
'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


lowercase__	:Union[str, Any]										=					{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}







class        lowercase	(      _UpperCAmelCase	):
							lowercase_ : Optional[Any]       ='''albert'''




							def __init__(       self     ,A__=3_0_0_0_0     ,A__=1_2_8     ,A__=4_0_9_6     ,A__=1_2     ,A__=1     ,A__=6_4     ,A__=1_6_3_8_4     ,A__=1     ,A__="gelu_new"     ,A__=0     ,A__=0     ,A__=5_1_2     ,A__=2     ,A__=0.02     ,A__=1E-12     ,A__=0.1     ,A__="absolute"     ,A__=0     ,A__=2     ,A__=3     ,**A__     ,):
												super().__init__(pad_token_id=lowercase_     ,bos_token_id=lowercase_     ,eos_token_id=lowercase_     ,**lowercase_)

												lowercase					=		vocab_size
												lowercase					=		embedding_size
												lowercase					=		hidden_size
												lowercase					=		num_hidden_layers
												lowercase					=		num_hidden_groups
												lowercase					=		num_attention_heads
												lowercase					=		inner_group_num
												lowercase					=		hidden_act
												lowercase					=		intermediate_size
												lowercase					=		hidden_dropout_prob
												lowercase					=		attention_probs_dropout_prob
												lowercase					=		max_position_embeddings
												lowercase					=		type_vocab_size
												lowercase					=		initializer_range
												lowercase					=		layer_norm_eps
												lowercase					=		classifier_dropout_prob
												lowercase					=		position_embedding_type







class        lowercase	(      _UpperCAmelCase	):




							@property
							def 				A__   (       self):
												if self.task == "multiple-choice":
																	lowercase					=		{0: """batch""", 1: """choice""", 2: """sequence"""}
												else:
																	lowercase					=		{0: """batch""", 1: """sequence"""}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												        ('''token_type_ids''', dynamic_axis),
												    ])






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")




