





'''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


__UpperCamelCase:  str   =   1_0_0_0


class 			__lowerCAmelCase				:


	'''simple docstring'''





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

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

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

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

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

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

			lowercase__				:			Tuple            =		(ya - ya) / np.sqrt(self.height * self.width )
			lowercase__				:			Dict            =		self._change_color_brightness(lowerCamelCase_,				brightness_factor=0.7 )
			lowercase__				:			str            =		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=lowerCamelCase_,				position=lowerCamelCase_,				color=lowerCamelCase_,				)

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

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

	def       snake_case__(	self:	Optional[Any],				lowerCamelCase_:	str=255 ):
			lowercase__				:			Tuple            =		np.random.randint(0,				len(_COLORS ) )
			lowercase__				:			Union[str, Any]            =		_COLORS[idx] * maximum
			if not self.rgb:
					lowercase__				:			Dict            =		ret[::-1]
			return ret

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

			lowercase__				:			Union[str, Any]            =		np.frombuffer(lowerCamelCase_,				dtype='uint8' )
			lowercase__				:			int            =		buffer.reshape(lowerCamelCase_,				lowerCamelCase_,				4 )
			lowercase__      ,							lowercase__				:			Union[str, Any]            =		np.split(lowerCamelCase_,				[3],				axis=2 )

			try:
					import numexpr as ne  # fuse them with numexpr

					lowercase__				:			Dict            =		ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)' )
			except ImportError:
					lowercase__				:			Optional[Any]            =		alpha.astype('float32' ) / 2_5_5.0
					lowercase__				:			str            =		img * (1 - alpha) + rgb * alpha

			return visualized_image.astype('uint8' )





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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase:  Dict   =   {
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  List[Any]   =   ["""DebertaV2TokenizerFast"""]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  str   =   [
			    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFDebertaV2ForMaskedLM""",
			    """TFDebertaV2ForQuestionAnswering""",
			    """TFDebertaV2ForSequenceClassification""",
			    """TFDebertaV2ForTokenClassification""",
			    """TFDebertaV2Model""",
			    """TFDebertaV2PreTrainedModel""",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  Tuple   =   [
			    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """DebertaV2ForMaskedLM""",
			    """DebertaV2ForMultipleChoice""",
			    """DebertaV2ForQuestionAnswering""",
			    """DebertaV2ForSequenceClassification""",
			    """DebertaV2ForTokenClassification""",
			    """DebertaV2Model""",
			    """DebertaV2PreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_deberta_va import (
			    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DebertaVaConfig,
			    DebertaVaOnnxConfig,
			)
			from .tokenization_deberta_va import DebertaVaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_deberta_va import (
						    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDebertaVaForMaskedLM,
						    TFDebertaVaForQuestionAnswering,
						    TFDebertaVaForSequenceClassification,
						    TFDebertaVaForTokenClassification,
						    TFDebertaVaModel,
						    TFDebertaVaPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deberta_va import (
						    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DebertaVaForMaskedLM,
						    DebertaVaForMultipleChoice,
						    DebertaVaForQuestionAnswering,
						    DebertaVaForSequenceClassification,
						    DebertaVaForTokenClassification,
						    DebertaVaModel,
						    DebertaVaPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:  Optional[Any]   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


