


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  )



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()

