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


SCREAMING_SNAKE_CASE							:List[str]							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Tuple							 =				{
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "transfo-xl"
	snake_case_					    =     ["mems"]
	snake_case_					    =     {
	    "n_token": "vocab_size",
	    "hidden_size": "d_model",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}






	def __init__(       self					: int							,A					: List[str]=26_77_35							,A					: Tuple=[2_00_00, 4_00_00, 20_00_00]							,A					: int=10_24							,A					: Any=10_24							,A					: Dict=16							,A					: Union[str, Any]=64							,A					: Union[str, Any]=40_96							,A					: Union[str, Any]=4							,A					: Optional[Any]=False							,A					: Any=18							,A					: Optional[int]=16_00							,A					: str=10_00							,A					: Optional[int]=True							,A					: Dict=True							,A					: Any=0							,A					: int=-1							,A					: str=True							,A					: str=0.1							,A					: Dict=0.0							,A					: str=True							,A					: Any="normal"							,A					: List[str]=0.01							,A					: int=0.01							,A					: Optional[int]=0.02							,A					: int=1E-5							,A					: Optional[Any]=0							,**A					: Optional[int]							,):
					__A   			= vocab_size
					__A   			= []
					self.cutoffs.extend(A )
					if proj_share_all_but_first:
									__A   			= [False] + [True] * len(self.cutoffs )
					else:
									__A   			= [False] + [False] * len(self.cutoffs )
					__A   			= d_model
					__A   			= d_embed
					__A   			= d_head
					__A   			= d_inner
					__A   			= div_val
					__A   			= pre_lnorm
					__A   			= n_layer
					__A   			= n_head
					__A   			= mem_len
					__A   			= same_length
					__A   			= attn_type
					__A   			= clamp_len
					__A   			= sample_softmax
					__A   			= adaptive
					__A   			= dropout
					__A   			= dropatt
					__A   			= untie_r
					__A   			= init
					__A   			= init_range
					__A   			= proj_init_std
					__A   			= init_std
					__A   			= layer_norm_epsilon
					super().__init__(eos_token_id=A							,**A )






	@property
	def 				UpperCamelCase_  (       self					: List[str] ):
					# Message copied from Transformer-XL documentation
					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 				UpperCamelCase_  (       self					: Any							,A					: Optional[Any] ):
					# Message copied from Transformer-XL documentation
					raise NotImplementedError(
					    f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )







from __future__ import annotations




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list[int]		,   __UpperCAmelCase:			int      )      ->	list[int]:
    UpperCamelCase__							:				Optional[int]          =      0
    UpperCamelCase__							:				Tuple          =      len(__UpperCAmelCase      ) - 1

    while i < j:
        if nums[i] + nums[j] == target:
            return [i, j]
        elif nums[i] + nums[j] < target:
            UpperCamelCase__							:				Tuple          =      i + 1
        else:
            UpperCamelCase__							:				str          =      j - 1

    return []


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(F'''{two_pointer([2, 7, 11, 15], 9) = }''')

