





'''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      :  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)

 lowerCAmelCase							,	lowerCAmelCase      :  List[Any]      							=parser.parse_known_args()

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

 # Set up logging
 lowerCAmelCase      :  Union[str, Any]      							=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      :  str      							=TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
 lowerCAmelCase      :  Optional[Any]      							=AutoTokenizer.from_pretrained(args.model_name_or_path)

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

 # Preprocess train dataset
 lowerCAmelCase      :  List[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'''])

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

 # Preprocess test dataset
 lowerCAmelCase      :  List[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'''])

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

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

 lowerCAmelCase      :  List[Any]      							=time.time()
 lowerCAmelCase      :  Optional[int]      							=model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
 lowerCAmelCase      :  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}''')









'''simple docstring'''


import math
def 							UpperCAmelCase_						(		__lowerCamelCase      :      int				):

   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 							UpperCAmelCase_						(		__lowerCamelCase      :      int = 2_00_00_00				):
   return sum(num for num in range(3			,__lowerCamelCase			,2				) if is_prime(__lowerCamelCase				)				) + 2 if n > 2 else 0


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


