






'''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'''

from __future__ import annotations

import collections
import pprint
from pathlib import Path







def    lowercase__			(				__UpperCamelCase   )->			str:
      return "".join(sorted(__UpperCamelCase   )   )







def    lowercase__			(				__UpperCamelCase   )->			list[str]:
      return word_by_signature[signature(__UpperCamelCase   )]


SCREAMING_SNAKE_CASE__     =       Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
SCREAMING_SNAKE_CASE__     =       sorted({word.strip().lower() for word in data.splitlines()})

SCREAMING_SNAKE_CASE__     =       collections.defaultdict(list)
for word in word_list:
     word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

     with open('anagrams.txt', 'w') as file:
          file.write('all_anagrams = \n ')
          file.write(pprint.pformat(all_anagrams))
