





import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_A :   Dict  			=  get_tests_dir('fixtures/test_sentencepiece_bpe.model')


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Optional[Any]			   =   BartphoTokenizer
       _UpperCAmelCase				:  Optional[Any]			   =   False
       _UpperCAmelCase				:  List[str]			   =   True







       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[Any]:
          super().setUp()

          lowerCamelCase__       :						Tuple        =							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
          lowerCamelCase__       :						Tuple        =							dict(zip(A		, range(len(A      )      )      )      )
          lowerCamelCase__       :						List[Any]        =							{'''unk_token''': '''<unk>'''}

          lowerCamelCase__       :						Union[str, Any]        =							os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['''monolingual_vocab_file''']      )
          with open(self.monolingual_vocab_file		, '''w'''		, encoding='''utf-8'''      ) as fp:
             for token in vocab_tokens:
                fp.write(F"{token} {vocab_tokens[token]}\n"      )

          lowerCamelCase__       :						List[str]        =							BartphoTokenizer(A		, self.monolingual_vocab_file		, **self.special_tokens_map      )
          tokenizer.save_pretrained(self.tmpdirname      )







       def 						__lowerCamelCase   (				self					:     str		, **A					:     Dict      )       ->int:
          kwargs.update(self.special_tokens_map      )
          return BartphoTokenizer.from_pretrained(self.tmpdirname		, **A      )







       def 						__lowerCamelCase   (				self					:     int		, A					:     Any      )       ->Optional[int]:
          lowerCamelCase__       :						Optional[Any]        =							'''This is a là test'''
          lowerCamelCase__       :						str        =							'''This is a<unk><unk> test'''
          return input_text, output_text







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->str:
          lowerCamelCase__       :						List[Any]        =							BartphoTokenizer(A		, self.monolingual_vocab_file		, **self.special_tokens_map      )
          lowerCamelCase__       :						List[Any]        =							'''This is a là test'''
          lowerCamelCase__       :						Dict        =							'''▁This ▁is ▁a ▁l à ▁t est'''.split()
          lowerCamelCase__       :						Dict        =							tokenizer.tokenize(A      )
          self.assertListEqual(A		, A      )

          lowerCamelCase__       :						Any        =							tokens + [tokenizer.unk_token]
          lowerCamelCase__       :						Optional[int]        =							[4, 5, 6, 3, 3, 7, 8, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A      )		, A      )










import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
       # Create universe of discourse in Python using linspace ()
       _A :   Optional[int]  			=  np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

       # Create two fuzzy sets by defining any membership function
       # (trapmf(), gbellmf(), gaussmf(), etc).
       _A :   Any  			=  [0, 25, 50]
       _A :   Dict  			=  [25, 50, 75]
       _A :   Any  			=  fuzz.membership.trimf(X, abca)
       _A :   List[Any]  			=  fuzz.membership.trimf(X, abca)

       # Compute the different operations using inbuilt functions.
       _A :   List[str]  			=  np.ones(75)
       _A :   Optional[int]  			=  np.zeros((75,))
       # 1. Union = max(µA(x), µB(x))
       _A :   Optional[Any]  			=  fuzz.fuzzy_or(X, young, X, middle_aged)[1]
       # 2. Intersection = min(µA(x), µB(x))
       _A :   Union[str, Any]  			=  fuzz.fuzzy_and(X, young, X, middle_aged)[1]
       # 3. Complement (A) = (1- min(µA(x))
       _A :   List[Any]  			=  fuzz.fuzzy_not(young)
       # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
       _A :   Tuple  			=  fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
       # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
       _A :   List[Any]  			=  young + middle_aged - (young * middle_aged)
       # 6. Algebraic Product = (µA(x) * µB(x))
       _A :   Optional[Any]  			=  young * middle_aged
       # 7. Bounded Sum = min[1,(µA(x), µB(x))]
       _A :   Dict  			=  fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
       # 8. Bounded difference = min[0,(µA(x), µB(x))]
       _A :   List[str]  			=  fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

       # max-min composition
       # max-product composition

       # Plot each set A, set B and each operation result using plot() and subplot().
       from matplotlib import pyplot as plt

       plt.figure()

       plt.subplot(4, 3, 1)
       plt.plot(X, young)
       plt.title('Young')
       plt.grid(True)

       plt.subplot(4, 3, 2)
       plt.plot(X, middle_aged)
       plt.title('Middle aged')
       plt.grid(True)

       plt.subplot(4, 3, 3)
       plt.plot(X, union)
       plt.title('union')
       plt.grid(True)

       plt.subplot(4, 3, 4)
       plt.plot(X, intersection)
       plt.title('intersection')
       plt.grid(True)

       plt.subplot(4, 3, 5)
       plt.plot(X, complement_a)
       plt.title('complement_a')
       plt.grid(True)

       plt.subplot(4, 3, 6)
       plt.plot(X, difference)
       plt.title('difference a/b')
       plt.grid(True)

       plt.subplot(4, 3, 7)
       plt.plot(X, alg_sum)
       plt.title('alg_sum')
       plt.grid(True)

       plt.subplot(4, 3, 8)
       plt.plot(X, alg_product)
       plt.title('alg_product')
       plt.grid(True)

       plt.subplot(4, 3, 9)
       plt.plot(X, bdd_sum)
       plt.title('bdd_sum')
       plt.grid(True)

       plt.subplot(4, 3, 10)
       plt.plot(X, bdd_difference)
       plt.title('bdd_difference')
       plt.grid(True)

       plt.subplots_adjust(hspace=0.5)
       plt.show()



