'''simple docstring'''





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			int      )   ->				list:


     '''simple docstring'''




     UpperCAmelCase_	   =       word.split()

     def justify(snake_case_	:			list ,   snake_case_	:			int ,   snake_case_	:			int      ) -> 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()





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_:					int  ={
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					List[str]  =['MaskFormerFeatureExtractor']
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =['MaskFormerImageProcessor']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Dict  =[
        'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MaskFormerForInstanceSegmentation',
        'MaskFormerModel',
        'MaskFormerPreTrainedModel',
    ]
    SCREAMING_SNAKE_CASE_:					List[str]  =[
        'MaskFormerSwinBackbone',
        'MaskFormerSwinModel',
        'MaskFormerSwinPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_:					Dict  =_LazyModule(__name__, globals()['__file__'], _import_structure)




