"""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     					=     10_00
class   UpperCAmelCase__				:


	"""simple docstring"""







	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       ,     )    ->	Tuple:
							if isinstance(_SCREAMING_SNAKE_CASE       ,     torch.Tensor ):
													a_							:			Union[str, Any]     		=					img.numpy().astype("np.uint8" )
							if isinstance(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE ):
													a_							:			Optional[Any]     		=					img_tensorize(_SCREAMING_SNAKE_CASE )
							assert isinstance(_SCREAMING_SNAKE_CASE       ,     np.ndarray )

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

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







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE=None )    ->	int:
							if color is None:
													a_							:			List[str]     		=					self.edgecolor
							((a_)		,					(a_)		,					(a_)		,					(a_))							:			Tuple     		=					box
							a_							:			Optional[int]     		=					xa - xa
							a_							:			List[Any]     		=					ya - ya
							self.ax.add_patch(
							    mpl.patches.Rectangle(
							        (xa, ya)       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     fill=_SCREAMING_SNAKE_CASE       ,     edgecolor=_SCREAMING_SNAKE_CASE       ,     linewidth=self.font_size // 3       ,     alpha=self.alpha       ,     linestyle=self.linestyle       ,     ) )







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

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







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	Dict:
							a_		,					a_		,					a_		,					a_							:			Tuple     		=					box
							a_							:			Tuple     		=					(xa, ya)
							a_							:			str     		=					(ya - ya) * (xa - xa)
							a_							:			str     		=					_SMALL_OBJ * self.scale
							if instance_area < small or ya - ya < 4_0 * self.scale:
													if ya >= self.height - 5:
																			a_							:			Any     		=					(xa, ya)
													else:
																			a_							:			Optional[Any]     		=					(xa, ya)

							a_							:			Any     		=					(ya - ya) / np.sqrt(self.height * self.width )
							a_							:			Optional[Any]     		=					self._change_color_brightness(_SCREAMING_SNAKE_CASE       ,     brightness_factor=0.7 )
							a_							:			Union[str, Any]     		=					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=_SCREAMING_SNAKE_CASE       ,     position=_SCREAMING_SNAKE_CASE       ,     color=_SCREAMING_SNAKE_CASE       ,     )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE="g"       ,     _SCREAMING_SNAKE_CASE="left"       ,     )    ->	List[Any]:
							a_							:			Optional[Any]     		=					0
							a_							:			Any     		=					self.font_size
							a_							:			List[Any]     		=					np.maximum(list(mplc.to_rgb(_SCREAMING_SNAKE_CASE ) )       ,     0.2 )
							a_							:			List[Any]     		=					max(0.8       ,     np.max(_SCREAMING_SNAKE_CASE ) )
							a_							:			int     		=					{
							    "facecolor": "black",
							    "alpha": self.alpha,
							    "pad": self.pad,
							    "edgecolor": "none",
							}
							a_		,					a_							:			Optional[int]     		=					position
							self.ax.text(
							    _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     size=font_size * self.scale       ,     family="sans-serif"       ,     bbox=_SCREAMING_SNAKE_CASE       ,     verticalalignment="top"       ,     horizontalalignment=_SCREAMING_SNAKE_CASE       ,     color=_SCREAMING_SNAKE_CASE       ,     zorder=1_0       ,     rotation=_SCREAMING_SNAKE_CASE       ,     )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE=None )    ->	Union[str, Any]:
							if saveas is None:
													a_							:			Dict     		=					self.saveas
							if saveas.lower().endswith(".jpg" ) or saveas.lower().endswith(".png" ):
													cva.imwrite(
													    _SCREAMING_SNAKE_CASE       ,     self._get_buffer()[:, :, ::-1]       ,     )
							else:
													self.fig.savefig(_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	Optional[int]:
							a_							:			Dict     		=					[self.idaobj[i] for i in classes]
							a_							:			Any     		=					[self.idaattr[i] for i in attr_classes]
							a_							:			List[str]     		=					[
							    f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
							    for label, score, attr, attr_score in zip(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
							]
							return labels







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	Dict:
							a_							:			Any     		=					[self.idaobj[i] for i in classes]
							if scores is not None:
													if labels is None:
																			a_							:			Tuple     		=					["{:.0f}%".format(s * 1_0_0 ) for s in scores]
													else:
																			a_							:			Optional[int]     		=					["{} {:.0f}%".format(_SCREAMING_SNAKE_CASE       ,     s * 1_0_0 ) for li, s in zip(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )]
							return labels







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE=2_5_5 )    ->	Union[str, Any]:
							a_							:			List[str]     		=					np.random.randint(0       ,     len(_COLORS ) )
							a_							:			Optional[int]     		=					_COLORS[idx] * maximum
							if not self.rgb:
													a_							:			Tuple     		=					ret[::-1]
							return ret







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

							a_							:			Optional[Any]     		=					np.frombuffer(_SCREAMING_SNAKE_CASE       ,     dtype="uint8" )
							a_							:			Optional[Any]     		=					buffer.reshape(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     4 )
							a_		,					a_							:			List[Any]     		=					np.split(_SCREAMING_SNAKE_CASE       ,     [3]       ,     axis=2 )

							try:
													import numexpr as ne  # fuse them with numexpr

													a_							:			List[Any]     		=					ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)" )
							except ImportError:
													a_							:			List[Any]     		=					alpha.astype("float32" ) / 2_5_5.0
													a_							:			Optional[int]     		=					img * (1 - alpha) + rgb * alpha

							return visualized_image.astype("uint8" )







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




UpperCamelCase     					=     (
    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)
)





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase     					=     {'configuration_levit': ['LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LevitConfig', 'LevitOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     ['LevitFeatureExtractor']
				UpperCamelCase     					=     ['LevitImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LevitForImageClassification',
				    'LevitForImageClassificationWithTeacher',
				    'LevitModel',
				    'LevitPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_levit import LevitFeatureExtractor
								from .image_processing_levit import LevitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_levit import (
								    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LevitForImageClassification,
								    LevitForImageClassificationWithTeacher,
								    LevitModel,
								    LevitPreTrainedModel,
								)
else:
				import sys

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




