from __future__ import annotations

import unittest
from collections.abc import Iterator





class 					__snake_case    :




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    None:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= label
									snake_case__   :      Union[str, Any]   				= parent
									snake_case__   :      Optional[Any]   				= None
									snake_case__   :      str   				= None





class 					__snake_case    :




					def __init__(  self						)	->    None:



									'''simple docstring'''







									snake_case__   :      Any   				= None




					def      __a     (  self						)	->    None:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= None




					def      __a     (  self						)	->    bool:



									'''simple docstring'''







									return self.root is None




					def      __a     (  self       ,			__UpperCamelCase						)	->    None:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= self._put(self.root       ,			UpperCAmelCase_						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase = None						)	->    Node:



									'''simple docstring'''







									if node is None:
													snake_case__   :      Optional[Any]   				= Node(UpperCAmelCase_       ,			UpperCAmelCase_						)
									else:
													if label < node.label:
																	snake_case__   :      Optional[Any]   				= self._put(node.left       ,			UpperCAmelCase_       ,			UpperCAmelCase_						)
													elif label > node.label:
																	snake_case__   :      List[str]   				= self._put(node.right       ,			UpperCAmelCase_       ,			UpperCAmelCase_						)
													else:
																	snake_case__   :      List[str]   				= F"""Node with label {label} already exists"""
																	raise Exception(UpperCAmelCase_						)

									return node




					def      __a     (  self       ,			__UpperCamelCase						)	->    Node:



									'''simple docstring'''







									return self._search(self.root       ,			UpperCAmelCase_						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Node:



									'''simple docstring'''







									if node is None:
													snake_case__   :      str   				= F"""Node with label {label} does not exist"""
													raise Exception(UpperCAmelCase_						)
									else:
													if label < node.label:
																	snake_case__   :      Tuple   				= self._search(node.left       ,			UpperCAmelCase_						)
													elif label > node.label:
																	snake_case__   :      Union[str, Any]   				= self._search(node.right       ,			UpperCAmelCase_						)

									return node




					def      __a     (  self       ,			__UpperCamelCase						)	->    None:



									'''simple docstring'''







									snake_case__   :      Dict   				= self.search(UpperCAmelCase_						)
									if node.right and node.left:
													snake_case__   :      Dict   				= self._get_lowest_node(node.right						)
													snake_case__   :      int   				= node.left
													snake_case__   :      Optional[int]   				= node.right
													snake_case__   :      Union[str, Any]   				= lowest_node
													if node.right:
																	snake_case__   :      Tuple   				= lowest_node
													self._reassign_nodes(UpperCAmelCase_       ,			UpperCAmelCase_						)
									elif not node.right and node.left:
													self._reassign_nodes(UpperCAmelCase_       ,			node.left						)
									elif node.right and not node.left:
													self._reassign_nodes(UpperCAmelCase_       ,			node.right						)
									else:
													self._reassign_nodes(UpperCAmelCase_       ,			UpperCAmelCase_						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    None:



									'''simple docstring'''







									if new_children:
													snake_case__   :      Optional[int]   				= node.parent

									if node.parent:
													if node.parent.right == node:
																	snake_case__   :      Tuple   				= new_children
													else:
																	snake_case__   :      int   				= new_children
									else:
													snake_case__   :      List[Any]   				= new_children




					def      __a     (  self       ,			__UpperCamelCase						)	->    Node:



									'''simple docstring'''







									if node.left:
													snake_case__   :      str   				= self._get_lowest_node(node.left						)
									else:
													snake_case__   :      List[str]   				= node
													self._reassign_nodes(UpperCAmelCase_       ,			node.right						)

									return lowest_node




					def      __a     (  self       ,			__UpperCamelCase						)	->    bool:



									'''simple docstring'''







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




					def      __a     (  self						)	->    int:



									'''simple docstring'''







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

									snake_case__   :      List[str]   				= self.root
									while node.right is not None:
													snake_case__   :      int   				= node.right

									return node.label




					def      __a     (  self						)	->    int:



									'''simple docstring'''







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

									snake_case__   :      List[str]   				= self.root
									while node.left is not None:
													snake_case__   :      Dict   				= node.left

									return node.label




					def      __a     (  self						)	->    Iterator[Node]:



									'''simple docstring'''







									return self._inorder_traversal(self.root						)




					def      __a     (  self       ,			__UpperCamelCase						)	->    Iterator[Node]:



									'''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						)	->    Iterator[Node]:



									'''simple docstring'''







									return self._preorder_traversal(self.root						)




					def      __a     (  self       ,			__UpperCamelCase						)	->    Iterator[Node]:



									'''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      ):




					@staticmethod
					def      __a     (  )	->    BinarySearchTree:



									'''simple docstring'''







									snake_case__   :      str   				= 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						)	->    None:



									'''simple docstring'''







									snake_case__   :      int   				= 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(UpperCAmelCase_						):  # noqa: B017
													t.put(1						)




					def      __a     (  self						)	->    None:



									'''simple docstring'''







									snake_case__   :      int   				= self._get_binary_search_tree()

									snake_case__   :      Optional[Any]   				= t.search(6						)
									assert node.label == 6

									snake_case__   :      List[Any]   				= t.search(13						)
									assert node.label == 13

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




					def      __a     (  self						)	->    None:



									'''simple docstring'''







									snake_case__   :      List[Any]   				= 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						)	->    None:



									'''simple docstring'''







									snake_case__   :      int   				= 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						)	->    None:



									'''simple docstring'''







									snake_case__   :      int   				= self._get_binary_search_tree()
									t.empty()
									assert t.root is None




					def      __a     (  self						)	->    None:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= self._get_binary_search_tree()
									assert not t.is_empty()

									t.empty()
									assert t.is_empty()




					def      __a     (  self						)	->    None:



									'''simple docstring'''







									snake_case__   :      Dict   				= self._get_binary_search_tree()

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




					def      __a     (  self						)	->    None:



									'''simple docstring'''







									snake_case__   :      Any   				= self._get_binary_search_tree()

									assert t.get_max_label() == 14

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




					def      __a     (  self						)	->    None:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= self._get_binary_search_tree()

									assert t.get_min_label() == 1

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




					def      __a     (  self						)	->    None:



									'''simple docstring'''







									snake_case__   :      int   				= self._get_binary_search_tree()

									snake_case__   :      str   				= [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						)	->    None:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= self._get_binary_search_tree()

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




def 					UpperCamelCase__		(							)  ->   None:
				snake_case__   :      Tuple   				= 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(
				    '\n            8\n           / \\\n          3   10\n         / \\    \\\n        1   6    14\n           / \\   /\n          4   7 13\n           \\\n            5\n        '       )

				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
				snake_case__   :      str   				= [i.label for i in t.inorder_traversal()]
				print('Inorder traversal:'     ,					_SCREAMING_SNAKE_CASE       )

				# Prints all the elements of the list in preorder traversal
				snake_case__   :      Union[str, Any]   				= [i.label for i in t.preorder_traversal()]
				print('Preorder traversal:'     ,					_SCREAMING_SNAKE_CASE       )

				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(
				    '\n          4\n         / \\\n        1   7\n             \\\n              5\n        '       )
				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
				snake_case__   :      Any   				= [i.label for i in t.inorder_traversal()]
				print('Inorder traversal after delete:'     ,					_SCREAMING_SNAKE_CASE       )

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

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


if __name__ == "__main__":
				binary_search_tree_example()






"""simple docstring"""
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


__SCREAMING_SNAKE_CASE				:     int		=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:     str		=     {
    '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   __A  (snake_case__):





							'''simple docstring'''

							__lowercase:		str				   =     """bigbird_pegasus"""
							__lowercase:		Any				   =     ["""past_key_values"""]
							__lowercase:		Optional[Any]				   =     {
							    """num_attention_heads""": """encoder_attention_heads""",
							    """hidden_size""": """d_model""",
							    """attention_probs_dropout_prob""": """attention_dropout""",
							}



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



														"""simple docstring"""



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

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

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





class   __A  (snake_case__):





							'''simple docstring'''

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



														"""simple docstring"""



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

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

																					if self.use_past:
																												self.fill_with_past_key_values_(UpperCAmelCase_		,							direction="""inputs"""							)
														elif self.task == "causal-lm":
																					# TODO: figure this case out.
																					snake_case_     							=		OrderedDict(
																					    [
																					        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																					        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																					    ]							)
																					if self.use_past:
																												snake_case_							,    snake_case_     							=		self.num_layers
																												for i in range(UpperCAmelCase_							):
																																			snake_case_     							=		{0: """batch""", 2: """past_sequence + sequence"""}
																																			snake_case_     							=		{0: """batch""", 2: """past_sequence + sequence"""}
														else:
																					snake_case_     							=		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 		lowerCAmelCase							(					self				:   Dict							)       ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														snake_case_     							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_							)

														# Generate decoder inputs
														snake_case_     							=		seq_length if not self.use_past else 1
														snake_case_     							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
														snake_case_     							=		dict(**UpperCAmelCase_		,							**UpperCAmelCase_							)

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

																					snake_case_     							=		torch.cat(
																					    [common_inputs["""decoder_attention_mask"""], torch.ones(UpperCAmelCase_		,							UpperCAmelCase_							)]		,							dim=1							)

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

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



							def 		lowerCAmelCase							(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														snake_case_     							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_							)

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

																					snake_case_     							=		common_inputs["""attention_mask"""].dtype
																					snake_case_     							=		torch.cat(
																					    [common_inputs["""attention_mask"""], torch.ones(UpperCAmelCase_		,							UpperCAmelCase_		,							dtype=UpperCAmelCase_							)]		,							dim=1							)
																					snake_case_     							=		[
																					    (torch.zeros(UpperCAmelCase_							), torch.zeros(UpperCAmelCase_							)) for _ in range(UpperCAmelCase_							)
																					]
														return common_inputs



							def 		lowerCAmelCase							(					self				:   Dict		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														snake_case_     							=		compute_effective_axis_dimension(
														    UpperCAmelCase_		,							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
														snake_case_     							=		tokenizer.num_special_tokens_to_add(UpperCAmelCase_							)
														snake_case_     							=		compute_effective_axis_dimension(
														    UpperCAmelCase_		,							fixed_dimension=OnnxConfig.default_fixed_sequence		,							num_token_to_add=UpperCAmelCase_							)

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



							def 		lowerCAmelCase							(					self				:   str		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



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

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

														return common_inputs



							def 		lowerCAmelCase							(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Any							)       ->Tuple:



														"""simple docstring"""



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



