






import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 							__A	(     a ,	unittest.TestCase					):





				"""simple docstring"""







				UpperCamelCase__					: Dict     					=MPNetTokenizer
				UpperCamelCase__					: Dict     					=MPNetTokenizerFast
				UpperCamelCase__					: Union[str, Any]     					=True
				UpperCamelCase__					: Tuple     					=True


				def  __lowercase				(					self       ):




									"""simple docstring"""

									super().setUp()

									__UpperCamelCase :   Dict									=[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    '[PAD]',
									    '[MASK]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									__UpperCamelCase :   Union[str, Any]									=os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['vocab_file']       )
									with open(self.vocab_file				,    'w'				,    encoding='utf-8'       ) as vocab_writer:
														vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   List[Any]									='UNwant\u00E9d,running'
									__UpperCamelCase :   Optional[int]									='unwanted, running'
									return input_text, output_text


				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Dict									=self.tokenizer_class(self.vocab_file       )

									__UpperCamelCase :   str									=tokenizer.tokenize('UNwant\u00E9d,running'       )
									self.assertListEqual(lowerCamelCase__				,    ['un', '##want', '##ed', ',', 'runn', '##ing']       )
									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__       )				,    [9, 6, 7, 12, 10, 11]       )







				@slow
				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   str									=self.tokenizer_class.from_pretrained('microsoft/mpnet-base'       )

									__UpperCamelCase :   str									=tokenizer.encode('sequence builders'				,    add_special_tokens=lowerCamelCase__       )
									__UpperCamelCase :   Union[str, Any]									=tokenizer.encode('multi-sequence build'				,    add_special_tokens=lowerCamelCase__       )

									__UpperCamelCase :   Dict									=tokenizer.build_inputs_with_special_tokens(lowerCamelCase__       )
									__UpperCamelCase :   Union[str, Any]									=tokenizer.build_inputs_with_special_tokens(lowerCamelCase__				,    lowerCamelCase__       )

									assert encoded_sentence == [0] + text + [2]
									assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]









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_			:Tuple	    =		1000




class 							__A	:





				"""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       ):
														__UpperCamelCase :   Union[str, Any]									=img.numpy().astype('np.uint8'       )
									if isinstance(lowerCamelCase__				,    lowerCamelCase__       ):
														__UpperCamelCase :   List[str]									=img_tensorize(lowerCamelCase__       )
									assert isinstance(lowerCamelCase__				,    np.ndarray       )

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

									__UpperCamelCase :   List[Any]									=saveas
									__UpperCamelCase :   Any									=rgb
									__UpperCamelCase :   int									=pynb
									__UpperCamelCase :   Optional[int]									=img
									__UpperCamelCase :   List[Any]									=edgecolor
									__UpperCamelCase :   List[str]									=0.5
									__UpperCamelCase :   Optional[Any]									=linestyle
									__UpperCamelCase :   Dict									=int(np.sqrt(min(lowerCamelCase__				,    lowerCamelCase__       )       ) * scale // 3       )
									__UpperCamelCase :   List[Any]									=width
									__UpperCamelCase :   List[Any]									=height
									__UpperCamelCase :   int									=scale
									__UpperCamelCase :   List[str]									=fig
									__UpperCamelCase :   Union[str, Any]									=ax
									__UpperCamelCase :   Dict									=pad
									__UpperCamelCase :   List[Any]									=idaobj
									__UpperCamelCase :   List[Any]									=idaattr
									__UpperCamelCase :   List[str]									=FigureCanvasAgg(lowerCamelCase__       )


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__=None       ):




									"""simple docstring"""

									if color is None:
														__UpperCamelCase :   Optional[Any]									=self.edgecolor
									((__UpperCamelCase)						,							(__UpperCamelCase)						,							(__UpperCamelCase)						,							(__UpperCamelCase)) :   str									=box
									__UpperCamelCase :   List[Any]									=xa - xa
									__UpperCamelCase :   List[Any]									=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  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__=None				,    lowerCamelCase__=None				,    lowerCamelCase__=None				,    lowerCamelCase__=None       ):




									"""simple docstring"""

									if len(boxes.shape       ) > 2:
														__UpperCamelCase :   List[str]									=boxes[0]
									if len(obj_ids.shape       ) > 1:
														__UpperCamelCase :   Tuple									=obj_ids[0]
									if len(obj_scores.shape       ) > 1:
														__UpperCamelCase :   int									=obj_scores[0]
									if len(attr_ids.shape       ) > 1:
														__UpperCamelCase :   str									=attr_ids[0]
									if len(attr_scores.shape       ) > 1:
														__UpperCamelCase :   Optional[int]									=attr_scores[0]
									if isinstance(lowerCamelCase__				,    torch.Tensor       ):
														__UpperCamelCase :   Dict									=boxes.numpy()
									if isinstance(lowerCamelCase__				,    lowerCamelCase__       ):
														__UpperCamelCase :   Tuple									=np.array(lowerCamelCase__       )
									assert isinstance(lowerCamelCase__				,    np.ndarray       )
									__UpperCamelCase :   Dict									=np.prod(boxes[:, 2:] - boxes[:, :2]				,    axis=1       )
									__UpperCamelCase :   int									=np.argsort(-areas       ).tolist()
									__UpperCamelCase :   Optional[Any]									=boxes[sorted_idxs] if boxes is not None else None
									__UpperCamelCase :   List[Any]									=obj_ids[sorted_idxs] if obj_ids is not None else None
									__UpperCamelCase :   Tuple									=obj_scores[sorted_idxs] if obj_scores is not None else None
									__UpperCamelCase :   Dict									=attr_ids[sorted_idxs] if attr_ids is not None else None
									__UpperCamelCase :   Optional[Any]									=attr_scores[sorted_idxs] if attr_scores is not None else None

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


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   Dict									=box
									__UpperCamelCase :   str									=(xa, ya)
									__UpperCamelCase :   Optional[int]									=(ya - ya) * (xa - xa)
									__UpperCamelCase :   Optional[Any]									=_SMALL_OBJ * self.scale
									if instance_area < small or ya - ya < 40 * self.scale:
														if ya >= self.height - 5:
																			__UpperCamelCase :   str									=(xa, ya)
														else:
																			__UpperCamelCase :   str									=(xa, ya)

									__UpperCamelCase :   Tuple									=(ya - ya) / np.sqrt(self.height * self.width       )
									__UpperCamelCase :   List[Any]									=self._change_color_brightness(lowerCamelCase__				,    brightness_factor=0.7       )
									__UpperCamelCase :   Any									=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  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__="g"				,    lowerCamelCase__="left"				,    ):




									"""simple docstring"""

									__UpperCamelCase :   int									=0
									__UpperCamelCase :   Optional[int]									=self.font_size
									__UpperCamelCase :   Optional[Any]									=np.maximum(list(mplc.to_rgb(lowerCamelCase__       )       )				,    0.2       )
									__UpperCamelCase :   int									=max(0.8				,    np.max(lowerCamelCase__       )       )
									__UpperCamelCase :   str									={
									    'facecolor': 'black',
									    'alpha': self.alpha,
									    'pad': self.pad,
									    'edgecolor': 'none',
									}
									__UpperCamelCase						,							__UpperCamelCase :   Union[str, Any]									=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  __lowercase				(					self				,    lowerCamelCase__=None       ):




									"""simple docstring"""

									if saveas is None:
														__UpperCamelCase :   Optional[int]									=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  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[int]									=[self.idaobj[i] for i in classes]
									__UpperCamelCase :   str									=[self.idaattr[i] for i in attr_classes]
									__UpperCamelCase :   Any									=[
									    f'{label} {score:.2f} {attr} {attr_score:.2f}'
									    for label, score, attr, attr_score in zip(lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       )
									]
									return labels


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Tuple									=[self.idaobj[i] for i in classes]
									if scores is not None:
														if labels is None:
																			__UpperCamelCase :   List[str]									=['{:.0f}%'.format(s * 100       ) for s in scores]
														else:
																			__UpperCamelCase :   Union[str, Any]									=['{} {:.0f}%'.format(lowerCamelCase__				,    s * 100       ) for li, s in zip(lowerCamelCase__				,    lowerCamelCase__       )]
									return labels


				def  __lowercase				(					self				,    lowerCamelCase__=255       ):




									"""simple docstring"""

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


				def  __lowercase				(					self       ):




									"""simple docstring"""

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

									__UpperCamelCase :   Dict									=np.frombuffer(lowerCamelCase__				,    dtype='uint8'       )
									__UpperCamelCase :   List[str]									=buffer.reshape(lowerCamelCase__				,    lowerCamelCase__				,    4       )
									__UpperCamelCase						,							__UpperCamelCase :   Union[str, Any]									=np.split(lowerCamelCase__				,    [3]				,    axis=2       )

									try:
														import numexpr as ne  # fuse them with numexpr

														__UpperCamelCase :   List[Any]									=ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'       )
									except ImportError:
														__UpperCamelCase :   int									=alpha.astype('float32'       ) / 255.0
														__UpperCamelCase :   Tuple									=img * (1 - alpha) + rgb * alpha

									return visualized_image.astype('uint8'       )







				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									assert brightness_factor >= -1.0 and brightness_factor <= 1.0
									__UpperCamelCase :   List[Any]									=mplc.to_rgb(lowerCamelCase__       )
									__UpperCamelCase :   Dict									=colorsys.rgb_to_hls(*mplc.to_rgb(lowerCamelCase__       )       )
									__UpperCamelCase :   Dict									=polygon_color[1] + (brightness_factor * polygon_color[1])
									__UpperCamelCase :   Tuple									=0.0 if modified_lightness < 0.0 else modified_lightness
									__UpperCamelCase :   str									=1.0 if modified_lightness > 1.0 else modified_lightness
									__UpperCamelCase :   Union[str, Any]									=colorsys.hls_to_rgb(polygon_color[0]				,    lowerCamelCase__				,    polygon_color[2]       )
									return modified_color
A_			:Union[str, Any]	    =		(
    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)
)

