"""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__":
					UpperCAmelCase       :    List[str]									=			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)

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

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

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

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

					# Preprocess train dataset
					UpperCAmelCase       :    Optional[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"])

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

					# Preprocess test dataset
					UpperCAmelCase       :    Optional[Any]									=			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"])

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

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

					UpperCAmelCase       :    int									=			time.time()
					UpperCAmelCase       :    Dict									=			model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
					UpperCAmelCase       :    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     _A    (    UpperCamelCase_	:			str)      ->		int:
   '''simple docstring'''


   __lowercase								=		0
   for plain_chr in plain_text:
      __lowercase								=		(
          ord(UpperCamelCase_) + (hash_value << 6) + (hash_value << 16) - hash_value
      )
   return hash_value




