



def   lowerCAmelCase__						(			_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		word.split()

				def justify(_SCREAMING_SNAKE_CASE       :			list				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			int    ) -> str:
								__a       =		max_width - width
								__a       =		len(__UpperCamelCase    )
								if len(__UpperCamelCase    ) == 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(__UpperCamelCase    ):
																num_spaces_between_words_list[i] += 1
												__a       =		[]
												for i in range(__UpperCamelCase    ):
																# 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(__UpperCamelCase    )

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


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

						testmod()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__			= {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= ["""MBartTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= ["""MBartTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MBartForCausalLM""",
						    """MBartForConditionalGeneration""",
						    """MBartForQuestionAnswering""",
						    """MBartForSequenceClassification""",
						    """MBartModel""",
						    """MBartPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """TFMBartForConditionalGeneration""",
						    """TFMBartModel""",
						    """TFMBartPreTrainedModel""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """FlaxMBartForConditionalGeneration""",
						    """FlaxMBartForQuestionAnswering""",
						    """FlaxMBartForSequenceClassification""",
						    """FlaxMBartModel""",
						    """FlaxMBartPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbart import MBartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbart_fast import MBartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mbart import (
												    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MBartForCausalLM,
												    MBartForConditionalGeneration,
												    MBartForQuestionAnswering,
												    MBartForSequenceClassification,
												    MBartModel,
												    MBartPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mbart import (
												    FlaxMBartForConditionalGeneration,
												    FlaxMBartForQuestionAnswering,
												    FlaxMBartForSequenceClassification,
												    FlaxMBartModel,
												    FlaxMBartPreTrainedModel,
												)

else:
						import sys

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