


"""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


a_											=			10_00
class snake_case					:







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





						'''simple docstring'''







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

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

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







		def        a_       (    self					:				str			,      a__					:				Optional[Any]			,      a__					:				List[Any]=None						)				->  List[Any]:





						'''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)			,      a__			,      a__			,      fill=a__			,      edgecolor=a__			,      linewidth=self.font_size // 3			,      alpha=self.alpha			,      linestyle=self.linestyle			,      )						)







		def        a_       (    self					:				int			,      a__					:				int			,      a__					:				Any=None			,      a__					:				List[Any]=None			,      a__					:				Any=None			,      a__					:				Tuple=None						)				->  int:





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







		def        a_       (    self					:				List[str]			,      a__					:				Any			,      a__					:				Any			,      a__					:				Any						)				->  Dict:





						'''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(a__			,      brightness_factor=0.7						)
						_A         =	np.clip((height_ratio - 0.0_2) / 0.0_8 + 1			,      1.2			,      2						)
						font_size *= 0.7_5 * self.font_size

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







		def        a_       (    self					:				List[Any]			,      a__					:				Any			,      a__					:				List[Any]			,      a__					:				str="g"			,      a__					:				Any="left"			,      )				->  List[Any]:





						'''simple docstring'''







						_A         =	0
						_A         =	self.font_size
						_A         =	np.maximum(list(mplc.to_rgb(a__						)						)			,      0.2						)
						_A         =	max(0.8			,      np.max(a__						)						)
						_A         =	{
						    "facecolor": "black",
						    "alpha": self.alpha,
						    "pad": self.pad,
						    "edgecolor": "none",
						}
						_A     ,						_A         =	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        a_       (    self					:				Tuple			,      a__					:				int=None						)				->  Tuple:





						'''simple docstring'''







						if saveas is None:
										_A         =	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        a_       (    self					:				Dict			,      a__					:				Tuple			,      a__					:				Optional[Any]			,      a__					:				str			,      a__					:				Union[str, Any]						)				->  Any:





						'''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(a__			,      a__			,      a__			,      a__						)
						]
						return labels







		def        a_       (    self					:				List[Any]			,      a__					:				str			,      a__					:				List[str]						)				->  List[Any]:





						'''simple docstring'''







						_A         =	[self.idaobj[i] for i in classes]
						if scores is not None:
										if labels is None:
														_A         =	["{:.0f}%".format(s * 1_00						) for s in scores]
										else:
														_A         =	["{} {:.0f}%".format(a__			,      s * 1_00						) for li, s in zip(a__			,      a__						)]
						return labels







		def        a_       (    self					:				str			,      a__					:				List[str]=2_55						)				->  Dict:





						'''simple docstring'''







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







		def        a_       (    self					:				Tuple						)				->  Any:





						'''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(a__						)
										_A     ,						_A         =	self.width, self.height
										_A         =	buf.getvalue()
										_A         =	self.img

						_A         =	np.frombuffer(a__			,      dtype="uint8"						)
						_A         =	buffer.reshape(a__			,      a__			,      4						)
						_A     ,						_A         =	np.split(a__			,      [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"						) / 2_5_5.0
										_A         =	img * (1 - alpha) + rgb * alpha

						return visualized_image.astype("uint8"						)







		def        a_       (    self					:				Tuple			,      a__					:				int			,      a__					:				Optional[int]						)				->  Optional[int]:





						'''simple docstring'''







						assert brightness_factor >= -1.0 and brightness_factor <= 1.0
						_A         =	mplc.to_rgb(a__						)
						_A         =	colorsys.rgb_to_hls(*mplc.to_rgb(a__						)						)
						_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]			,      a__			,      polygon_color[2]						)
						return modified_color





a_											=			(
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)



"""simple docstring"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		a_											=			None


a_											=			logging.get_logger(__name__)


a_											=			"▁"

a_											=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=			{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

a_											=			{
    "google/reformer-crime-and-punishment": 52_42_88,
}
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	VOCAB_FILES_NAMES
		__UpperCamelCase							=	PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							=	['input_ids', 'attention_mask']
		__UpperCamelCase							=	ReformerTokenizer







		def __init__(    self					:				Dict			,      a__					:				List[Any]=None			,      a__					:				int=None			,      a__					:				int="</s>"			,      a__					:				List[Any]="<unk>"			,      a__					:				Optional[Any]=[]			,      **a__					:				Tuple			,      )				->  Tuple:





						'''simple docstring'''







						super().__init__(
						    a__			,      tokenizer_file=a__			,      eos_token=a__			,      unk_token=a__			,      additional_special_tokens=a__			,      **a__			,      )

						_A         =	vocab_file
						_A         =	False if not self.vocab_file else True







		def        a_       (    self					:				Any			,      a__					:				str			,      a__					:				Optional[str] = None						)				->  Tuple[str]:





						'''simple docstring'''







						if not self.can_save_slow_tokenizer:
										raise ValueError(
										    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
										    "tokenizer."						)

						if not os.path.isdir(a__						):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
										return
						_A         =	os.path.join(
						    a__			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(a__						):
										copyfile(self.vocab_file			,      a__						)

						return (out_vocab_file,)