
"""simple docstring"""





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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		'''mra'''




		def __init__(     self							,  lowerCAmelCase__=5_0_2_6_5							,  lowerCAmelCase__=7_6_8							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=3_0_7_2							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=5_1_2							,  lowerCAmelCase__=1							,  lowerCAmelCase__=0.02							,  lowerCAmelCase__=1E-5							,  lowerCAmelCase__="absolute"							,  lowerCAmelCase__=4							,  lowerCAmelCase__="full"							,  lowerCAmelCase__=0							,  lowerCAmelCase__=0							,  lowerCAmelCase__=1							,  lowerCAmelCase__=0							,  lowerCAmelCase__=2							,  **lowerCAmelCase__							,  ):
				super().__init__(pad_token_id=lowerCAmelCase__							,  bos_token_id=lowerCAmelCase__							,  eos_token_id=lowerCAmelCase__							,  **lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = vocab_size
				__SCREAMING_SNAKE_CASE            = max_position_embeddings
				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = intermediate_size
				__SCREAMING_SNAKE_CASE            = hidden_act
				__SCREAMING_SNAKE_CASE            = hidden_dropout_prob
				__SCREAMING_SNAKE_CASE            = attention_probs_dropout_prob
				__SCREAMING_SNAKE_CASE            = initializer_range
				__SCREAMING_SNAKE_CASE            = type_vocab_size
				__SCREAMING_SNAKE_CASE            = layer_norm_eps
				__SCREAMING_SNAKE_CASE            = position_embedding_type
				__SCREAMING_SNAKE_CASE            = block_per_row
				__SCREAMING_SNAKE_CASE            = approx_mode
				__SCREAMING_SNAKE_CASE            = initial_prior_first_n_blocks
				__SCREAMING_SNAKE_CASE            = initial_prior_diagonal_n_blocks








"""simple docstring"""





from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def    _snake_case  (  _snake_case :	np.ndarray       ,      _snake_case :	np.ndarray	):
      return math.sqrt(sum(pow(a - b       ,      2	) for a, b in zip(_snake_case       ,      _snake_case	)	)	)


def    _snake_case  (  _snake_case :	np.ndarray       ,      _snake_case :	np.ndarray	):

      if dataset.ndim != value_array.ndim:
            lowerCAmelCase    :		List[Any]				=      (
                '''Wrong input data\'s dimensions... '''
                f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
            )
            raise ValueError(_snake_case	)

      try:
            if dataset.shape[1] != value_array.shape[1]:
                  lowerCAmelCase    :		Dict				=      (
                      '''Wrong input data\'s shape... '''
                      f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
                  )
                  raise ValueError(_snake_case	)
      except IndexError:
            if dataset.ndim != value_array.ndim:
                  raise TypeError('''Wrong shape'''	)

      if dataset.dtype != value_array.dtype:
            lowerCAmelCase    :		Optional[Any]				=      (
                '''Input data have different datatype... '''
                f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
            )
            raise TypeError(_snake_case	)

      lowerCAmelCase    :		str				=      []

      for value in value_array:
            lowerCAmelCase    :		int				=      euclidean(_snake_case       ,      dataset[0]	)
            lowerCAmelCase    :		Union[str, Any]				=      dataset[0].tolist()

            for dataset_value in dataset[1:]:
                  lowerCAmelCase    :		Any				=      euclidean(_snake_case       ,      _snake_case	)

                  if dist > temp_dist:
                        lowerCAmelCase    :		List[Any]				=      temp_dist
                        lowerCAmelCase    :		Tuple				=      dataset_value.tolist()

            answer.append([vector, dist]	)

      return answer


def    _snake_case  (  _snake_case :	np.ndarray       ,      _snake_case :	np.ndarray	):
      return np.dot(_snake_case       ,      _snake_case	) / (norm(_snake_case	) * norm(_snake_case	))


if __name__ == "__main__":
 import doctest

 doctest.testmod()


