





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_    =    {
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase_    =    {
    '''yjernite/retribert-base-uncased''': 5_1_2,
}


lowerCAmelCase_    =    {
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Optional[int]	      =				VOCAB_FILES_NAMES
			lowerCamelCase_					:					List[Any]	      =				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_					:					str	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_					:					Tuple	      =				PRETRAINED_INIT_CONFIGURATION
			lowerCamelCase_					:					str	      =				RetriBertTokenizer
			lowerCamelCase_					:					Dict	      =				['''input_ids''', '''attention_mask''']






			def __init__(self ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=True ,							__magic_name__="[UNK]" ,							__magic_name__="[SEP]" ,							__magic_name__="[PAD]" ,							__magic_name__="[CLS]" ,							__magic_name__="[MASK]" ,							__magic_name__=True ,							__magic_name__=None ,							**__magic_name__ ,							)					->       Dict:


							'''simple docstring'''



							super().__init__(
							    __magic_name__ ,							tokenizer_file=__magic_name__ ,							do_lower_case=__magic_name__ ,							unk_token=__magic_name__ ,							sep_token=__magic_name__ ,							pad_token=__magic_name__ ,							cls_token=__magic_name__ ,							mask_token=__magic_name__ ,							tokenize_chinese_chars=__magic_name__ ,							strip_accents=__magic_name__ ,							**__magic_name__ ,							)

							snake_case_						:   Optional[int]           =  json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
							if (
							    normalizer_state.get('''lowercase''' ,							__magic_name__   ) != do_lower_case
							    or normalizer_state.get('''strip_accents''' ,							__magic_name__   ) != strip_accents
							    or normalizer_state.get('''handle_chinese_chars''' ,							__magic_name__   ) != tokenize_chinese_chars
							):
											snake_case_						:   Optional[int]           =  getattr(__magic_name__ ,							normalizer_state.pop('''type'''   )   )
											snake_case_						:   str           =  do_lower_case
											snake_case_						:   List[Any]           =  strip_accents
											snake_case_						:   int           =  tokenize_chinese_chars
											snake_case_						:   List[Any]           =  normalizer_class(**__magic_name__   )

							snake_case_						:   int           =  do_lower_case






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
											output += token_ids_a + [self.sep_token_id]

							return output






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       List[int]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  [self.sep_token_id]
							snake_case_						:   List[Any]           =  [self.cls_token_id]
							if token_ids_a is None:
											return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       Tuple[str]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  self._tokenizer.model.save(__magic_name__ ,							name=__magic_name__   )
							return tuple(__magic_name__   )







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			)		->						tuple:






				"""simple docstring"""


				return (data["data"], data["target"])






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						XGBClassifier:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  XGBClassifier()
				classifier.fit(_UpperCamelCase			,    _UpperCamelCase			)
				return classifier






def 					lowerCamelCase_						(    )		->						None:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  load_iris()
				snake_case_      ,							snake_case_						:   str           =  data_handling(_UpperCamelCase			)
				snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Dict           =  train_test_split(
				    _UpperCamelCase			,    _UpperCamelCase			,    test_size=0.25			)

				snake_case_						:   List[str]           =  iris['''target_names''']

				# Create an XGBoost Classifier from the training data
				snake_case_						:   int           =  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()
