






"""simple docstring"""






def       _A    (__a					,       __a							)				-> List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[Any]							=	word.split()

    def justify(__a					,       __a					,       __a							) -> str:
        SCREAMING_SNAKE_CASE_   :						Any							=	max_width - width
        SCREAMING_SNAKE_CASE_   :						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:
            SCREAMING_SNAKE_CASE_   :						List[str]							=	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_   :						Optional[Any]							=	spaces_to_insert_between_words * [
                overall_spaces_count // spaces_to_insert_between_words
            ]
            SCREAMING_SNAKE_CASE_   :						Dict							=	(
                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
            SCREAMING_SNAKE_CASE_   :						List[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_							)

    SCREAMING_SNAKE_CASE_   :						int							=	[]
    SCREAMING_SNAKE_CASE_   :						Tuple							=	[]
    SCREAMING_SNAKE_CASE_   :						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
            SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[word], len(A_							)
    SCREAMING_SNAKE_CASE_   :						List[str]							=	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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_     :				List[str]         =				logging.get_logger(__name__)

UpperCAmelCase_     :				Dict         =				{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						"data2vec-text"
      def __init__(		self  :						Any		,			lowercase_  :						Any=30522		,			lowercase_  :						Any=768		,			lowercase_  :						Union[str, Any]=12		,			lowercase_  :						Dict=12		,			lowercase_  :						List[Any]=3072		,			lowercase_  :						str="gelu"		,			lowercase_  :						int=0.1		,			lowercase_  :						Dict=0.1		,			lowercase_  :						str=512		,			lowercase_  :						Optional[int]=2		,			lowercase_  :						int=0.02		,			lowercase_  :						int=1e-12		,			lowercase_  :						Any=1		,			lowercase_  :						Any=0		,			lowercase_  :						List[Any]=2		,			lowercase_  :						Tuple="absolute"		,			lowercase_  :						Optional[int]=True		,			lowercase_  :						int=None		,			**lowercase_  :						Tuple		,			):




          '''simple docstring'''


          super().__init__(pad_token_id=lowercase_		,			bos_token_id=lowercase_		,			eos_token_id=lowercase_		,			**lowercase_)

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	vocab_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	hidden_size
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	num_hidden_layers
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	num_attention_heads
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	hidden_act
          SCREAMING_SNAKE_CASE_   :						Any							=	intermediate_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Tuple							=	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	max_position_embeddings
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	type_vocab_size
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	initializer_range
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	layer_norm_eps
          SCREAMING_SNAKE_CASE_   :						str							=	position_embedding_type
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	use_cache
          SCREAMING_SNAKE_CASE_   :						str							=	classifier_dropout




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          if self.task == "multiple-choice":
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              SCREAMING_SNAKE_CASE_   :						Any							=	{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ])


