

import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):
  _a									=  """MCTCTFeatureExtractor"""
  _a									=  """AutoTokenizer"""




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        super().__init__(lowerCAmelCase							,   lowerCAmelCase					)
        _lowercase	       =self.feature_extractor
        _lowercase	       =False




  def __call__(				self							,   *lowerCAmelCase							,   **lowerCAmelCase					)  -> Any:
        '''simple docstring'''






        if self._in_target_context_manager:
              return self.current_processor(*lowerCAmelCase							,   **lowerCAmelCase					)

        if "raw_speech" in kwargs:
              warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'					)
              _lowercase	       =kwargs.pop('raw_speech'					)
        else:
              _lowercase	       =kwargs.pop('audio'							,   lowerCAmelCase					)
        _lowercase	       =kwargs.pop('sampling_rate'							,   lowerCAmelCase					)
        _lowercase	       =kwargs.pop('text'							,   lowerCAmelCase					)
        if len(lowerCAmelCase					) > 0:
              _lowercase	       =args[0]
              _lowercase	       =args[1:]

        if audio is None and text is None:
              raise ValueError('You need to specify either an `audio` or `text` input to process.'					)

        if audio is not None:
              _lowercase	       =self.feature_extractor(lowerCAmelCase							,   *lowerCAmelCase							,   sampling_rate=lowerCAmelCase							,   **lowerCAmelCase					)
        if text is not None:
              _lowercase	       =self.tokenizer(lowerCAmelCase							,   **lowerCAmelCase					)

        if text is None:
              return inputs
        elif audio is None:
              return encodings
        else:
              _lowercase	       =encodings['input_ids']
              return inputs




  def   A__     (				self							,   *lowerCAmelCase							,   **lowerCAmelCase					)  -> Any:
        '''simple docstring'''






        return self.tokenizer.batch_decode(*lowerCAmelCase							,   **lowerCAmelCase					)




  def   A__     (				self							,   *lowerCAmelCase							,   **lowerCAmelCase					)  -> Union[str, Any]:
        '''simple docstring'''






        if self._in_target_context_manager:
              return self.current_processor.pad(*lowerCAmelCase							,   **lowerCAmelCase					)

        _lowercase	       =kwargs.pop('input_features'							,   lowerCAmelCase					)
        _lowercase	       =kwargs.pop('labels'							,   lowerCAmelCase					)
        if len(lowerCAmelCase					) > 0:
              _lowercase	       =args[0]
              _lowercase	       =args[1:]

        if input_features is not None:
              _lowercase	       =self.feature_extractor.pad(lowerCAmelCase							,   *lowerCAmelCase							,   **lowerCAmelCase					)
        if labels is not None:
              _lowercase	       =self.tokenizer.pad(lowerCAmelCase							,   **lowerCAmelCase					)

        if labels is None:
              return input_features
        elif input_features is None:
              return labels
        else:
              _lowercase	       =labels['input_ids']
              return input_features




  def   A__     (				self							,   *lowerCAmelCase							,   **lowerCAmelCase					)  -> Any:
        '''simple docstring'''






        return self.tokenizer.decode(*lowerCAmelCase							,   **lowerCAmelCase					)




  @contextmanager
  def   A__     (				self					)  -> int:
        '''simple docstring'''






        warnings.warn(
            '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
            'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
            'your audio inputs, or in a separate call.'					)
        _lowercase	       =True
        _lowercase	       =self.tokenizer
        yield
        _lowercase	       =self.feature_extractor
        _lowercase	       =False





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def      a      (			A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int     )    ->      np.ndarray:





      """simple docstring"""



      if (ksize % 2) == 0:
            _lowercase	       =ksize + 1
      _lowercase	       =np.zeros((ksize, ksize)      ,   dtype=np.floataa     )

      # each value
      for y in range(A__     ):
            for x in range(A__     ):
                  # distance from center
                  _lowercase	       =x - ksize // 2
                  _lowercase	       =y - ksize // 2

                  # degree to radiant
                  _lowercase	       =theta / 180 * np.pi
                  _lowercase	       =np.cos(_theta     )
                  _lowercase	       =np.sin(_theta     )

                  # get kernel x
                  _lowercase	       =cos_theta * px + sin_theta * py

                  # get kernel y
                  _lowercase	       =-sin_theta * px + cos_theta * py

                  # fill kernel
                  _lowercase	       =np.exp(
                      -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)     ) * np.cos(2 * np.pi * _x / lambd + psi     )

      return gabor


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # read original image
  lowercase_									=   imread('../image_data/lena.jpg')
  # turn image in gray scale value
  lowercase_									=   cvtColor(img, COLOR_BGR2GRAY)

  # Apply multiple Kernel to detect edges
  lowercase_									=   np.zeros(gray.shape[:2])
  for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
    lowercase_									=   gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
    out += filteraD(gray, CV_8UC3, kernel_aa)
  lowercase_									=   out / out.max() * 2_5_5
  lowercase_									=   out.astype(np.uinta)

  imshow('Original', gray)
  imshow('Gabor filter with 20x20 mask and 6 directions', out)

  waitKey(0)


