Tuple       		=     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__    :							Optional[Any]       		=     TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   UpperCAmelCase__    :							List[Any]       		=     AutoTokenizer.from_pretrained(args.model_name_or_path)

   # Load dataset
   UpperCAmelCase__		,  UpperCAmelCase__    :							str       		=     load_dataset('imdb', split=['train', 'test'])
   UpperCAmelCase__    :							Optional[int]       		=     train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
   UpperCAmelCase__    :							Dict       		=     test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

   # Preprocess train dataset
   UpperCAmelCase__    :							Optional[Any]       		=     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__    :							Any       		=     {
       x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['input_ids', 'attention_mask']
   }
   UpperCAmelCase__    :							List[Any]       		=     tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
       args.per_device_train_batch_size
   )

   # Preprocess test dataset
   UpperCAmelCase__    :							str       		=     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__    :							Dict       		=     {
       x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['input_ids', 'attention_mask']
   }
   UpperCAmelCase__    :							List[str]       		=     tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
       args.per_device_eval_batch_size
   )

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

   UpperCAmelCase__    :							Tuple       		=     time.time()
   UpperCAmelCase__    :							str       		=     model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   UpperCAmelCase__    :							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}''')






def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		int:





  """simple docstring"""





  return (input_a, input_a).count(0				) % 2


def 		_lowerCamelCase					(       )      ->		None:





  """simple docstring"""





  assert xor_gate(0       ,  0				) == 0
  assert xor_gate(0       ,  1				) == 1
  assert xor_gate(1       ,  0				) == 1
  assert xor_gate(1       ,  1				) == 0


if __name__ == "__main__":
   print(xor_gate(0, 0))
   print(xor_gate(0, 1))
