





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


A__:    int  	=	logging.get_logger(__name__)

A__:    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:			List[str]	,    __lowerCamelCase:			Dict=3_2128	,    __lowerCamelCase:			Optional[int]=768	,    __lowerCamelCase:			int=64	,    __lowerCamelCase:			List[Any]=2048	,    __lowerCamelCase:			List[Any]=64	,    __lowerCamelCase:			str=12	,    __lowerCamelCase:			Tuple=3	,    __lowerCamelCase:			Any=12	,    __lowerCamelCase:			List[str]=3	,    __lowerCamelCase:			Optional[Any]=12	,    __lowerCamelCase:			str=8	,    __lowerCamelCase:			Optional[Any]=False	,    __lowerCamelCase:			Union[str, Any]=0.01	,    __lowerCamelCase:			str="float32"	,    __lowerCamelCase:			Union[str, Any]=False	,    __lowerCamelCase:			Optional[int]=32	,    __lowerCamelCase:			Optional[int]=128	,    __lowerCamelCase:			Dict=0.1	,    __lowerCamelCase:			Union[str, Any]=1e-6	,    __lowerCamelCase:			Optional[Any]=0.001	,    __lowerCamelCase:			Optional[int]=0.001	,    __lowerCamelCase:			Optional[Any]=1.0	,    __lowerCamelCase:			Any="relu"	,    __lowerCamelCase:			Union[str, Any]=True	,    __lowerCamelCase:			Dict=False	,    __lowerCamelCase:			Tuple=True	,    __lowerCamelCase:			Tuple=0	,    __lowerCamelCase:			List[Any]=1	,    **__lowerCamelCase:			Optional[Any]	,    ):
          '''simple docstring'''


          UpperCamelCase__:	int					=     vocab_size
          UpperCamelCase__:	List[Any]					=     d_model
          UpperCamelCase__:	Tuple					=     d_kv
          UpperCamelCase__:	str					=     d_ff

          UpperCamelCase__:	Union[str, Any]					=     num_sparse_encoder_layers

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

          UpperCamelCase__:	Dict					=     num_heads
          UpperCamelCase__:	Union[str, Any]					=     num_experts
          UpperCamelCase__:	Tuple					=     expert_capacity
          UpperCamelCase__:	int					=     router_bias
          UpperCamelCase__:	List[Any]					=     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__:	Any					=     router_dtype

          UpperCamelCase__:	Tuple					=     router_ignore_padding_tokens
          UpperCamelCase__:	Union[str, Any]					=     relative_attention_num_buckets
          UpperCamelCase__:	str					=     relative_attention_max_distance

          UpperCamelCase__:	str					=     dropout_rate
          UpperCamelCase__:	Tuple					=     layer_norm_epsilon
          UpperCamelCase__:	int					=     initializer_factor
          UpperCamelCase__:	Dict					=     feed_forward_proj
          UpperCamelCase__:	str					=     use_cache
          UpperCamelCase__:	str					=     add_router_probs

          UpperCamelCase__:	List[Any]					=     router_z_loss_coef
          UpperCamelCase__:	List[Any]					=     router_aux_loss_coef

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

          if len(__lowerCamelCase					) > 1 and act_info[0] != "gated" or len(__lowerCamelCase					) > 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__:	Optional[Any]					=     "gelu_new"

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








import math



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	Dict					=     len(A_)
    UpperCamelCase__:	Optional[Any]					=     int(math.floor(math.sqrt(A_)))
    UpperCamelCase__:	Union[str, Any]					=     0
    while arr[min(A_    ,A_) - 1] < x:
        UpperCamelCase__:	Any					=     step
        step += int(math.floor(math.sqrt(A_)))
        if prev >= n:
            return -1

    while arr[prev] < x:
        UpperCamelCase__:	Dict					=     prev + 1
        if prev == min(A_    ,A_):
            return -1
    if arr[prev] == x:
        return prev
    return -1


if __name__ == "__main__":
   A__:    Tuple  	=	input('''Enter numbers separated by a comma:\n''').strip()
   A__:    List[Any]  	=	[int(item) for item in user_input.split(''',''')]
   A__:    int  	=	int(input('''Enter the number to be searched:\n'''))
   A__:    Any  	=	jump_search(arr, x)
   if res == -1:
      print('''Number not found!''')
   else:
      print(f"Number {x} is at index {res}")

