

"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


a__       :							str        =  logging.get_logger(__name__)

a__       :							Union[str, Any]        =  {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a__       :							List[str]        =  {
    '''vocab_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
        ),
        '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
        ),
    },
}

a__       :							List[str]        =  {
    '''squeezebert/squeezebert-uncased''': 5_1_2,
    '''squeezebert/squeezebert-mnli''': 5_1_2,
    '''squeezebert/squeezebert-mnli-headless''': 5_1_2,
}


a__       :							List[Any]        =  {
    '''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Any					      =    VOCAB_FILES_NAMES
	snake_case__       :						List[str]					      =    PRETRAINED_VOCAB_FILES_MAP
	snake_case__       :						Any					      =    PRETRAINED_INIT_CONFIGURATION
	snake_case__       :						List[str]					      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__       :						str					      =    SqueezeBertTokenizer




	def __init__(				self    :       Union[str, Any]    ,    UpperCAmelCase__    :       Tuple=None    ,    UpperCAmelCase__    :       List[Any]=None    ,    UpperCAmelCase__    :       Any=True    ,    UpperCAmelCase__    :       Tuple="[UNK]"    ,    UpperCAmelCase__    :       Dict="[SEP]"    ,    UpperCAmelCase__    :       List[str]="[PAD]"    ,    UpperCAmelCase__    :       Optional[int]="[CLS]"    ,    UpperCAmelCase__    :       Any="[MASK]"    ,    UpperCAmelCase__    :       Tuple=True    ,    UpperCAmelCase__    :       int=None    ,    **UpperCAmelCase__    :       str    ,    ) ->			Union[str, Any]:
						super().__init__(
						    UpperCAmelCase__    ,    tokenizer_file=UpperCAmelCase__    ,    do_lower_case=UpperCAmelCase__    ,    unk_token=UpperCAmelCase__    ,    sep_token=UpperCAmelCase__    ,    pad_token=UpperCAmelCase__    ,    cls_token=UpperCAmelCase__    ,    mask_token=UpperCAmelCase__    ,    tokenize_chinese_chars=UpperCAmelCase__    ,    strip_accents=UpperCAmelCase__    ,    **UpperCAmelCase__    ,    )

						__SCREAMING_SNAKE_CASE		     =       json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
						if (
						    normalizer_state.get("lowercase"    ,    UpperCAmelCase__     ) != do_lower_case
						    or normalizer_state.get("strip_accents"    ,    UpperCAmelCase__     ) != strip_accents
						    or normalizer_state.get("handle_chinese_chars"    ,    UpperCAmelCase__     ) != tokenize_chinese_chars
						):
											__SCREAMING_SNAKE_CASE		     =       getattr(UpperCAmelCase__    ,    normalizer_state.pop("type"     )     )
											__SCREAMING_SNAKE_CASE		     =       do_lower_case
											__SCREAMING_SNAKE_CASE		     =       strip_accents
											__SCREAMING_SNAKE_CASE		     =       tokenize_chinese_chars
											__SCREAMING_SNAKE_CASE		     =       normalizer_class(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       do_lower_case




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       List[Any]    ,    UpperCAmelCase__    :       Any=None     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       [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 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       List[int]    ,    UpperCAmelCase__    :       Optional[List[int]] = None     ) ->			List[int]:
						__SCREAMING_SNAKE_CASE		     =       [self.sep_token_id]
						__SCREAMING_SNAKE_CASE		     =       [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 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Optional[str] = None     ) ->			Tuple[str]:
						__SCREAMING_SNAKE_CASE		     =       self._tokenizer.model.save(UpperCAmelCase__    ,    name=UpperCAmelCase__     )
						return tuple(UpperCAmelCase__     )




"""simple docstring"""







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      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       XGBClassifier()
					classifier.fit(lowerCAmelCase_						,		lowerCAmelCase_     )
					return classifier




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       load_iris()
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       data_handling(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       train_test_split(
					    lowerCAmelCase_						,		lowerCAmelCase_						,		test_size=0.25     )

					__SCREAMING_SNAKE_CASE		     =       iris["target_names"]

					# Create an XGBoost Classifier from the training data
					__SCREAMING_SNAKE_CASE		     =       xgboost(lowerCAmelCase_						,		lowerCAmelCase_     )

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


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)
		main()
