
'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__	:Any										=					logging.get_logger(__name__)

lowercase__	:str										=					{
    "google/vit-base-patch16-224": "https://huggingface.co/vit-base-patch16-224/resolve/main/config.json",
    # See all ViT models at https://huggingface.co/models?filter=vit
}







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : List[Any]       ='''vit'''




							def __init__(       self     ,A__=7_6_8     ,A__=1_2     ,A__=1_2     ,A__=3_0_7_2     ,A__="gelu"     ,A__=0.0     ,A__=0.0     ,A__=0.02     ,A__=1E-12     ,A__=2_2_4     ,A__=1_6     ,A__=3     ,A__=True     ,A__=1_6     ,**A__     ,):
												super().__init__(**A__)

												lowercase					=		hidden_size
												lowercase					=		num_hidden_layers
												lowercase					=		num_attention_heads
												lowercase					=		intermediate_size
												lowercase					=		hidden_act
												lowercase					=		hidden_dropout_prob
												lowercase					=		attention_probs_dropout_prob
												lowercase					=		initializer_range
												lowercase					=		layer_norm_eps
												lowercase					=		image_size
												lowercase					=		patch_size
												lowercase					=		num_channels
												lowercase					=		qkv_bias
												lowercase					=		encoder_stride







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Optional[int]       =version.parse('''1.11'''	)




							@property
							def 				A__   (       self):
												return OrderedDict(
												    [
												        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
												    ])





							@property
							def 				A__   (       self):
												return 1E-4






from pathlib import Path

import numpy as np
from PIL import Image



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase			,		lowercase					=		rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
					return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return (gray > 127) & (gray <= 255)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		np.zeros_like(lowerCAmelCase__	)
					lowercase					=		np.zeros(
					    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

					# Copy image to padded image
					lowercase					=		image

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


if __name__ == "__main__":
							# read original image
							lowercase__	:str										=					Path(__file__).resolve().parent / "image_data" / "lena.jpg"
							lowercase__	:List[str]										=					np.array(Image.open(lena_path))
							# kernel to be applied
							lowercase__	:Union[str, Any]										=					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__	:str										=					Image.fromarray(output).convert("RGB")
							pil_img.save("result_dilation.png")




