






'''simple docstring'''

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


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}






class     a_						(      __lowercase    ):
		lowercase							=				"""trocr"""
		lowercase							=				["""past_key_values"""]
		lowercase							=				{
		    """num_attention_heads""": """decoder_attention_heads""",
		    """hidden_size""": """d_model""",
		    """num_hidden_layers""": """decoder_layers""",
		}

		def __init__(				self      ,		_SCREAMING_SNAKE_CASE=50265      ,		_SCREAMING_SNAKE_CASE=1024      ,		_SCREAMING_SNAKE_CASE=12      ,		_SCREAMING_SNAKE_CASE=16      ,		_SCREAMING_SNAKE_CASE=4096      ,		_SCREAMING_SNAKE_CASE="gelu"      ,		_SCREAMING_SNAKE_CASE=512      ,		_SCREAMING_SNAKE_CASE=0.1      ,		_SCREAMING_SNAKE_CASE=0.0      ,		_SCREAMING_SNAKE_CASE=0.0      ,		_SCREAMING_SNAKE_CASE=2      ,		_SCREAMING_SNAKE_CASE=0.0_2      ,		_SCREAMING_SNAKE_CASE=0.0      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=1      ,		_SCREAMING_SNAKE_CASE=0      ,		_SCREAMING_SNAKE_CASE=2      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Dict:



								"""simple docstring"""






								UpperCamelCase      = vocab_size
								UpperCamelCase      = d_model
								UpperCamelCase      = decoder_layers
								UpperCamelCase      = decoder_attention_heads
								UpperCamelCase      = decoder_ffn_dim
								UpperCamelCase      = activation_function
								UpperCamelCase      = max_position_embeddings
								UpperCamelCase      = dropout
								UpperCamelCase      = attention_dropout
								UpperCamelCase      = activation_dropout
								UpperCamelCase      = init_std
								UpperCamelCase      = decoder_layerdrop
								UpperCamelCase      = use_cache
								UpperCamelCase      = scale_embedding
								UpperCamelCase      = use_learned_position_embeddings
								UpperCamelCase      = layernorm_embedding

								super().__init__(
								    pad_token_id=_a      ,		bos_token_id=_a      ,		eos_token_id=_a      ,		decoder_start_token_id=_a      ,		**_a      ,		)








'''simple docstring'''

def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			list:
      UpperCamelCase      = word.split()

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

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


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

     testmod()
