


'''simple docstring'''



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


__lowercase     :						List[str]	=     get_tests_dir('fixtures/test_sentencepiece_bpe.model')





class 							__UpperCamelCase    (		lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   BartphoTokenizer
	A_           =   False
	A_           =   True




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().setUp()

						__a      :	Dict    =  ['▁This', '▁is', '▁a', '▁t', 'est']
						__a      :	List[Any]    =  dict(zip(__a						,    range(len(__a							)							)							)							)
						__a      :	Union[str, Any]    =  {'unk_token': '<unk>'}

						__a      :	List[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"""							)

						__a      :	Union[str, Any]    =  BartphoTokenizer(__a						,    self.monolingual_vocab_file						,    **self.special_tokens_map							)
						tokenizer.save_pretrained(self.tmpdirname							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						kwargs.update(self.special_tokens_map							)
						return BartphoTokenizer.from_pretrained(self.tmpdirname						,    **__a							)




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	int    =  'This is a là test'
						__a      :	Dict    =  'This is a<unk><unk> test'
						return input_text, output_text





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  BartphoTokenizer(__a						,    self.monolingual_vocab_file						,    **self.special_tokens_map							)
						__a      :	int    =  'This is a là test'
						__a      :	Optional[Any]    =  '▁This ▁is ▁a ▁l à ▁t est'.split()
						__a      :	Any    =  tokenizer.tokenize(__a							)
						self.assertListEqual(__a						,    __a							)

						__a      :	Union[str, Any]    =  tokens + [tokenizer.unk_token]
						__a      :	List[str]    =  [4, 5, 6, 3, 3, 7, 8, 3]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__a							)						,    __a							)







'''simple docstring'''



import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
	# Create universe of discourse in Python using linspace ()
	__lowercase     :						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).
	__lowercase     :						Any	=     [0, 25, 50]
	__lowercase     :						int	=     [25, 50, 75]
	__lowercase     :						List[str]	=     fuzz.membership.trimf(X, abca)
	__lowercase     :						Any	=     fuzz.membership.trimf(X, abca)

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



