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


A__           =							logging.get_logger(__name__)

A__           =							{
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class   _lowerCAmelCase		(     UpperCAmelCase_				):
							_UpperCAmelCase						    =     "transfo-xl"
							_UpperCAmelCase						    =     ["mems"]
							_UpperCAmelCase						    =     {
							    "n_token": "vocab_size",
							    "hidden_size": "d_model",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}







							def __init__(				self     :    Optional[Any]	,					__snake_case     :    Tuple=267735	,					__snake_case     :    Union[str, Any]=[20000, 40000, 200000]	,					__snake_case     :    int=1024	,					__snake_case     :    int=1024	,					__snake_case     :    int=16	,					__snake_case     :    Any=64	,					__snake_case     :    int=4096	,					__snake_case     :    Dict=4	,					__snake_case     :    int=False	,					__snake_case     :    Optional[int]=18	,					__snake_case     :    List[str]=1600	,					__snake_case     :    Optional[Any]=1000	,					__snake_case     :    Union[str, Any]=True	,					__snake_case     :    List[Any]=True	,					__snake_case     :    Optional[Any]=0	,					__snake_case     :    Union[str, Any]=-1	,					__snake_case     :    Tuple=True	,					__snake_case     :    Tuple=0.1	,					__snake_case     :    Union[str, Any]=0.0	,					__snake_case     :    str=True	,					__snake_case     :    Union[str, Any]="normal"	,					__snake_case     :    Optional[Any]=0.0_1	,					__snake_case     :    List[str]=0.0_1	,					__snake_case     :    Union[str, Any]=0.0_2	,					__snake_case     :    Optional[int]=1e-5	,					__snake_case     :    int=0	,					**__snake_case     :    Union[str, Any]	,					):
										lowerCamelCase						:Optional[Any]						  =     vocab_size
										lowerCamelCase						:Any						  =     []
										self.cutoffs.extend(_snake_case		)
										if proj_share_all_but_first:
													lowerCamelCase						:str						  =     [False] + [True] * len(self.cutoffs		)
										else:
													lowerCamelCase						:int						  =     [False] + [False] * len(self.cutoffs		)
										lowerCamelCase						:List[Any]						  =     d_model
										lowerCamelCase						:int						  =     d_embed
										lowerCamelCase						:Optional[int]						  =     d_head
										lowerCamelCase						:List[str]						  =     d_inner
										lowerCamelCase						:List[str]						  =     div_val
										lowerCamelCase						:Tuple						  =     pre_lnorm
										lowerCamelCase						:Tuple						  =     n_layer
										lowerCamelCase						:Tuple						  =     n_head
										lowerCamelCase						:List[Any]						  =     mem_len
										lowerCamelCase						:Dict						  =     same_length
										lowerCamelCase						:Optional[Any]						  =     attn_type
										lowerCamelCase						:Optional[Any]						  =     clamp_len
										lowerCamelCase						:Optional[int]						  =     sample_softmax
										lowerCamelCase						:Union[str, Any]						  =     adaptive
										lowerCamelCase						:int						  =     dropout
										lowerCamelCase						:Union[str, Any]						  =     dropatt
										lowerCamelCase						:int						  =     untie_r
										lowerCamelCase						:int						  =     init
										lowerCamelCase						:Optional[int]						  =     init_range
										lowerCamelCase						:str						  =     proj_init_std
										lowerCamelCase						:Tuple						  =     init_std
										lowerCamelCase						:Optional[int]						  =     layer_norm_epsilon
										super().__init__(eos_token_id=_snake_case	,					**_snake_case		)







							@property
							def 				snake_case  (				self     :    Optional[Any]		):
										logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit."		)
										return -1







							@max_position_embeddings.setter
							def 				snake_case  (				self     :    Any	,					__snake_case     :    Any		):
										raise NotImplementedError(
										    F"The model {self.model_type} is one of the few models that has no sequence length limit."		)




from maths.prime_factors import prime_factors





def  _lowerCamelCase					(   a_				: int):
			if not isinstance(a_						,			a_):
						lowerCamelCase						:Tuple						  =     F"Input value of [number={number}] must be an integer"
						raise TypeError(a_)
			if number < 1:
						raise ValueError('''Input must be a positive integer''')
			return -1 if len(prime_factors(a_)) % 2 else 1


if __name__ == "__main__":
			import doctest

			doctest.testmod()



