




'''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__":
       lowercase__								=						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)

       lowercase__								=						parser.parse_known_args()

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

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

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

       # Preprocess train dataset
       lowercase__								=						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'''])

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

       # Preprocess test dataset
       lowercase__								=						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'''])

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

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

       lowercase__								=						time.time()
       lowercase__								=						model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
       lowercase__								=						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}")







class        snake_case__				:
				"""simple docstring"""



				def __init__( self							:	List[str]   , __lowerCamelCase							:	int				)   ->	None:
							a       =     data
							a       =     42
							a       =     42




def      __magic_name__							(					A						:   Node  ):






			'''simple docstring'''







			a       =     0
			a       =     x




def      __magic_name__							(					A						:   Node,    A						:   Node  ):






			'''simple docstring'''







			a       ,						a       =     find_set(A  ), find_set(A  )
			if x == y:
						return

			elif x.rank > y.rank:
						a       =     x
			else:
						a       =     y
						if x.rank == y.rank:
									y.rank += 1




def      __magic_name__							(					A						:   Node  ):






			'''simple docstring'''







			if x != x.parent:
						a       =     find_set(x.parent  )
			return x.parent




def      __magic_name__							(					A						:   Node  ):






			'''simple docstring'''







			a       =     ({0, 1, 2}, {3, 4, 5})
			for s in sets:
						if node.data in s:
									return s
			a       =     F"""{node.data} is not in {sets}"""
			raise ValueError(A  )




def      __magic_name__							(					):






			'''simple docstring'''







			a       =     [Node(A  ) for i in range(6  )]
			for v in vertex:
						make_set(A  )

			union_set(vertex[0],    vertex[1]  )
			union_set(vertex[1],    vertex[2]  )
			union_set(vertex[3],    vertex[4]  )
			union_set(vertex[3],    vertex[5]  )

			for nodea in vertex:
						for nodea in vertex:
									if find_python_set(A  ).isdisjoint(find_python_set(A  )  ):
												assert find_set(A  ) != find_set(A  )
									else:
												assert find_set(A  ) == find_set(A  )


if __name__ == "__main__":
		test_disjoint_set()






