

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


a__				=		logging.get_logger(__name__)

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

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    "gptsan-japanese"
							UpperCAmelCase__       :						List[Any]	    =    [
							    "past_key_values",
							]
							UpperCAmelCase__       :						List[Any]	    =    {
							    "hidden_size": "d_model",
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(			self       ,							_a=3_6_0_0_0       ,							_a=1_2_8_0       ,							_a=1_0_2_4       ,							_a=8_1_9_2       ,							_a=4_0_9_6       ,							_a=1_2_8       ,							_a=1_0       ,							_a=0       ,							_a=1_6       ,							_a=1_6       ,							_a=1_2_8       ,							_a=0.0       ,							_a=1e-5       ,							_a=False       ,							_a=0.0       ,							_a="float32"       ,							_a=False       ,							_a=False       ,							_a=False       ,							_a=0.002       ,							_a=False       ,							_a=True       ,							_a=3_5_9_9_8       ,							_a=3_5_9_9_5       ,							_a=3_5_9_9_9       ,							**_a       ,							)    ->    str:
												_a	:    List[Any]         =		vocab_size
												_a	:    Tuple         =		max_position_embeddings
												_a	:    Tuple         =		d_model
												_a	:    Dict         =		d_ff
												_a	:    Any         =		d_ext
												_a	:    str         =		d_spout
												_a	:    str         =		num_switch_layers
												_a	:    Optional[Any]         =		num_ext_layers
												_a	:    Dict         =		num_switch_layers + num_ext_layers
												_a	:    Any         =		num_heads
												_a	:    Optional[int]         =		num_experts
												_a	:    Dict         =		expert_capacity
												_a	:    Optional[int]         =		dropout_rate
												_a	:    Tuple         =		layer_norm_epsilon
												_a	:    str         =		router_bias
												_a	:    List[Any]         =		router_jitter_noise
												_a	:    Optional[int]         =		router_dtype
												_a	:    int         =		router_ignore_padding_tokens
												_a	:    Tuple         =		output_hidden_states
												_a	:    int         =		output_attentions
												_a	:    Tuple         =		initializer_factor
												_a	:    List[str]         =		output_router_logits
												_a	:    List[str]         =		use_cache

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






from math import ceil



def    __UpperCAmelCase (	__a      :			int = 1_001	)			->     int:

					"""simple docstring"""

					_a	:    List[Any]         =		1

					for i in range(1		,int(ceil(n / 2.0	)	)	):
										_a	:    Optional[Any]         =		2 * i + 1
										_a	:    Optional[int]         =		2 * i
										_a	:    str         =		total + 4 * odd**2 - 6 * even

					return total


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						print(solution())
			else:
						try:
									a__				=		int(sys.argv[1])
									print(solution(n))
						except ValueError:
									print('''Invalid entry - please enter a number''')



