



'''simple docstring'''





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 TensorType, logging


_UpperCamelCase							    =						logging.get_logger(__name__)






class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  a_							 =["""input_values""", """attention_mask"""]




  def __init__(			self				:     List[Any]      ,     _a				:     int=1      ,     _a				:     List[Any]=1_6000      ,     _a				:     Optional[Any]=128      ,     _a				:     str=1024      ,     _a				:     List[Any]=0.0      ,     _a				:     Dict=True      ,     _a				:     List[str]=-4.2677393      ,     _a				:     List[Any]=4.5689974      ,     _a				:     List[str]=False      ,     **_a				:     Tuple      ,     ) -> Dict:
    super().__init__(feature_size=_a      ,     sampling_rate=_a      ,     padding_value=_a      ,     **_a       )
    __lowerCamelCase	:	str   	=			num_mel_bins
    __lowerCamelCase	:	str   	=			max_length
    __lowerCamelCase	:	Any   	=			do_normalize
    __lowerCamelCase	:	Any   	=			mean
    __lowerCamelCase	:	Optional[int]   	=			std
    __lowerCamelCase	:	Dict   	=			return_attention_mask




  def 						_lowercase					(			self				:     List[str]      ,     _a				:     np.ndarray      ,     _a				:     int      ,     ) -> np.ndarray:
    __lowerCamelCase	:	Optional[Any]   	=			torch.from_numpy(_a       ).unsqueeze(0       )
    __lowerCamelCase	:	Optional[int]   	=			ta_kaldi.fbank(
        _a      ,     htk_compat=_a      ,     sample_frequency=self.sampling_rate      ,     use_energy=_a      ,     window_type='hanning'      ,     num_mel_bins=self.num_mel_bins      ,     dither=0.0      ,     frame_shift=10      ,     )

    __lowerCamelCase	:	Any   	=			fbank.shape[0]
    __lowerCamelCase	:	Any   	=			max_length - n_frames

    # pad or truncate, depending on difference
    if difference > 0:
      __lowerCamelCase	:	Optional[int]   	=			torch.nn.ZeroPadad((0, 0, 0, difference)       )
      __lowerCamelCase	:	int   	=			pad_module(_a       )
    elif difference < 0:
      __lowerCamelCase	:	int   	=			fbank[0:max_length, :]

    __lowerCamelCase	:	str   	=			fbank.numpy()

    return fbank




  def 						_lowercase					(			self				:     List[str]      ,     _a				:     np.ndarray       ) -> np.ndarray:
    return (input_values - (self.mean)) / (self.std * 2)




  def __call__(			self				:     Any      ,     _a				:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,     _a				:     Optional[int] = None      ,     _a				:     Optional[Union[str, TensorType]] = None      ,     **_a				:     Union[str, Any]      ,     ) -> BatchFeature:

    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	:	Dict   	=			isinstance(_a      ,     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	:	Dict   	=			is_batched_numpy or (
        isinstance(_a      ,     (list, tuple)       ) and (isinstance(raw_speech[0]      ,     (np.ndarray, tuple, list)       ))
    )

    if is_batched:
      __lowerCamelCase	:	int   	=			[np.asarray(_a      ,     dtype=np.floataa       ) for speech in raw_speech]
    elif not is_batched and not isinstance(_a      ,     np.ndarray       ):
      __lowerCamelCase	:	Any   	=			np.asarray(_a      ,     dtype=np.floataa       )
    elif isinstance(_a      ,     np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
      __lowerCamelCase	:	Optional[int]   	=			raw_speech.astype(np.floataa       )

    # always return batch
    if not is_batched:
      __lowerCamelCase	:	Optional[int]   	=			[raw_speech]

    # extract fbank features and pad/truncate to max_length
    __lowerCamelCase	:	Any   	=			[self._extract_fbank_features(_a      ,     max_length=self.max_length       ) for waveform in raw_speech]

    # convert into BatchFeature
    __lowerCamelCase	:	str   	=			BatchFeature({'input_values': features}       )

    # make sure list is in array format
    __lowerCamelCase	:	Union[str, Any]   	=			padded_inputs.get('input_values'       )
    if isinstance(input_values[0]      ,     _a       ):
      __lowerCamelCase	:	Any   	=			[np.asarray(_a      ,     dtype=np.floataa       ) for feature in input_values]

    # normalization
    if self.do_normalize:
      __lowerCamelCase	:	int   	=			[self.normalize(_a       ) for feature in input_values]

    if return_tensors is not None:
      __lowerCamelCase	:	Dict   	=			padded_inputs.convert_to_tensors(_a       )

    return padded_inputs











'''simple docstring'''





from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
_UpperCamelCase							    =						load_iris()
iris.keys()


print(f'''Target names: \n {iris.target_names} ''')
print(f'''\n Features: \n {iris.feature_names}''')

# Train set e Test set
_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase							    =						train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

_UpperCamelCase							    =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
_UpperCamelCase							    =						[[1, 2, 1, 4], [2, 3, 4, 5]]

_UpperCamelCase							    =						knn.predict(X_new)

print(
    f'''\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'''
    f''' {iris["target_names"][prediction]}'''
)






