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


lowerCamelCase_						: List[str]         =  logging.get_logger(__name__)

lowerCamelCase_						: str         =  {
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						int					      = """switch_transformers"""
			__UpperCamelCase      :						Dict					      = ["""past_key_values"""]
			__UpperCamelCase      :						List[Any]					      = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

			def __init__(       self :       Optional[Any]						,	snake_case_ :       Optional[Any]=3_2128						,	snake_case_ :       List[str]=768						,	snake_case_ :       int=64						,	snake_case_ :       Optional[int]=2048						,	snake_case_ :       Dict=64						,	snake_case_ :       Dict=12						,	snake_case_ :       Optional[int]=3						,	snake_case_ :       str=12						,	snake_case_ :       Tuple=3						,	snake_case_ :       Optional[int]=12						,	snake_case_ :       Union[str, Any]=8						,	snake_case_ :       List[Any]=False						,	snake_case_ :       List[str]=0.01						,	snake_case_ :       Tuple="float32"						,	snake_case_ :       Union[str, Any]=False						,	snake_case_ :       Union[str, Any]=32						,	snake_case_ :       Union[str, Any]=128						,	snake_case_ :       Any=0.1						,	snake_case_ :       int=1e-6						,	snake_case_ :       List[str]=0.001						,	snake_case_ :       List[str]=0.001						,	snake_case_ :       Union[str, Any]=1.0						,	snake_case_ :       str="relu"						,	snake_case_ :       int=True						,	snake_case_ :       Dict=False						,	snake_case_ :       Optional[Any]=True						,	snake_case_ :       Dict=0						,	snake_case_ :       Tuple=1						,	**snake_case_ :       Union[str, Any]						,	):
							UpperCamelCase_:							Tuple	=						vocab_size
							UpperCamelCase_:							Any	=						d_model
							UpperCamelCase_:							Any	=						d_kv
							UpperCamelCase_:							Union[str, Any]	=						d_ff

							UpperCamelCase_:							Optional[Any]	=						num_sparse_encoder_layers

							UpperCamelCase_:							Tuple	=						num_layers
							UpperCamelCase_:							List[Any]	=						(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							UpperCamelCase_:							str	=						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:
											UpperCamelCase_:							Any	=						self.num_layers // self.num_sparse_encoder_layers
							else:
											UpperCamelCase_:							Dict	=						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:
											UpperCamelCase_:							Tuple	=						self.num_decoder_layers // self.num_sparse_decoder_layers
							else:
											UpperCamelCase_:							Optional[int]	=						self.num_decoder_layers  # HACK: this will create 0 sparse layers

							UpperCamelCase_:							List[str]	=						num_heads
							UpperCamelCase_:							Optional[int]	=						num_experts
							UpperCamelCase_:							Union[str, Any]	=						expert_capacity
							UpperCamelCase_:							Tuple	=						router_bias
							UpperCamelCase_:							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}'''      )
							UpperCamelCase_:							Tuple	=						router_dtype

							UpperCamelCase_:							Optional[int]	=						router_ignore_padding_tokens
							UpperCamelCase_:							Optional[Any]	=						relative_attention_num_buckets
							UpperCamelCase_:							List[Any]	=						relative_attention_max_distance

							UpperCamelCase_:							List[Any]	=						dropout_rate
							UpperCamelCase_:							Optional[int]	=						layer_norm_epsilon
							UpperCamelCase_:							Any	=						initializer_factor
							UpperCamelCase_:							Optional[int]	=						feed_forward_proj
							UpperCamelCase_:							Tuple	=						use_cache
							UpperCamelCase_:							Tuple	=						add_router_probs

							UpperCamelCase_:							Union[str, Any]	=						router_z_loss_coef
							UpperCamelCase_:							Tuple	=						router_aux_loss_coef

							UpperCamelCase_:							Tuple	=						self.feed_forward_proj.split("""-"""      )
							UpperCamelCase_:							str	=						act_info[-1]
							UpperCamelCase_:							Dict	=						act_info[0] == """gated"""

							if len(snake_case_      ) > 1 and act_info[0] != "gated" or len(snake_case_      ) > 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":
											UpperCamelCase_:							Any	=						"""gelu_new"""

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






from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = "x"     ,    lowerCamelCase = 10**-10     ,    lowerCamelCase = 1     ,    )  ->	complex:
				UpperCamelCase_:							Optional[Any]	=						symbols(lowerCamelCase						)
				UpperCamelCase_:							int	=						lambdify(lowerCamelCase     ,    lowerCamelCase						)
				UpperCamelCase_:							Optional[Any]	=						lambdify(lowerCamelCase     ,    diff(lowerCamelCase     ,    lowerCamelCase						)						)

				UpperCamelCase_:							Tuple	=						starting_point

				while True:
								if diff_function(lowerCamelCase						) != 0:
												UpperCamelCase_:							List[Any]	=						prev_guess - multiplicity * func(lowerCamelCase						) / diff_function(
												    lowerCamelCase						)
								else:
												raise ZeroDivisionError("""Could not find root"""						) from None

								# Precision is checked by comparing the difference of consecutive guesses
								if abs(next_guess - prev_guess						) < precision:
												return next_guess

								UpperCamelCase_:							Any	=						next_guess


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

							# Find root of polynomial
							# Find fourth Root of 5
							print(F"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}""")

							# Find value of e
							print(
							    """The root of log(y) - 1 = 0 is """,
							    F"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
							)

							# Exponential Roots
							print(
							    """The root of exp(x) - 1 = 0 is""",
							    F"""{newton_raphson('exp(x) - 1', 10, precision=0.005)}""",
							)

							# Find root of cos(x)
							print(F"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")






