"""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        =     imread(R'digital_image_processing/image_data/lena_small.jpg')
_A        =     cvtColor(img, COLOR_BGR2GRAY)




def     SCREAMING_SNAKE_CASE       (	)				-> Any:
				SCREAMING_SNAKE_CASE__			=					cn.convert_to_negative(_UpperCAmelCase							)
				# assert negative_img array for at least one True
				assert negative_img.any()




def     SCREAMING_SNAKE_CASE       (	)				-> Tuple:

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




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




def     SCREAMING_SNAKE_CASE       (	)				-> int:
				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(_UpperCAmelCase							)
				# assert canny array for at least one True
				assert canny_array.any()




def     SCREAMING_SNAKE_CASE       (	)				-> Optional[Any]:
				assert gg.gaussian_filter(_UpperCAmelCase	,       5	,       sigma=0.9							).all()




def     SCREAMING_SNAKE_CASE       (	)				-> Tuple:
				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(_UpperCAmelCase	,       _UpperCAmelCase							).astype(_UpperCAmelCase							)
				assert res.any()




def     SCREAMING_SNAKE_CASE       (	)				-> Tuple:
				assert med.median_filter(_UpperCAmelCase	,       3							).any()




def     SCREAMING_SNAKE_CASE       (	)				-> List[Any]:
				SCREAMING_SNAKE_CASE__			=					sob.sobel_filter(_UpperCAmelCase							)
				assert grad.any() and theta.any()




def     SCREAMING_SNAKE_CASE       (	)				-> int:
				SCREAMING_SNAKE_CASE__			=					sp.make_sepia(_UpperCAmelCase	,       20							)
				assert sepia.all()




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase = "digital_image_processing/image_data/lena_small.jpg"							)				-> Union[str, Any]:
				SCREAMING_SNAKE_CASE__			=					bs.Burkes(imread(_UpperCAmelCase	,       1							)	,       120							)
				burkes.process()
				assert burkes.output_img.any()




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase = "digital_image_processing/image_data/lena_small.jpg"	,       )				-> Optional[int]:
				SCREAMING_SNAKE_CASE__			=					rs.NearestNeighbour(imread(_UpperCAmelCase	,       1							)	,       400	,       200							)
				nn.process()
				assert nn.output.any()




def     SCREAMING_SNAKE_CASE       (	)				-> Tuple:
				SCREAMING_SNAKE_CASE__			=					"digital_image_processing/image_data/lena.jpg"

				# Reading the image and converting it to grayscale.
				SCREAMING_SNAKE_CASE__			=					imread(_UpperCAmelCase	,       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(
				    _UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase							)

				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(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase							)

				assert lbp_image.any()






from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from ..image_utils import load_image


if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

UpperCamelCase_						:     int	       =							logging.get_logger(__name__)


UpperCamelCase_						:     List[Any]	       =							Dict[str, Any]
UpperCamelCase_						:     Optional[int]	       =							List[Prediction]




@add_end_docstrings(__snake_case       )
class   __lowercase						(						__snake_case       ):






	def __init__(self				: Dict      ,	*snake_case				: List[Any]      ,	**snake_case				: Tuple    ) ->			Any:
							super().__init__(*snake_case      ,	**snake_case    )

							if self.framework == "tf":
													raise ValueError(F"""The {self.__class__} is only available in PyTorch."""    )

							requires_backends(self      ,	"vision"    )
							self.check_model_type(
							    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()    )    )






	def       _a(self				: Optional[int]      ,	**snake_case				: Union[str, Any]    ) ->			Any:
							_lowercase       :	List[Any]           =   {}
							if "threshold" in kwargs:
													_lowercase       :	Optional[Any]           =   kwargs["threshold"]
							return {}, {}, postprocess_kwargs






	def __call__(self				: str      ,	*snake_case				: Dict      ,	**snake_case				: Optional[int]    ) ->			Union[Predictions, List[Prediction]]:
							return super().__call__(*snake_case      ,	**snake_case    )






	def       _a(self				: Tuple      ,	snake_case				: str    ) ->			Dict:
							_lowercase       :	List[str]           =   load_image(snake_case    )
							_lowercase       :	str           =   torch.IntTensor([[image.height, image.width]]    )
							_lowercase       :	int           =   self.image_processor(images=[image]      ,	return_tensors="pt"    )
							if self.tokenizer is not None:
													_lowercase       :	List[Any]           =   self.tokenizer(text=inputs["words"]      ,	boxes=inputs["boxes"]      ,	return_tensors="pt"    )
							_lowercase       :	str           =   target_size
							return inputs






	def       _a(self				: List[Any]      ,	snake_case				: Any    ) ->			str:
							_lowercase       :	Dict           =   model_inputs.pop("target_size"    )
							_lowercase       :	str           =   self.model(**snake_case    )
							_lowercase       :	Union[str, Any]           =   outputs.__class__({"target_size": target_size, **outputs}    )
							if self.tokenizer is not None:
													_lowercase       :	str           =   model_inputs["bbox"]
							return model_outputs






	def       _a(self				: str      ,	snake_case				: Tuple      ,	snake_case				: Dict=0.9    ) ->			List[str]:
							_lowercase       :	Optional[Any]           =   model_outputs["target_size"]
							if self.tokenizer is not None:
													# This is a LayoutLMForTokenClassification variant.
													# The OCR got the boxes and the model classified the words.
													_lowercase      ,    _lowercase       :	str           =   target_size[0].tolist()

													def unnormalize(snake_case				: str    ):
																			return self._get_bounding_box(
																			    torch.Tensor(
																			        [
																			            (width * bbox[0] / 1000),
																			            (height * bbox[1] / 1000),
																			            (width * bbox[2] / 1000),
																			            (height * bbox[3] / 1000),
																			        ]    )    )

													_lowercase      ,    _lowercase       :	Any           =   model_outputs["logits"].squeeze(0    ).softmax(dim=-1    ).max(dim=-1    )
													_lowercase       :	Optional[Any]           =   [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
													_lowercase       :	Dict           =   [unnormalize(snake_case    ) for bbox in model_outputs["bbox"].squeeze(0    )]
													_lowercase       :	Tuple           =   ["score", "label", "box"]
													_lowercase       :	int           =   [dict(zip(snake_case      ,	snake_case    )    ) for vals in zip(scores.tolist()      ,	snake_case      ,	snake_case    ) if vals[0] > threshold]
							else:
													# This is a regular ForObjectDetectionModel
													_lowercase       :	str           =   self.image_processor.post_process_object_detection(snake_case      ,	snake_case      ,	snake_case    )
													_lowercase       :	List[Any]           =   raw_annotations[0]
													_lowercase       :	str           =   raw_annotation["scores"]
													_lowercase       :	Optional[int]           =   raw_annotation["labels"]
													_lowercase       :	Dict           =   raw_annotation["boxes"]

													_lowercase       :	int           =   scores.tolist()
													_lowercase       :	List[str]           =   [self.model.config.idalabel[label.item()] for label in labels]
													_lowercase       :	List[Any]           =   [self._get_bounding_box(snake_case    ) for box in boxes]

													# {"scores": [...], ...} --> [{"score":x, ...}, ...]
													_lowercase       :	Union[str, Any]           =   ["score", "label", "box"]
													_lowercase       :	Tuple           =   [
													    dict(zip(snake_case      ,	snake_case    )    )
													    for vals in zip(raw_annotation["scores"]      ,	raw_annotation["labels"]      ,	raw_annotation["boxes"]    )
													]

							return annotation






	def       _a(self				: int      ,	snake_case				: "torch.Tensor"    ) ->			Dict[str, int]:
							if self.framework != "pt":
													raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."    )
							_lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase       :	Optional[int]           =   box.int().tolist()
							_lowercase       :	Tuple           =   {
							    "xmin": xmin,
							    "ymin": ymin,
							    "xmax": xmax,
							    "ymax": ymax,
							}
							return bbox




