






"""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__":
						_snake_case       :      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)

						_snake_case       :      Union[str, Any]       =					parser.parse_known_args()

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

						# Set up logging
						_snake_case       :      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
						_snake_case       :      Tuple       =					TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						_snake_case       :      Optional[Any]       =					AutoTokenizer.from_pretrained(args.model_name_or_path)

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

						# Preprocess train dataset
						_snake_case       :      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"])

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

						# Preprocess test dataset
						_snake_case       :      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"])

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

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

						_snake_case       :      Dict       =					time.time()
						_snake_case       :      Dict       =					model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
						_snake_case       :      Optional[Any]       =					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"""



_snake_case       :      Dict       =					[0.485, 0.456, 0.406]
_snake_case       :      int       =					[0.229, 0.224, 0.225]
_snake_case       :      Optional[int]       =					[0.5, 0.5, 0.5]
_snake_case       :      str       =					[0.5, 0.5, 0.5]
_snake_case       :      str       =					[0.4814_5466, 0.457_8275, 0.4082_1073]
_snake_case       :      str       =					[0.2686_2954, 0.2613_0258, 0.2757_7711]