



import numpy as np
import skfuzzy as fuzz

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

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





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
   from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase         =			get_tests_dir('''fixtures/test_sentencepiece.model''')

__lowerCamelCase         =			{'''target_lang''': '''fi''', '''source_lang''': '''en'''}
__lowerCamelCase         =			'''>>zh<<'''
__lowerCamelCase         =			'''Helsinki-NLP/'''

if is_torch_available():
   __lowerCamelCase         =			'''pt'''
elif is_tf_available():
   __lowerCamelCase         =			'''tf'''
else:
   __lowerCamelCase         =			'''jax'''


@require_sentencepiece
class   snake_case_ (lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				MarianTokenizer
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				True



       def  A_   (     self):

             """simple docstring"""





             super().setUp()
             UpperCAmelCase_  :	Union[str, Any]				      =		["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
             UpperCAmelCase_  :	Union[str, Any]				      =		dict(zip(lowercase     ,range(len(lowercase))))
             UpperCAmelCase_  :	int				      =		Path(self.tmpdirname)
             save_json(lowercase     ,save_dir / VOCAB_FILES_NAMES["vocab"])
             save_json(lowercase     ,save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"])
             if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
                   copyfile(lowercase     ,save_dir / VOCAB_FILES_NAMES["source_spm"])
                   copyfile(lowercase     ,save_dir / VOCAB_FILES_NAMES["target_spm"])

             UpperCAmelCase_  :	str				      =		MarianTokenizer.from_pretrained(self.tmpdirname)
             tokenizer.save_pretrained(self.tmpdirname)



       def  A_   (     self     ,**lowercase):

             """simple docstring"""





             return MarianTokenizer.from_pretrained(self.tmpdirname     ,**lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             return (
                 "This is a test",
                 "This is a test",
             )



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		"</s>"
             UpperCAmelCase_  :	List[str]				      =		0

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase)     ,lowercase)
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase)     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		list(self.get_tokenizer().get_vocab().keys())

             self.assertEqual(vocab_keys[0]     ,"</s>")
             self.assertEqual(vocab_keys[1]     ,"<unk>")
             self.assertEqual(vocab_keys[-1]     ,"<pad>")
             self.assertEqual(len(lowercase)     ,9)



       def  A_   (     self):

             """simple docstring"""





             self.assertEqual(self.get_tokenizer().vocab_size     ,9)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de""")
             UpperCAmelCase_  :	Optional[int]				      =		en_de_tokenizer(["I am a small frog"]     ,return_tensors=lowercase)
             self.assertIsInstance(lowercase     ,lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		[38, 121, 14, 697, 38848, 0]
             self.assertListEqual(lowercase     ,batch.input_ids[0])

             UpperCAmelCase_  :	Optional[int]				      =		tempfile.mkdtemp()
             en_de_tokenizer.save_pretrained(lowercase)
             UpperCAmelCase_  :	Any				      =		[x.name for x in Path(lowercase).glob("*")]
             self.assertIn("source.spm"     ,lowercase)
             MarianTokenizer.from_pretrained(lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		self.get_tokenizer()

             UpperCAmelCase_  :	List[Any]				      =		tok(
                 ["I am a small frog" * 1000, "I am a small frog"]     ,padding=lowercase     ,truncation=lowercase     ,return_tensors=lowercase)
             self.assertIsInstance(lowercase     ,lowercase)
             self.assertEqual(batch.input_ids.shape     ,(2, 512))



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		self.get_tokenizer()
             UpperCAmelCase_  :	Optional[int]				      =		tok(["I am a tiny frog", "I am a small frog"]     ,padding=lowercase     ,return_tensors=lowercase)
             self.assertIsInstance(lowercase     ,lowercase)
             self.assertEqual(batch_smaller.input_ids.shape     ,(2, 10))



       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		{"input_ids": [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
             # fmt: on

             self.tokenizer_integration_test_util(
                 expected_encoding=lowercase     ,model_name="Helsinki-NLP/opus-mt-en-de"     ,revision="1a8c2263da11e68e50938f97e10cd57820bd504c"     ,decode_kwargs={"use_source_tokenizer": True}     ,)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs")

             UpperCAmelCase_  :	Any				      =		"Tämä on testi"
             UpperCAmelCase_  :	List[str]				      =		"This is a test"

             UpperCAmelCase_  :	int				      =		[76, 7, 2047, 2]
             UpperCAmelCase_  :	Any				      =		[69, 12, 11, 940, 2]

             UpperCAmelCase_  :	Any				      =		tokenizer(lowercase).input_ids
             self.assertListEqual(lowercase     ,lowercase)

             UpperCAmelCase_  :	Any				      =		tokenizer(text_target=lowercase).input_ids
             self.assertListEqual(lowercase     ,lowercase)

             UpperCAmelCase_  :	List[Any]				      =		tokenizer.decode(lowercase     ,skip_special_tokens=lowercase)
             self.assertEqual(lowercase     ,lowercase)
