






"""simple docstring"""


import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






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





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      a					:      Optional[int]    =       ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
      a					:      Optional[int]    =       dict(zip(A    ,		range(len(A      )      )      )      )
      a					:      Optional[Any]    =       ['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
      a					:      List[str]    =       {'unk_token': '<unk>'}

      a					:      Tuple    =       os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['vocab_file']      )
      a					:      str    =       os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['merges_file']      )
      with open(self.vocab_file    ,		'w'    ,		encoding='utf-8'      ) as fp:
        fp.write(json.dumps(A      ) + '\n'      )
      with open(self.merges_file    ,		'w'    ,		encoding='utf-8'      ) as fp:
        fp.write('\n'.join(A      )      )





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


      '''simple docstring'''




      kwargs.update(self.special_tokens_map      )
      return CTRLTokenizer.from_pretrained(self.tmpdirname    ,		**A      )





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : str      ):


      '''simple docstring'''




      a					:      Tuple    =       'adapt react readapt apt'
      a					:      Dict    =       'adapt react readapt apt'
      return input_text, output_text





    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       CTRLTokenizer(self.vocab_file    ,		self.merges_file    ,		**self.special_tokens_map      )
      a					:      List[Any]    =       'adapt react readapt apt'
      a					:      List[str]    =       'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
      a					:      Any    =       tokenizer.tokenize(A      )
      self.assertListEqual(A    ,		A      )

      a					:      Optional[Any]    =       tokens + [tokenizer.unk_token]

      a					:      Tuple    =       [0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A      )    ,		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()




