def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	word.split()

 def justify(_A				,					_A				,					_A       ) -> str:
  __a							=	max_width - width
  __a							=	len(_A       )
  if len(_A       ) == 1:
   # if there is only word in line
   # just insert overall_spaces_count for the remainder of line
   return line[0] + " " * overall_spaces_count
  else:
   __a							=	words_count - 1
   # num_spaces_between_words_list[i] : tells you to insert
   # num_spaces_between_words_list[i] spaces
   # after word on line[i]
   __a							=	spaces_to_insert_between_words * [
       overall_spaces_count // spaces_to_insert_between_words
   ]
   __a							=	(
       overall_spaces_count % spaces_to_insert_between_words
   )
   # distribute spaces via round robin to the left words
   for i in range(_A       ):
    num_spaces_between_words_list[i] += 1
   __a							=	[]
   for i in range(_A       ):
    # add the word
    aligned_words_list.append(line[i]       )
    # add the spaces to insert
    aligned_words_list.append(num_spaces_between_words_list[i] * " "       )
   # just add the last word to the sentence
   aligned_words_list.append(line[-1]       )
   # join the aligned words list to form a justified line
   return "".join(_A       )

 __a							=	[]
 __a							=	[]
 __a							=	0
 for word in words:
  if width + len(_A       ) + len(_A       ) <= max_width:
   # keep adding words until we can fill out max_width
   # width = sum of length of all words (without overall_spaces_count)
   # len(word) = length of current word
   # len(line) = number of overall_spaces_count to insert between words
   line.append(_A       )
   width += len(_A       )
  else:
   # justify the line and add it to result
   answer.append(justify(_A				,					_A				,					_A       )       )
   # reset new line and new width
   __a							,  __a							=	[word], len(_A       )
 __a							=	max_width - width - len(_A       )
 answer.append(" ".join(_A       ) + (remaining_spaces + 1) * " "       )
 return answer


if __name__ == "__main__":
  from doctest import testmod

  testmod()



from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Dict				  =		{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """canine"""

  def __init__(		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   List[str]=7_68  ,				__SCREAMING_SNAKE_CASE			:   Any=12  ,				__SCREAMING_SNAKE_CASE			:   str=12  ,				__SCREAMING_SNAKE_CASE			:   int=30_72  ,				__SCREAMING_SNAKE_CASE			:   int="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1_63_84  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=16  ,				__SCREAMING_SNAKE_CASE			:   int=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=1E-12  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0xE000  ,				__SCREAMING_SNAKE_CASE			:   int=0xE001  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=8  ,				__SCREAMING_SNAKE_CASE			:   List[str]=1_63_84  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=1_28  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   __a							=	max_position_embeddings
   __a							=	hidden_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	intermediate_size
   __a							=	hidden_act
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	initializer_range
   __a							=	type_vocab_size
   __a							=	layer_norm_eps

   # Character config:
   __a							=	downsampling_rate
   __a							=	upsampling_kernel_size
   __a							=	num_hash_functions
   __a							=	num_hash_buckets
   __a							=	local_transformer_stride


