






from pathlib import Path

import numpy as np
from PIL import Image







def       lowercase__	(					A_:						int		)	->				np.ndarray:







       """simple docstring"""






       __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=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       lowercase__	(					A_:						List[str]		)	->				np.ndarray:







       """simple docstring"""






       return (gray > 127) & (gray <= 255)







def       lowercase__	(					A_:						List[Any]  ,      A_:						Tuple		)	->				np.ndarray:







       """simple docstring"""






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

       # Copy image to padded image
       __UpperCAmelCase     				=image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]		):
              for y in range(image.shape[0]		):
                     __UpperCAmelCase     				=(
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     __UpperCAmelCase     				=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 dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer




@dataclass
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  torch.FloatTensor




class 						a__							(				a__							,							a__    ):





							'''simple docstring'''






							@register_to_config
							def __init__(						self   ,       lowerCamelCase_ = 3   ,       lowerCamelCase_ = 3   ,       lowerCamelCase_ = ("DownEncoderBlock2D",)   ,       lowerCamelCase_ = ("UpDecoderBlock2D",)   ,       lowerCamelCase_ = (64,)   ,       lowerCamelCase_ = 1   ,       lowerCamelCase_ = "silu"   ,       lowerCamelCase_ = 3   ,       lowerCamelCase_ = 32   ,       lowerCamelCase_ = 2_56   ,       lowerCamelCase_ = 32   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = 0.18_215   ,       lowerCamelCase_ = "group"   ,       )			->				Union[str, Any]:
														super().__init__()

														# pass init params to Encoder
														lowerCAmelCase__			  =      Encoder(
														    in_channels=lowerCamelCase_   ,       out_channels=lowerCamelCase_   ,       down_block_types=lowerCamelCase_   ,       block_out_channels=lowerCamelCase_   ,       layers_per_block=lowerCamelCase_   ,       act_fn=lowerCamelCase_   ,       norm_num_groups=lowerCamelCase_   ,       double_z=lowerCamelCase_   ,       )

														lowerCAmelCase__			  =      vq_embed_dim if vq_embed_dim is not None else latent_channels

														lowerCAmelCase__			  =      nn.Convad(lowerCamelCase_   ,       lowerCamelCase_   ,       1  )
														lowerCAmelCase__			  =      VectorQuantizer(lowerCamelCase_   ,       lowerCamelCase_   ,       beta=0.25   ,       remap=lowerCamelCase_   ,       sane_index_shape=lowerCamelCase_  )
														lowerCAmelCase__			  =      nn.Convad(lowerCamelCase_   ,       lowerCamelCase_   ,       1  )

														# pass init params to Decoder
														lowerCAmelCase__			  =      Decoder(
														    in_channels=lowerCamelCase_   ,       out_channels=lowerCamelCase_   ,       up_block_types=lowerCamelCase_   ,       block_out_channels=lowerCamelCase_   ,       layers_per_block=lowerCamelCase_   ,       act_fn=lowerCamelCase_   ,       norm_num_groups=lowerCamelCase_   ,       norm_type=lowerCamelCase_   ,       )







							@apply_forward_hook
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = True  )			->				VQEncoderOutput:
														lowerCAmelCase__			  =      self.encoder(lowerCamelCase_  )
														lowerCAmelCase__			  =      self.quant_conv(lowerCamelCase_  )

														if not return_dict:
																					return (h,)

														return VQEncoderOutput(latents=lowerCamelCase_  )







							@apply_forward_hook
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = False   ,       lowerCamelCase_ = True  )			->				Union[DecoderOutput, torch.FloatTensor]:
														# also go through quantization layer
														if not force_not_quantize:
																					lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.quantize(lowerCamelCase_  )
														else:
																					lowerCAmelCase__			  =      h
														lowerCAmelCase__			  =      self.post_quant_conv(lowerCamelCase_  )
														lowerCAmelCase__			  =      self.decoder(lowerCamelCase_   ,       quant if self.config.norm_type == '''spatial''' else None  )

														if not return_dict:
																					return (dec,)

														return DecoderOutput(sample=lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = True  )			->				Union[DecoderOutput, torch.FloatTensor]:
														lowerCAmelCase__			  =      sample
														lowerCAmelCase__			  =      self.encode(lowerCamelCase_  ).latents
														lowerCAmelCase__			  =      self.decode(lowerCamelCase_  ).sample

														if not return_dict:
																					return (dec,)

														return DecoderOutput(sample=lowerCamelCase_  )