




'''simple docstring'''


def 						lowerCamelCase__   (				a    ,	a							):
						__snake_case						 =   word.split()

						def justify(a    ,	a    ,	a							) -> str:
												__snake_case						 =   max_width - width
												__snake_case						 =   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:
																		__snake_case						 =   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]
																		__snake_case						 =   spaces_to_insert_between_words * [
																		    overall_spaces_count // spaces_to_insert_between_words
																		]
																		__snake_case						 =   (
																		    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
																		__snake_case						 =   []
																		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							)

						__snake_case						 =   []
						__snake_case						 =   []
						__snake_case						 =   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
																		__snake_case ,       __snake_case						 =   [word], len(a							)
						__snake_case						 =   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__)

