

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


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"switch_transformers"
			SCREAMING_SNAKE_CASE     :   Tuple									=				["past_key_values"]
			SCREAMING_SNAKE_CASE     :   int									=				{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}







			def __init__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]=3_2_1_2_8							,   _UpperCamelCase :				Any=7_6_8							,   _UpperCamelCase :				Optional[Any]=6_4							,   _UpperCamelCase :				List[Any]=2_0_4_8							,   _UpperCamelCase :				Union[str, Any]=6_4							,   _UpperCamelCase :				Union[str, Any]=1_2							,   _UpperCamelCase :				List[Any]=3							,   _UpperCamelCase :				str=1_2							,   _UpperCamelCase :				Union[str, Any]=3							,   _UpperCamelCase :				Tuple=1_2							,   _UpperCamelCase :				Dict=8							,   _UpperCamelCase :				Any=False							,   _UpperCamelCase :				Dict=0.01							,   _UpperCamelCase :				Optional[Any]="float32"							,   _UpperCamelCase :				Optional[int]=False							,   _UpperCamelCase :				List[str]=3_2							,   _UpperCamelCase :				str=1_2_8							,   _UpperCamelCase :				Tuple=0.1							,   _UpperCamelCase :				List[str]=1e-6							,   _UpperCamelCase :				Optional[int]=0.001							,   _UpperCamelCase :				Optional[int]=0.001							,   _UpperCamelCase :				Any=1.0							,   _UpperCamelCase :				Optional[int]="relu"							,   _UpperCamelCase :				Dict=True							,   _UpperCamelCase :				Optional[int]=False							,   _UpperCamelCase :				Dict=True							,   _UpperCamelCase :				Tuple=0							,   _UpperCamelCase :				List[Any]=1							,   **_UpperCamelCase :				Tuple							,   )      ->str:
									snake_case_   =	vocab_size
									snake_case_   =	d_model
									snake_case_   =	d_kv
									snake_case_   =	d_ff

									snake_case_   =	num_sparse_encoder_layers

									snake_case_   =	num_layers
									snake_case_   =	(
									    num_decoder_layers if num_decoder_layers is not None else self.num_layers
									)  # default = symmetry
									snake_case_   =	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:
															snake_case_   =	self.num_layers // self.num_sparse_encoder_layers
									else:
															snake_case_   =	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:
															snake_case_   =	self.num_decoder_layers // self.num_sparse_decoder_layers
									else:
															snake_case_   =	self.num_decoder_layers  # HACK: this will create 0 sparse layers

									snake_case_   =	num_heads
									snake_case_   =	num_experts
									snake_case_   =	expert_capacity
									snake_case_   =	router_bias
									snake_case_   =	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}'''							)
									snake_case_   =	router_dtype

									snake_case_   =	router_ignore_padding_tokens
									snake_case_   =	relative_attention_num_buckets
									snake_case_   =	relative_attention_max_distance

									snake_case_   =	dropout_rate
									snake_case_   =	layer_norm_epsilon
									snake_case_   =	initializer_factor
									snake_case_   =	feed_forward_proj
									snake_case_   =	use_cache
									snake_case_   =	add_router_probs

									snake_case_   =	router_z_loss_coef
									snake_case_   =	router_aux_loss_coef

									snake_case_   =	self.feed_forward_proj.split('''-'''							)
									snake_case_   =	act_info[-1]
									snake_case_   =	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":
															snake_case_   =	'''gelu_new'''

									super().__init__(
									    pad_token_id=_UpperCamelCase							,   eos_token_id=_UpperCamelCase							,   is_encoder_decoder=_UpperCamelCase							,   **_UpperCamelCase							,   )


from decimal import Decimal, getcontext
from math import ceil, factorial


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

						if not isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
												raise TypeError('''Undefined for non-integers'''       )
						elif precision < 1:
												raise ValueError('''Undefined for non-natural numbers'''       )

						snake_case_   =	precision
						snake_case_   =	ceil(precision / 14       )
						snake_case_   =	426880 * Decimal(10005       ).sqrt()
						snake_case_   =	1
						snake_case_   =	13591409
						snake_case_   =	Decimal(SCREAMING_SNAKE_CASE__       )
						for k in range(1  ,		SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	factorial(6 * k       ) // (factorial(3 * k       ) * factorial(SCREAMING_SNAKE_CASE__       ) ** 3)
												linear_term += 545140134
												exponential_term *= -262537412640768000
												partial_sum += Decimal(multinomial_term * linear_term       ) / exponential_term
						return str(constant_term / partial_sum       )[:-1]


if __name__ == "__main__":
					lowerCAmelCase_       	=	50
					print(f"""The first {n} digits of pi is: {pi(n)}""")