
from typing import List, Optional, Union

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


__lowerCamelCase				=			logging.get_logger(__name__)

__lowerCamelCase				=			{
    """huggingface/time-series-transformer-tourism-monthly""": (
        """https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}






class     UpperCAmelCase						(		A_					):
   A__   :						Tuple			=	"time_series_transformer"
   A__   :						Optional[int]			=	{
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
       "num_hidden_layers": "encoder_layers",
   }



   def __init__(self						:   List[Any]       ,		snake_case__						:   Optional[int] = None       ,		snake_case__						:   Optional[int] = None       ,		snake_case__						:   str = "student_t"       ,		snake_case__						:   str = "nll"       ,		snake_case__						:   int = 1       ,		snake_case__						:   List[int] = [1, 2, 3, 4, 5, 6, 7]       ,		snake_case__						:   Optional[Union[str, bool]] = "mean"       ,		snake_case__						:   int = 0       ,		snake_case__						:   int = 0       ,		snake_case__						:   int = 0       ,		snake_case__						:   int = 0       ,		snake_case__						:   Optional[List[int]] = None       ,		snake_case__						:   Optional[List[int]] = None       ,		snake_case__						:   int = 32       ,		snake_case__						:   int = 32       ,		snake_case__						:   int = 2       ,		snake_case__						:   int = 2       ,		snake_case__						:   int = 2       ,		snake_case__						:   int = 2       ,		snake_case__						:   bool = True       ,		snake_case__						:   str = "gelu"       ,		snake_case__						:   int = 64       ,		snake_case__						:   float = 0.1       ,		snake_case__						:   float = 0.1       ,		snake_case__						:   float = 0.1       ,		snake_case__						:   float = 0.1       ,		snake_case__						:   float = 0.1       ,		snake_case__						:   int = 1_00       ,		snake_case__						:   float = 0.02       ,		snake_case__						:   List[str]=True       ,		**snake_case__						:   str       ,		)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= prediction_length
          snake_case			:  Union[str, Any]     							= context_length or prediction_length
          snake_case			:  Dict     							= distribution_output
          snake_case			:  Optional[int]     							= loss
          snake_case			:  Dict     							= input_size
          snake_case			:  int     							= num_time_features
          snake_case			:  Any     							= lags_sequence
          snake_case			:  Union[str, Any]     							= scaling
          snake_case			:  Optional[int]     							= num_dynamic_real_features
          snake_case			:  Optional[Any]     							= num_static_real_features
          snake_case			:  Any     							= num_static_categorical_features
          if cardinality and num_static_categorical_features > 0:
                 if len(snake_case__					) != 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]
          if embedding_dimension and num_static_categorical_features > 0:
                 if len(snake_case__					) != 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			:  List[Any]     							= [min(50       ,		(cat + 1) // 2					) for cat in self.cardinality]
          snake_case			:  Dict     							= num_parallel_samples

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

          snake_case			:  str     							= dropout
          snake_case			:  Tuple     							= attention_dropout
          snake_case			:  Tuple     							= activation_dropout
          snake_case			:  int     							= encoder_layerdrop
          snake_case			:  Union[str, Any]     							= decoder_layerdrop

          snake_case			:  Optional[Any]     							= activation_function
          snake_case			:  Optional[Any]     							= init_std

          snake_case			:  Dict     							= use_cache

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



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      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
          )







class     UpperCAmelCase						:



   def __init__(self						:   Optional[Any]       ,		snake_case__						:   str = ""       ,		snake_case__						:   bool = False					)						->      None:


          '''simple docstring'''


          snake_case			:  dict[str, RadixNode]     							= {}

          # A node will be a leaf if the tree contains its word
          snake_case			:  int     							= is_leaf

          snake_case			:  Dict     							= prefix



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   str					)						->      tuple[str, str, str]:


          '''simple docstring'''


          snake_case			:  Any     							= 0
          for q, w in zip(self.prefix       ,		snake_case__					):
                 if q != w:
                        break

                 x += 1

          return self.prefix[:x], self.prefix[x:], word[x:]



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   list[str]					)						->      None:


          '''simple docstring'''


          for word in words:
                 self.insert(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int       ,		snake_case__						:   str					)						->      None:


          '''simple docstring'''


          if self.prefix == word:
                 snake_case			:  str     							= True

          # Case 2: The node has no edges that have a prefix to the word
          # Solution: We create an edge from the current node to a new one
          # containing the word
          elif word[0] not in self.nodes:
                 snake_case			:  Optional[int]     							= RadixNode(prefix=snake_case__       ,		is_leaf=snake_case__					)

          else:
                 snake_case			:  Dict     							= self.nodes[word[0]]
                 snake_case							,   snake_case							,   snake_case			:  int     							= incoming_node.match(
                     snake_case__					)

                 # Case 3: The node prefix is equal to the matching
                 # Solution: We insert remaining word on the next node
                 if remaining_prefix == "":
                        self.nodes[matching_string[0]].insert(snake_case__					)

                 # Case 4: The word is greater equal to the matching
                 # Solution: Create a node in between both nodes, change
                 # prefixes and add the new node for the remaining word
                 else:
                        snake_case			:  Tuple     							= remaining_prefix

                        snake_case			:  Dict     							= self.nodes[matching_string[0]]
                        snake_case			:  Tuple     							= RadixNode(snake_case__       ,		snake_case__					)
                        snake_case			:  List[str]     							= aux_node

                        if remaining_word == "":
                               snake_case			:  Tuple     							= True
                        else:
                               self.nodes[matching_string[0]].insert(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   str					)						->      bool:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.nodes.get(word[0]       ,		snake_case__					)
          if not incoming_node:
                 return False
          else:
                 snake_case							,   snake_case							,   snake_case			:  Optional[int]     							= incoming_node.match(
                     snake_case__					)
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                        return False
                 # This applies when the word and the prefix are equal
                 elif remaining_word == "":
                        return incoming_node.is_leaf
                 # We have word remaining so we check the next node
                 else:
                        return incoming_node.find(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   str					)						->      bool:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.nodes.get(word[0]       ,		snake_case__					)
          if not incoming_node:
                 return False
          else:
                 snake_case							,   snake_case							,   snake_case			:  Optional[Any]     							= incoming_node.match(
                     snake_case__					)
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                        return False
                 # We have word remaining so we check the next node
                 elif remaining_word != "":
                        return incoming_node.delete(snake_case__					)
                 else:
                        # If it is not a leaf, we don't have to delete
                        if not incoming_node.is_leaf:
                               return False
                        else:
                               # We delete the nodes if no edges go from it
                               if len(incoming_node.nodes					) == 0:
                                      del self.nodes[word[0]]
                                      # We merge the current node with its only child
                                      if len(self.nodes					) == 1 and not self.is_leaf:
                                             snake_case			:  List[Any]     							= list(self.nodes.values()					)[0]
                                             snake_case			:  Optional[int]     							= merging_node.is_leaf
                                             self.prefix += merging_node.prefix
                                             snake_case			:  str     							= merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                               elif len(incoming_node.nodes					) > 1:
                                      snake_case			:  List[Any]     							= False
                               # If there is 1 edge, we merge it with its child
                               else:
                                      snake_case			:  Any     							= list(incoming_node.nodes.values()					)[0]
                                      snake_case			:  Any     							= merging_node.is_leaf
                                      incoming_node.prefix += merging_node.prefix
                                      snake_case			:  Union[str, Any]     							= merging_node.nodes

                               return True



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   int = 0					)						->      None:


          '''simple docstring'''


          if self.prefix != "":
                 print("-" * height       ,		self.prefix       ,		"  (leaf)" if self.is_leaf else ""					)

          for value in self.nodes.values():
                 value.print_tree(height + 1					)



def      UpperCamelCase   (       ):
       snake_case			:  Dict     							= "banana bananas bandana band apple all beast".split()
       snake_case			:  Tuple     							= RadixNode()
       root.insert_many(__lowerCamelCase       )

       assert all(root.find(__lowerCamelCase       ) for word in words       )
       assert not root.find("bandanas"       )
       assert not root.find("apps"       )
       root.delete("all"       )
       assert not root.find("all"       )
       root.delete("banana"       )
       assert not root.find("banana"       )
       assert root.find("bananas"       )

       return True


def      UpperCamelCase   (       ):
       assert test_trie()


def      UpperCamelCase   (       ):
       snake_case			:  Union[str, Any]     							= RadixNode()
       snake_case			:  List[str]     							= "banana bananas bandanas bandana band apple all beast".split()
       root.insert_many(__lowerCamelCase       )

       print("Words:"							,   __lowerCamelCase       )
       print("Tree:"       )
       root.print_tree()


if __name__ == "__main__":
   main()





