


'''simple docstring'''



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

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
	from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
	__lowercase     :						Optional[int]	=     get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
	from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__lowercase     :						Any	=     12_80_22
__lowercase     :						List[str]	=     12_80_28





@require_sentencepiece
class 							__UpperCamelCase    (		lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   MaMaaaTokenizer
	A_           =   False
	A_           =   False
	A_           =   True




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().setUp()

						__a      :	Any    =  ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
						__a      :	List[Any]    =  dict(zip(__a						,    range(len(__a							)							)							)							)
						__a      :	List[Any]    =  Path(self.tmpdirname							)
						save_json(__a						,    save_dir / VOCAB_FILES_NAMES['vocab_file']							)
						if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
											copyfile(__a						,    save_dir / VOCAB_FILES_NAMES['spm_file']							)

						__a      :	int    =  MaMaaaTokenizer.from_pretrained(self.tmpdirname							)
						tokenizer.save_pretrained(self.tmpdirname							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return MaMaaaTokenizer.from_pretrained(self.tmpdirname						,    **__a							)




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  '</s>'
						__a      :	List[str]    =  0

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.get_tokenizer()
						__a      :	Any    =  list(tokenizer.get_vocab().keys()							)

						self.assertEqual(vocab_keys[0]						,    '</s>'							)
						self.assertEqual(vocab_keys[1]						,    '<unk>'							)
						self.assertEqual(vocab_keys[-1]						,    '<s>'							)
						self.assertEqual(len(__a							)						,    tokenizer.vocab_size + len(tokenizer.get_added_vocab()							)							)




	@unittest.skip('Skip this test while all models are still to be uploaded.'							)
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	List[str]    =  tokenizer.tokenize('This is a test'							)
						self.assertListEqual(__a						,    ['▁This', '▁is', '▁a', '▁t', 'est']							)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(__a							)						,    [2, 3, 4, 5, 6]						,    )

						__a      :	Union[str, Any]    =  tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]							)
						self.assertListEqual(__a						,    ['▁This', '▁is', '▁a', '▁t', 'est']							)

						__a      :	Optional[int]    =  tokenizer.convert_tokens_to_string(__a							)
						self.assertEqual(__a						,    'This is a test'							)





	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  {'input_ids': [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 397, 5, 2, 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], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 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]], '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, 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], [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, 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=__a						,    model_name='facebook/m2m100_418M'						,    revision='c168bae485c864188cf9aa0e4108b0b6934dc91e'						,    )





@require_torch
@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase    (		unittest.TestCase ):
	A_           =   "facebook/m2m100_418M"
	A_           =   [
	    "In my opinion, there are two levels of response from the French government.",
	    "NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
	]
	A_           =   [
	    "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
	    "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
	]

	# fmt: off
	A_           =   [EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2]




	@classmethod
	def 				__UpperCAmelCase							(       cls							):

						'''simple docstring'''





						__a      :	MaMaaaTokenizer    =  MaMaaaTokenizer.from_pretrained(
						    cls.checkpoint_name						,    src_lang='en'						,    tgt_lang='fr'							)
						__a      :	str    =  1
						return cls




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						self.assertEqual(self.tokenizer.get_lang_id('ar'							)						,    12_8006							)
						self.assertEqual(self.tokenizer.get_lang_id('en'							)						,    12_8022							)
						self.assertEqual(self.tokenizer.get_lang_id('ro'							)						,    12_8076							)
						self.assertEqual(self.tokenizer.get_lang_id('mr'							)						,    12_8063							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  self.tokenizer.get_vocab()
						self.assertEqual(len(__a							)						,    self.tokenizer.vocab_size							)
						self.assertEqual(vocab['<unk>']						,    3							)
						self.assertIn(self.tokenizer.get_lang_token('en'							)						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  'en'
						__a      :	Union[str, Any]    =  self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
						self.assertListEqual(self.expected_src_tokens						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						self.assertIn(__a						,    self.tokenizer.all_special_ids							)
						# fmt: off
						__a      :	Tuple    =  [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
						# fmt: on
						__a      :	List[str]    =  self.tokenizer.decode(__a						,    skip_special_tokens=__a							)
						__a      :	Any    =  self.tokenizer.decode(generated_ids[1:]						,    skip_special_tokens=__a							)
						self.assertEqual(__a						,    __a							)
						self.assertNotIn(self.tokenizer.eos_token						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  tempfile.mkdtemp()
						__a      :	List[str]    =  self.tokenizer.lang_token_to_id
						self.tokenizer.save_pretrained(__a							)
						__a      :	Optional[Any]    =  MaMaaaTokenizer.from_pretrained(__a							)
						self.assertDictEqual(new_tok.lang_token_to_id						,    __a							)




	@require_torch
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  'en'
						__a      :	Optional[int]    =  'fr'

						__a      :	Any    =  self.tokenizer(self.src_text						,    text_target=self.tgt_text						,    padding=__a						,    return_tensors='pt'							)

						__a      :	Optional[int]    =  shift_tokens_right(
						    batch['labels']						,    self.tokenizer.pad_token_id						,    self.tokenizer.eos_token_id							)

						for k in batch:
											__a      :	List[str]    =  batch[k].tolist()
						# batch = {k: v.tolist() for k,v in batch.items()}
						# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
						# batch.decoder_inputs_ids[0][0] ==
						assert batch.input_ids[1][0] == EN_CODE
						assert batch.input_ids[1][-1] == 2
						assert batch.labels[1][0] == FR_CODE
						assert batch.labels[1][-1] == 2
						assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]




	@require_torch
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  'mr'
						self.assertListEqual(self.tokenizer.prefix_tokens						,    [self.tokenizer.get_lang_id('mr'							)]							)
						self.assertListEqual(self.tokenizer.suffix_tokens						,    [self.tokenizer.eos_token_id]							)

						__a      :	Optional[int]    =  'zh'
						self.assertListEqual(self.tokenizer.prefix_tokens						,    [self.tokenizer.get_lang_id('zh'							)]							)
						self.assertListEqual(self.tokenizer.suffix_tokens						,    [self.tokenizer.eos_token_id]							)




	@require_torch
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  'mr'
						self.tokenizer._switch_to_target_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens						,    [self.tokenizer.get_lang_id('mr'							)]							)
						self.assertListEqual(self.tokenizer.suffix_tokens						,    [self.tokenizer.eos_token_id]							)
						self.tokenizer._switch_to_input_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens						,    [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)

						__a      :	int    =  'zh'
						self.tokenizer._switch_to_target_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens						,    [self.tokenizer.get_lang_id('zh'							)]							)
						self.assertListEqual(self.tokenizer.suffix_tokens						,    [self.tokenizer.eos_token_id]							)
						self.tokenizer._switch_to_input_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens						,    [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)





	@require_torch
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  self.tokenizer._build_translation_inputs('A test'						,    return_tensors='pt'						,    src_lang='en'						,    tgt_lang='ar'							)

						self.assertEqual(
						    nested_simplify(__a							)						,    {
						        # en_XX, A, test, EOS
						        'input_ids': [[12_8022, 58, 4183, 2]],
						        'attention_mask': [[1, 1, 1, 1]],
						        # ar_AR
						        'forced_bos_token_id': 12_8006,
						    }						,    )







'''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()



