
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


__lowerCAmelCase      :					Tuple   	=       1000




class 	UpperCAmelCase_      :


  '''simple docstring'''







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







    """simple docstring"""






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

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

    __magic_name__    					=	saveas
    __magic_name__    					=	rgb
    __magic_name__    					=	pynb
    __magic_name__    					=	img
    __magic_name__    					=	edgecolor
    __magic_name__    					=	0.5
    __magic_name__    					=	linestyle
    __magic_name__    					=	int(np.sqrt(min(UpperCamelCase__      ,							UpperCamelCase__   )   ) * scale // 3   )
    __magic_name__    					=	width
    __magic_name__    					=	height
    __magic_name__    					=	scale
    __magic_name__    					=	fig
    __magic_name__    					=	ax
    __magic_name__    					=	pad
    __magic_name__    					=	idaobj
    __magic_name__    					=	idaattr
    __magic_name__    					=	FigureCanvasAgg(UpperCamelCase__   )
  def 							_lowercase							(			self      :			int      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Tuple=None   )      ->     str:







    """simple docstring"""






    if color is None:
      __magic_name__    					=	self.edgecolor
    ((__magic_name__)					,   (__magic_name__)					,   (__magic_name__)					,   (__magic_name__))    					=	box
    __magic_name__    					=	xa - xa
    __magic_name__    					=	ya - ya
    self.ax.add_patch(
        mpl.patches.Rectangle(
            (xa, ya)      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							fill=UpperCamelCase__      ,							edgecolor=UpperCamelCase__      ,							linewidth=self.font_size // 3      ,							alpha=self.alpha      ,							linestyle=self.linestyle      ,							)   )
  def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			Any=None      ,							UpperCamelCase__      :			Optional[int]=None      ,							UpperCamelCase__      :			int=None      ,							UpperCamelCase__      :			List[str]=None   )      ->     Dict:







    """simple docstring"""






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

    __magic_name__    					=	[self._random_color(maximum=1   ) for _ in range(len(UpperCamelCase__   )   )]
    __magic_name__    					=	[assigned_colors[idx] for idx in sorted_idxs]
    if obj_ids is not None:
      __magic_name__    					=	self._create_text_labels_attr(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__   )
      for i in range(len(UpperCamelCase__   )   ):
        __magic_name__    					=	assigned_colors[i]
        self.add_box(boxes[i]      ,							UpperCamelCase__   )
        self.draw_labels(labels[i]      ,							boxes[i]      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			int      ,							UpperCamelCase__      :			Any   )      ->     Tuple:







    """simple docstring"""






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

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

    self.draw_text(
        text=UpperCamelCase__      ,							position=UpperCamelCase__      ,							color=UpperCamelCase__      ,							)
  def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			Union[str, Any]="g"      ,							UpperCamelCase__      :			Dict="left"      ,							)      ->     Tuple:







    """simple docstring"""






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







    """simple docstring"""






    if saveas is None:
      __magic_name__    					=	self.saveas
    if saveas.lower().endswith(""".jpg"""   ) or saveas.lower().endswith(""".png"""   ):
      cva.imwrite(
          UpperCamelCase__      ,							self._get_buffer()[:, :, ::-1]      ,							)
    else:
      self.fig.savefig(UpperCamelCase__   )
  def 							_lowercase							(			self      :			List[str]      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			int      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Optional[Any]   )      ->     Dict:







    """simple docstring"""






    __magic_name__    					=	[self.idaobj[i] for i in classes]
    __magic_name__    					=	[self.idaattr[i] for i in attr_classes]
    __magic_name__    					=	[
        F'''{label} {score:.2f} {attr} {attr_score:.2f}'''
        for label, score, attr, attr_score in zip(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__   )
    ]
    return labels
  def 							_lowercase							(			self      :			List[str]      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Tuple   )      ->     Optional[Any]:







    """simple docstring"""






    __magic_name__    					=	[self.idaobj[i] for i in classes]
    if scores is not None:
      if labels is None:
        __magic_name__    					=	["""{:.0f}%""".format(s * 100   ) for s in scores]
      else:
        __magic_name__    					=	["""{} {:.0f}%""".format(UpperCamelCase__      ,							s * 100   ) for li, s in zip(UpperCamelCase__      ,							UpperCamelCase__   )]
    return labels
  def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			List[Any]=255   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	np.random.randint(0      ,							len(_COLORS   )   )
    __magic_name__    					=	_COLORS[idx] * maximum
    if not self.rgb:
      __magic_name__    					=	ret[::-1]
    return ret
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     int:







    """simple docstring"""






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

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

    try:
      import numexpr as ne  # fuse them with numexpr

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

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





  def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Optional[Any]   )      ->     Tuple:







    """simple docstring"""






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







__lowerCAmelCase      :					Optional[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)
)



import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
    import transformer_engine.pytorch as te

def a__   (	A_,					A_=True,					A_=True,					A_=True ):






  '''simple docstring'''





  if not is_fpa_available():
    raise ImportError("""Using `convert_model` requires transformer_engine to be installed.""" )
  for name, module in model.named_children():
    if isinstance(A_,					nn.Linear ) and to_transformer_engine and _convert_linear:
      # Return early if the linear layer weights are not multiples of 16
      if any(p % 16 != 0 for p in module.weight.shape ):
        return
      __magic_name__    					=	module.bias is not None
      __magic_name__    					=	te.Linear(
          module.in_features,					module.out_features,					bias=A_,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      if has_bias:
        __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    elif isinstance(A_,					nn.LayerNorm ) and to_transformer_engine and _convert_ln:
      __magic_name__    					=	te.LayerNorm(module.normalized_shape[0],					eps=module.eps,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    elif isinstance(A_,					te.Linear ) and not to_transformer_engine and _convert_linear:
      __magic_name__    					=	module.bias is not None
      __magic_name__    					=	nn.Linear(
          module.in_features,					module.out_features,					bias=A_,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      if has_bias:
        __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    elif isinstance(A_,					te.LayerNorm ) and not to_transformer_engine and _convert_ln:
      __magic_name__    					=	nn.LayerNorm(module.normalized_shape[0],					eps=module.eps,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    else:
      convert_model(
          A_,					to_transformer_engine=A_,					_convert_linear=_convert_linear,					_convert_ln=_convert_ln,					)





def a__   (	A_ ):






  '''simple docstring'''





  if not is_fpa_available():
    raise ImportError("""Using `has_transformer_engine_layers` requires transformer_engine to be installed.""" )
  for m in model.modules():
    if isinstance(A_,					(te.LayerNorm, te.Linear) ):
      return True
  return False

