



'''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											=			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											=			parser.parse_known_args()

					# overwrite batch size until we have tf_glue.py
					_UpperCamelCase											=			16
					_UpperCamelCase											=			16

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

					# Load dataset
					_UpperCamelCase											=			load_dataset('''imdb''', split=['''train''', '''test'''])
					_UpperCamelCase											=			train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
					_UpperCamelCase											=			test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

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

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

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

					_UpperCamelCase											=			time.time()
					_UpperCamelCase											=			model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
					_UpperCamelCase											=			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 itertools import permutations


def   lowercase_	(   lowerCAmelCase__	:  int = 5 ):

			"""simple docstring"""





			if gon_side < 3 or gon_side > 5:
						raise ValueError("""gon_side must be in the range [3, 5]""" )

			# Since it's 16, we know 10 is on the outer ring
			# Put the big numbers at the end so that they are never the first number
			__UpperCAmelCase   :      int										=     list(range(gon_side + 1			,							0			,							-1 ) )
			__UpperCAmelCase   :      int										=     list(range(gon_side + 2			,							gon_side * 2 + 1 ) )

			for perm in permutations(small_numbers + big_numbers ):
						__UpperCAmelCase   :      Optional[int]										=     generate_gon_ring(lowerCAmelCase__			,							list(lowerCAmelCase__ ) )
						if is_magic_gon(lowerCAmelCase__ ):
									return int("""""".join(str(lowerCAmelCase__ ) for n in numbers ) )

			__UpperCAmelCase   :      Optional[int]										=     f'Magic {gon_side}-gon ring is impossible'
			raise ValueError(lowerCAmelCase__ )


def   lowercase_	(   lowerCAmelCase__	:  int			,							lowerCAmelCase__	:  list[int] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Tuple										=     [0] * (gon_side * 3)
			__UpperCAmelCase   :      Optional[Any]										=     perm[0:3]
			perm.append(perm[1] )

			__UpperCAmelCase   :      Union[str, Any]										=     1 if gon_side < 5 else 2

			for i in range(1			,							len(lowerCAmelCase__ ) // 3 + magic_number ):
						__UpperCAmelCase   :      List[str]										=     perm[2 * i + 1]
						__UpperCAmelCase   :      Optional[int]										=     result[3 * i - 1]
						__UpperCAmelCase   :      Optional[int]										=     perm[2 * i + 2]

			return result


def   lowercase_	(   lowerCAmelCase__	:  list[int] ):

			"""simple docstring"""





			if len(lowerCAmelCase__ ) % 3 != 0:
						raise ValueError("""a gon ring should have a length that is a multiple of 3""" )

			if min(numbers[::3] ) != numbers[0]:
						return False

			__UpperCAmelCase   :      Optional[int]										=     sum(numbers[:3] )

			return all(sum(numbers[i : i + 3] ) == total for i in range(3			,							len(lowerCAmelCase__ )			,							3 ) )


if __name__ == "__main__":
					print(solution())




