

'''simple docstring'''
from __future__ import annotations

import queue


class    a__  :


   def __init__( self			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      int			     =   data
      _lowercase			:      Dict			     =   None
      _lowercase			:      str			     =   None







def 					_A		(	)			->	Optional[Any]:
   print("\n********Press N to stop entering at any point of time********\n"     )
   _lowercase			:      List[str]			     =   input("Enter the value of the root node: "     ).strip().lower()
   _lowercase			:      queue.Queue			     =   queue.Queue()
   _lowercase			:      str			     =   TreeNode(int(snake_case     )     )
   q.put(snake_case     )
   while not q.empty():
      _lowercase			:      List[Any]			     =   q.get()
      _lowercase			:      Any			     =   F'''Enter the left node of {node_found.data}: '''
      _lowercase			:      int			     =   input(snake_case     ).strip().lower() or """n"""
      if check == "n":
         return tree_node
      _lowercase			:      Dict			     =   TreeNode(int(snake_case     )     )
      _lowercase			:      List[str]			     =   left_node
      q.put(snake_case     )
      _lowercase			:      List[str]			     =   F'''Enter the right node of {node_found.data}: '''
      _lowercase			:      Optional[Any]			     =   input(snake_case     ).strip().lower() or """n"""
      if check == "n":
         return tree_node
      _lowercase			:      Union[str, Any]			     =   TreeNode(int(snake_case     )     )
      _lowercase			:      List[str]			     =   right_node
      q.put(snake_case     )
   raise







def 					_A		(	snake_case     )			->	Optional[Any]:
   if not isinstance(snake_case		,							snake_case     ) or not node:
      return
   print(node.data		,							end=","     )
   pre_order(node.left     )
   pre_order(node.right     )







def 					_A		(	snake_case     )			->	int:
   if not isinstance(snake_case		,							snake_case     ) or not node:
      return
   in_order(node.left     )
   print(node.data		,							end=","     )
   in_order(node.right     )







def 					_A		(	snake_case     )			->	Union[str, Any]:
   if not isinstance(snake_case		,							snake_case     ) or not node:
      return
   post_order(node.left     )
   post_order(node.right     )
   print(node.data		,							end=","     )







def 					_A		(	snake_case     )			->	Optional[Any]:
   if not isinstance(snake_case		,							snake_case     ) or not node:
      return
   _lowercase			:      queue.Queue			     =   queue.Queue()
   q.put(snake_case     )
   while not q.empty():
      _lowercase			:      str			     =   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 					_A		(	snake_case     )			->	List[Any]:
   if not isinstance(snake_case		,							snake_case     ) or not node:
      return
   _lowercase			:      queue.Queue			     =   queue.Queue()
   q.put(snake_case     )
   while not q.empty():
      _lowercase			:      Optional[int]			     =   []
      while not q.empty():
         _lowercase			:      Any			     =   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(snake_case     )







def 					_A		(	snake_case     )			->	str:
   if not isinstance(snake_case		,							snake_case     ) or not node:
      return
   _lowercase			:      list[TreeNode]			     =   []
   _lowercase			:      Optional[int]			     =   node
   while n or stack:
      while n:  # start from root node, find its left child
         print(n.data		,							end=","     )
         stack.append(snake_case     )
         _lowercase			:      Optional[int]			     =   n.left
      # end of while means current node doesn't have left child
      _lowercase			:      Optional[Any]			     =   stack.pop()
      # start to traverse its right child
      _lowercase			:      Optional[Any]			     =   n.right







def 					_A		(	snake_case     )			->	Any:
   if not isinstance(snake_case		,							snake_case     ) or not node:
      return
   _lowercase			:      list[TreeNode]			     =   []
   _lowercase			:      Optional[Any]			     =   node
   while n or stack:
      while n:
         stack.append(snake_case     )
         _lowercase			:      Optional[Any]			     =   n.left
      _lowercase			:      Tuple			     =   stack.pop()
      print(n.data		,							end=","     )
      _lowercase			:      int			     =   n.right







def 					_A		(	snake_case     )			->	Any:
   if not isinstance(snake_case		,							snake_case     ) or not node:
      return
   _lowercase			:      Optional[int]			     =   [], []
   _lowercase			:      str			     =   node
   stacka.append(snake_case     )
   while stacka:  # to find the reversed order of post order, store it in stack2
      _lowercase			:      str			     =   stacka.pop()
      if n.left:
         stacka.append(n.left     )
      if n.right:
         stacka.append(n.right     )
      stacka.append(snake_case     )
   while stacka:  # pop up from stack2 will be the post order
      print(stacka.pop().data		,							end=","     )







def 					_A		(	snake_case = ""		,							snake_case=50		,							snake_case="*"     )			->	Optional[int]:
   if not s:
      return "\n" + width * char
   _lowercase			:      Optional[int]			     =   divmod(width - len(snake_case     ) - 2		,							2     )
   return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
    import doctest

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

    _snake_case     =  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())











"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


A:   Tuple								=		logging.get_logger(__name__)

A:   List[Any]								=		{
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   int						=						'blenderbot-small'
   __lowerCAmelCase  :   Any						=						['past_key_values']
   __lowerCAmelCase  :   str						=						{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=50265      ,     _SCREAMING_SNAKE_CASE=512      ,     _SCREAMING_SNAKE_CASE=8      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=16      ,     _SCREAMING_SNAKE_CASE=8      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=16      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE="gelu"      ,     _SCREAMING_SNAKE_CASE=512      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.02      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE=0      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=2      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	vocab_size
    UpperCAmelCase	:					Any	=	max_position_embeddings
    UpperCAmelCase	:					str	=	d_model
    UpperCAmelCase	:					List[Any]	=	encoder_ffn_dim
    UpperCAmelCase	:					Union[str, Any]	=	encoder_layers
    UpperCAmelCase	:					List[str]	=	encoder_attention_heads
    UpperCAmelCase	:					Optional[Any]	=	decoder_ffn_dim
    UpperCAmelCase	:					List[Any]	=	decoder_layers
    UpperCAmelCase	:					Any	=	decoder_attention_heads
    UpperCAmelCase	:					Union[str, Any]	=	dropout
    UpperCAmelCase	:					Union[str, Any]	=	attention_dropout
    UpperCAmelCase	:					int	=	activation_dropout
    UpperCAmelCase	:					int	=	activation_function
    UpperCAmelCase	:					Optional[Any]	=	init_std
    UpperCAmelCase	:					List[str]	=	encoder_layerdrop
    UpperCAmelCase	:					List[str]	=	decoder_layerdrop
    UpperCAmelCase	:					List[str]	=	use_cache
    UpperCAmelCase	:					Union[str, Any]	=	encoder_layers
    UpperCAmelCase	:					Any	=	scale_embedding  # scale factor will be sqrt(d_model) if True

    super().__init__(
        pad_token_id=_SCREAMING_SNAKE_CASE      ,     bos_token_id=_SCREAMING_SNAKE_CASE      ,     eos_token_id=_SCREAMING_SNAKE_CASE      ,     is_encoder_decoder=_SCREAMING_SNAKE_CASE      ,     decoder_start_token_id=_SCREAMING_SNAKE_CASE      ,     forced_eos_token_id=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     )



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Mapping[str, Mapping[int, str]]:





    '''simple docstring'''




    if self.task in ["default", "seq2seq-lm"]:
     UpperCAmelCase	:					Dict	=	OrderedDict(
         [
             ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
             ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
         ]						)

     if self.use_past:
      UpperCAmelCase	:					int	=	{0: """batch"""}
      UpperCAmelCase	:					List[Any]	=	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
     else:
      UpperCAmelCase	:					Optional[Any]	=	{0: """batch""", 1: """decoder_sequence"""}
      UpperCAmelCase	:					Any	=	{0: """batch""", 1: """decoder_sequence"""}

     if self.use_past:
      self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE      ,     direction="""inputs"""						)
    elif self.task == "causal-lm":
     # TODO: figure this case out.
     UpperCAmelCase	:					int	=	OrderedDict(
         [
             ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
             ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
         ]						)
     if self.use_past:
      UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	self.num_layers
      for i in range(_SCREAMING_SNAKE_CASE						):
       UpperCAmelCase	:					Optional[Any]	=	{0: """batch""", 2: """past_sequence + sequence"""}
       UpperCAmelCase	:					List[Any]	=	{0: """batch""", 2: """past_sequence + sequence"""}
    else:
     UpperCAmelCase	:					Dict	=	OrderedDict(
         [
             ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
             ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
             ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
             ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
         ]						)

    return common_inputs

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Mapping[str, Mapping[int, str]]:





    '''simple docstring'''




    if self.task in ["default", "seq2seq-lm"]:
     UpperCAmelCase	:					Union[str, Any]	=	super().outputs
    else:
     UpperCAmelCase	:					int	=	super(_SCREAMING_SNAKE_CASE      ,     self						).outputs
     if self.use_past:
      UpperCAmelCase	,		UpperCAmelCase	:					Any	=	self.num_layers
      for i in range(_SCREAMING_SNAKE_CASE						):
       UpperCAmelCase	:					List[str]	=	{0: """batch""", 2: """past_sequence + sequence"""}
       UpperCAmelCase	:					List[str]	=	{0: """batch""", 2: """past_sequence + sequence"""}
    return common_outputs

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = None      ,     )  ->      Mapping[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

    # Generate decoder inputs
    UpperCAmelCase	:					Optional[Any]	=	seq_length if not self.use_past else 1
    UpperCAmelCase	:					Dict	=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Any	=	{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
    UpperCAmelCase	:					Union[str, Any]	=	dict(**_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

    if self.use_past:
     if not is_torch_available():
      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
     else:
      import torch
     UpperCAmelCase	,		UpperCAmelCase	:					int	=	common_inputs["""input_ids"""].shape
     UpperCAmelCase	:					Any	=	common_inputs["""decoder_input_ids"""].shape[1]
     UpperCAmelCase	,		UpperCAmelCase	:					str	=	self.num_attention_heads
     UpperCAmelCase	:					Union[str, Any]	=	(
         batch,
         num_encoder_attention_heads,
         encoder_seq_length,
         self._config.hidden_size // num_encoder_attention_heads,
     )
     UpperCAmelCase	:					Union[str, Any]	=	decoder_seq_length + 3
     UpperCAmelCase	:					Union[str, Any]	=	(
         batch,
         num_decoder_attention_heads,
         decoder_past_length,
         self._config.hidden_size // num_decoder_attention_heads,
     )

     UpperCAmelCase	:					Optional[Any]	=	torch.cat(
         [common_inputs["""decoder_attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)]      ,     dim=1						)

     UpperCAmelCase	:					List[str]	=	[]
     # If the number of encoder and decoder layers are present in the model configuration, both are considered
     UpperCAmelCase	,		UpperCAmelCase	:					List[Any]	=	self.num_layers
     UpperCAmelCase	:					Optional[Any]	=	min(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Optional[Any]	=	max(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						) - min_num_layers
     UpperCAmelCase	:					Optional[int]	=	"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

     for _ in range(_SCREAMING_SNAKE_CASE						):
      common_inputs["past_key_values"].append(
          (
              torch.zeros(_SCREAMING_SNAKE_CASE						),
              torch.zeros(_SCREAMING_SNAKE_CASE						),
              torch.zeros(_SCREAMING_SNAKE_CASE						),
              torch.zeros(_SCREAMING_SNAKE_CASE						),
          )						)
     # TODO: test this.
     UpperCAmelCase	:					Dict	=	encoder_shape if remaining_side_name == """encoder""" else decoder_shape
     for _ in range(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
      common_inputs["past_key_values"].append((torch.zeros(_SCREAMING_SNAKE_CASE						), torch.zeros(_SCREAMING_SNAKE_CASE						))						)
    return common_inputs

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = None      ,     )  ->      Mapping[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

    if self.use_past:
     if not is_torch_available():
      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
     else:
      import torch
     UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	common_inputs["""input_ids"""].shape
     # Not using the same length for past_key_values
     UpperCAmelCase	:					Union[str, Any]	=	seqlen + 2
     UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	self.num_layers
     UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	self.num_attention_heads
     UpperCAmelCase	:					Any	=	(
         batch,
         num_encoder_attention_heads,
         past_key_values_length,
         self._config.hidden_size // num_encoder_attention_heads,
     )

     UpperCAmelCase	:					List[Any]	=	common_inputs["""attention_mask"""].dtype
     UpperCAmelCase	:					Any	=	torch.cat(
         [common_inputs["""attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     dtype=_SCREAMING_SNAKE_CASE						)]      ,     dim=1						)
     UpperCAmelCase	:					List[str]	=	[
         (torch.zeros(_SCREAMING_SNAKE_CASE						), torch.zeros(_SCREAMING_SNAKE_CASE						)) for _ in range(_SCREAMING_SNAKE_CASE						)
     ]
    return common_inputs

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = None      ,     )  ->      Mapping[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	compute_effective_axis_dimension(
        _SCREAMING_SNAKE_CASE      ,     fixed_dimension=OnnxConfig.default_fixed_batch      ,     num_token_to_add=0						)

    # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
    UpperCAmelCase	:					List[str]	=	tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	compute_effective_axis_dimension(
        _SCREAMING_SNAKE_CASE      ,     fixed_dimension=OnnxConfig.default_fixed_sequence      ,     num_token_to_add=_SCREAMING_SNAKE_CASE						)

    # Generate dummy inputs according to compute batch and sequence
    UpperCAmelCase	:					Dict	=	[""" """.join([tokenizer.unk_token]						) * seq_length] * batch_size
    UpperCAmelCase	:					Optional[int]	=	dict(tokenizer(_SCREAMING_SNAKE_CASE      ,     return_tensors=_SCREAMING_SNAKE_CASE						)						)
    return common_inputs

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = -1      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = None      ,     )  ->      Mapping[str, Any]:





    '''simple docstring'''




    if self.task in ["default", "seq2seq-lm"]:
     UpperCAmelCase	:					Optional[int]	=	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
         _SCREAMING_SNAKE_CASE      ,     batch_size=_SCREAMING_SNAKE_CASE      ,     seq_length=_SCREAMING_SNAKE_CASE      ,     is_pair=_SCREAMING_SNAKE_CASE      ,     framework=_SCREAMING_SNAKE_CASE						)

    elif self.task == "causal-lm":
     UpperCAmelCase	:					Optional[int]	=	self._generate_dummy_inputs_for_causal_lm(
         _SCREAMING_SNAKE_CASE      ,     batch_size=_SCREAMING_SNAKE_CASE      ,     seq_length=_SCREAMING_SNAKE_CASE      ,     is_pair=_SCREAMING_SNAKE_CASE      ,     framework=_SCREAMING_SNAKE_CASE						)
    else:
     UpperCAmelCase	:					Tuple	=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         _SCREAMING_SNAKE_CASE      ,     batch_size=_SCREAMING_SNAKE_CASE      ,     seq_length=_SCREAMING_SNAKE_CASE      ,     is_pair=_SCREAMING_SNAKE_CASE      ,     framework=_SCREAMING_SNAKE_CASE						)

    return common_inputs




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    if self.task in ["default", "seq2seq-lm"]:
     UpperCAmelCase	:					List[Any]	=	super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
    else:
     UpperCAmelCase	:					Dict	=	super(_SCREAMING_SNAKE_CASE      ,     self						)._flatten_past_key_values_(
         _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
