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				 =   1000






class 				_SCREAMING_SNAKE_CASE:
   def __init__(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=1.2				,  lowerCAmelCase_="g"				,  lowerCAmelCase_=0.5				,  lowerCAmelCase_="-"				,  lowerCAmelCase_="test_out.jpg"				,  lowerCAmelCase_=True				,  lowerCAmelCase_=False				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  lowerCAmelCase_=0.7				,  )						->int:
        if isinstance(lowerCAmelCase_				,  torch.Tensor     ):
             a	:     Union[str, Any]						      =    img.numpy().astype('''np.uint8'''     )
        if isinstance(lowerCAmelCase_				,  lowerCAmelCase_     ):
             a	:     Dict						      =    img_tensorize(lowerCAmelCase_     )
        assert isinstance(lowerCAmelCase_				,  np.ndarray     )

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

        a	:     List[str]						      =    saveas
        a	:     Optional[Any]						      =    rgb
        a	:     Optional[int]						      =    pynb
        a	:     List[Any]						      =    img
        a	:     List[str]						      =    edgecolor
        a	:     int						      =    0.5
        a	:     List[Any]						      =    linestyle
        a	:     str						      =    int(np.sqrt(min(lowerCAmelCase_				,  lowerCAmelCase_     )     ) * scale // 3     )
        a	:     Optional[Any]						      =    width
        a	:     List[str]						      =    height
        a	:     int						      =    scale
        a	:     Tuple						      =    fig
        a	:     Optional[Any]						      =    ax
        a	:     int						      =    pad
        a	:     Any						      =    idaobj
        a	:     Optional[int]						      =    idaattr
        a	:     List[str]						      =    FigureCanvasAgg(lowerCAmelCase_     )
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=None     )						->Dict:
        if color is None:
             a	:     Union[str, Any]						      =    self.edgecolor
        ((a)      ,				(a)      ,				(a)      ,				(a))	:     int						      =    box
        a	:     Tuple						      =    xa - xa
        a	:     Dict						      =    ya - ya
        self.ax.add_patch(
            mpl.patches.Rectangle(
                (xa, ya)				,  lowerCAmelCase_				,  lowerCAmelCase_				,  fill=lowerCAmelCase_				,  edgecolor=lowerCAmelCase_				,  linewidth=self.font_size // 3				,  alpha=self.alpha				,  linestyle=self.linestyle				,  )     )
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None     )						->Dict:
        if len(boxes.shape     ) > 2:
             a	:     str						      =    boxes[0]
        if len(obj_ids.shape     ) > 1:
             a	:     int						      =    obj_ids[0]
        if len(obj_scores.shape     ) > 1:
             a	:     List[Any]						      =    obj_scores[0]
        if len(attr_ids.shape     ) > 1:
             a	:     Dict						      =    attr_ids[0]
        if len(attr_scores.shape     ) > 1:
             a	:     Any						      =    attr_scores[0]
        if isinstance(lowerCAmelCase_				,  torch.Tensor     ):
             a	:     Optional[Any]						      =    boxes.numpy()
        if isinstance(lowerCAmelCase_				,  lowerCAmelCase_     ):
             a	:     Any						      =    np.array(lowerCAmelCase_     )
        assert isinstance(lowerCAmelCase_				,  np.ndarray     )
        a	:     Optional[int]						      =    np.prod(boxes[:, 2:] - boxes[:, :2]				,  axis=1     )
        a	:     Optional[int]						      =    np.argsort(-areas     ).tolist()
        a	:     List[str]						      =    boxes[sorted_idxs] if boxes is not None else None
        a	:     Any						      =    obj_ids[sorted_idxs] if obj_ids is not None else None
        a	:     List[Any]						      =    obj_scores[sorted_idxs] if obj_scores is not None else None
        a	:     int						      =    attr_ids[sorted_idxs] if attr_ids is not None else None
        a	:     List[Any]						      =    attr_scores[sorted_idxs] if attr_scores is not None else None

        a	:     Optional[Any]						      =    [self._random_color(maximum=1     ) for _ in range(len(lowerCAmelCase_     )     )]
        a	:     Dict						      =    [assigned_colors[idx] for idx in sorted_idxs]
        if obj_ids is not None:
             a	:     Union[str, Any]						      =    self._create_text_labels_attr(lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )
             for i in range(len(lowerCAmelCase_     )     ):
                  a	:     Any						      =    assigned_colors[i]
                  self.add_box(boxes[i]				,  lowerCAmelCase_     )
                  self.draw_labels(labels[i]				,  boxes[i]				,  lowerCAmelCase_     )
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        a      ,				a      ,				a      ,				a	:     List[str]						      =    box
        a	:     Tuple						      =    (xa, ya)
        a	:     List[Any]						      =    (ya - ya) * (xa - xa)
        a	:     Tuple						      =    _SMALL_OBJ * self.scale
        if instance_area < small or ya - ya < 40 * self.scale:
             if ya >= self.height - 5:
                  a	:     List[Any]						      =    (xa, ya)
             else:
                  a	:     str						      =    (xa, ya)

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

        self.draw_text(
            text=lowerCAmelCase_				,  position=lowerCAmelCase_				,  color=lowerCAmelCase_				,  )
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_="g"				,  lowerCAmelCase_="left"				,  )						->Tuple:
        a	:     Dict						      =    0
        a	:     Tuple						      =    self.font_size
        a	:     Optional[Any]						      =    np.maximum(list(mplc.to_rgb(lowerCAmelCase_     )     )				,  0.2     )
        a	:     List[Any]						      =    max(0.8				,  np.max(lowerCAmelCase_     )     )
        a	:     Union[str, Any]						      =    {
            '''facecolor''': '''black''',
            '''alpha''': self.alpha,
            '''pad''': self.pad,
            '''edgecolor''': '''none''',
        }
        a      ,				a	:     Any						      =    position
        self.ax.text(
            lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  size=font_size * self.scale				,  family='''sans-serif'''				,  bbox=lowerCAmelCase_				,  verticalalignment='''top'''				,  horizontalalignment=lowerCAmelCase_				,  color=lowerCAmelCase_				,  zorder=10				,  rotation=lowerCAmelCase_				,  )
   def _lowercase(     self				,  lowerCAmelCase_=None     )						->Union[str, Any]:
        if saveas is None:
             a	:     Dict						      =    self.saveas
        if saveas.lower().endswith('''.jpg'''     ) or saveas.lower().endswith('''.png'''     ):
             cva.imwrite(
                 lowerCAmelCase_				,  self._get_buffer()[:, :, ::-1]				,  )
        else:
             self.fig.savefig(lowerCAmelCase_     )
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->str:
        a	:     Optional[int]						      =    [self.idaobj[i] for i in classes]
        a	:     Union[str, Any]						      =    [self.idaattr[i] for i in attr_classes]
        a	:     Optional[Any]						      =    [
            F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
            for label, score, attr, attr_score in zip(lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )
        ]
        return labels
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        a	:     List[Any]						      =    [self.idaobj[i] for i in classes]
        if scores is not None:
             if labels is None:
                  a	:     Optional[int]						      =    ['''{:.0f}%'''.format(s * 1_00     ) for s in scores]
             else:
                  a	:     Optional[int]						      =    ['''{} {:.0f}%'''.format(lowerCAmelCase_				,  s * 1_00     ) for li, s in zip(lowerCAmelCase_				,  lowerCAmelCase_     )]
        return labels
   def _lowercase(     self				,  lowerCAmelCase_=2_55     )						->Any:
        a	:     List[str]						      =    np.random.randint(0				,  len(_COLORS     )     )
        a	:     Dict						      =    _COLORS[idx] * maximum
        if not self.rgb:
             a	:     Union[str, Any]						      =    ret[::-1]
        return ret
   def _lowercase(     self     )						->Optional[Any]:
        if not self.pynb:
             a      ,				a	:     Dict						      =    self.canvas.print_to_buffer()
             if (width, height) != (self.width, self.height):
                  a	:     str						      =    cva.resize(self.img				,  (width, height)     )
             else:
                  a	:     Dict						      =    self.img
        else:
             a	:     int						      =    io.BytesIO()  # works for cairo backend
             self.canvas.print_rgba(lowerCAmelCase_     )
             a      ,				a	:     Any						      =    self.width, self.height
             a	:     Optional[Any]						      =    buf.getvalue()
             a	:     int						      =    self.img

        a	:     str						      =    np.frombuffer(lowerCAmelCase_				,  dtype='''uint8'''     )
        a	:     Any						      =    buffer.reshape(lowerCAmelCase_				,  lowerCAmelCase_				,  4     )
        a      ,				a	:     Any						      =    np.split(lowerCAmelCase_				,  [3]				,  axis=2     )

        try:
             import numexpr as ne  # fuse them with numexpr

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

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


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

UpperCAmelCase				 =   (
    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 torch
from torch import autograd






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[str]:
        a	:     str						      =    inputs.numel()
        a	:     str						      =    int(0.0_05 * nb_elems     ) + 1
        if sigmoid:
             a	:     Optional[Any]						      =    (torch.sigmoid(lowerCAmelCase_     ) > threshold).type(inputs.type()     )
        else:
             a	:     Union[str, Any]						      =    (inputs > threshold).type(inputs.type()     )
        if mask.sum() < nb_min:
             # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
             a	:     str						      =    inputs.flatten().kthvalue(max(nb_elems - nb_min				,  1     )     ).values
             a	:     Tuple						      =    (inputs > k_threshold).type(inputs.type()     )
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        return gradOutput, None, None






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[Any]:
        a	:     List[Any]						      =    inputs.clone()
        a      ,				a	:     Optional[Any]						      =    inputs.flatten().sort(descending=lowerCAmelCase_     )
        a	:     Tuple						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Optional[Any]						      =    0
        a	:     List[Any]						      =    1
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        return gradOutput, None






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        a	:     Union[str, Any]						      =    inputs.clone()
        a      ,				a	:     List[Any]						      =    inputs.abs().flatten().sort(descending=lowerCAmelCase_     )
        a	:     Optional[int]						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Tuple						      =    0
        a	:     List[str]						      =    1
        return mask