
"""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"""




from itertools import combinations, product



def 						__magic_name__		(				UpperCamelCase    :		int       ,		UpperCamelCase    :		int       ,		UpperCamelCase    :		int       ,		UpperCamelCase    :		int    )    ->	bool:
    if xa == ya == 0 or xa == ya == 0:
        return False
    a__    = xa * xa + ya * ya
    a__    = xa * xa + ya * ya
    a__    = (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
    return (
        a_square + b_square == c_square
        or a_square + c_square == b_square
        or b_square + c_square == a_square
    )



def 						__magic_name__		(				UpperCamelCase    :		int = 50    )    ->	int:
    return sum(
        1
        for pta, pta in combinations(product(range(limit + 1    )       ,		repeat=2    )       ,		2    )
        if is_right(*UpperCamelCase       ,		*UpperCamelCase    )    )


if __name__ == "__main__":
    print(F'''{solution() = }''')


