





'''simple docstring'''







from typing import List, Optional, Union

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


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

snake_case_   :				List[str]		=     {
    """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     __a					(a__   ):
							__a    :      Union[str, Any]  	=				"informer"
							__a    :      Optional[int]  	=				{
							    "hidden_size": "d_model",
							    "num_attention_heads": "encoder_attention_heads",
							    "num_hidden_layers": "encoder_layers",
							}

							def __init__(							self							:   Union[str, Any]				,   __magic_name__							:   List[str] = None				,   __magic_name__							:   List[str] = None				,   __magic_name__							:   Optional[Any] = "student_t"				,   __magic_name__							:   Optional[int] = "nll"				,   __magic_name__							:   Optional[Any] = 1				,   __magic_name__							:   Dict = None				,   __magic_name__							:   Dict = "mean"				,   __magic_name__							:   Union[str, Any] = 0				,   __magic_name__							:   Any = 0				,   __magic_name__							:   str = 0				,   __magic_name__							:   List[str] = 0				,   __magic_name__							:   Tuple = None				,   __magic_name__							:   List[Any] = None				,   __magic_name__							:   str = 64				,   __magic_name__							:   Tuple = 32				,   __magic_name__							:   List[Any] = 32				,   __magic_name__							:   Any = 2				,   __magic_name__							:   List[str] = 2				,   __magic_name__							:   List[str] = 2				,   __magic_name__							:   int = 2				,   __magic_name__							:   Dict = True				,   __magic_name__							:   Dict = "gelu"				,   __magic_name__							:   Union[str, Any] = 0.0_5				,   __magic_name__							:   Optional[Any] = 0.1				,   __magic_name__							:   Dict = 0.1				,   __magic_name__							:   List[str] = 0.1				,   __magic_name__							:   Any = 0.1				,   __magic_name__							:   Optional[Any] = 1_00				,   __magic_name__							:   List[Any] = 0.0_2				,   __magic_name__							:   Any=True				,   __magic_name__							:   Optional[Any] = "prob"				,   __magic_name__							:   Optional[Any] = 5				,   __magic_name__							:   Dict = True				,   **__magic_name__							:   Optional[Any]				,   )    ->				Dict:







													"""simple docstring"""
													# time series specific configuration
													UpperCAmelCase_	:					List[Any]     =     prediction_length
													UpperCAmelCase_	:					int     =     context_length or prediction_length
													UpperCAmelCase_	:					Union[str, Any]     =     distribution_output
													UpperCAmelCase_	:					List[str]     =     loss
													UpperCAmelCase_	:					Optional[int]     =     input_size
													UpperCAmelCase_	:					Optional[Any]     =     num_time_features
													UpperCAmelCase_	:					Optional[int]     =     lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
													UpperCAmelCase_	:					List[str]     =     scaling
													UpperCAmelCase_	:					Any     =     num_dynamic_real_features
													UpperCAmelCase_	:					int     =     num_static_real_features
													UpperCAmelCase_	:					Optional[int]     =     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`'''      )
																			UpperCAmelCase_	:					List[Any]     =     cardinality
													else:
																			UpperCAmelCase_	:					Optional[int]     =     [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`'''      )
																			UpperCAmelCase_	:					List[str]     =     embedding_dimension
													else:
																			UpperCAmelCase_	:					Optional[int]     =     [min(50				,   (cat + 1) // 2      ) for cat in self.cardinality]

													UpperCAmelCase_	:					Any     =     num_parallel_samples

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

													UpperCAmelCase_	:					Tuple     =     dropout
													UpperCAmelCase_	:					Optional[Any]     =     attention_dropout
													UpperCAmelCase_	:					Dict     =     activation_dropout
													UpperCAmelCase_	:					List[str]     =     encoder_layerdrop
													UpperCAmelCase_	:					Union[str, Any]     =     decoder_layerdrop

													UpperCAmelCase_	:					int     =     activation_function
													UpperCAmelCase_	:					Union[str, Any]     =     init_std

													UpperCAmelCase_	:					List[Any]     =     use_cache

													# Informer
													UpperCAmelCase_	:					Any     =     attention_type
													UpperCAmelCase_	:					List[str]     =     sampling_factor
													UpperCAmelCase_	:					List[str]     =     distil

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



							@property
							def 			UpperCAmelCase__		(							self							:   Dict      )    ->				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

from random import random



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase = None )  ->  str:
        lowercase__				:    Any  =					value
        lowercase__				:    Union[str, Any]  =					random()
        lowercase__				:    Node | None  =					None
        lowercase__				:    Node | None  =					None

       def __repr__(						self )  ->  str:
        from pprint import pformat

        if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
        else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}	,   indent=1 )


       def __str__(						self )  ->  str:
        lowercase__				:    Any  =					str(self.value ) + ''' '''
        lowercase__				:    str  =					str(self.left or '''''' )
        lowercase__				:    List[str]  =					str(self.right or '''''' )
        return value + left + right


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 if root is None:  # None tree is split into 2 Nones
  return None, None
 elif root.value is None:
  return None, None
 else:
  if value < root.value:
   lowercase__ ,							lowercase__				:    int  =					split(root.left		,     UpperCAmelCase     )
   return left, root
  else:
   lowercase__ ,							lowercase__				:    Optional[Any]  =					split(root.right		,     UpperCAmelCase     )
   return root, right
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 if (not left) or (not right):  # If one node is None, return the other
  return left or right
 elif left.prior < right.prior:
  lowercase__				:    Any  =					merge(left.right		,     UpperCAmelCase     )
  return left
 else:
  lowercase__				:    Dict  =					merge(UpperCAmelCase		,     right.left     )
  return right
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Dict  =					Node(UpperCAmelCase     )
 lowercase__ ,							lowercase__				:    Tuple  =					split(UpperCAmelCase		,     UpperCAmelCase     )
 return merge(merge(UpperCAmelCase		,     UpperCAmelCase     )		,     UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__ ,							lowercase__				:    Dict  =					split(UpperCAmelCase		,     value - 1     )
 lowercase__ ,							lowercase__				:    int  =					split(UpperCAmelCase		,     UpperCAmelCase     )
 return merge(UpperCAmelCase		,     UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 if not root:  # None
  return
 else:
  inorder(root.left     )
  print(root.value		,     end=''','''     )
  inorder(root.right     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 for arg in args.split():
  if arg[0] == "+":
   lowercase__				:    Tuple  =					insert(UpperCAmelCase		,     int(arg[1:]     )     )

  elif arg[0] == "-":
   lowercase__				:    Union[str, Any]  =					erase(UpperCAmelCase		,     int(arg[1:]     )     )

  else:
   print('''Unknown command'''     )

 return root
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					None
 print(
     '''enter numbers to create a tree, + value to add value into treap, '''
     '''- value to erase all nodes with value. \'q\' to quit. '''     )

 lowercase__				:    Dict  =					input()
 while args != "q":
  lowercase__				:    Any  =					interact_treap(UpperCAmelCase		,     UpperCAmelCase     )
  print(UpperCAmelCase     )
  lowercase__				:    List[Any]  =					input()

 print('''good by!'''     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

