
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(50_00))  # smaller the size for train dataset to 5k
			_A       =					test_dataset.shuffle().select(range(5_00))  # 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}""")





import math






def        lowerCamelCase__    (    __lowerCAmelCase  :  int    ):




			"""simple docstring"""


			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5   ,							int(math.sqrt(__lowerCAmelCase    ) + 1    )   ,							6    ):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True






def        lowerCamelCase__    (    __lowerCAmelCase  :  int = 600851475143    ):




			"""simple docstring"""


			try:
						lowerCAmelCase_      =     int(__lowerCAmelCase    )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."    )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."    )
			lowerCAmelCase_      =     0
			if is_prime(__lowerCAmelCase    ):
						return n
			while n % 2 == 0:
						n //= 2
			if is_prime(__lowerCAmelCase    ):
						return n
			for i in range(3   ,							int(math.sqrt(__lowerCAmelCase    )    ) + 1   ,							2    ):
						if n % i == 0:
									if is_prime(n // i    ):
												lowerCAmelCase_      =     n // i
												break
									elif is_prime(__lowerCAmelCase    ):
												lowerCAmelCase_      =     i
			return max_number


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



