


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


A      = logging.get_logger(__name__)

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


class     lowercase__  (					__a		):
  A__=  "switch_transformers"
  A__=  ["past_key_values"]
  A__=  {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






  def __init__(					self      :					Optional[int]		,						_lowercase      :					Any=3_21_28		,						_lowercase      :					str=7_68		,						_lowercase      :					Tuple=64		,						_lowercase      :					int=20_48		,						_lowercase      :					Any=64		,						_lowercase      :					Optional[Any]=12		,						_lowercase      :					Any=3		,						_lowercase      :					List[str]=12		,						_lowercase      :					int=3		,						_lowercase      :					Dict=12		,						_lowercase      :					Optional[int]=8		,						_lowercase      :					List[Any]=False		,						_lowercase      :					Any=0.0_1		,						_lowercase      :					List[Any]="float32"		,						_lowercase      :					Tuple=False		,						_lowercase      :					Union[str, Any]=32		,						_lowercase      :					Dict=1_28		,						_lowercase      :					Dict=0.1		,						_lowercase      :					Any=1E-6		,						_lowercase      :					Optional[int]=0.0_0_1		,						_lowercase      :					Dict=0.0_0_1		,						_lowercase      :					Dict=1.0		,						_lowercase      :					str="relu"		,						_lowercase      :					Tuple=True		,						_lowercase      :					int=False		,						_lowercase      :					Dict=True		,						_lowercase      :					Tuple=0		,						_lowercase      :					str=1		,						**_lowercase      :					List[str]		,						):




         """simple docstring"""







         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(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__										=     "gelu_new"

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









from __future__ import annotations

import math





def __UpperCAmelCase       (		__A ,						__A			)						->					float:



       '''simple docstring'''





       UpperCAmelCase__										=     u
       for i in range(1 ,						__A			):
              UpperCAmelCase__										=     temp * (u - i)
       return temp





def __UpperCAmelCase       (		)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     int(input("enter the numbers of values: "			)			)
       UpperCAmelCase__										=     []
       for _ in range(__A			):
              y.append([]			)
       for i in range(__A			):
              for j in range(__A			):
                     y[i].append(__A			)
                     UpperCAmelCase__										=     0

       print("enter the values of parameters in a list: "			)
       UpperCAmelCase__										=     list(map(__A ,						input().split()			)			)

       print("enter the values of corresponding parameters: "			)
       for i in range(__A			):
              UpperCAmelCase__										=     float(input()			)

       UpperCAmelCase__										=     int(input("enter the value to interpolate: "			)			)
       UpperCAmelCase__										=     (value - x[0]) / (x[1] - x[0])

       # for calculating forward difference table

       for i in range(1 ,						__A			):
              for j in range(n - i			):
                     UpperCAmelCase__										=     y[j + 1][i - 1] - y[j][i - 1]

       UpperCAmelCase__										=     y[0][0]
       for i in range(1 ,						__A			):
              summ += (ucal(__A ,						__A			) * y[0][i]) / math.factorial(__A			)

       print(F"""the value at {value} is {summ}"""			)


if __name__ == "__main__":
    main()





