






'''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	:					Optional[int]					=  1_000

class      lowerCamelCase_     :
      '''simple docstring'''

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


             if isinstance(__lowerCamelCase						, torch.Tensor	):
                    _UpperCAmelCase					:  List[Any]											=   img.numpy().astype("np.uint8"	)
             if isinstance(__lowerCamelCase						, __lowerCamelCase	):
                    _UpperCAmelCase					:  List[str]											=   img_tensorize(__lowerCamelCase	)
             assert isinstance(__lowerCamelCase						, np.ndarray	)

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

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




      def 					_A     (	self						:    Any						, A						:    Tuple						, A						:    int=None	):


             if color is None:
                    _UpperCAmelCase					:  List[str]											=   self.edgecolor
             (_UpperCAmelCase)					:  List[str]											=   box
             _UpperCAmelCase					:  int											=   xa - xa
             _UpperCAmelCase					:  List[str]											=   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 					_A     (	self						:    str						, A						:    str						, A						:    str=None						, A						:    Union[str, Any]=None						, A						:    Optional[Any]=None						, A						:    int=None	):


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

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




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

             _UpperCAmelCase					:  Optional[Any]											=   (ya - ya) / np.sqrt(self.height * self.width	)
             _UpperCAmelCase					:  int											=   self._change_color_brightness(__lowerCamelCase						, brightness_factor=0.7	)
             _UpperCAmelCase					:  List[str]											=   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 					_A     (	self						:    Union[str, Any]						, A						:    List[Any]						, A						:    str						, A						:    Optional[Any]="g"						, A						:    Optional[Any]="left"						, ):
             _UpperCAmelCase					:  Union[str, Any]											=   0
             _UpperCAmelCase					:  Union[str, Any]											=   self.font_size
             _UpperCAmelCase					:  int											=   np.maximum(list(mplc.to_rgb(__lowerCamelCase	)	)						, 0.2	)
             _UpperCAmelCase					:  List[str]											=   max(0.8						, np.max(__lowerCamelCase	)	)
             _UpperCAmelCase					:  Tuple											=   {
                 "facecolor": "black",
                 "alpha": self.alpha,
                 "pad": self.pad,
                 "edgecolor": "none",
             }
             _UpperCAmelCase					:  Tuple											=   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 					_A     (	self						:    Optional[int]						, A						:    Dict=None	):


             if saveas is None:
                    _UpperCAmelCase					:  Optional[int]											=   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 					_A     (	self						:    Any						, A						:    int						, A						:    Optional[int]						, A						:    int						, A						:    Optional[int]	):
             _UpperCAmelCase					:  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(__lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase	)
             ]
             return labels




      def 					_A     (	self						:    int						, A						:    int						, A						:    Any	):
             _UpperCAmelCase					:  Any											=   [self.idaobj[i] for i in classes]
             if scores is not None:
                    if labels is None:
                           _UpperCAmelCase					:  Dict											=   ["{:.0f}%".format(s * 100	) for s in scores]
                    else:
                           _UpperCAmelCase					:  str											=   ["{} {:.0f}%".format(__lowerCamelCase						, s * 100	) for li, s in zip(__lowerCamelCase						, __lowerCamelCase	)]
             return labels




      def 					_A     (	self						:    Any						, A						:    Optional[Any]=255	):
             _UpperCAmelCase					:  Any											=   np.random.randint(0						, len(_COLORS	)	)
             _UpperCAmelCase					:  Any											=   _COLORS[idx] * maximum
             if not self.rgb:
                    _UpperCAmelCase					:  Any											=   ret[::-1]
             return ret




      def 					_A     (	self						:    List[Any]	):


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

             _UpperCAmelCase					:  Optional[Any]											=   np.frombuffer(__lowerCamelCase						, dtype="uint8"	)
             _UpperCAmelCase					:  str											=   buffer.reshape(__lowerCamelCase						, __lowerCamelCase						, 4	)
             _UpperCAmelCase					:  Tuple											=   np.split(__lowerCamelCase						, [3]						, axis=2	)

             try:
                    import numexpr as ne  # fuse them with numexpr

                    _UpperCAmelCase					:  List[Any]											=   ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"	)
             except ImportError:
                    _UpperCAmelCase					:  Union[str, Any]											=   alpha.astype("float32"	) / 255.0
                    _UpperCAmelCase					:  Optional[int]											=   img * (1 - alpha) + rgb * alpha

             return visualized_image.astype("uint8"	)





      def 					_A     (	self						:    Optional[Any]						, A						:    Any						, A						:    str	):
             assert brightness_factor >= -1.0 and brightness_factor <= 1.0
             _UpperCAmelCase					:  Tuple											=   mplc.to_rgb(__lowerCamelCase	)
             _UpperCAmelCase					:  Optional[Any]											=   colorsys.rgb_to_hls(*mplc.to_rgb(__lowerCamelCase	)	)
             _UpperCAmelCase					:  Optional[int]											=   polygon_color[1] + (brightness_factor * polygon_color[1])
             _UpperCAmelCase					:  int											=   0.0 if modified_lightness < 0.0 else modified_lightness
             _UpperCAmelCase					:  int											=   1.0 if modified_lightness > 1.0 else modified_lightness
             _UpperCAmelCase					:  int											=   colorsys.hls_to_rgb(polygon_color[0]						, __lowerCamelCase						, polygon_color[2]	)
             return modified_color

__SCREAMING_SNAKE_CASE	:					str					=  (
    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)
)


'''simple docstring'''




from sklearn.metrics import accuracy_score

import datasets


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


__SCREAMING_SNAKE_CASE	:					Any					=  """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,			_KWARGS_DESCRIPTION   )
class      lowerCamelCase_     (datasets.Metric   ):
      '''simple docstring'''

      def 					_A     (	self						:    int	):
             return datasets.MetricInfo(
                 description=_DESCRIPTION						, citation=_CITATION						, inputs_description=_KWARGS_DESCRIPTION						, features=datasets.Features(
                     {
                         "predictions": datasets.Sequence(datasets.Value("int32"	)	),
                         "references": datasets.Sequence(datasets.Value("int32"	)	),
                     }
                     if self.config_name == "multilabel"
                     else {
                         "predictions": datasets.Value("int32"	),
                         "references": datasets.Value("int32"	),
                     }	)						, reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]						, )





      def 					_A     (	self						:    str						, A						:    List[str]						, A						:    int						, A						:    List[Any]=True						, A						:    List[str]=None	):
             return {
                 "accuracy": float(
                     accuracy_score(A						, A						, normalize=A						, sample_weight=A	)	)
             }

