





from __future__ import annotations

import unittest
from collections.abc import Iterator

class   __snake_case				:






    '''simple docstring'''





    def __init__(   self						:		List[str]			,						snake_case						:		int			,						snake_case						:		Node | None							):

      """simple docstring"""





      __lowerCAmelCase			=				label
      __lowerCAmelCase			=				parent
      __lowerCAmelCase			=				None
      __lowerCAmelCase			=				None


class   __snake_case				:






    '''simple docstring'''





    def __init__(   self						:		int							):

      """simple docstring"""





      __lowerCAmelCase			=				None





    def 	A		(   self						:		Optional[int]							):

      """simple docstring"""





      __lowerCAmelCase			=				None





    def 	A		(   self						:		List[Any]							):

      """simple docstring"""





      return self.root is None





    def 	A		(   self						:		Optional[Any]			,						snake_case						:		int							):

      """simple docstring"""





      __lowerCAmelCase			=				self._put(self.root			,						snake_case							)





    def 	A		(   self						:		Any			,						snake_case						:		Node | None			,						snake_case						:		int			,						snake_case						:		Node | None = None							):

      """simple docstring"""





      if node is None:
        __lowerCAmelCase			=				Node(snake_case			,						snake_case							)
      else:
        if label < node.label:
          __lowerCAmelCase			=				self._put(node.left			,						snake_case			,						snake_case							)
        elif label > node.label:
          __lowerCAmelCase			=				self._put(node.right			,						snake_case			,						snake_case							)
        else:
          __lowerCAmelCase			=				f'''Node with label {label} already exists'''
          raise Exception(snake_case							)

      return node





    def 	A		(   self						:		Optional[int]			,						snake_case						:		int							):

      """simple docstring"""





      return self._search(self.root			,						snake_case							)





    def 	A		(   self						:		Union[str, Any]			,						snake_case						:		Node | None			,						snake_case						:		int							):

      """simple docstring"""





      if node is None:
        __lowerCAmelCase			=				f'''Node with label {label} does not exist'''
        raise Exception(snake_case							)
      else:
        if label < node.label:
          __lowerCAmelCase			=				self._search(node.left			,						snake_case							)
        elif label > node.label:
          __lowerCAmelCase			=				self._search(node.right			,						snake_case							)

      return node





    def 	A		(   self						:		Union[str, Any]			,						snake_case						:		int							):

      """simple docstring"""





      __lowerCAmelCase			=				self.search(snake_case							)
      if node.right and node.left:
        __lowerCAmelCase			=				self._get_lowest_node(node.right							)
        __lowerCAmelCase			=				node.left
        __lowerCAmelCase			=				node.right
        __lowerCAmelCase			=				lowest_node
        if node.right:
          __lowerCAmelCase			=				lowest_node
        self._reassign_nodes(snake_case			,						snake_case							)
      elif not node.right and node.left:
        self._reassign_nodes(snake_case			,						node.left							)
      elif node.right and not node.left:
        self._reassign_nodes(snake_case			,						node.right							)
      else:
        self._reassign_nodes(snake_case			,						snake_case							)





    def 	A		(   self						:		Optional[int]			,						snake_case						:		Node			,						snake_case						:		Node | None							):

      """simple docstring"""





      if new_children:
        __lowerCAmelCase			=				node.parent

      if node.parent:
        if node.parent.right == node:
          __lowerCAmelCase			=				new_children
        else:
          __lowerCAmelCase			=				new_children
      else:
        __lowerCAmelCase			=				new_children





    def 	A		(   self						:		List[str]			,						snake_case						:		Node							):

      """simple docstring"""





      if node.left:
        __lowerCAmelCase			=				self._get_lowest_node(node.left							)
      else:
        __lowerCAmelCase			=				node
        self._reassign_nodes(snake_case			,						node.right							)

      return lowest_node





    def 	A		(   self						:		Union[str, Any]			,						snake_case						:		int							):

      """simple docstring"""





      try:
        self.search(snake_case							)
        return True
      except Exception:
        return False





    def 	A		(   self						:		List[Any]							):

      """simple docstring"""





      if self.root is None:
        raise Exception('''Binary search tree is empty'''							)

      __lowerCAmelCase			=				self.root
      while node.right is not None:
        __lowerCAmelCase			=				node.right

      return node.label





    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      if self.root is None:
        raise Exception('''Binary search tree is empty'''							)

      __lowerCAmelCase			=				self.root
      while node.left is not None:
        __lowerCAmelCase			=				node.left

      return node.label





    def 	A		(   self						:		List[Any]							):

      """simple docstring"""





      return self._inorder_traversal(self.root							)





    def 	A		(   self						:		Dict			,						snake_case						:		Node | None							):

      """simple docstring"""





      if node is not None:
        yield from self._inorder_traversal(node.left							)
        yield node
        yield from self._inorder_traversal(node.right							)





    def 	A		(   self						:		Optional[int]							):

      """simple docstring"""





      return self._preorder_traversal(self.root							)





    def 	A		(   self						:		List[str]			,						snake_case						:		Node | None							):

      """simple docstring"""





      if node is not None:
        yield node
        yield from self._preorder_traversal(node.left							)
        yield from self._preorder_traversal(node.right							)


class   __snake_case				(			unittest.TestCase   ):






    '''simple docstring'''





    @staticmethod
    def 	A		(   ):

      """simple docstring"""





      __lowerCAmelCase			=				BinarySearchTree()
      t.put(8							)
      t.put(3							)
      t.put(6							)
      t.put(1							)
      t.put(10							)
      t.put(14							)
      t.put(13							)
      t.put(4							)
      t.put(7							)
      t.put(5							)

      return t





    def 	A		(   self						:		List[str]							):

      """simple docstring"""





      __lowerCAmelCase			=				BinarySearchTree()
      assert t.is_empty()

      t.put(8							)
      assert t.root is not None
      assert t.root.parent is None
      assert t.root.label == 8

      t.put(10							)
      assert t.root.right is not None
      assert t.root.right.parent == t.root
      assert t.root.right.label == 10

      t.put(3							)
      assert t.root.left is not None
      assert t.root.left.parent == t.root
      assert t.root.left.label == 3

      t.put(6							)
      assert t.root.left.right is not None
      assert t.root.left.right.parent == t.root.left
      assert t.root.left.right.label == 6

      t.put(1							)
      assert t.root.left.left is not None
      assert t.root.left.left.parent == t.root.left
      assert t.root.left.left.label == 1

      with self.assertRaises(snake_case							):  # noqa: B017
        t.put(1							)





    def 	A		(   self						:		str							):

      """simple docstring"""





      __lowerCAmelCase			=				self._get_binary_search_tree()

      __lowerCAmelCase			=				t.search(6							)
      assert node.label == 6

      __lowerCAmelCase			=				t.search(13							)
      assert node.label == 13

      with self.assertRaises(snake_case							):  # noqa: B017
        t.search(2							)





    def 	A		(   self						:		str							):

      """simple docstring"""





      __lowerCAmelCase			=				self._get_binary_search_tree()

      t.remove(13							)
      assert t.root is not None
      assert t.root.right is not None
      assert t.root.right.right is not None
      assert t.root.right.right.right is None
      assert t.root.right.right.left is None

      t.remove(7							)
      assert t.root.left is not None
      assert t.root.left.right is not None
      assert t.root.left.right.left is not None
      assert t.root.left.right.right is None
      assert t.root.left.right.left.label == 4

      t.remove(6							)
      assert t.root.left.left is not None
      assert t.root.left.right.right is not None
      assert t.root.left.left.label == 1
      assert t.root.left.right.label == 4
      assert t.root.left.right.right.label == 5
      assert t.root.left.right.left is None
      assert t.root.left.left.parent == t.root.left
      assert t.root.left.right.parent == t.root.left

      t.remove(3							)
      assert t.root is not None
      assert t.root.left.label == 4
      assert t.root.left.right.label == 5
      assert t.root.left.left.label == 1
      assert t.root.left.parent == t.root
      assert t.root.left.left.parent == t.root.left
      assert t.root.left.right.parent == t.root.left

      t.remove(4							)
      assert t.root.left is not None
      assert t.root.left.left is not None
      assert t.root.left.label == 5
      assert t.root.left.right is None
      assert t.root.left.left.label == 1
      assert t.root.left.parent == t.root
      assert t.root.left.left.parent == t.root.left





    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				self._get_binary_search_tree()

      t.remove(3							)
      assert t.root is not None
      assert t.root.left is not None
      assert t.root.left.left is not None
      assert t.root.left.right is not None
      assert t.root.left.right.left is not None
      assert t.root.left.right.right is not None
      assert t.root.left.label == 4
      assert t.root.left.right.label == 6
      assert t.root.left.left.label == 1
      assert t.root.left.right.right.label == 7
      assert t.root.left.right.left.label == 5
      assert t.root.left.parent == t.root
      assert t.root.left.right.parent == t.root.left
      assert t.root.left.left.parent == t.root.left
      assert t.root.left.right.left.parent == t.root.left.right





    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				self._get_binary_search_tree()
      t.empty()
      assert t.root is None





    def 	A		(   self						:		Tuple							):

      """simple docstring"""





      __lowerCAmelCase			=				self._get_binary_search_tree()
      assert not t.is_empty()

      t.empty()
      assert t.is_empty()





    def 	A		(   self						:		Any							):

      """simple docstring"""





      __lowerCAmelCase			=				self._get_binary_search_tree()

      assert t.exists(6							)
      assert not t.exists(-1							)





    def 	A		(   self						:		List[Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				self._get_binary_search_tree()

      assert t.get_max_label() == 14

      t.empty()
      with self.assertRaises(snake_case							):  # noqa: B017
        t.get_max_label()





    def 	A		(   self						:		Optional[int]							):

      """simple docstring"""





      __lowerCAmelCase			=				self._get_binary_search_tree()

      assert t.get_min_label() == 1

      t.empty()
      with self.assertRaises(snake_case							):  # noqa: B017
        t.get_min_label()





    def 	A		(   self						:		Tuple							):

      """simple docstring"""





      __lowerCAmelCase			=				self._get_binary_search_tree()

      __lowerCAmelCase			=				[i.label for i in t.inorder_traversal()]
      assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]





    def 	A		(   self						:		Tuple							):

      """simple docstring"""





      __lowerCAmelCase			=				self._get_binary_search_tree()

      __lowerCAmelCase			=				[i.label for i in t.preorder_traversal()]
      assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]


def 					__SCREAMING_SNAKE_CASE  (							):



  '''simple docstring'''






  __lowerCAmelCase			=				BinarySearchTree()
  t.put(8)
  t.put(3)
  t.put(6)
  t.put(1)
  t.put(10)
  t.put(14)
  t.put(13)
  t.put(4)
  t.put(7)
  t.put(5)

  print(
      '''
            8
           / \\
          3   10
         / \\    \\
        1   6    14
           / \\   /
          4   7 13
           \\
            5
        ''')

  print('''Label 6 exists:'''     ,							t.exists(6))
  print('''Label 13 exists:'''     ,							t.exists(13))
  print('''Label -1 exists:'''     ,							t.exists(-1))
  print('''Label 12 exists:'''     ,							t.exists(12))

  # Prints all the elements of the list in inorder traversal
  __lowerCAmelCase			=				[i.label for i in t.inorder_traversal()]
  print('''Inorder traversal:'''     ,							__lowercase)

  # Prints all the elements of the list in preorder traversal
  __lowerCAmelCase			=				[i.label for i in t.preorder_traversal()]
  print('''Preorder traversal:'''     ,							__lowercase)

  print('''Max. label:'''     ,							t.get_max_label())
  print('''Min. label:'''     ,							t.get_min_label())

  # Delete elements
  print('''\nDeleting elements 13, 10, 8, 3, 6, 14''')
  print(
      '''
          4
         / \\
        1   7
             \\
              5
        ''')
  t.remove(13)
  t.remove(10)
  t.remove(8)
  t.remove(3)
  t.remove(6)
  t.remove(14)

  # Prints all the elements of the list in inorder traversal after delete
  __lowerCAmelCase			=				[i.label for i in t.inorder_traversal()]
  print('''Inorder traversal after delete:'''     ,							__lowercase)

  # Prints all the elements of the list in preorder traversal after delete
  __lowerCAmelCase			=				[i.label for i in t.preorder_traversal()]
  print('''Preorder traversal after delete:'''     ,							__lowercase)

  print('''Max. label:'''     ,							t.get_max_label())
  print('''Min. label:'''     ,							t.get_min_label())


if __name__ == "__main__":
      binary_search_tree_example()







from collections import OrderedDict
from typing import Any, Mapping, Optional

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


__lowercase													=			logging.get_logger(__name__)

__lowercase													=			{
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class   __snake_case				(			_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	"""bigbird_pegasus"""
    _lowerCAmelCase     =	["""past_key_values"""]
    _lowerCAmelCase     =	{
        """num_attention_heads""": """encoder_attention_heads""",
        """hidden_size""": """d_model""",
        """attention_probs_dropout_prob""": """attention_dropout""",
    }





    def __init__(   self						:		Any			,						snake_case						:		Optional[int]=96_103			,						snake_case						:		Tuple=4_096			,						snake_case						:		str=16			,						snake_case						:		Any=4_096			,						snake_case						:		Any=16			,						snake_case						:		Dict=16			,						snake_case						:		str=4_096			,						snake_case						:		Tuple=16			,						snake_case						:		Dict=0.0			,						snake_case						:		str=0.0			,						snake_case						:		Union[str, Any]=True			,						snake_case						:		List[Any]=True			,						snake_case						:		Optional[Any]="gelu_new"			,						snake_case						:		Union[str, Any]=1_024			,						snake_case						:		Union[str, Any]=0.1			,						snake_case						:		int=0.0			,						snake_case						:		Optional[Any]=0.0			,						snake_case						:		str=0.02			,						snake_case						:		int=2			,						snake_case						:		Any=0.0			,						snake_case						:		str=True			,						snake_case						:		Tuple=0			,						snake_case						:		Any=2			,						snake_case						:		int=1			,						snake_case						:		Any="block_sparse"			,						snake_case						:		List[Any]=64			,						snake_case						:		Dict=3			,						snake_case						:		Optional[Any]=False			,						**snake_case						:		Dict			,						):

      """simple docstring"""





      __lowerCAmelCase			=				vocab_size
      __lowerCAmelCase			=				max_position_embeddings
      __lowerCAmelCase			=				d_model
      __lowerCAmelCase			=				encoder_ffn_dim
      __lowerCAmelCase			=				encoder_layers
      __lowerCAmelCase			=				encoder_attention_heads
      __lowerCAmelCase			=				decoder_ffn_dim
      __lowerCAmelCase			=				decoder_layers
      __lowerCAmelCase			=				decoder_attention_heads
      __lowerCAmelCase			=				dropout
      __lowerCAmelCase			=				attention_dropout
      __lowerCAmelCase			=				activation_dropout
      __lowerCAmelCase			=				activation_function
      __lowerCAmelCase			=				init_std
      __lowerCAmelCase			=				encoder_layerdrop
      __lowerCAmelCase			=				decoder_layerdrop
      __lowerCAmelCase			=				classifier_dropout
      __lowerCAmelCase			=				use_cache
      __lowerCAmelCase			=				encoder_layers
      __lowerCAmelCase			=				scale_embedding  # scale factor will be sqrt(d_model) if True

      # extra config
      __lowerCAmelCase			=				attention_type
      __lowerCAmelCase			=				block_size
      __lowerCAmelCase			=				num_random_blocks
      __lowerCAmelCase			=				use_bias

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


class   __snake_case				(			_a   ):






    '''simple docstring'''





    @property
    def 	A		(   self						:		List[str]							):

      """simple docstring"""





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

        if self.use_past:
          __lowerCAmelCase			=				{0: '''batch'''}
          __lowerCAmelCase			=				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
        else:
          __lowerCAmelCase			=				{0: '''batch''', 1: '''decoder_sequence'''}
          __lowerCAmelCase			=				{0: '''batch''', 1: '''decoder_sequence'''}

        if self.use_past:
          self.fill_with_past_key_values_(snake_case			,						direction='''inputs'''							)
      elif self.task == "causal-lm":
        # TODO: figure this case out.
        __lowerCAmelCase			=				OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ]							)
        if self.use_past:
          __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
          for i in range(snake_case							):
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
      else:
        __lowerCAmelCase			=				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 	A		(   self						:		Optional[int]							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				super().outputs
      else:
        __lowerCAmelCase			=				super(snake_case			,						self							).outputs
        if self.use_past:
          __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
          for i in range(snake_case							):
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
      return common_outputs





    def 	A		(   self						:		List[Any]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)

      # Generate decoder inputs
      __lowerCAmelCase			=				seq_length if not self.use_past else 1
      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)
      __lowerCAmelCase			=				{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      __lowerCAmelCase			=				dict(**snake_case			,						**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
        __lowerCAmelCase	,						__lowerCAmelCase			=				common_inputs['''input_ids'''].shape
        __lowerCAmelCase			=				common_inputs['''decoder_input_ids'''].shape[1]
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_attention_heads
        __lowerCAmelCase			=				(
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __lowerCAmelCase			=				decoder_seq_length + 3
        __lowerCAmelCase			=				(
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        __lowerCAmelCase			=				torch.cat(
            [common_inputs['''decoder_attention_mask'''], torch.ones(snake_case			,						snake_case							)]			,						dim=1							)

        __lowerCAmelCase			=				[]
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
        __lowerCAmelCase			=				min(snake_case			,						snake_case							)
        __lowerCAmelCase			=				max(snake_case			,						snake_case							) - min_num_layers
        __lowerCAmelCase			=				'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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





    def 	A		(   self						:		Tuple			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						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
        __lowerCAmelCase	,						__lowerCAmelCase			=				common_inputs['''input_ids'''].shape
        # Not using the same length for past_key_values
        __lowerCAmelCase			=				seqlen + 2
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_attention_heads
        __lowerCAmelCase			=				(
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        __lowerCAmelCase			=				common_inputs['''attention_mask'''].dtype
        __lowerCAmelCase			=				torch.cat(
            [common_inputs['''attention_mask'''], torch.ones(snake_case			,						snake_case			,						dtype=snake_case							)]			,						dim=1							)
        __lowerCAmelCase			=				[
            (torch.zeros(snake_case							), torch.zeros(snake_case							)) for _ in range(snake_case							)
        ]
      return common_inputs





    def 	A		(   self						:		Optional[Any]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				compute_effective_axis_dimension(
          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
      __lowerCAmelCase			=				tokenizer.num_special_tokens_to_add(snake_case							)
      __lowerCAmelCase			=				compute_effective_axis_dimension(
          snake_case			,						fixed_dimension=OnnxConfig.default_fixed_sequence			,						num_token_to_add=snake_case							)

      # Generate dummy inputs according to compute batch and sequence
      __lowerCAmelCase			=				[''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
      __lowerCAmelCase			=				dict(tokenizer(snake_case			,						return_tensors=snake_case							)							)
      return common_inputs





    def 	A		(   self						:		List[str]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)

      elif self.task == "causal-lm":
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_causal_lm(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)
      else:
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)

      return common_inputs





    def 	A		(   self						:		int			,						snake_case						:		Optional[int]			,						snake_case						:		List[Any]			,						snake_case						:		str			,						snake_case						:		Union[str, Any]							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				super()._flatten_past_key_values_(snake_case			,						snake_case			,						snake_case			,						snake_case							)
      else:
        __lowerCAmelCase			=				super(snake_case			,						self							)._flatten_past_key_values_(
            snake_case			,						snake_case			,						snake_case			,						snake_case							)
