




'''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'''

import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


a__         =     logging.get_logger(__name__)





class __magic_name__(						__lowerCAmelCase      ):






						def __init__(   self						:						List[str]					,   *__UpperCamelCase						:						Tuple					,   **__UpperCamelCase						:						Tuple       ):



									'''simple docstring'''






									warnings.warn(
									    """The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
									    """ use ViTImageProcessor instead."""					,   __UpperCamelCase					,   )
									super().__init__(*__UpperCamelCase					,   **__UpperCamelCase       )