
'''simple docstring'''


import warnings

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


__lowercase							:   Tuple					=     logging.get_logger(__name__)

__lowercase							:   Dict					=     {
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}

class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		Any	      =			"xlnet"
			lowerCamelCase			:		Optional[int]	      =			["mems"]
			lowerCamelCase			:		Any	      =			{
			    "n_token": "vocab_size",  # Backward compatibility
			    "hidden_size": "d_model",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}

			def __init__(self			,   A=3_2_0_0_0			,   A=1_0_2_4			,   A=2_4			,   A=1_6			,   A=4_0_9_6			,   A="gelu"			,   A=True			,   A="bi"			,   A=0.02			,   A=1E-12			,   A=0.1			,   A=5_1_2			,   A=None			,   A=True			,   A=False			,   A=False			,   A=-1			,   A=False			,   A="last"			,   A=True			,   A="tanh"			,   A=0.1			,   A=5			,   A=5			,   A=5			,   A=1			,   A=2			,   **A			,   ):
								lowerCamelCase_					:      int     =		vocab_size
								lowerCamelCase_					:      List[str]     =		d_model
								lowerCamelCase_					:      List[Any]     =		n_layer
								lowerCamelCase_					:      str     =		n_head
								if d_model % n_head != 0:
													raise ValueError(F"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""      )
								if "d_head" in kwargs:
													if kwargs["d_head"] != d_model // n_head:
																		raise ValueError(
																		    F"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})"""      )
								lowerCamelCase_					:      Optional[Any]     =		d_model // n_head
								lowerCamelCase_					:      List[Any]     =		ff_activation
								lowerCamelCase_					:      str     =		d_inner
								lowerCamelCase_					:      List[Any]     =		untie_r
								lowerCamelCase_					:      str     =		attn_type

								lowerCamelCase_					:      str     =		initializer_range
								lowerCamelCase_					:      Tuple     =		layer_norm_eps

								lowerCamelCase_					:      str     =		dropout
								lowerCamelCase_					:      Dict     =		mem_len
								lowerCamelCase_					:      List[Any]     =		reuse_len
								lowerCamelCase_					:      str     =		bi_data
								lowerCamelCase_					:      Dict     =		clamp_len
								lowerCamelCase_					:      Dict     =		same_length

								lowerCamelCase_					:      List[str]     =		summary_type
								lowerCamelCase_					:      List[str]     =		summary_use_proj
								lowerCamelCase_					:      int     =		summary_activation
								lowerCamelCase_					:      Tuple     =		summary_last_dropout
								lowerCamelCase_					:      List[str]     =		start_n_top
								lowerCamelCase_					:      List[Any]     =		end_n_top

								lowerCamelCase_					:      Union[str, Any]     =		bos_token_id
								lowerCamelCase_					:      Dict     =		pad_token_id
								lowerCamelCase_					:      List[str]     =		eos_token_id

								if "use_cache" in kwargs:
													warnings.warn(
													    '''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
													    ''' instead.'''			,   A			,   )
													lowerCamelCase_					:      Tuple     =		kwargs['''use_cache''']

								lowerCamelCase_					:      List[str]     =		use_mems_eval
								lowerCamelCase_					:      List[str]     =		use_mems_train
								super().__init__(pad_token_id=A			,   bos_token_id=A			,   eos_token_id=A			,   **A      )

			@property
			def        UpperCAmelCase__ (self      ):
								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			,   A      ):
								# 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."""      )




'''simple docstring'''


def lowercase_	(   _lowercase = 1_000				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      Any     =		-1
					lowerCamelCase_					:      Optional[Any]     =		0
					for a in range(1			,       n // 3				):
										# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
										lowerCamelCase_					:      Any     =		(n * n - 2 * a * n) // (2 * n - 2 * a)
										lowerCamelCase_					:      int     =		n - a - b
										if c * c == (a * a + b * b):
															lowerCamelCase_					:      int     =		a * b * c
															if candidate >= product:
																				lowerCamelCase_					:      Any     =		candidate
					return product


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


