
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

_snake_case				:   Tuple     =				imread(R"""digital_image_processing/image_data/lena_small.jpg""")
_snake_case				:   str     =				cvtColor(img, COLOR_BGR2GRAY)



def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= cn.convert_to_negative(_SCREAMING_SNAKE_CASE  )
						# assert negative_img array for at least one True
						assert negative_img.any()



def 			_a		(	):
						with Image.open("digital_image_processing/image_data/lena_small.jpg"  ) as img:
												# Work around assertion for response
												assert str(cc.change_contrast(_SCREAMING_SNAKE_CASE	,			110  )  ).startswith(
												    "<PIL.Image.Image image mode=RGB size=100x100 at"  )



def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= canny.gen_gaussian_kernel(9	,			sigma=1.4  )
						# Assert ambiguous array
						assert resp.all()



def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= imread("digital_image_processing/image_data/lena_small.jpg"	,			0  )
						# assert ambiguous array for all == True
						assert canny_img.all()
						_SCREAMING_SNAKE_CASE				= canny.canny(_SCREAMING_SNAKE_CASE  )
						# assert canny array for at least one True
						assert canny_array.any()



def 			_a		(	):
						assert gg.gaussian_filter(_SCREAMING_SNAKE_CASE	,			5	,			sigma=0.9  ).all()



def 			_a		(	):
						# laplace diagonals
						_SCREAMING_SNAKE_CASE				= array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]  )
						_SCREAMING_SNAKE_CASE				= conv.img_convolve(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ).astype(_SCREAMING_SNAKE_CASE  )
						assert res.any()



def 			_a		(	):
						assert med.median_filter(_SCREAMING_SNAKE_CASE	,			3  ).any()



def 			_a		(	):
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= sob.sobel_filter(_SCREAMING_SNAKE_CASE  )
						assert grad.any() and theta.any()



def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= sp.make_sepia(_SCREAMING_SNAKE_CASE	,			20  )
						assert sepia.all()



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str = "digital_image_processing/image_data/lena_small.jpg"  ):
						_SCREAMING_SNAKE_CASE				= bs.Burkes(imread(_SCREAMING_SNAKE_CASE	,			1  )	,			120  )
						burkes.process()
						assert burkes.output_img.any()



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str = "digital_image_processing/image_data/lena_small.jpg"	,			):
						_SCREAMING_SNAKE_CASE				= rs.NearestNeighbour(imread(_SCREAMING_SNAKE_CASE	,			1  )	,			400	,			200  )
						nn.process()
						assert nn.output.any()




def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= "digital_image_processing/image_data/lena.jpg"

						# Reading the image and converting it to grayscale.
						_SCREAMING_SNAKE_CASE				= imread(_SCREAMING_SNAKE_CASE	,			0  )

						# Test for get_neighbors_pixel function() return not None
						_SCREAMING_SNAKE_CASE				= 0
						_SCREAMING_SNAKE_CASE				= 0
						_SCREAMING_SNAKE_CASE				= image[x_coordinate][y_coordinate]

						_SCREAMING_SNAKE_CASE				= lbp.get_neighbors_pixel(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

						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
						_SCREAMING_SNAKE_CASE				= 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]  ):
																		_SCREAMING_SNAKE_CASE				= lbp.local_binary_value(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

						assert lbp_image.any()

'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


_snake_case				:   List[Any]     =				logging.get_logger(__name__)

_snake_case				:   int     =				{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Tuple       =					"""convbert"""




		def __init__(				self					,						UpperCamelCase=30_522					,						UpperCamelCase=768					,						UpperCamelCase=12					,						UpperCamelCase=12					,						UpperCamelCase=3_072					,						UpperCamelCase="gelu"					,						UpperCamelCase=0.1					,						UpperCamelCase=0.1					,						UpperCamelCase=512					,						UpperCamelCase=2					,						UpperCamelCase=0.02					,						UpperCamelCase=1e-12					,						UpperCamelCase=1					,						UpperCamelCase=0					,						UpperCamelCase=2					,						UpperCamelCase=768					,						UpperCamelCase=2					,						UpperCamelCase=9					,						UpperCamelCase=1					,						UpperCamelCase=None					,						**UpperCamelCase					,						):
								super().__init__(
								    pad_token_id=UpperCamelCase					,						bos_token_id=UpperCamelCase					,						eos_token_id=UpperCamelCase					,						**UpperCamelCase					,						)

								_SCREAMING_SNAKE_CASE				= vocab_size
								_SCREAMING_SNAKE_CASE				= hidden_size
								_SCREAMING_SNAKE_CASE				= num_hidden_layers
								_SCREAMING_SNAKE_CASE				= num_attention_heads
								_SCREAMING_SNAKE_CASE				= intermediate_size
								_SCREAMING_SNAKE_CASE				= hidden_act
								_SCREAMING_SNAKE_CASE				= hidden_dropout_prob
								_SCREAMING_SNAKE_CASE				= attention_probs_dropout_prob
								_SCREAMING_SNAKE_CASE				= max_position_embeddings
								_SCREAMING_SNAKE_CASE				= type_vocab_size
								_SCREAMING_SNAKE_CASE				= initializer_range
								_SCREAMING_SNAKE_CASE				= layer_norm_eps
								_SCREAMING_SNAKE_CASE				= embedding_size
								_SCREAMING_SNAKE_CASE				= head_ratio
								_SCREAMING_SNAKE_CASE				= conv_kernel_size
								_SCREAMING_SNAKE_CASE				= num_groups
								_SCREAMING_SNAKE_CASE				= classifier_dropout


class 			lowerCAmelCase (				__UpperCAmelCase			):




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