





'''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


a__							:							int			=logging.get_logger(__name__)

class 					snake_case					(   __lowerCamelCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Union[str, Any]      		=["input_values", "attention_mask"]






 def __init__(	self	:							Optional[int]							,     __A	:							Union[str, Any]=1							,     __A	:							Optional[Any]=1_6_0_0_0							,     __A	:							Optional[int]=1_2_8							,     __A	:							Any=1_0_2_4							,     __A	:							Optional[int]=0.0							,     __A	:							int=True							,     __A	:							Union[str, Any]=-4.267_7393							,     __A	:							int=4.568_9974							,     __A	:							Tuple=False							,     **__A	:							Optional[Any]							,     ):
        super().__init__(feature_size=__A							,     sampling_rate=__A							,     padding_value=__A							,     **__A     )
        __UpperCamelCase	=  num_mel_bins
        __UpperCamelCase	=  max_length
        __UpperCamelCase	=  do_normalize
        __UpperCamelCase	=  mean
        __UpperCamelCase	=  std
        __UpperCamelCase	=  return_attention_mask






 def 	_lowerCamelCase (	self	:							List[str]							,     __A	:							np.ndarray							,     __A	:							int							,     ):
        __UpperCamelCase	=  torch.from_numpy(__A     ).unsqueeze(0     )
        __UpperCamelCase	=  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=1_0							,     )

        __UpperCamelCase	=  fbank.shape[0]
        __UpperCamelCase	=  max_length - n_frames

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

        __UpperCamelCase	=  fbank.numpy()

        return fbank






 def 	_lowerCamelCase (	self	:							Optional[Any]							,     __A	:							np.ndarray     ):
        return (input_values - (self.mean)) / (self.std * 2)






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

        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.'     )

        __UpperCamelCase	=  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}'''     )
        __UpperCamelCase	=  is_batched_numpy or (
            isinstance(__A							,     (list, tuple)     ) and (isinstance(raw_speech[0]							,     (np.ndarray, tuple, list)     ))
        )

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

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

        # extract fbank features and pad/truncate to max_length
        __UpperCamelCase	=  [self._extract_fbank_features(__A							,     max_length=self.max_length     ) for waveform in raw_speech]

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

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

        # normalization
        if self.do_normalize:
               __UpperCamelCase	=  [self.normalize(__A     ) for feature in input_values]

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

        return padded_inputs







'''simple docstring'''
import numpy as np
from PIL import Image
def     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
def     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       return (gray > 127) & (gray <= 255)
def     lowercase__       (       __lowercase						:  np.array    ,  __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __UpperCamelCase	=  np.zeros_like(__lowercase					)
       __UpperCamelCase	=  np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

       # Copy image to padded image
       __UpperCamelCase	=  image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]					):
              for y in range(image.shape[0]					):
                     __UpperCamelCase	=  (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     __UpperCamelCase	=  int(summation == 5					)
       return output


# kernel to be applied
a__							:							Dict			=np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
    # read original image
    a__							:							Any			=np.array(Image.open(r'''..\image_data\lena.jpg'''))
    # Apply erosion operation to a binary image
    a__							:							Union[str, Any]			=erosion(grayabinary(rgbagray(image)), structuring_element)
    # Save the output image
    a__							:							Tuple			=Image.fromarray(output).convert('''RGB''')
    pil_img.save('''result_erosion.png''')
