






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__":
					snake_case_   :	List[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=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)

					snake_case_      ,	snake_case_   :	Tuple							      =parser.parse_known_args()

					# overwrite batch size until we have tf_glue.py
					snake_case_   :	Optional[int]							      =16
					snake_case_   :	Any							      =16

					# Set up logging
					snake_case_   :	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
					snake_case_   :	Dict							      =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
					snake_case_   :	Dict							      =AutoTokenizer.from_pretrained(args.model_name_or_path)

					# Load dataset
					snake_case_      ,	snake_case_   :	str							      =load_dataset('''imdb''', split=['''train''', '''test'''])
					snake_case_   :	Union[str, Any]							      =train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
					snake_case_   :	List[Any]							      =test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

					# Preprocess train dataset
					snake_case_   :	Optional[int]							      =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'''])

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

					# Preprocess test dataset
					snake_case_   :	List[Any]							      =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'''])

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

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

					snake_case_   :	List[Any]							      =time.time()
					snake_case_   :	Union[str, Any]							      =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
					snake_case_   :	List[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}""")






import math
from timeit import timeit


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  str     					=	0
				_UpperCAmelCase						:  Tuple     					=	abs(_UpperCamelCase			)
				while True:
								_UpperCAmelCase						:  str     					=	n // 10
								digits += 1
								if n == 0:
												break
				return digits


def 	_A	(   _UpperCamelCase			):
				return 1 if n == 0 else math.floor(math.log(abs(_UpperCamelCase			)      ,					10			) + 1			)


def 	_A	(   _UpperCamelCase			):
				return len(str(abs(_UpperCamelCase			)			)			)


def 	_A	(   ):
				from collections.abc import Callable

				def benchmark_a_function(_UpperCamelCase      ,					_UpperCamelCase			) -> None:
								_UpperCAmelCase						:  List[str]     					=	F'''{func.__name__}({value})'''
								_UpperCAmelCase						:  Any     					=	timeit(F'''__main__.{call}'''      ,					setup='''import __main__'''			)
								print(F'''{call}: {func(_UpperCamelCase			)} -- {timing} seconds'''			)

				for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
								for func in (num_digits, num_digits_fast, num_digits_faster):
												benchmark_a_function(_UpperCamelCase      ,					_UpperCamelCase			)
								print()


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()
