

'''simple docstring'''
from pathlib import Path

import numpy as np
from PIL import Image


def 	_lowerCAmelCase (						_UpperCamelCase						:					np.ndarray    )				->      np.ndarray:
     """simple docstring"""




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


def 	_lowerCAmelCase (						_UpperCamelCase						:					np.ndarray    )				->      np.ndarray:
     """simple docstring"""




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


def 	_lowerCAmelCase (						_UpperCamelCase						:					np.ndarray							,			_UpperCamelCase						:					np.ndarray    )				->      np.ndarray:
     """simple docstring"""




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

     # Copy image to padded image
     _SCREAMING_SNAKE_CASE							   =image

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


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



'''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


lowerCamelCase			:				Optional[Any] 			=					logging.get_logger(__name__)

lowerCamelCase			:				Optional[Any] 			=					"T5Config"







class 							A__   (	A__  ):
  A__					=    'mt5'
  A__					=    MTaConfig







class 							A__   (	A__  ):
  A__					=    'mt5'
  A__					=    MTaConfig







class 							A__   (	A__  ):
  A__					=    'mt5'
  A__					=    MTaConfig
