


'''simple docstring'''



import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 							__UpperCamelCase    (		lowerCamelCase_		,				unittest.TestCase ):
	A_           =   GPTSanJapaneseTokenizer
	A_           =   False
	A_           =   {"""do_clean_text""": False, """add_prefix_space""": False}




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().setUp()

						# fmt: off
						__a      :	List[str]    =  ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
						# fmt: on
						__a      :	Any    =  {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
						__a      :	Optional[Any]    =  {'unk_token': '<unk>'}

						__a      :	List[str]    =  os.path.join(self.tmpdirname						,    VOCAB_FILES_NAMES['vocab_file']							)
						__a      :	str    =  os.path.join(self.tmpdirname						,    VOCAB_FILES_NAMES['emoji_file']							)
						with open(self.vocab_file						,    'w'						,    encoding='utf-8'							) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)
						with open(self.emoji_file						,    'w'							) as emoji_writer:
											emoji_writer.write(json.dumps(_UpperCAmelCase							)							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						kwargs.update(self.special_tokens_map							)
						return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname						,    **_UpperCAmelCase							)




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	Any    =  'こんにちは、世界。 \nこんばんは、㔺界。😀'
						__a      :	Union[str, Any]    =  'こんにちは、世界。 \nこんばんは、世界。😀'
						return input_text, output_text




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a   ,    __a      :	List[str]    =  self.get_input_output_texts(_UpperCAmelCase							)
						__a      :	int    =  tokenizer.encode(_UpperCAmelCase						,    add_special_tokens=_UpperCAmelCase							)
						__a      :	List[str]    =  tokenizer.decode(_UpperCAmelCase						,    clean_up_tokenization_spaces=_UpperCAmelCase							)
						return text, ids




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass  # TODO add if relevant




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass  # TODO add if relevant




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass  # TODO add if relevant




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  self.get_tokenizer()

						# Testing tokenization
						__a      :	int    =  'こんにちは、世界。　こんばんは、㔺界。'
						__a      :	Union[str, Any]    =  ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
						__a      :	str    =  tokenizer.tokenize(_UpperCAmelCase							)
						self.assertListEqual(_UpperCAmelCase						,    _UpperCAmelCase							)

						# Testing conversion to ids without special tokens
						__a      :	List[Any]    =  [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
						__a      :	List[Any]    =  tokenizer.convert_tokens_to_ids(_UpperCAmelCase							)
						self.assertListEqual(_UpperCAmelCase						,    _UpperCAmelCase							)

						# Testing conversion to ids with special tokens
						__a      :	int    =  tokens + [tokenizer.unk_token]
						__a      :	Tuple    =  [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
						__a      :	Dict    =  tokenizer.convert_tokens_to_ids(_UpperCAmelCase							)
						self.assertListEqual(_UpperCAmelCase						,    _UpperCAmelCase							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Dict    =  self.get_tokenizer()

						# Testing tokenization
						__a      :	Optional[Any]    =  'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
						__a      :	Tuple    =  'こんにちは、、、、世界。こんばんは、、、、世界。'
						__a      :	Union[str, Any]    =  tokenizer.encode(_UpperCAmelCase							)
						__a      :	Optional[Any]    =  tokenizer.decode(_UpperCAmelCase							)
						self.assertEqual(_UpperCAmelCase						,    _UpperCAmelCase							)




	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'							)

						# Testing tokenization
						__a      :	int    =  'こんにちは、世界。'
						__a      :	str    =  'こんばんは、㔺界。😀'
						__a      :	Optional[int]    =  'こんにちは、世界。こんばんは、世界。😀'
						__a      :	List[Any]    =  tokenizer.encode(prefix_text + input_text							)
						__a      :	Union[str, Any]    =  tokenizer.encode(''						,    prefix_text=prefix_text + input_text							)
						__a      :	Tuple    =  tokenizer.encode(_UpperCAmelCase						,    prefix_text=_UpperCAmelCase							)
						__a      :	Dict    =  tokenizer.decode(_UpperCAmelCase							)
						__a      :	List[str]    =  tokenizer.decode(_UpperCAmelCase							)
						__a      :	Dict    =  tokenizer.decode(_UpperCAmelCase							)
						self.assertEqual(_UpperCAmelCase						,    _UpperCAmelCase							)
						self.assertEqual(_UpperCAmelCase						,    _UpperCAmelCase							)
						self.assertEqual(_UpperCAmelCase						,    _UpperCAmelCase							)




	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'							)

						# Testing tokenization
						__a      :	Optional[Any]    =  'こんにちは、世界。'
						__a      :	Optional[Any]    =  'こんばんは、㔺界。😀'

						__a      :	str    =  len(tokenizer.encode(_UpperCAmelCase							)							) - 2
						__a      :	Optional[Any]    =  len(tokenizer.encode(_UpperCAmelCase							)							) - 2

						__a      :	int    =  [1] + [0] * (len_prefix + len_text + 1)
						__a      :	Union[str, Any]    =  [1] * (len_prefix + len_text + 1) + [0]
						__a      :	Optional[Any]    =  [1] + [1] * (len_prefix) + [0] * (len_text + 1)

						__a      :	Any    =  tokenizer(prefix_text + input_text							).token_type_ids
						__a      :	List[Any]    =  tokenizer(''						,    prefix_text=prefix_text + input_text							).token_type_ids
						__a      :	str    =  tokenizer(_UpperCAmelCase						,    prefix_text=_UpperCAmelCase							).token_type_ids
						self.assertListEqual(_UpperCAmelCase						,    _UpperCAmelCase							)
						self.assertListEqual(_UpperCAmelCase						,    _UpperCAmelCase							)
						self.assertListEqual(_UpperCAmelCase						,    _UpperCAmelCase							)




	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'							)

						__a      :	Optional[int]    =  tokenizer.encode('あンいワ'							)
						__a      :	Tuple    =  tokenizer.encode(''						,    prefix_text='あンいワ'							)
						__a      :	List[str]    =  tokenizer.encode('いワ'						,    prefix_text='あン'							)

						self.assertEqual(tokenizer.decode(_UpperCAmelCase							)						,    tokenizer.decode(_UpperCAmelCase							)							)
						self.assertEqual(tokenizer.decode(_UpperCAmelCase							)						,    tokenizer.decode(_UpperCAmelCase							)							)
						self.assertNotEqual(_UpperCAmelCase						,    _UpperCAmelCase							)
						self.assertNotEqual(_UpperCAmelCase						,    _UpperCAmelCase							)
						self.assertEqual(x_token_a[1]						,    x_token_a[-1]							)  # SEG token
						self.assertEqual(x_token_a[1]						,    x_token_a[3]							)  # SEG token




	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'							)

						__a      :	Optional[Any]    =  [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
						__a      :	Optional[int]    =  tokenizer(_UpperCAmelCase						,    padding=_UpperCAmelCase							)
						__a      :	Optional[int]    =  tokenizer.batch_encode_plus(_UpperCAmelCase						,    padding=_UpperCAmelCase							)

						# fmt: off
						__a      :	Optional[Any]    =  [[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
						__a      :	Union[str, Any]    =  [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
						__a      :	List[str]    =  [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
						# fmt: on
						self.assertListEqual(x_token.input_ids						,    _UpperCAmelCase							)
						self.assertListEqual(x_token.token_type_ids						,    _UpperCAmelCase							)
						self.assertListEqual(x_token.attention_mask						,    _UpperCAmelCase							)
						self.assertListEqual(x_token_a.input_ids						,    _UpperCAmelCase							)
						self.assertListEqual(x_token_a.token_type_ids						,    _UpperCAmelCase							)
						self.assertListEqual(x_token_a.attention_mask						,    _UpperCAmelCase							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass








'''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()



