


'''simple docstring'''

def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					str )		->       str:





   '''simple docstring'''







   UpperCamelCase__			    =    word.upper()
   UpperCamelCase__			    =    0
   UpperCamelCase__			    =    {}
   UpperCamelCase__			    =    []

   for letter in word:
      if letter not in letter_nums:
         UpperCamelCase__			    =    str(_UpperCamelCase )
         next_num += 1
      word_pattern.append(letter_nums[letter] )
   return ".".join(_UpperCamelCase )


if __name__ == "__main__":
       import pprint
       import time

       __lowercase:		Optional[Any]    						=  time.time()
       with open("dictionary.txt") as in_file:
              __lowercase:		Optional[int]    						=  in_file.read().splitlines()

       __lowercase:		dict    						=  {}
       for word in word_list:
              __lowercase:		List[str]    						=  get_word_pattern(word)
              if pattern in all_patterns:
                     all_patterns[pattern].append(word)
              else:
                     __lowercase:		Optional[int]    						=  [word]

       with open("word_patterns.txt", "w") as out_file:
              out_file.write(pprint.pformat(all_patterns))

       __lowercase:		str    						=  round(time.time() - start_time, 2)
       print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
       # Done!  9,581 word patterns found in 0.58 seconds.

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


__magic_name__  :  Dict     			=   1_0_0_0


class 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase=1.2						,    __UpperCamelCase="g"						,    __UpperCamelCase=0.5						,    __UpperCamelCase="-"						,    __UpperCamelCase="test_out.jpg"						,    __UpperCamelCase=True						,    __UpperCamelCase=False						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=0.7						,    ):
      if isinstance(__UpperCamelCase						,    torch.Tensor					):
          A_								=					img.numpy().astype("np.uint8"					)
      if isinstance(__UpperCamelCase						,    __UpperCamelCase					):
          A_								=					img_tensorize(__UpperCamelCase					)
      assert isinstance(__UpperCamelCase						,    np.ndarray					)

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

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

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase=None					):
      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)						,    __UpperCamelCase						,    __UpperCamelCase						,    fill=__UpperCamelCase						,    edgecolor=__UpperCamelCase						,    linewidth=self.font_size // 3						,    alpha=self.alpha						,    linestyle=self.linestyle						,    )					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=None					):
      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(__UpperCamelCase						,    torch.Tensor					):
          A_								=					boxes.numpy()
      if isinstance(__UpperCamelCase						,    __UpperCamelCase					):
          A_								=					np.array(__UpperCamelCase					)
      assert isinstance(__UpperCamelCase						,    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(__UpperCamelCase					)					)]
      A_								=					[assigned_colors[idx] for idx in sorted_idxs]
      if obj_ids is not None:
          A_								=					self._create_text_labels_attr(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)
          for i in range(len(__UpperCamelCase					)					):
              A_								=					assigned_colors[i]
              self.add_box(boxes[i]						,    __UpperCamelCase					)
              self.draw_labels(labels[i]						,    boxes[i]						,    __UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      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(__UpperCamelCase						,    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=__UpperCamelCase						,    position=__UpperCamelCase						,    color=__UpperCamelCase						,    )

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase="g"						,    __UpperCamelCase="left"						,    ):
      A_								=					0
      A_								=					self.font_size
      A_								=					np.maximum(list(mplc.to_rgb(__UpperCamelCase					)					)						,    0.2					)
      A_								=					max(0.8						,    np.max(__UpperCamelCase					)					)
      A_								=					{
          "facecolor": "black",
          "alpha": self.alpha,
          "pad": self.pad,
          "edgecolor": "none",
      }
      A_	,	A_								=					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						,    __UpperCamelCase=None					):
      if saveas is None:
          A_								=					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						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      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(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)
      ]
      return labels

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      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(__UpperCamelCase						,    s * 100					) for li, s in zip(__UpperCamelCase						,    __UpperCamelCase					)]
      return labels

  def 					lowercase_						(					self						,    __UpperCamelCase=255					):
      A_								=					np.random.randint(0						,    len(_COLORS					)					)
      A_								=					_COLORS[idx] * maximum
      if not self.rgb:
          A_								=					ret[::-1]
      return ret

  def 					lowercase_						(					self					):
      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(__UpperCamelCase					)
          A_	,	A_								=					self.width, self.height
          A_								=					buf.getvalue()
          A_								=					self.img

      A_								=					np.frombuffer(__UpperCamelCase						,    dtype="uint8"					)
      A_								=					buffer.reshape(__UpperCamelCase						,    __UpperCamelCase						,    4					)
      A_	,	A_								=					np.split(__UpperCamelCase						,    [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 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      assert brightness_factor >= -1.0 and brightness_factor <= 1.0
      A_								=					mplc.to_rgb(__UpperCamelCase					)
      A_								=					colorsys.rgb_to_hls(*mplc.to_rgb(__UpperCamelCase					)					)
      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]						,    __UpperCamelCase						,    polygon_color[2]					)
      return modified_color



__magic_name__  :  List[str]     			=   (
    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)
)






