




def    A						(__A   :    str   ,				__A   :    int       )   ->      list:







     """simple docstring"""


     UpperCAmelCase_          =							word.split()

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

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


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

    testmod()












snake_case_ : Dict    		=       {
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}






