


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


lowerCAmelCase__						:Dict         =							1_0_0_0

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




									"""simple docstring"""





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

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

									_UpperCAmelCase					=    saveas
									_UpperCAmelCase					=    rgb
									_UpperCAmelCase					=    pynb
									_UpperCAmelCase					=    img
									_UpperCAmelCase					=    edgecolor
									_UpperCAmelCase					=    0.5
									_UpperCAmelCase					=    linestyle
									_UpperCAmelCase					=    int(np.sqrt(min(__A    ,					__A		)		) * scale // 3		)
									_UpperCAmelCase					=    width
									_UpperCAmelCase					=    height
									_UpperCAmelCase					=    scale
									_UpperCAmelCase					=    fig
									_UpperCAmelCase					=    ax
									_UpperCAmelCase					=    pad
									_UpperCAmelCase					=    idaobj
									_UpperCAmelCase					=    idaattr
									_UpperCAmelCase					=    FigureCanvasAgg(__A		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> Optional[Any]:




									"""simple docstring"""





									if color is None:
																_UpperCAmelCase					=    self.edgecolor
									(_UpperCAmelCase)					=    box
									_UpperCAmelCase					=    xa - xa
									_UpperCAmelCase					=    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 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None		)				-> Any:




									"""simple docstring"""





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

									_UpperCAmelCase					=    [self._random_color(maximum=1		) for _ in range(len(__A		)		)]
									_UpperCAmelCase					=    [assigned_colors[idx] for idx in sorted_idxs]
									if obj_ids is not None:
																_UpperCAmelCase					=    self._create_text_labels_attr(__A    ,					__A    ,					__A    ,					__A		)
																for i in range(len(__A		)		):
																							_UpperCAmelCase					=    assigned_colors[i]
																							self.add_box(boxes[i]    ,					__A		)
																							self.draw_labels(labels[i]    ,					boxes[i]    ,					__A		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Optional[Any]:




									"""simple docstring"""





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

									_UpperCAmelCase					=    (ya - ya) / np.sqrt(self.height * self.width		)
									_UpperCAmelCase					=    self._change_color_brightness(__A    ,					brightness_factor=0.7		)
									_UpperCAmelCase					=    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 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE="g"    ,					_SCREAMING_SNAKE_CASE="left"    ,					)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    0
									_UpperCAmelCase					=    self.font_size
									_UpperCAmelCase					=    np.maximum(list(mplc.to_rgb(__A		)		)    ,					0.2		)
									_UpperCAmelCase					=    max(0.8    ,					np.max(__A		)		)
									_UpperCAmelCase					=    {
									    "facecolor": "black",
									    "alpha": self.alpha,
									    "pad": self.pad,
									    "edgecolor": "none",
									}
									_UpperCAmelCase					=    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 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=None		)				-> List[str]:




									"""simple docstring"""





									if saveas is None:
																_UpperCAmelCase					=    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 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Any:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.idaobj[i] for i in classes]
									_UpperCAmelCase					=    [self.idaattr[i] for i in attr_classes]
									_UpperCAmelCase					=    [
									    f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
									    for label, score, attr, attr_score in zip(__A    ,					__A    ,					__A    ,					__A		)
									]
									return labels
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.idaobj[i] for i in classes]
									if scores is not None:
																if labels is None:
																							_UpperCAmelCase					=    ["{:.0f}%".format(s * 100		) for s in scores]
																else:
																							_UpperCAmelCase					=    ["{} {:.0f}%".format(__A    ,					s * 100		) for li, s in zip(__A    ,					__A		)]
									return labels
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=255		)				-> int:




									"""simple docstring"""





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




									"""simple docstring"""





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

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

									try:
																import numexpr as ne  # fuse them with numexpr

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

									return visualized_image.astype('uint8'		)






		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> List[Any]:




									"""simple docstring"""





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



lowerCAmelCase__						:List[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)
)




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

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


if is_sentencepiece_available():
		from .tokenization_deberta_va import DebertaVaTokenizer
else:
		lowerCAmelCase__						:int         =							None

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

lowerCAmelCase__						:List[str]         =							{'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase__						:List[str]         =							{
    '''vocab_file''': {
        '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
        ),
        '''microsoft/deberta-v2-xxlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
        ),
    }
}

lowerCAmelCase__						:List[str]         =							{
    '''microsoft/deberta-v2-xlarge''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge''': 5_1_2,
    '''microsoft/deberta-v2-xlarge-mnli''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge-mnli''': 5_1_2,
}

lowerCAmelCase__						:Any         =							{
    '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
}

class 	__a   (       UpperCAmelCase    ):
		_a			:						Tuple    			=			VOCAB_FILES_NAMES
		_a			:						int    			=			PRETRAINED_VOCAB_FILES_MAP
		_a			:						Optional[Any]    			=			PRETRAINED_INIT_CONFIGURATION
		_a			:						str    			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a			:						Optional[int]    			=			DebertaVaTokenizer
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[UNK]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[PAD]"    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[MASK]"    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> None:




									"""simple docstring"""





									super().__init__(
									    _SCREAMING_SNAKE_CASE    ,					tokenizer_file=_SCREAMING_SNAKE_CASE    ,					do_lower_case=_SCREAMING_SNAKE_CASE    ,					bos_token=_SCREAMING_SNAKE_CASE    ,					eos_token=_SCREAMING_SNAKE_CASE    ,					unk_token=_SCREAMING_SNAKE_CASE    ,					sep_token=_SCREAMING_SNAKE_CASE    ,					pad_token=_SCREAMING_SNAKE_CASE    ,					cls_token=_SCREAMING_SNAKE_CASE    ,					mask_token=_SCREAMING_SNAKE_CASE    ,					split_by_punct=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)

									_UpperCAmelCase					=    do_lower_case
									_UpperCAmelCase					=    split_by_punct
									_UpperCAmelCase					=    vocab_file
									_UpperCAmelCase					=    False if not self.vocab_file else True
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> str:




									"""simple docstring"""





									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_UpperCAmelCase					=    [self.cls_token_id]
									_UpperCAmelCase					=    [self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False		)				-> Any:




									"""simple docstring"""





									if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_SCREAMING_SNAKE_CASE    ,					token_ids_a=_SCREAMING_SNAKE_CASE    ,					already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

									if token_ids_a is not None:
																return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
									return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.sep_token_id]
									_UpperCAmelCase					=    [self.cls_token_id]
									if token_ids_a is None:
																return len(cls + token_ids_a + sep		) * [0]
									return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]






		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = 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(_SCREAMING_SNAKE_CASE		):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
																return
									_UpperCAmelCase					=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

									return (out_vocab_file,)
