




'''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


a__         =     1_000





class __magic_name__:






						def __init__(   self						:						Dict					,   __UpperCamelCase						:						Tuple					,   __UpperCamelCase						:						Dict=1.2					,   __UpperCamelCase						:						Union[str, Any]="g"					,   __UpperCamelCase						:						Optional[Any]=0.5					,   __UpperCamelCase						:						Optional[Any]="-"					,   __UpperCamelCase						:						str="test_out.jpg"					,   __UpperCamelCase						:						Tuple=True					,   __UpperCamelCase						:						Dict=False					,   __UpperCamelCase						:						Optional[Any]=None					,   __UpperCamelCase						:						int=None					,   __UpperCamelCase						:						int=0.7					,   ):



									'''simple docstring'''






									if isinstance(__UpperCamelCase					,   torch.Tensor       ):
												snake_case__          =						img.numpy().astype("""np.uint8"""       )
									if isinstance(__UpperCamelCase					,   __UpperCamelCase       ):
												snake_case__          =						img_tensorize(__UpperCamelCase       )
									assert isinstance(__UpperCamelCase					,   np.ndarray       )

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

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






						def       __lowerCAmelCase(   self						:						Optional[int]					,   __UpperCamelCase						:						Optional[int]					,   __UpperCamelCase						:						Any=None       ):



									'''simple docstring'''






									if color is None:
												snake_case__          =						self.edgecolor
									((snake_case__)    ,			(snake_case__)    ,			(snake_case__)    ,			(snake_case__))          =						box
									snake_case__          =						xa - xa
									snake_case__          =						ya - ya
									self.ax.add_patch(
									    mpl.patches.Rectangle(
									        (xa, ya)					,   __UpperCamelCase					,   __UpperCamelCase					,   fill=__UpperCamelCase					,   edgecolor=__UpperCamelCase					,   linewidth=self.font_size // 3					,   alpha=self.alpha					,   linestyle=self.linestyle					,   )       )






						def       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						str					,   __UpperCamelCase						:						List[str]=None					,   __UpperCamelCase						:						Dict=None					,   __UpperCamelCase						:						Any=None					,   __UpperCamelCase						:						List[str]=None       ):



									'''simple docstring'''






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

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






						def       __lowerCAmelCase(   self						:						Any					,   __UpperCamelCase						:						List[str]					,   __UpperCamelCase						:						List[Any]					,   __UpperCamelCase						:						List[str]       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__          =						box
									snake_case__          =						(xa, ya)
									snake_case__          =						(ya - ya) * (xa - xa)
									snake_case__          =						_SMALL_OBJ * self.scale
									if instance_area < small or ya - ya < 4_0 * self.scale:
												if ya >= self.height - 5:
															snake_case__          =						(xa, ya)
												else:
															snake_case__          =						(xa, ya)

									snake_case__          =						(ya - ya) / np.sqrt(self.height * self.width       )
									snake_case__          =						self._change_color_brightness(__UpperCamelCase					,   brightness_factor=0.7       )
									snake_case__          =						np.clip((height_ratio - 0.02) / 0.08 + 1					,   1.2					,   2       )
									font_size *= 0.75 * self.font_size

									self.draw_text(
									    text=__UpperCamelCase					,   position=__UpperCamelCase					,   color=__UpperCamelCase					,   )






						def       __lowerCAmelCase(   self						:						int					,   __UpperCamelCase						:						int					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						Any="g"					,   __UpperCamelCase						:						Dict="left"					,   ):



									'''simple docstring'''






									snake_case__          =						0
									snake_case__          =						self.font_size
									snake_case__          =						np.maximum(list(mplc.to_rgb(__UpperCamelCase       )       )					,   0.2       )
									snake_case__          =						max(0.8					,   np.max(__UpperCamelCase       )       )
									snake_case__          =						{
									    """facecolor""": """black""",
									    """alpha""": self.alpha,
									    """pad""": self.pad,
									    """edgecolor""": """none""",
									}
									snake_case__    ,			snake_case__          =						position
									self.ax.text(
									    __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   size=font_size * self.scale					,   family="""sans-serif"""					,   bbox=__UpperCamelCase					,   verticalalignment="""top"""					,   horizontalalignment=__UpperCamelCase					,   color=__UpperCamelCase					,   zorder=1_0					,   rotation=__UpperCamelCase					,   )






						def       __lowerCAmelCase(   self						:						Any					,   __UpperCamelCase						:						Optional[int]=None       ):



									'''simple docstring'''






									if saveas is None:
												snake_case__          =						self.saveas
									if saveas.lower().endswith(""".jpg"""       ) or saveas.lower().endswith(""".png"""       ):
												cva.imwrite(
												    __UpperCamelCase					,   self._get_buffer()[:, :, ::-1]					,   )
									else:
												self.fig.savefig(__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Optional[int]					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						Optional[Any]					,   __UpperCamelCase						:						int					,   __UpperCamelCase						:						Union[str, Any]       ):



									'''simple docstring'''






									snake_case__          =						[self.idaobj[i] for i in classes]
									snake_case__          =						[self.idaattr[i] for i in attr_classes]
									snake_case__          =						[
									    f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
									    for label, score, attr, attr_score in zip(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase       )
									]
									return labels






						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						Tuple					,   __UpperCamelCase						:						Optional[Any]       ):



									'''simple docstring'''






									snake_case__          =						[self.idaobj[i] for i in classes]
									if scores is not None:
												if labels is None:
															snake_case__          =						["""{:.0f}%""".format(s * 1_0_0       ) for s in scores]
												else:
															snake_case__          =						["""{} {:.0f}%""".format(__UpperCamelCase					,   s * 1_0_0       ) for li, s in zip(__UpperCamelCase					,   __UpperCamelCase       )]
									return labels






						def       __lowerCAmelCase(   self						:						List[Any]					,   __UpperCamelCase						:						int=2_5_5       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






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

									snake_case__          =						np.frombuffer(__UpperCamelCase					,   dtype="""uint8"""       )
									snake_case__          =						buffer.reshape(__UpperCamelCase					,   __UpperCamelCase					,   4       )
									snake_case__    ,			snake_case__          =						np.split(__UpperCamelCase					,   [3]					,   axis=2       )

									try:
												import numexpr as ne  # fuse them with numexpr

												snake_case__          =						ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""       )
									except ImportError:
												snake_case__          =						alpha.astype("""float32"""       ) / 2_55.0
												snake_case__          =						img * (1 - alpha) + rgb * alpha

									return visualized_image.astype("""uint8"""       )







						def       __lowerCAmelCase(   self						:						Union[str, Any]					,   __UpperCamelCase						:						List[Any]					,   __UpperCamelCase						:						Optional[int]       ):



									'''simple docstring'''






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







a__         =     (
    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 MobileNetVaImageProcessor
class SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







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

			'''simple docstring'''






			a__   		=     size if size is not None else {'''shortest_edge''': 20}
			a__   		=     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
			a__   		=     parent
			a__   		=     batch_size
			a__   		=     num_channels
			a__   		=     image_size
			a__   		=     min_resolution
			a__   		=     max_resolution
			a__   		=     do_resize
			a__   		=     size
			a__   		=     do_center_crop
			a__   		=     crop_size






	def    _UpperCamelCase							(  self :Tuple			)			->   Any:

			'''simple docstring'''






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

@require_torch
@require_vision
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					,   unittest.TestCase					):






	'''simple docstring'''







	snake_case__							:		str         =		MobileNetVaImageProcessor if is_vision_available() else None


	def    _UpperCamelCase							(  self :Union[str, Any]			)			->   str:

			'''simple docstring'''






			a__   		=     MobileNetVaImageProcessingTester(self			)


	@property
	def    _UpperCamelCase							(  self :List[str]			)			->   Optional[Any]:

			'''simple docstring'''






			return self.image_processor_tester.prepare_image_processor_dict()


	def    _UpperCamelCase							(  self :Tuple			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_resize'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''size'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_center_crop'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''center_crop'''			)			)


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

			'''simple docstring'''






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

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


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

			'''simple docstring'''






			pass


	def    _UpperCamelCase							(  self :Dict			)			->   Tuple:

			'''simple docstring'''






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

			# Test not batched input
			a__   		=     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
			a__   		=     image_processing(__magic_name__       ,    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    _UpperCamelCase							(  self :Optional[Any]			)			->   Union[str, Any]:

			'''simple docstring'''






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

			# Test not batched input
			a__   		=     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
			a__   		=     image_processing(__magic_name__       ,    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    _UpperCamelCase							(  self :List[Any]			)			->   str:

			'''simple docstring'''






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

			# Test not batched input
			a__   		=     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
			a__   		=     image_processing(__magic_name__       ,    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'''],
			    )       ,    )




