
'''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          =    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          =    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'''



def  _A		(							lowercase__	):

						# validate:
						if not isinstance(lowercase__		,		lowercase__	) or not all(
						    isinstance(lowercase__		,		lowercase__	) and item >= 0 for item in citations	):
												raise ValueError("""The citations should be a list of non negative integers."""	)

						citations.sort()
						lowercase__							 =       len(lowercase__	)

						for i in range(lowercase__	):
												if citations[len_citations - 1 - i] <= i:
																		return i

						return len_citations


if __name__ == "__main__":
				import doctest

				doctest.testmod()






