






'''simple docstring'''

from typing import List, Optional

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


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}






class     a_						(      lowerCamelCase    ):
  lowercase							=				"""autoformer"""
  lowercase							=				{
      """hidden_size""": """d_model""",
      """num_attention_heads""": """encoder_attention_heads""",
      """num_hidden_layers""": """encoder_layers""",
  }

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = "student_t"      ,		_SCREAMING_SNAKE_CASE = "nll"      ,		_SCREAMING_SNAKE_CASE = 1      ,		_SCREAMING_SNAKE_CASE = [1, 2, 3, 4, 5, 6, 7]      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = 0      ,		_SCREAMING_SNAKE_CASE = 0      ,		_SCREAMING_SNAKE_CASE = 0      ,		_SCREAMING_SNAKE_CASE = 0      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 64      ,		_SCREAMING_SNAKE_CASE = 2      ,		_SCREAMING_SNAKE_CASE = 2      ,		_SCREAMING_SNAKE_CASE = 2      ,		_SCREAMING_SNAKE_CASE = 2      ,		_SCREAMING_SNAKE_CASE = 32      ,		_SCREAMING_SNAKE_CASE = 32      ,		_SCREAMING_SNAKE_CASE = "gelu"      ,		_SCREAMING_SNAKE_CASE = 0.1      ,		_SCREAMING_SNAKE_CASE = 0.1      ,		_SCREAMING_SNAKE_CASE = 0.1      ,		_SCREAMING_SNAKE_CASE = 0.1      ,		_SCREAMING_SNAKE_CASE = 0.1      ,		_SCREAMING_SNAKE_CASE = 100      ,		_SCREAMING_SNAKE_CASE = 0.0_2      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE = 10      ,		_SCREAMING_SNAKE_CASE = 25      ,		_SCREAMING_SNAKE_CASE = 3      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = prediction_length
        UpperCamelCase      = context_length if context_length is not None else prediction_length
        UpperCamelCase      = distribution_output
        UpperCamelCase      = loss
        UpperCamelCase      = input_size
        UpperCamelCase      = num_time_features
        UpperCamelCase      = lags_sequence
        UpperCamelCase      = scaling
        UpperCamelCase      = num_dynamic_real_features
        UpperCamelCase      = num_static_real_features
        UpperCamelCase      = num_static_categorical_features
        if cardinality is not None and num_static_categorical_features > 0:
              if len(_SCREAMING_SNAKE_CASE				) != num_static_categorical_features:
                    raise ValueError(
                        """The cardinality should be a list of the same length as `num_static_categorical_features`"""				)
              UpperCamelCase      = cardinality
        else:
              UpperCamelCase      = [0]
        if embedding_dimension is not None and num_static_categorical_features > 0:
              if len(_SCREAMING_SNAKE_CASE				) != num_static_categorical_features:
                    raise ValueError(
                        """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""				)
              UpperCamelCase      = embedding_dimension
        else:
              UpperCamelCase      = [min(50      ,		(cat + 1) // 2				) for cat in self.cardinality]
        UpperCamelCase      = num_parallel_samples

        # Transformer architecture configuration
        UpperCamelCase      = input_size * len(self.lags_sequence				) + self._number_of_features
        UpperCamelCase      = d_model
        UpperCamelCase      = encoder_attention_heads
        UpperCamelCase      = decoder_attention_heads
        UpperCamelCase      = encoder_ffn_dim
        UpperCamelCase      = decoder_ffn_dim
        UpperCamelCase      = encoder_layers
        UpperCamelCase      = decoder_layers

        UpperCamelCase      = dropout
        UpperCamelCase      = attention_dropout
        UpperCamelCase      = activation_dropout
        UpperCamelCase      = encoder_layerdrop
        UpperCamelCase      = decoder_layerdrop

        UpperCamelCase      = activation_function
        UpperCamelCase      = init_std

        UpperCamelCase      = use_cache

        # Autoformer
        UpperCamelCase      = label_length
        UpperCamelCase      = moving_average
        UpperCamelCase      = autocorrelation_factor

        super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)


  @property
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        return (
            sum(self.embedding_dimension				)
            + self.num_dynamic_real_features
            + self.num_time_features
            + self.num_static_real_features
            + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
        )








'''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()
