




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				list:
						_UpperCAmelCase      =			word.split()

						def justify(snake_case			,     snake_case			,     snake_case       ) -> str:
												_UpperCAmelCase      =			max_width - width
												_UpperCAmelCase      =			len(snake_case       )
												if len(snake_case       ) == 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(snake_case       ):
																								num_spaces_between_words_list[i] += 1
																		_UpperCAmelCase      =			[]
																		for i in range(snake_case       ):
																								# 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(snake_case       )

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


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

				testmod()





from typing import Dict

from .base import GenericTensor, Pipeline




class       _A				(		__lowercase     ):






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE			):
								if tokenize_kwargs is None:
														_UpperCAmelCase      =			{}

								if truncation is not None:
														if "truncation" in tokenize_kwargs:
																				raise ValueError(
																				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""			)
														_UpperCAmelCase      =			truncation

								_UpperCAmelCase      =			tokenize_kwargs

								_UpperCAmelCase      =			{}
								if return_tensors is not None:
														_UpperCAmelCase      =			return_tensors

								return preprocess_params, {}, postprocess_params






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.framework
								_UpperCAmelCase      =			self.tokenizer(_SCREAMING_SNAKE_CASE						,		return_tensors=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								return model_inputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.model(**_SCREAMING_SNAKE_CASE			)
								return model_outputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False			):
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
														return model_outputs[0]
								if self.framework == "pt":
														return model_outputs[0].tolist()
								elif self.framework == "tf":
														return model_outputs[0].numpy().tolist()






		def __call__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								return super().__call__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)