from pathlib import Path

import numpy as np
from PIL import Image
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
   return 0.2989 * r + 0.5870 * g + 0.1140 * b
def 		snake_case__	(		lowercase					):
   return (gray > 127) & (gray <= 255)
def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							int       =	np.zeros_like(lowercase					)
   lowerCAmelCase_:							Tuple       =	np.zeros(
       (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

   # Copy image to padded image
   lowerCAmelCase_:							str       =	image

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


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

from packaging import version

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


a  :      Optional[int]            =    logging.get_logger(__name__)

a  :      Optional[Any]            =    {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Dict          =    'efficientnet'




    def __init__(    self       ,						lowerCamelCase__ = 3       ,						lowerCamelCase__ = 600       ,						lowerCamelCase__ = 2.0       ,						lowerCamelCase__ = 3.1       ,						lowerCamelCase__ = 8       ,						lowerCamelCase__ = [3, 3, 5, 3, 5, 5, 3]       ,						lowerCamelCase__ = [32, 16, 24, 40, 80, 112, 192]       ,						lowerCamelCase__ = [16, 24, 40, 80, 112, 192, 320]       ,						lowerCamelCase__ = []       ,						lowerCamelCase__ = [1, 2, 2, 2, 1, 2, 1]       ,						lowerCamelCase__ = [1, 2, 2, 3, 3, 4, 1]       ,						lowerCamelCase__ = [1, 6, 6, 6, 6, 6, 6]       ,						lowerCamelCase__ = 0.2_5       ,						lowerCamelCase__ = "swish"       ,						lowerCamelCase__ = 2_560       ,						lowerCamelCase__ = "mean"       ,						lowerCamelCase__ = 0.0_2       ,						lowerCamelCase__ = 0.0_0_1       ,						lowerCamelCase__ = 0.9_9       ,						lowerCamelCase__ = 0.5       ,						lowerCamelCase__ = 0.2       ,						**lowerCamelCase__       ,						):
       super().__init__(**lowerCamelCase__ )

       lowerCAmelCase_:							str       =	num_channels
       lowerCAmelCase_:							str       =	image_size
       lowerCAmelCase_:							int       =	width_coefficient
       lowerCAmelCase_:							Union[str, Any]       =	depth_coefficient
       lowerCAmelCase_:							int       =	depth_divisor
       lowerCAmelCase_:							List[str]       =	kernel_sizes
       lowerCAmelCase_:							Tuple       =	in_channels
       lowerCAmelCase_:							List[str]       =	out_channels
       lowerCAmelCase_:							List[str]       =	depthwise_padding
       lowerCAmelCase_:							Optional[int]       =	strides
       lowerCAmelCase_:							List[str]       =	num_block_repeats
       lowerCAmelCase_:							Any       =	expand_ratios
       lowerCAmelCase_:							List[Any]       =	squeeze_expansion_ratio
       lowerCAmelCase_:							Optional[int]       =	hidden_act
       lowerCAmelCase_:							Optional[int]       =	hidden_dim
       lowerCAmelCase_:							Dict       =	pooling_type
       lowerCAmelCase_:							Optional[Any]       =	initializer_range
       lowerCAmelCase_:							int       =	batch_norm_eps
       lowerCAmelCase_:							List[str]       =	batch_norm_momentum
       lowerCAmelCase_:							List[Any]       =	dropout_rate
       lowerCAmelCase_:							Union[str, Any]       =	drop_connect_rate
       lowerCAmelCase_:							Tuple       =	sum(lowerCamelCase__ ) * 4





class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    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-5