


import math

class      __snake_case				:
					def __init__(						self       :						List[Any]	,      _lowerCAmelCase       :						str  )							->					int:



											'''simple docstring'''





											__magic_name__					=      len(_lowerCAmelCase  )
											__magic_name__					=      [0] * (
											    4 * self.N
											)  # approximate the overall size of segment tree with array N
											self.build(1	,      0	,      self.N - 1  )
					def 	a			(						self       :						List[Any]	,      _lowerCAmelCase       :						List[Any]  )							->					Any:



											'''simple docstring'''





											return idx * 2
					def 	a			(						self       :						str	,      _lowerCAmelCase       :						Any  )							->					List[Any]:



											'''simple docstring'''





											return idx * 2 + 1
					def 	a			(						self       :						Any	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						Union[str, Any]	,      _lowerCAmelCase       :						Optional[Any]  )							->					List[Any]:  # noqa: E741



											'''simple docstring'''





											if l == r:
																	__magic_name__					=      A[l]
											else:
																	__magic_name__					=      (l + r) // 2
																	self.build(self.left(_lowerCAmelCase  )	,      _lowerCAmelCase	,      _lowerCAmelCase  )
																	self.build(self.right(_lowerCAmelCase  )	,      mid + 1	,      _lowerCAmelCase  )
																	__magic_name__					=      max(self.st[self.left(_lowerCAmelCase  )]	,      self.st[self.right(_lowerCAmelCase  )]  )
					def 	a			(						self       :						List[Any]	,      _lowerCAmelCase       :						Optional[int]	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						Dict  )							->					Any:



											'''simple docstring'''





											return self.update_recursive(1	,      0	,      self.N - 1	,      a - 1	,      b - 1	,      _lowerCAmelCase  )
					def 	a			(						self       :						List[Any]	,      _lowerCAmelCase       :						str	,      _lowerCAmelCase       :						int	,      _lowerCAmelCase       :						List[Any]	,      _lowerCAmelCase       :						int	,      _lowerCAmelCase       :						str	,      _lowerCAmelCase       :						List[str]  )							->					Tuple:  # noqa: E741



											'''simple docstring'''





											if r < a or l > b:
																	return True
											if l == r:
																	__magic_name__					=      val
																	return True
											__magic_name__					=      (l + r) // 2
											self.update_recursive(self.left(_lowerCAmelCase  )	,      _lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase  )
											self.update_recursive(self.right(_lowerCAmelCase  )	,      mid + 1	,      _lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase  )
											__magic_name__					=      max(self.st[self.left(_lowerCAmelCase  )]	,      self.st[self.right(_lowerCAmelCase  )]  )
											return True
					def 	a			(						self       :						Tuple	,      _lowerCAmelCase       :						Dict	,      _lowerCAmelCase       :						Optional[Any]  )							->					Any:



											'''simple docstring'''





											return self.query_recursive(1	,      0	,      self.N - 1	,      a - 1	,      b - 1  )
					def 	a			(						self       :						List[Any]	,      _lowerCAmelCase       :						str	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						int	,      _lowerCAmelCase       :						int	,      _lowerCAmelCase       :						Dict  )							->					Dict:  # noqa: E741



											'''simple docstring'''





											if r < a or l > b:
																	return -math.inf
											if l >= a and r <= b:
																	return self.st[idx]
											__magic_name__					=      (l + r) // 2
											__magic_name__					=      self.query_recursive(self.left(_lowerCAmelCase  )	,      _lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase  )
											__magic_name__					=      self.query_recursive(self.right(_lowerCAmelCase  )	,      mid + 1	,      _lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase  )
											return max(_lowerCAmelCase	,      _lowerCAmelCase  )







					def 	a			(						self       :						Optional[int]  )							->					int:



											'''simple docstring'''





											__magic_name__					=      []
											for i in range(1	,      N + 1  ):
																	show_list += [self.query(_lowerCAmelCase	,      _lowerCAmelCase  )]
											print(_lowerCAmelCase  )


if __name__ == "__main__":
					UpperCamelCase			:					Union[str, Any]         =							[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
					UpperCamelCase			:					Union[str, Any]         =							15
					UpperCamelCase			:					Union[str, Any]         =							SegmentTree(A)
					print(segt.query(4, 6))
					print(segt.query(7, 11))
					print(segt.query(7, 12))
					segt.update(1, 3, 111)
					print(segt.query(1, 15))
					segt.update(7, 8, 235)
					segt.show_data()







'''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__":
   A					       =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)

   A       , A					       =parser.parse_known_args()

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

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

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

   # Preprocess train dataset
   A					       =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'])

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

   # Preprocess test dataset
   A					       =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'])

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

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

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




