
'''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 							_snake_case	(    _a  ):
   _A    :       List[str]      				=   '''plbart'''
   _A    :       Optional[Any]      				=   ['''past_key_values''']
   _A    :       List[str]      				=   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
   def __init__(	self     : int       ,SCREAMING_SNAKE_CASE__     : List[Any]=50_005       ,SCREAMING_SNAKE_CASE__     : List[str]=1_024       ,SCREAMING_SNAKE_CASE__     : str=6       ,SCREAMING_SNAKE_CASE__     : Any=3_072       ,SCREAMING_SNAKE_CASE__     : List[Any]=12       ,SCREAMING_SNAKE_CASE__     : Optional[Any]=6       ,SCREAMING_SNAKE_CASE__     : int=3_072       ,SCREAMING_SNAKE_CASE__     : List[Any]=12       ,SCREAMING_SNAKE_CASE__     : Dict=0.0       ,SCREAMING_SNAKE_CASE__     : Optional[Any]=0.0       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]=True       ,SCREAMING_SNAKE_CASE__     : Optional[Any]=True       ,SCREAMING_SNAKE_CASE__     : List[str]="gelu"       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]=768       ,SCREAMING_SNAKE_CASE__     : Optional[Any]=0.1       ,SCREAMING_SNAKE_CASE__     : Optional[Any]=0.1       ,SCREAMING_SNAKE_CASE__     : List[str]=0.0       ,SCREAMING_SNAKE_CASE__     : Any=0.02       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]=0.0       ,SCREAMING_SNAKE_CASE__     : Optional[int]=True       ,SCREAMING_SNAKE_CASE__     : Dict=1       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]=0       ,SCREAMING_SNAKE_CASE__     : Optional[int]=2       ,SCREAMING_SNAKE_CASE__     : Any=2       ,**SCREAMING_SNAKE_CASE__     : Optional[int]       ,):
     SCREAMING_SNAKE_CASE:Tuple   =   vocab_size
     SCREAMING_SNAKE_CASE:Optional[Any]   =   max_position_embeddings
     SCREAMING_SNAKE_CASE:Optional[int]   =   d_model
     SCREAMING_SNAKE_CASE:Optional[int]   =   encoder_ffn_dim
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   encoder_layers
     SCREAMING_SNAKE_CASE:Dict   =   encoder_attention_heads
     SCREAMING_SNAKE_CASE:List[Any]   =   decoder_ffn_dim
     SCREAMING_SNAKE_CASE:Optional[Any]   =   decoder_layers
     SCREAMING_SNAKE_CASE:int   =   decoder_attention_heads
     SCREAMING_SNAKE_CASE:List[Any]   =   dropout
     SCREAMING_SNAKE_CASE:Optional[Any]   =   attention_dropout
     SCREAMING_SNAKE_CASE:Tuple   =   activation_dropout
     SCREAMING_SNAKE_CASE:Tuple   =   activation_function
     SCREAMING_SNAKE_CASE:Tuple   =   init_std
     SCREAMING_SNAKE_CASE:str   =   encoder_layerdrop
     SCREAMING_SNAKE_CASE:List[str]   =   decoder_layerdrop
     SCREAMING_SNAKE_CASE:Optional[int]   =   classifier_dropout
     SCREAMING_SNAKE_CASE:Any   =   use_cache
     SCREAMING_SNAKE_CASE:Any   =   encoder_layers
     SCREAMING_SNAKE_CASE:Dict   =   scale_embedding  # scale factor will be sqrt(d_model) if True
     super().__init__(
         pad_token_id=SCREAMING_SNAKE_CASE__       ,bos_token_id=SCREAMING_SNAKE_CASE__       ,eos_token_id=SCREAMING_SNAKE_CASE__       ,is_encoder_decoder=SCREAMING_SNAKE_CASE__       ,forced_eos_token_id=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__       ,)





class 							_snake_case	(    _a  ):
   @property
   def       __UpperCamelCase  (	self     : Dict							):
     return OrderedDict(
         [
             ("input_ids", {0: "batch", 1: "sequence"}),
             ("attention_mask", {0: "batch", 1: "sequence"}),
         ]							)







   @property
   def       __UpperCamelCase  (	self     : List[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_					(	snake_case	):
  return "".join(chr(ord(snake_case	) + 32	) if "A" <= char <= "Z" else char for char in word	)


if __name__ == "__main__":
    from doctest import testmod

    testmod()






