



import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase						  = get_tests_dir("fixtures/test_sentencepiece.model")
_lowercase						  = get_tests_dir("fixtures/test_sentencepiece_bpe.model")

_lowercase						  = "pt" if is_torch_available() else "tf"



@require_sentencepiece
@require_tokenizers
class     _UpperCAmelCase			(       A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				CamembertTokenizer
     UpperCamelCase__		    =				CamembertTokenizerFast
     UpperCamelCase__		    =				True
     UpperCamelCase__		    =				True




     def 		snake_case_		(				self):
         super().setUp()

         # We have a SentencePiece fixture for testing
         A__		 =					CamembertTokenizer(a__)
         tokenizer.save_pretrained(self.tmpdirname)




     def 		snake_case_		(				self):
         A__		 =					'''<pad>'''
         A__		 =					1

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




     def 		snake_case_		(				self):
         A__		 =					list(self.get_tokenizer().get_vocab().keys())

         self.assertEqual(vocab_keys[0] ,						'''<s>NOTUSED''')
         self.assertEqual(vocab_keys[1] ,						'''<pad>''')
         self.assertEqual(vocab_keys[-1] ,						'''<mask>''')
         self.assertEqual(len(a__) ,						1_0_0_4)




     def 		snake_case_		(				self):
         self.assertEqual(self.get_tokenizer().vocab_size ,						1_0_0_5)




     def 		snake_case_		(				self):
         A__		 =					CamembertTokenizer(a__)
         tokenizer.save_pretrained(self.tmpdirname)
         A__		 =					CamembertTokenizerFast.from_pretrained(self.tmpdirname)

         A__		 =					'''I was born in 92000, and this is falsé.'''

         A__		 =					tokenizer.encode(a__)
         A__		 =					rust_tokenizer.encode(a__)
         self.assertListEqual(a__ ,						a__)

         A__		 =					tokenizer.encode(a__ ,						add_special_tokens=a__)
         A__		 =					rust_tokenizer.encode(a__ ,						add_special_tokens=a__)
         self.assertListEqual(a__ ,						a__)

         # <unk> tokens are not the same for `rust` than for `slow`.
         # Because spm gives back raw token instead of `unk` in EncodeAsPieces
         # tokens = tokenizer.tokenize(sequence)
         A__		 =					tokenizer.convert_ids_to_tokens(a__)
         A__		 =					rust_tokenizer.tokenize(a__)
         self.assertListEqual(a__ ,						a__)




     def 		snake_case_		(				self):
         if not self.test_rust_tokenizer:
             return

         A__		 =					self.get_tokenizer()
         A__		 =					self.get_rust_tokenizer()

         A__		 =					'''I was born in 92000, and this is falsé.'''

         A__		 =					tokenizer.tokenize(a__)
         A__		 =					rust_tokenizer.tokenize(a__)
         self.assertListEqual(a__ ,						a__)

         A__		 =					tokenizer.encode(a__ ,						add_special_tokens=a__)
         A__		 =					rust_tokenizer.encode(a__ ,						add_special_tokens=a__)
         self.assertListEqual(a__ ,						a__)

         A__		 =					self.get_rust_tokenizer()
         A__		 =					tokenizer.encode(a__)
         A__		 =					rust_tokenizer.encode(a__)
         self.assertListEqual(a__ ,						a__)




     @slow
     def 		snake_case_		(				self):
         # fmt: off
         A__		 =					{'''input_ids''': [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], '''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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
         # fmt: on

         # camembert is a french model. So we also use french texts.
         A__		 =					[
             '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
             '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
             '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
             '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
             '''telles que la traduction et la synthèse de texte.''',
         ]

         self.tokenizer_integration_test_util(
             expected_encoding=a__ ,						model_name='''camembert-base''' ,						revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf''' ,						sequences=a__ ,						)





import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier



def 		lowerCAmelCase__    (					UpperCamelCase_				:       dict	)->  tuple:
    return (data["data"], data["target"])



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.ndarray ,       UpperCamelCase_				:       np.ndarray	)->  XGBClassifier:
    A__		 =					XGBClassifier()
    classifier.fit(UpperCamelCase_ ,       UpperCamelCase_	)
    return classifier



def 		lowerCAmelCase__    (					)->  None:
    A__		 =					load_iris()
    A__ ,	A__		 =					data_handling(UpperCamelCase_	)
    A__ ,	A__ ,	A__ ,	A__		 =					train_test_split(
        UpperCamelCase_ ,       UpperCamelCase_ ,       test_size=0.25	)

    A__		 =					iris['''target_names''']

    # Create an XGBoost Classifier from the training data
    A__		 =					xgboost(UpperCamelCase_ ,       UpperCamelCase_	)

    # Display the confusion matrix of the classifier with both training and test sets
    ConfusionMatrixDisplay.from_estimator(
        UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       display_labels=UpperCamelCase_ ,       cmap='''Blues''' ,       normalize='''true''' ,       )
    plt.title('''Normalized Confusion Matrix - IRIS Dataset'''	)
    plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()
