



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


lowerCAmelCase__		:					Optional[Any]							=logging.get_logger(__name__)

lowerCAmelCase__		:					Any							={
    '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
        '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
    ),
}

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[Any]      =  '''gptsan-japanese'''
						UpperCamelCase__      :			List[str]      =  [
						    '''past_key_values''',
						]
						UpperCamelCase__      :			int      =  {
						    '''hidden_size''': '''d_model''',
						    '''num_attention_heads''': '''num_heads''',
						    '''num_hidden_layers''': '''num_layers''',
						}






						def __init__(   self				,							_A=36_000				,							_A=1_280				,							_A=1_024				,							_A=8_192				,							_A=4_096				,							_A=128				,							_A=10				,							_A=0				,							_A=16				,							_A=16				,							_A=128				,							_A=0.0				,							_A=1e-5				,							_A=False				,							_A=0.0				,							_A="float32"				,							_A=False				,							_A=False				,							_A=False				,							_A=0.0_0_2				,							_A=False				,							_A=True				,							_A=35_998				,							_A=35_995				,							_A=35_999				,							**_A				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= vocab_size
													__SCREAMING_SNAKE_CASE									= max_position_embeddings
													__SCREAMING_SNAKE_CASE									= d_model
													__SCREAMING_SNAKE_CASE									= d_ff
													__SCREAMING_SNAKE_CASE									= d_ext
													__SCREAMING_SNAKE_CASE									= d_spout
													__SCREAMING_SNAKE_CASE									= num_switch_layers
													__SCREAMING_SNAKE_CASE									= num_ext_layers
													__SCREAMING_SNAKE_CASE									= num_switch_layers + num_ext_layers
													__SCREAMING_SNAKE_CASE									= num_heads
													__SCREAMING_SNAKE_CASE									= num_experts
													__SCREAMING_SNAKE_CASE									= expert_capacity
													__SCREAMING_SNAKE_CASE									= dropout_rate
													__SCREAMING_SNAKE_CASE									= layer_norm_epsilon
													__SCREAMING_SNAKE_CASE									= router_bias
													__SCREAMING_SNAKE_CASE									= router_jitter_noise
													__SCREAMING_SNAKE_CASE									= router_dtype
													__SCREAMING_SNAKE_CASE									= router_ignore_padding_tokens
													__SCREAMING_SNAKE_CASE									= output_hidden_states
													__SCREAMING_SNAKE_CASE									= output_attentions
													__SCREAMING_SNAKE_CASE									= initializer_factor
													__SCREAMING_SNAKE_CASE									= output_router_logits
													__SCREAMING_SNAKE_CASE									= use_cache

													super().__init__(
													    separator_token_id=_A				,							pad_token_id=_A				,							eos_token_id=_A				,							**_A				,							)











def      __lowercase       (			a__ = 10_00       )    ->			int:
							__SCREAMING_SNAKE_CASE									= -1
							__SCREAMING_SNAKE_CASE									= 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
														__SCREAMING_SNAKE_CASE									= (n * n - 2 * a * n) // (2 * n - 2 * a)
														__SCREAMING_SNAKE_CASE									= n - a - b
														if c * c == (a * a + b * b):
																					__SCREAMING_SNAKE_CASE									= a * b * c
																					if candidate >= product:
																												__SCREAMING_SNAKE_CASE									= candidate
							return product


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






