



"""simple docstring"""


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


snake_case_				     =       logging.get_logger(__name__)

snake_case_				     =       {
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}







class       A_			(     SCREAMING_SNAKE_CASE_				):



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






						@property
						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			Any:
												# 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 :Union[str, Any]   ,  lowercase_ :Any					)				->			Tuple:
												# 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

from math import ceil, floor, sqrt




def 	__UpperCamelCase	(  _lowerCAmelCase = 200_0000		)       ->  int:



    """simple docstring"""






    A :       list[int]          =   [0]
    A :       int

    for idx in range(1      ,     ceil(sqrt(target * 2		) * 1.1		)		):
        triangle_numbers.append(triangle_numbers[-1] + idx		)

    # we want this to be as close as possible to target
    A :       int          =   0
    # the area corresponding to the grid that gives the product closest to target
    A :       int          =   0
    # an estimate of b, using the quadratic formula
    A :       float
    # the largest integer less than b_estimate
    A :       int
    # the largest integer less than b_estimate
    A :       int
    # the triangle number corresponding to b_floor
    A :       int
    # the triangle number corresponding to b_ceil
    A :       int

    for idx_a, triangle_a in enumerate(triangle_numbers[1:]      ,     1		):
        A :       Union[str, Any]          =   (-1 + sqrt(1 + 8 * target / triangle_a		)) / 2
        A :       List[Any]          =   floor(_lowerCAmelCase		)
        A :       Tuple          =   ceil(_lowerCAmelCase		)
        A :       int          =   triangle_numbers[b_floor]
        A :       Dict          =   triangle_numbers[b_ceil]

        if abs(target - triangle_b_first_guess * triangle_a		) < abs(
            target - best_product		):
            A :       Optional[int]          =   triangle_b_first_guess * triangle_a
            A :       Optional[int]          =   idx_a * b_floor

        if abs(target - triangle_b_second_guess * triangle_a		) < abs(
            target - best_product		):
            A :       Tuple          =   triangle_b_second_guess * triangle_a
            A :       Tuple          =   idx_a * b_ceil

    return area


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


