from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_SCREAMING_SNAKE_CASE     =     logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     =     {
    """uclanlp/plbart-base""": """https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json""",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}







class        UpperCAmelCase_					(				_a     ):
    UpperCAmelCase_      :							Tuple    			=				'plbart'
    UpperCAmelCase_      :							List[Any]    			=				['past_key_values']
    UpperCAmelCase_      :							Dict    			=				{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




    def __init__(       self	: Dict   ,   lowerCAmelCase	: Optional[Any]=50005   ,   lowerCAmelCase	: Dict=1024   ,   lowerCAmelCase	: Any=6   ,   lowerCAmelCase	: List[Any]=3072   ,   lowerCAmelCase	: Any=12   ,   lowerCAmelCase	: Any=6   ,   lowerCAmelCase	: Any=3072   ,   lowerCAmelCase	: Any=12   ,   lowerCAmelCase	: List[str]=0.0   ,   lowerCAmelCase	: List[Any]=0.0   ,   lowerCAmelCase	: List[str]=True   ,   lowerCAmelCase	: Any=True   ,   lowerCAmelCase	: Optional[Any]="gelu"   ,   lowerCAmelCase	: Optional[int]=768   ,   lowerCAmelCase	: int=0.1   ,   lowerCAmelCase	: List[str]=0.1   ,   lowerCAmelCase	: List[Any]=0.0   ,   lowerCAmelCase	: str=0.02   ,   lowerCAmelCase	: int=0.0   ,   lowerCAmelCase	: Union[str, Any]=True   ,   lowerCAmelCase	: str=1   ,   lowerCAmelCase	: List[str]=0   ,   lowerCAmelCase	: Optional[Any]=2   ,   lowerCAmelCase	: List[str]=2   ,   **lowerCAmelCase	: Union[str, Any]   ,   )						->			List[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_					(				_a     ):




    @property
    def     __lowerCamelCase			(       self	: Any )						->			Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ("input_ids", {0: "batch", 1: "sequence"}),
              ("attention_mask", {0: "batch", 1: "sequence"}),
          ] )




    @property
    def     __lowerCamelCase			(       self	: Union[str, Any] )						->			Mapping[str, Mapping[int, str]]:
      if self.use_past:
        return OrderedDict(
            [
                ("last_hidden_state", {0: "batch", 1: "sequence"}),
                ("past_keys", {0: "batch", 2: "sequence"}),
                ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
            ] )
      else:
        return OrderedDict(
            [
                ("last_hidden_state", {0: "batch", 1: "sequence"}),
                ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
            ] )


def 					__lowercase						(    lowerCamelCase__       )					->int:

  '''simple docstring'''




  if not isinstance(lowerCamelCase__			,					lowerCamelCase__       ):
    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()

