


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def    a__    (		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    a__    (		snake_case__					:							np.ndarray						,   snake_case__					:							np.ndarray     ):

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

  _UpperCAmelCase		:							List[str]							=     []

  for value in value_array:
    _UpperCAmelCase		:							Tuple							=     euclidean(snake_case__						,   dataset[0]     )
    _UpperCAmelCase		:							Optional[Any]							=     dataset[0].tolist()

    for dataset_value in dataset[1:]:
      _UpperCAmelCase		:							Tuple							=     euclidean(snake_case__						,   snake_case__     )

      if dist > temp_dist:
        _UpperCAmelCase		:							Optional[int]							=     temp_dist
        _UpperCAmelCase		:							int							=     dataset_value.tolist()

    answer.append([vector, dist]     )

  return answer




def    a__    (		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()









from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''ernie_m'''
     __SCREAMING_SNAKE_CASE     					=    {"dropout": "classifier_dropout", "num_classes": "num_labels"}


     def __init__(  self							,					A_ = 25_00_02							,					A_ = 7_68							,					A_ = 12							,					A_ = 12							,					A_ = 30_72							,					A_ = "gelu"							,					A_ = 0.1							,					A_ = 0.1							,					A_ = 5_14							,					A_ = 0.0_2							,					A_ = 1							,					A_ = 1e-05							,					A_=None							,					A_=False							,					A_=0.0							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					**A_    )
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							List[str]							=     hidden_size
       _UpperCAmelCase		:							Optional[int]							=     num_hidden_layers
       _UpperCAmelCase		:							Tuple							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     intermediate_size
       _UpperCAmelCase		:							List[Any]							=     hidden_act
       _UpperCAmelCase		:							Any							=     hidden_dropout_prob
       _UpperCAmelCase		:							Any							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Tuple							=     max_position_embeddings
       _UpperCAmelCase		:							Dict							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							str							=     classifier_dropout
       _UpperCAmelCase		:							Any							=     is_decoder
       _UpperCAmelCase		:							Union[str, Any]							=     act_dropout





