





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__":
	lowerCAmelCase_												=       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=1_6)
	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)

	lowerCAmelCase_						,      lowerCAmelCase_												=       parser.parse_known_args()

	# overwrite batch size until we have tf_glue.py
	lowerCAmelCase_												=       1_6
	lowerCAmelCase_												=       1_6

	# Set up logging
	lowerCAmelCase_												=       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
	lowerCAmelCase_												=       TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
	lowerCAmelCase_												=       AutoTokenizer.from_pretrained(args.model_name_or_path)

	# Load dataset
	lowerCAmelCase_						,      lowerCAmelCase_												=       load_dataset('''imdb''', split=['''train''', '''test'''])
	lowerCAmelCase_												=       train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
	lowerCAmelCase_												=       test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

	# Preprocess train dataset
	lowerCAmelCase_												=       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'''])

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

	# Preprocess test dataset
	lowerCAmelCase_												=       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'''])

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

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

	lowerCAmelCase_												=       time.time()
	lowerCAmelCase_												=       model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
	lowerCAmelCase_												=       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}''')







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowerCAmelCase_												=       logging.get_logger(__name__)



class       __lowerCAmelCase (    _a  ):






			def __init__(self      ,							*__magic_name__      ,							**__magic_name__					)  ->     None:


							'''simple docstring'''



							warnings.warn(
							    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
							    ''' Please use MobileNetV2ImageProcessor instead.'''      ,							__magic_name__      ,							)
							super().__init__(*__magic_name__      ,							**__magic_name__					)
