

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


__A     						=      10_00



class     lowercase_       :



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

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

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



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							Dict     ,   A__ :							int=None						)       ->	int:
        if color is None:
          _snake_case		=				self.edgecolor
        ((_snake_case),	(_snake_case),	(_snake_case),	(_snake_case))		=				box
        _snake_case		=				xa - xa
        _snake_case		=				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 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							List[Any]     ,   A__ :							Any=None     ,   A__ :							Union[str, Any]=None     ,   A__ :							List[str]=None     ,   A__ :							Optional[Any]=None						)       ->	str:
        if len(boxes.shape						) > 2:
          _snake_case		=				boxes[0]
        if len(obj_ids.shape						) > 1:
          _snake_case		=				obj_ids[0]
        if len(obj_scores.shape						) > 1:
          _snake_case		=				obj_scores[0]
        if len(attr_ids.shape						) > 1:
          _snake_case		=				attr_ids[0]
        if len(attr_scores.shape						) > 1:
          _snake_case		=				attr_scores[0]
        if isinstance(A__     ,   torch.Tensor						):
          _snake_case		=				boxes.numpy()
        if isinstance(A__     ,   A__						):
          _snake_case		=				np.array(A__						)
        assert isinstance(A__     ,   np.ndarray						)
        _snake_case		=				np.prod(boxes[:, 2:] - boxes[:, :2]     ,   axis=1						)
        _snake_case		=				np.argsort(-areas						).tolist()
        _snake_case		=				boxes[sorted_idxs] if boxes is not None else None
        _snake_case		=				obj_ids[sorted_idxs] if obj_ids is not None else None
        _snake_case		=				obj_scores[sorted_idxs] if obj_scores is not None else None
        _snake_case		=				attr_ids[sorted_idxs] if attr_ids is not None else None
        _snake_case		=				attr_scores[sorted_idxs] if attr_scores is not None else None

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



      def 		UpperCamelCase_						(     self :							Optional[Any]     ,   A__ :							Union[str, Any]     ,   A__ :							Optional[int]     ,   A__ :							Dict						)       ->	Union[str, Any]:
        _snake_case,	_snake_case,	_snake_case,	_snake_case		=				box
        _snake_case		=				(xa, ya)
        _snake_case		=				(ya - ya) * (xa - xa)
        _snake_case		=				_SMALL_OBJ * self.scale
        if instance_area < small or ya - ya < 40 * self.scale:
          if ya >= self.height - 5:
            _snake_case		=				(xa, ya)
          else:
            _snake_case		=				(xa, ya)

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

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



      def 		UpperCamelCase_						(     self :							Dict     ,   A__ :							Optional[Any]     ,   A__ :							Dict     ,   A__ :							List[Any]="g"     ,   A__ :							Any="left"     ,   )       ->	Tuple:
        _snake_case		=				0
        _snake_case		=				self.font_size
        _snake_case		=				np.maximum(list(mplc.to_rgb(A__						)						)     ,   0.2						)
        _snake_case		=				max(0.8     ,   np.max(A__						)						)
        _snake_case		=				{
            '''facecolor''': '''black''',
            '''alpha''': self.alpha,
            '''pad''': self.pad,
            '''edgecolor''': '''none''',
        }
        _snake_case,	_snake_case		=				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 		UpperCamelCase_						(     self :							List[str]     ,   A__ :							Any=None						)       ->	Dict:
        if saveas is None:
          _snake_case		=				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 		UpperCamelCase_						(     self :							List[str]     ,   A__ :							Optional[Any]     ,   A__ :							List[Any]     ,   A__ :							List[Any]     ,   A__ :							int						)       ->	Optional[Any]:
        _snake_case		=				[self.idaobj[i] for i in classes]
        _snake_case		=				[self.idaattr[i] for i in attr_classes]
        _snake_case		=				[
            f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
            for label, score, attr, attr_score in zip(A__     ,   A__     ,   A__     ,   A__						)
        ]
        return labels



      def 		UpperCamelCase_						(     self :							Tuple     ,   A__ :							List[str]     ,   A__ :							str						)       ->	Dict:
        _snake_case		=				[self.idaobj[i] for i in classes]
        if scores is not None:
          if labels is None:
            _snake_case		=				['''{:.0f}%'''.format(s * 100						) for s in scores]
          else:
            _snake_case		=				['''{} {:.0f}%'''.format(A__     ,   s * 100						) for li, s in zip(A__     ,   A__						)]
        return labels



      def 		UpperCamelCase_						(     self :							Optional[int]     ,   A__ :							str=255						)       ->	Any:
        _snake_case		=				np.random.randint(0     ,   len(_COLORS						)						)
        _snake_case		=				_COLORS[idx] * maximum
        if not self.rgb:
          _snake_case		=				ret[::-1]
        return ret



      def 		UpperCamelCase_						(     self :							Dict						)       ->	Dict:
        if not self.pynb:
          _snake_case,	_snake_case		=				self.canvas.print_to_buffer()
          if (width, height) != (self.width, self.height):
            _snake_case		=				cva.resize(self.img     ,   (width, height)						)
          else:
            _snake_case		=				self.img
        else:
          _snake_case		=				io.BytesIO()  # works for cairo backend
          self.canvas.print_rgba(A__						)
          _snake_case,	_snake_case		=				self.width, self.height
          _snake_case		=				buf.getvalue()
          _snake_case		=				self.img

        _snake_case		=				np.frombuffer(A__     ,   dtype='''uint8'''						)
        _snake_case		=				buffer.reshape(A__     ,   A__     ,   4						)
        _snake_case,	_snake_case		=				np.split(A__     ,   [3]     ,   axis=2						)

        try:
          import numexpr as ne  # fuse them with numexpr

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

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







      def 		UpperCamelCase_						(     self :							int     ,   A__ :							Optional[int]     ,   A__ :							Any						)       ->	Optional[int]:
        assert brightness_factor >= -1.0 and brightness_factor <= 1.0
        _snake_case		=				mplc.to_rgb(A__						)
        _snake_case		=				colorsys.rgb_to_hls(*mplc.to_rgb(A__						)						)
        _snake_case		=				polygon_color[1] + (brightness_factor * polygon_color[1])
        _snake_case		=				0.0 if modified_lightness < 0.0 else modified_lightness
        _snake_case		=				1.0 if modified_lightness > 1.0 else modified_lightness
        _snake_case		=				colorsys.hls_to_rgb(polygon_color[0]     ,   A__     ,   polygon_color[2]						)
        return modified_color

__A     						=      (
    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 warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


__A     						=      logging.get_logger(__name__)



class     lowercase_       (    __lowercase	):



      def __init__(     self :							List[str]     ,   *A__ :							Tuple     ,   **A__ :							int						)       ->	None:
        warnings.warn(
            '''The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use DetrImageProcessor instead.'''     ,   A__     ,   )
        super().__init__(*A__     ,   **A__						)

