
"""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(5000))  # 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"""


def     _lowerCamelCase(__UpperCamelCase = 4000000       )				-> int:
	_lowerCAmelCase								=1
	_lowerCAmelCase								=2
	_lowerCAmelCase								=0
	while j <= n:
		if j % 2 == 0:
			total += j
		_lowerCAmelCase						,							_lowerCAmelCase								=j, i + j

	return total


if __name__ == "__main__":
			print(F"""{solution() = }""")



