



"""simple docstring"""


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


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

__A   :							Optional[int]										=   {
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
class 			_a (					__UpperCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""switch_transformers"""
			UpperCamelCase__			      =	["""past_key_values"""]
			UpperCamelCase__			      =	{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



			def __init__(		self     :    Tuple				,   __UpperCamelCase     :    Optional[Any]=3_2_1_2_8				,   __UpperCamelCase     :    Optional[Any]=7_6_8				,   __UpperCamelCase     :    Union[str, Any]=6_4				,   __UpperCamelCase     :    Tuple=2_0_4_8				,   __UpperCamelCase     :    Optional[Any]=6_4				,   __UpperCamelCase     :    Union[str, Any]=1_2				,   __UpperCamelCase     :    Optional[int]=3				,   __UpperCamelCase     :    int=1_2				,   __UpperCamelCase     :    Optional[Any]=3				,   __UpperCamelCase     :    Tuple=1_2				,   __UpperCamelCase     :    Optional[Any]=8				,   __UpperCamelCase     :    Union[str, Any]=False				,   __UpperCamelCase     :    Optional[int]=0.0_1				,   __UpperCamelCase     :    Optional[Any]="float32"				,   __UpperCamelCase     :    List[str]=False				,   __UpperCamelCase     :    Tuple=3_2				,   __UpperCamelCase     :    str=1_2_8				,   __UpperCamelCase     :    Dict=0.1				,   __UpperCamelCase     :    Union[str, Any]=1e-6				,   __UpperCamelCase     :    List[Any]=0.0_0_1				,   __UpperCamelCase     :    str=0.0_0_1				,   __UpperCamelCase     :    str=1.0				,   __UpperCamelCase     :    Tuple="relu"				,   __UpperCamelCase     :    Any=True				,   __UpperCamelCase     :    Optional[int]=False				,   __UpperCamelCase     :    Any=True				,   __UpperCamelCase     :    Union[str, Any]=0				,   __UpperCamelCase     :    Optional[Any]=1				,   **__UpperCamelCase     :    int				,   )->Optional[Any]:
							_UpperCAmelCase					=    vocab_size
							_UpperCAmelCase					=    d_model
							_UpperCAmelCase					=    d_kv
							_UpperCAmelCase					=    d_ff

							_UpperCAmelCase					=    num_sparse_encoder_layers

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

							_UpperCAmelCase					=    num_heads
							_UpperCAmelCase					=    num_experts
							_UpperCAmelCase					=    expert_capacity
							_UpperCAmelCase					=    router_bias
							_UpperCAmelCase					=    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					=    router_dtype

							_UpperCAmelCase					=    router_ignore_padding_tokens
							_UpperCAmelCase					=    relative_attention_num_buckets
							_UpperCAmelCase					=    relative_attention_max_distance

							_UpperCAmelCase					=    dropout_rate
							_UpperCAmelCase					=    layer_norm_epsilon
							_UpperCAmelCase					=    initializer_factor
							_UpperCAmelCase					=    feed_forward_proj
							_UpperCAmelCase					=    use_cache
							_UpperCAmelCase					=    add_router_probs

							_UpperCAmelCase					=    router_z_loss_coef
							_UpperCAmelCase					=    router_aux_loss_coef

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

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








"""simple docstring"""


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				def is_in_circle(_SCREAMING_SNAKE_CASE   :						float					,     _SCREAMING_SNAKE_CASE   :						float				) -> bool:
								_UpperCAmelCase					=    sqrt((x**2) + (y**2)				)
								# Our circle has a radius of 1, so a distance
								# greater than 1 would land outside the circle.
								return distance_from_centre <= 1

				# The proportion of guesses that landed in the circle
				_UpperCAmelCase					=    mean(
				    int(is_in_circle(uniform(-1.0					,     1.0				)					,     uniform(-1.0					,     1.0				)				)				)
				    for _ in range(_SCREAMING_SNAKE_CASE				)				)
				# The ratio of the area for circle to square is pi/4.
				_UpperCAmelCase					=    proportion * 4
				print(f'The estimated value of pi is {pi_estimate}'				)
				print(f'The numpy value of pi is {pi}'				)
				print(f'The total error is {abs(pi - pi_estimate				)}'				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Callable[[float], float]					,     _SCREAMING_SNAKE_CASE   :						float = 0.0					,     _SCREAMING_SNAKE_CASE   :						float = 1.0					,     ):






				'''simple docstring'''



				return mean(
				    function_to_integrate(uniform(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)				) for _ in range(_SCREAMING_SNAKE_CASE				)				) * (max_value - min_value)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						float = 0.0					,     _SCREAMING_SNAKE_CASE   :						float = 1.0				):






				'''simple docstring'''



				def identity_function(_SCREAMING_SNAKE_CASE   :						float				) -> float:
								return x

				_UpperCAmelCase					=    area_under_curve_estimator(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    (max_value * max_value - min_value * min_value) / 2

				print('''******************'''				)
				print(f'Estimating area under y=x where x varies from {min_value} to {max_value}'				)
				print(f'Estimated value is {estimated_value}'				)
				print(f'Expected value is {expected_value}'				)
				print(f'Total error is {abs(estimated_value - expected_value				)}'				)
				print('''******************'''				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				def function_to_integrate(_SCREAMING_SNAKE_CASE   :						float				) -> float:
								return sqrt(4.0 - x * x				)

				_UpperCAmelCase					=    area_under_curve_estimator(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     0.0					,     2.0				)

				print('''******************'''				)
				print('''Estimating pi using area_under_curve_estimator'''				)
				print(f'Estimated value is {estimated_value}'				)
				print(f'Expected value is {pi}'				)
				print(f'Total error is {abs(estimated_value - pi				)}'				)
				print('''******************'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



