




from typing import List, Optional

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


_lowerCamelCase     :		List[str]								=       logging.get_logger(__name__)

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


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''autoformer'''
    UpperCAmelCase__							  =				{
        '''hidden_size''': '''d_model''',
        '''num_attention_heads''': '''encoder_attention_heads''',
        '''num_hidden_layers''': '''encoder_layers''',
    }


    def __init__(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Optional[int] = None ,			UpperCAmelCase__				:     Optional[int] = None ,			UpperCAmelCase__				:     str = "student_t" ,			UpperCAmelCase__				:     str = "nll" ,			UpperCAmelCase__				:     int = 1 ,			UpperCAmelCase__				:     List[int] = [1, 2, 3, 4, 5, 6, 7] ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     int = 0 ,			UpperCAmelCase__				:     int = 0 ,			UpperCAmelCase__				:     int = 0 ,			UpperCAmelCase__				:     int = 0 ,			UpperCAmelCase__				:     Optional[List[int]] = None ,			UpperCAmelCase__				:     Optional[List[int]] = None ,			UpperCAmelCase__				:     int = 64 ,			UpperCAmelCase__				:     int = 2 ,			UpperCAmelCase__				:     int = 2 ,			UpperCAmelCase__				:     int = 2 ,			UpperCAmelCase__				:     int = 2 ,			UpperCAmelCase__				:     int = 32 ,			UpperCAmelCase__				:     int = 32 ,			UpperCAmelCase__				:     str = "gelu" ,			UpperCAmelCase__				:     float = 0.1 ,			UpperCAmelCase__				:     float = 0.1 ,			UpperCAmelCase__				:     float = 0.1 ,			UpperCAmelCase__				:     float = 0.1 ,			UpperCAmelCase__				:     float = 0.1 ,			UpperCAmelCase__				:     int = 100 ,			UpperCAmelCase__				:     float = 0.02 ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     Dict=True ,			UpperCAmelCase__				:     int = 10 ,			UpperCAmelCase__				:     int = 25 ,			UpperCAmelCase__				:     int = 3 ,			**UpperCAmelCase__				:     Tuple ,			)						->str:





           '''simple docstring'''

           A__							=   prediction_length
           A__							=   context_length if context_length is not None else prediction_length
           A__							=   distribution_output
           A__							=   loss
           A__							=   input_size
           A__							=   num_time_features
           A__							=   lags_sequence
           A__							=   scaling
           A__							=   num_dynamic_real_features
           A__							=   num_static_real_features
           A__							=   num_static_categorical_features
           if cardinality is not None and num_static_categorical_features > 0:
                  if len(UpperCAmelCase__) != num_static_categorical_features:
                         raise ValueError(
                             '''The cardinality should be a list of the same length as `num_static_categorical_features`''')
                  A__							=   cardinality
           else:
                  A__							=   [0]
           if embedding_dimension is not None and num_static_categorical_features > 0:
                  if len(UpperCAmelCase__) != num_static_categorical_features:
                         raise ValueError(
                             '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''')
                  A__							=   embedding_dimension
           else:
                  A__							=   [min(50 ,			(cat + 1) // 2) for cat in self.cardinality]
           A__							=   num_parallel_samples

           # Transformer architecture configuration
           A__							=   input_size * len(self.lags_sequence) + self._number_of_features
           A__							=   d_model
           A__							=   encoder_attention_heads
           A__							=   decoder_attention_heads
           A__							=   encoder_ffn_dim
           A__							=   decoder_ffn_dim
           A__							=   encoder_layers
           A__							=   decoder_layers

           A__							=   dropout
           A__							=   attention_dropout
           A__							=   activation_dropout
           A__							=   encoder_layerdrop
           A__							=   decoder_layerdrop

           A__							=   activation_function
           A__							=   init_std

           A__							=   use_cache

           # Autoformer
           A__							=   label_length
           A__							=   moving_average
           A__							=   autocorrelation_factor

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





    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->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
           )








from __future__ import annotations

import queue


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Dict)						->Any:





           '''simple docstring'''

           A__							=   data
           A__							=   None
           A__							=   None







def SCREAMING_SNAKE_CASE    (	)       ->	TreeNode:



       """simple docstring"""

       print('''\n********Press N to stop entering at any point of time********\n'''						)
       A__							=   input('''Enter the value of the root node: '''						).strip().lower()
       A__							=   queue.Queue()
       A__							=   TreeNode(int(lowercase_						)						)
       q.put(lowercase_						)
       while not q.empty():
              A__							=   q.get()
              A__							=   f"""Enter the left node of {node_found.data}: """
              A__							=   input(lowercase_						).strip().lower() or '''n'''
              if check == "n":
                     return tree_node
              A__							=   TreeNode(int(lowercase_						)						)
              A__							=   left_node
              q.put(lowercase_						)
              A__							=   f"""Enter the right node of {node_found.data}: """
              A__							=   input(lowercase_						).strip().lower() or '''n'''
              if check == "n":
                     return tree_node
              A__							=   TreeNode(int(lowercase_						)						)
              A__							=   right_node
              q.put(lowercase_						)
       raise







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       print(node.data			,    end=''','''						)
       pre_order(node.left						)
       pre_order(node.right						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       in_order(node.left						)
       print(node.data			,    end=''','''						)
       in_order(node.right						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       post_order(node.left						)
       post_order(node.right						)
       print(node.data			,    end=''','''						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   queue.Queue()
       q.put(lowercase_						)
       while not q.empty():
              A__							=   q.get()
              print(node_dequeued.data			,    end=''','''						)
              if node_dequeued.left:
                     q.put(node_dequeued.left						)
              if node_dequeued.right:
                     q.put(node_dequeued.right						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   queue.Queue()
       q.put(lowercase_						)
       while not q.empty():
              A__							=   []
              while not q.empty():
                     A__							=   q.get()
                     print(node_dequeued.data			,    end=''','''						)
                     if node_dequeued.left:
                            list_.append(node_dequeued.left						)
                     if node_dequeued.right:
                            list_.append(node_dequeued.right						)
              print()
              for node in list_:
                     q.put(lowercase_						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   []
       A__							=   node
       while n or stack:
              while n:  # start from root node, find its left child
                     print(n.data			,    end=''','''						)
                     stack.append(lowercase_						)
                     A__							=   n.left
              # end of while means current node doesn't have left child
              A__							=   stack.pop()
              # start to traverse its right child
              A__							=   n.right







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   []
       A__							=   node
       while n or stack:
              while n:
                     stack.append(lowercase_						)
                     A__							=   n.left
              A__							=   stack.pop()
              print(n.data			,    end=''','''						)
              A__							=   n.right







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__			,							A__							=   [], []
       A__							=   node
       stacka.append(lowercase_						)
       while stacka:  # to find the reversed order of post order, store it in stack2
              A__							=   stacka.pop()
              if n.left:
                     stacka.append(n.left						)
              if n.right:
                     stacka.append(n.right						)
              stacka.append(lowercase_						)
       while stacka:  # pop up from stack2 will be the post order
              print(stacka.pop().data			,    end=''','''						)







def SCREAMING_SNAKE_CASE    (	lowercase_ = ""			,    lowercase_=50			,    lowercase_="*"						)       ->	str:



       """simple docstring"""

       if not s:
              return "\n" + width * char
       A__			,							A__							=   divmod(width - len(lowercase_						) - 2			,    2						)
       return f"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(prompt("""Binary Tree Traversals"""))

  _lowerCamelCase     :		TreeNode								=       build_tree()
  print(prompt("""Pre Order Traversal"""))
  pre_order(node)
  print(prompt() + """\n""")

  print(prompt("""In Order Traversal"""))
  in_order(node)
  print(prompt() + """\n""")

  print(prompt("""Post Order Traversal"""))
  post_order(node)
  print(prompt() + """\n""")

  print(prompt("""Level Order Traversal"""))
  level_order(node)
  print(prompt() + """\n""")

  print(prompt("""Actual Level Order Traversal"""))
  level_order_actual(node)
  print("""*""" * 50 + """\n""")

  print(prompt("""Pre Order Traversal - Iteration Version"""))
  pre_order_iter(node)
  print(prompt() + """\n""")

  print(prompt("""In Order Traversal - Iteration Version"""))
  in_order_iter(node)
  print(prompt() + """\n""")

  print(prompt("""Post Order Traversal - Iteration Version"""))
  post_order_iter(node)
  print(prompt())


