
def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Optional[Any]    ,							SCREAMING_SNAKE_CASE       :      Optional[int]			)     ->						list:
				__lowercase								=					word.split()

				def justify(SCREAMING_SNAKE_CASE       :      List[Any]    ,							SCREAMING_SNAKE_CASE       :      str    ,							SCREAMING_SNAKE_CASE       :      Union[str, Any]			) -> str:
								__lowercase								=					max_width - width
								__lowercase								=					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:
												__lowercase								=					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]
												__lowercase								=					spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__lowercase								=					(
												    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
												__lowercase								=					[]
												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			)

				__lowercase								=					[]
				__lowercase								=					[]
				__lowercase								=					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
												__lowercase								=					[word], len(__snake_case			)
				__lowercase								=					max_width - width - len(__snake_case			)
				answer.append(' '.join(__snake_case			) + (remaining_spaces + 1) * ' '			)
				return answer


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

							testmod()










"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
	import optax
	from flax.training.common_utils import onehot

	from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
	from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)