




from typing import List, Optional, Union

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


lowerCamelCase	:	str				=   logging.get_logger(__name__)

lowerCamelCase	:	Tuple				=   {
    'huggingface/informer-tourism-monthly': (
        'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						"""informer"""
      _snake_case												=						{
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
          """num_hidden_layers""": """encoder_layers""",
      }

      def __init__(				self    ,				A = None    ,				A = None    ,				A = "student_t"    ,				A = "nll"    ,				A = 1    ,				A = None    ,				A = "mean"    ,				A = 0    ,				A = 0    ,				A = 0    ,				A = 0    ,				A = None    ,				A = None    ,				A = 6_4    ,				A = 3_2    ,				A = 3_2    ,				A = 2    ,				A = 2    ,				A = 2    ,				A = 2    ,				A = True    ,				A = "gelu"    ,				A = 0.05    ,				A = 0.1    ,				A = 0.1    ,				A = 0.1    ,				A = 0.1    ,				A = 1_0_0    ,				A = 0.02    ,				A=True    ,				A = "prob"    ,				A = 5    ,				A = True    ,				**A    ,				)		->    List[Any]:
         # time series specific configuration
         snake_case				:    Union[str, Any]           =	prediction_length
         snake_case				:    str           =	context_length or prediction_length
         snake_case				:    str           =	distribution_output
         snake_case				:    Optional[Any]           =	loss
         snake_case				:    Optional[int]           =	input_size
         snake_case				:    str           =	num_time_features
         snake_case				:    Optional[int]           =	lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
         snake_case				:    List[Any]           =	scaling
         snake_case				:    Optional[int]           =	num_dynamic_real_features
         snake_case				:    List[Any]           =	num_static_real_features
         snake_case				:    int           =	num_static_categorical_features

         # set cardinality
         if cardinality and num_static_categorical_features > 0:
            if len(A     ) != num_static_categorical_features:
               raise ValueError(
                   """The cardinality should be a list of the same length as `num_static_categorical_features`"""     )
            snake_case				:    Union[str, Any]           =	cardinality
         else:
            snake_case				:    Union[str, Any]           =	[0]

         # set embedding_dimension
         if embedding_dimension and num_static_categorical_features > 0:
            if len(A     ) != num_static_categorical_features:
               raise ValueError(
                   """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""     )
            snake_case				:    Dict           =	embedding_dimension
         else:
            snake_case				:    int           =	[min(5_0    ,				(cat + 1) // 2     ) for cat in self.cardinality]

         snake_case				:    str           =	num_parallel_samples

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

         snake_case				:    Tuple           =	dropout
         snake_case				:    Optional[Any]           =	attention_dropout
         snake_case				:    Dict           =	activation_dropout
         snake_case				:    Any           =	encoder_layerdrop
         snake_case				:    Any           =	decoder_layerdrop

         snake_case				:    List[str]           =	activation_function
         snake_case				:    Any           =	init_std

         snake_case				:    Dict           =	use_cache

         # Informer
         snake_case				:    Optional[int]           =	attention_type
         snake_case				:    Dict           =	sampling_factor
         snake_case				:    Optional[int]           =	distil

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

      @property
      def      UpperCAmelCase				(				self     )		->    int:
         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
         )











# using dfs for finding eulerian path traversal




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase=None					)  ->       Any:
   snake_case				:    Union[str, Any]           =	(path or []) + [u]
   for v in graph[u]:
      if visited_edge[u][v] is False:
         snake_case     , snake_case				:    int           =	True, True
         snake_case				:    List[Any]           =	dfs(lowercase						,lowercase						,lowercase						,lowercase					)
   return path




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Tuple:
   snake_case				:    Union[str, Any]           =	0
   snake_case				:    Union[str, Any]           =	-1
   for i in range(lowercase					):
      if i not in graph.keys():
         continue
      if len(graph[i]					) % 2 == 1:
         odd_degree_nodes += 1
         snake_case				:    str           =	i
   if odd_degree_nodes == 0:
      return 1, odd_node
   if odd_degree_nodes == 2:
      return 2, odd_node
   return 3, odd_node




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       List[str]:
   snake_case				:    Tuple           =	[[False for _ in range(max_node + 1					)] for _ in range(max_node + 1					)]
   snake_case     , snake_case				:    Any           =	check_circuit_or_path(lowercase						,lowercase					)
   if check == 3:
      print("""graph is not Eulerian"""					)
      print("""no path"""					)
      return
   snake_case				:    str           =	1
   if check == 2:
      snake_case				:    Optional[int]           =	odd_node
      print("""graph has a Euler path"""					)
   if check == 1:
      print("""graph has a Euler cycle"""					)
   snake_case				:    Dict           =	dfs(lowercase						,lowercase						,lowercase					)
   print(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   )  ->       Optional[Any]:
   snake_case				:    Union[str, Any]           =	{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
   snake_case				:    Optional[int]           =	{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
   snake_case				:    Optional[int]           =	{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
   snake_case				:    List[Any]           =	{1: [2, 3], 2: [1, 3], 3: [1, 2]}
   snake_case				:    Tuple           =	{
       1: [],
       2: []
       # all degree is zero
   }
   snake_case				:    Tuple           =	10
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)


if __name__ == "__main__":
       main()





