






"""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_						:					Tuple      				=			1000


class   _lowercase			:


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





            """simple docstring"""







            if isinstance(a				, torch.Tensor    ):
                  __snake_case   :     int	      =img.numpy().astype('''np.uint8'''    )
            if isinstance(a				, a    ):
                  __snake_case   :     int	      =img_tensorize(a    )
            assert isinstance(a				, np.ndarray    )

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

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


      def _UpperCamelCase    (     self					:						str				, a					:						List[str]				, a					:						List[str]=None    ):





            """simple docstring"""







            if color is None:
                  __snake_case   :     int	      =self.edgecolor
            ((__snake_case)      ,					(__snake_case)      ,					(__snake_case)      ,					(__snake_case))   :     Optional[Any]	      =box
            __snake_case   :     List[Any]	      =xa - xa
            __snake_case   :     Dict	      =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 _UpperCamelCase    (     self					:						int				, a					:						str				, a					:						Optional[int]=None				, a					:						List[Any]=None				, a					:						str=None				, a					:						Dict=None    ):





            """simple docstring"""







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

            __snake_case   :     List[str]	      =[self._random_color(maximum=1    ) for _ in range(len(a    )    )]
            __snake_case   :     Optional[int]	      =[assigned_colors[idx] for idx in sorted_idxs]
            if obj_ids is not None:
                  __snake_case   :     List[str]	      =self._create_text_labels_attr(a				, a				, a				, a    )
                  for i in range(len(a    )    ):
                        __snake_case   :     Optional[int]	      =assigned_colors[i]
                        self.add_box(boxes[i]				, a    )
                        self.draw_labels(labels[i]				, boxes[i]				, a    )


      def _UpperCamelCase    (     self					:						Tuple				, a					:						Optional[int]				, a					:						Any				, a					:						str    ):





            """simple docstring"""







            __snake_case      ,					__snake_case      ,					__snake_case      ,					__snake_case   :     Dict	      =box
            __snake_case   :     Tuple	      =(xa, ya)
            __snake_case   :     int	      =(ya - ya) * (xa - xa)
            __snake_case   :     Any	      =_SMALL_OBJ * self.scale
            if instance_area < small or ya - ya < 4_0 * self.scale:
                  if ya >= self.height - 5:
                        __snake_case   :     str	      =(xa, ya)
                  else:
                        __snake_case   :     str	      =(xa, ya)

            __snake_case   :     List[str]	      =(ya - ya) / np.sqrt(self.height * self.width    )
            __snake_case   :     Optional[int]	      =self._change_color_brightness(a				, brightness_factor=0.7    )
            __snake_case   :     Union[str, Any]	      =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 _UpperCamelCase    (     self					:						Dict				, a					:						str				, a					:						List[str]				, a					:						List[Any]="g"				, a					:						List[Any]="left"				, ):





            """simple docstring"""







            __snake_case   :     List[str]	      =0
            __snake_case   :     Dict	      =self.font_size
            __snake_case   :     Any	      =np.maximum(list(mplc.to_rgb(a    )    )				, 0.2    )
            __snake_case   :     Union[str, Any]	      =max(0.8				, np.max(a    )    )
            __snake_case   :     str	      ={
                '''facecolor''': '''black''',
                '''alpha''': self.alpha,
                '''pad''': self.pad,
                '''edgecolor''': '''none''',
            }
            __snake_case      ,					__snake_case   :     Dict	      =position
            self.ax.text(
                a				, a				, a				, size=font_size * self.scale				, family='''sans-serif'''				, bbox=a				, verticalalignment='''top'''				, horizontalalignment=a				, color=a				, zorder=1_0				, rotation=a				, )


      def _UpperCamelCase    (     self					:						int				, a					:						str=None    ):





            """simple docstring"""







            if saveas is None:
                  __snake_case   :     List[str]	      =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 _UpperCamelCase    (     self					:						str				, a					:						List[str]				, a					:						Optional[Any]				, a					:						int				, a					:						Union[str, Any]    ):





            """simple docstring"""







            __snake_case   :     Dict	      =[self.idaobj[i] for i in classes]
            __snake_case   :     Dict	      =[self.idaattr[i] for i in attr_classes]
            __snake_case   :     Any	      =[
                f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
                for label, score, attr, attr_score in zip(a				, a				, a				, a    )
            ]
            return labels


      def _UpperCamelCase    (     self					:						Tuple				, a					:						List[Any]				, a					:						Optional[Any]    ):





            """simple docstring"""







            __snake_case   :     Optional[int]	      =[self.idaobj[i] for i in classes]
            if scores is not None:
                  if labels is None:
                        __snake_case   :     List[Any]	      =['''{:.0f}%'''.format(s * 1_0_0    ) for s in scores]
                  else:
                        __snake_case   :     Tuple	      =['''{} {:.0f}%'''.format(a				, s * 1_0_0    ) for li, s in zip(a				, a    )]
            return labels


      def _UpperCamelCase    (     self					:						int				, a					:						Union[str, Any]=2_5_5    ):





            """simple docstring"""







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


      def _UpperCamelCase    (     self					:						Dict    ):





            """simple docstring"""







            if not self.pynb:
                  __snake_case      ,					__snake_case   :     int	      =self.canvas.print_to_buffer()
                  if (width, height) != (self.width, self.height):
                        __snake_case   :     Dict	      =cva.resize(self.img				, (width, height)    )
                  else:
                        __snake_case   :     Union[str, Any]	      =self.img
            else:
                  __snake_case   :     Tuple	      =io.BytesIO()  # works for cairo backend
                  self.canvas.print_rgba(a    )
                  __snake_case      ,					__snake_case   :     Any	      =self.width, self.height
                  __snake_case   :     Any	      =buf.getvalue()
                  __snake_case   :     Optional[int]	      =self.img

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

            try:
                  import numexpr as ne  # fuse them with numexpr

                  __snake_case   :     Dict	      =ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''    )
            except ImportError:
                  __snake_case   :     Tuple	      =alpha.astype('''float32'''    ) / 2_5_5.0
                  __snake_case   :     str	      =img * (1 - alpha) + rgb * alpha

            return visualized_image.astype('''uint8'''    )







      def _UpperCamelCase    (     self					:						List[str]				, a					:						str				, a					:						Union[str, Any]    ):





            """simple docstring"""







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



UpperCamelCase_						:					Dict      				=			(
    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 importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCamelCase_						:					Optional[int]      				=			"""\
@inproceedings{ramshaw-marcus-1995-text,
    title = \"Text Chunking using Transformation-Based Learning\",
    author = \"Ramshaw, Lance  and
      Marcus, Mitch\",
    booktitle = \"Third Workshop on Very Large Corpora\",
    year = \"1995\",
    url = \"https://www.aclweb.org/anthology/W95-0107\",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
"""

UpperCamelCase_						:					Any      				=			"""\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

UpperCamelCase_						:					int      				=			"""
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,	_KWARGS_DESCRIPTION    )
class   _lowercase			(  datasets.Metric    ):


      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            return datasets.MetricInfo(
                description=_DESCRIPTION				, citation=_CITATION				, homepage='''https://github.com/chakki-works/seqeval'''				, inputs_description=_KWARGS_DESCRIPTION				, features=datasets.Features(
                    {
                        '''predictions''': datasets.Sequence(datasets.Value('''string'''				, id='''label'''    )				, id='''sequence'''    ),
                        '''references''': datasets.Sequence(datasets.Value('''string'''				, id='''label'''    )				, id='''sequence'''    ),
                    }    )				, codebase_urls=['''https://github.com/chakki-works/seqeval''']				, reference_urls=['''https://github.com/chakki-works/seqeval''']				, )







      def _UpperCamelCase    (     self					:						List[Any]				, a					:						List[str]				, a					:						Optional[Any]				, a					:						bool = False				, a					:						Optional[str] = None				, a					:						Optional[str] = None				, a					:						Optional[List[int]] = None				, a					:						Union[str, int] = "warn"				, ):





            """simple docstring"""







            if scheme is not None:
                  try:
                        __snake_case   :     List[str]	      =importlib.import_module('''seqeval.scheme'''    )
                        __snake_case   :     int	      =getattr(a				, a    )
                  except AttributeError:
                        raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''    )
            __snake_case   :     Optional[Any]	      =classification_report(
                y_true=a				, y_pred=a				, suffix=a				, output_dict=a				, scheme=a				, mode=a				, sample_weight=a				, zero_division=a				, )
            report.pop('''macro avg'''    )
            report.pop('''weighted avg'''    )
            __snake_case   :     Union[str, Any]	      =report.pop('''micro avg'''    )

            __snake_case   :     str	      ={
                type_name: {
                    '''precision''': score['''precision'''],
                    '''recall''': score['''recall'''],
                    '''f1''': score['''f1-score'''],
                    '''number''': score['''support'''],
                }
                for type_name, score in report.items()
            }
            __snake_case   :     str	      =overall_score['''precision''']
            __snake_case   :     int	      =overall_score['''recall''']
            __snake_case   :     str	      =overall_score['''f1-score''']
            __snake_case   :     Union[str, Any]	      =accuracy_score(y_true=a				, y_pred=a    )

            return scores


