


'''simple docstring'''







def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Tuple  )  ->      int:
							_a   :			Tuple					 =len(_a  )
							for i in range(length - 1  ):
														_a   :			Tuple					 =False
														for j in range(length - 1 - i  ):
																					if collection[j] > collection[j + 1]:
																												_a   :			Union[str, Any]					 =True
																												_a   :			int					 =collection[j + 1], collection[j]
														if not swapped:
																					break  # Stop iteration if the collection is sorted.
							return collection


if __name__ == "__main__":
			import doctest
			import time

			doctest.testmod()

			A__:   Any    =	input('''Enter numbers separated by a comma:''').strip()
			A__:   str    =	[int(item) for item in user_input.split(''',''')]
			A__:   str    =	time.process_time()
			print(*bubble_sort(unsorted), sep=''',''')
			print(F"Processing time: {(time.process_time() - start)%1E9 + 7}")






'''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__":
   A__:   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=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__:   List[Any]    =	parser.parse_known_args()

   # overwrite batch size until we have tf_glue.py
   A__:   Tuple    =	16
   A__:   List[str]    =	16

   # Set up logging
   A__:   str    =	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__:   int    =	TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   A__:   Any    =	AutoTokenizer.from_pretrained(args.model_name_or_path)

   # Load dataset
   A__   ,	A__:   Optional[int]    =	load_dataset('''imdb''', split=['''train''', '''test'''])
   A__:   int    =	train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
   A__:   int    =	test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

   # Preprocess train dataset
   A__:   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'''])

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

   # Preprocess test dataset
   A__:   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'''])

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

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

   A__:   str    =	time.time()
   A__:   Dict    =	model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   A__:   Tuple    =	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}")


