


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 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=1.2   ,			__SCREAMING_SNAKE_CASE="g"   ,			__SCREAMING_SNAKE_CASE=0.5   ,			__SCREAMING_SNAKE_CASE="-"   ,			__SCREAMING_SNAKE_CASE="test_out.jpg"   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=False   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=0.7   ,			)					->						Any:







         """simple docstring"""







         if isinstance(__SCREAMING_SNAKE_CASE   ,			torch.Tensor):
            UpperCamelCase__ :			Dict 						=img.numpy().astype("np.uint8")
         if isinstance(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE):
            UpperCamelCase__ :			List[str] 						=img_tensorize(__SCREAMING_SNAKE_CASE)
         assert isinstance(__SCREAMING_SNAKE_CASE   ,			np.ndarray)

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

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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=None)					->						Optional[Any]:







         """simple docstring"""







         if color is None:
            UpperCamelCase__ :			Tuple 						=self.edgecolor
         ((UpperCamelCase__)						,	(UpperCamelCase__)						,	(UpperCamelCase__)						,	(UpperCamelCase__)) :			str 						=box
         UpperCamelCase__ :			List[str] 						=xa - xa
         UpperCamelCase__ :			Union[str, Any] 						=ya - ya
         self.ax.add_patch(
             mpl.patches.Rectangle(
                 (xa, ya)   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			fill=__SCREAMING_SNAKE_CASE   ,			edgecolor=__SCREAMING_SNAKE_CASE   ,			linewidth=self.font_size // 3   ,			alpha=self.alpha   ,			linestyle=self.linestyle   ,			))




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None)					->						str:







         """simple docstring"""







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

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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Optional[Any]:







         """simple docstring"""







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

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

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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE="g"   ,			__SCREAMING_SNAKE_CASE="left"   ,			)					->						int:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE=None)					->						Optional[int]:







         """simple docstring"""







         if saveas is None:
            UpperCamelCase__ :			Any 						=self.saveas
         if saveas.lower().endswith(".jpg") or saveas.lower().endswith(".png"):
            cva.imwrite(
                __SCREAMING_SNAKE_CASE   ,			self._get_buffer()[:, :, ::-1]   ,			)
         else:
            self.fig.savefig(__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						List[str]:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			str 						=[self.idaobj[i] for i in classes]
         if scores is not None:
            if labels is None:
               UpperCamelCase__ :			List[str] 						=["{:.0f}%".format(s * 1_00) for s in scores]
            else:
               UpperCamelCase__ :			List[str] 						=["{} {:.0f}%".format(__SCREAMING_SNAKE_CASE   ,			s * 1_00) for li, s in zip(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)]
         return labels




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE=2_55)					->						int:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







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

         UpperCamelCase__ :			Dict 						=np.frombuffer(__SCREAMING_SNAKE_CASE   ,			dtype="uint8")
         UpperCamelCase__ :			Tuple 						=buffer.reshape(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			4)
         UpperCamelCase__						,	UpperCamelCase__ :			List[Any] 						=np.split(__SCREAMING_SNAKE_CASE   ,			[3]   ,			axis=2)

         try:
            import numexpr as ne  # fuse them with numexpr

            UpperCamelCase__ :			Union[str, Any] 						=ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)")
         except ImportError:
            UpperCamelCase__ :			Union[str, Any] 						=alpha.astype("float32") / 2_55.0
            UpperCamelCase__ :			str 						=img * (1 - alpha) + rgb * alpha

         return visualized_image.astype("uint8")




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						List[Any]:







         """simple docstring"""







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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase      			=  {"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase      			=  [
         """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SEWDForCTC""",
         """SEWDForSequenceClassification""",
         """SEWDModel""",
         """SEWDPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_sew_d import (
              SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
              SEWDForCTC,
              SEWDForSequenceClassification,
              SEWDModel,
              SEWDPreTrainedModel,
          )


else:
     import sys

     __UpperCAmelCase      			=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

