

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

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

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"switch_transformers"
						_lowercase    						=			["past_key_values"]
						_lowercase    						=			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


						def __init__(       self						,					A_=32128						,					A_=768						,					A_=64						,					A_=2048						,					A_=64						,					A_=12						,					A_=3						,					A_=12						,					A_=3						,					A_=12						,					A_=8						,					A_=False						,					A_=0.01						,					A_="float32"						,					A_=False						,					A_=32						,					A_=128						,					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'''


										_UpperCAmelCase       :		Dict 		=    vocab_size
										_UpperCAmelCase       :		Union[str, Any] 		=    d_model
										_UpperCAmelCase       :		Any 		=    d_kv
										_UpperCAmelCase       :		List[str] 		=    d_ff

										_UpperCAmelCase       :		str 		=    num_sparse_encoder_layers

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

										_UpperCAmelCase       :		Optional[int] 		=    num_heads
										_UpperCAmelCase       :		Optional[int] 		=    num_experts
										_UpperCAmelCase       :		str 		=    expert_capacity
										_UpperCAmelCase       :		Dict 		=    router_bias
										_UpperCAmelCase       :		int 		=    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       :		Union[str, Any] 		=    router_ignore_padding_tokens
										_UpperCAmelCase       :		List[Any] 		=    relative_attention_num_buckets
										_UpperCAmelCase       :		Optional[Any] 		=    relative_attention_max_distance

										_UpperCAmelCase       :		Optional[int] 		=    dropout_rate
										_UpperCAmelCase       :		str 		=    layer_norm_epsilon
										_UpperCAmelCase       :		Any 		=    initializer_factor
										_UpperCAmelCase       :		Union[str, Any] 		=    feed_forward_proj
										_UpperCAmelCase       :		Optional[int] 		=    use_cache
										_UpperCAmelCase       :		Optional[int] 		=    add_router_probs

										_UpperCAmelCase       :		Union[str, Any] 		=    router_z_loss_coef
										_UpperCAmelCase       :		Optional[int] 		=    router_aux_loss_coef

										_UpperCAmelCase       :		int 		=    self.feed_forward_proj.split("-"	)
										_UpperCAmelCase       :		Dict 		=    act_info[-1]
										_UpperCAmelCase       :		Optional[Any] 		=    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":
														_UpperCAmelCase       :		Any 		=    "gelu_new"

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





from __future__ import annotations

import numpy as np

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[float] )   ->       Dict:
				return np.maximum(0  ,      lowerCAmelCase )


if __name__ == "__main__":
					print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


