



import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


__A             =1_0_0_0

class 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			Optional[Any]	,	a_							:			Optional[int]	,	a_							:			Tuple=1.2	,	a_							:			Optional[Any]="g"	,	a_							:			str=0.5	,	a_							:			Tuple="-"	,	a_							:			Union[str, Any]="test_out.jpg"	,	a_							:			Optional[int]=True	,	a_							:			Any=False	,	a_							:			Optional[Any]=None	,	a_							:			Tuple=None	,	a_							:			Tuple=0.7	,	):
					'''simple docstring'''





					if isinstance(a_	,	torch.Tensor			):
							__UpperCAmelCase   :					Dict													=     img.numpy().astype('''np.uint8'''			)
					if isinstance(a_	,	a_			):
							__UpperCAmelCase   :					Union[str, Any]													=     img_tensorize(a_			)
					assert isinstance(a_	,	np.ndarray			)

					__UpperCAmelCase   :					Union[str, Any]													=     img.shape[1], img.shape[0]
					__UpperCAmelCase   :					Union[str, Any]													=     mplfigure.Figure(frameon=a_			)
					__UpperCAmelCase   :					str													=     fig.get_dpi()
					__UpperCAmelCase   :					Any													=     (width * scale + 1e-2) / dpi
					__UpperCAmelCase   :					List[str]													=     (height * scale + 1e-2) / dpi
					fig.set_size_inches(a_	,	a_			)
					__UpperCAmelCase   :					Dict													=     fig.add_axes([0.0, 0.0, 1.0, 1.0]			)
					ax.axis('''off'''			)
					ax.set_xlim(0.0	,	a_			)
					ax.set_ylim(a_			)

					__UpperCAmelCase   :					Optional[Any]													=     saveas
					__UpperCAmelCase   :					Optional[int]													=     rgb
					__UpperCAmelCase   :					Optional[Any]													=     pynb
					__UpperCAmelCase   :					List[str]													=     img
					__UpperCAmelCase   :					Optional[int]													=     edgecolor
					__UpperCAmelCase   :					Tuple													=     0.5
					__UpperCAmelCase   :					List[Any]													=     linestyle
					__UpperCAmelCase   :					List[Any]													=     int(np.sqrt(min(a_	,	a_			)			) * scale // 3			)
					__UpperCAmelCase   :					List[Any]													=     width
					__UpperCAmelCase   :					Dict													=     height
					__UpperCAmelCase   :					Optional[Any]													=     scale
					__UpperCAmelCase   :					Tuple													=     fig
					__UpperCAmelCase   :					Any													=     ax
					__UpperCAmelCase   :					Optional[int]													=     pad
					__UpperCAmelCase   :					Any													=     idaobj
					__UpperCAmelCase   :					str													=     idaattr
					__UpperCAmelCase   :					int													=     FigureCanvasAgg(a_			)




			def 			snake_case__					(       self							:			Union[str, Any]	,	a_							:			str	,	a_							:			Tuple=None			):
					'''simple docstring'''





					if color is None:
							__UpperCAmelCase   :					Union[str, Any]													=     self.edgecolor
					(__UpperCAmelCase)   :					Dict													=     box
					__UpperCAmelCase   :					Optional[int]													=     xa - xa
					__UpperCAmelCase   :					Dict													=     ya - ya
					self.ax.add_patch(
					    mpl.patches.Rectangle(
					        (xa, ya)	,	a_	,	a_	,	fill=a_	,	edgecolor=a_	,	linewidth=self.font_size // 3	,	alpha=self.alpha	,	linestyle=self.linestyle	,	)			)




			def 			snake_case__					(       self							:			Optional[int]	,	a_							:			Tuple	,	a_							:			Optional[int]=None	,	a_							:			List[Any]=None	,	a_							:			Tuple=None	,	a_							:			Dict=None			):
					'''simple docstring'''





					if len(boxes.shape			) > 2:
							__UpperCAmelCase   :					Optional[Any]													=     boxes[0]
					if len(obj_ids.shape			) > 1:
							__UpperCAmelCase   :					int													=     obj_ids[0]
					if len(obj_scores.shape			) > 1:
							__UpperCAmelCase   :					Tuple													=     obj_scores[0]
					if len(attr_ids.shape			) > 1:
							__UpperCAmelCase   :					Optional[Any]													=     attr_ids[0]
					if len(attr_scores.shape			) > 1:
							__UpperCAmelCase   :					Tuple													=     attr_scores[0]
					if isinstance(a_	,	torch.Tensor			):
							__UpperCAmelCase   :					str													=     boxes.numpy()
					if isinstance(a_	,	a_			):
							__UpperCAmelCase   :					str													=     np.array(a_			)
					assert isinstance(a_	,	np.ndarray			)
					__UpperCAmelCase   :					Optional[int]													=     np.prod(boxes[:, 2:] - boxes[:, :2]	,	axis=1			)
					__UpperCAmelCase   :					int													=     np.argsort(-areas			).tolist()
					__UpperCAmelCase   :					Union[str, Any]													=     boxes[sorted_idxs] if boxes is not None else None
					__UpperCAmelCase   :					int													=     obj_ids[sorted_idxs] if obj_ids is not None else None
					__UpperCAmelCase   :					List[str]													=     obj_scores[sorted_idxs] if obj_scores is not None else None
					__UpperCAmelCase   :					List[str]													=     attr_ids[sorted_idxs] if attr_ids is not None else None
					__UpperCAmelCase   :					Tuple													=     attr_scores[sorted_idxs] if attr_scores is not None else None

					__UpperCAmelCase   :					Optional[Any]													=     [self._random_color(maximum=1			) for _ in range(len(a_			)			)]
					__UpperCAmelCase   :					Tuple													=     [assigned_colors[idx] for idx in sorted_idxs]
					if obj_ids is not None:
							__UpperCAmelCase   :					List[str]													=     self._create_text_labels_attr(a_	,	a_	,	a_	,	a_			)
							for i in range(len(a_			)			):
									__UpperCAmelCase   :					Any													=     assigned_colors[i]
									self.add_box(boxes[i]	,	a_			)
									self.draw_labels(labels[i]	,	boxes[i]	,	a_			)




			def 			snake_case__					(       self							:			Tuple	,	a_							:			Tuple	,	a_							:			Union[str, Any]	,	a_							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					Any													=     box
					__UpperCAmelCase   :					Dict													=     (xa, ya)
					__UpperCAmelCase   :					List[Any]													=     (ya - ya) * (xa - xa)
					__UpperCAmelCase   :					Dict													=     _SMALL_OBJ * self.scale
					if instance_area < small or ya - ya < 40 * self.scale:
							if ya >= self.height - 5:
									__UpperCAmelCase   :					Dict													=     (xa, ya)
							else:
									__UpperCAmelCase   :					Dict													=     (xa, ya)

					__UpperCAmelCase   :					str													=     (ya - ya) / np.sqrt(self.height * self.width			)
					__UpperCAmelCase   :					List[Any]													=     self._change_color_brightness(a_	,	brightness_factor=0.7			)
					__UpperCAmelCase   :					Tuple													=     np.clip((height_ratio - 0.0_2) / 0.0_8 + 1	,	1.2	,	2			)
					font_size *= 0.7_5 * self.font_size

					self.draw_text(
					    text=a_	,	position=a_	,	color=a_	,	)




			def 			snake_case__					(       self							:			int	,	a_							:			Optional[Any]	,	a_							:			List[str]	,	a_							:			List[str]="g"	,	a_							:			Optional[int]="left"	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Union[str, Any]													=     0
					__UpperCAmelCase   :					List[str]													=     self.font_size
					__UpperCAmelCase   :					Dict													=     np.maximum(list(mplc.to_rgb(a_			)			)	,	0.2			)
					__UpperCAmelCase   :					List[Any]													=     max(0.8	,	np.max(a_			)			)
					__UpperCAmelCase   :					Tuple													=     {
					    '''facecolor''': '''black''',
					    '''alpha''': self.alpha,
					    '''pad''': self.pad,
					    '''edgecolor''': '''none''',
					}
					__UpperCAmelCase   :					Dict													=     position
					self.ax.text(
					    a_	,	a_	,	a_	,	size=font_size * self.scale	,	family='''sans-serif'''	,	bbox=a_	,	verticalalignment='''top'''	,	horizontalalignment=a_	,	color=a_	,	zorder=10	,	rotation=a_	,	)




			def 			snake_case__					(       self							:			Any	,	a_							:			Optional[Any]=None			):
					'''simple docstring'''





					if saveas is None:
							__UpperCAmelCase   :					str													=     self.saveas
					if saveas.lower().endswith('''.jpg'''			) or saveas.lower().endswith('''.png'''			):
							cva.imwrite(
							    a_	,	self._get_buffer()[:, :, ::-1]	,	)
					else:
							self.fig.savefig(a_			)




			def 			snake_case__					(       self							:			Optional[Any]	,	a_							:			List[str]	,	a_							:			List[str]	,	a_							:			Any	,	a_							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     [self.idaobj[i] for i in classes]
					__UpperCAmelCase   :					Optional[Any]													=     [self.idaattr[i] for i in attr_classes]
					__UpperCAmelCase   :					Dict													=     [
					    F'{label} {score:.2f} {attr} {attr_score:.2f}'
					    for label, score, attr, attr_score in zip(a_	,	a_	,	a_	,	a_			)
					]
					return labels




			def 			snake_case__					(       self							:			Dict	,	a_							:			Optional[Any]	,	a_							:			int			):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     [self.idaobj[i] for i in classes]
					if scores is not None:
							if labels is None:
									__UpperCAmelCase   :					List[str]													=     ['''{:.0f}%'''.format(s * 1_00			) for s in scores]
							else:
									__UpperCAmelCase   :					Any													=     ['''{} {:.0f}%'''.format(a_	,	s * 1_00			) for li, s in zip(a_	,	a_			)]
					return labels




			def 			snake_case__					(       self							:			Dict	,	a_							:			Dict=2_55			):
					'''simple docstring'''





					__UpperCAmelCase   :					Dict													=     np.random.randint(0	,	len(_COLORS			)			)
					__UpperCAmelCase   :					Tuple													=     _COLORS[idx] * maximum
					if not self.rgb:
							__UpperCAmelCase   :					Union[str, Any]													=     ret[::-1]
					return ret




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					if not self.pynb:
							__UpperCAmelCase   :					Optional[int]													=     self.canvas.print_to_buffer()
							if (width, height) != (self.width, self.height):
									__UpperCAmelCase   :					Union[str, Any]													=     cva.resize(self.img	,	(width, height)			)
							else:
									__UpperCAmelCase   :					List[str]													=     self.img
					else:
							__UpperCAmelCase   :					Dict													=     io.BytesIO()  # works for cairo backend
							self.canvas.print_rgba(a_			)
							__UpperCAmelCase   :					int													=     self.width, self.height
							__UpperCAmelCase   :					int													=     buf.getvalue()
							__UpperCAmelCase   :					Union[str, Any]													=     self.img

					__UpperCAmelCase   :					List[str]													=     np.frombuffer(a_	,	dtype='''uint8'''			)
					__UpperCAmelCase   :					List[str]													=     buffer.reshape(a_	,	a_	,	4			)
					__UpperCAmelCase   :					Dict													=     np.split(a_	,	[3]	,	axis=2			)

					try:
							import numexpr as ne  # fuse them with numexpr

							__UpperCAmelCase   :					List[Any]													=     ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''			)
					except ImportError:
							__UpperCAmelCase   :					List[str]													=     alpha.astype('''float32'''			) / 2_55.0
							__UpperCAmelCase   :					str													=     img * (1 - alpha) + rgb * alpha

					return visualized_image.astype('''uint8'''			)




			def 			snake_case__					(       self							:			str	,	a_							:			Union[str, Any]	,	a_							:			Union[str, Any]			):
					'''simple docstring'''





					assert brightness_factor >= -1.0 and brightness_factor <= 1.0
					__UpperCAmelCase   :					Optional[int]													=     mplc.to_rgb(a_			)
					__UpperCAmelCase   :					int													=     colorsys.rgb_to_hls(*mplc.to_rgb(a_			)			)
					__UpperCAmelCase   :					Union[str, Any]													=     polygon_color[1] + (brightness_factor * polygon_color[1])
					__UpperCAmelCase   :					List[str]													=     0.0 if modified_lightness < 0.0 else modified_lightness
					__UpperCAmelCase   :					Optional[Any]													=     1.0 if modified_lightness > 1.0 else modified_lightness
					__UpperCAmelCase   :					List[Any]													=     colorsys.hls_to_rgb(polygon_color[0]	,	a_	,	polygon_color[2]			)
					return modified_color
__A             =(
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)










import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			int	,	a_							:			str	,	a_							:			List[str]=sys.maxsize			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     '''bilinear'''
					__UpperCAmelCase   :					str													=     max_size
					__UpperCAmelCase   :					Dict													=     short_edge_length




			def __call__(       self							:			str	,	a_							:			Union[str, Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     []
					for img in imgs:
							__UpperCAmelCase							, __UpperCAmelCase   :					List[str]													=     img.shape[:2]
							# later: provide list and randomly choose index for resize
							__UpperCAmelCase   :					Optional[Any]													=     np.random.randint(self.short_edge_length[0]	,	self.short_edge_length[1] + 1			)
							if size == 0:
									return img
							__UpperCAmelCase   :					Optional[Any]													=     size * 1.0 / min(a_	,	a_			)
							if h < w:
									__UpperCAmelCase							, __UpperCAmelCase   :					List[str]													=     size, scale * w
							else:
									__UpperCAmelCase							, __UpperCAmelCase   :					Union[str, Any]													=     scale * h, size
							if max(a_	,	a_			) > self.max_size:
									__UpperCAmelCase   :					Union[str, Any]													=     self.max_size * 1.0 / max(a_	,	a_			)
									__UpperCAmelCase   :					int													=     newh * scale
									__UpperCAmelCase   :					Union[str, Any]													=     neww * scale
							__UpperCAmelCase   :					Any													=     int(neww + 0.5			)
							__UpperCAmelCase   :					Optional[int]													=     int(newh + 0.5			)

							if img.dtype == np.uinta:
									__UpperCAmelCase   :					Any													=     Image.fromarray(a_			)
									__UpperCAmelCase   :					Any													=     pil_image.resize((neww, newh)	,	PILImageResampling.BILINEAR			)
									__UpperCAmelCase   :					List[Any]													=     np.asarray(a_			)
							else:
									__UpperCAmelCase   :					Union[str, Any]													=     img.permute(2	,	0	,	1			).unsqueeze(0			)  # 3, 0, 1)  # hw(c) -> nchw
									__UpperCAmelCase   :					Optional[int]													=     nn.functional.interpolate(
									    a_	,	(newh, neww)	,	mode=self.interp_method	,	align_corners=a_			).squeeze(0			)
							img_augs.append(a_			)

					return img_augs

class 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			Tuple	,	a_							:			List[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]	,	cfg.INPUT.MAX_SIZE_TEST			)
					__UpperCAmelCase   :					List[str]													=     cfg.INPUT.FORMAT
					__UpperCAmelCase   :					int													=     cfg.SIZE_DIVISIBILITY
					__UpperCAmelCase   :					int													=     cfg.PAD_VALUE
					__UpperCAmelCase   :					Any													=     cfg.INPUT.MAX_SIZE_TEST
					__UpperCAmelCase   :					Tuple													=     cfg.MODEL.DEVICE
					__UpperCAmelCase   :					Union[str, Any]													=     torch.tensor(cfg.MODEL.PIXEL_STD			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)	,	1	,	1			)
					__UpperCAmelCase   :					List[Any]													=     torch.tensor(cfg.MODEL.PIXEL_MEAN			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)	,	1	,	1			)
					__UpperCAmelCase   :					Union[str, Any]													=     lambda a_			: (x - self.pixel_mean) / self.pixel_std




			def 			snake_case__					(       self							:			int	,	a_							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     tuple(max(a_			) for s in zip(*[img.shape for img in images]			)			)
					__UpperCAmelCase   :					Any													=     [im.shape[-2:] for im in images]
					__UpperCAmelCase   :					Union[str, Any]													=     [
					    nn.functional.pad(
					        a_	,	[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]	,	value=self.pad_value	,	)
					    for size, im in zip(a_	,	a_			)
					]

					return torch.stack(a_			), torch.tensor(a_			)




			def __call__(       self							:			List[str]	,	a_							:			int	,	a_							:			int=False			):
					'''simple docstring'''





					with torch.no_grad():
							if not isinstance(a_	,	a_			):
									__UpperCAmelCase   :					Optional[Any]													=     [images]
							if single_image:
									assert len(a_			) == 1
							for i in range(len(a_			)			):
									if isinstance(images[i]	,	torch.Tensor			):
											images.insert(a_	,	images.pop(a_			).to(self.device			).float()			)
									elif not isinstance(images[i]	,	torch.Tensor			):
											images.insert(
											    a_	,	torch.as_tensor(img_tensorize(images.pop(a_			)	,	input_format=self.input_format			)			)
											    .to(self.device			)
											    .float()	,	)
            # resize smallest edge
							__UpperCAmelCase   :					Optional[Any]													=     torch.tensor([im.shape[:2] for im in images]			)
							__UpperCAmelCase   :					Dict													=     self.aug(a_			)
							# transpose images and convert to torch tensors
							# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
							# now normalize before pad to avoid useless arithmetic
							__UpperCAmelCase   :					Optional[int]													=     [self.normalizer(a_			) for x in images]
							# now pad them to do the following operations
							__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     self.pad(a_			)
							# Normalize

							if self.size_divisibility > 0:
									raise NotImplementedError()
							# pad
							__UpperCAmelCase   :					str													=     torch.true_divide(a_	,	a_			)
							if single_image:
									return images[0], sizes[0], scales_yx[0]
							else:
									return images, sizes, scales_yx

def 	a						(				_UpperCAmelCase							: str			,       _UpperCAmelCase							: Optional[int]						):







		'''simple docstring'''





		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes

def 	a						(				_UpperCAmelCase							: Any			,       _UpperCAmelCase							: Tuple[int, int]						):







		'''simple docstring'''





		assert torch.isfinite(_UpperCAmelCase						).all(), "Box tensor contains infinite or NaN!"
		__UpperCAmelCase							, __UpperCAmelCase   :					List[str]													=     box_size
		tensor[:, 0].clamp_(min=0			,       max=_UpperCAmelCase						)
		tensor[:, 1].clamp_(min=0			,       max=_UpperCAmelCase						)
		tensor[:, 2].clamp_(min=0			,       max=_UpperCAmelCase						)
		tensor[:, 3].clamp_(min=0			,       max=_UpperCAmelCase						)





