





'''simple docstring'''


from typing import Callable, Optional, Union

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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

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

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Optional[Any]								=       "xlm-prophetnet"
 __lowerCamelCase    :				Dict								=       ["past_key_values"]
 __lowerCamelCase    :				Tuple								=       {
     "num_attention_heads": "num_encoder_attention_heads",
 }




 def __init__(						self      ,  _lowerCAmelCase = 0.1      ,  _lowerCAmelCase = "gelu"      ,  _lowerCAmelCase = 30522      ,  _lowerCAmelCase = 1024      ,  _lowerCAmelCase = 4096      ,  _lowerCAmelCase = 12      ,  _lowerCAmelCase = 16      ,  _lowerCAmelCase = 4096      ,  _lowerCAmelCase = 12      ,  _lowerCAmelCase = 16      ,  _lowerCAmelCase = 0.1      ,  _lowerCAmelCase = 0.1      ,  _lowerCAmelCase = 512      ,  _lowerCAmelCase = 0.02      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = 0      ,  _lowerCAmelCase = 2      ,  _lowerCAmelCase = 32      ,  _lowerCAmelCase = 128      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = 0.0      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = 0      ,  _lowerCAmelCase = 1      ,  _lowerCAmelCase = 2      ,  **_lowerCAmelCase      ,  )     ->				str:
     _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=_lowerCAmelCase      ,  bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase      ,  is_encoder_decoder=_lowerCAmelCase      ,  add_cross_attention=_lowerCAmelCase      ,  decoder_start_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase      ,  )




 @property
 def 				_snake_case (						self		)     ->				int:
     return self.num_encoder_layers + self.num_decoder_layers





 @num_hidden_layers.setter
 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				int:
     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


def 						__a(SCREAMING_SNAKE_CASE_	:       int | float | str		,							SCREAMING_SNAKE_CASE_	:       int | float | str     ):



    '''simple docstring'''





    if nth_term == "":
        return [""]
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   []
    for temp in range(int(SCREAMING_SNAKE_CASE_     )     ):
        series.append(F'''1 / {pow(temp + 1		,							int(SCREAMING_SNAKE_CASE_     )     )}''' if series else "1"     )
    return series


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _SCREAMING_SNAKE_CASE									=				int(input("Enter the last number (nth term) of the P-Series"))
       _SCREAMING_SNAKE_CASE									=				int(input("Enter the power for  P-Series"))
       print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
       print(p_series(nth_term, power))


