
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							:Tuple											=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Union[str, 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 							UpperCAmelCase   (						UpperCamelCase__						):




	'''simple docstring'''





	snake_case_      					=  """mbart"""
	snake_case_      					=  ["""past_key_values"""]
	snake_case_      					=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






	def __init__( self      :				int							,A      :				Dict=5_02_65							,A      :				List[Any]=10_24							,A      :				Optional[int]=12							,A      :				str=40_96							,A      :				List[str]=16							,A      :				List[str]=12							,A      :				int=40_96							,A      :				str=16							,A      :				Dict=0.0							,A      :				Any=0.0							,A      :				int=True							,A      :				Union[str, Any]=True							,A      :				Tuple="gelu"							,A      :				Any=10_24							,A      :				int=0.1							,A      :				Any=0.0							,A      :				List[str]=0.0							,A      :				int=0.02							,A      :				int=0.0							,A      :				Tuple=False							,A      :				int=1							,A      :				Any=0							,A      :				int=2							,A      :				Dict=2							,**A      :				Optional[Any]							,):
					__A         =				vocab_size
					__A         =				max_position_embeddings
					__A         =				d_model
					__A         =				encoder_ffn_dim
					__A         =				encoder_layers
					__A         =				encoder_attention_heads
					__A         =				decoder_ffn_dim
					__A         =				decoder_layers
					__A         =				decoder_attention_heads
					__A         =				dropout
					__A         =				attention_dropout
					__A         =				activation_dropout
					__A         =				activation_function
					__A         =				init_std
					__A         =				encoder_layerdrop
					__A         =				decoder_layerdrop
					__A         =				classifier_dropout
					__A         =				use_cache
					__A         =				encoder_layers
					__A         =				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 							UpperCAmelCase   (						UpperCamelCase__						):




	'''simple docstring'''





	@property
	def        UpperCamelCase_						( self      :				Optional[Any]				):




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

									if self.use_past:
													__A         =				{0: "batch"}
													__A         =				{0: "batch", 1: "past_decoder_sequence + sequence"}
									else:
													__A         =				{0: "batch", 1: "decoder_sequence"}
													__A         =				{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.
									__A         =				OrderedDict(
									    [
									        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
									        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
									    ]				)
									if self.use_past:
													__A         =				self.num_layers
													for i in range(lowerCAmelCase__				):
																	__A         =				{0: "batch", 2: "past_sequence + sequence"}
																	__A         =				{0: "batch", 2: "past_sequence + sequence"}
					else:
									__A         =				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      :				Any				):




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






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				List[Any]							,A      :				str = -1							,A      :				List[str] = -1							,A      :				int = False							,A      :				Tuple = None							,):
					__A         =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__				)

					# Generate decoder inputs
					__A         =				seq_length if not self.use_past else 1
					__A         =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__				)
					__A         =				{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
					__A         =				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
									__A         =				common_inputs["input_ids"].shape
									__A         =				common_inputs["decoder_input_ids"].shape[1]
									__A         =				self.num_attention_heads
									__A         =				(
									    batch,
									    num_encoder_attention_heads,
									    encoder_seq_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									__A         =				decoder_seq_length + 3
									__A         =				(
									    batch,
									    num_decoder_attention_heads,
									    decoder_past_length,
									    self._config.hidden_size // num_decoder_attention_heads,
									)

									__A         =				torch.cat(
									    [common_inputs["decoder_attention_mask"], torch.ones(lowerCAmelCase__							,lowerCAmelCase__				)]							,dim=1				)

									__A         =				[]
									# If the number of encoder and decoder layers are present in the model configuration, both are considered
									__A         =				self.num_layers
									__A         =				min(lowerCAmelCase__							,lowerCAmelCase__				)
									__A         =				max(lowerCAmelCase__							,lowerCAmelCase__				) - min_num_layers
									__A         =				"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.
									__A         =				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        UpperCamelCase_						( self      :				int							,A      :				Optional[int]							,A      :				Any = -1							,A      :				str = -1							,A      :				Any = False							,A      :				Optional[int] = None							,):
					__A         =				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
									__A         =				common_inputs["input_ids"].shape
									# Not using the same length for past_key_values
									__A         =				seqlen + 2
									__A         =				self.num_layers
									__A         =				self.num_attention_heads
									__A         =				(
									    batch,
									    num_encoder_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)

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






	def        UpperCamelCase_						( self      :				Dict							,A      :				Union[str, Any]							,A      :				Optional[Any] = -1							,A      :				Dict = -1							,A      :				List[Any] = False							,A      :				List[str] = None							,):
					__A         =				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
					__A         =				tokenizer.num_special_tokens_to_add(lowerCAmelCase__				)
					__A         =				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
					__A         =				[" ".join([tokenizer.unk_token]				) * seq_length] * batch_size
					__A         =				dict(tokenizer(lowerCAmelCase__							,return_tensors=lowerCAmelCase__				)				)
					return common_inputs






	def        UpperCamelCase_						( self      :				int							,A      :				List[Any]							,A      :				int = -1							,A      :				Union[str, Any] = -1							,A      :				int = False							,A      :				str = None							,):




					if self.task in ["default", "seq2seq-lm"]:
									__A         =				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":
									__A         =				self._generate_dummy_inputs_for_causal_lm(
									    lowerCAmelCase__							,batch_size=lowerCAmelCase__							,seq_length=lowerCAmelCase__							,is_pair=lowerCAmelCase__							,framework=lowerCAmelCase__				)
					else:
									__A         =				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        UpperCamelCase_						( self      :				Optional[int]							,A      :				List[str]							,A      :				List[str]							,A      :				List[Any]							,A      :				int				):




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


from typing import Any





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Union[str, Any]				):
					__A         =				[]
					__A         =				0






	def __str__( self      :				Tuple				):
					__A         =				"<" + str(self.stack				)[1:-1] + ">"
					return printed






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				Any				):
					self.stack.append(A				)
					__A         =				self.length + 1






	def        UpperCamelCase_						( self      :				Any				):
					self.rotate(1				)
					__A         =				self.stack[self.length - 1]
					__A         =				self.stack[:-1]
					self.rotate(self.length - 1				)
					__A         =				self.length - 1
					return dequeued






	def        UpperCamelCase_						( self      :				Tuple							,A      :				int				):
					for _ in range(A				):
									__A         =				self.stack[0]
									__A         =				self.stack[1:]
									self.put(A				)
									__A         =				self.length - 1






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					__A         =				self.get()
					self.put(A				)
					self.rotate(self.length - 1				)
					return front






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					return self.length
