



"""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_								=			1000







class  A_     :



						"""simple docstring"""






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

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

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






						def       UpperCAmelCase__	(   self					:Tuple  ,    lowercase_					:Tuple  ,    lowercase_					:Optional[int]=None   )							->     Dict:
												if color is None:
																		UpperCAmelCase			 =					self.edgecolor
												((UpperCAmelCase) ,       (UpperCAmelCase) ,       (UpperCAmelCase) ,       (UpperCAmelCase))			 =					box
												UpperCAmelCase			 =					xa - xa
												UpperCAmelCase			 =					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       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:Tuple  ,    lowercase_					:Optional[int]=None  ,    lowercase_					:Any=None  ,    lowercase_					:Optional[int]=None  ,    lowercase_					:Optional[int]=None   )							->     int:
												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(lowercase_  ,    torch.Tensor   ):
																		UpperCAmelCase			 =					boxes.numpy()
												if isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					np.array(lowercase_   )
												assert isinstance(lowercase_  ,    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(lowercase_   )   )]
												UpperCAmelCase			 =					[assigned_colors[idx] for idx in sorted_idxs]
												if obj_ids is not None:
																		UpperCAmelCase			 =					self._create_text_labels_attr(lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_   )
																		for i in range(len(lowercase_   )   ):
																								UpperCAmelCase			 =					assigned_colors[i]
																								self.add_box(boxes[i]  ,    lowercase_   )
																								self.draw_labels(labels[i]  ,    boxes[i]  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:int  ,    lowercase_					:List[Any]  ,    lowercase_					:Optional[int]  ,    lowercase_					:Tuple   )							->     List[str]:
												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       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(lowercase_  ,    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=lowercase_  ,    position=lowercase_  ,    color=lowercase_  ,    )






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:Optional[int]  ,    lowercase_					:Dict  ,    lowercase_					:List[Any]="g"  ,    lowercase_					:List[Any]="left"  ,    )							->     Any:
												UpperCAmelCase			 =					0
												UpperCAmelCase			 =					self.font_size
												UpperCAmelCase			 =					np.maximum(list(mplc.to_rgb(lowercase_   )   )  ,    0.2   )
												UpperCAmelCase			 =					max(0.8  ,    np.max(lowercase_   )   )
												UpperCAmelCase			 =					{
												    'facecolor': 'black',
												    'alpha': self.alpha,
												    'pad': self.pad,
												    'edgecolor': 'none',
												}
												UpperCAmelCase ,       UpperCAmelCase			 =					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       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:Tuple=None   )							->     Tuple:
												if saveas is None:
																		UpperCAmelCase			 =					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       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:str  ,    lowercase_					:str  ,    lowercase_					:Optional[int]  ,    lowercase_					:Optional[Any]   )							->     Tuple:
												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(lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_   )
												]
												return labels






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:str  ,    lowercase_					:List[str]   )							->     int:
												UpperCAmelCase			 =					[self.idaobj[i] for i in classes]
												if scores is not None:
																		if labels is None:
																								UpperCAmelCase			 =					['{:.0f}%'.format(s * 1_00   ) for s in scores]
																		else:
																								UpperCAmelCase			 =					['{} {:.0f}%'.format(lowercase_  ,    s * 1_00   ) for li, s in zip(lowercase_  ,    lowercase_   )]
												return labels






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:int=2_55   )							->     Any:
												UpperCAmelCase			 =					np.random.randint(0  ,    len(_COLORS   )   )
												UpperCAmelCase			 =					_COLORS[idx] * maximum
												if not self.rgb:
																		UpperCAmelCase			 =					ret[::-1]
												return ret






						def       UpperCAmelCase__	(   self					:Any   )							->     Tuple:
												if not self.pynb:
																		UpperCAmelCase ,       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(lowercase_   )
																		UpperCAmelCase ,       UpperCAmelCase			 =					self.width, self.height
																		UpperCAmelCase			 =					buf.getvalue()
																		UpperCAmelCase			 =					self.img

												UpperCAmelCase			 =					np.frombuffer(lowercase_  ,    dtype='uint8'   )
												UpperCAmelCase			 =					buffer.reshape(lowercase_  ,    lowercase_  ,    4   )
												UpperCAmelCase ,       UpperCAmelCase			 =					np.split(lowercase_  ,    [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					:int  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     Optional[Any]:
												assert brightness_factor >= -1.0 and brightness_factor <= 1.0
												UpperCAmelCase			 =					mplc.to_rgb(lowercase_   )
												UpperCAmelCase			 =					colorsys.rgb_to_hls(*mplc.to_rgb(lowercase_   )   )
												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]  ,    lowercase_  ,    polygon_color[2]   )
												return modified_color


snake_case_								=			(
    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'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import DeiTImageProcessor

class      lowerCamelCase__     (     unittest.TestCase				):






	'''simple docstring'''

	def __init__(  self		:  List[str]   ,		__A		:  Any   ,		__A		:  Tuple=7   ,		__A		:  Dict=3   ,		__A		:  List[str]=18   ,		__A		:  Optional[int]=30   ,		__A		:  int=400   ,		__A		:  Union[str, Any]=True   ,		__A		:  Tuple=None   ,		__A		:  int=True   ,		__A		:  int=None   ,		__A		:  List[str]=True   ,		__A		:  Union[str, Any]=[0.5, 0.5, 0.5]   ,		__A		:  List[Any]=[0.5, 0.5, 0.5]   ,		)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   size if size is not None else {"""height""": 20, """width""": 20}
								lowerCAmelCase__  =   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

								lowerCAmelCase__  =   parent
								lowerCAmelCase__  =   batch_size
								lowerCAmelCase__  =   num_channels
								lowerCAmelCase__  =   image_size
								lowerCAmelCase__  =   min_resolution
								lowerCAmelCase__  =   max_resolution
								lowerCAmelCase__  =   do_resize
								lowerCAmelCase__  =   size
								lowerCAmelCase__  =   do_center_crop
								lowerCAmelCase__  =   crop_size
								lowerCAmelCase__  =   do_normalize
								lowerCAmelCase__  =   image_mean
								lowerCAmelCase__  =   image_std




	def      lowercase__							(  self		:  Tuple					)     ->			Optional[int]:




								'''simple docstring'''





								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}





@require_torch
@require_vision
class      lowerCamelCase__     (     _A,     unittest.TestCase				):






	'''simple docstring'''

	A__		=	DeiTImageProcessor if is_vision_available() else None
	A__		=	True




	def      lowercase__							(  self		:  List[Any]					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   DeiTImageProcessingTester(self					)




	@property
	def      lowercase__							(  self		:  Any					)     ->			int:




								'''simple docstring'''





								return self.image_processor_tester.prepare_image_processor_dict()




	def      lowercase__							(  self		:  str					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(__A   ,		"""do_resize"""					)					)
								self.assertTrue(hasattr(__A   ,		"""size"""					)					)
								self.assertTrue(hasattr(__A   ,		"""do_center_crop"""					)					)
								self.assertTrue(hasattr(__A   ,		"""center_crop"""					)					)
								self.assertTrue(hasattr(__A   ,		"""do_normalize"""					)					)
								self.assertTrue(hasattr(__A   ,		"""image_mean"""					)					)
								self.assertTrue(hasattr(__A   ,		"""image_std"""					)					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size   ,		{"""height""": 20, """width""": 20}					)
								self.assertEqual(image_processor.crop_size   ,		{"""height""": 18, """width""": 18}					)

								lowerCAmelCase__  =   self.image_processing_class.from_dict(self.image_processor_dict   ,		size=42   ,		crop_size=84					)
								self.assertEqual(image_processor.size   ,		{"""height""": 42, """width""": 42}					)
								self.assertEqual(image_processor.crop_size   ,		{"""height""": 84, """width""": 84}					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			List[str]:




								'''simple docstring'''





								pass




	def      lowercase__							(  self		:  Dict					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		Image.Image					)

								# Test not batched input
								lowerCAmelCase__  =   image_processing(image_inputs[0]   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)

								# Test batched
								lowerCAmelCase__  =   image_processing(__A   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)




	def      lowercase__							(  self		:  List[Any]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A   ,		numpify=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		np.ndarray					)

								# Test not batched input
								lowerCAmelCase__  =   image_processing(image_inputs[0]   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)

								# Test batched
								lowerCAmelCase__  =   image_processing(__A   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)




	def      lowercase__							(  self		:  List[str]					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A   ,		torchify=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		torch.Tensor					)

								# Test not batched input
								lowerCAmelCase__  =   image_processing(image_inputs[0]   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)

								# Test batched
								lowerCAmelCase__  =   image_processing(__A   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)





