


"""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_  :  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=1_6)
       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_  :  Tuple     		=    parser.parse_known_args()

       # overwrite batch size until we have tf_glue.py
       lowerCamelCase_  :  Optional[int]     		=    1_6
       lowerCamelCase_  :  int     		=    1_6

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

       # Load dataset
       lowerCamelCase_  :  Dict     		=    load_dataset("""imdb""", split=["""train""", """test"""])
       lowerCamelCase_  :  int     		=    train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
       lowerCamelCase_  :  Union[str, Any]     		=    test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

       # Preprocess train dataset
       lowerCamelCase_  :  Union[str, 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_  :  Union[str, Any]     		=    {
           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_  :  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_  :  Dict     		=    {
           x: test_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((test_features, test_dataset["""label"""])).batch(
           args.per_device_eval_batch_size
       )

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

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






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =len(__lowerCamelCase      )
			for i in range(length - 1      ):
						lowerCamelCase__						:						Any						      =False
						for j in range(length - 1 - i      ):
									if collection[j] > collection[j + 1]:
												lowerCamelCase__						:						List[str]						      =True
												lowerCamelCase__      ,  lowerCamelCase__						:						List[str]						      =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()

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




