"""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         =				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)

       __A     ,__A         =				parser.parse_known_args()

       # overwrite batch size until we have tf_glue.py
       __A         =				1_6
       __A         =				1_6

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

       # Load dataset
       __A     ,__A         =				load_dataset("imdb", split=["train", "test"])
       __A         =				train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
       __A         =				test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

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

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

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

       __A         =				time.time()
       __A         =				model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
       __A         =				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 SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						Optional[int]:
   lowercase__:     str      =   [7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
   for gap in gaps:
      for i in range(__UpperCAmelCase ,					len(__UpperCAmelCase				)				):
         lowercase__:     Any      =   collection[i]
         lowercase__:     Tuple      =   i
         while j >= gap and collection[j - gap] > insert_value:
            lowercase__:     List[str]      =   collection[j - gap]
            j -= gap
         if j != i:
            lowercase__:     List[str]      =   insert_value
   return collection


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       __A         =				input("Enter numbers separated by a comma:\n").strip()
       __A         =				[int(item) for item in user_input.split(",")]
       print(shell_sort(unsorted))
