




from typing import Callable, Optional, Union

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


lowerCamelCase							:						Dict					       =  logging.get_logger(__name__)

lowerCamelCase							:						Any					       =  {
    "microsoft/xprophetnet-large-wiki100-cased": (
        "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
    ),
}







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		'''xlm-prophetnet'''
 UpperCamelCase		  =		['''past_key_values''']
 UpperCamelCase		  =		{
     '''num_attention_heads''': '''num_encoder_attention_heads''',
 }

 def __init__(							self   :    Optional[int]		,				A_   :    Optional[float] = 0.1		,				A_   :    Optional[Union[str, Callable]] = "gelu"		,				A_   :    Optional[int] = 30522		,				A_   :    Optional[int] = 1024		,				A_   :    Optional[int] = 4096		,				A_   :    Optional[int] = 12		,				A_   :    Optional[int] = 16		,				A_   :    Optional[int] = 4096		,				A_   :    Optional[int] = 12		,				A_   :    Optional[int] = 16		,				A_   :    Optional[float] = 0.1		,				A_   :    Optional[float] = 0.1		,				A_   :    Optional[int] = 512		,				A_   :    Optional[float] = 0.02		,				A_   :    Optional[bool] = True		,				A_   :    Optional[bool] = True		,				A_   :    Optional[int] = 0		,				A_   :    Optional[int] = 2		,				A_   :    Optional[int] = 32		,				A_   :    Optional[int] = 128		,				A_   :    Optional[bool] = False		,				A_   :    Optional[float] = 0.0		,				A_   :    Optional[bool] = True		,				A_   :    Optional[int] = 0		,				A_   :    Optional[int] = 1		,				A_   :    Optional[int] = 2		,				**A_   :    str		,				)  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       vocab_size
       lowerCamelCase_							=       hidden_size
       lowerCamelCase_							=       encoder_ffn_dim
       lowerCamelCase_							=       num_encoder_layers
       lowerCamelCase_							=       num_encoder_attention_heads
       lowerCamelCase_							=       decoder_ffn_dim
       lowerCamelCase_							=       num_decoder_layers
       lowerCamelCase_							=       num_decoder_attention_heads
       lowerCamelCase_							=       max_position_embeddings
       lowerCamelCase_							=       init_std  # Normal(0, this parameter)
       lowerCamelCase_							=       activation_function

       # parameters for xlmprophetnet
       lowerCamelCase_							=       ngram
       lowerCamelCase_							=       num_buckets
       lowerCamelCase_							=       relative_max_distance
       lowerCamelCase_							=       disable_ngram_loss
       lowerCamelCase_							=       eps

       # 3 Types of Dropout
       lowerCamelCase_							=       attention_dropout
       lowerCamelCase_							=       activation_dropout
       lowerCamelCase_							=       dropout

       lowerCamelCase_							=       use_cache

       super().__init__(
           pad_token_id=A_		,				bos_token_id=A_		,				eos_token_id=A_		,				is_encoder_decoder=A_		,				add_cross_attention=A_		,				decoder_start_token_id=A_		,				**A_		,				)

 @property
 def 							a__					(							self   :    str    )  ->      int:

       """simple docstring"""






       return self.num_encoder_layers + self.num_decoder_layers





 @num_hidden_layers.setter
 def 							a__					(							self   :    List[Any]		,				A_   :    Optional[Any]    )  ->      Union[str, Any]:

       """simple docstring"""






       raise NotImplementedError(
           'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
           ' `num_decoder_layers`.'    )











'''simple docstring'''

from __future__ import annotations

from math import pi, sqrt



def 				snake_case       (  a_				:			float     , a_				:			float						)							->					tuple:





      """simple docstring"""

      if inductance <= 0:
            raise ValueError("""Inductance cannot be 0 or negative"""						)

      elif capacitance <= 0:
            raise ValueError("""Capacitance cannot be 0 or negative"""						)

      else:
            return (
                "Resonant frequency",
                float(1 / (2 * pi * (sqrt(inductance * capacitance						)))						),
            )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






