

def      A__					(			__A  :						List[Any]     ,				__A  :						str			)					->Dict:
      __A    =word.split()

      def justify(__A  :						int     ,				__A  :						Dict     ,				__A  :						List[Any]			) -> 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()








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase  =			{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BioGptForCausalLM""",
	    """BioGptForTokenClassification""",
	    """BioGptForSequenceClassification""",
	    """BioGptModel""",
	    """BioGptPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
	from .tokenization_biogpt import BioGptTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_biogpt import (
		    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BioGptForCausalLM,
		    BioGptForSequenceClassification,
		    BioGptForTokenClassification,
		    BioGptModel,
		    BioGptPreTrainedModel,
		)


else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

