

def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						word.split()

    def justify(_SCREAMING_SNAKE_CASE					:		list ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int       ) -> str:
        SCREAMING_SNAKE_CASE_									=						max_width - width
        SCREAMING_SNAKE_CASE_									=						len(_SCREAMING_SNAKE_CASE       )
        if len(_SCREAMING_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:
            SCREAMING_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]
            SCREAMING_SNAKE_CASE_									=						spaces_to_insert_between_words * [
                overall_spaces_count // spaces_to_insert_between_words
            ]
            SCREAMING_SNAKE_CASE_									=						(
                overall_spaces_count % spaces_to_insert_between_words
            )
            # distribute spaces via round robin to the left words
            for i in range(_SCREAMING_SNAKE_CASE       ):
                num_spaces_between_words_list[i] += 1
            SCREAMING_SNAKE_CASE_									=						[]
            for i in range(_SCREAMING_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(_SCREAMING_SNAKE_CASE       )

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


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

       testmod()





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__  :      List[Any]			=    logging.get_logger(__name__)

UpperCamelCase__  :      List[str]			=    {
    "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Any						 =  'biogpt'

  def __init__(					self						,		_A=42384						,		_A=1024						,		_A=24						,		_A=16						,		_A=4096						,		_A="gelu"						,		_A=0.1						,		_A=0.1						,		_A=1024						,		_A=0.0_2						,		_A=1E-12						,		_A=True						,		_A=True						,		_A=0.0						,		_A=0.0						,		_A=1						,		_A=0						,		_A=2						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						vocab_size
      SCREAMING_SNAKE_CASE_									=						max_position_embeddings
      SCREAMING_SNAKE_CASE_									=						hidden_size
      SCREAMING_SNAKE_CASE_									=						num_hidden_layers
      SCREAMING_SNAKE_CASE_									=						num_attention_heads
      SCREAMING_SNAKE_CASE_									=						intermediate_size
      SCREAMING_SNAKE_CASE_									=						hidden_act
      SCREAMING_SNAKE_CASE_									=						hidden_dropout_prob
      SCREAMING_SNAKE_CASE_									=						attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_									=						initializer_range
      SCREAMING_SNAKE_CASE_									=						layer_norm_eps
      SCREAMING_SNAKE_CASE_									=						scale_embedding
      SCREAMING_SNAKE_CASE_									=						use_cache
      SCREAMING_SNAKE_CASE_									=						layerdrop
      SCREAMING_SNAKE_CASE_									=						activation_dropout
      super().__init__(pad_token_id=_A						,		bos_token_id=_A						,		eos_token_id=_A						,		**_A)


