





'''simple docstring'''
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  int     ) ->      list:



       """simple docstring"""






       __UpperCamelCase       =  word.split()

       def justify(__lowercase						:  list				,  __lowercase						:  int				,  __lowercase						:  int     ) -> str:
              __UpperCamelCase       =  max_width - width
              __UpperCamelCase       =  len(__lowercase     )
              if len(__lowercase     ) == 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:
                     __UpperCamelCase       =  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]
                     __UpperCamelCase       =  spaces_to_insert_between_words * [
                         overall_spaces_count // spaces_to_insert_between_words
                     ]
                     __UpperCamelCase       =  (
                         overall_spaces_count % spaces_to_insert_between_words
                     )
                     # distribute spaces via round robin to the left words
                     for i in range(__lowercase     ):
                            num_spaces_between_words_list[i] += 1
                     __UpperCamelCase       =  []
                     for i in range(__lowercase     ):
                            # 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(__lowercase     )

       __UpperCamelCase       =  []
       __UpperCamelCase       =  []
       __UpperCamelCase       =  0
       for word in words:
              if width + len(__lowercase     ) + len(__lowercase     ) <= 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(__lowercase     )
                     width += len(__lowercase     )
              else:
                     # justify the line and add it to result
                     answer.append(justify(__lowercase				,  __lowercase				,  __lowercase     )     )
                     # reset new line and new width
                     __UpperCamelCase       ,						__UpperCamelCase       =  [word], len(__lowercase     )
       __UpperCamelCase       =  max_width - width - len(__lowercase     )
       answer.append(' '.join(__lowercase     ) + (remaining_spaces + 1) * ' '     )
       return answer


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

    testmod()





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase	:       Tuple      	=	{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       Dict      	=	[
       "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "IBertForMaskedLM",
       "IBertForMultipleChoice",
       "IBertForQuestionAnswering",
       "IBertForSequenceClassification",
       "IBertForTokenClassification",
       "IBertModel",
       "IBertPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase	:       Union[str, Any]      	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

