



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


_lowercase	:   Any    	=1000

class      snake_case__       :







						"""simple docstring"""




						def __init__(     self      ,     __lowercase      ,     __lowercase=1.2      ,     __lowercase="g"      ,     __lowercase=0.5      ,     __lowercase="-"      ,     __lowercase="test_out.jpg"      ,     __lowercase=True      ,     __lowercase=False      ,     __lowercase=None      ,     __lowercase=None      ,     __lowercase=0.7      ,     )      ->   Optional[Any]:
									"""simple docstring"""




									if isinstance(__lowercase      ,     torch.Tensor						):
												a__     :		int		 =						img.numpy().astype("""np.uint8"""						)
									if isinstance(__lowercase      ,     __lowercase						):
												a__     :		List[Any]		 =						img_tensorize(__lowercase						)
									assert isinstance(__lowercase      ,     np.ndarray						)

									a__ ,		a__     :		List[str]		 =						img.shape[1], img.shape[0]
									a__     :		Tuple		 =						mplfigure.Figure(frameon=__lowercase						)
									a__     :		Any		 =						fig.get_dpi()
									a__     :		int		 =						(width * scale + 1E-2) / dpi
									a__     :		str		 =						(height * scale + 1E-2) / dpi
									fig.set_size_inches(__lowercase      ,     __lowercase						)
									a__     :		Tuple		 =						fig.add_axes([0.0, 0.0, 1.0, 1.0]						)
									ax.axis("""off"""						)
									ax.set_xlim(0.0      ,     __lowercase						)
									ax.set_ylim(__lowercase						)

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

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase=None						)      ->   Optional[Any]:
									"""simple docstring"""




									if color is None:
												a__     :		List[Any]		 =						self.edgecolor
									((a__) ,		(a__) ,		(a__) ,		(a__))     :		List[Any]		 =						box
									a__     :		Any		 =						xa - xa
									a__     :		List[str]		 =						ya - ya
									self.ax.add_patch(
									    mpl.patches.Rectangle(
									        (xa, ya)      ,     __lowercase      ,     __lowercase      ,     fill=__lowercase      ,     edgecolor=__lowercase      ,     linewidth=self.font_size // 3      ,     alpha=self.alpha      ,     linestyle=self.linestyle      ,     )						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase=None      ,     __lowercase=None      ,     __lowercase=None      ,     __lowercase=None						)      ->   Tuple:
									"""simple docstring"""




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

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

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase						)      ->   Tuple:
									"""simple docstring"""




									a__ ,		a__ ,		a__ ,		a__     :		Tuple		 =						box
									a__     :		Dict		 =						(xa, ya)
									a__     :		Optional[int]		 =						(ya - ya) * (xa - xa)
									a__     :		str		 =						_SMALL_OBJ * self.scale
									if instance_area < small or ya - ya < 4_0 * self.scale:
												if ya >= self.height - 5:
															a__     :		Any		 =						(xa, ya)
												else:
															a__     :		Dict		 =						(xa, ya)

									a__     :		int		 =						(ya - ya) / np.sqrt(self.height * self.width						)
									a__     :		List[Any]		 =						self._change_color_brightness(__lowercase      ,     brightness_factor=0.7						)
									a__     :		Optional[int]		 =						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=__lowercase      ,     position=__lowercase      ,     color=__lowercase      ,     )

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase="g"      ,     __lowercase="left"      ,     )      ->   List[str]:
									"""simple docstring"""




									a__     :		Union[str, Any]		 =						0
									a__     :		Any		 =						self.font_size
									a__     :		Tuple		 =						np.maximum(list(mplc.to_rgb(__lowercase						)						)      ,     0.2						)
									a__     :		Tuple		 =						max(0.8      ,     np.max(__lowercase						)						)
									a__     :		str		 =						{
									    """facecolor""": """black""",
									    """alpha""": self.alpha,
									    """pad""": self.pad,
									    """edgecolor""": """none""",
									}
									a__ ,		a__     :		Any		 =						position
									self.ax.text(
									    __lowercase      ,     __lowercase      ,     __lowercase      ,     size=font_size * self.scale      ,     family="""sans-serif"""      ,     bbox=__lowercase      ,     verticalalignment="""top"""      ,     horizontalalignment=__lowercase      ,     color=__lowercase      ,     zorder=1_0      ,     rotation=__lowercase      ,     )

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase=None						)      ->   int:
									"""simple docstring"""




									if saveas is None:
												a__     :		int		 =						self.saveas
									if saveas.lower().endswith(""".jpg"""						) or saveas.lower().endswith(""".png"""						):
												cva.imwrite(
												    __lowercase      ,     self._get_buffer()[:, :, ::-1]      ,     )
									else:
												self.fig.savefig(__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase						)      ->   List[str]:
									"""simple docstring"""




									a__     :		int		 =						[self.idaobj[i] for i in classes]
									a__     :		Any		 =						[self.idaattr[i] for i in attr_classes]
									a__     :		List[Any]		 =						[
									    F'''{label} {score:.2f} {attr} {attr_score:.2f}'''
									    for label, score, attr, attr_score in zip(__lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase						)
									]
									return labels

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase						)      ->   Optional[Any]:
									"""simple docstring"""




									a__     :		List[Any]		 =						[self.idaobj[i] for i in classes]
									if scores is not None:
												if labels is None:
															a__     :		Any		 =						["""{:.0f}%""".format(s * 1_0_0						) for s in scores]
												else:
															a__     :		int		 =						["""{} {:.0f}%""".format(__lowercase      ,     s * 1_0_0						) for li, s in zip(__lowercase      ,     __lowercase						)]
									return labels

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase=2_5_5						)      ->   List[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   int:
									"""simple docstring"""




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

									a__     :		Union[str, Any]		 =						np.frombuffer(__lowercase      ,     dtype="""uint8"""						)
									a__     :		List[Any]		 =						buffer.reshape(__lowercase      ,     __lowercase      ,     4						)
									a__ ,		a__     :		Dict		 =						np.split(__lowercase      ,     [3]      ,     axis=2						)

									try:
												import numexpr as ne  # fuse them with numexpr

												a__     :		List[Any]		 =						ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""						)
									except ImportError:
												a__     :		Any		 =						alpha.astype("""float32"""						) / 2_5_5.0
												a__     :		List[str]		 =						img * (1 - alpha) + rgb * alpha

									return visualized_image.astype("""uint8"""						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase						)      ->   Any:
									"""simple docstring"""




									assert brightness_factor >= -1.0 and brightness_factor <= 1.0
									a__     :		List[str]		 =						mplc.to_rgb(__lowercase						)
									a__     :		Optional[Any]		 =						colorsys.rgb_to_hls(*mplc.to_rgb(__lowercase						)						)
									a__     :		str		 =						polygon_color[1] + (brightness_factor * polygon_color[1])
									a__     :		Optional[int]		 =						0.0 if modified_lightness < 0.0 else modified_lightness
									a__     :		Optional[int]		 =						1.0 if modified_lightness > 1.0 else modified_lightness
									a__     :		Optional[int]		 =						colorsys.hls_to_rgb(polygon_color[0]      ,     __lowercase      ,     polygon_color[2]						)
									return modified_color




_lowercase	:   Tuple    	=(
    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)
)









def 			lowerCAmelCase_  (						_lowercase       :     list)							-> bool:







			"""simple docstring"""
			if not isinstance(_lowercase		,     _lowercase):
						raise ValueError("""Input series is not valid, valid series - [1, 2/3, 2]""")
			if len(_lowercase) == 0:
						raise ValueError("""Input list must be a non empty list""")
			if len(_lowercase) == 1 and series[0] != 0:
						return True
			a__     :		Tuple		 =						[]
			a__     :		Union[str, Any]		 =						len(_lowercase)
			for i in range(0		,     _lowercase):
						if series[i] == 0:
									raise ValueError("""Input series cannot have 0 as an element""")
						rec_series.append(1 / series[i])
			a__     :		Dict		 =						rec_series[1] - rec_series[0]
			for index in range(2		,     _lowercase):
						if rec_series[index] - rec_series[index - 1] != common_diff:
									return False
			return True

def 			lowerCAmelCase_  (						_lowercase       :     list)							-> float:







			"""simple docstring"""
			if not isinstance(_lowercase		,     _lowercase):
						raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""")
			if len(_lowercase) == 0:
						raise ValueError("""Input list must be a non empty list""")
			a__     :		Optional[Any]		 =						0
			for val in series:
						answer += 1 / val
			return len(_lowercase) / answer


if __name__ == "__main__":
		import doctest

		doctest.testmod()




