
"""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


__a			 =   logging.get_logger(__name__)

__a			 =   {
    "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 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      """mbart"""
							SCREAMING_SNAKE_CASE										=      ["""past_key_values"""]
							SCREAMING_SNAKE_CASE										=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






							def __init__(						self :     Any		, lowerCamelCase_ :     List[Any]=50265		, lowerCamelCase_ :     str=1024		, lowerCamelCase_ :     str=12		, lowerCamelCase_ :     Dict=4096		, lowerCamelCase_ :     Optional[Any]=16		, lowerCamelCase_ :     List[Any]=12		, lowerCamelCase_ :     str=4096		, lowerCamelCase_ :     int=16		, lowerCamelCase_ :     int=0.0		, lowerCamelCase_ :     Optional[Any]=0.0		, lowerCamelCase_ :     int=True		, lowerCamelCase_ :     Dict=True		, lowerCamelCase_ :     List[str]="gelu"		, lowerCamelCase_ :     List[Any]=1024		, lowerCamelCase_ :     Tuple=0.1		, lowerCamelCase_ :     Any=0.0		, lowerCamelCase_ :     int=0.0		, lowerCamelCase_ :     str=0.02		, lowerCamelCase_ :     Any=0.0		, lowerCamelCase_ :     Optional[Any]=False		, lowerCamelCase_ :     List[str]=1		, lowerCamelCase_ :     str=0		, lowerCamelCase_ :     Optional[Any]=2		, lowerCamelCase_ :     Any=2		, **lowerCamelCase_ :     str		, ):
													UpperCAmelCase__		=    vocab_size
													UpperCAmelCase__		=    max_position_embeddings
													UpperCAmelCase__		=    d_model
													UpperCAmelCase__		=    encoder_ffn_dim
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    encoder_attention_heads
													UpperCAmelCase__		=    decoder_ffn_dim
													UpperCAmelCase__		=    decoder_layers
													UpperCAmelCase__		=    decoder_attention_heads
													UpperCAmelCase__		=    dropout
													UpperCAmelCase__		=    attention_dropout
													UpperCAmelCase__		=    activation_dropout
													UpperCAmelCase__		=    activation_function
													UpperCAmelCase__		=    init_std
													UpperCAmelCase__		=    encoder_layerdrop
													UpperCAmelCase__		=    decoder_layerdrop
													UpperCAmelCase__		=    classifier_dropout
													UpperCAmelCase__		=    use_cache
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    scale_embedding  # scale factor will be sqrt(d_model) if True
													super().__init__(
													    pad_token_id=lowerCamelCase_		, bos_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, is_encoder_decoder=lowerCamelCase_		, forced_eos_token_id=lowerCamelCase_		, **lowerCamelCase_		, )




class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""






							@property
							def 					lowerCAmelCase     (						self :     List[Any]				):
													if self.task in ["default", "seq2seq-lm"]:
																			UpperCAmelCase__		=    OrderedDict(
																			    [
																			        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																			        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																			    ]				)

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

																			if self.use_past:
																									self.fill_with_past_key_values_(lowerCamelCase_		, direction='''inputs'''				)
													elif self.task == "causal-lm":
																			# TODO: figure this case out.
																			UpperCAmelCase__		=    OrderedDict(
																			    [
																			        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																			        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																			    ]				)
																			if self.use_past:
																									UpperCAmelCase__	,				UpperCAmelCase__		=    self.num_layers
																									for i in range(lowerCamelCase_				):
																															UpperCAmelCase__		=    {0: '''batch''', 2: '''past_sequence + sequence'''}
																															UpperCAmelCase__		=    {0: '''batch''', 2: '''past_sequence + sequence'''}
													else:
																			UpperCAmelCase__		=    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				):
													if self.task in ["default", "seq2seq-lm"]:
																			UpperCAmelCase__		=    super().outputs
													else:
																			UpperCAmelCase__		=    super(lowerCamelCase_		, self				).outputs
																			if self.use_past:
																									UpperCAmelCase__	,				UpperCAmelCase__		=    self.num_layers
																									for i in range(lowerCamelCase_				):
																															UpperCAmelCase__		=    {0: '''batch''', 2: '''past_sequence + sequence'''}
																															UpperCAmelCase__		=    {0: '''batch''', 2: '''past_sequence + sequence'''}
													return common_outputs






							def 					lowerCAmelCase     (						self :     Tuple		, lowerCamelCase_ :     PreTrainedTokenizer		, lowerCamelCase_ :     int = -1		, lowerCamelCase_ :     int = -1		, lowerCamelCase_ :     bool = False		, lowerCamelCase_ :     Optional[TensorType] = None		, ):
													UpperCAmelCase__		=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_				)

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

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

																			UpperCAmelCase__		=    torch.cat(
																			    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCamelCase_		, lowerCamelCase_				)]		, dim=1				)

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

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






							def 					lowerCAmelCase     (						self :     Tuple		, lowerCamelCase_ :     PreTrainedTokenizer		, lowerCamelCase_ :     int = -1		, lowerCamelCase_ :     int = -1		, lowerCamelCase_ :     bool = False		, lowerCamelCase_ :     Optional[TensorType] = None		, ):
													UpperCAmelCase__		=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_				)

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

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






							def 					lowerCAmelCase     (						self :     str		, lowerCamelCase_ :     PreTrainedTokenizer		, lowerCamelCase_ :     int = -1		, lowerCamelCase_ :     int = -1		, lowerCamelCase_ :     bool = False		, lowerCamelCase_ :     Optional[TensorType] = None		, ):
													# Copied from OnnxConfig.generate_dummy_inputs
													# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
													# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
													UpperCAmelCase__		=    compute_effective_axis_dimension(
													    lowerCamelCase_		, fixed_dimension=OnnxConfig.default_fixed_batch		, num_token_to_add=0				)

													# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
													UpperCAmelCase__		=    tokenizer.num_special_tokens_to_add(lowerCamelCase_				)
													UpperCAmelCase__		=    compute_effective_axis_dimension(
													    lowerCamelCase_		, fixed_dimension=OnnxConfig.default_fixed_sequence		, num_token_to_add=lowerCamelCase_				)

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






							def 					lowerCAmelCase     (						self :     Tuple		, lowerCamelCase_ :     PreTrainedTokenizer		, lowerCamelCase_ :     int = -1		, lowerCamelCase_ :     int = -1		, lowerCamelCase_ :     bool = False		, lowerCamelCase_ :     Optional[TensorType] = None		, ):
													if self.task in ["default", "seq2seq-lm"]:
																			UpperCAmelCase__		=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																			    lowerCamelCase_		, batch_size=lowerCamelCase_		, seq_length=lowerCamelCase_		, is_pair=lowerCamelCase_		, framework=lowerCamelCase_				)

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

													return common_inputs






							def 					lowerCAmelCase     (						self :     Any		, lowerCamelCase_ :     Dict		, lowerCamelCase_ :     List[str]		, lowerCamelCase_ :     int		, lowerCamelCase_ :     List[Any]				):
													if self.task in ["default", "seq2seq-lm"]:
																			UpperCAmelCase__		=    super()._flatten_past_key_values_(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_				)
													else:
																			UpperCAmelCase__		=    super(lowerCamelCase_		, self				)._flatten_past_key_values_(
																			    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_				)



"""simple docstring"""







from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

__a			 =   TypeVar("T")
__a			 =   TypeVar("U")
class 							lowerCamelCase       (			Generic[T, U] ):


							"""simple docstring"""






							def __init__(						self :     str		, lowerCamelCase_ :     T | None		, lowerCamelCase_ :     U | None				):
													UpperCAmelCase__		=    key
													UpperCAmelCase__		=    val
													UpperCAmelCase__		=    0
													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    None






							def __repr__(						self :     Tuple				):
													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 							lowerCamelCase       (			Generic[T, U] ):


							"""simple docstring"""






							def __init__(						self :     List[str]				):
													UpperCAmelCase__		=    DoubleLinkedListNode(lowerCamelCase_		, lowerCamelCase_				)
													UpperCAmelCase__		=    DoubleLinkedListNode(lowerCamelCase_		, lowerCamelCase_				)
													UpperCAmelCase__	,				UpperCAmelCase__		=    self.rear, self.head






							def __repr__(						self :     int				):
													UpperCAmelCase__		=    ['''DoubleLinkedList''']
													UpperCAmelCase__		=    self.head
													while node.next is not None:
																			rep.append(str(lowerCamelCase_				)				)
																			UpperCAmelCase__		=    node.next
													rep.append(str(self.rear				)				)
													return ",\n    ".join(lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     List[Any]		, lowerCamelCase_ :     DoubleLinkedListNode[T, U]				):
													UpperCAmelCase__		=    self.rear.prev

													# All nodes other than self.head are guaranteed to have non-None previous
													assert previous is not None

													UpperCAmelCase__		=    node
													UpperCAmelCase__		=    previous
													UpperCAmelCase__		=    node
													UpperCAmelCase__		=    self.rear
													node.freq += 1
													self._position_node(lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     Optional[Any]		, lowerCamelCase_ :     DoubleLinkedListNode[T, U]				):

													while node.prev is not None and node.prev.freq > node.freq:
																			# swap node with previous node
																			UpperCAmelCase__		=    node.prev

																			UpperCAmelCase__		=    previous_node.prev
																			UpperCAmelCase__		=    node.prev
																			UpperCAmelCase__		=    previous_node
																			UpperCAmelCase__		=    node






							def 					lowerCAmelCase     (						self :     Union[str, Any]		, lowerCamelCase_ :     DoubleLinkedListNode[T, U]				):

													if node.prev is None or node.next is None:
																			return None

													UpperCAmelCase__		=    node.next
													UpperCAmelCase__		=    node.prev
													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    None
													return node




class 							lowerCamelCase       (			Generic[T, U] ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      {}






							def __init__(						self :     str		, lowerCamelCase_ :     int				):
													UpperCAmelCase__		=    DoubleLinkedList()
													UpperCAmelCase__		=    capacity
													UpperCAmelCase__		=    0
													UpperCAmelCase__		=    0
													UpperCAmelCase__		=    0
													UpperCAmelCase__		=    {}






							def __repr__(						self :     Tuple				):
													return (
													    f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
													    f"""capacity={self.capacity}, current_size={self.num_keys})"""
													)






							def __contains__(						self :     Optional[Any]		, lowerCamelCase_ :     T				):
													return key in self.cache






							def 					lowerCAmelCase     (						self :     Any		, lowerCamelCase_ :     T				):

													if key in self.cache:
																			self.hits += 1
																			UpperCAmelCase__		=    self.cache[key]
																			UpperCAmelCase__		=    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(lowerCamelCase_				)
																			return node.val
													self.miss += 1
													return None






							def 					lowerCAmelCase     (						self :     Tuple		, lowerCamelCase_ :     T		, lowerCamelCase_ :     U				):

													if key not in self.cache:
																			if self.num_keys >= self.capacity:
																									# delete first node when over capacity
																									UpperCAmelCase__		=    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(lowerCamelCase_				) is not None
																									# first_node guaranteed to be in list

																									del self.cache[first_node.key]
																									self.num_keys -= 1
																			UpperCAmelCase__		=    DoubleLinkedListNode(lowerCamelCase_		, lowerCamelCase_				)
																			self.list.add(self.cache[key]				)
																			self.num_keys += 1

													else:
																			UpperCAmelCase__		=    self.list.remove(self.cache[key]				)
																			assert node is not None  # node guaranteed to be in list
																			UpperCAmelCase__		=    value
																			self.list.add(lowerCamelCase_				)






							@classmethod
							def 					lowerCAmelCase     (						cls :     type[LFUCache[T, U]]		, lowerCamelCase_ :     int = 128				):

													def cache_decorator_inner(lowerCamelCase_ :     Callable[[T], U]				) -> Callable[..., U]:
																			def cache_decorator_wrapper(*lowerCamelCase_ :     T				) -> U:
																									if func not in cls.decorator_function_to_instance_map:
																															UpperCAmelCase__		=    LFUCache(lowerCamelCase_				)

																									UpperCAmelCase__		=    cls.decorator_function_to_instance_map[func].get(args[0]				)
																									if result is None:
																															UpperCAmelCase__		=    func(*lowerCamelCase_				)
																															cls.decorator_function_to_instance_map[func].put(args[0]		, lowerCamelCase_				)
																									return result

																			def cache_info() -> LFUCache[T, U]:
																									return cls.decorator_function_to_instance_map[func]

																			setattr(lowerCamelCase_		, '''cache_info'''		, lowerCamelCase_				)  # noqa: B010

																			return cache_decorator_wrapper

													return cache_decorator_inner


if __name__ == "__main__":
						import doctest

						doctest.testmod()