
from typing import List, Optional

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


lowerCamelCase      =logging.get_logger(__name__)

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




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''autoformer'''
     SCREAMING_SNAKE_CASE_		   =  {
         '''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 = 6_4						,						__SCREAMING_SNAKE_CASE = 2						,						__SCREAMING_SNAKE_CASE = 2						,						__SCREAMING_SNAKE_CASE = 2						,						__SCREAMING_SNAKE_CASE = 2						,						__SCREAMING_SNAKE_CASE = 3_2						,						__SCREAMING_SNAKE_CASE = 3_2						,						__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 = 1_0_0						,						__SCREAMING_SNAKE_CASE = 0.02						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE = 1_0						,						__SCREAMING_SNAKE_CASE = 2_5						,						__SCREAMING_SNAKE_CASE = 3						,						**__SCREAMING_SNAKE_CASE						,						)  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				prediction_length
           UpperCamelCase__    :   Union[str, Any]					=				context_length if context_length is not None else prediction_length
           UpperCamelCase__    :   Tuple					=				distribution_output
           UpperCamelCase__    :   Optional[Any]					=				loss
           UpperCamelCase__    :   str					=				input_size
           UpperCamelCase__    :   str					=				num_time_features
           UpperCamelCase__    :   Tuple					=				lags_sequence
           UpperCamelCase__    :   Tuple					=				scaling
           UpperCamelCase__    :   List[str]					=				num_dynamic_real_features
           UpperCamelCase__    :   Any					=				num_static_real_features
           UpperCamelCase__    :   int					=				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__    :   List[str]					=				cardinality
           else:
                 UpperCamelCase__    :   Optional[Any]					=				[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__    :   Optional[int]					=				embedding_dimension
           else:
                 UpperCamelCase__    :   str					=				[min(5_0						,						(cat + 1) // 2  ) for cat in self.cardinality]
           UpperCamelCase__    :   List[Any]					=				num_parallel_samples

           # Transformer architecture configuration
           UpperCamelCase__    :   Any					=				input_size * len(self.lags_sequence  ) + self._number_of_features
           UpperCamelCase__    :   List[str]					=				d_model
           UpperCamelCase__    :   str					=				encoder_attention_heads
           UpperCamelCase__    :   Any					=				decoder_attention_heads
           UpperCamelCase__    :   List[Any]					=				encoder_ffn_dim
           UpperCamelCase__    :   Optional[Any]					=				decoder_ffn_dim
           UpperCamelCase__    :   Tuple					=				encoder_layers
           UpperCamelCase__    :   int					=				decoder_layers

           UpperCamelCase__    :   Tuple					=				dropout
           UpperCamelCase__    :   Any					=				attention_dropout
           UpperCamelCase__    :   List[str]					=				activation_dropout
           UpperCamelCase__    :   int					=				encoder_layerdrop
           UpperCamelCase__    :   List[Any]					=				decoder_layerdrop

           UpperCamelCase__    :   Optional[Any]					=				activation_function
           UpperCamelCase__    :   Optional[int]					=				init_std

           UpperCamelCase__    :   str					=				use_cache

           # Autoformer
           UpperCamelCase__    :   Optional[int]					=				label_length
           UpperCamelCase__    :   Tuple					=				moving_average
           UpperCamelCase__    :   Optional[Any]					=				autocorrelation_factor

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






     @property
     def    __SCREAMING_SNAKE_CASE			(							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
           )




lowerCamelCase      ={"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
lowerCamelCase      =["a", "b", "c", "d", "e"]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   str					=				start
      # add current to visited
      visited.append(UpperCamelCase__		)
      UpperCamelCase__    :   int					=				edges[current]
      for neighbor in neighbors:
            # if neighbor not in visited, visit
            if neighbor not in visited:
                  UpperCamelCase__    :   int					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # if all neighbors visited add current to sort
      sort.append(UpperCamelCase__		)
      # if all vertices haven't been visited select a new one to visit
      if len(UpperCamelCase__		) != len(UpperCamelCase__		):
            for vertice in vertices:
                  if vertice not in visited:
                        UpperCamelCase__    :   Optional[int]					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # return sort
      return sort


if __name__ == "__main__":
    lowerCamelCase      =topological_sort("a", [], [])
    print(sort)


