




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


UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      {
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
class       a__   ( a_				):
						_a							:   Tuple =   "switch_transformers"
						_a							:   Tuple =   ["past_key_values"]
						_a							:   Any =   {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






						def __init__(       self		,      _A=3_2_1_2_8		,      _A=7_6_8		,      _A=6_4		,      _A=2_0_4_8		,      _A=6_4		,      _A=1_2		,      _A=3		,      _A=1_2		,      _A=3		,      _A=1_2		,      _A=8		,      _A=False		,      _A=0.01		,      _A="float32"		,      _A=False		,      _A=3_2		,      _A=1_2_8		,      _A=0.1		,      _A=1E-6		,      _A=0.0_01		,      _A=0.0_01		,      _A=1.0		,      _A="relu"		,      _A=True		,      _A=False		,      _A=True		,      _A=0		,      _A=1		,      **_A		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	vocab_size
											__lowerCAmelCase           =	d_model
											__lowerCAmelCase           =	d_kv
											__lowerCAmelCase           =	d_ff

											__lowerCAmelCase           =	num_sparse_encoder_layers

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

											__lowerCAmelCase           =	num_heads
											__lowerCAmelCase           =	num_experts
											__lowerCAmelCase           =	expert_capacity
											__lowerCAmelCase           =	router_bias
											__lowerCAmelCase           =	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           =	router_dtype

											__lowerCAmelCase           =	router_ignore_padding_tokens
											__lowerCAmelCase           =	relative_attention_num_buckets
											__lowerCAmelCase           =	relative_attention_max_distance

											__lowerCAmelCase           =	dropout_rate
											__lowerCAmelCase           =	layer_norm_epsilon
											__lowerCAmelCase           =	initializer_factor
											__lowerCAmelCase           =	feed_forward_proj
											__lowerCAmelCase           =	use_cache
											__lowerCAmelCase           =	add_router_probs

											__lowerCAmelCase           =	router_z_loss_coef
											__lowerCAmelCase           =	router_aux_loss_coef

											__lowerCAmelCase           =	self.feed_forward_proj.split("-"      )
											__lowerCAmelCase           =	act_info[-1]
											__lowerCAmelCase           =	act_info[0] == "gated"

											if len(_A      ) > 1 and act_info[0] != "gated" or len(_A      ) > 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           =	"gelu_new"

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










import math







def      _a				(						SCREAMING_SNAKE_CASE_							:    int					):

					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or number % 2 == 0 or number % 3 == 0:
										# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
										return False

					# All primes number are in format of 6k +/- 1
					for i in range(5				,				int(math.sqrt(SCREAMING_SNAKE_CASE_					) + 1					)				,				6					):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True







def      _a				(						SCREAMING_SNAKE_CASE_							:    float = 0.1					):
					__lowerCAmelCase           =	3
					__lowerCAmelCase           =	3

					while primes / (2 * j - 1) >= ratio:
										for i in range(j * j + j + 1				,				(j + 2) * (j + 2)				,				j + 1					):
															primes += is_prime(SCREAMING_SNAKE_CASE_					)
										j += 2
					return j


if __name__ == "__main__":
					import doctest

					doctest.testmod()




