





'''simple docstring'''


import warnings

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


_lowerCamelCase :		str 			=				logging.get_logger(__name__)

_lowerCamelCase :		Union[str, Any] 			=				{
    '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 							__UpperCAmelCase     (				lowerCAmelCase__  ):







		'''simple docstring'''







		__lowerCAmelCase        =			'''xlnet'''
		__lowerCAmelCase        =			['''mems''']
		__lowerCAmelCase        =			{
		    '''n_token''': '''vocab_size''',  # Backward compatibility
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''n_head''',
		    '''num_hidden_layers''': '''n_layer''',
		}







		def __init__(self  :					List[str]					,					_lowerCAmelCase  :					Tuple=3_2000					,					_lowerCAmelCase  :					Tuple=1024					,					_lowerCAmelCase  :					Union[str, Any]=24					,					_lowerCAmelCase  :					Tuple=16					,					_lowerCAmelCase  :					List[Any]=4096					,					_lowerCAmelCase  :					Optional[Any]="gelu"					,					_lowerCAmelCase  :					List[str]=True					,					_lowerCAmelCase  :					Tuple="bi"					,					_lowerCAmelCase  :					List[str]=0.02					,					_lowerCAmelCase  :					List[Any]=1e-12					,					_lowerCAmelCase  :					Optional[Any]=0.1					,					_lowerCAmelCase  :					Optional[Any]=512					,					_lowerCAmelCase  :					List[Any]=None					,					_lowerCAmelCase  :					int=True					,					_lowerCAmelCase  :					Any=False					,					_lowerCAmelCase  :					Tuple=False					,					_lowerCAmelCase  :					List[Any]=-1					,					_lowerCAmelCase  :					Optional[Any]=False					,					_lowerCAmelCase  :					Optional[int]="last"					,					_lowerCAmelCase  :					Union[str, Any]=True					,					_lowerCAmelCase  :					Optional[Any]="tanh"					,					_lowerCAmelCase  :					Dict=0.1					,					_lowerCAmelCase  :					Optional[Any]=5					,					_lowerCAmelCase  :					Tuple=5					,					_lowerCAmelCase  :					Dict=5					,					_lowerCAmelCase  :					List[Any]=1					,					_lowerCAmelCase  :					Dict=2					,					**_lowerCAmelCase  :					Optional[Any]					,					):
			A							     =					vocab_size
			A							     =					d_model
			A							     =					n_layer
			A							     =					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})"""					)
			A							     =					d_model // n_head
			A							     =					ff_activation
			A							     =					d_inner
			A							     =					untie_r
			A							     =					attn_type

			A							     =					initializer_range
			A							     =					layer_norm_eps

			A							     =					dropout
			A							     =					mem_len
			A							     =					reuse_len
			A							     =					bi_data
			A							     =					clamp_len
			A							     =					same_length

			A							     =					summary_type
			A							     =					summary_use_proj
			A							     =					summary_activation
			A							     =					summary_last_dropout
			A							     =					start_n_top
			A							     =					end_n_top

			A							     =					bos_token_id
			A							     =					pad_token_id
			A							     =					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."""					,					__lowerCAmelCase					,					)
				A							     =					kwargs["""use_cache"""]

			A							     =					use_mems_eval
			A							     =					use_mems_train
			super().__init__(pad_token_id=__lowerCAmelCase					,					bos_token_id=__lowerCAmelCase					,					eos_token_id=__lowerCAmelCase					,					**__lowerCAmelCase					)







		@property
		def 				A   (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 				A   (self  :					Any					,					_lowerCAmelCase  :					Optional[Any]					):
			raise NotImplementedError(
			    F"""The model {self.model_type} is one of the few models that has no sequence length limit."""					)


'''simple docstring'''




from math import sqrt






def 				snake_case__							(			_A:					int = 1000000 )    ->	int:

		'''simple docstring'''


		lowerCAmelCase   					=  0
		lowerCAmelCase   					=  0
		lowerCAmelCase   					=  42

		while num_cuboids <= limit:
				max_cuboid_size += 1
				for sum_shortest_sides in range(2      ,   2 * max_cuboid_size + 1 ):
						if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
								num_cuboids += (
								    min(_A      ,   sum_shortest_sides // 2 )
								    - max(1      ,   sum_shortest_sides - max_cuboid_size )
								    + 1
								)

		return max_cuboid_size


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




