



"""simple docstring"""
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						return math.sqrt(sum(pow(a - b,					2	) for a, b in zip(_lowercase,					_lowercase	)	)	)
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						if dataset.ndim != value_array.ndim:
												snake_case_ :Tuple						=			(
												    """Wrong input data's dimensions... """
												    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
												)
												raise ValueError(_lowercase	)

						try:
												if dataset.shape[1] != value_array.shape[1]:
																		snake_case_ :Tuple						=			(
																		    """Wrong input data's shape... """
																		    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
																		)
																		raise ValueError(_lowercase	)
						except IndexError:
												if dataset.ndim != value_array.ndim:
																		raise TypeError("""Wrong shape"""	)

						if dataset.dtype != value_array.dtype:
												snake_case_ :Any						=			(
												    """Input data have different datatype... """
												    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
												)
												raise TypeError(_lowercase	)

						snake_case_ :List[Any]						=			[]

						for value in value_array:
												snake_case_ :Union[str, Any]						=			euclidean(_lowercase,					dataset[0]	)
												snake_case_ :List[Any]						=			dataset[0].tolist()

												for dataset_value in dataset[1:]:
																		snake_case_ :Dict						=			euclidean(_lowercase,					_lowercase	)

																		if dist > temp_dist:
																								snake_case_ :Any						=			temp_dist
																								snake_case_ :Tuple						=			dataset_value.tolist()

												answer.append([vector, dist]	)

						return answer
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						return np.dot(_lowercase,					_lowercase	) / (norm(_lowercase	) * norm(_lowercase	))


if __name__ == "__main__":
						import doctest

						doctest.testmod()



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


snake_case_			  = logging.get_logger(__name__)

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


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       int														=     """switch_transformers"""
    __lowerCamelCase						:       Optional[Any]														=     ["""past_key_values"""]
    __lowerCamelCase						:       Any														=     {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





    def __init__(      self						,   a=3_2128						,   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=0.001						,   a=0.001						,   a=1.0						,   a="relu"						,   a=True						,   a=False						,   a=True						,   a=0						,   a=1						,   **a						,   ):
         lowercase__    :   Optional[int]      =       vocab_size
         lowercase__    :   List[Any]      =       d_model
         lowercase__    :   List[Any]      =       d_kv
         lowercase__    :   Any      =       d_ff

         lowercase__    :   Optional[int]      =       num_sparse_encoder_layers

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

         lowercase__    :   List[Any]      =       num_heads
         lowercase__    :   Union[str, Any]      =       num_experts
         lowercase__    :   str      =       expert_capacity
         lowercase__    :   List[Any]      =       router_bias
         lowercase__    :   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}""")
         lowercase__    :   str      =       router_dtype

         lowercase__    :   Optional[int]      =       router_ignore_padding_tokens
         lowercase__    :   int      =       relative_attention_num_buckets
         lowercase__    :   Optional[Any]      =       relative_attention_max_distance

         lowercase__    :   List[str]      =       dropout_rate
         lowercase__    :   str      =       layer_norm_epsilon
         lowercase__    :   int      =       initializer_factor
         lowercase__    :   int      =       feed_forward_proj
         lowercase__    :   Dict      =       use_cache
         lowercase__    :   int      =       add_router_probs

         lowercase__    :   int      =       router_z_loss_coef
         lowercase__    :   List[Any]      =       router_aux_loss_coef

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

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

