
from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCAmelCase__		:     Union[str, Any]     			=      TypeVar('T')






class UpperCAmelCase   (						Generic[T]   ):





   '''simple docstring'''
   __UpperCamelCase		:    deque[T]  # Cache store of keys
   __UpperCamelCase		:    set[T]  # References of the keys in cache
   __UpperCamelCase		:    int 				=    10  # Maximum capacity of cache


   def __init__(					self     :							List[str]			,			lowerCAmelCase_     :							int     ):

      """simple docstring"""
      _A:  Tuple					=       deque()
      _A:  List[Any]					=       set()
      if not n:
         _A:  str					=       sys.maxsize
      elif n < 0:
         raise ValueError('''n should be an integer greater than 0.'''     )
      else:
         _A:  Dict					=       n


   def  __magic_name__				(					self     :							Optional[int]			,			lowerCAmelCase_     :							T     ):

      """simple docstring"""
      if x not in self.key_reference:
         if len(self.dq_store     ) == LRUCache._MAX_CAPACITY:
            _A:  Optional[Any]					=       self.dq_store.pop()
            self.key_reference.remove(lowerCAmelCase_     )
      else:
         self.dq_store.remove(lowerCAmelCase_     )

      self.dq_store.appendleft(lowerCAmelCase_     )
      self.key_reference.add(lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      for k in self.dq_store:
         print(lowerCAmelCase_     )


   def __repr__(					self     :							Dict     ):

      """simple docstring"""
      return F"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store     )}"""


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase__		:     LRUCache[str | int]     			=      LRUCache(4)
   lru_cache.refer('A')
   lru_cache.refer(2)
   lru_cache.refer(3)
   lru_cache.refer('A')
   lru_cache.refer(4)
   lru_cache.refer(5)
   lru_cache.display()

   print(lru_cache)
   assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"





from typing import List, Optional, Union

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


UpperCAmelCase__		:     Optional[Any]     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     Any     			=      {
    '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 UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    List[Any] 				=    '''informer'''
   __UpperCamelCase		:    List[str] 				=    {
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
       '''num_hidden_layers''': '''encoder_layers''',
   }


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Optional[int] = None			,			lowerCAmelCase_     :							Optional[int] = None			,			lowerCAmelCase_     :							str = "student_t"			,			lowerCAmelCase_     :							str = "nll"			,			lowerCAmelCase_     :							int = 1			,			lowerCAmelCase_     :							List[int] = None			,			lowerCAmelCase_     :							Optional[Union[str, bool]] = "mean"			,			lowerCAmelCase_     :							int = 0			,			lowerCAmelCase_     :							int = 0			,			lowerCAmelCase_     :							int = 0			,			lowerCAmelCase_     :							int = 0			,			lowerCAmelCase_     :							Optional[List[int]] = None			,			lowerCAmelCase_     :							Optional[List[int]] = None			,			lowerCAmelCase_     :							int = 6_4			,			lowerCAmelCase_     :							int = 3_2			,			lowerCAmelCase_     :							int = 3_2			,			lowerCAmelCase_     :							int = 2			,			lowerCAmelCase_     :							int = 2			,			lowerCAmelCase_     :							int = 2			,			lowerCAmelCase_     :							int = 2			,			lowerCAmelCase_     :							bool = True			,			lowerCAmelCase_     :							str = "gelu"			,			lowerCAmelCase_     :							float = 0.05			,			lowerCAmelCase_     :							float = 0.1			,			lowerCAmelCase_     :							float = 0.1			,			lowerCAmelCase_     :							float = 0.1			,			lowerCAmelCase_     :							float = 0.1			,			lowerCAmelCase_     :							int = 1_0_0			,			lowerCAmelCase_     :							float = 0.02			,			lowerCAmelCase_     :							Tuple=True			,			lowerCAmelCase_     :							str = "prob"			,			lowerCAmelCase_     :							int = 5			,			lowerCAmelCase_     :							bool = True			,			**lowerCAmelCase_     :							str			,			):

      """simple docstring"""
      # time series specific configuration
      _A:  Optional[Any]					=       prediction_length
      _A:  Optional[Any]					=       context_length or prediction_length
      _A:  Dict					=       distribution_output
      _A:  List[str]					=       loss
      _A:  int					=       input_size
      _A:  List[str]					=       num_time_features
      _A:  Optional[Any]					=       lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
      _A:  str					=       scaling
      _A:  Optional[Any]					=       num_dynamic_real_features
      _A:  List[Any]					=       num_static_real_features
      _A:  Tuple					=       num_static_categorical_features

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

      # set embedding_dimension
      if embedding_dimension and num_static_categorical_features > 0:
         if len(lowerCAmelCase_     ) != num_static_categorical_features:
            raise ValueError(
                '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''     )
         _A:  List[str]					=       embedding_dimension
      else:
         _A:  Union[str, Any]					=       [min(5_0			,			(cat + 1) // 2     ) for cat in self.cardinality]

      _A:  int					=       num_parallel_samples

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

      _A:  List[str]					=       dropout
      _A:  Any					=       attention_dropout
      _A:  Optional[int]					=       activation_dropout
      _A:  List[Any]					=       encoder_layerdrop
      _A:  str					=       decoder_layerdrop

      _A:  int					=       activation_function
      _A:  Tuple					=       init_std

      _A:  Union[str, Any]					=       use_cache

      # Informer
      _A:  Union[str, Any]					=       attention_type
      _A:  str					=       sampling_factor
      _A:  List[str]					=       distil

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


   @property
   def  __magic_name__				(					self     :							List[str]     ):

      """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
      )



