"""simple docstring"""





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


__A  =				logging.get_logger(__name__)

__A  =				{
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}
class  _snake_case     (					a__		):
						snake_case__			  =			"switch_transformers"
						snake_case__			  =			["past_key_values"]
						snake_case__			  =			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






						def __init__(       self      :			int							,					UpperCAmelCase      :			str=32128							,					UpperCAmelCase      :			Dict=768							,					UpperCAmelCase      :			Optional[int]=64							,					UpperCAmelCase      :			Tuple=2048							,					UpperCAmelCase      :			Dict=64							,					UpperCAmelCase      :			Any=12							,					UpperCAmelCase      :			Any=3							,					UpperCAmelCase      :			Any=12							,					UpperCAmelCase      :			Tuple=3							,					UpperCAmelCase      :			Optional[Any]=12							,					UpperCAmelCase      :			List[Any]=8							,					UpperCAmelCase      :			List[Any]=False							,					UpperCAmelCase      :			str=0.0_1							,					UpperCAmelCase      :			str="float32"							,					UpperCAmelCase      :			Optional[int]=False							,					UpperCAmelCase      :			Optional[Any]=32							,					UpperCAmelCase      :			str=128							,					UpperCAmelCase      :			Dict=0.1							,					UpperCAmelCase      :			Any=1E-6							,					UpperCAmelCase      :			List[Any]=0.0_0_1							,					UpperCAmelCase      :			List[Any]=0.0_0_1							,					UpperCAmelCase      :			str=1.0							,					UpperCAmelCase      :			Dict="relu"							,					UpperCAmelCase      :			Any=True							,					UpperCAmelCase      :			List[str]=False							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			str=0							,					UpperCAmelCase      :			List[Any]=1							,					**UpperCAmelCase      :			int							,					):
													__lowerCamelCase					:		List[str]		   =     vocab_size
													__lowerCamelCase					:		Optional[int]		   =     d_model
													__lowerCamelCase					:		Optional[Any]		   =     d_kv
													__lowerCamelCase					:		Any		   =     d_ff

													__lowerCamelCase					:		Any		   =     num_sparse_encoder_layers

													__lowerCamelCase					:		Union[str, Any]		   =     num_layers
													__lowerCamelCase					:		List[str]		   =     (
													    num_decoder_layers if num_decoder_layers is not None else self.num_layers
													)  # default = symmetry
													__lowerCamelCase					:		Dict		   =     num_sparse_decoder_layers

													# This tells us, each how many encoder layer we'll have to set a sparse layer.
													if self.num_sparse_encoder_layers > 0:
																				__lowerCamelCase					:		Union[str, Any]		   =     self.num_layers // self.num_sparse_encoder_layers
													else:
																				__lowerCamelCase					:		List[Any]		   =     self.num_layers  # HACK: this will create 0 sparse layers

													# This tells us, each how many encoder layer we'll have to set a sparse layer.
													if self.num_sparse_decoder_layers > 0:
																				__lowerCamelCase					:		int		   =     self.num_decoder_layers // self.num_sparse_decoder_layers
													else:
																				__lowerCamelCase					:		List[str]		   =     self.num_decoder_layers  # HACK: this will create 0 sparse layers

													__lowerCamelCase					:		Optional[int]		   =     num_heads
													__lowerCamelCase					:		List[Any]		   =     num_experts
													__lowerCamelCase					:		str		   =     expert_capacity
													__lowerCamelCase					:		Optional[Any]		   =     router_bias
													__lowerCamelCase					:		List[str]		   =     router_jitter_noise
													if router_dtype not in ["float32", "float16", "bfloat16"]:
																				raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""    )
													__lowerCamelCase					:		Any		   =     router_dtype

													__lowerCamelCase					:		Tuple		   =     router_ignore_padding_tokens
													__lowerCamelCase					:		Dict		   =     relative_attention_num_buckets
													__lowerCamelCase					:		Optional[Any]		   =     relative_attention_max_distance

													__lowerCamelCase					:		Union[str, Any]		   =     dropout_rate
													__lowerCamelCase					:		int		   =     layer_norm_epsilon
													__lowerCamelCase					:		Any		   =     initializer_factor
													__lowerCamelCase					:		str		   =     feed_forward_proj
													__lowerCamelCase					:		List[str]		   =     use_cache
													__lowerCamelCase					:		str		   =     add_router_probs

													__lowerCamelCase					:		str		   =     router_z_loss_coef
													__lowerCamelCase					:		Any		   =     router_aux_loss_coef

													__lowerCamelCase					:		Dict		   =     self.feed_forward_proj.split("-"    )
													__lowerCamelCase					:		Tuple		   =     act_info[-1]
													__lowerCamelCase					:		Optional[int]		   =     act_info[0] == "gated"

													if len(UpperCAmelCase    ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase    ) > 2:
																				raise ValueError(
																				    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																				    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
																				    "'gated-gelu' or 'relu'"    )

													# for backwards compatibility
													if feed_forward_proj == "gated-gelu":
																				__lowerCamelCase					:		int		   =     "gelu_new"

													super().__init__(
													    pad_token_id=UpperCAmelCase							,					eos_token_id=UpperCAmelCase							,					is_encoder_decoder=UpperCAmelCase							,					**UpperCAmelCase							,					)
"""simple docstring"""





def    lowercase_   (   _lowerCamelCase:	int = 600851475143      ) ->      int:



							'''simple docstring'''

							try:
														__lowerCamelCase					:		Optional[Any]		   =     int(_lowerCamelCase      )
							except (TypeError, ValueError):
														raise TypeError("Parameter n must be int or castable to int."      )
							if n <= 0:
														raise ValueError("Parameter n must be greater than or equal to one."      )
							__lowerCamelCase					:		Union[str, Any]		   =     2
							__lowerCamelCase					:		int		   =     0
							if n == 2:
														return 2
							while n > 2:
														while n % i != 0:
																					i += 1
														__lowerCamelCase					:		Dict		   =     i
														while n % i == 0:
																					__lowerCamelCase					:		Union[str, Any]		   =     n // i
														i += 1
							return int(_lowerCamelCase      )


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