



"""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=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)

		_lowercase		,      _lowercase					:					Tuple 		=		parser.parse_known_args()

		# overwrite batch size until we have tf_glue.py
		_lowercase					:					Optional[int] 		=		1_6
		_lowercase					:					int 		=		1_6

		# Set up logging
		_lowercase					:					int 		=		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					:					Any 		=		TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
		_lowercase					:					Dict 		=		AutoTokenizer.from_pretrained(args.model_name_or_path)

		# Load dataset
		_lowercase		,      _lowercase					:					Dict 		=		load_dataset("imdb", split=["train", "test"])
		_lowercase					:					int 		=		train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
		_lowercase					:					Union[str, Any] 		=		test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

		# Preprocess train dataset
		_lowercase					:					Union[str, 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					:					Union[str, Any] 		=		{
		    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
		    for x in ["input_ids", "attention_mask"]
		}
		_lowercase					:					Dict 		=		tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])).batch(
		    args.per_device_train_batch_size
		)

		# Preprocess test dataset
		_lowercase					:					str 		=		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					:					Dict 		=		{
		    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
		    for x in ["input_ids", "attention_mask"]
		}
		_lowercase					:					Dict 		=		tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])).batch(
		    args.per_device_eval_batch_size
		)

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

		_lowercase					:					Union[str, Any] 		=		time.time()
		_lowercase					:					Union[str, 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 _A		(		lowercase						):







    """simple docstring"""
    a											=[]
    a											=1

    for i in range(len(lowercase						)						):
        if i + 1 < len(lowercase						) and text[i] == text[i + 1]:
            count += 1
        else:
            encoded.append((text[i], count)						)
            a											=1

    return encoded
def _A		(		lowercase						):







    """simple docstring"""
    return "".join(char * length for char, length in encoded						)


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

       testmod(name="""run_length_encode""", verbose=True)
       testmod(name="""run_length_decode""", verbose=True)