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


lowerCamelCase_  :	Dict 				=					logging.get_logger(__name__)

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

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

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

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

							UpperCamelCase_:							Dict		=  num_sparse_encoder_layers

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

							UpperCamelCase_:							List[Any]		=  num_heads
							UpperCamelCase_:							List[Any]		=  num_experts
							UpperCamelCase_:							int		=  expert_capacity
							UpperCamelCase_:							List[str]		=  router_bias
							UpperCamelCase_:							Optional[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_:							int		=  router_dtype

							UpperCamelCase_:							List[Any]		=  router_ignore_padding_tokens
							UpperCamelCase_:							List[Any]		=  relative_attention_num_buckets
							UpperCamelCase_:							Tuple		=  relative_attention_max_distance

							UpperCamelCase_:							int		=  dropout_rate
							UpperCamelCase_:							str		=  layer_norm_epsilon
							UpperCamelCase_:							Union[str, Any]		=  initializer_factor
							UpperCamelCase_:							Tuple		=  feed_forward_proj
							UpperCamelCase_:							Tuple		=  use_cache
							UpperCamelCase_:							Union[str, Any]		=  add_router_probs

							UpperCamelCase_:							Any		=  router_z_loss_coef
							UpperCamelCase_:							Dict		=  router_aux_loss_coef

							UpperCamelCase_:							Dict		=  self.feed_forward_proj.split("""-""" )
							UpperCamelCase_:							List[str]		=  act_info[-1]
							UpperCamelCase_:							Union[str, Any]		=  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_:							Dict		=  """gelu_new"""

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








def     A__   (  lowerCamelCase  )					->		list:
				return [
				    txt[:a] + txt[a].upper() + txt[a + 1 :]
				    for a in range(len(lowerCamelCase  )  )
				    if txt[a].isalpha()
				]


if __name__ == "__main__":
							__import__("""doctest""").testmod()






