


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""ClapFeatureExtractor"""
							_UpperCAmelCase      =						("""RobertaTokenizer""", """RobertaTokenizerFast""")






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										super().__init__(lowerCAmelCase__			,						lowerCAmelCase__       )






							def __call__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					kwargs.pop('sampling_rate'			,						lowerCAmelCase__       )

										if text is None and audios is None:
													raise ValueError('You have to specify either text or audios. Both cannot be none.'       )

										if text is not None:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.tokenizer(lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if audios is not None:
													SCREAMING_SNAKE_CASE_		:     Tuple					=					self.feature_extractor(
													    lowerCAmelCase__			,						sampling_rate=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if text is not None and audios is not None:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					audio_features.input_features
													return encoding
										elif text is not None:
													return encoding
										else:
													return BatchEncoding(data=dict(**lowerCAmelCase__       )			,						tensor_type=lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					self.tokenizer.model_input_names
										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.feature_extractor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names       )       )










"""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_					(				__a   :			dict						):




			'''simple docstring'''



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

def     UpperCAmelCase_					(				__a   :			np.ndarray      ,							__a   :			np.ndarray						):




			'''simple docstring'''



			_lowerCamelCase					:					Dict	       =    XGBClassifier()
			classifier.fit(__a      ,							__a						)
			return classifier

def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					List[str]	       =    load_iris()
			_lowerCamelCase       ,							_lowerCamelCase					:					Optional[Any]	       =    data_handling(__a						)
			_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase					:					int	       =    train_test_split(
			    __a      ,							__a      ,							test_size=0.2_5						)

			_lowerCamelCase					:					Optional[Any]	       =    iris['target_names']

			# Create an XGBoost Classifier from the training data
			_lowerCamelCase					:					Tuple	       =    xgboost(__a      ,							__a						)

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


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()





