
"""simple docstring"""




from typing import Callable, Optional, Union

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


a		:					int    	=					logging.get_logger(__name__)

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







class 						lowercase(_lowercase					):
     __snake_case:  Dict  				=    'prophetnet'
     __snake_case:  Dict  				=    ['past_key_values']
     __snake_case:  str  				=    {
         'num_attention_heads': 'num_encoder_attention_heads',
     }



     def __init__(       self    ,							__SCREAMING_SNAKE_CASE = 0.1    ,							__SCREAMING_SNAKE_CASE = "gelu"    ,							__SCREAMING_SNAKE_CASE = 3_0_5_2_2    ,							__SCREAMING_SNAKE_CASE = 1_0_2_4    ,							__SCREAMING_SNAKE_CASE = 4_0_9_6    ,							__SCREAMING_SNAKE_CASE = 1_2    ,							__SCREAMING_SNAKE_CASE = 1_6    ,							__SCREAMING_SNAKE_CASE = 4_0_9_6    ,							__SCREAMING_SNAKE_CASE = 1_2    ,							__SCREAMING_SNAKE_CASE = 1_6    ,							__SCREAMING_SNAKE_CASE = 0.1    ,							__SCREAMING_SNAKE_CASE = 0.1    ,							__SCREAMING_SNAKE_CASE = 5_1_2    ,							__SCREAMING_SNAKE_CASE = 0.02    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = 0    ,							__SCREAMING_SNAKE_CASE = 2    ,							__SCREAMING_SNAKE_CASE = 3_2    ,							__SCREAMING_SNAKE_CASE = 1_2_8    ,							__SCREAMING_SNAKE_CASE = False    ,							__SCREAMING_SNAKE_CASE = 0.0    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = 0    ,							__SCREAMING_SNAKE_CASE = 1    ,							__SCREAMING_SNAKE_CASE = 2    ,							**__SCREAMING_SNAKE_CASE    ,							)		->    Union[str, Any]:







         """simple docstring"""



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

         # parameters for prophetnet
         a__    = ngram
         a__    = num_buckets
         a__    = relative_max_distance
         a__    = disable_ngram_loss
         a__    = eps

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

         a__    = 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  lowercase__     (       self      )		->    int:







         """simple docstring"""



         return self.num_encoder_layers + self.num_decoder_layers




     @num_hidden_layers.setter
     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[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"""




a		:					str    	=					(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
a		:					tuple    	=					(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)



def 						__magic_name__		(				UpperCamelCase    :		int    )    ->	bool:
    if not isinstance(UpperCamelCase       ,		UpperCamelCase    ) or n < 1:
        return False

    # Initialization of sum and number of digits.
    a__    = 0
    a__    = 0
    a__    = n
    # Calculation of digits of the number
    while temp > 0:
        number_of_digits += 1
        temp //= 10
    # Dividing number into separate digits and find Armstrong number
    a__    = n
    while temp > 0:
        a__    = temp % 10
        total += rem**number_of_digits
        temp //= 10
    return n == total



def 						__magic_name__		(				UpperCamelCase    :		int    )    ->	bool:
    if not isinstance(UpperCamelCase       ,		UpperCamelCase    ) or n < 1:
        return False

    # Init a "histogram" of the digits
    a__    = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    a__    = 0
    a__    = 0
    a__    = n
    while temp > 0:
        a__  ,    a__    = divmod(UpperCamelCase       ,		10    )
        digit_histogram[rem] += 1
        digit_total += 1

    for cnt, i in zip(UpperCamelCase       ,		range(len(UpperCamelCase    )    )    ):
        total += cnt * i**digit_total

    return n == total



def 						__magic_name__		(				UpperCamelCase    :		int    )    ->	bool:
    if not isinstance(UpperCamelCase       ,		UpperCamelCase    ) or n < 1:
        return False
    a__    = len(str(UpperCamelCase    )    )  # the power that all digits will be raised to
    # check if sum of each digit multiplied expo times is equal to number
    return n == sum(int(UpperCamelCase    ) ** expo for i in str(UpperCamelCase    )    )



def 						__magic_name__		(				)    ->	List[Any]:
    a__    = int(input('Enter an integer to see if it is an Armstrong number: '    ).strip()    )
    print(f'{num} is {"" if armstrong_number(UpperCamelCase    ) else "not "}an Armstrong number.'    )
    print(f'{num} is {"" if narcissistic_number(UpperCamelCase    ) else "not "}an Armstrong number.'    )
    print(f'{num} is {"" if pluperfect_number(UpperCamelCase    ) else "not "}an Armstrong number.'    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


