



"""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						)





"""simple docstring"""
from __future__ import annotations



class       SCREAMING_SNAKE_CASE__  :

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE						)  ->      None:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	data
    UpperCAmelCase	:					Node | None	=	None
    UpperCAmelCase	:					Node | None	=	None

def       _snake_case						(						UpperCamelCase						:		Node | None						):  # In Order traversal of the tree
 if tree:
  display(tree.left						)
  print(tree.data						)
  display(tree.right						)

def       _snake_case						(						UpperCamelCase						:		Node | None						):
 return 1 + max(depth_of_tree(tree.left						)			,		depth_of_tree(tree.right						)						) if tree else 0

def       _snake_case						(						UpperCamelCase						:		Node						):
 if not tree:
  return True
 if tree.left and tree.right:
  return is_full_binary_tree(tree.left						) and is_full_binary_tree(tree.right						)
 else:
  return not tree.left and not tree.right

def       _snake_case						(						):  # Main function for testing.
 UpperCAmelCase	:					int	=	Node(1						)
 UpperCAmelCase	:					Tuple	=	Node(2						)
 UpperCAmelCase	:					Any	=	Node(3						)
 UpperCAmelCase	:					Optional[int]	=	Node(4						)
 UpperCAmelCase	:					Any	=	Node(5						)
 UpperCAmelCase	:					Optional[int]	=	Node(6						)
 UpperCAmelCase	:					int	=	Node(7						)
 UpperCAmelCase	:					str	=	Node(8						)
 UpperCAmelCase	:					str	=	Node(9						)

 print(is_full_binary_tree(UpperCamelCase						)						)
 print(depth_of_tree(UpperCamelCase						)						)
 print("""Tree is: """						)
 display(UpperCamelCase						)


if __name__ == "__main__":
      main()
