


"""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


SCREAMING_SNAKE_CASE__				:  List[str]						= 1000


class 	A_       :





  """simple docstring"""

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

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

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

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

     a   :	Optional[int]						=    (ya - ya) / np.sqrt(self.height * self.width						)
     a   :	Any						=    self._change_color_brightness(lowerCAmelCase_  ,					brightness_factor=0.7						)
     a   :	Optional[Any]						=    np.clip((height_ratio - 0.02) / 0.08 + 1  ,					1.2  ,					2						)
     font_size *= 0.75 * self.font_size

     self.draw_text(
         text=lowerCAmelCase_  ,					position=lowerCAmelCase_  ,					color=lowerCAmelCase_  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase="g"  ,					__UpperCAmelCase="left"  ,					)				->					Tuple:
     a   :	Dict						=    0
     a   :	Tuple						=    self.font_size
     a   :	Optional[Any]						=    np.maximum(list(mplc.to_rgb(lowerCAmelCase_						)						)  ,					0.2						)
     a   :	List[Any]						=    max(0.8  ,					np.max(lowerCAmelCase_						)						)
     a   :	Union[str, Any]						=    {
         '''facecolor''': '''black''',
         '''alpha''': self.alpha,
         '''pad''': self.pad,
         '''edgecolor''': '''none''',
     }
     a   :	Any						=    position
     self.ax.text(
         lowerCAmelCase_  ,					lowerCAmelCase_  ,					lowerCAmelCase_  ,					size=font_size * self.scale  ,					family='sans-serif'  ,					bbox=lowerCAmelCase_  ,					verticalalignment='top'  ,					horizontalalignment=lowerCAmelCase_  ,					color=lowerCAmelCase_  ,					zorder=10  ,					rotation=lowerCAmelCase_  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase=None						)				->					Union[str, Any]:
     if saveas is None:
        a   :	Dict						=    self.saveas
     if saveas.lower().endswith('.jpg'						) or saveas.lower().endswith('.png'						):
        cva.imwrite(
            lowerCAmelCase_  ,					self._get_buffer()[:, :, ::-1]  ,					)
     else:
        self.fig.savefig(lowerCAmelCase_						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					str:
     a   :	Optional[int]						=    [self.idaobj[i] for i in classes]
     a   :	Union[str, Any]						=    [self.idaattr[i] for i in attr_classes]
     a   :	Optional[Any]						=    [
         f'{label} {score:.2f} {attr} {attr_score:.2f}'
         for label, score, attr, attr_score in zip(lowerCAmelCase_  ,					lowerCAmelCase_  ,					lowerCAmelCase_  ,					lowerCAmelCase_						)
     ]
     return labels
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Union[str, Any]:
     a   :	List[Any]						=    [self.idaobj[i] for i in classes]
     if scores is not None:
        if labels is None:
           a   :	Optional[int]						=    ['''{:.0f}%'''.format(s * 1_00						) for s in scores]
        else:
           a   :	Optional[int]						=    ['''{} {:.0f}%'''.format(lowerCAmelCase_  ,					s * 1_00						) for li, s in zip(lowerCAmelCase_  ,					lowerCAmelCase_						)]
     return labels
  def 							lowercase_     (						self  ,					__UpperCAmelCase=2_55						)				->					Any:
     a   :	List[str]						=    np.random.randint(0  ,					len(_COLORS						)						)
     a   :	Dict						=    _COLORS[idx] * maximum
     if not self.rgb:
        a   :	Union[str, Any]						=    ret[::-1]
     return ret
  def 							lowercase_     (						self						)				->					Optional[Any]:
     if not self.pynb:
        a   :	Dict						=    self.canvas.print_to_buffer()
        if (width, height) != (self.width, self.height):
           a   :	str						=    cva.resize(self.img  ,					(width, height)						)
        else:
           a   :	Dict						=    self.img
     else:
        a   :	int						=    io.BytesIO()  # works for cairo backend
        self.canvas.print_rgba(lowerCAmelCase_						)
        a   :	Any						=    self.width, self.height
        a   :	Optional[Any]						=    buf.getvalue()
        a   :	int						=    self.img

     a   :	str						=    np.frombuffer(lowerCAmelCase_  ,					dtype='uint8'						)
     a   :	Any						=    buffer.reshape(lowerCAmelCase_  ,					lowerCAmelCase_  ,					4						)
     a   :	Any						=    np.split(lowerCAmelCase_  ,					[3]  ,					axis=2						)

     try:
        import numexpr as ne  # fuse them with numexpr

        a   :	str						=    ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'						)
     except ImportError:
        a   :	Any						=    alpha.astype('float32'						) / 2_55.0
        a   :	Optional[int]						=    img * (1 - alpha) + rgb * alpha

     return visualized_image.astype('uint8'						)






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					str:
     assert brightness_factor >= -1.0 and brightness_factor <= 1.0
     a   :	List[Any]						=    mplc.to_rgb(lowerCAmelCase_						)
     a   :	Optional[Any]						=    colorsys.rgb_to_hls(*mplc.to_rgb(lowerCAmelCase_						)						)
     a   :	Tuple						=    polygon_color[1] + (brightness_factor * polygon_color[1])
     a   :	Optional[int]						=    0.0 if modified_lightness < 0.0 else modified_lightness
     a   :	Union[str, Any]						=    1.0 if modified_lightness > 1.0 else modified_lightness
     a   :	Optional[int]						=    colorsys.hls_to_rgb(polygon_color[0]  ,					lowerCAmelCase_  ,					polygon_color[2]						)
     return modified_color



SCREAMING_SNAKE_CASE__				:  List[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 List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


UpperCAmelCase				 =   datasets.utils.logging.get_logger(__name__)






class 				_SCREAMING_SNAKE_CASE( folder_based_builder.FolderBasedBuilderConfig						):
   UpperCamelCase_					:    bool			      = None
   UpperCamelCase_					:    bool			      = None






class 				_SCREAMING_SNAKE_CASE( folder_based_builder.FolderBasedBuilder						):
   UpperCamelCase_					:    List[str]			      = datasets.Image()
   UpperCamelCase_					:    Any			      = 'image'
   UpperCamelCase_					:    str			      = ImageFolderConfig
   UpperCamelCase_					:    List[str]  # definition at the bottom of the script
   UpperCamelCase_					:    Union[str, Any]			      = ImageClassification(image_column='image'							,   label_column='label'						)

UpperCAmelCase				 =   [
    """.blp""",
    """.bmp""",
    """.dib""",
    """.bufr""",
    """.cur""",
    """.pcx""",
    """.dcx""",
    """.dds""",
    """.ps""",
    """.eps""",
    """.fit""",
    """.fits""",
    """.fli""",
    """.flc""",
    """.ftc""",
    """.ftu""",
    """.gbr""",
    """.gif""",
    """.grib""",
    """.h5""",
    """.hdf""",
    """.png""",
    """.apng""",
    """.jp2""",
    """.j2k""",
    """.jpc""",
    """.jpf""",
    """.jpx""",
    """.j2c""",
    """.icns""",
    """.ico""",
    """.im""",
    """.iim""",
    """.tif""",
    """.tiff""",
    """.jfif""",
    """.jpe""",
    """.jpg""",
    """.jpeg""",
    """.mpg""",
    """.mpeg""",
    """.msp""",
    """.pcd""",
    """.pxr""",
    """.pbm""",
    """.pgm""",
    """.ppm""",
    """.pnm""",
    """.psd""",
    """.bw""",
    """.rgb""",
    """.rgba""",
    """.sgi""",
    """.ras""",
    """.tga""",
    """.icb""",
    """.vda""",
    """.vst""",
    """.webp""",
    """.wmf""",
    """.emf""",
    """.xbm""",
    """.xpm""",
]
UpperCAmelCase				 =   IMAGE_EXTENSIONS