


'''simple docstring'''







def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							str   			=       word.split()

       def justify(_A   ,						_A   ,						_A					) -> str:
              _lowerCAmelCase    :							Optional[int]   			=       max_width - width
              _lowerCAmelCase    :							List[Any]   			=       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:
                     _lowerCAmelCase    :							Optional[int]   			=       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]
                     _lowerCAmelCase    :							str   			=       spaces_to_insert_between_words * [
                         overall_spaces_count // spaces_to_insert_between_words
                     ]
                     _lowerCAmelCase    :							List[Any]   			=       (
                         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
                     _lowerCAmelCase    :							str   			=       []
                     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					)

       _lowerCAmelCase    :							Optional[Any]   			=       []
       _lowerCAmelCase    :							list[str]   			=       []
       _lowerCAmelCase    :							Any   			=       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
                     _lowerCAmelCase			,     _lowerCAmelCase    :							int   			=       [word], len(_A					)
       _lowerCAmelCase    :							int   			=       max_width - width - len(_A					)
       answer.append(' '.join(_A					) + (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_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCAmelCase    :     int					=						{
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    :     Optional[Any]					=						[
           """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TrOCRForCausalLM""",
           """TrOCRPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

       lowerCAmelCase    :     Any					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

