'''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


_UpperCAmelCase						:    Optional[Any]     					=					10_00







class lowercase_					:







   """simple docstring"""

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

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

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

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

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

        self.draw_text(
            text=UpperCamelCase__,   position=UpperCamelCase__,   color=UpperCamelCase__,   )
   def   __UpperCAmelCase      (			self			: List[Any],   UpperCamelCase__			: int,   UpperCamelCase__			: Dict,   UpperCamelCase__			: str="g",   UpperCamelCase__			: List[str]="left",   )     -> List[str]:
        _A								=     0
        _A								=     self.font_size
        _A								=     np.maximum(list(mplc.to_rgb(UpperCamelCase__    )    ),   0.2    )
        _A								=     max(0.8,   np.max(UpperCamelCase__    )    )
        _A								=     {
            'facecolor': 'black',
            'alpha': self.alpha,
            'pad': self.pad,
            'edgecolor': 'none',
        }
        _A		,					_A								=     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   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: List[Any]=None    )     -> List[str]:
        if saveas is None:
             _A								=     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   __UpperCAmelCase      (			self			: Dict,   UpperCamelCase__			: Optional[Any],   UpperCamelCase__			: Tuple,   UpperCamelCase__			: Optional[Any],   UpperCamelCase__			: List[Any]    )     -> str:
        _A								=     [self.idaobj[i] for i in classes]
        _A								=     [self.idaattr[i] for i in attr_classes]
        _A								=     [
            f'{label} {score:.2f} {attr} {attr_score:.2f}'
            for label, score, attr, attr_score in zip(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__    )
        ]
        return labels
   def   __UpperCAmelCase      (			self			: Union[str, Any],   UpperCamelCase__			: Any,   UpperCamelCase__			: Any    )     -> str:
        _A								=     [self.idaobj[i] for i in classes]
        if scores is not None:
             if labels is None:
                  _A								=     ['{:.0f}%'.format(s * 1_00    ) for s in scores]
             else:
                  _A								=     ['{} {:.0f}%'.format(UpperCamelCase__,   s * 1_00    ) for li, s in zip(UpperCamelCase__,   UpperCamelCase__    )]
        return labels
   def   __UpperCAmelCase      (			self			: Tuple,   UpperCamelCase__			: List[str]=2_55    )     -> str:
        _A								=     np.random.randint(0,   len(_COLORS    )    )
        _A								=     _COLORS[idx] * maximum
        if not self.rgb:
             _A								=     ret[::-1]
        return ret
   def   __UpperCAmelCase      (			self			: List[str]    )     -> List[str]:
        if not self.pynb:
             _A		,					_A								=     self.canvas.print_to_buffer()
             if (width, height) != (self.width, self.height):
                  _A								=     cva.resize(self.img,   (width, height)    )
             else:
                  _A								=     self.img
        else:
             _A								=     io.BytesIO()  # works for cairo backend
             self.canvas.print_rgba(UpperCamelCase__    )
             _A		,					_A								=     self.width, self.height
             _A								=     buf.getvalue()
             _A								=     self.img

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

        try:
             import numexpr as ne  # fuse them with numexpr

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

        return visualized_image.astype('uint8'    )

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






_UpperCAmelCase						:    Optional[Any]     					=					(
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)




'''simple docstring'''






import torch

from accelerate import Accelerator, DistributedType







class lowercase_					:







   """simple docstring"""

   def __enter__(			self			: Optional[int]    )     -> Union[str, Any]:
        if self.enabled:
             _A								=     self.model.no_sync()
             self.model_sync_obj.__enter__()

        return self
   def __exit__(			self			: Dict,   UpperCamelCase__			: Any,   UpperCamelCase__			: int,   UpperCamelCase__			: Any    )     -> str:
        if self.enabled:
             # Average all models on exit
             self._sync_and_avg_model_params()
             self.model_sync_obj.__exit__(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__    )
   def __init__(			self			: Optional[int],   UpperCamelCase__			: Accelerator,   UpperCamelCase__			: torch.nn.Module,   UpperCamelCase__			: int,   UpperCamelCase__			: bool = True    )     -> str:
        if accelerator.distributed_type not in [
            DistributedType.NO,
            DistributedType.MULTI_CPU,
            DistributedType.MULTI_GPU,
        ]:
             raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'    )
        _A								=     enabled and accelerator.distributed_type != DistributedType.NO
        _A								=     0
        if self.enabled:
             _A								=     accelerator
             _A								=     model
             _A								=     local_sgd_steps
   def   __UpperCAmelCase      (			self			: Any    )     -> int:
        self.num_steps += 1
        if not self.enabled:
             return

        if self.num_steps % self.local_sgd_steps == 0:
             self._sync_and_avg_model_params()

   def   __UpperCAmelCase      (			self			: Dict    )     -> int:
        self.accelerator.wait_for_everyone()
        with self.accelerator.autocast():
             for param in self.model.parameters():
                  _A								=     self.accelerator.reduce(param.data,   reduction='mean'    )



