



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:					Any      =						1000



class       _lowercase				:





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						,			lowerCamelCase_=1.2						,			lowerCamelCase_="g"						,			lowerCamelCase_=0.5						,			lowerCamelCase_="-"						,			lowerCamelCase_="test_out.jpg"						,			lowerCamelCase_=True						,			lowerCamelCase_=False						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_=0.7						,			):
						"""simple docstring"""

						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 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_=None						):
						"""simple docstring"""

						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 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_=None						):
						"""simple docstring"""

						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 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						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 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_="g"						,			lowerCamelCase_="left"						,			):
						"""simple docstring"""

						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 	UpperCamelCase_			(self						,			lowerCamelCase_=None						):
						"""simple docstring"""

						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 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						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 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						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 	UpperCamelCase_			(self						,			lowerCamelCase_=255						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						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 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						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




_lowercase:					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)
)





from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


_lowercase:					List[str]      =						logging.get_logger(__name__)

_lowercase:					str      =						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowercase:					Union[str, Any]      =						{
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
    "tokenizer_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"},
}

_lowercase:					int      =						{
    "openai-gpt": 512,
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				VOCAB_FILES_NAMES
	__A		      =				PRETRAINED_VOCAB_FILES_MAP
	__A		      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A		      =				["input_ids", "attention_mask"]
	__A		      =				OpenAIGPTTokenizer




	def __init__(self						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_="<unk>"						,			**lowerCamelCase_						):
						"""simple docstring"""

						super().__init__(lowerCamelCase_						,			lowerCamelCase_						,			tokenizer_file=lowerCamelCase_						,			unk_token=lowerCamelCase_						,			**lowerCamelCase_						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return True




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						):
						"""simple docstring"""

						a   	=  self._tokenizer.model.save(lowerCamelCase_						,			name=lowerCamelCase_						)
						return tuple(lowerCamelCase_						)
