



'''simple docstring'''




from typing import Callable, Optional, Union

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


lowerCamelCase							:str       = logging.get_logger(__name__)

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







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		int		      = 'xlm-prophetnet'
	__SCREAMING_SNAKE_CASE       :		List[Any]		      = ['past_key_values']
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = {
	    'num_attention_heads': 'num_encoder_attention_heads',
	}


	def __init__(self	,				lowercase = 0.1	,				lowercase = "gelu"	,				lowercase = 30522	,				lowercase = 1024	,				lowercase = 4096	,				lowercase = 12	,				lowercase = 16	,				lowercase = 4096	,				lowercase = 12	,				lowercase = 16	,				lowercase = 0.1	,				lowercase = 0.1	,				lowercase = 512	,				lowercase = 0.02	,				lowercase = True	,				lowercase = True	,				lowercase = 0	,				lowercase = 2	,				lowercase = 32	,				lowercase = 128	,				lowercase = False	,				lowercase = 0.0	,				lowercase = True	,				lowercase = 0	,				lowercase = 1	,				lowercase = 2	,				**lowercase	,				):
				A_     :						str     =					vocab_size
				A_     :						Optional[Any]     =					hidden_size
				A_     :						Optional[Any]     =					encoder_ffn_dim
				A_     :						Union[str, Any]     =					num_encoder_layers
				A_     :						Any     =					num_encoder_attention_heads
				A_     :						List[str]     =					decoder_ffn_dim
				A_     :						Optional[int]     =					num_decoder_layers
				A_     :						Any     =					num_decoder_attention_heads
				A_     :						Optional[int]     =					max_position_embeddings
				A_     :						List[Any]     =					init_std  # Normal(0, this parameter)
				A_     :						Optional[Any]     =					activation_function

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

				# 3 Types of Dropout
				A_     :						Optional[int]     =					attention_dropout
				A_     :						Dict     =					activation_dropout
				A_     :						Tuple     =					dropout

				A_     :						Dict     =					use_cache

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


	@property
	def    _a     (self							):
				return self.num_encoder_layers + self.num_decoder_layers


	@num_hidden_layers.setter
	def    _a     (self	,				lowercase							):
				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 maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						List[Any]     =					prime_factors(lowerCamelCase__   )
			if is_square_free(lowerCamelCase__   ):
						return -1 if len(lowerCamelCase__   ) % 2 else 1
			return 0


if __name__ == "__main__":
						import doctest

						doctest.testmod()