
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__":
		__SCREAMING_SNAKE_CASE	:      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)

		__SCREAMING_SNAKE_CASE   , __SCREAMING_SNAKE_CASE	:      str			   =   parser.parse_known_args()

		# overwrite batch size until we have tf_glue.py
		__SCREAMING_SNAKE_CASE	:      Tuple			   =   16
		__SCREAMING_SNAKE_CASE	:      Union[str, Any]			   =   16

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

		# Load dataset
		__SCREAMING_SNAKE_CASE   , __SCREAMING_SNAKE_CASE	:      Optional[Any]			   =   load_dataset('imdb', split=['train', 'test'])
		__SCREAMING_SNAKE_CASE	:      Optional[Any]			   =   train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
		__SCREAMING_SNAKE_CASE	:      List[str]			   =   test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

		# Preprocess train dataset
		__SCREAMING_SNAKE_CASE	:      List[str]			   =   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'])

		__SCREAMING_SNAKE_CASE	:      Tuple			   =   {
		    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
		    for x in ['input_ids', 'attention_mask']
		}
		__SCREAMING_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
		__SCREAMING_SNAKE_CASE	:      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'])

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

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

		__SCREAMING_SNAKE_CASE	:      Dict			   =   time.time()
		__SCREAMING_SNAKE_CASE	:      Tuple			   =   model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
		__SCREAMING_SNAKE_CASE	:      List[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'''

from __future__ import annotations






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						list[int]							,		_SCREAMING_SNAKE_CASE     :						int							)			->						list[int]:







	"""simple docstring"""
	lowerCAmelCase    =	{}
	for index, val in enumerate(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase    =	target - val
		if compl in chk_map:
			return [chk_map[compl], index]
		lowerCAmelCase    =	index
	return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(F'''{two_sum([2, 7, 11, 15], 9) = }''')