





import numpy as np
import skfuzzy as fuzz

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

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






import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class   UpperCamelCase_       (     UpperCamelCase__				,  unittest.TestCase    ):
							lowerCamelCase_           =					OpenAIGPTTokenizer
							lowerCamelCase_           =					OpenAIGPTTokenizerFast
							lowerCamelCase_           =					True
							lowerCamelCase_           =					False


							def      _snake_case  (							self    :Optional[Any]		)   ->   Dict:



									"""simple docstring"""



									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									SCREAMING_SNAKE_CASE__			=      [
									    """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>""",
									]
									SCREAMING_SNAKE_CASE__			=      dict(zip(__A							,				range(len(__A		)		)		)		)
									SCREAMING_SNAKE_CASE__			=      ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

									SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				VOCAB_FILES_NAMES["""vocab_file"""]		)
									SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				VOCAB_FILES_NAMES["""merges_file"""]		)
									with open(self.vocab_file							,				"""w"""		) as fp:
											fp.write(json.dumps(__A		)		)
									with open(self.merges_file							,				"""w"""		) as fp:
											fp.write("""\n""".join(__A		)		)


							def      _snake_case  (							self    :Union[str, Any]							,				__A    :str		)   ->   List[Any]:



									"""simple docstring"""



									return "lower newer", "lower newer"


							def      _snake_case  (							self    :Optional[Any]		)   ->   Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      OpenAIGPTTokenizer(self.vocab_file							,				self.merges_file		)

									SCREAMING_SNAKE_CASE__			=      """lower"""
									SCREAMING_SNAKE_CASE__			=      ["""low""", """er</w>"""]
									SCREAMING_SNAKE_CASE__			=      tokenizer.tokenize(__A		)
									self.assertListEqual(__A							,				__A		)

									SCREAMING_SNAKE_CASE__			=      tokens + ["""<unk>"""]
									SCREAMING_SNAKE_CASE__			=      [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__A		)							,				__A		)


							def      _snake_case  (							self    :Optional[Any]							,				__A    :Optional[Any]=15		)   ->   Any:



									"""simple docstring"""



									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
													SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(__A							,				**__A		)

													# Simple input
													SCREAMING_SNAKE_CASE__			=      """This is a simple input"""
													SCREAMING_SNAKE_CASE__			=      ["""This is a simple input 1""", """This is a simple input 2"""]
													SCREAMING_SNAKE_CASE__			=      ("""This is a simple input""", """This is a pair""")
													SCREAMING_SNAKE_CASE__			=      [
													    ("""This is a simple input 1""", """This is a simple input 2"""),
													    ("""This is a simple pair 1""", """This is a simple pair 2"""),
													]

													# Simple input tests
													self.assertRaises(__A							,				tokenizer_r.encode							,				__A							,				max_length=__A							,				padding="""max_length"""		)

													# Simple input
													self.assertRaises(__A							,				tokenizer_r.encode_plus							,				__A							,				max_length=__A							,				padding="""max_length"""		)

													# Simple input
													self.assertRaises(
													    __A							,				tokenizer_r.batch_encode_plus							,				__A							,				max_length=__A							,				padding="""max_length"""							,				)

													# Pair input
													self.assertRaises(__A							,				tokenizer_r.encode							,				__A							,				max_length=__A							,				padding="""max_length"""		)

													# Pair input
													self.assertRaises(__A							,				tokenizer_r.encode_plus							,				__A							,				max_length=__A							,				padding="""max_length"""		)

													# Pair input
													self.assertRaises(
													    __A							,				tokenizer_r.batch_encode_plus							,				__A							,				max_length=__A							,				padding="""max_length"""							,				)


							def      _snake_case  (							self    :Dict		)   ->   List[Any]:



									"""simple docstring"""



									pass




@require_ftfy
@require_spacy
@require_tokenizers
class   UpperCamelCase_       (     UpperCamelCase__    ):
							pass