





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase_    =    logging.get_logger(__name__)



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Optional[int]	      =				['''input_features''']






			def __init__(self ,							__magic_name__=80 ,							__magic_name__=1_6000 ,							__magic_name__=160 ,							__magic_name__=30 ,							__magic_name__=400 ,							__magic_name__=0.0 ,							__magic_name__=False ,							**__magic_name__ ,							)					->       Optional[int]:


							'''simple docstring'''



							super().__init__(
							    feature_size=__magic_name__ ,							sampling_rate=__magic_name__ ,							padding_value=__magic_name__ ,							return_attention_mask=__magic_name__ ,							**__magic_name__ ,							)
							snake_case_						:   Optional[int]           =  n_fft
							snake_case_						:   List[str]           =  hop_length
							snake_case_						:   List[Any]           =  chunk_length
							snake_case_						:   Any           =  chunk_length * sampling_rate
							snake_case_						:   Dict           =  self.n_samples // hop_length
							snake_case_						:   int           =  sampling_rate
							snake_case_						:   Optional[int]           =  mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2 ,							num_mel_filters=__magic_name__ ,							min_frequency=0.0 ,							max_frequency=8000.0 ,							sampling_rate=__magic_name__ ,							norm='''slaney''' ,							mel_scale='''slaney''' ,							)






			def       lowerCamelCase  (self ,							__magic_name__   )					->       np.ndarray:


							'''simple docstring'''



							snake_case_						:   int           =  spectrogram(
							    __magic_name__ ,							window_function(self.n_fft ,							'''hann'''   ) ,							frame_length=self.n_fft ,							hop_length=self.hop_length ,							power=2.0 ,							mel_filters=self.mel_filters ,							log_mel='''log10''' ,							)
							snake_case_						:   Any           =  log_spec[:, :-1]
							snake_case_						:   Any           =  np.maximum(__magic_name__ ,							log_spec.max() - 8.0   )
							snake_case_						:   int           =  (log_spec + 4.0) / 4.0
							return log_spec






			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def       lowerCamelCase  (__magic_name__ ,							__magic_name__ ,							__magic_name__ = 0.0   )					->       List[np.ndarray]:


							'''simple docstring'''



							if attention_mask is not None:
											snake_case_						:   str           =  np.array(__magic_name__ ,							np.intaa   )
											snake_case_						:   Any           =  []

											for vector, length in zip(__magic_name__ ,							attention_mask.sum(-1   )   ):
															snake_case_						:   Tuple           =  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7   )
															if length < normed_slice.shape[0]:
																			snake_case_						:   Tuple           =  padding_value

															normed_input_values.append(__magic_name__   )
							else:
											snake_case_						:   Optional[Any]           =  [(x - x.mean()) / np.sqrt(x.var() + 1e-7   ) for x in input_values]

							return normed_input_values






			def __call__(self ,							__magic_name__ ,							__magic_name__ = True ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = "max_length" ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       BatchFeature:


							'''simple docstring'''



							if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
															raise ValueError(
															    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
															    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
															    F''' was sampled with {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.'''   )

							snake_case_						:   List[str]           =  isinstance(__magic_name__ ,							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}'''   )
							snake_case_						:   List[Any]           =  is_batched_numpy or (
							    isinstance(__magic_name__ ,							(list, tuple)   ) and (isinstance(raw_speech[0] ,							(np.ndarray, tuple, list)   ))
							)

							if is_batched:
											snake_case_						:   List[Any]           =  [np.asarray([speech] ,							dtype=np.floataa   ).T for speech in raw_speech]
							elif not is_batched and not isinstance(__magic_name__ ,							np.ndarray   ):
											snake_case_						:   List[str]           =  np.asarray(__magic_name__ ,							dtype=np.floataa   )
							elif isinstance(__magic_name__ ,							np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
											snake_case_						:   List[str]           =  raw_speech.astype(np.floataa   )

							# always return batch
							if not is_batched:
											snake_case_						:   Union[str, Any]           =  [np.asarray([raw_speech]   ).T]

							snake_case_						:   List[Any]           =  BatchFeature({'''input_features''': raw_speech}   )

							# convert into correct format for padding

							snake_case_						:   Any           =  self.pad(
							    __magic_name__ ,							padding=__magic_name__ ,							max_length=max_length if max_length else self.n_samples ,							truncation=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=return_attention_mask or do_normalize ,							)

							# zero-mean and unit-variance normalization
							if do_normalize:
											snake_case_						:   List[str]           =  self.zero_mean_unit_var_norm(
											    padded_inputs['''input_features'''] ,							attention_mask=padded_inputs['''attention_mask'''] ,							padding_value=self.padding_value ,							)
											snake_case_						:   Union[str, Any]           =  np.stack(padded_inputs['''input_features'''] ,							axis=0   )

							# make sure list is in array format
							snake_case_						:   int           =  padded_inputs.get('''input_features'''   ).transpose(2 ,							0 ,							1   )

							snake_case_						:   Dict           =  [self._np_extract_fbank_features(__magic_name__   ) for waveform in input_features[0]]

							if isinstance(input_features[0] ,							__magic_name__   ):
											snake_case_						:   Union[str, Any]           =  [np.asarray(__magic_name__ ,							dtype=np.floataa   ) for feature in input_features]
							else:
											snake_case_						:   Optional[int]           =  input_features

							if return_attention_mask:
											# rescale from sample (48000) to feature (3000)
											snake_case_						:   Dict           =  padded_inputs['''attention_mask'''][:, :: self.hop_length]

							if return_tensors is not None:
											snake_case_						:   Dict           =  padded_inputs.convert_to_tensors(__magic_name__   )

							return padded_inputs






			def       lowerCamelCase  (self   )					->       Dict[str, Any]:


							'''simple docstring'''



							snake_case_						:   int           =  copy.deepcopy(self.__dict__   )
							snake_case_						:   Dict           =  self.__class__.__name__
							if "mel_filters" in output:
											del output["mel_filters"]
							return output







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()
