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   						=    1000







class 					UpperCAmelCase_				:




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

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

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




    def 						__lowerCamelCase (					self  :   Optional[int]      ,      lowerCAmelCase  :   Any      ,      lowerCAmelCase  :   Any=None   )	-> int:
      if color is None:
        a__     		=     self.edgecolor
      ((a__)		,(a__)		,(a__)		,(a__))     		=     box
      a__     		=     xa - xa
      a__     		=     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 						__lowerCamelCase (					self  :   Any      ,      lowerCAmelCase  :   Dict      ,      lowerCAmelCase  :   Dict=None      ,      lowerCAmelCase  :   Optional[int]=None      ,      lowerCAmelCase  :   Optional[int]=None      ,      lowerCAmelCase  :   List[Any]=None   )	-> List[Any]:
      if len(boxes.shape   ) > 2:
        a__     		=     boxes[0]
      if len(obj_ids.shape   ) > 1:
        a__     		=     obj_ids[0]
      if len(obj_scores.shape   ) > 1:
        a__     		=     obj_scores[0]
      if len(attr_ids.shape   ) > 1:
        a__     		=     attr_ids[0]
      if len(attr_scores.shape   ) > 1:
        a__     		=     attr_scores[0]
      if isinstance(lowerCAmelCase      ,      torch.Tensor   ):
        a__     		=     boxes.numpy()
      if isinstance(lowerCAmelCase      ,      lowerCAmelCase   ):
        a__     		=     np.array(lowerCAmelCase   )
      assert isinstance(lowerCAmelCase      ,      np.ndarray   )
      a__     		=     np.prod(boxes[:, 2:] - boxes[:, :2]      ,      axis=1   )
      a__     		=     np.argsort(-areas   ).tolist()
      a__     		=     boxes[sorted_idxs] if boxes is not None else None
      a__     		=     obj_ids[sorted_idxs] if obj_ids is not None else None
      a__     		=     obj_scores[sorted_idxs] if obj_scores is not None else None
      a__     		=     attr_ids[sorted_idxs] if attr_ids is not None else None
      a__     		=     attr_scores[sorted_idxs] if attr_scores is not None else None

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




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

      a__     		=     (ya - ya) / np.sqrt(self.height * self.width   )
      a__     		=     self._change_color_brightness(lowerCAmelCase      ,      brightness_factor=0.7   )
      a__     		=     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 						__lowerCamelCase (					self  :   List[str]      ,      lowerCAmelCase  :   int      ,      lowerCAmelCase  :   Union[str, Any]      ,      lowerCAmelCase  :   str="g"      ,      lowerCAmelCase  :   Tuple="left"      ,      )	-> List[Any]:
      a__     		=     0
      a__     		=     self.font_size
      a__     		=     np.maximum(list(mplc.to_rgb(lowerCAmelCase   )   )      ,      0.2   )
      a__     		=     max(0.8      ,      np.max(lowerCAmelCase   )   )
      a__     		=     {
          "facecolor": "black",
          "alpha": self.alpha,
          "pad": self.pad,
          "edgecolor": "none",
      }
      a__		,a__     		=     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 						__lowerCamelCase (					self  :   Tuple      ,      lowerCAmelCase  :   Dict=None   )	-> Union[str, Any]:
      if saveas is None:
        a__     		=     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 						__lowerCamelCase (					self  :   int      ,      lowerCAmelCase  :   int      ,      lowerCAmelCase  :   Union[str, Any]      ,      lowerCAmelCase  :   Any      ,      lowerCAmelCase  :   Tuple   )	-> Optional[int]:
      a__     		=     [self.idaobj[i] for i in classes]
      a__     		=     [self.idaattr[i] for i in attr_classes]
      a__     		=     [
          f"{label} {score:.2f} {attr} {attr_score:.2f}"
          for label, score, attr, attr_score in zip(lowerCAmelCase      ,      lowerCAmelCase      ,      lowerCAmelCase      ,      lowerCAmelCase   )
      ]
      return labels




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




    def 						__lowerCamelCase (					self  :   Optional[Any]      ,      lowerCAmelCase  :   str=255   )	-> Union[str, Any]:
      a__     		=     np.random.randint(0      ,      len(_COLORS   )   )
      a__     		=     _COLORS[idx] * maximum
      if not self.rgb:
        a__     		=     ret[::-1]
      return ret




    def 						__lowerCamelCase (					self  :   Tuple   )	-> Dict:
      if not self.pynb:
        a__		,a__     		=     self.canvas.print_to_buffer()
        if (width, height) != (self.width, self.height):
          a__     		=     cva.resize(self.img      ,      (width, height)   )
        else:
          a__     		=     self.img
      else:
        a__     		=     io.BytesIO()  # works for cairo backend
        self.canvas.print_rgba(lowerCAmelCase   )
        a__		,a__     		=     self.width, self.height
        a__     		=     buf.getvalue()
        a__     		=     self.img

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

      try:
        import numexpr as ne  # fuse them with numexpr

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

      return visualized_image.astype("uint8"   )




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

_SCREAMING_SNAKE_CASE   						=    (
    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)
)


def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->Optional[int]:

  '''simple docstring'''




  sequence.append(lowerCamelCase__					)

  a__     		=     0
  while sequence[index] != target:
    index += 1

  sequence.pop()

  if index == len(lowerCamelCase__					):
    return None

  return index


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE   						=    input("""Enter numbers separated by comma:\n""").strip()
     _SCREAMING_SNAKE_CASE   						=    [int(item) for item in user_input.split(""",""")]

     _SCREAMING_SNAKE_CASE   						=    input("""Enter a single number to be found in the list:\n""")
     _SCREAMING_SNAKE_CASE   						=    int(target_input)
     _SCREAMING_SNAKE_CASE   						=    sentinel_linear_search(sequence, target)
     if result is not None:
          print(F"""{target} found at positions: {result}""")
     else:
          print("""Not found""")

