

"""simple docstring"""





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__	:						List[Any]  				=	word.split()

	def justify(A_						,     A_						,     A_ ) -> str:
		lowerCAmelCase__	:						Union[str, Any]  				=	max_width - width
		lowerCAmelCase__	:						str  				=	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:
			lowerCAmelCase__	:						Optional[int]  				=	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]
			lowerCAmelCase__	:						int  				=	spaces_to_insert_between_words * [
			    overall_spaces_count // spaces_to_insert_between_words
			]
			lowerCAmelCase__	:						str  				=	(
			    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
			lowerCAmelCase__	:						Any  				=	[]
			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_ )

	lowerCAmelCase__	:						List[str]  				=	[]
	lowerCAmelCase__	:						list[str]  				=	[]
	lowerCAmelCase__	:						Optional[int]  				=	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
			lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	[word], len(A_ )
	lowerCAmelCase__	:						str  				=	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_flax_available, is_torch_available


__UpperCamelCase  :      str							      =		{
    '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      str							      =		[
							    '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LongT5EncoderModel''',
							    '''LongT5ForConditionalGeneration''',
							    '''LongT5Model''',
							    '''LongT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''FlaxLongT5ForConditionalGeneration''',
							    '''FlaxLongT5Model''',
							    '''FlaxLongT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_longta import (
														    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LongTaEncoderModel,
														    LongTaForConditionalGeneration,
														    LongTaModel,
														    LongTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_longta import (
														    FlaxLongTaForConditionalGeneration,
														    FlaxLongTaModel,
														    FlaxLongTaPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      int							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




