


'''simple docstring'''



class       a							:


     """simple docstring"""






     def __init__(							self	:						List[Any]     )		->				None:
          # numbermap keys are the number and its values are lists of indexes sorted
          # in ascending order
          __UpperCAmelCase      :					int    				=     {}
          # indexmap keys are an index and it's values are the number at that index
          __UpperCAmelCase      :					Union[str, Any]    				=     {}

     def 					lowerCamelCase__					(							self	:						Dict				,					snake_case	:						int				,					snake_case	:						List[Any]     )		->				list[int]:
          if isinstance(_SCREAMING_SNAKE_CASE				,					(range, str)     ):
               __UpperCAmelCase      :					Tuple    				=     list(_SCREAMING_SNAKE_CASE     )
          elif not isinstance(_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     ):
               raise TypeError(
                   '''binary_search_delete() only accepts either a list, range or str'''     )

          __UpperCAmelCase      :					Tuple    				=     0
          __UpperCAmelCase      :					str    				=     len(_SCREAMING_SNAKE_CASE     ) - 1

          while low <= high:
               __UpperCAmelCase      :					int    				=     (low + high) // 2
               if array[mid] == item:
                    array.pop(_SCREAMING_SNAKE_CASE     )
                    return array
               elif array[mid] < item:
                    __UpperCAmelCase      :					Any    				=     mid + 1
               else:
                    __UpperCAmelCase      :					Any    				=     mid - 1
          raise ValueError(
              '''Either the item is not in the array or the array was unsorted'''     )

     def 					lowerCamelCase__					(							self	:						Optional[int]				,					snake_case	:						str				,					snake_case	:						Optional[Any]     )		->				list[int]:
          if isinstance(_SCREAMING_SNAKE_CASE				,					(range, str)     ):
               __UpperCAmelCase      :					Any    				=     list(_SCREAMING_SNAKE_CASE     )
          elif not isinstance(_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     ):
               raise TypeError(
                   '''binary_search_insert() only accepts either a list, range or str'''     )

          __UpperCAmelCase      :					Tuple    				=     0
          __UpperCAmelCase      :					str    				=     len(_SCREAMING_SNAKE_CASE     ) - 1

          while low <= high:
               __UpperCAmelCase      :					List[str]    				=     (low + high) // 2
               if array[mid] == index:
                    # If the item already exists in the array,
                    # insert it after the existing item
                    array.insert(mid + 1				,					_SCREAMING_SNAKE_CASE     )
                    return array
               elif array[mid] < index:
                    __UpperCAmelCase      :					Any    				=     mid + 1
               else:
                    __UpperCAmelCase      :					List[str]    				=     mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
          array.insert(_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     )
          return array

     def 					lowerCamelCase__					(							self	:						List[Any]				,					snake_case	:						Union[str, Any]				,					snake_case	:						List[Any]     )		->				None:
          # Remove previous index
          if index in self.indexmap:
               __UpperCAmelCase      :					List[str]    				=     self.indexmap[index]
               if len(self.numbermap[n]     ) == 1:
                    del self.numbermap[n]
               else:
                    __UpperCAmelCase      :					Optional[int]    				=     self.binary_search_delete(self.numbermap[n]				,					_SCREAMING_SNAKE_CASE     )

        # Set new index
          __UpperCAmelCase      :					Tuple    				=     number

          # Number not seen before or empty so insert number value
          if number not in self.numbermap:
               __UpperCAmelCase      :					List[str]    				=     [index]

          # Here we need to perform a binary search insertion in order to insert
          # The item in the correct place
          else:
               __UpperCAmelCase      :					Optional[Any]    				=     self.binary_search_insert(
                   self.numbermap[number]				,					_SCREAMING_SNAKE_CASE     )






     def 					lowerCamelCase__					(							self	:						int				,					snake_case	:						List[Any]     )		->				int:
          return self.numbermap.get(_SCREAMING_SNAKE_CASE				,					[-1]     )[0]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
'''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  : 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)

  __snake_case       ,       __snake_case  : Dict		       =    parser.parse_known_args()

  # overwrite batch size until we have tf_glue.py
  __snake_case  : Any		       =    16
  __snake_case  : Optional[Any]		       =    16

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

  # Load dataset
  __snake_case       ,       __snake_case  : Optional[int]		       =    load_dataset('imdb', split=['train', 'test'])
  __snake_case  : List[str]		       =    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  : int		       =    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  : Dict		       =    {
      x: train_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((train_features, train_dataset['label'])).batch(
      args.per_device_train_batch_size
  )

  # Preprocess test dataset
  __snake_case  : Tuple		       =    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  : List[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  : Optional[int]		       =    tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  __snake_case  : Union[str, Any]		       =    tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  __snake_case  : int		       =    [tf.keras.metrics.SparseCategoricalAccuracy()]
  model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

  __snake_case  : Any		       =    time.time()
  __snake_case  : int		       =    model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
  __snake_case  : Union[str, 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}""")






