



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     :Union[str, Any]			       =imread(r'digital_image_processing/image_data/lena_small.jpg')
__snake_case     :List[str]			       =cvtColor(img, COLOR_BGR2GRAY)






def  lowerCamelCase_	(      )       ->							Tuple:

							'''simple docstring'''






							A      						=    cn.convert_to_negative(lowerCAmelCase__				)
							# assert negative_img array for at least one True
							assert negative_img.any()






def  lowerCamelCase_	(      )       ->							List[str]:

							'''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(lowerCAmelCase__			,	110				)				).startswith(
														    '<PIL.Image.Image image mode=RGB size=100x100 at'				)






def  lowerCamelCase_	(      )       ->							List[Any]:

							'''simple docstring'''






							A      						=    canny.gen_gaussian_kernel(9			,	sigma=1.4				)
							# Assert ambiguous array
							assert resp.all()






def  lowerCamelCase_	(      )       ->							Dict:

							'''simple docstring'''






							A      						=    imread('digital_image_processing/image_data/lena_small.jpg'			,	0				)
							# assert ambiguous array for all == True
							assert canny_img.all()
							A      						=    canny.canny(lowerCAmelCase__				)
							# assert canny array for at least one True
							assert canny_array.any()






def  lowerCamelCase_	(      )       ->							Union[str, Any]:

							'''simple docstring'''






							assert gg.gaussian_filter(lowerCAmelCase__			,	5			,	sigma=0.9				).all()






def  lowerCamelCase_	(      )       ->							List[Any]:

							'''simple docstring'''






							A      						=    array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
							A      						=    conv.img_convolve(lowerCAmelCase__			,	lowerCAmelCase__				).astype(lowerCAmelCase__				)
							assert res.any()






def  lowerCamelCase_	(      )       ->							Optional[int]:

							'''simple docstring'''






							assert med.median_filter(lowerCAmelCase__			,	3				).any()






def  lowerCamelCase_	(      )       ->							Optional[Any]:

							'''simple docstring'''






							A						,   A      						=    sob.sobel_filter(lowerCAmelCase__				)
							assert grad.any() and theta.any()






def  lowerCamelCase_	(      )       ->							Union[str, Any]:

							'''simple docstring'''






							A      						=    sp.make_sepia(lowerCAmelCase__			,	20				)
							assert sepia.all()






def  lowerCamelCase_	(      lowerCAmelCase__	:	str = "digital_image_processing/image_data/lena_small.jpg"				)       ->							Any:

							'''simple docstring'''






							A      						=    bs.Burkes(imread(lowerCAmelCase__			,	1				)			,	120				)
							burkes.process()
							assert burkes.output_img.any()






def  lowerCamelCase_	(      lowerCAmelCase__	:	str = "digital_image_processing/image_data/lena_small.jpg"			,	)       ->							str:

							'''simple docstring'''






							A      						=    rs.NearestNeighbour(imread(lowerCAmelCase__			,	1				)			,	400			,	200				)
							nn.process()
							assert nn.output.any()






def  lowerCamelCase_	(      )       ->							int:

							'''simple docstring'''






							A      						=    'digital_image_processing/image_data/lena.jpg'

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

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

							A      						=    lbp.get_neighbors_pixel(
							    lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)

							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
							A      						=    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]				):
																					A      						=    lbp.local_binary_value(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)

							assert lbp_image.any()




"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

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


if TYPE_CHECKING:
			from ...processing_utils import ProcessorMixin
			from ...utils import TensorType


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					int							  =     "layoutlmv3"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=50265						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=3072						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-5						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=1024						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=64						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=224						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Any:
				super().__init__(
				    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__						,	pad_token_id=SCREAMING_SNAKE_CASE__						,	bos_token_id=SCREAMING_SNAKE_CASE__						,	eos_token_id=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__						,	)
				A__											=					max_ad_position_embeddings
				A__											=					coordinate_size
				A__											=					shape_size
				A__											=					has_relative_attention_bias
				A__											=					rel_pos_bins
				A__											=					max_rel_pos
				A__											=					has_spatial_attention_bias
				A__											=					rel_ad_pos_bins
				A__											=					max_rel_ad_pos
				A__											=					text_embed
				A__											=					visual_embed
				A__											=					input_size
				A__											=					num_channels
				A__											=					patch_size
				A__											=					classifier_dropout






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     version.parse("1.12"      )


			@property
			def   snake_case__	(      self		)      ->	Mapping[str, Mapping[int, str]]:
				# The order of inputs is different for question answering and sequence classification
				if self.task in ["question-answering", "sequence-classification"]:
					return OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "sequence"}),
					        ("attention_mask", {0: "batch", 1: "sequence"}),
					        ("bbox", {0: "batch", 1: "sequence"}),
					        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
					    ]		)
				else:
					return OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "sequence"}),
					        ("bbox", {0: "batch", 1: "sequence"}),
					        ("attention_mask", {0: "batch", 1: "sequence"}),
					        ("pixel_values", {0: "batch", 1: "num_channels"}),
					    ]		)


			@property
			def   snake_case__	(      self		)      ->	float:
				return 1e-5


			@property
			def   snake_case__	(      self		)      ->	int:
				return 12







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 3						,	SCREAMING_SNAKE_CASE__ = 40						,	SCREAMING_SNAKE_CASE__ = 40						,	)      ->	Mapping[str, Any]:
				setattr(processor.image_processor						,	"apply_ocr"						,	SCREAMING_SNAKE_CASE__		)

				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				A__											=					compute_effective_axis_dimension(
				    SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_batch						,	num_token_to_add=0		)
				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
				A__											=					processor.tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__		)
				A__											=					compute_effective_axis_dimension(
				    SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_sequence						,	num_token_to_add=SCREAMING_SNAKE_CASE__		)
				# Generate dummy inputs according to compute batch and sequence
				A__											=					[[" ".join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

				# Generate dummy bounding boxes
				A__											=					[[[48, 84, 73, 128]]] * batch_size

				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
				A__											=					self._generate_dummy_images(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				A__											=					dict(
				    processor(
				        SCREAMING_SNAKE_CASE__						,	text=SCREAMING_SNAKE_CASE__						,	boxes=SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__						,	)		)

				return inputs




