



'''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__":
							__lowerCAmelCase		    =     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)

							__lowerCAmelCase    ,      __lowerCAmelCase		    =     parser.parse_known_args()

							# overwrite batch size until we have tf_glue.py
							__lowerCAmelCase		    =     16
							__lowerCAmelCase		    =     16

							# Set up logging
							__lowerCAmelCase		    =     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
							__lowerCAmelCase		    =     TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							__lowerCAmelCase		    =     AutoTokenizer.from_pretrained(args.model_name_or_path)

							# Load dataset
							__lowerCAmelCase    ,      __lowerCAmelCase		    =     load_dataset('''imdb''', split=['''train''', '''test'''])
							__lowerCAmelCase		    =     train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
							__lowerCAmelCase		    =     test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

							# Preprocess train dataset
							__lowerCAmelCase		    =     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'''])

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

							# Preprocess test dataset
							__lowerCAmelCase		    =     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'''])

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

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

							__lowerCAmelCase		    =     time.time()
							__lowerCAmelCase		    =     model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							__lowerCAmelCase		    =     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 math



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   list:
		if n <= 0:
				raise ValueError('Only positive integers have prime factors'						)
		_a  :   Tuple				 =							[]
		while n % 2 == 0:
				pf.append(2						)
				_a  :   str				 =							int(n / 2						)
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				while n % i == 0:
						pf.append(lowerCAmelCase_						)
						_a  :   List[Any]				 =							int(n / i						)
		if n > 2:
				pf.append(lowerCAmelCase_						)
		return pf



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		if n <= 0:
				raise ValueError('Only positive numbers are accepted'						)
		_a  :   int				 =							1
		_a  :   Union[str, Any]				 =							1
		while n % 2 == 0:
				temp += 1
				_a  :   str				 =							int(n / 2						)
		div *= temp
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				_a  :   List[str]				 =							1
				while n % i == 0:
						temp += 1
						_a  :   List[str]				 =							int(n / i						)
				div *= temp
		if n > 1:
				div *= 2
		return div



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		if n <= 0:
				raise ValueError('Only positive numbers are accepted'						)
		_a  :   List[str]				 =							1
		_a  :   Union[str, Any]				 =							1
		while n % 2 == 0:
				temp += 1
				_a  :   Tuple				 =							int(n / 2						)
		if temp > 1:
				s *= (2**temp - 1) / (2 - 1)
		for i in range(3		,    int(math.sqrt(lowerCAmelCase_						)						) + 1		,    2						):
				_a  :   str				 =							1
				while n % i == 0:
						temp += 1
						_a  :   Any				 =							int(n / i						)
				if temp > 1:
						s *= (i**temp - 1) / (i - 1)
		return int(lowerCAmelCase_						)



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   int:
		_a  :   Dict				 =							n
		for x in set(prime_factors(lowerCAmelCase_						)						):
				s *= (x - 1) / x
		return int(lowerCAmelCase_						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




