



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


snake_case     :    Optional[int]						=    1_0_0_0




class 				lowerCAmelCase__	:







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

											A__     ,							A__						:				int         =			img.shape[1], img.shape[0]
											A__						:				Union[str, Any]         =			mplfigure.Figure(frameon=_A)
											A__						:				Optional[Any]         =			fig.get_dpi()
											A__						:				Optional[Any]         =			(width * scale + 1e-2) / dpi
											A__						:				Union[str, Any]         =			(height * scale + 1e-2) / dpi
											fig.set_size_inches(_A      ,		_A)
											A__						:				List[str]         =			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__						:				str         =			saveas
											A__						:				List[str]         =			rgb
											A__						:				Optional[Any]         =			pynb
											A__						:				Any         =			img
											A__						:				str         =			edgecolor
											A__						:				Union[str, Any]         =			0.5
											A__						:				int         =			linestyle
											A__						:				List[Any]         =			int(np.sqrt(min(_A      ,		_A)) * scale // 3)
											A__						:				int         =			width
											A__						:				Optional[int]         =			height
											A__						:				List[Any]         =			scale
											A__						:				str         =			fig
											A__						:				str         =			ax
											A__						:				Tuple         =			pad
											A__						:				Union[str, Any]         =			idaobj
											A__						:				Union[str, Any]         =			idaattr
											A__						:				Optional[Any]         =			FigureCanvasAgg(_A)







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

											A__						:				Any         =			[self._random_color(maximum=1) for _ in range(len(_A))]
											A__						:				Dict         =			[assigned_colors[idx] for idx in sorted_idxs]
											if obj_ids is not None:
																	A__						:				Tuple         =			self._create_text_labels_attr(_A      ,		_A      ,		_A      ,		_A)
																	for i in range(len(_A)):
																							A__						:				List[Any]         =			assigned_colors[i]
																							self.add_box(boxes[i]      ,		_A)
																							self.draw_labels(labels[i]      ,		boxes[i]      ,		_A)







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

											A__						:				Any         =			(ya - ya) / np.sqrt(self.height * self.width)
											A__						:				List[str]         =			self._change_color_brightness(_A      ,		brightness_factor=0.7)
											A__						:				str         =			np.clip((height_ratio - 0.02) / 0.08 + 1      ,		1.2      ,		2)
											font_size *= 0.75 * self.font_size

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







					def 			_lowercase      (							self   :  Any      ,		_A   :  Union[str, Any]      ,		_A   :  Optional[Any]      ,		_A   :  str="g"      ,		_A   :  Dict="left"      ,		):
											A__						:				Any         =			0
											A__						:				List[Any]         =			self.font_size
											A__						:				List[Any]         =			np.maximum(list(mplc.to_rgb(_A))      ,		0.2)
											A__						:				List[str]         =			max(0.8      ,		np.max(_A))
											A__						:				Tuple         =			{
											    "facecolor": "black",
											    "alpha": self.alpha,
											    "pad": self.pad,
											    "edgecolor": "none",
											}
											A__     ,							A__						:				List[str]         =			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 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  Union[str, Any]=None):
											if saveas is None:
																	A__						:				List[str]         =			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 			_lowercase      (							self   :  Tuple      ,		_A   :  Union[str, Any]      ,		_A   :  int      ,		_A   :  str      ,		_A   :  Optional[int]):
											A__						:				List[str]         =			[self.idaobj[i] for i in classes]
											A__						:				Optional[Any]         =			[self.idaattr[i] for i in attr_classes]
											A__						:				Optional[Any]         =			[
											    F'{label} {score:.2f} {attr} {attr_score:.2f}'
											    for label, score, attr, attr_score in zip(_A      ,		_A      ,		_A      ,		_A)
											]
											return labels







					def 			_lowercase      (							self   :  Optional[int]      ,		_A   :  Optional[int]      ,		_A   :  List[Any]):
											A__						:				List[str]         =			[self.idaobj[i] for i in classes]
											if scores is not None:
																	if labels is None:
																							A__						:				Dict         =			["{:.0f}%".format(s * 100) for s in scores]
																	else:
																							A__						:				Dict         =			["{} {:.0f}%".format(_A      ,		s * 100) for li, s in zip(_A      ,		_A)]
											return labels







					def 			_lowercase      (							self   :  List[str]      ,		_A   :  Optional[int]=255):
											A__						:				int         =			np.random.randint(0      ,		len(_COLORS))
											A__						:				List[Any]         =			_COLORS[idx] * maximum
											if not self.rgb:
																	A__						:				Dict         =			ret[::-1]
											return ret







					def 			_lowercase      (							self   :  List[str]):
											if not self.pynb:
																	A__     ,							A__						:				List[str]         =			self.canvas.print_to_buffer()
																	if (width, height) != (self.width, self.height):
																							A__						:				Optional[int]         =			cva.resize(self.img      ,		(width, height))
																	else:
																							A__						:				List[str]         =			self.img
											else:
																	A__						:				Union[str, Any]         =			io.BytesIO()  # works for cairo backend
																	self.canvas.print_rgba(_A)
																	A__     ,							A__						:				Dict         =			self.width, self.height
																	A__						:				Union[str, Any]         =			buf.getvalue()
																	A__						:				int         =			self.img

											A__						:				List[str]         =			np.frombuffer(_A      ,		dtype="uint8")
											A__						:				Optional[Any]         =			buffer.reshape(_A      ,		_A      ,		4)
											A__     ,							A__						:				Dict         =			np.split(_A      ,		[3]      ,		axis=2)

											try:
																	import numexpr as ne  # fuse them with numexpr

																	A__						:				int         =			ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)")
											except ImportError:
																	A__						:				Dict         =			alpha.astype("float32") / 2_55.0
																	A__						:				List[Any]         =			img * (1 - alpha) + rgb * alpha

											return visualized_image.astype("uint8")







					def 			_lowercase      (							self   :  Optional[Any]      ,		_A   :  Dict      ,		_A   :  List[Any]):
											assert brightness_factor >= -1.0 and brightness_factor <= 1.0
											A__						:				Any         =			mplc.to_rgb(_A)
											A__						:				Optional[int]         =			colorsys.rgb_to_hls(*mplc.to_rgb(_A))
											A__						:				Optional[Any]         =			polygon_color[1] + (brightness_factor * polygon_color[1])
											A__						:				Dict         =			0.0 if modified_lightness < 0.0 else modified_lightness
											A__						:				Optional[int]         =			1.0 if modified_lightness > 1.0 else modified_lightness
											A__						:				Tuple         =			colorsys.hls_to_rgb(polygon_color[0]      ,		_A      ,		polygon_color[2])
											return modified_color




snake_case     :    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 json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 				lowerCAmelCase__	(						UpperCamelCase       ,				unittest.TestCase    ):
					__A  :     Any     =    BartTokenizer
					__A  :     str     =    BartTokenizerFast
					__A  :     Optional[int]     =    True
					__A  :     Optional[int]     =    filter_roberta_detectors







					def 			_lowercase      (							self   :  Dict):
											super().setUp()
											A__						:				Union[str, Any]         =			[
											    "l",
											    "o",
											    "w",
											    "e",
											    "r",
											    "s",
											    "t",
											    "i",
											    "d",
											    "n",
											    "\u0120",
											    "\u0120l",
											    "\u0120n",
											    "\u0120lo",
											    "\u0120low",
											    "er",
											    "\u0120lowest",
											    "\u0120newer",
											    "\u0120wider",
											    "<unk>",
											]
											A__						:				List[str]         =			dict(zip(_A      ,		range(len(_A))))
											A__						:				List[str]         =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
											A__						:				str         =			{"unk_token": "<unk>"}

											A__						:				Dict         =			os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["vocab_file"])
											A__						:				Optional[Any]         =			os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["merges_file"])
											with open(self.vocab_file      ,		"w"      ,		encoding="utf-8") as fp:
																	fp.write(json.dumps(_A) + "\n")
											with open(self.merges_file      ,		"w"      ,		encoding="utf-8") as fp:
																	fp.write("\n".join(_A))







					def 			_lowercase      (							self   :  Union[str, Any]      ,		**_A   :  str):
											kwargs.update(self.special_tokens_map)
											return self.tokenizer_class.from_pretrained(self.tmpdirname      ,		**_A)







					def 			_lowercase      (							self   :  Union[str, Any]      ,		**_A   :  List[Any]):
											kwargs.update(self.special_tokens_map)
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,		**_A)







					def 			_lowercase      (							self   :  Tuple      ,		_A   :  Union[str, Any]):
											return "lower newer", "lower newer"







					@cached_property
					def 			_lowercase      (							self   :  Optional[int]):
											return BartTokenizer.from_pretrained("facebook/bart-large")







					@cached_property
					def 			_lowercase      (							self   :  Optional[int]):
											return BartTokenizerFast.from_pretrained("facebook/bart-large")







					@require_torch
					def 			_lowercase      (							self   :  Dict):
											A__						:				Optional[int]         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
											A__						:				List[Any]         =			[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Tuple         =			tokenizer(_A      ,		max_length=len(_A)      ,		padding=_A      ,		return_tensors="pt")
																	self.assertIsInstance(_A      ,		_A)

																	self.assertEqual((2, 9)      ,		batch.input_ids.shape)
																	self.assertEqual((2, 9)      ,		batch.attention_mask.shape)
																	A__						:				Dict         =			batch.input_ids.tolist()[0]
																	self.assertListEqual(_A      ,		_A)
																	# Test that special tokens are reset







					@require_torch
					def 			_lowercase      (							self   :  List[Any]):
											A__						:				Optional[Any]         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Tuple         =			tokenizer(_A      ,		padding=_A      ,		return_tensors="pt")
																	# check if input_ids are returned and no labels
																	self.assertIn("input_ids"      ,		_A)
																	self.assertIn("attention_mask"      ,		_A)
																	self.assertNotIn("labels"      ,		_A)
																	self.assertNotIn("decoder_attention_mask"      ,		_A)







					@require_torch
					def 			_lowercase      (							self   :  int):
											A__						:				Optional[Any]         =			[
											    "Summary of the text.",
											    "Another summary.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				int         =			tokenizer(text_target=_A      ,		max_length=32      ,		padding="max_length"      ,		return_tensors="pt")
																	self.assertEqual(32      ,		targets["input_ids"].shape[1])







					@require_torch
					def 			_lowercase      (							self   :  List[str]):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				List[str]         =			tokenizer(
																	    ["I am a small frog" * 1024, "I am a small frog"]      ,		padding=_A      ,		truncation=_A      ,		return_tensors="pt")
																	self.assertIsInstance(_A      ,		_A)
																	self.assertEqual(batch.input_ids.shape      ,		(2, 1024))







					@require_torch
					def 			_lowercase      (							self   :  List[str]):
											A__						:				int         =			["A long paragraph for summarization."]
											A__						:				List[str]         =			[
											    "Summary of the text.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Optional[Any]         =			tokenizer(_A      ,		return_tensors="pt")
																	A__						:				Union[str, Any]         =			tokenizer(text_target=_A      ,		return_tensors="pt")
																	A__						:				Tuple         =			inputs["input_ids"]
																	A__						:				str         =			targets["input_ids"]
																	self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
																	self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())







					def 			_lowercase      (							self   :  Optional[int]):
											pass







					def 			_lowercase      (							self   :  Any):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
																							A__						:				Union[str, Any]         =			self.rust_tokenizer_class.from_pretrained(_A      ,		**_A)
																							A__						:				int         =			self.tokenizer_class.from_pretrained(_A      ,		**_A)
																							A__						:				Union[str, Any]         =			"A, <mask> AllenNLP sentence."
																							A__						:				Dict         =			tokenizer_r.encode_plus(_A      ,		add_special_tokens=_A      ,		return_token_type_ids=_A)
																							A__						:				Optional[Any]         =			tokenizer_p.encode_plus(_A      ,		add_special_tokens=_A      ,		return_token_type_ids=_A)

																							# token_type_ids should put 0 everywhere
																							self.assertEqual(sum(tokens_r["token_type_ids"])      ,		sum(tokens_p["token_type_ids"]))

																							# attention_mask should put 1 everywhere, so sum over length should be 1
																							self.assertEqual(
																							    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"])      ,		sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"])      ,		)

																							A__						:				Optional[int]         =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
																							A__						:				Dict         =			tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

																							# Rust correctly handles the space before the mask while python doesnt
																							self.assertSequenceEqual(tokens_p["input_ids"]      ,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
																							self.assertSequenceEqual(tokens_r["input_ids"]      ,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

																							self.assertSequenceEqual(
																							    _A      ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
																							self.assertSequenceEqual(
																							    _A      ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])