




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


SCREAMING_SNAKE_CASE_						:					List[Any]			   =       10_00







class 		__UpperCAmelCase:





     """simple docstring"""



     def __init__(      self :       str						,				A :       Tuple						,				A :       Union[str, Any]=1.2						,				A :       Optional[Any]="g"						,				A :       Tuple=0.5						,				A :       Union[str, Any]="-"						,				A :       Any="test_out.jpg"						,				A :       List[str]=True						,				A :       Union[str, Any]=False						,				A :       Union[str, Any]=None						,				A :       Dict=None						,				A :       str=0.7						,				)			->			Optional[int]:






         """simple docstring"""
         if isinstance(A						,				torch.Tensor    ):
             a__      =       img.numpy().astype("""np.uint8"""    )
         if isinstance(A						,				A    ):
             a__      =       img_tensorize(A    )
         assert isinstance(A						,				np.ndarray    )

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

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

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]						,				A :       Union[str, Any]						,				A :       Optional[Any]=None    )			->			Dict:






         """simple docstring"""
         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)						,				A						,				A						,				fill=A						,				edgecolor=A						,				linewidth=self.font_size // 3						,				alpha=self.alpha						,				linestyle=self.linestyle						,				)    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       int						,				A :       Optional[Any]						,				A :       List[str]=None						,				A :       Dict=None						,				A :       Optional[int]=None						,				A :       Any=None    )			->			Optional[Any]:






         """simple docstring"""
         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(A						,				torch.Tensor    ):
             a__      =       boxes.numpy()
         if isinstance(A						,				A    ):
             a__      =       np.array(A    )
         assert isinstance(A						,				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(A    )    )]
         a__      =       [assigned_colors[idx] for idx in sorted_idxs]
         if obj_ids is not None:
             a__      =       self._create_text_labels_attr(A						,				A						,				A						,				A    )
             for i in range(len(A    )    ):
                 a__      =       assigned_colors[i]
                 self.add_box(boxes[i]						,				A    )
                 self.draw_labels(labels[i]						,				boxes[i]						,				A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       int						,				A :       Tuple						,				A :       Optional[int]						,				A :       Optional[int]    )			->			Optional[int]:






         """simple docstring"""
         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(A						,				brightness_factor=0.7    )
         a__      =       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=A						,				position=A						,				color=A						,				)

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]						,				A :       List[str]						,				A :       Tuple						,				A :       Union[str, Any]="g"						,				A :       Optional[int]="left"						,				)			->			Optional[Any]:






         """simple docstring"""
         a__      =       0
         a__      =       self.font_size
         a__      =       np.maximum(list(mplc.to_rgb(A    )    )						,				0.2    )
         a__      =       max(0.8						,				np.max(A    )    )
         a__      =       {
             """facecolor""": """black""",
             """alpha""": self.alpha,
             """pad""": self.pad,
             """edgecolor""": """none""",
         }
         a__ ,		a__      =       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 				__SCREAMING_SNAKE_CASE		(      self :       Any						,				A :       Optional[Any]=None    )			->			List[Any]:






         """simple docstring"""
         if saveas is None:
             a__      =       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 				__SCREAMING_SNAKE_CASE		(      self :       List[str]						,				A :       Tuple						,				A :       str						,				A :       Any						,				A :       List[str]    )			->			Optional[int]:






         """simple docstring"""
         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(A						,				A						,				A						,				A    )
         ]
         return labels

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       List[str]						,				A :       Tuple    )			->			Optional[Any]:






         """simple docstring"""
         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(A						,				s * 1_00    ) for li, s in zip(A						,				A    )]
         return labels

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]						,				A :       List[str]=2_55    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       np.random.randint(0						,				len(_COLORS    )    )
         a__      =       _COLORS[idx] * maximum
         if not self.rgb:
             a__      =       ret[::-1]
         return ret

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			Dict:






         """simple docstring"""
         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(A    )
             a__ ,		a__      =       self.width, self.height
             a__      =       buf.getvalue()
             a__      =       self.img

         a__      =       np.frombuffer(A						,				dtype="""uint8"""    )
         a__      =       buffer.reshape(A						,				A						,				4    )
         a__ ,		a__      =       np.split(A						,				[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"""    ) / 2_5_5.0
             a__      =       img * (1 - alpha) + rgb * alpha

         return visualized_image.astype("""uint8"""    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Any						,				A :       Optional[int]						,				A :       List[Any]    )			->			Tuple:






         """simple docstring"""
         assert brightness_factor >= -1.0 and brightness_factor <= 1.0
         a__      =       mplc.to_rgb(A    )
         a__      =       colorsys.rgb_to_hls(*mplc.to_rgb(A    )    )
         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]						,				A						,				polygon_color[2]    )
         return modified_color






SCREAMING_SNAKE_CASE_						:					Any			   =       (
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)












from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


SCREAMING_SNAKE_CASE_						:					Any			   =       logging.get_logger(__name__)







class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     __UpperCAmelCase	: Optional[Any]						=    ["""pixel_values"""]

     def __init__(      self :       int						,				A :       bool = True						,				A :       Dict[str, int] = None						,				A :       PILImageResampling = PIL.Image.NEAREST						,				A :       bool = False						,				A :       Dict[str, int] = None						,				A :       Union[int, float] = 1 / 2_55						,				A :       bool = False						,				A :       bool = True						,				A :       bool = True						,				A :       Optional[Union[float, List[float]]] = None						,				A :       Optional[Union[float, List[float]]] = None						,				A :       bool = True						,				**A :       Optional[int]						,				)			->			None:






         """simple docstring"""
         super().__init__(**A    )
         a__      =       size if size is not None else {"""height""": 3_46, """width""": 3_46}
         a__      =       get_size_dict(A    )
         a__      =       crop_size if crop_size is not None else {"""height""": 2_89, """width""": 2_89}
         a__      =       get_size_dict(A						,				param_name="""crop_size"""    )

         a__      =       do_resize
         a__      =       size
         a__      =       resample
         a__      =       do_center_crop
         a__      =       crop_size
         a__      =       do_rescale
         a__      =       rescale_factor
         a__      =       rescale_offset
         a__      =       do_normalize
         a__      =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         a__      =       image_std if image_std is not None else IMAGENET_STANDARD_STD
         a__      =       include_top

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]						,				A :       np.ndarray						,				A :       Dict[str, int]						,				A :       PILImageResampling = PIL.Image.NEAREST						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       Dict						,				)			->			np.ndarray:






         """simple docstring"""
         a__      =       get_size_dict(A    )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
         return resize(
             A						,				size=(size["""height"""], size["""width"""])						,				resample=A						,				data_format=A						,				**A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple						,				A :       np.ndarray						,				A :       Dict[str, int]						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       Optional[Any]						,				)			->			np.ndarray:






         """simple docstring"""
         a__      =       get_size_dict(A    )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
         return center_crop(A						,				size=(size["""height"""], size["""width"""])						,				data_format=A						,				**A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict						,				A :       np.ndarray						,				A :       Union[int, float]						,				A :       bool = True						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       List[str]						,				)			->			Any:






         """simple docstring"""
         if offset:
             a__      =       (image - 1_2_7.5) * scale
             if data_format is not None:
                 a__      =       to_channel_dimension_format(A						,				A    )
             a__      =       rescaled_image.astype(np.floataa    )
         else:
             a__      =       rescale(A						,				scale=A						,				data_format=A						,				**A    )
         return rescaled_image

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       np.ndarray						,				A :       Union[float, List[float]]						,				A :       Union[float, List[float]]						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       str						,				)			->			np.ndarray:






         """simple docstring"""
         return normalize(A						,				mean=A						,				std=A						,				data_format=A						,				**A    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]						,				A :       ImageInput						,				A :       bool = None						,				A :       Dict[str, int] = None						,				A :       Any=None						,				A :       bool = None						,				A :       Dict[str, int] = None						,				A :       bool = None						,				A :       float = None						,				A :       bool = None						,				A :       bool = None						,				A :       Optional[Union[float, List[float]]] = None						,				A :       Optional[Union[float, List[float]]] = None						,				A :       bool = None						,				A :       Optional[Union[str, TensorType]] = None						,				A :       ChannelDimension = ChannelDimension.FIRST						,				**A :       Tuple						,				)			->			PIL.Image.Image:






         """simple docstring"""
         a__      =       do_resize if do_resize is not None else self.do_resize
         a__      =       resample if resample is not None else self.resample
         a__      =       do_center_crop if do_center_crop is not None else self.do_center_crop
         a__      =       do_rescale if do_rescale is not None else self.do_rescale
         a__      =       rescale_factor if rescale_factor is not None else self.rescale_factor
         a__      =       rescale_offset if rescale_offset is not None else self.rescale_offset
         a__      =       do_normalize if do_normalize is not None else self.do_normalize
         a__      =       image_mean if image_mean is not None else self.image_mean
         a__      =       image_std if image_std is not None else self.image_std
         a__      =       include_top if include_top is not None else self.include_top

         a__      =       size if size is not None else self.size
         a__      =       get_size_dict(A    )
         a__      =       crop_size if crop_size is not None else self.crop_size
         a__      =       get_size_dict(A						,				param_name="""crop_size"""    )

         a__      =       make_list_of_images(A    )

         if not valid_images(A    ):
             raise ValueError(
                 """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                 """torch.Tensor, tf.Tensor or jax.ndarray."""    )

         if do_resize and size is None or resample is None:
             raise ValueError("""Size and resample must be specified if do_resize is True."""    )

         if do_center_crop and crop_size is None:
             raise ValueError("""Crop size must be specified if do_center_crop is True."""    )

         if do_rescale and rescale_factor is None:
             raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError("""Image mean and std must be specified if do_normalize is True."""    )

         # All transformations expect numpy arrays.
         a__      =       [to_numpy_array(A    ) for image in images]

         if do_resize:
             a__      =       [self.resize(image=A						,				size=A						,				resample=A    ) for image in images]

         if do_center_crop:
             a__      =       [self.center_crop(image=A						,				size=A    ) for image in images]

         if do_rescale:
             a__      =       [self.rescale(image=A						,				scale=A						,				offset=A    ) for image in images]

         if do_normalize:
             a__      =       [self.normalize(image=A						,				mean=A						,				std=A    ) for image in images]

         if include_top:
             a__      =       [self.normalize(image=A						,				mean=[0, 0, 0]						,				std=A    ) for image in images]

         a__      =       [to_channel_dimension_format(A						,				A    ) for image in images]

         a__      =       {"""pixel_values""": images}
         return BatchFeature(data=A						,				tensor_type=A    )






