






from pathlib import Path

import numpy as np
from PIL import Image






def 				lowerCamelCase__      (    __lowerCamelCase			:     np.ndarray		):
  __UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase							:	Union[str, Any]           =  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 				lowerCamelCase__      (    __lowerCamelCase			:     np.ndarray		):
  return (gray > 127) & (gray <= 255)






def 				lowerCamelCase__      (    __lowerCamelCase			:     np.ndarray  ,		__lowerCamelCase			:     np.ndarray		):
  __UpperCAmelCase							:	int           =  np.zeros_like(__lowerCamelCase		)
  __UpperCAmelCase							:	str           =  np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)		)

  # Copy image to padded image
  __UpperCAmelCase							:	Tuple           =  image

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


if __name__ == "__main__":
       # read original image
       a		:       Optional[Any]			     = Path(__file__).resolve().parent / "image_data" / "lena.jpg"
       a		:       Dict			     = np.array(Image.open(lena_path))
       # kernel to be applied
       a		:       Any			     = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
       a		:       Optional[Any]			     = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
       # Save the output image
       a		:       Union[str, Any]			     = Image.fromarray(output).convert("RGB")
       pil_img.save("result_dilation.png")










import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase__   )
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : str							       =	field(default='automatic-speech-recognition'							,       metadata={'include_in_asdict_even_if_is_default': True}   )
      a  : ClassVar[Features]							       =	Features({'audio': Audio()}   )
      a  : ClassVar[Features]							       =	Features({'transcription': Value('string'   )}   )
      a  : str							       =	"audio"
      a  : str							       =	"transcription"
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			Optional[int]					)							->      str:
        if self.audio_column not in features:
          raise ValueError(f"""Column {self.audio_column} is not present in features."""					)
        if not isinstance(features[self.audio_column]		,      __lowercase					):
          raise ValueError(f"""Column {self.audio_column} is not an Audio type."""					)
        __UpperCAmelCase							:	int           =  copy.deepcopy(self					)
        __UpperCAmelCase							:	str           =  self.input_schema.copy()
        __UpperCAmelCase							:	List[str]           =  features[self.audio_column]
        __UpperCAmelCase							:	Optional[Any]           =  input_schema
        return task_template




      @property
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      Dict[str, str]:
        return {self.audio_column: "audio", self.transcription_column: "transcription"}


