


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


__snake_case     =		1_0_0_0



class 					__lowerCamelCase	:







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






            '''simple docstring'''




            if isinstance(A_,torch.Tensor			):
                 __UpperCamelCase				=	img.numpy().astype('np.uint8'			)
            if isinstance(A_,A_			):
                 __UpperCamelCase				=	img_tensorize(A_			)
            assert isinstance(A_,np.ndarray			)

            __UpperCamelCase,  __UpperCamelCase				=	img.shape[1], img.shape[0]
            __UpperCamelCase				=	mplfigure.Figure(frameon=A_			)
            __UpperCamelCase				=	fig.get_dpi()
            __UpperCamelCase				=	(width * scale + 1E-2) / dpi
            __UpperCamelCase				=	(height * scale + 1E-2) / dpi
            fig.set_size_inches(A_,A_			)
            __UpperCamelCase				=	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				=	saveas
            __UpperCamelCase				=	rgb
            __UpperCamelCase				=	pynb
            __UpperCamelCase				=	img
            __UpperCamelCase				=	edgecolor
            __UpperCamelCase				=	0.5
            __UpperCamelCase				=	linestyle
            __UpperCamelCase				=	int(np.sqrt(min(A_,A_			)			) * scale // 3			)
            __UpperCamelCase				=	width
            __UpperCamelCase				=	height
            __UpperCamelCase				=	scale
            __UpperCamelCase				=	fig
            __UpperCamelCase				=	ax
            __UpperCamelCase				=	pad
            __UpperCamelCase				=	idaobj
            __UpperCamelCase				=	idaattr
            __UpperCamelCase				=	FigureCanvasAgg(A_			)







       def 						snake_case_					(	self:				Dict,A_:				Optional[Any],A_:				int=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),A_,A_,fill=A_,edgecolor=A_,linewidth=self.font_size // 3,alpha=self.alpha,linestyle=self.linestyle,)			)







       def 						snake_case_					(	self:				str,A_:				str,A_:				Union[str, Any]=None,A_:				List[Any]=None,A_:				Optional[int]=None,A_:				List[str]=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(A_,torch.Tensor			):
                 __UpperCamelCase				=	boxes.numpy()
            if isinstance(A_,A_			):
                 __UpperCamelCase				=	np.array(A_			)
            assert isinstance(A_,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(A_			)			)]
            __UpperCamelCase				=	[assigned_colors[idx] for idx in sorted_idxs]
            if obj_ids is not None:
                 __UpperCamelCase				=	self._create_text_labels_attr(A_,A_,A_,A_			)
                 for i in range(len(A_			)			):
                      __UpperCamelCase				=	assigned_colors[i]
                      self.add_box(boxes[i],A_			)
                      self.draw_labels(labels[i],boxes[i],A_			)







       def 						snake_case_					(	self:				Optional[int],A_:				List[Any],A_:				Union[str, Any],A_:				Union[str, Any]			):






            '''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 < 40 * 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(A_,brightness_factor=0.7			)
            __UpperCamelCase				=	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:				str,A_:				List[Any],A_:				str,A_:				List[Any]="g",A_:				int="left",):






            '''simple docstring'''




            __UpperCamelCase				=	0
            __UpperCamelCase				=	self.font_size
            __UpperCamelCase				=	np.maximum(list(mplc.to_rgb(A_			)			),0.2			)
            __UpperCamelCase				=	max(0.8,np.max(A_			)			)
            __UpperCamelCase				=	{
                'facecolor': 'black',
                'alpha': self.alpha,
                'pad': self.pad,
                'edgecolor': 'none',
            }
            __UpperCamelCase,  __UpperCamelCase				=	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:				Optional[int],A_:				Union[str, Any]=None			):






            '''simple docstring'''




            if saveas is None:
                 __UpperCamelCase				=	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:				List[str],A_:				Optional[Any],A_:				Optional[Any],A_:				int,A_:				Optional[Any]			):






            '''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(A_,A_,A_,A_			)
            ]
            return labels







       def 						snake_case_					(	self:				List[str],A_:				Optional[int],A_:				int			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				str,A_:				Optional[Any]=255			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				Dict			):






            '''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(A_			)
                 __UpperCamelCase,  __UpperCamelCase				=	self.width, self.height
                 __UpperCamelCase				=	buf.getvalue()
                 __UpperCamelCase				=	self.img

            __UpperCamelCase				=	np.frombuffer(A_,dtype='uint8'			)
            __UpperCamelCase				=	buffer.reshape(A_,A_,4			)
            __UpperCamelCase,  __UpperCamelCase				=	np.split(A_,[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'			) / 2_5_5.0
                 __UpperCamelCase				=	img * (1 - alpha) + rgb * alpha

            return visualized_image.astype('uint8'			)







       def 						snake_case_					(	self:				Optional[Any],A_:				Optional[Any],A_:				Tuple			):






            '''simple docstring'''




            assert brightness_factor >= -1.0 and brightness_factor <= 1.0
            __UpperCamelCase				=	mplc.to_rgb(A_			)
            __UpperCamelCase				=	colorsys.rgb_to_hls(*mplc.to_rgb(A_			)			)
            __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],A_,polygon_color[2]			)
            return modified_color



__snake_case     =		(
    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 matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	len(_lowercase				)  # Number of training samples
     __UpperCamelCase				=	np.eye(_lowercase				)  # Initialize weights as identity matrix
     for j in range(_lowercase				):
          __UpperCamelCase				=	point - x_train[j]
          __UpperCamelCase				=	np.exp(diff @ diff.T / (-2.0 * tau**2)				)

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	np.zeros(len(_lowercase				)				)  # Initialize array of predictions
     for i, item in enumerate(_lowercase				):
          __UpperCamelCase				=	item @ local_weight(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

     # pairing elements of one and x_data
     __UpperCamelCase				=	np.column_stack((one, x_data)				)

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	np.sort(_lowercase			,       axis=0				)
     plt.scatter(_lowercase			,       _lowercase			,       color='blue'				)
     plt.plot(
         x_train_sorted[:, 1]			,       preds[x_train[:, 1].argsort(0				)]			,       color='yellow'			,       linewidth=5			,       )
     plt.title('Local Weighted Regression'				)
     plt.xlabel(_lowercase				)
     plt.ylabel(_lowercase				)
     plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    __snake_case    ,					__snake_case    ,					__snake_case     =		load_data('''tips''', '''total_bill''', '''tip''')
    __snake_case     =		local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')


