"""simple docstring"""


def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase					)				->      list:
     snake_case_					       =					word.split()

     def justify(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					) -> str:
          snake_case_					       =					max_width - width
          snake_case_					       =					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:
               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(UpperCAmelCase					):
                    num_spaces_between_words_list[i] += 1
               snake_case_					       =					[]
               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					)

     snake_case_					       =					[]
     snake_case_					       =					[]
     snake_case_					       =					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
               snake_case_       ,	snake_case_					       =					[word], len(UpperCAmelCase					)
     snake_case_					       =					max_width - width - len(UpperCAmelCase					)
     answer.append(' '.join(UpperCAmelCase					) + (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_tf_available,
    is_torch_available,
)


lowercase_			     =  {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "UniSpeechForCTC",
				    "UniSpeechForPreTraining",
				    "UniSpeechForSequenceClassification",
				    "UniSpeechModel",
				    "UniSpeechPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_unispeech import (
								    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
								    UniSpeechForCTC,
								    UniSpeechForPreTraining,
								    UniSpeechForSequenceClassification,
								    UniSpeechModel,
								    UniSpeechPreTrainedModel,
								)

else:
				import sys

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






