





'''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


A__       :     Dict          =10_00




class 				UpperCAmelCase  :




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

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

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




						def        lowercase__					(    self    :				Any    ,    __snake_case    :				Dict    ,    __snake_case    :				Dict=None						)       ->   Tuple:
												if color is None:
																		_lowerCAmelCase							  =							self.edgecolor
												((_lowerCAmelCase)  ,							(_lowerCAmelCase)  ,							(_lowerCAmelCase)  ,							(_lowerCAmelCase))							  =							box
												_lowerCAmelCase							  =							xa - xa
												_lowerCAmelCase							  =							ya - ya
												self.ax.add_patch(
												    mpl.patches.Rectangle(
												        (xa, ya)    ,    __snake_case    ,    __snake_case    ,    fill=__snake_case    ,    edgecolor=__snake_case    ,    linewidth=self.font_size // 3    ,    alpha=self.alpha    ,    linestyle=self.linestyle    ,    )						)




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

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




						def        lowercase__					(    self    :				str    ,    __snake_case    :				Optional[int]    ,    __snake_case    :				Tuple    ,    __snake_case    :				Optional[int]						)       ->   int:
												_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase							  =							box
												_lowerCAmelCase							  =							(xa, ya)
												_lowerCAmelCase							  =							(ya - ya) * (xa - xa)
												_lowerCAmelCase							  =							_SMALL_OBJ * self.scale
												if instance_area < small or ya - ya < 40 * self.scale:
																		if ya >= self.height - 5:
																								_lowerCAmelCase							  =							(xa, ya)
																		else:
																								_lowerCAmelCase							  =							(xa, ya)

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

												self.draw_text(
												    text=__snake_case    ,    position=__snake_case    ,    color=__snake_case    ,    )




						def        lowercase__					(    self    :				Union[str, Any]    ,    __snake_case    :				List[Any]    ,    __snake_case    :				Optional[Any]    ,    __snake_case    :				str="g"    ,    __snake_case    :				List[str]="left"    ,    )       ->   Optional[int]:
												_lowerCAmelCase							  =							0
												_lowerCAmelCase							  =							self.font_size
												_lowerCAmelCase							  =							np.maximum(list(mplc.to_rgb(__snake_case						)						)    ,    0.2						)
												_lowerCAmelCase							  =							max(0.8    ,    np.max(__snake_case						)						)
												_lowerCAmelCase							  =							{
												    """facecolor""": """black""",
												    """alpha""": self.alpha,
												    """pad""": self.pad,
												    """edgecolor""": """none""",
												}
												_lowerCAmelCase  ,							_lowerCAmelCase							  =							position
												self.ax.text(
												    __snake_case    ,    __snake_case    ,    __snake_case    ,    size=font_size * self.scale    ,    family="""sans-serif"""    ,    bbox=__snake_case    ,    verticalalignment="""top"""    ,    horizontalalignment=__snake_case    ,    color=__snake_case    ,    zorder=10    ,    rotation=__snake_case    ,    )




						def        lowercase__					(    self    :				Optional[int]    ,    __snake_case    :				Dict=None						)       ->   Optional[Any]:
												if saveas is None:
																		_lowerCAmelCase							  =							self.saveas
												if saveas.lower().endswith(""".jpg"""						) or saveas.lower().endswith(""".png"""						):
																		cva.imwrite(
																		    __snake_case    ,    self._get_buffer()[:, :, ::-1]    ,    )
												else:
																		self.fig.savefig(__snake_case						)




						def        lowercase__					(    self    :				Union[str, Any]    ,    __snake_case    :				List[Any]    ,    __snake_case    :				str    ,    __snake_case    :				Tuple    ,    __snake_case    :				Dict						)       ->   Union[str, Any]:
												_lowerCAmelCase							  =							[self.idaobj[i] for i in classes]
												_lowerCAmelCase							  =							[self.idaattr[i] for i in attr_classes]
												_lowerCAmelCase							  =							[
												    f"{label} {score:.2f} {attr} {attr_score:.2f}"
												    for label, score, attr, attr_score in zip(__snake_case    ,    __snake_case    ,    __snake_case    ,    __snake_case						)
												]
												return labels




						def        lowercase__					(    self    :				Optional[int]    ,    __snake_case    :				Dict    ,    __snake_case    :				Tuple						)       ->   Dict:
												_lowerCAmelCase							  =							[self.idaobj[i] for i in classes]
												if scores is not None:
																		if labels is None:
																								_lowerCAmelCase							  =							["""{:.0f}%""".format(s * 1_00						) for s in scores]
																		else:
																								_lowerCAmelCase							  =							["""{} {:.0f}%""".format(__snake_case    ,    s * 1_00						) for li, s in zip(__snake_case    ,    __snake_case						)]
												return labels




						def        lowercase__					(    self    :				Optional[int]    ,    __snake_case    :				List[str]=2_55						)       ->   Optional[Any]:
												_lowerCAmelCase							  =							np.random.randint(0    ,    len(_COLORS						)						)
												_lowerCAmelCase							  =							_COLORS[idx] * maximum
												if not self.rgb:
																		_lowerCAmelCase							  =							ret[::-1]
												return ret




						def        lowercase__					(    self    :				Any						)       ->   List[Any]:
												if not self.pynb:
																		_lowerCAmelCase  ,							_lowerCAmelCase							  =							self.canvas.print_to_buffer()
																		if (width, height) != (self.width, self.height):
																								_lowerCAmelCase							  =							cva.resize(self.img    ,    (width, height)						)
																		else:
																								_lowerCAmelCase							  =							self.img
												else:
																		_lowerCAmelCase							  =							io.BytesIO()  # works for cairo backend
																		self.canvas.print_rgba(__snake_case						)
																		_lowerCAmelCase  ,							_lowerCAmelCase							  =							self.width, self.height
																		_lowerCAmelCase							  =							buf.getvalue()
																		_lowerCAmelCase							  =							self.img

												_lowerCAmelCase							  =							np.frombuffer(__snake_case    ,    dtype="""uint8"""						)
												_lowerCAmelCase							  =							buffer.reshape(__snake_case    ,    __snake_case    ,    4						)
												_lowerCAmelCase  ,							_lowerCAmelCase							  =							np.split(__snake_case    ,    [3]    ,    axis=2						)

												try:
																		import numexpr as ne  # fuse them with numexpr

																		_lowerCAmelCase							  =							ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""						)
												except ImportError:
																		_lowerCAmelCase							  =							alpha.astype("""float32"""						) / 2_55.0
																		_lowerCAmelCase							  =							img * (1 - alpha) + rgb * alpha

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




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




A__       :     int          =(
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__       :     Tuple          ={
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Tuple          =['''MPNetTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[Any]          =[
          '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MPNetForMaskedLM''',
          '''MPNetForMultipleChoice''',
          '''MPNetForQuestionAnswering''',
          '''MPNetForSequenceClassification''',
          '''MPNetForTokenClassification''',
          '''MPNetLayer''',
          '''MPNetModel''',
          '''MPNetPreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     str          =[
          '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFMPNetEmbeddings''',
          '''TFMPNetForMaskedLM''',
          '''TFMPNetForMultipleChoice''',
          '''TFMPNetForQuestionAnswering''',
          '''TFMPNetForSequenceClassification''',
          '''TFMPNetForTokenClassification''',
          '''TFMPNetMainLayer''',
          '''TFMPNetModel''',
          '''TFMPNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

      A__       :     List[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






