



'''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


_lowercase       :						Dict						=1000






class 			_SCREAMING_SNAKE_CASE				:



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





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

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

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



			def 				lowerCAmelCase (     self		:						Union[str, Any]    ,			__UpperCamelCase		:						Optional[Any]    ,			__UpperCamelCase		:						Optional[Any]=None			)      ->      str:
							"""simple docstring"""





							if color is None:
											snake_case__      :   str      =    self.edgecolor
							((snake_case__)  ,      (snake_case__)  ,      (snake_case__)  ,      (snake_case__))      :   Any      =    box
							snake_case__      :   Dict      =    xa - xa
							snake_case__      :   Any      =    ya - ya
							self.ax.add_patch(
							    mpl.patches.Rectangle(
							        (xa, ya)    ,			_UpperCamelCase    ,			_UpperCamelCase    ,			fill=_UpperCamelCase    ,			edgecolor=_UpperCamelCase    ,			linewidth=self.font_size // 3    ,			alpha=self.alpha    ,			linestyle=self.linestyle    ,			)			)



			def 				lowerCAmelCase (     self		:						Optional[Any]    ,			__UpperCamelCase		:						Dict    ,			__UpperCamelCase		:						Optional[int]=None    ,			__UpperCamelCase		:						Any=None    ,			__UpperCamelCase		:						Optional[int]=None    ,			__UpperCamelCase		:						Optional[Any]=None			)      ->      Tuple:
							"""simple docstring"""





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

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



			def 				lowerCAmelCase (     self		:						int    ,			__UpperCamelCase		:						Union[str, Any]    ,			__UpperCamelCase		:						List[str]    ,			__UpperCamelCase		:						Any			)      ->      int:
							"""simple docstring"""





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

							snake_case__      :   Optional[int]      =    (ya - ya) / np.sqrt(self.height * self.width			)
							snake_case__      :   List[str]      =    self._change_color_brightness(_UpperCamelCase    ,			brightness_factor=0.7			)
							snake_case__      :   Union[str, Any]      =    np.clip((height_ratio - 0.02) / 0.08 + 1    ,			1.2    ,			2			)
							font_size *= 0.75 * self.font_size

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



			def 				lowerCAmelCase (     self		:						List[str]    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						Dict    ,			__UpperCamelCase		:						Any="g"    ,			__UpperCamelCase		:						str="left"    ,			)      ->      str:
							"""simple docstring"""





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



			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						List[Any]=None			)      ->      Any:
							"""simple docstring"""





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



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Optional[Any]    ,			__UpperCamelCase		:						Dict    ,			__UpperCamelCase		:						List[str]    ,			__UpperCamelCase		:						str			)      ->      List[str]:
							"""simple docstring"""





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



			def 				lowerCAmelCase (     self		:						Dict    ,			__UpperCamelCase		:						List[Any]    ,			__UpperCamelCase		:						Any			)      ->      Dict:
							"""simple docstring"""





							snake_case__      :   Any      =    [self.idaobj[i] for i in classes]
							if scores is not None:
											if labels is None:
															snake_case__      :   Union[str, Any]      =    ['''{:.0f}%'''.format(s * 100			) for s in scores]
											else:
															snake_case__      :   int      =    ['''{} {:.0f}%'''.format(_UpperCamelCase    ,			s * 100			) for li, s in zip(_UpperCamelCase    ,			_UpperCamelCase			)]
							return labels



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						Optional[Any]=255			)      ->      List[Any]:
							"""simple docstring"""





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



			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      int:
							"""simple docstring"""





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

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

							try:
											import numexpr as ne  # fuse them with numexpr

											snake_case__      :   Tuple      =    ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''			)
							except ImportError:
											snake_case__      :   Union[str, Any]      =    alpha.astype('''float32'''			) / 255.0
											snake_case__      :   str      =    img * (1 - alpha) + rgb * alpha

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







			def 				lowerCAmelCase (     self		:						List[str]    ,			__UpperCamelCase		:						str    ,			__UpperCamelCase		:						str			)      ->      List[Any]:
							"""simple docstring"""





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



_lowercase       :						str						=(
    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 numpy as np


class      __SCREAMING_SNAKE_CASE       :






						def __init__(						self  ,    _UpperCamelCase=5  ,    _UpperCamelCase=5 ):






												"""simple docstring"""
												lowerCAmelCase__         =    depth
												lowerCAmelCase__         =    0
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    min_leaf_size
												lowerCAmelCase__         =    None






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												if labels.ndim != 1:
																		print('Error: Input labels must be one dimensional' )

												return np.mean((labels - prediction) ** 2 )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												if x.ndim != 1:
																		print('Error: Input data set must be one dimensional' )
																		return
												if len(_UpperCamelCase ) != len(_UpperCamelCase ):
																		print('Error: X and y have different lengths' )
																		return
												if y.ndim != 1:
																		print('Error: Data set labels must be one dimensional' )
																		return

												if len(_UpperCamelCase ) < 2 * self.min_leaf_size:
																		lowerCAmelCase__         =    np.mean(_UpperCamelCase )
																		return

												if self.depth == 1:
																		lowerCAmelCase__         =    np.mean(_UpperCamelCase )
																		return

												lowerCAmelCase__         =    0
												lowerCAmelCase__         =    self.mean_squared_error(_UpperCamelCase  ,    np.mean(_UpperCamelCase ) ) * 2
												for i in range(len(_UpperCamelCase ) ):
																		if len(x[:i] ) < self.min_leaf_size:
																								continue
																		elif len(x[i:] ) < self.min_leaf_size:
																								continue
																		else:
																								lowerCAmelCase__         =    self.mean_squared_error(x[:i]  ,    np.mean(y[:i] ) )
																								lowerCAmelCase__         =    self.mean_squared_error(x[i:]  ,    np.mean(y[i:] ) )
																								lowerCAmelCase__         =    error_left + error_right
																								if error < min_error:
																														lowerCAmelCase__         =    i
																														lowerCAmelCase__         =    error

												if best_split != 0:
																		lowerCAmelCase__         =    x[:best_split]
																		lowerCAmelCase__         =    y[:best_split]
																		lowerCAmelCase__         =    x[best_split:]
																		lowerCAmelCase__         =    y[best_split:]

																		lowerCAmelCase__         =    x[best_split]
																		lowerCAmelCase__         =    DecisionTree(
																		    depth=self.depth - 1  ,    min_leaf_size=self.min_leaf_size )
																		lowerCAmelCase__         =    DecisionTree(
																		    depth=self.depth - 1  ,    min_leaf_size=self.min_leaf_size )
																		self.left.train(_UpperCamelCase  ,    _UpperCamelCase )
																		self.right.train(_UpperCamelCase  ,    _UpperCamelCase )
												else:
																		lowerCAmelCase__         =    np.mean(_UpperCamelCase )

												return







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												if self.prediction is not None:
																		return self.prediction
												elif self.left or self.right is not None:
																		if x >= self.decision_boundary:
																								return self.right.predict(_UpperCamelCase )
																		else:
																								return self.left.predict(_UpperCamelCase )
												else:
																		print('Error: Decision tree not yet trained' )
																		return None




class      __SCREAMING_SNAKE_CASE       :






						@staticmethod
						def    UpperCamelCase__   (						_UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    float(0 )
												for label in labels:
																		squared_error_sum += (label - prediction) ** 2

												return float(squared_error_sum / labels.size )







def 						_UpperCamelCase (     )	-> List[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    np.arange(-1.0     ,							1.0     ,							0.005		)
						lowerCAmelCase__         =    np.sin(UpperCamelCase_		)

						lowerCAmelCase__         =    DecisionTree(depth=10     ,							min_leaf_size=10		)
						tree.train(UpperCamelCase_     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    (np.random.rand(10		) * 2) - 1
						lowerCAmelCase__         =    np.array([tree.predict(UpperCamelCase_		) for x in test_cases]		)
						lowerCAmelCase__         =    np.mean((predictions - test_cases) ** 2		)

						print('Test values: ' + str(UpperCamelCase_		)		)
						print('Predictions: ' + str(UpperCamelCase_		)		)
						print('Average error: ' + str(UpperCamelCase_		)		)


if __name__ == "__main__":
					main()
					import doctest

					doctest.testmod(name="""mean_squarred_error""", verbose=True)





