code: 
  
  
  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
  
  
  UpperCAmelCase      :						str    =		logging.get_logger(__name__)
  
  UpperCAmelCase      :						List[Any]    =		{
      "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
      # See all MBART models at https://huggingface.co/models?filter=mbart
  }
  
  
  
  
  
  
  
  class 					__lowercase					(       A_     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				Dict 					=		'''mbart'''
  		UpperCamelCase    :				Any 					=		['''past_key_values''']
  		UpperCamelCase    :				Union[str, Any] 					=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
  
  
  
  
  
  		def __init__(   self		,			A=5_02_65		,			A=10_24		,			A=12		,			A=40_96		,			A=16		,			A=12		,			A=40_96		,			A=16		,			A=0.0		,			A=0.0		,			A=True		,			A=True		,			A="gelu"		,			A=10_24		,			A=0.1		,			A=0.0		,			A=0.0		,			A=0.02		,			A=0.0		,			A=False		,			A=1		,			A=0		,			A=2		,			A=2		,			**A		,			)     ->   str:
  
  
  
  
  
  
  
  					'''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
  					super().__init__(
  					    pad_token_id=A		,			bos_token_id=A		,			eos_token_id=A		,			is_encoder_decoder=A		,			forced_eos_token_id=A		,			**A		,			)
  
  
  
  
  
  
  
  class 					__lowercase					(       A_     ):
  
  
  
  		"""simple docstring"""
  
  		@property
  		def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
  					'''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_(A		,			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(A  ):
  														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  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if self.task in ["default", "seq2seq-lm"]:
  								lowerCamelCase								=     super().outputs
  					else:
  								lowerCamelCase								=     super(A		,			self  ).outputs
  								if self.use_past:
  											lowerCamelCase		,		lowerCamelCase								=     self.num_layers
  											for i in range(A  ):
  														lowerCamelCase								=     {0: """batch""", 2: """past_sequence + sequence"""}
  														lowerCamelCase								=     {0: """batch""", 2: """past_sequence + sequence"""}
  					return common_outputs
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A = -1		,			A = -1		,			A = False		,			A = None		,			)     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  					    A		,			A		,			A		,			A		,			A  )
  
  					# Generate decoder inputs
  					lowerCamelCase								=     seq_length if not self.use_past else 1
  					lowerCamelCase								=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  					    A		,			A		,			A		,			A		,			A  )
  					lowerCamelCase								=     {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
  					lowerCamelCase								=     dict(**A		,			**A  )
  
  					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(A		,			A  )]		,			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(A		,			A  )
  								lowerCamelCase								=     max(A		,			A  ) - min_num_layers
  								lowerCamelCase								=     """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
  
  								for _ in range(A  ):
  											common_inputs["past_key_values"].append(
  											    (
  											        torch.zeros(A  ),
  											        torch.zeros(A  ),
  											        torch.zeros(A  ),
  											        torch.zeros(A  ),
  											    )  )
  								# TODO: test this.
  								lowerCamelCase								=     encoder_shape if remaining_side_name == """encoder""" else decoder_shape
  								for _ in range(A		,			A  ):
  											common_inputs["past_key_values"].append((torch.zeros(A  ), torch.zeros(A  ))  )
  					return common_inputs
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A = -1		,			A = -1		,			A = False		,			A = None		,			)     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  					    A		,			A		,			A		,			A		,			A  )
  
  					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(A		,			A		,			dtype=A  )]		,			dim=1  )
  								lowerCamelCase								=     [
  								    (torch.zeros(A  ), torch.zeros(A  )) for _ in range(A  )
  								]
  					return common_inputs
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A = -1		,			A = -1		,			A = False		,			A = None		,			)     ->   List[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     compute_effective_axis_dimension(
  					    A		,			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(A  )
  					lowerCamelCase								=     compute_effective_axis_dimension(
  					    A		,			fixed_dimension=OnnxConfig.default_fixed_sequence		,			num_token_to_add=A  )
  
  					# Generate dummy inputs according to compute batch and sequence
  					lowerCamelCase								=     [""" """.join([tokenizer.unk_token]  ) * seq_length] * batch_size
  					lowerCamelCase								=     dict(tokenizer(A		,			return_tensors=A  )  )
  					return common_inputs
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A = -1		,			A = -1		,			A = False		,			A = None		,			)     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if self.task in ["default", "seq2seq-lm"]:
  								lowerCamelCase								=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
  								    A		,			batch_size=A		,			seq_length=A		,			is_pair=A		,			framework=A  )
  
  					elif self.task == "causal-lm":
  								lowerCamelCase								=     self._generate_dummy_inputs_for_causal_lm(
  								    A		,			batch_size=A		,			seq_length=A		,			is_pair=A		,			framework=A  )
  					else:
  								lowerCamelCase								=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  								    A		,			batch_size=A		,			seq_length=A		,			is_pair=A		,			framework=A  )
  
  					return common_inputs
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A		,			A		,			A  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if self.task in ["default", "seq2seq-lm"]:
  								lowerCamelCase								=     super()._flatten_past_key_values_(A		,			A		,			A		,			A  )
  					else:
  								lowerCamelCase								=     super(A		,			self  )._flatten_past_key_values_(
  								    A		,			A		,			A		,			A  )
style_context: 
  
  
  from __future__ import annotations
  
  from collections.abc import Callable
  from typing import Generic, TypeVar
  
  UpperCAmelCase      :						List[str]    =		TypeVar("T")
  UpperCAmelCase      :						Dict    =		TypeVar("U")
  
  
  
  
  
  
  
  class 					__lowercase					(       Generic[T, U]     ):
  
  
  
  		"""simple docstring"""
  
  		def __init__(   self		,			A		,			A  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     key
  					lowerCamelCase								=     val
  					lowerCamelCase								=     0
  					lowerCamelCase								=     None
  					lowerCamelCase								=     None
  
  
  
  
  
  
  
  		def __repr__(   self  )     ->   str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
  					    self.key		,			self.val		,			self.freq		,			self.next is not None		,			self.prev is not None  )
  
  
  
  
  
  
  
  class 					__lowercase					(       Generic[T, U]     ):
  
  
  
  		"""simple docstring"""
  
  		def __init__(   self  )     ->   None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     DoubleLinkedListNode(A		,			A  )
  					lowerCamelCase								=     DoubleLinkedListNode(A		,			A  )
  					lowerCamelCase		,		lowerCamelCase								=     self.rear, self.head
  
  
  
  
  
  
  
  		def __repr__(   self  )     ->   str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     ["""DoubleLinkedList"""]
  					lowerCamelCase								=     self.head
  					while node.next is not None:
  								rep.append(str(A  )  )
  								lowerCamelCase								=     node.next
  					rep.append(str(self.rear  )  )
  					return ",\n    ".join(A  )
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A  )     ->   None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.rear.prev
  
  					# All nodes other than self.head are guaranteed to have non-None previous
  					assert previous is not None
  
  					lowerCamelCase								=     node
  					lowerCamelCase								=     previous
  					lowerCamelCase								=     node
  					lowerCamelCase								=     self.rear
  					node.freq += 1
  					self._position_node(A  )
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A  )     ->   None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					while node.prev is not None and node.prev.freq > node.freq:
  								# swap node with previous node
  								lowerCamelCase								=     node.prev
  
  								lowerCamelCase								=     previous_node.prev
  								lowerCamelCase								=     node.prev
  								lowerCamelCase								=     previous_node
  								lowerCamelCase								=     node
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A  )     ->   DoubleLinkedListNode[T, U] | None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if node.prev is None or node.next is None:
  								return None
  
  					lowerCamelCase								=     node.next
  					lowerCamelCase								=     node.prev
  					lowerCamelCase								=     None
  					lowerCamelCase								=     None
  					return node
  
  
  
  
  
  
  
  class 					__lowercase					(       Generic[T, U]     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				dict[Callable[[T], U], LFUCache[T, U]] 					=		{}
  
  
  
  
  
  
  
  		def __init__(   self		,			A  )     ->   Union[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     DoubleLinkedList()
  					lowerCamelCase								=     capacity
  					lowerCamelCase								=     0
  					lowerCamelCase								=     0
  					lowerCamelCase								=     0
  					lowerCamelCase								=     {}
  
  
  
  
  
  
  
  		def __repr__(   self  )     ->   str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return (
  					    F'CacheInfo(hits={self.hits}, misses={self.miss}, '
  					    F'capacity={self.capacity}, current_size={self.num_keys})'
  					)
  
  
  
  
  
  
  
  		def __contains__(   self		,			A  )     ->   bool:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return key in self.cache
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A  )     ->   U | None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if key in self.cache:
  								self.hits += 1
  								lowerCamelCase								=     self.cache[key]
  								lowerCamelCase								=     self.list.remove(self.cache[key]  )
  								assert node == value_node
  
  								# node is guaranteed not None because it is in self.cache
  								assert node is not None
  								self.list.add(A  )
  								return node.val
  					self.miss += 1
  					return None
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A  )     ->   None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if key not in self.cache:
  								if self.num_keys >= self.capacity:
  											# delete first node when over capacity
  											lowerCamelCase								=     self.list.head.next
  
  											# guaranteed to have a non-None first node when num_keys > 0
  											# explain to type checker via assertions
  											assert first_node is not None
  											assert first_node.key is not None
  											assert self.list.remove(A  ) is not None
  											# first_node guaranteed to be in list
  
  											del self.cache[first_node.key]
  											self.num_keys -= 1
  								lowerCamelCase								=     DoubleLinkedListNode(A		,			A  )
  								self.list.add(self.cache[key]  )
  								self.num_keys += 1
  
  					else:
  								lowerCamelCase								=     self.list.remove(self.cache[key]  )
  								assert node is not None  # node guaranteed to be in list
  								lowerCamelCase								=     value
  								self.list.add(A  )
  
  
  
  
  
  
  
  		@classmethod
  		def 				__A      (   cls		,			A = 1_28  )     ->   Callable[[Callable[[T], U]], Callable[..., U]]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					def cache_decorator_inner(A  ) -> Callable[..., U]:
  								def cache_decorator_wrapper(*A  ) -> U:
  											if func not in cls.decorator_function_to_instance_map:
  														lowerCamelCase								=     LFUCache(A  )
  
  											lowerCamelCase								=     cls.decorator_function_to_instance_map[func].get(args[0]  )
  											if result is None:
  														lowerCamelCase								=     func(*A  )
  														cls.decorator_function_to_instance_map[func].put(args[0]		,			A  )
  											return result
  
  								def cache_info() -> LFUCache[T, U]:
  											return cls.decorator_function_to_instance_map[func]
  
  								setattr(A		,			"""cache_info"""		,			A  )  # noqa: B010
  
  								return cache_decorator_wrapper
  
  					return cache_decorator_inner
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  