




"""simple docstring"""


from pathlib import Path

import numpy as np
from PIL import Image


def  UpperCAmelCase__	(snake_case__							: np.ndarray   ):
  """simple docstring"""

  _snake_case							:		Dict								=  rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b


def  UpperCAmelCase__	(snake_case__							: np.ndarray   ):
  """simple docstring"""

  return (gray > 1_27) & (gray <= 2_55)


def  UpperCAmelCase__	(snake_case__							: np.ndarray		,       snake_case__							: np.ndarray   ):
  """simple docstring"""

  _snake_case							:		Dict								=  np.zeros_like(snake_case__   )
  _snake_case							:		List[str]								=  np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)   )

  # Copy image to padded image
  _snake_case							:		Union[str, Any]								=  image

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


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








"""simple docstring"""


from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)


