





'''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


A						    =							10_00



class 							__SCREAMING_SNAKE_CASE			:
      '''simple docstring'''



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

           _lowercase						, _lowercase							:      Optional[int]  						=							img.shape[1], img.shape[0]
           _lowercase							:      Optional[int]  						=							mplfigure.Figure(frameon=UpperCamelCase  )
           _lowercase							:      Optional[int]  						=							fig.get_dpi()
           _lowercase							:      Optional[int]  						=							(width * scale + 1e-2) / dpi
           _lowercase							:      Any  						=							(height * scale + 1e-2) / dpi
           fig.set_size_inches(UpperCamelCase   ,UpperCamelCase  )
           _lowercase							:      Optional[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  )

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

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

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

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

           _lowercase							:      List[Any]  						=							(ya - ya) / np.sqrt(self.height * self.width  )
           _lowercase							:      Optional[int]  						=							self._change_color_brightness(UpperCamelCase   ,brightness_factor=0.7  )
           _lowercase							:      Union[str, Any]  						=							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=UpperCamelCase   ,position=UpperCamelCase   ,color=UpperCamelCase   ,)

      def   _lowerCamelCase		(							self    : int   ,UpperCamelCase    : Optional[int]   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : Any="g"   ,UpperCamelCase    : Optional[int]="left"   ,)		->  Dict:
           _lowercase							:      str  						=							0
           _lowercase							:      Optional[int]  						=							self.font_size
           _lowercase							:      Optional[Any]  						=							np.maximum(list(mplc.to_rgb(UpperCamelCase  )  )   ,0.2  )
           _lowercase							:      Dict  						=							max(0.8   ,np.max(UpperCamelCase  )  )
           _lowercase							:      Optional[int]  						=							{
               'facecolor': 'black',
               'alpha': self.alpha,
               'pad': self.pad,
               'edgecolor': 'none',
           }
           _lowercase						, _lowercase							:      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    : List[Any]   ,UpperCamelCase    : List[Any]=None  )		->  Union[str, Any]:
           if saveas is None:
                _lowercase							:      int  						=							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    : Optional[int]   ,UpperCamelCase    : List[str]   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : List[str]   ,UpperCamelCase    : Union[str, Any]  )		->  str:
           _lowercase							:      List[str]  						=							[self.idaobj[i] for i in classes]
           _lowercase							:      Tuple  						=							[self.idaattr[i] for i in attr_classes]
           _lowercase							:      Tuple  						=							[
               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    : str   ,UpperCamelCase    : Any   ,UpperCamelCase    : List[str]  )		->  Any:
           _lowercase							:      Optional[Any]  						=							[self.idaobj[i] for i in classes]
           if scores is not None:
                if labels is None:
                     _lowercase							:      int  						=							['{:.0f}%'.format(s * 100  ) for s in scores]
                else:
                     _lowercase							:      str  						=							['{} {:.0f}%'.format(UpperCamelCase   ,s * 100  ) for li, s in zip(UpperCamelCase   ,UpperCamelCase  )]
           return labels

      def   _lowerCamelCase		(							self    : List[str]   ,UpperCamelCase    : Union[str, Any]=255  )		->  List[Any]:
           _lowercase							:      Union[str, Any]  						=							np.random.randint(0   ,len(_COLORS  )  )
           _lowercase							:      List[str]  						=							_COLORS[idx] * maximum
           if not self.rgb:
                _lowercase							:      Any  						=							ret[::-1]
           return ret

      def   _lowerCamelCase		(							self    : Dict  )		->  int:
           if not self.pynb:
                _lowercase						, _lowercase							:      Union[str, Any]  						=							self.canvas.print_to_buffer()
                if (width, height) != (self.width, self.height):
                     _lowercase							:      Union[str, Any]  						=							cva.resize(self.img   ,(width, height)  )
                else:
                     _lowercase							:      Tuple  						=							self.img
           else:
                _lowercase							:      Tuple  						=							io.BytesIO()  # works for cairo backend
                self.canvas.print_rgba(UpperCamelCase  )
                _lowercase						, _lowercase							:      List[str]  						=							self.width, self.height
                _lowercase							:      str  						=							buf.getvalue()
                _lowercase							:      Optional[int]  						=							self.img

           _lowercase							:      int  						=							np.frombuffer(UpperCamelCase   ,dtype='uint8'  )
           _lowercase							:      List[Any]  						=							buffer.reshape(UpperCamelCase   ,UpperCamelCase   ,4  )
           _lowercase						, _lowercase							:      Union[str, Any]  						=							np.split(UpperCamelCase   ,[3]   ,axis=2  )

           try:
                import numexpr as ne  # fuse them with numexpr

                _lowercase							:      Dict  						=							ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'  )
           except ImportError:
                _lowercase							:      List[Any]  						=							alpha.astype('float32'  ) / 2_5_5.0
                _lowercase							:      Dict  						=							img * (1 - alpha) + rgb * alpha

           return visualized_image.astype('uint8'  )



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






'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


A						    =							logging.get_logger(__name__)



class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      def __init__(							self    : Tuple   ,*UpperCamelCase    : str   ,**UpperCamelCase    : List[Any]  )		->  None:
           warnings.warn(
               'The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
               ' Please use MobileNetV2ImageProcessor instead.'   ,UpperCamelCase   ,)
           super().__init__(*UpperCamelCase   ,**UpperCamelCase  )