'''simple docstring'''




import warnings

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


__lowercase          =  logging.get_logger(__name__)

__lowercase          =  {
    '''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 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






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



			def __init__(   self       ,    __lowerCAmelCase=32000       ,    __lowerCAmelCase=1024       ,    __lowerCAmelCase=24       ,    __lowerCAmelCase=16       ,    __lowerCAmelCase=4096       ,    __lowerCAmelCase="gelu"       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase="bi"       ,    __lowerCAmelCase=0.02       ,    __lowerCAmelCase=1E-1_2       ,    __lowerCAmelCase=0.1       ,    __lowerCAmelCase=512       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase=-1       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase="last"       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase="tanh"       ,    __lowerCAmelCase=0.1       ,    __lowerCAmelCase=5       ,    __lowerCAmelCase=5       ,    __lowerCAmelCase=5       ,    __lowerCAmelCase=1       ,    __lowerCAmelCase=2       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  vocab_size
					lowerCAmelCase   					=  d_model
					lowerCAmelCase   					=  n_layer
					lowerCAmelCase   					=  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   					=  d_model // n_head
					lowerCAmelCase   					=  ff_activation
					lowerCAmelCase   					=  d_inner
					lowerCAmelCase   					=  untie_r
					lowerCAmelCase   					=  attn_type

					lowerCAmelCase   					=  initializer_range
					lowerCAmelCase   					=  layer_norm_eps

					lowerCAmelCase   					=  dropout
					lowerCAmelCase   					=  mem_len
					lowerCAmelCase   					=  reuse_len
					lowerCAmelCase   					=  bi_data
					lowerCAmelCase   					=  clamp_len
					lowerCAmelCase   					=  same_length

					lowerCAmelCase   					=  summary_type
					lowerCAmelCase   					=  summary_use_proj
					lowerCAmelCase   					=  summary_activation
					lowerCAmelCase   					=  summary_last_dropout
					lowerCAmelCase   					=  start_n_top
					lowerCAmelCase   					=  end_n_top

					lowerCAmelCase   					=  bos_token_id
					lowerCAmelCase   					=  pad_token_id
					lowerCAmelCase   					=  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       ,    )
							lowerCAmelCase   					=  kwargs["""use_cache"""]

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



			@property
			def 							a_       (   self):




					"""simple docstring"""


					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       ,    __lowerCAmelCase):




					"""simple docstring"""


					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() = }')




