






"""simple docstring"""


import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 						snake_case  (  UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					BioGptTokenizer
    __magic_name__   					=					False





    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      a					:      Dict    =       [
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          'w</w>',
          'r</w>',
          't</w>',
          'lo',
          'low',
          'er</w>',
          'low</w>',
          'lowest</w>',
          'newer</w>',
          'wider</w>',
          '<unk>',
      ]
      a					:      int    =       dict(zip(A    ,		range(len(A      )      )      )      )
      a					:      int    =       ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

      a					:      str    =       os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['vocab_file']      )
      a					:      Optional[int]    =       os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['merges_file']      )
      with open(self.vocab_file    ,		'w'      ) as fp:
        fp.write(json.dumps(A      )      )
      with open(self.merges_file    ,		'w'      ) as fp:
        fp.write('\n'.join(A      )      )





    def     lowerCamelCase__				(							self     : int    ,		A     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Optional[int]    =       'lower newer'
      a					:      List[Any]    =       'lower newer'
      return input_text, output_text





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      List[Any]    =       BioGptTokenizer(self.vocab_file    ,		self.merges_file      )

      a					:      int    =       'lower'
      a					:      Union[str, Any]    =       ['low', 'er</w>']
      a					:      List[str]    =       tokenizer.tokenize(A      )
      self.assertListEqual(A    ,		A      )

      a					:      List[str]    =       tokens + ['<unk>']
      a					:      Dict    =       [1_4, 1_5, 2_0]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A      )    ,		A      )





    @slow
    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       BioGptTokenizer.from_pretrained('microsoft/biogpt'      )

      a					:      Any    =       tokenizer.encode('sequence builders'    ,		add_special_tokens=A      )
      a					:      List[str]    =       tokenizer.encode('multi-sequence build'    ,		add_special_tokens=A      )

      a					:      List[str]    =       tokenizer.build_inputs_with_special_tokens(A      )
      a					:      str    =       tokenizer.build_inputs_with_special_tokens(A    ,		A      )

      self.assertTrue(encoded_sentence == [2] + text      )
      self.assertTrue(encoded_pair == [2] + text + [2] + text_a      )












"""simple docstring"""


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

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




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

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

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

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

  a					:      Tuple    =       []

  for value in value_array:
    a					:      List[Any]    =       euclidean(A_     ,      dataset[0]				)
    a					:      int    =       dataset[0].tolist()

    for dataset_value in dataset[1:]:
      a					:      Optional[int]    =       euclidean(A_     ,      A_				)

      if dist > temp_dist:
        a					:      List[str]    =       temp_dist
        a					:      int    =       dataset_value.tolist()

    answer.append([vector, dist]				)

  return answer




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()




