

"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


_a				   =							logging.get_logger(__name__)

_a				   =							{
    '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       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 __UpperCAmelCase			:      Any     	=			"plbart"
 __UpperCAmelCase			:      int     	=			["past_key_values"]
 __UpperCAmelCase			:      Union[str, Any]     	=			{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


 def __init__(    self							:    Any,      UpperCAmelCase__							:    List[Any]=5_0_0_0_5,      UpperCAmelCase__							:    List[Any]=1_0_2_4,      UpperCAmelCase__							:    Union[str, Any]=6,      UpperCAmelCase__							:    Any=3_0_7_2,      UpperCAmelCase__							:    Dict=1_2,      UpperCAmelCase__							:    str=6,      UpperCAmelCase__							:    Any=3_0_7_2,      UpperCAmelCase__							:    Tuple=1_2,      UpperCAmelCase__							:    Union[str, Any]=0.0,      UpperCAmelCase__							:    Tuple=0.0,      UpperCAmelCase__							:    int=True,      UpperCAmelCase__							:    List[str]=True,      UpperCAmelCase__							:    int="gelu",      UpperCAmelCase__							:    Optional[int]=7_6_8,      UpperCAmelCase__							:    Tuple=0.1,      UpperCAmelCase__							:    Tuple=0.1,      UpperCAmelCase__							:    int=0.0,      UpperCAmelCase__							:    List[str]=0.02,      UpperCAmelCase__							:    Tuple=0.0,      UpperCAmelCase__							:    Tuple=True,      UpperCAmelCase__							:    Optional[Any]=1,      UpperCAmelCase__							:    str=0,      UpperCAmelCase__							:    Dict=2,      UpperCAmelCase__							:    str=2,      **UpperCAmelCase__							:    Optional[Any],      ):
    __lowercase			=		vocab_size
    __lowercase			=		max_position_embeddings
    __lowercase			=		d_model
    __lowercase			=		encoder_ffn_dim
    __lowercase			=		encoder_layers
    __lowercase			=		encoder_attention_heads
    __lowercase			=		decoder_ffn_dim
    __lowercase			=		decoder_layers
    __lowercase			=		decoder_attention_heads
    __lowercase			=		dropout
    __lowercase			=		attention_dropout
    __lowercase			=		activation_dropout
    __lowercase			=		activation_function
    __lowercase			=		init_std
    __lowercase			=		encoder_layerdrop
    __lowercase			=		decoder_layerdrop
    __lowercase			=		classifier_dropout
    __lowercase			=		use_cache
    __lowercase			=		encoder_layers
    __lowercase			=		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       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 @property
 def       _lowercase				(    self							:    Any			):
    return OrderedDict(
        [
            ("input_ids", {0: "batch", 1: "sequence"}),
            ("attention_mask", {0: "batch", 1: "sequence"}),
        ]			)



 @property
 def       _lowercase				(    self							:    Union[str, Any]			):
    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"}),
           ]			)







"""simple docstring"""





def 						_A						(						UpperCamelCase_  : list,    UpperCamelCase_  : list,    UpperCamelCase_  : list,    UpperCamelCase_  : int,    UpperCamelCase_  : int)			->		None:
   '''simple docstring'''


   if target == 0:
      answer.append(path.copy())
   else:
      for index in range(UpperCamelCase_,    len(UpperCamelCase_)):
         if target >= candidates[index]:
            path.append(candidates[index])
            backtrack(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    target - candidates[index],    UpperCamelCase_)
            path.pop(len(UpperCamelCase_) - 1)




def 						_A						(						UpperCamelCase_  : list,    UpperCamelCase_  : int)			->		list:
   '''simple docstring'''


   __lowercase			=		[]  # type: list[int]
   __lowercase			=		[]  # type: list[int]
   backtrack(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    0)
   return answer




def 						_A						(						)			->		None:
   '''simple docstring'''


   print(combination_sum([-8, 2.3, 0],    1))


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()




