
from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowercase        =  logging.get_logger(__name__)





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''input_features''', '''attention_mask''']

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Union[str, Any]=8_0   ,					__UpperCamelCase					:     Tuple=1_6_0_0_0   ,					__UpperCamelCase					:     str=8_0   ,					__UpperCamelCase					:     Tuple=0.0   ,					__UpperCamelCase					:     Union[str, Any]=True   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     Any=True   ,					**__UpperCamelCase					:     Any   ,					):
												super().__init__(feature_size=__UpperCamelCase   ,					sampling_rate=__UpperCamelCase   ,					padding_value=__UpperCamelCase   ,					**__UpperCamelCase   )
												lowerCamelCase_							  =					num_mel_bins
												lowerCamelCase_							  =					do_ceptral_normalize
												lowerCamelCase_							  =					normalize_means
												lowerCamelCase_							  =					normalize_vars
												lowerCamelCase_							  =					True

					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     np.ndarray   ,					):
												lowerCamelCase_							  =					waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
												lowerCamelCase_							  =					torch.from_numpy(__UpperCamelCase   ).unsqueeze(0   )
												lowerCamelCase_							  =					ta_kaldi.fbank(__UpperCamelCase   ,					num_mel_bins=self.num_mel_bins   ,					sample_frequency=self.sampling_rate   )
												return features.numpy()

					@staticmethod
					def    lowercase__      (	__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Optional[bool] = True   ,					__UpperCamelCase					:     Optional[bool] = True   ,					__UpperCamelCase					:     float = 0.0   ,					):
												# make sure we normalize float32 arrays
												if normalize_means:
																			lowerCamelCase_							  =					x[:input_length].mean(axis=0   )
																			lowerCamelCase_							  =					np.subtract(__UpperCamelCase   ,					__UpperCamelCase   )
												if normalize_vars:
																			lowerCamelCase_							  =					x[:input_length].std(axis=0   )
																			lowerCamelCase_							  =					np.divide(__UpperCamelCase   ,					__UpperCamelCase   )

												if input_length < x.shape[0]:
																			lowerCamelCase_							  =					padding_value

												# make sure array is in float32
												lowerCamelCase_							  =					x.astype(np.floataa   )

												return x

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     List[np.ndarray]   ,					__UpperCamelCase					:     Optional[np.ndarray] = None   ):
												lowerCamelCase_							  =					attention_mask.sum(-1   ) if attention_mask is not None else [x.shape[0] for x in input_features]
												return [
												    self.utterance_cmvn(__UpperCamelCase   ,					__UpperCamelCase   ,					self.normalize_means   ,					self.normalize_vars   ,					self.padding_value   )
												    for x, n in zip(__UpperCamelCase   ,					__UpperCamelCase   )
												]



					def __call__(	self					:     Dict   ,					__UpperCamelCase					:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,					__UpperCamelCase					:     Union[bool, str, PaddingStrategy] = False   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					**__UpperCamelCase					:     List[Any]   ,					):

												if sampling_rate is not None:
																			if sampling_rate != self.sampling_rate:
																										raise ValueError(
																										    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																										    F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
																										    F''' {self.sampling_rate} and not {sampling_rate}.'''   )
												else:
																			logger.warning(
																			    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
																			    """Failing to do so can result in silent errors that might be hard to debug."""   )

												lowerCamelCase_							  =					isinstance(__UpperCamelCase   ,					np.ndarray   ) and len(raw_speech.shape   ) > 1
												if is_batched_numpy and len(raw_speech.shape   ) > 2:
																			raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''   )
												lowerCamelCase_							  =					is_batched_numpy or (
												    isinstance(__UpperCamelCase   ,					(list, tuple)   ) and (isinstance(raw_speech[0]   ,					(np.ndarray, tuple, list)   ))
												)

												if is_batched:
																			lowerCamelCase_							  =					[np.asarray(__UpperCamelCase   ,					dtype=np.floataa   ) for speech in raw_speech]
												elif not is_batched and not isinstance(__UpperCamelCase   ,					np.ndarray   ):
																			lowerCamelCase_							  =					np.asarray(__UpperCamelCase   ,					dtype=np.floataa   )
												elif isinstance(__UpperCamelCase   ,					np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
																			lowerCamelCase_							  =					raw_speech.astype(np.floataa   )

												# always return batch
												if not is_batched:
																			lowerCamelCase_							  =					[raw_speech]

												# extract fbank features
												lowerCamelCase_							  =					[self._extract_fbank_features(__UpperCamelCase   ) for waveform in raw_speech]

												# convert into correct format for padding
												lowerCamelCase_							  =					BatchFeature({"""input_features""": features}   )

												lowerCamelCase_							  =					self.pad(
												    __UpperCamelCase   ,					padding=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					pad_to_multiple_of=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					**__UpperCamelCase   ,					)

												# make sure list is in array format
												lowerCamelCase_							  =					padded_inputs.get("""input_features"""   )
												if isinstance(input_features[0]   ,					__UpperCamelCase   ):
																			lowerCamelCase_							  =					[np.asarray(__UpperCamelCase   ,					dtype=np.floataa   ) for feature in input_features]

												lowerCamelCase_							  =					padded_inputs.get("""attention_mask"""   )
												if attention_mask is not None:
																			lowerCamelCase_							  =					[np.asarray(__UpperCamelCase   ,					dtype=np.intaa   ) for array in attention_mask]

												# Utterance-level cepstral mean and variance normalization
												if self.do_ceptral_normalize:
																			lowerCamelCase_							  =					(
																			    np.array(__UpperCamelCase   ,					dtype=np.intaa   )
																			    if self._get_padding_strategies(__UpperCamelCase   ,					max_length=__UpperCamelCase   ) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			lowerCamelCase_							  =					self.normalize(
																			    padded_inputs["""input_features"""]   ,					attention_mask=__UpperCamelCase   )

												if return_tensors is not None:
																			lowerCamelCase_							  =					padded_inputs.convert_to_tensors(__UpperCamelCase   )

												return padded_inputs






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:
							lowerCamelCase_							  =					XGBClassifier()
							classifier.fit(UpperCAmelCase__			,				UpperCAmelCase__       )
							return classifier



def      __lowerCAmelCase						(							)  ->    None:
							lowerCamelCase_							  =					load_iris()
							lowerCamelCase_  ,   lowerCamelCase_							  =					data_handling(UpperCAmelCase__       )
							lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					train_test_split(
							    UpperCAmelCase__			,				UpperCAmelCase__			,				test_size=0.2_5       )

							lowerCamelCase_							  =					iris["""target_names"""]

							# Create an XGBoost Classifier from the training data
							lowerCamelCase_							  =					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()





