

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 _UpperCamelCase							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          ='mbart'
			lowerCamelCase__          =['past_key_values']
			lowerCamelCase__          ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
			def __init__(     self							:	Tuple , a							:	Any=5_0265 , a							:	Optional[int]=1024 , a							:	Union[str, Any]=12 , a							:	Optional[Any]=4096 , a							:	List[str]=16 , a							:	int=12 , a							:	Dict=4096 , a							:	int=16 , a							:	int=0.0 , a							:	Tuple=0.0 , a							:	Any=True , a							:	Tuple=True , a							:	List[str]="gelu" , a							:	Tuple=1024 , a							:	str=0.1 , a							:	str=0.0 , a							:	List[str]=0.0 , a							:	Dict=0.02 , a							:	Dict=0.0 , a							:	Union[str, Any]=False , a							:	Optional[int]=1 , a							:	Optional[Any]=0 , a							:	Optional[Any]=2 , a							:	int=2 , **a							:	Optional[Any] , )							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    vocab_size
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    max_position_embeddings
				SCREAMING_SNAKE_CASE				: str			       =    d_model
				SCREAMING_SNAKE_CASE				: str			       =    encoder_ffn_dim
				SCREAMING_SNAKE_CASE				: str			       =    encoder_layers
				SCREAMING_SNAKE_CASE				: Tuple			       =    encoder_attention_heads
				SCREAMING_SNAKE_CASE				: str			       =    decoder_ffn_dim
				SCREAMING_SNAKE_CASE				: str			       =    decoder_layers
				SCREAMING_SNAKE_CASE				: int			       =    decoder_attention_heads
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    dropout
				SCREAMING_SNAKE_CASE				: Any			       =    attention_dropout
				SCREAMING_SNAKE_CASE				: int			       =    activation_dropout
				SCREAMING_SNAKE_CASE				: List[Any]			       =    activation_function
				SCREAMING_SNAKE_CASE				: Tuple			       =    init_std
				SCREAMING_SNAKE_CASE				: List[Any]			       =    encoder_layerdrop
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    decoder_layerdrop
				SCREAMING_SNAKE_CASE				: List[Any]			       =    classifier_dropout
				SCREAMING_SNAKE_CASE				: List[Any]			       =    use_cache
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    encoder_layers
				SCREAMING_SNAKE_CASE				: str			       =    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 _UpperCamelCase							(							__A				):
			'''simple docstring'''







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

				"""simple docstring"""

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

					if self.use_past:
						SCREAMING_SNAKE_CASE				: List[str]			       =    {0: "batch"}
						SCREAMING_SNAKE_CASE				: Optional[int]			       =    {0: "batch", 1: "past_decoder_sequence + sequence"}
					else:
						SCREAMING_SNAKE_CASE				: str			       =    {0: "batch", 1: "decoder_sequence"}
						SCREAMING_SNAKE_CASE				: Optional[int]			       =    {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.
					SCREAMING_SNAKE_CASE				: List[Any]			       =    OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
					    ]		)
					if self.use_past:
						SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: List[str]			       =    self.num_layers
						for i in range(a		):
							SCREAMING_SNAKE_CASE				: str			       =    {0: "batch", 2: "past_sequence + sequence"}
							SCREAMING_SNAKE_CASE				: Any			       =    {0: "batch", 2: "past_sequence + sequence"}
				else:
					SCREAMING_SNAKE_CASE				: Tuple			       =    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 							__UpperCamelCase	(     self							:	List[Any]		)							->    Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    super().outputs
				else:
					SCREAMING_SNAKE_CASE				: str			       =    super(a , self		).outputs
					if self.use_past:
						SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: int			       =    self.num_layers
						for i in range(a		):
							SCREAMING_SNAKE_CASE				: Optional[int]			       =    {0: "batch", 2: "past_sequence + sequence"}
							SCREAMING_SNAKE_CASE				: Optional[Any]			       =    {0: "batch", 2: "past_sequence + sequence"}
				return common_outputs
			def 							__UpperCamelCase	(     self							:	Tuple , a							:	PreTrainedTokenizer , a							:	int = -1 , a							:	int = -1 , a							:	bool = False , a							:	Optional[TensorType] = None , )							->    Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    a , a , a , a , a		)

				# Generate decoder inputs
				SCREAMING_SNAKE_CASE				: Dict			       =    seq_length if not self.use_past else 1
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    a , a , a , a , a		)
				SCREAMING_SNAKE_CASE				: int			       =    {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
				SCREAMING_SNAKE_CASE				: int			       =    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
					SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Optional[int]			       =    common_inputs["input_ids"].shape
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    common_inputs["decoder_input_ids"].shape[1]
					SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Tuple			       =    self.num_attention_heads
					SCREAMING_SNAKE_CASE				: Dict			       =    (
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					SCREAMING_SNAKE_CASE				: Dict			       =    decoder_seq_length + 3
					SCREAMING_SNAKE_CASE				: Any			       =    (
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)

					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    torch.cat(
					    [common_inputs["decoder_attention_mask"], torch.ones(a , a		)] , dim=1		)

					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    []
					# If the number of encoder and decoder layers are present in the model configuration, both are considered
					SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Any			       =    self.num_layers
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    min(a , a		)
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    max(a , a		) - min_num_layers
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    "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.
					SCREAMING_SNAKE_CASE				: Any			       =    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 							__UpperCamelCase	(     self							:	Optional[Any] , a							:	PreTrainedTokenizer , a							:	int = -1 , a							:	int = -1 , a							:	bool = False , a							:	Optional[TensorType] = None , )							->    Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    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
					SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Dict			       =    common_inputs["input_ids"].shape
					# Not using the same length for past_key_values
					SCREAMING_SNAKE_CASE				: Dict			       =    seqlen + 2
					SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Any			       =    self.num_layers
					SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: str			       =    self.num_attention_heads
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    (
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)

					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    common_inputs["attention_mask"].dtype
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    torch.cat(
					    [common_inputs["attention_mask"], torch.ones(a , a , dtype=a		)] , dim=1		)
					SCREAMING_SNAKE_CASE				: Dict			       =    [
					    (torch.zeros(a		), torch.zeros(a		)) for _ in range(a		)
					]
				return common_inputs
			def 							__UpperCamelCase	(     self							:	List[str] , a							:	PreTrainedTokenizer , a							:	int = -1 , a							:	int = -1 , a							:	bool = False , a							:	Optional[TensorType] = None , )							->    Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    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
				SCREAMING_SNAKE_CASE				: str			       =    tokenizer.num_special_tokens_to_add(a		)
				SCREAMING_SNAKE_CASE				: Dict			       =    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
				SCREAMING_SNAKE_CASE				: Dict			       =    [" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    dict(tokenizer(a , return_tensors=a		)		)
				return common_inputs
			def 							__UpperCamelCase	(     self							:	int , a							:	PreTrainedTokenizer , a							:	int = -1 , a							:	int = -1 , a							:	bool = False , a							:	Optional[TensorType] = None , )							->    Mapping[str, Any]:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    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":
					SCREAMING_SNAKE_CASE				: Any			       =    self._generate_dummy_inputs_for_causal_lm(
					    a , batch_size=a , seq_length=a , is_pair=a , framework=a		)
				else:
					SCREAMING_SNAKE_CASE				: Dict			       =    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 							__UpperCamelCase	(     self							:	str , a							:	Optional[Any] , a							:	Dict , a							:	Optional[int] , a							:	Union[str, Any]		)							->    Tuple:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE				: List[str]			       =    super()._flatten_past_key_values_(a , a , a , a		)
				else:
					SCREAMING_SNAKE_CASE				: List[Any]			       =    super(a , self		)._flatten_past_key_values_(
					    a , a , a , a		)


import re

def 	lowerCamelCase__	(   _a):
	def docstring_decorator(_a):
		SCREAMING_SNAKE_CASE				: List[Any]			       =    fn.__doc__
		SCREAMING_SNAKE_CASE				: List[str]			       =    func_doc.split("\n")
		SCREAMING_SNAKE_CASE				: Dict			       =    0
		while i < len(_a) and re.search(r"^\s*Examples?:\s*$"							,     lines[i]) is None:
			i += 1
		if i < len(_a):
			SCREAMING_SNAKE_CASE				: Tuple			       =    example_docstring
			SCREAMING_SNAKE_CASE				: List[Any]			       =    "\n".join(_a)
		else:
			raise ValueError(
			    f"The function {fn} should have an empty 'Examples:' in its docstring as placeholder, "
			    f"current docstring is:\n{func_doc}")
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    func_doc
		return fn

	return docstring_decorator