import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class  __magic_name__  (		__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[int]  			=   XLMTokenizer
   lowerCAmelCase      :    List[Any]  			=   False



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    _UpperCamelCase:     Union[str, Any]							= [
        '''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>''',
    ]
    _UpperCamelCase:     str							= dict(zip(_lowercase		,  range(len(_lowercase							)							)							)							)
    _UpperCamelCase:     Optional[Any]							= ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

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



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= '''lower newer'''
    _UpperCamelCase:     Union[str, Any]							= '''lower newer'''
    return input_text, output_text



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= XLMTokenizer(self.vocab_file		,  self.merges_file							)

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

    _UpperCamelCase:     List[Any]							= tokens + ['''<unk>''']
    _UpperCamelCase:     List[str]							= [14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase							)		,  _lowercase							)



   @slow
   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''							)

    _UpperCamelCase:     Any							= tokenizer.encode('''sequence builders'''		,  add_special_tokens=_lowercase							)
    _UpperCamelCase:     Optional[Any]							= tokenizer.encode('''multi-sequence build'''		,  add_special_tokens=_lowercase							)

    _UpperCamelCase:     Optional[Any]							= tokenizer.build_inputs_with_special_tokens(_lowercase							)
    _UpperCamelCase:     Any							= tokenizer.build_inputs_with_special_tokens(_lowercase		,  _lowercase							)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize




def       lowerCAmelCase_     (		lowercase:							ndarray		)    ->				float:







 '''simple docstring'''
 return np.dot(lowercase  ,					lowercase		)




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  int		,  *,
       _lowercase					:  float = np.inf		,  _lowercase					:  str = "linear"		,  _lowercase					:  float = 0.0		,  ):



    """simple docstring"""



    _UpperCamelCase:     int							= regularization
    _UpperCamelCase:     Optional[int]							= gamma
    if kernel == "linear":
     _UpperCamelCase:     Optional[Any]							= self.__linear
    elif kernel == "rbf":
     if self.gamma == 0:
      raise ValueError('''rbf kernel requires gamma'''							)
     if not isinstance(self.gamma		,  (float, int)							):
      raise ValueError('''gamma must be float or int'''							)
     if not self.gamma > 0:
      raise ValueError('''gamma must be > 0'''							)
     _UpperCamelCase:     Optional[Any]							= self.__rbf
     # in the future, there could be a default value like in sklearn
     # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
     # previously it was 1/(n_features)
    else:
     _UpperCamelCase:     Optional[int]							= f"""Unknown kernel: {kernel}"""
     raise ValueError(_lowercase							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  ndarray		,  _lowercase					:  ndarray							):



    """simple docstring"""



    return np.dot(_lowercase		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  ndarray		,  _lowercase					:  ndarray							):



    """simple docstring"""



    return np.exp(-(self.gamma * norm_squared(vectora - vectora							))							)



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  list[ndarray]		,  _lowercase					:  ndarray							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= observations
    _UpperCamelCase:     Optional[int]							= classes

    # using Wolfe's Dual to calculate w.
    # Primal problem: minimize 1/2*norm_squared(w)
    #   constraint: yn(w . xn + b) >= 1
    #
    # With l a vector
    # Dual problem: maximize sum_n(ln) -
    #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
    #   constraint: self.C >= ln >= 0
    #           and sum_n(ln*yn) = 0
    # Then we get w using w = sum_n(ln*yn*xn)
    # At the end we can get b ~= mean(yn - w . xn)
    #
    # Since we use kernels, we only need l_star to calculate b
    # and to classify observations

    ((_UpperCamelCase)	,							):     Any							= np.shape(_lowercase							)

    def to_minimize(_lowercase					:  ndarray							) -> float:
     _UpperCamelCase:     Optional[int]							= 0
     ((_UpperCamelCase)	,							):     str							= np.shape(_lowercase							)
     for i in range(_lowercase							):
      for j in range(_lowercase							):
       s += (
           candidate[i]
           * candidate[j]
           * classes[i]
           * classes[j]
           * self.kernel(observations[i]		,  observations[j]							)
       )
     return 1 / 2 * s - sum(_lowercase							)

    _UpperCamelCase:     Optional[Any]							= LinearConstraint(_lowercase		,  0		,  0							)
    _UpperCamelCase:     Optional[int]							= Bounds(0		,  self.regularization							)

    _UpperCamelCase:     Dict							= minimize(
        _lowercase		,  np.ones(_lowercase							)		,  bounds=_lowercase		,  constraints=[ly_contraint]							).x
    _UpperCamelCase:     Union[str, Any]							= l_star

    # calculating mean offset of separation plane to points
    _UpperCamelCase:     List[str]							= 0
    for i in range(_lowercase							):
     for j in range(_lowercase							):
      s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
          observations[i]		,  observations[j]							)
    _UpperCamelCase:     str							= s / n



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  ndarray							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= sum(
        self.optimum[n]
        * self.classes[n]
        * self.kernel(self.observations[n]		,  _lowercase							)
        for n in range(len(self.classes							)							)							)
    return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
  import doctest

  doctest.testmod()