


'''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__":
	__lowercase			:		Optional[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)

	__lowercase ,   __lowercase			:		Any =			parser.parse_known_args()

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

	# Set up logging
	__lowercase			:		Optional[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
	__lowercase			:		List[Any] =			TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
	__lowercase			:		Dict =			AutoTokenizer.from_pretrained(args.model_name_or_path)

	# Load dataset
	__lowercase ,   __lowercase			:		List[str] =			load_dataset('imdb', split=['train', 'test'])
	__lowercase			:		int =			train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
	__lowercase			:		Union[str, Any] =			test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

	# Preprocess train dataset
	__lowercase			:		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'])

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

	# Preprocess test dataset
	__lowercase			:		Optional[Any] =			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'])

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

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

	__lowercase			:		Union[str, Any] =			time.time()
	__lowercase			:		Any =			model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
	__lowercase			:		str =			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'''



def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     set		,   _SCREAMING_SNAKE_CASE   :     set     ):
					assert isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ), F"""The input value of [set_a={set_a}] is not a set"""
					assert isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ), F"""The input value of [set_b={set_b}] is not a set"""

					return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
	from doctest import testmod

	testmod()



