





'''simple docstring'''


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

import numpy as np

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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Optional[Any]								=       ["input_values", "attention_mask"]




 def __init__(						self      ,  _lowerCAmelCase = 1      ,  _lowerCAmelCase = 16000      ,  _lowerCAmelCase = 0.0      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = 80      ,  _lowerCAmelCase = 16      ,  _lowerCAmelCase = 64      ,  _lowerCAmelCase = "hann_window"      ,  _lowerCAmelCase = 1.0      ,  _lowerCAmelCase = 80      ,  _lowerCAmelCase = 7600      ,  _lowerCAmelCase = 1E-10      ,  _lowerCAmelCase = 2      ,  _lowerCAmelCase = True      ,  **_lowerCAmelCase      ,  )     ->				Optional[int]:
     super().__init__(feature_size=_lowerCAmelCase      ,  sampling_rate=_lowerCAmelCase      ,  padding_value=_lowerCAmelCase      ,  **_lowerCAmelCase		)
     _lowerCAmelCase						      =   do_normalize
     _lowerCAmelCase						      =   return_attention_mask

     _lowerCAmelCase						      =   num_mel_bins
     _lowerCAmelCase						      =   hop_length
     _lowerCAmelCase						      =   win_length
     _lowerCAmelCase						      =   win_function
     _lowerCAmelCase						      =   frame_signal_scale
     _lowerCAmelCase						      =   fmin
     _lowerCAmelCase						      =   fmax
     _lowerCAmelCase						      =   mel_floor
     _lowerCAmelCase						      =   reduction_factor

     _lowerCAmelCase						      =   win_length * sampling_rate // 1000
     _lowerCAmelCase						      =   hop_length * sampling_rate // 1000
     _lowerCAmelCase						      =   optimal_fft_length(self.sample_size		)
     _lowerCAmelCase						      =   (self.n_fft // 2) + 1

     _lowerCAmelCase						      =   window_function(window_length=self.sample_size      ,  name=self.win_function      ,  periodic=_lowerCAmelCase		)

     _lowerCAmelCase						      =   mel_filter_bank(
         num_frequency_bins=self.n_freqs      ,  num_mel_filters=self.num_mel_bins      ,  min_frequency=self.fmin      ,  max_frequency=self.fmax      ,  sampling_rate=self.sampling_rate      ,  norm="slaney"      ,  mel_scale="slaney"      ,  )

     if frame_signal_scale != 1.0:
         warnings.warn(
             "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"      ,  _lowerCAmelCase      ,  )
     if reduction_factor != 2.0:
         warnings.warn(
             "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"      ,  _lowerCAmelCase      ,  )




 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def 				_snake_case (						_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = 0.0		)     ->				List[np.ndarray]:
     if attention_mask is not None:
         _lowerCAmelCase						      =   np.array(_lowerCAmelCase      ,  np.intaa		)
         _lowerCAmelCase						      =   []

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

             normed_input_values.append(_lowerCAmelCase		)
     else:
         _lowerCAmelCase						      =   [(x - x.mean()) / np.sqrt(x.var() + 1E-7		) for x in input_values]

     return normed_input_values




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  )     ->				np.ndarray:
     _lowerCAmelCase						      =   spectrogram(
         _lowerCAmelCase      ,  window=self.window      ,  frame_length=self.sample_size      ,  hop_length=self.sample_stride      ,  fft_length=self.n_fft      ,  mel_filters=self.mel_filters      ,  mel_floor=self.mel_floor      ,  log_mel="log10"      ,  )
     return log_mel_spec.T




 def __call__(						self      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				BatchFeature:
     if audio is None and audio_target is None:
         raise ValueError("You must provide either `audio` or `audio_target` values."		)

     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 audio 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."		)

     if audio is not None:
         _lowerCAmelCase						      =   self._process_audio(
             _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase      ,  )
     else:
         _lowerCAmelCase						      =   None

     if audio_target is not None:
         _lowerCAmelCase						      =   self._process_audio(
             _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase      ,  )

         if inputs is None:
             return inputs_target
         else:
             _lowerCAmelCase						      =   inputs_target["input_values"]
             _lowerCAmelCase						      =   inputs_target.get("attention_mask"		)
             if decoder_attention_mask is not None:
                 _lowerCAmelCase						      =   decoder_attention_mask

     return inputs




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				BatchFeature:
     _lowerCAmelCase						      =   isinstance(_lowerCAmelCase      ,  np.ndarray		) and len(speech.shape		) > 1
     if is_batched_numpy and len(speech.shape		) > 2:
         raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''		)
     _lowerCAmelCase						      =   is_batched_numpy or (
         isinstance(_lowerCAmelCase      ,  (list, tuple)		) and (isinstance(speech[0]      ,  (np.ndarray, tuple, list)		))
     )

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

     # always return batch
     if not is_batched:
         _lowerCAmelCase						      =   [speech]

     # needed to make pad() work on spectrogram inputs
     _lowerCAmelCase						      =   self.feature_size

     # convert into correct format for padding
     if is_target:
         _lowerCAmelCase						      =   [self._extract_mel_features(_lowerCAmelCase		) for waveform in speech]
         _lowerCAmelCase						      =   BatchFeature({"input_values": features}		)
         _lowerCAmelCase						      =   self.num_mel_bins
     else:
         _lowerCAmelCase						      =   BatchFeature({"input_values": speech}		)

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

     _lowerCAmelCase						      =   feature_size_hack

     # convert input values to correct format
     _lowerCAmelCase						      =   padded_inputs["input_values"]
     if not isinstance(input_values[0]      ,  np.ndarray		):
         _lowerCAmelCase						      =   [np.asarray(_lowerCAmelCase      ,  dtype=np.floataa		) for array in input_values]
     elif (
         not isinstance(_lowerCAmelCase      ,  np.ndarray		)
         and isinstance(input_values[0]      ,  np.ndarray		)
         and input_values[0].dtype is np.dtype(np.floataa		)
     ):
         _lowerCAmelCase						      =   [array.astype(np.floataa		) for array in input_values]
     elif isinstance(_lowerCAmelCase      ,  np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
         _lowerCAmelCase						      =   input_values.astype(np.floataa		)

     # convert attention_mask to correct format
     _lowerCAmelCase						      =   padded_inputs.get("attention_mask"		)
     if attention_mask is not None:
         _lowerCAmelCase						      =   [np.asarray(_lowerCAmelCase      ,  dtype=np.intaa		) for array in attention_mask]

     # zero-mean and unit-variance normalization
     if not is_target and self.do_normalize:
         _lowerCAmelCase						      =   (
             attention_mask
             if self._get_padding_strategies(_lowerCAmelCase      ,  max_length=_lowerCAmelCase		) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         _lowerCAmelCase						      =   self.zero_mean_unit_var_norm(
             padded_inputs["input_values"]      ,  attention_mask=_lowerCAmelCase      ,  padding_value=self.padding_value		)

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

     return padded_inputs





 def 				_snake_case (						self		)     ->				Dict[str, Any]:
     _lowerCAmelCase						      =   super().to_dict()

     # Don't serialize these as they are derived from the other properties.
     _lowerCAmelCase						      =   ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
     for name in names:
         if name in output:
             del output[name]

     return output







from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

lowercase_													=      datasets.load_iris()

lowercase_													=      np.array(data['''data'''])
lowercase_													=      np.array(data['''target'''])
lowercase_													=      data['''target_names''']

lowercase_							,							lowercase_							,							lowercase_							,							lowercase_													=      train_test_split(X, y)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[Any]						)	->    Optional[int]:
					return np.linalg.norm(np.array(__lowerCamelCase						) - np.array(__lowerCamelCase						)						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     int=5						)	->    str:
					__lowerCAmelCase						 =zip(__lowerCamelCase					, __lowerCamelCase						)
					# List of distances of all points from the point to be classified
					__lowerCAmelCase						 =[]
					for data_point in data:
										__lowerCAmelCase						 =euclidean_distance(data_point[0]					, __lowerCamelCase						)
										distances.append((distance, data_point[1])						)
					# Choosing 'k' points with the least distances.
					__lowerCAmelCase						 =[i[1] for i in sorted(__lowerCamelCase						)[:k]]
					# Most commonly occurring class among them
					# is the class into which the point is classified
					__lowerCAmelCase						 =Counter(__lowerCamelCase						).most_common(1						)[0][0]
					return classes[result]


if __name__ == "__main__":
				print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))




