
"""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


snake_case    =		1_0_0_0




class      UpperCamelCase	:

				"""simple docstring"""



				def __init__(				self		,	lowercase__		,	lowercase__=1.2		,	lowercase__="g"		,	lowercase__=0.5		,	lowercase__="-"		,	lowercase__="test_out.jpg"		,	lowercase__=True		,	lowercase__=False		,	lowercase__=None		,	lowercase__=None		,	lowercase__=0.7		,	)    ->    Optional[Any]:
									"""simple docstring"""







									if isinstance(lowercase__		,	torch.Tensor			):
														SCREAMING_SNAKE_CASE				=	img.numpy().astype('np.uint8'			)
									if isinstance(lowercase__		,	lowercase__			):
														SCREAMING_SNAKE_CASE				=	img_tensorize(lowercase__			)
									assert isinstance(lowercase__		,	np.ndarray			)

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

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


				def 							A					(				self		,	lowercase__		,	lowercase__=None			)    ->    Dict:
									"""simple docstring"""







									if color is None:
														SCREAMING_SNAKE_CASE				=	self.edgecolor
									((SCREAMING_SNAKE_CASE)	,    (SCREAMING_SNAKE_CASE)	,    (SCREAMING_SNAKE_CASE)	,    (SCREAMING_SNAKE_CASE))				=	box
									SCREAMING_SNAKE_CASE				=	xa - xa
									SCREAMING_SNAKE_CASE				=	ya - ya
									self.ax.add_patch(
									    mpl.patches.Rectangle(
									        (xa, ya)		,	lowercase__		,	lowercase__		,	fill=lowercase__		,	edgecolor=lowercase__		,	linewidth=self.font_size // 3		,	alpha=self.alpha		,	linestyle=self.linestyle		,	)			)


				def 							A					(				self		,	lowercase__		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None			)    ->    List[str]:
									"""simple docstring"""







									if len(boxes.shape			) > 2:
														SCREAMING_SNAKE_CASE				=	boxes[0]
									if len(obj_ids.shape			) > 1:
														SCREAMING_SNAKE_CASE				=	obj_ids[0]
									if len(obj_scores.shape			) > 1:
														SCREAMING_SNAKE_CASE				=	obj_scores[0]
									if len(attr_ids.shape			) > 1:
														SCREAMING_SNAKE_CASE				=	attr_ids[0]
									if len(attr_scores.shape			) > 1:
														SCREAMING_SNAKE_CASE				=	attr_scores[0]
									if isinstance(lowercase__		,	torch.Tensor			):
														SCREAMING_SNAKE_CASE				=	boxes.numpy()
									if isinstance(lowercase__		,	lowercase__			):
														SCREAMING_SNAKE_CASE				=	np.array(lowercase__			)
									assert isinstance(lowercase__		,	np.ndarray			)
									SCREAMING_SNAKE_CASE				=	np.prod(boxes[:, 2:] - boxes[:, :2]		,	axis=1			)
									SCREAMING_SNAKE_CASE				=	np.argsort(-areas			).tolist()
									SCREAMING_SNAKE_CASE				=	boxes[sorted_idxs] if boxes is not None else None
									SCREAMING_SNAKE_CASE				=	obj_ids[sorted_idxs] if obj_ids is not None else None
									SCREAMING_SNAKE_CASE				=	obj_scores[sorted_idxs] if obj_scores is not None else None
									SCREAMING_SNAKE_CASE				=	attr_ids[sorted_idxs] if attr_ids is not None else None
									SCREAMING_SNAKE_CASE				=	attr_scores[sorted_idxs] if attr_scores is not None else None

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


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__			)    ->    Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	box
									SCREAMING_SNAKE_CASE				=	(xa, ya)
									SCREAMING_SNAKE_CASE				=	(ya - ya) * (xa - xa)
									SCREAMING_SNAKE_CASE				=	_SMALL_OBJ * self.scale
									if instance_area < small or ya - ya < 40 * self.scale:
														if ya >= self.height - 5:
																			SCREAMING_SNAKE_CASE				=	(xa, ya)
														else:
																			SCREAMING_SNAKE_CASE				=	(xa, ya)

									SCREAMING_SNAKE_CASE				=	(ya - ya) / np.sqrt(self.height * self.width			)
									SCREAMING_SNAKE_CASE				=	self._change_color_brightness(lowercase__		,	brightness_factor=0.7			)
									SCREAMING_SNAKE_CASE				=	np.clip((height_ratio - 0.02) / 0.08 + 1		,	1.2		,	2			)
									font_size *= 0.75 * self.font_size

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


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__="g"		,	lowercase__="left"		,	)    ->    Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	0
									SCREAMING_SNAKE_CASE				=	self.font_size
									SCREAMING_SNAKE_CASE				=	np.maximum(list(mplc.to_rgb(lowercase__			)			)		,	0.2			)
									SCREAMING_SNAKE_CASE				=	max(0.8		,	np.max(lowercase__			)			)
									SCREAMING_SNAKE_CASE				=	{
									    'facecolor': 'black',
									    'alpha': self.alpha,
									    'pad': self.pad,
									    'edgecolor': 'none',
									}
									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	position
									self.ax.text(
									    lowercase__		,	lowercase__		,	lowercase__		,	size=font_size * self.scale		,	family='sans-serif'		,	bbox=lowercase__		,	verticalalignment='top'		,	horizontalalignment=lowercase__		,	color=lowercase__		,	zorder=10		,	rotation=lowercase__		,	)


				def 							A					(				self		,	lowercase__=None			)    ->    int:
									"""simple docstring"""







									if saveas is None:
														SCREAMING_SNAKE_CASE				=	self.saveas
									if saveas.lower().endswith('.jpg'			) or saveas.lower().endswith('.png'			):
														cva.imwrite(
														    lowercase__		,	self._get_buffer()[:, :, ::-1]		,	)
									else:
														self.fig.savefig(lowercase__			)


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__			)    ->    List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.idaobj[i] for i in classes]
									SCREAMING_SNAKE_CASE				=	[self.idaattr[i] for i in attr_classes]
									SCREAMING_SNAKE_CASE				=	[
									    f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
									    for label, score, attr, attr_score in zip(lowercase__		,	lowercase__		,	lowercase__		,	lowercase__			)
									]
									return labels


				def 							A					(				self		,	lowercase__		,	lowercase__			)    ->    Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.idaobj[i] for i in classes]
									if scores is not None:
														if labels is None:
																			SCREAMING_SNAKE_CASE				=	['{:.0f}%'.format(s * 100			) for s in scores]
														else:
																			SCREAMING_SNAKE_CASE				=	['{} {:.0f}%'.format(lowercase__		,	s * 100			) for li, s in zip(lowercase__		,	lowercase__			)]
									return labels


				def 							A					(				self		,	lowercase__=255			)    ->    int:
									"""simple docstring"""







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


				def 							A					(				self			)    ->    Any:
									"""simple docstring"""







									if not self.pynb:
														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	self.canvas.print_to_buffer()
														if (width, height) != (self.width, self.height):
																			SCREAMING_SNAKE_CASE				=	cva.resize(self.img		,	(width, height)			)
														else:
																			SCREAMING_SNAKE_CASE				=	self.img
									else:
														SCREAMING_SNAKE_CASE				=	io.BytesIO()  # works for cairo backend
														self.canvas.print_rgba(lowercase__			)
														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	self.width, self.height
														SCREAMING_SNAKE_CASE				=	buf.getvalue()
														SCREAMING_SNAKE_CASE				=	self.img

									SCREAMING_SNAKE_CASE				=	np.frombuffer(lowercase__		,	dtype='uint8'			)
									SCREAMING_SNAKE_CASE				=	buffer.reshape(lowercase__		,	lowercase__		,	4			)
									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	np.split(lowercase__		,	[3]		,	axis=2			)

									try:
														import numexpr as ne  # fuse them with numexpr

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

									return visualized_image.astype('uint8'			)




				def 							A					(				self		,	lowercase__		,	lowercase__			)    ->    Any:
									"""simple docstring"""







									assert brightness_factor >= -1.0 and brightness_factor <= 1.0
									SCREAMING_SNAKE_CASE				=	mplc.to_rgb(lowercase__			)
									SCREAMING_SNAKE_CASE				=	colorsys.rgb_to_hls(*mplc.to_rgb(lowercase__			)			)
									SCREAMING_SNAKE_CASE				=	polygon_color[1] + (brightness_factor * polygon_color[1])
									SCREAMING_SNAKE_CASE				=	0.0 if modified_lightness < 0.0 else modified_lightness
									SCREAMING_SNAKE_CASE				=	1.0 if modified_lightness > 1.0 else modified_lightness
									SCREAMING_SNAKE_CASE				=	colorsys.hls_to_rgb(polygon_color[0]		,	lowercase__		,	polygon_color[2]			)
									return modified_color






snake_case    =		(
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import CamembertModel


@require_torch
@require_sentencepiece
@require_tokenizers
class        A_					(       unittest.TestCase    ):




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							Dict							=						CamembertModel.from_pretrained('camembert-base'				)
       model.to(a				)

       __lowerCamelCase		:							List[Any]							=						torch.tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]						,      device=a						,      dtype=torch.long						,      )  # J'aime le camembert !
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						model(a				)['last_hidden_state']
       __lowerCamelCase		:							Union[str, Any]							=						torch.Size((1, 10, 768)				)
       self.assertEqual(output.shape						,      a				)
       # compare the actual values for a slice.
       __lowerCamelCase		:							Tuple							=						torch.tensor(
           [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,      device=a						,      dtype=torch.float						,      )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(torch.allclose(output[:, :3, :3]						,      a						,      atol=1e-4				)				)

