





'''simple docstring'''






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   : Union[str, Any]        =			[0, 25, 50]
		A   : Optional[int]        =			[25, 50, 75]
		A   : str        =			fuzz.membership.trimf(X, abca)
		A   : Any        =			fuzz.membership.trimf(X, abca)

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












'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


A   : Tuple             =			['bert-base-uncased', 'bert-base-cased']
A   : str             =			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():







		class 			lowerCamelCase					(						tf.keras.Model  ):


							def __init__(					self	:  Union[str, Any]       ,     __snake_case	:  List[Any]       ):





													'''simple docstring'''





													super().__init__()
													_snake_case:   List[Any]  					=		tokenizer
													_snake_case:   str  					=		AutoConfig.from_pretrained(__snake_case       )
													_snake_case:   List[Any]  					=		TFAutoModel.from_config(__snake_case       )




							def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     __snake_case	:  List[str]       ):





													'''simple docstring'''





													_snake_case:   Optional[int]  					=		self.tokenizer(__snake_case       )
													_snake_case:   Tuple  					=		self.bert(**__snake_case       )
													return out["pooler_output"]







@require_tf
@require_tensorflow_text
class 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											super().setUp()

											_snake_case:   List[Any]  					=		[
											    BertTokenizer.from_pretrained(__snake_case       ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
											]  # repeat for when fast_bert_tokenizer=false
											_snake_case:   List[Any]  					=		[TFBertTokenizer.from_pretrained(__snake_case       ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
											    TFBertTokenizer.from_pretrained(__snake_case       ,     use_fast_bert_tokenizer=__snake_case       )
											    for checkpoint in TOKENIZER_CHECKPOINTS
											]
											assert len(self.tokenizers       ) == len(self.tf_tokenizers       )

											_snake_case:   int  					=		[
											    'This is a straightforward English test sentence.',
											    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
											    'Now we\'re going to add some Chinese: 一 二 三 一二三',
											    'And some much more rare Chinese: 齉 堃 齉堃',
											    'Je vais aussi écrire en français pour tester les accents',
											    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
											]
											_snake_case:   str  					=		list(zip(self.test_sentences       ,     self.test_sentences[::-1]       )       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											for tokenizer, tf_tokenizer in zip(self.tokenizers       ,     self.tf_tokenizers       ):
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																							_snake_case:   List[Any]  					=		tokenizer(__snake_case       ,     return_tensors='tf'       ,     padding='longest'       )
																							_snake_case:   Optional[Any]  					=		tf_tokenizer(__snake_case       )

																							for key in python_outputs.keys():
																													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape       )       )
																													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]       ,     tf.intaa       ) == tf_outputs[key]       )       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   str  					=		tf_tokenizer(self.paired_sentences       )
																	_snake_case:   Any  					=		tf_tokenizer(
																	    text=[sentence[0] for sentence in self.paired_sentences]       ,     text_pair=[sentence[1] for sentence in self.paired_sentences]       ,     )
																	for key in merged_outputs.keys():
																							self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]       ,     tf.intaa       ) == separated_outputs[key]       )       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   List[str]  					=		tf.function(__snake_case       )
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																							_snake_case:   Union[str, Any]  					=		tf.constant(__snake_case       )
																							_snake_case:   Any  					=		compiled_tokenizer(__snake_case       )
																							_snake_case:   Union[str, Any]  					=		tf_tokenizer(__snake_case       )

																							for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]       )       )




					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   Optional[Any]  					=		ModelToSave(tokenizer=__snake_case       )
																	_snake_case:   Tuple  					=		tf.convert_to_tensor(self.test_sentences       )
																	_snake_case:   List[str]  					=		model(__snake_case       )  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																							_snake_case:   List[str]  					=		Path(__snake_case       ) / 'saved.model'
																							model.save(__snake_case       )
																							_snake_case:   int  					=		tf.keras.models.load_model(__snake_case       )
																	_snake_case:   int  					=		loaded_model(__snake_case       )
																	# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																	self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output       )       )       ,     1e-5       )





