def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			str      )				->   int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= SCREAMING_SNAKE_CASE__     				= 0
  for letter in data:
    SCREAMING_SNAKE_CASE__     				= (hash_ << 4) + ord(__UpperCamelCase      )
    SCREAMING_SNAKE_CASE__     				= hash_ & 0xf0_00_00_00
    if x != 0:
      hash_ ^= x >> 24
    hash_ &= ~x
  return hash_


if __name__ == "__main__":
   import doctest

   doctest.testmod()







'''simple docstring'''


import sys

from transformers import AutoTokenizer


_lowerCamelCase					:						Any					     =							sys.argv[1]
_lowerCamelCase					:						Union[str, Any]					     =							sys.argv[2]
_lowerCamelCase					:						Optional[Any]					     =							int(sys.argv[3])

_lowerCamelCase					:						Union[str, Any]					     =							0

_lowerCamelCase					:						Tuple					     =							AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
				for line in f_p:
								_lowerCamelCase					:						Optional[int]					     =							line.rstrip()

								if not line:
												print(line)
												_lowerCamelCase					:						Optional[int]					     =							0
												continue

								_lowerCamelCase					:						int					     =							line.split()[0]

								_lowerCamelCase					:						Union[str, Any]					     =							len(tokenizer.tokenize(token))

								# Token contains strange control characters like \x96 or \x95
								# Just filter out the complete line
								if current_subwords_len == 0:
												continue

								if (subword_len_counter + current_subwords_len) > max_len:
												print('')
												print(line)
												_lowerCamelCase					:						Optional[int]					     =							current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

