

'''simple docstring'''

from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_      =							logging.get_logger(__name__)

A_      =							{
    "microsoft/prophetnet-large-uncased": (
        "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"
    ),
}





class 							_snake_case	(    _a  ):
   _A    :       List[Any]      				=   '''prophetnet'''
   _A    :       Tuple      				=   ['''past_key_values''']
   _A    :       Tuple      				=   {
       '''num_attention_heads''': '''num_encoder_attention_heads''',
   }
   def __init__(	self     : Union[str, Any]       ,SCREAMING_SNAKE_CASE__     : Optional[float] = 0.1       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, Callable]] = "gelu"       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 30_522       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 1_024       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 4_096       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 12       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 16       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 4_096       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 12       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 16       ,SCREAMING_SNAKE_CASE__     : Optional[float] = 0.1       ,SCREAMING_SNAKE_CASE__     : Optional[float] = 0.1       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 512       ,SCREAMING_SNAKE_CASE__     : Optional[float] = 0.02       ,SCREAMING_SNAKE_CASE__     : Optional[bool] = True       ,SCREAMING_SNAKE_CASE__     : Optional[bool] = True       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 0       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 2       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 32       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 128       ,SCREAMING_SNAKE_CASE__     : Optional[bool] = False       ,SCREAMING_SNAKE_CASE__     : Optional[float] = 0.0       ,SCREAMING_SNAKE_CASE__     : Optional[bool] = True       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 0       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 1       ,SCREAMING_SNAKE_CASE__     : Optional[int] = 2       ,**SCREAMING_SNAKE_CASE__     : Any       ,):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   vocab_size
     SCREAMING_SNAKE_CASE:Dict   =   hidden_size
     SCREAMING_SNAKE_CASE:int   =   encoder_ffn_dim
     SCREAMING_SNAKE_CASE:Optional[int]   =   num_encoder_layers
     SCREAMING_SNAKE_CASE:Optional[Any]   =   num_encoder_attention_heads
     SCREAMING_SNAKE_CASE:List[Any]   =   decoder_ffn_dim
     SCREAMING_SNAKE_CASE:Dict   =   num_decoder_layers
     SCREAMING_SNAKE_CASE:str   =   num_decoder_attention_heads
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   max_position_embeddings
     SCREAMING_SNAKE_CASE:Any   =   init_std  # Normal(0, this parameter)
     SCREAMING_SNAKE_CASE:Optional[Any]   =   activation_function

     # parameters for prophetnet
     SCREAMING_SNAKE_CASE:Tuple   =   ngram
     SCREAMING_SNAKE_CASE:int   =   num_buckets
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   relative_max_distance
     SCREAMING_SNAKE_CASE:List[Any]   =   disable_ngram_loss
     SCREAMING_SNAKE_CASE:Tuple   =   eps

     # 3 Types of Dropout
     SCREAMING_SNAKE_CASE:str   =   attention_dropout
     SCREAMING_SNAKE_CASE:List[Any]   =   activation_dropout
     SCREAMING_SNAKE_CASE:Optional[int]   =   dropout

     SCREAMING_SNAKE_CASE:Dict   =   use_cache

     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__       ,add_cross_attention=SCREAMING_SNAKE_CASE__       ,decoder_start_token_id=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__       ,)
   @property
   def       __UpperCamelCase  (	self     : str							):
     return self.num_encoder_layers + self.num_decoder_layers







   @num_hidden_layers.setter
   def       __UpperCamelCase  (	self     : Union[str, Any]       ,SCREAMING_SNAKE_CASE__     : List[str]							):
     raise NotImplementedError(
         "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
         " `num_decoder_layers`."							)









'''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()






