

"""simple docstring"""







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


UpperCamelCase_       :       Union[str, Any]							   =		1000


class 			_lowercase		:

				def __init__(     self       :    List[str]       ,    _lowercase       :    Any       ,    _lowercase       :    int=1.2       ,    _lowercase       :    List[str]="g"       ,    _lowercase       :    Dict=0.5       ,    _lowercase       :    Dict="-"       ,    _lowercase       :    Optional[int]="test_out.jpg"       ,    _lowercase       :    int=True       ,    _lowercase       :    Optional[int]=False       ,    _lowercase       :    Optional[int]=None       ,    _lowercase       :    List[str]=None       ,    _lowercase       :    Any=0.7       ,    ):


										'''simple docstring'''
										if isinstance(_lowercase       ,    torch.Tensor     ):
																UpperCAmelCase__						=		img.numpy().astype("""np.uint8"""     )
										if isinstance(_lowercase       ,    _lowercase     ):
																UpperCAmelCase__						=		img_tensorize(_lowercase     )
										assert isinstance(_lowercase       ,    np.ndarray     )

										UpperCAmelCase__	,      UpperCAmelCase__						=		img.shape[1], img.shape[0]
										UpperCAmelCase__						=		mplfigure.Figure(frameon=_lowercase     )
										UpperCAmelCase__						=		fig.get_dpi()
										UpperCAmelCase__						=		(width * scale + 1e-2) / dpi
										UpperCAmelCase__						=		(height * scale + 1e-2) / dpi
										fig.set_size_inches(_lowercase       ,    _lowercase     )
										UpperCAmelCase__						=		fig.add_axes([0.0, 0.0, 1.0, 1.0]     )
										ax.axis("""off"""     )
										ax.set_xlim(0.0       ,    _lowercase     )
										ax.set_ylim(_lowercase     )

										UpperCAmelCase__						=		saveas
										UpperCAmelCase__						=		rgb
										UpperCAmelCase__						=		pynb
										UpperCAmelCase__						=		img
										UpperCAmelCase__						=		edgecolor
										UpperCAmelCase__						=		0.5
										UpperCAmelCase__						=		linestyle
										UpperCAmelCase__						=		int(np.sqrt(min(_lowercase       ,    _lowercase     )     ) * scale // 3     )
										UpperCAmelCase__						=		width
										UpperCAmelCase__						=		height
										UpperCAmelCase__						=		scale
										UpperCAmelCase__						=		fig
										UpperCAmelCase__						=		ax
										UpperCAmelCase__						=		pad
										UpperCAmelCase__						=		idaobj
										UpperCAmelCase__						=		idaattr
										UpperCAmelCase__						=		FigureCanvasAgg(_lowercase     )

				def        A (     self       :    Optional[int]       ,    _lowercase       :    Any       ,    _lowercase       :    Any=None     ):


										'''simple docstring'''
										if color is None:
																UpperCAmelCase__						=		self.edgecolor
										((UpperCAmelCase__)	,      (UpperCAmelCase__)	,      (UpperCAmelCase__)	,      (UpperCAmelCase__))						=		box
										UpperCAmelCase__						=		xa - xa
										UpperCAmelCase__						=		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        A (     self       :    Any       ,    _lowercase       :    Dict       ,    _lowercase       :    Dict=None       ,    _lowercase       :    Optional[int]=None       ,    _lowercase       :    Optional[int]=None       ,    _lowercase       :    List[Any]=None     ):


										'''simple docstring'''
										if len(boxes.shape     ) > 2:
																UpperCAmelCase__						=		boxes[0]
										if len(obj_ids.shape     ) > 1:
																UpperCAmelCase__						=		obj_ids[0]
										if len(obj_scores.shape     ) > 1:
																UpperCAmelCase__						=		obj_scores[0]
										if len(attr_ids.shape     ) > 1:
																UpperCAmelCase__						=		attr_ids[0]
										if len(attr_scores.shape     ) > 1:
																UpperCAmelCase__						=		attr_scores[0]
										if isinstance(_lowercase       ,    torch.Tensor     ):
																UpperCAmelCase__						=		boxes.numpy()
										if isinstance(_lowercase       ,    _lowercase     ):
																UpperCAmelCase__						=		np.array(_lowercase     )
										assert isinstance(_lowercase       ,    np.ndarray     )
										UpperCAmelCase__						=		np.prod(boxes[:, 2:] - boxes[:, :2]       ,    axis=1     )
										UpperCAmelCase__						=		np.argsort(-areas     ).tolist()
										UpperCAmelCase__						=		boxes[sorted_idxs] if boxes is not None else None
										UpperCAmelCase__						=		obj_ids[sorted_idxs] if obj_ids is not None else None
										UpperCAmelCase__						=		obj_scores[sorted_idxs] if obj_scores is not None else None
										UpperCAmelCase__						=		attr_ids[sorted_idxs] if attr_ids is not None else None
										UpperCAmelCase__						=		attr_scores[sorted_idxs] if attr_scores is not None else None

										UpperCAmelCase__						=		[self._random_color(maximum=1     ) for _ in range(len(_lowercase     )     )]
										UpperCAmelCase__						=		[assigned_colors[idx] for idx in sorted_idxs]
										if obj_ids is not None:
																UpperCAmelCase__						=		self._create_text_labels_attr(_lowercase       ,    _lowercase       ,    _lowercase       ,    _lowercase     )
																for i in range(len(_lowercase     )     ):
																						UpperCAmelCase__						=		assigned_colors[i]
																						self.add_box(boxes[i]       ,    _lowercase     )
																						self.draw_labels(labels[i]       ,    boxes[i]       ,    _lowercase     )

				def        A (     self       :    List[Any]       ,    _lowercase       :    Tuple       ,    _lowercase       :    Optional[int]       ,    _lowercase       :    Optional[int]     ):


										'''simple docstring'''
										UpperCAmelCase__	,      UpperCAmelCase__	,      UpperCAmelCase__	,      UpperCAmelCase__						=		box
										UpperCAmelCase__						=		(xa, ya)
										UpperCAmelCase__						=		(ya - ya) * (xa - xa)
										UpperCAmelCase__						=		_SMALL_OBJ * self.scale
										if instance_area < small or ya - ya < 4_0 * self.scale:
																if ya >= self.height - 5:
																						UpperCAmelCase__						=		(xa, ya)
																else:
																						UpperCAmelCase__						=		(xa, ya)

										UpperCAmelCase__						=		(ya - ya) / np.sqrt(self.height * self.width     )
										UpperCAmelCase__						=		self._change_color_brightness(_lowercase       ,    brightness_factor=0.7     )
										UpperCAmelCase__						=		np.clip((height_ratio - 0.02) / 0.08 + 1       ,    1.2       ,    2     )
										font_size *= 0.75 * self.font_size

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

				def        A (     self       :    List[str]       ,    _lowercase       :    int       ,    _lowercase       :    Union[str, Any]       ,    _lowercase       :    str="g"       ,    _lowercase       :    Tuple="left"       ,    ):


										'''simple docstring'''
										UpperCAmelCase__						=		0
										UpperCAmelCase__						=		self.font_size
										UpperCAmelCase__						=		np.maximum(list(mplc.to_rgb(_lowercase     )     )       ,    0.2     )
										UpperCAmelCase__						=		max(0.8       ,    np.max(_lowercase     )     )
										UpperCAmelCase__						=		{
										    """facecolor""": """black""",
										    """alpha""": self.alpha,
										    """pad""": self.pad,
										    """edgecolor""": """none""",
										}
										UpperCAmelCase__	,      UpperCAmelCase__						=		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        A (     self       :    Tuple       ,    _lowercase       :    Dict=None     ):


										'''simple docstring'''
										if saveas is None:
																UpperCAmelCase__						=		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        A (     self       :    int       ,    _lowercase       :    int       ,    _lowercase       :    Union[str, Any]       ,    _lowercase       :    Any       ,    _lowercase       :    Tuple     ):


										'''simple docstring'''
										UpperCAmelCase__						=		[self.idaobj[i] for i in classes]
										UpperCAmelCase__						=		[self.idaattr[i] for i in attr_classes]
										UpperCAmelCase__						=		[
										    f'{label} {score:.2f} {attr} {attr_score:.2f}'
										    for label, score, attr, attr_score in zip(_lowercase       ,    _lowercase       ,    _lowercase       ,    _lowercase     )
										]
										return labels

				def        A (     self       :    Union[str, Any]       ,    _lowercase       :    List[Any]       ,    _lowercase       :    Tuple     ):


										'''simple docstring'''
										UpperCAmelCase__						=		[self.idaobj[i] for i in classes]
										if scores is not None:
																if labels is None:
																						UpperCAmelCase__						=		["""{:.0f}%""".format(s * 1_0_0     ) for s in scores]
																else:
																						UpperCAmelCase__						=		["""{} {:.0f}%""".format(_lowercase       ,    s * 1_0_0     ) for li, s in zip(_lowercase       ,    _lowercase     )]
										return labels

				def        A (     self       :    Optional[Any]       ,    _lowercase       :    str=2_5_5     ):


										'''simple docstring'''
										UpperCAmelCase__						=		np.random.randint(0       ,    len(_COLORS     )     )
										UpperCAmelCase__						=		_COLORS[idx] * maximum
										if not self.rgb:
																UpperCAmelCase__						=		ret[::-1]
										return ret

				def        A (     self       :    Tuple     ):


										'''simple docstring'''
										if not self.pynb:
																UpperCAmelCase__	,      UpperCAmelCase__						=		self.canvas.print_to_buffer()
																if (width, height) != (self.width, self.height):
																						UpperCAmelCase__						=		cva.resize(self.img       ,    (width, height)     )
																else:
																						UpperCAmelCase__						=		self.img
										else:
																UpperCAmelCase__						=		io.BytesIO()  # works for cairo backend
																self.canvas.print_rgba(_lowercase     )
																UpperCAmelCase__	,      UpperCAmelCase__						=		self.width, self.height
																UpperCAmelCase__						=		buf.getvalue()
																UpperCAmelCase__						=		self.img

										UpperCAmelCase__						=		np.frombuffer(_lowercase       ,    dtype="""uint8"""     )
										UpperCAmelCase__						=		buffer.reshape(_lowercase       ,    _lowercase       ,    4     )
										UpperCAmelCase__	,      UpperCAmelCase__						=		np.split(_lowercase       ,    [3]       ,    axis=2     )

										try:
																import numexpr as ne  # fuse them with numexpr

																UpperCAmelCase__						=		ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""     )
										except ImportError:
																UpperCAmelCase__						=		alpha.astype("""float32"""     ) / 255.0
																UpperCAmelCase__						=		img * (1 - alpha) + rgb * alpha

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

				def        A (     self       :    Tuple       ,    _lowercase       :    Any       ,    _lowercase       :    str     ):


										'''simple docstring'''
										assert brightness_factor >= -1.0 and brightness_factor <= 1.0
										UpperCAmelCase__						=		mplc.to_rgb(_lowercase     )
										UpperCAmelCase__						=		colorsys.rgb_to_hls(*mplc.to_rgb(_lowercase     )     )
										UpperCAmelCase__						=		polygon_color[1] + (brightness_factor * polygon_color[1])
										UpperCAmelCase__						=		0.0 if modified_lightness < 0.0 else modified_lightness
										UpperCAmelCase__						=		1.0 if modified_lightness > 1.0 else modified_lightness
										UpperCAmelCase__						=		colorsys.hls_to_rgb(polygon_color[0]       ,    _lowercase       ,    polygon_color[2]     )
										return modified_color
UpperCamelCase_       :       List[Any]							   =		(
    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)
)


"""simple docstring"""







from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
UpperCamelCase_       :       Optional[int]							   =		load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_       :       int							   =		train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

UpperCamelCase_       :       str							   =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
UpperCamelCase_       :       Union[str, Any]							   =		[[1, 2, 1, 4], [2, 3, 4, 5]]

UpperCamelCase_       :       Union[str, Any]							   =		knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris["target_names"][prediction]}"""
)

