




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


lowerCamelCase_  						=   logging.get_logger(__name__)

lowerCamelCase_  						=   {
    "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       __a					( __lowerCamelCase     ):


     """simple docstring"""


     _A				:    Any									=  "mbart"
     _A				:    str									=  ["past_key_values"]
     _A				:    int									=  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




     def __init__(	self			: Tuple				,_UpperCamelCase			: Union[str, Any]=5_0_2_6_5				,_UpperCamelCase			: List[str]=1_0_2_4				,_UpperCamelCase			: List[str]=1_2				,_UpperCamelCase			: Tuple=4_0_9_6				,_UpperCamelCase			: str=1_6				,_UpperCamelCase			: List[Any]=1_2				,_UpperCamelCase			: Optional[int]=4_0_9_6				,_UpperCamelCase			: List[Any]=1_6				,_UpperCamelCase			: Any=0.0				,_UpperCamelCase			: str=0.0				,_UpperCamelCase			: List[Any]=True				,_UpperCamelCase			: Optional[Any]=True				,_UpperCamelCase			: int="gelu"				,_UpperCamelCase			: int=1_0_2_4				,_UpperCamelCase			: Union[str, Any]=0.1				,_UpperCamelCase			: Optional[Any]=0.0				,_UpperCamelCase			: Tuple=0.0				,_UpperCamelCase			: List[str]=0.02				,_UpperCamelCase			: Union[str, Any]=0.0				,_UpperCamelCase			: Optional[int]=False				,_UpperCamelCase			: List[str]=1				,_UpperCamelCase			: Tuple=0				,_UpperCamelCase			: Dict=2				,_UpperCamelCase			: int=2				,**_UpperCamelCase			: int				,)		->   Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=vocab_size
         SCREAMING_SNAKE_CASE__								=max_position_embeddings
         SCREAMING_SNAKE_CASE__								=d_model
         SCREAMING_SNAKE_CASE__								=encoder_ffn_dim
         SCREAMING_SNAKE_CASE__								=encoder_layers
         SCREAMING_SNAKE_CASE__								=encoder_attention_heads
         SCREAMING_SNAKE_CASE__								=decoder_ffn_dim
         SCREAMING_SNAKE_CASE__								=decoder_layers
         SCREAMING_SNAKE_CASE__								=decoder_attention_heads
         SCREAMING_SNAKE_CASE__								=dropout
         SCREAMING_SNAKE_CASE__								=attention_dropout
         SCREAMING_SNAKE_CASE__								=activation_dropout
         SCREAMING_SNAKE_CASE__								=activation_function
         SCREAMING_SNAKE_CASE__								=init_std
         SCREAMING_SNAKE_CASE__								=encoder_layerdrop
         SCREAMING_SNAKE_CASE__								=decoder_layerdrop
         SCREAMING_SNAKE_CASE__								=classifier_dropout
         SCREAMING_SNAKE_CASE__								=use_cache
         SCREAMING_SNAKE_CASE__								=encoder_layers
         SCREAMING_SNAKE_CASE__								=scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=_UpperCamelCase				,bos_token_id=_UpperCamelCase				,eos_token_id=_UpperCamelCase				,is_encoder_decoder=_UpperCamelCase				,forced_eos_token_id=_UpperCamelCase				,**_UpperCamelCase				,)






class       __a					( __lowerCamelCase     ):


     """simple docstring"""


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


         '''simple docstring'''





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

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

             if self.use_past:
                 self.fill_with_past_key_values_(_UpperCamelCase				,direction="""inputs"""						)
         elif self.task == "causal-lm":
             # TODO: figure this case out.
             SCREAMING_SNAKE_CASE__								=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__								=self.num_layers
                 for i in range(_UpperCamelCase						):
                     SCREAMING_SNAKE_CASE__								={0: """batch""", 2: """past_sequence + sequence"""}
                     SCREAMING_SNAKE_CASE__								={0: """batch""", 2: """past_sequence + sequence"""}
         else:
             SCREAMING_SNAKE_CASE__								=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			: int						)		->   Mapping[str, Mapping[int, str]]:


         '''simple docstring'''





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




     def   __A				(	self			: Tuple				,_UpperCamelCase			: PreTrainedTokenizer				,_UpperCamelCase			: int = -1				,_UpperCamelCase			: int = -1				,_UpperCamelCase			: bool = False				,_UpperCamelCase			: Optional[TensorType] = None				,)		->   Mapping[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase						)

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

         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__								=common_inputs["""input_ids"""].shape
             SCREAMING_SNAKE_CASE__								=common_inputs["""decoder_input_ids"""].shape[1]
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=self.num_attention_heads
             SCREAMING_SNAKE_CASE__								=(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             SCREAMING_SNAKE_CASE__								=decoder_seq_length + 3
             SCREAMING_SNAKE_CASE__								=(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )

             SCREAMING_SNAKE_CASE__								=torch.cat(
                 [common_inputs["""decoder_attention_mask"""], torch.ones(_UpperCamelCase				,_UpperCamelCase						)]				,dim=1						)

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

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




     def   __A				(	self			: List[str]				,_UpperCamelCase			: PreTrainedTokenizer				,_UpperCamelCase			: int = -1				,_UpperCamelCase			: int = -1				,_UpperCamelCase			: bool = False				,_UpperCamelCase			: Optional[TensorType] = None				,)		->   Mapping[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase						)

         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__								=common_inputs["""input_ids"""].shape
             # Not using the same length for past_key_values
             SCREAMING_SNAKE_CASE__								=seqlen + 2
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=self.num_layers
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=self.num_attention_heads
             SCREAMING_SNAKE_CASE__								=(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

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




     def   __A				(	self			: List[Any]				,_UpperCamelCase			: PreTrainedTokenizer				,_UpperCamelCase			: int = -1				,_UpperCamelCase			: int = -1				,_UpperCamelCase			: bool = False				,_UpperCamelCase			: Optional[TensorType] = None				,)		->   Mapping[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=compute_effective_axis_dimension(
             _UpperCamelCase				,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__								=tokenizer.num_special_tokens_to_add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=compute_effective_axis_dimension(
             _UpperCamelCase				,fixed_dimension=OnnxConfig.default_fixed_sequence				,num_token_to_add=_UpperCamelCase						)

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




     def   __A				(	self			: Union[str, Any]				,_UpperCamelCase			: PreTrainedTokenizer				,_UpperCamelCase			: int = -1				,_UpperCamelCase			: int = -1				,_UpperCamelCase			: bool = False				,_UpperCamelCase			: Optional[TensorType] = None				,)		->   Mapping[str, Any]:


         '''simple docstring'''





         if self.task in ["default", "seq2seq-lm"]:
             SCREAMING_SNAKE_CASE__								=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _UpperCamelCase				,batch_size=_UpperCamelCase				,seq_length=_UpperCamelCase				,is_pair=_UpperCamelCase				,framework=_UpperCamelCase						)

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

         return common_inputs




     def   __A				(	self			: Union[str, Any]				,_UpperCamelCase			: str				,_UpperCamelCase			: str				,_UpperCamelCase			: str				,_UpperCamelCase			: List[str]						)		->   str:


         '''simple docstring'''





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












def 	UpperCAmelCase_    (	__UpperCamelCase	):
    if not isinstance(__UpperCamelCase,				__UpperCamelCase	):
        raise ValueError("""double_factorial() only accepts integral values"""	)
    if n < 0:
        raise ValueError("""double_factorial() not defined for negative values"""	)
    return 1 if n <= 1 else n * double_factorial(n - 2	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






