from pathlib import Path

import numpy as np
from PIL import Image



def A__  (			lowercase:     np.ndarray					)				->		np.ndarray:
       A  ,  A  ,  A						:     Dict         =rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b



def A__  (			lowercase:     np.ndarray					)				->		np.ndarray:
       return (gray > 127) & (gray <= 255)



def A__  (			lowercase:     np.ndarray,		lowercase:     np.ndarray					)				->		np.ndarray:
       A						:     int         =np.zeros_like(lowercase					)
       A						:     Optional[int]         =np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

       # Copy image to padded image
       A						:     Optional[int]         =image

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


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







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase							:      Any      							=logging.getLogger(__name__)



@dataclass
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[float]            =				field(
      default=0.0   ,						metadata={"help": "The label smoothing epsilon to apply (if not zero)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Whether to SortishSamler or not."}			)
  lowercase		:     bool            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "whether to use adafactor"}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Encoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Decoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Attention dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[str]            =				field(
      default="linear"   ,						metadata={"help": f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()			)}'}   ,						)






