


'''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


__lowercase:		List[str]    						=  1_000







class 			UpperCAmelCase			:

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





    """simple docstring"""



    if isinstance(a_,       torch.Tensor       ):
       UpperCamelCase__			    =    img.numpy().astype("np.uint8"       )
    if isinstance(a_,       a_       ):
       UpperCamelCase__			    =    img_tensorize(a_       )
    assert isinstance(a_,       np.ndarray       )

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

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

 def 		lowercase_    (			self			:						Optional[int],       a_			:						Union[str, Any],       a_			:						List[str]=None       ):





    """simple docstring"""



    if color is None:
       UpperCamelCase__			    =    self.edgecolor
    ((UpperCamelCase__)    ,		(UpperCamelCase__)    ,		(UpperCamelCase__)    ,		(UpperCamelCase__))			    =    box
    UpperCamelCase__			    =    xa - xa
    UpperCamelCase__			    =    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 		lowercase_    (			self			:						Optional[int],       a_			:						List[Any],       a_			:						Optional[Any]=None,       a_			:						Dict=None,       a_			:						Tuple=None,       a_			:						Dict=None       ):





    """simple docstring"""



    if len(boxes.shape       ) > 2:
       UpperCamelCase__			    =    boxes[0]
    if len(obj_ids.shape       ) > 1:
       UpperCamelCase__			    =    obj_ids[0]
    if len(obj_scores.shape       ) > 1:
       UpperCamelCase__			    =    obj_scores[0]
    if len(attr_ids.shape       ) > 1:
       UpperCamelCase__			    =    attr_ids[0]
    if len(attr_scores.shape       ) > 1:
       UpperCamelCase__			    =    attr_scores[0]
    if isinstance(a_,       torch.Tensor       ):
       UpperCamelCase__			    =    boxes.numpy()
    if isinstance(a_,       a_       ):
       UpperCamelCase__			    =    np.array(a_       )
    assert isinstance(a_,       np.ndarray       )
    UpperCamelCase__			    =    np.prod(boxes[:, 2:] - boxes[:, :2],       axis=1       )
    UpperCamelCase__			    =    np.argsort(-areas       ).tolist()
    UpperCamelCase__			    =    boxes[sorted_idxs] if boxes is not None else None
    UpperCamelCase__			    =    obj_ids[sorted_idxs] if obj_ids is not None else None
    UpperCamelCase__			    =    obj_scores[sorted_idxs] if obj_scores is not None else None
    UpperCamelCase__			    =    attr_ids[sorted_idxs] if attr_ids is not None else None
    UpperCamelCase__			    =    attr_scores[sorted_idxs] if attr_scores is not None else None

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

 def 		lowercase_    (			self			:						Dict,       a_			:						Dict,       a_			:						Any,       a_			:						int       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    box
    UpperCamelCase__			    =    (xa, ya)
    UpperCamelCase__			    =    (ya - ya) * (xa - xa)
    UpperCamelCase__			    =    _SMALL_OBJ * self.scale
    if instance_area < small or ya - ya < 40 * self.scale:
       if ya >= self.height - 5:
          UpperCamelCase__			    =    (xa, ya)
       else:
          UpperCamelCase__			    =    (xa, ya)

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

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

 def 		lowercase_    (			self			:						Any,       a_			:						List[Any],       a_			:						str,       a_			:						Dict="g",       a_			:						str="left",       ):





    """simple docstring"""



    UpperCamelCase__			    =    0
    UpperCamelCase__			    =    self.font_size
    UpperCamelCase__			    =    np.maximum(list(mplc.to_rgb(a_       )       ),       0.2       )
    UpperCamelCase__			    =    max(0.8,       np.max(a_       )       )
    UpperCamelCase__			    =    {
        "facecolor": "black",
        "alpha": self.alpha,
        "pad": self.pad,
        "edgecolor": "none",
    }
    UpperCamelCase__    ,		UpperCamelCase__			    =    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 		lowercase_    (			self			:						Dict,       a_			:						List[Any]=None       ):





    """simple docstring"""



    if saveas is None:
       UpperCamelCase__			    =    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 		lowercase_    (			self			:						str,       a_			:						Tuple,       a_			:						List[str],       a_			:						Union[str, Any],       a_			:						List[str]       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						List[Any],       a_			:						Union[str, Any],       a_			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    [self.idaobj[i] for i in classes]
    if scores is not None:
       if labels is None:
          UpperCamelCase__			    =    ["{:.0f}%".format(s * 100       ) for s in scores]
       else:
          UpperCamelCase__			    =    ["{} {:.0f}%".format(a_,       s * 100       ) for li, s in zip(a_,       a_       )]
    return labels

 def 		lowercase_    (			self			:						Dict,       a_			:						str=255       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    if not self.pynb:
       UpperCamelCase__    ,		UpperCamelCase__			    =    self.canvas.print_to_buffer()
       if (width, height) != (self.width, self.height):
          UpperCamelCase__			    =    cva.resize(self.img,       (width, height)       )
       else:
          UpperCamelCase__			    =    self.img
    else:
       UpperCamelCase__			    =    io.BytesIO()  # works for cairo backend
       self.canvas.print_rgba(a_       )
       UpperCamelCase__    ,		UpperCamelCase__			    =    self.width, self.height
       UpperCamelCase__			    =    buf.getvalue()
       UpperCamelCase__			    =    self.img

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

    try:
       import numexpr as ne  # fuse them with numexpr

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

    return visualized_image.astype("uint8"       )

 def 		lowercase_    (			self			:						Union[str, Any],       a_			:						str,       a_			:						List[Any]       ):





    """simple docstring"""



    assert brightness_factor >= -1.0 and brightness_factor <= 1.0
    UpperCamelCase__			    =    mplc.to_rgb(a_       )
    UpperCamelCase__			    =    colorsys.rgb_to_hls(*mplc.to_rgb(a_       )       )
    UpperCamelCase__			    =    polygon_color[1] + (brightness_factor * polygon_color[1])
    UpperCamelCase__			    =    0.0 if modified_lightness < 0.0 else modified_lightness
    UpperCamelCase__			    =    1.0 if modified_lightness > 1.0 else modified_lightness
    UpperCamelCase__			    =    colorsys.hls_to_rgb(polygon_color[0],       a_,       polygon_color[2]       )
    return modified_color







__lowercase:		Optional[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 TYPE_CHECKING

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


__lowercase				    =      {
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      ["""BartTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BartForCausalLM""",
          """BartForConditionalGeneration""",
          """BartForQuestionAnswering""",
          """BartForSequenceClassification""",
          """BartModel""",
          """BartPretrainedModel""",
          """PretrainedBartModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """TFBartForConditionalGeneration""",
          """TFBartForSequenceClassification""",
          """TFBartModel""",
          """TFBartPretrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """FlaxBartDecoderPreTrainedModel""",
          """FlaxBartForCausalLM""",
          """FlaxBartForConditionalGeneration""",
          """FlaxBartForQuestionAnswering""",
          """FlaxBartForSequenceClassification""",
          """FlaxBartModel""",
          """FlaxBartPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
      from .tokenization_bart import BartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bart_fast import BartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bart import (
                BART_PRETRAINED_MODEL_ARCHIVE_LIST,
                BartForCausalLM,
                BartForConditionalGeneration,
                BartForQuestionAnswering,
                BartForSequenceClassification,
                BartModel,
                BartPretrainedModel,
                PretrainedBartModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bart import (
                TFBartForConditionalGeneration,
                TFBartForSequenceClassification,
                TFBartModel,
                TFBartPretrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bart import (
                FlaxBartDecoderPreTrainedModel,
                FlaxBartForCausalLM,
                FlaxBartForConditionalGeneration,
                FlaxBartForQuestionAnswering,
                FlaxBartForSequenceClassification,
                FlaxBartModel,
                FlaxBartPreTrainedModel,
            )

else:
      import sys

      __lowercase				    =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
