


"""simple docstring"""
from typing import Callable, Optional, Union

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


SCREAMING_SNAKE_CASE :    Union[str, Any]									=				logging.get_logger(__name__)

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



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

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



		def __init__(self				,       a_ = 0.1				,       a_ = "gelu"				,       a_ = 3_05_22				,       a_ = 10_24				,       a_ = 40_96				,       a_ = 12				,       a_ = 16				,       a_ = 40_96				,       a_ = 12				,       a_ = 16				,       a_ = 0.1				,       a_ = 0.1				,       a_ = 5_12				,       a_ = 0.02				,       a_ = True				,       a_ = True				,       a_ = 0				,       a_ = 2				,       a_ = 32				,       a_ = 1_28				,       a_ = False				,       a_ = 0.0				,       a_ = True				,       a_ = 0				,       a_ = 1				,       a_ = 2				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						str						=				vocab_size
			__snake_case	:						Optional[int]						=				hidden_size
			__snake_case	:						List[str]						=				encoder_ffn_dim
			__snake_case	:						List[str]						=				num_encoder_layers
			__snake_case	:						Dict						=				num_encoder_attention_heads
			__snake_case	:						Dict						=				decoder_ffn_dim
			__snake_case	:						List[Any]						=				num_decoder_layers
			__snake_case	:						Optional[int]						=				num_decoder_attention_heads
			__snake_case	:						List[str]						=				max_position_embeddings
			__snake_case	:						Any						=				init_std  # Normal(0, this parameter)
			__snake_case	:						str						=				activation_function

			# parameters for xlmprophetnet
			__snake_case	:						Union[str, Any]						=				ngram
			__snake_case	:						Union[str, Any]						=				num_buckets
			__snake_case	:						Optional[int]						=				relative_max_distance
			__snake_case	:						List[Any]						=				disable_ngram_loss
			__snake_case	:						int						=				eps

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

			__snake_case	:						List[str]						=				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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self.num_encoder_layers + self.num_decoder_layers



		@num_hidden_layers.setter
		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''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"""
def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      int     )     ->str:
	"""simple docstring"""





	if a < 0 or b < 0:
		raise ValueError('''the value of both inputs must be positive'''     )
	__snake_case	:						Tuple						=				str(bin(_snake_case     )     )[2:]  # remove the leading "0b"
	__snake_case	:						List[Any]						=				str(bin(_snake_case     )     )[2:]
	__snake_case	:						Any						=				max(len(_snake_case     )     ,   len(_snake_case     )     )
	return "0b" + "".join(
	    str(int('''1''' in (char_a, char_b)     )     )
	    for char_a, char_b in zip(a_binary.zfill(_snake_case     )     ,   b_binary.zfill(_snake_case     )     )     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





