"""simple docstring"""







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

_a					:					List[Any]=			imread(R"digital_image_processing/image_data/lena_small.jpg")
_a					:					Union[str, Any]=			cvtColor(img, COLOR_BGR2GRAY)
def   __UpperCAmelCase    (   )							->      Optional[int]:

	'''simple docstring'''





	__snake_case      :							Tuple						=			cn.convert_to_negative(a__  )
	# assert negative_img array for at least one True
	assert negative_img.any()
def   __UpperCAmelCase    (   )							->      int:

	'''simple docstring'''





	with Image.open('digital_image_processing/image_data/lena_small.jpg'  ) as img:
		# Work around assertion for response
		assert str(cc.change_contrast(a__      ,							1_10  )  ).startswith(
		    '<PIL.Image.Image image mode=RGB size=100x100 at'  )
def   __UpperCAmelCase    (   )							->      Optional[Any]:

	'''simple docstring'''





	__snake_case      :							int						=			canny.gen_gaussian_kernel(9      ,							sigma=1.4  )
	# Assert ambiguous array
	assert resp.all()
def   __UpperCAmelCase    (   )							->      Dict:

	'''simple docstring'''





	__snake_case      :							List[str]						=			imread('digital_image_processing/image_data/lena_small.jpg'      ,							0  )
	# assert ambiguous array for all == True
	assert canny_img.all()
	__snake_case      :							int						=			canny.canny(a__  )
	# assert canny array for at least one True
	assert canny_array.any()
def   __UpperCAmelCase    (   )							->      Optional[Any]:

	'''simple docstring'''





	assert gg.gaussian_filter(a__      ,							5      ,							sigma=0.9  ).all()
def   __UpperCAmelCase    (   )							->      Any:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]  )
	__snake_case      :							Any						=			conv.img_convolve(a__      ,							a__  ).astype(a__  )
	assert res.any()
def   __UpperCAmelCase    (   )							->      Union[str, Any]:

	'''simple docstring'''





	assert med.median_filter(a__      ,							3  ).any()
def   __UpperCAmelCase    (   )							->      Any:

	'''simple docstring'''





	__snake_case		,    __snake_case      :							Tuple						=			sob.sobel_filter(a__  )
	assert grad.any() and theta.any()
def   __UpperCAmelCase    (   )							->      List[str]:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			sp.make_sepia(a__      ,							20  )
	assert sepia.all()
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str = "digital_image_processing/image_data/lena_small.jpg"  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							int						=			bs.Burkes(imread(a__      ,							1  )      ,							1_20  )
	burkes.process()
	assert burkes.output_img.any()
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str = "digital_image_processing/image_data/lena_small.jpg"      ,							)							->      List[str]:

	'''simple docstring'''





	__snake_case      :							Any						=			rs.NearestNeighbour(imread(a__      ,							1  )      ,							4_00      ,							2_00  )
	nn.process()
	assert nn.output.any()

def   __UpperCAmelCase    (   )							->      Tuple:

	'''simple docstring'''





	__snake_case      :							str						=			'digital_image_processing/image_data/lena.jpg'

	# Reading the image and converting it to grayscale.
	__snake_case      :							Optional[Any]						=			imread(a__      ,							0  )

	# Test for get_neighbors_pixel function() return not None
	__snake_case      :							Dict						=			0
	__snake_case      :							int						=			0
	__snake_case      :							List[str]						=			image[x_coordinate][y_coordinate]

	__snake_case      :							int						=			lbp.get_neighbors_pixel(
	    a__      ,							a__      ,							a__      ,							a__  )

	assert neighbors_pixels is not None

	# Test for local_binary_pattern function()
	# Create a numpy array as the same height and width of read image
	__snake_case      :							int						=			np.zeros((image.shape[0], image.shape[1])  )

	# Iterating through the image and calculating the local binary pattern value
	# for each pixel.
	for i in range(0      ,							image.shape[0]  ):
		for j in range(0      ,							image.shape[1]  ):
			__snake_case      :							Optional[int]						=			lbp.local_binary_value(a__      ,							a__      ,							a__  )

	assert lbp_image.any()






"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


_a					:					Tuple=			{
    "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 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				str   =		"""albert"""



				def __init__(self    :				Union[str, Any]     ,  _A    :				int=3_00_00     ,  _A    :				Any=1_28     ,  _A    :				Tuple=40_96     ,  _A    :				int=12     ,  _A    :				Tuple=1     ,  _A    :				int=64     ,  _A    :				Optional[Any]=1_63_84     ,  _A    :				Optional[Any]=1     ,  _A    :				List[str]="gelu_new"     ,  _A    :				Any=0     ,  _A    :				Optional[Any]=0     ,  _A    :				List[Any]=5_12     ,  _A    :				List[Any]=2     ,  _A    :				Dict=0.02     ,  _A    :				Union[str, Any]=1E-12     ,  _A    :				Tuple=0.1     ,  _A    :				int="absolute"     ,  _A    :				List[Any]=0     ,  _A    :				str=2     ,  _A    :				int=3     ,  **_A    :				Tuple     ,  )   ->     Union[str, Any]:
					super().__init__(pad_token_id=_A     ,  bos_token_id=_A     ,  eos_token_id=_A     ,  **_A)

					__snake_case      :							str						=			vocab_size
					__snake_case      :							List[Any]						=			embedding_size
					__snake_case      :							Tuple						=			hidden_size
					__snake_case      :							Optional[Any]						=			num_hidden_layers
					__snake_case      :							List[str]						=			num_hidden_groups
					__snake_case      :							Dict						=			num_attention_heads
					__snake_case      :							Any						=			inner_group_num
					__snake_case      :							Union[str, Any]						=			hidden_act
					__snake_case      :							List[Any]						=			intermediate_size
					__snake_case      :							List[str]						=			hidden_dropout_prob
					__snake_case      :							int						=			attention_probs_dropout_prob
					__snake_case      :							Any						=			max_position_embeddings
					__snake_case      :							Optional[int]						=			type_vocab_size
					__snake_case      :							Optional[Any]						=			initializer_range
					__snake_case      :							List[Any]						=			layer_norm_eps
					__snake_case      :							Optional[Any]						=			classifier_dropout_prob
					__snake_case      :							List[str]						=			position_embedding_type







class 		UpperCamelCase      (   lowercase						):



				@property
				def  _lowercase					(self    :				Tuple)   ->     Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						__snake_case      :							int						=			{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
						__snake_case      :							Optional[Any]						=			{0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					        ('token_type_ids', dynamic_axis),
					    ])





