


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						:       Any        =					10_00





class 						_A:


				"""simple docstring"""


				def __init__(      self					, _A					, _A=1.2					, _A="g"					, _A=0.5					, _A="-"					, _A="test_out.jpg"					, _A=True					, _A=False					, _A=None					, _A=None					, _A=0.7					, ):






									if isinstance(UpperCamelCase__					, torch.Tensor					):
														__A     :						Optional[int]          =    img.numpy().astype('np.uint8'					)
									if isinstance(UpperCamelCase__					, UpperCamelCase__					):
														__A     :						int          =    img_tensorize(UpperCamelCase__					)
									assert isinstance(UpperCamelCase__					, np.ndarray					)

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

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



				def  UpperCAmelCase_		(      self					, _A					, _A=None					):






									if color is None:
														__A     :						Optional[Any]          =    self.edgecolor
									((__A)   ,    (__A)   ,    (__A)   ,    (__A))     :						Union[str, Any]          =    box
									__A     :						Optional[int]          =    xa - xa
									__A     :						Optional[Any]          =    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  UpperCAmelCase_		(      self					, _A					, _A=None					, _A=None					, _A=None					, _A=None					):






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

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



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

									__A     :						Dict          =    (ya - ya) / np.sqrt(self.height * self.width					)
									__A     :						List[Any]          =    self._change_color_brightness(UpperCamelCase__					, brightness_factor=0.7					)
									__A     :						List[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=UpperCamelCase__					, position=UpperCamelCase__					, color=UpperCamelCase__					, )



				def  UpperCAmelCase_		(      self					, _A					, _A					, _A="g"					, _A="left"					, ):
									__A     :						Union[str, Any]          =    0
									__A     :						Optional[int]          =    self.font_size
									__A     :						int          =    np.maximum(list(mplc.to_rgb(UpperCamelCase__					)					)					, 0.2					)
									__A     :						int          =    max(0.8					, np.max(UpperCamelCase__					)					)
									__A     :						List[str]          =    {
									    'facecolor': 'black',
									    'alpha': self.alpha,
									    'pad': self.pad,
									    'edgecolor': 'none',
									}
									__A   ,    __A     :						Optional[int]          =    position
									self.ax.text(
									    UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, size=font_size * self.scale					, family='sans-serif'					, bbox=UpperCamelCase__					, verticalalignment='top'					, horizontalalignment=UpperCamelCase__					, color=UpperCamelCase__					, zorder=10					, rotation=UpperCamelCase__					, )



				def  UpperCAmelCase_		(      self					, _A=None					):






									if saveas is None:
														__A     :						Union[str, Any]          =    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  UpperCAmelCase_		(      self					, _A					, _A					, _A					, _A					):
									__A     :						Optional[int]          =    [self.idaobj[i] for i in classes]
									__A     :						int          =    [self.idaattr[i] for i in attr_classes]
									__A     :						Dict          =    [
									    F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
									    for label, score, attr, attr_score in zip(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					)
									]
									return labels



				def  UpperCAmelCase_		(      self					, _A					, _A					):
									__A     :						Tuple          =    [self.idaobj[i] for i in classes]
									if scores is not None:
														if labels is None:
																			__A     :						Dict          =    ['{:.0f}%'.format(s * 100					) for s in scores]
														else:
																			__A     :						Tuple          =    ['{} {:.0f}%'.format(UpperCamelCase__					, s * 100					) for li, s in zip(UpperCamelCase__					, UpperCamelCase__					)]
									return labels



				def  UpperCAmelCase_		(      self					, _A=255					):
									__A     :						Dict          =    np.random.randint(0					, len(_COLORS					)					)
									__A     :						str          =    _COLORS[idx] * maximum
									if not self.rgb:
														__A     :						List[Any]          =    ret[::-1]
									return ret



				def  UpperCAmelCase_		(      self					):






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

									__A     :						List[str]          =    np.frombuffer(UpperCamelCase__					, dtype='uint8'					)
									__A     :						Tuple          =    buffer.reshape(UpperCamelCase__					, UpperCamelCase__					, 4					)
									__A   ,    __A     :						Optional[int]          =    np.split(UpperCamelCase__					, [3]					, axis=2					)

									try:
														import numexpr as ne  # fuse them with numexpr

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

									return visualized_image.astype('uint8'					)





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




UpperCAmelCase						:       Tuple        =					(
    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 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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import TvltImageProcessor







def 			_SCREAMING_SNAKE_CASE    (       a ,						a=10 ,						a=10 ,						a=False ,						a=False  )				->				Union[str, Any]:
					__A     :						str          =    []
					for i in range(image_processor_tester.num_frames  ):
										video.append(np.random.randint(2_55 ,						size=(image_processor_tester.num_channels, width, height) ,						dtype=np.uinta  )  )

					if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										__A     :						Union[str, Any]          =    [Image.fromarray(np.moveaxis(a ,						0 ,						-1  )  ) for frame in video]

					if torchify:
										__A     :						Dict          =    [torch.from_numpy(a  ) for frame in video]

					return video







def 			_SCREAMING_SNAKE_CASE    (       a ,						a=False ,						a=False ,						a=False  )				->				Union[str, Any]:
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					__A     :						List[Any]          =    []
					for i in range(image_processor_tester.batch_size  ):
										if equal_resolution:
															__A     :						int          =    image_processor_tester.max_resolution
										else:
															__A   ,    __A     :						Tuple          =    np.random.choice(
															    np.arange(image_processor_tester.min_resolution ,						image_processor_tester.max_resolution  ) ,						2  )
															__A     :						Optional[int]          =    prepare_video(
															    image_processor_tester=a ,						width=a ,						height=a ,						numpify=a ,						torchify=a ,						)
										video_inputs.append(a  )

					return video_inputs





class 						_A( unittest.TestCase       ):


				"""simple docstring"""


				def __init__(      self					, _A					, _A=7					, _A=3					, _A=4					, _A=18					, _A=30					, _A=400					, _A=True					, _A=None					, _A=True					, _A=[0.5, 0.5, 0.5]					, _A=[0.5, 0.5, 0.5]					, _A=True					, _A=None					, ):
									__A     :						List[str]          =    size if size is not None else {'shortest_edge': 18}
									__A     :						Tuple          =    crop_size if crop_size is not None else {'height': 18, 'width': 18}

									__A     :						Dict          =    parent
									__A     :						str          =    batch_size
									__A     :						int          =    num_channels
									__A     :						Optional[Any]          =    num_frames
									__A     :						Any          =    image_size
									__A     :						List[str]          =    min_resolution
									__A     :						List[Any]          =    max_resolution
									__A     :						Tuple          =    do_resize
									__A     :						List[str]          =    size
									__A     :						str          =    do_normalize
									__A     :						str          =    image_mean
									__A     :						int          =    image_std
									__A     :						Optional[Any]          =    do_center_crop
									__A     :						Union[str, Any]          =    crop_size





				def  UpperCAmelCase_		(      self					):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "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 						_A( snake_case__    ,			unittest.TestCase       ):


				"""simple docstring"""


				UpperCamelCase   :  Optional[Any]    =     TvltImageProcessor if is_vision_available() else None



				def  UpperCAmelCase_		(      self					):
									__A     :						str          =    TvltImageProcessorTester(self					)



				@property
				def  UpperCAmelCase_		(      self					):
									return self.image_processor_tester.prepare_image_processor_dict()



				def  UpperCAmelCase_		(      self					):
									__A     :						Union[str, Any]          =    self.image_processing_class(**self.image_processor_dict					)
									self.assertTrue(hasattr(_A					, 'image_mean'					)					)
									self.assertTrue(hasattr(_A					, 'image_std'					)					)
									self.assertTrue(hasattr(_A					, 'do_normalize'					)					)
									self.assertTrue(hasattr(_A					, 'do_resize'					)					)
									self.assertTrue(hasattr(_A					, 'do_center_crop'					)					)
									self.assertTrue(hasattr(_A					, 'size'					)					)



				def  UpperCAmelCase_		(      self					):
									# Initialize image_processor
									__A     :						List[str]          =    self.image_processing_class(**self.image_processor_dict					)
									# create random PIL videos
									__A     :						List[str]          =    prepare_video_inputs(self.image_processor_tester					, equal_resolution=_A					)
									for video in video_inputs:
														self.assertIsInstance(_A					, _A					)
														self.assertIsInstance(video[0]					, Image.Image					)

									# Test not batched input
									__A     :						str          =    image_processor(video_inputs[0]					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )

									# Test batched
									__A     :						List[Any]          =    image_processor(_A					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )



				def  UpperCAmelCase_		(      self					):
									# Initialize image_processor
									__A     :						Any          =    self.image_processing_class(**self.image_processor_dict					)
									# create random numpy tensors
									__A     :						Dict          =    prepare_video_inputs(self.image_processor_tester					, equal_resolution=_A					, numpify=_A					)
									for video in video_inputs:
														self.assertIsInstance(_A					, _A					)
														self.assertIsInstance(video[0]					, np.ndarray					)

									# Test not batched input
									__A     :						Dict          =    image_processor(video_inputs[0]					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )

									# Test batched
									__A     :						Optional[int]          =    image_processor(_A					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )





				def  UpperCAmelCase_		(      self					):
									# Initialize image_processor
									__A     :						Any          =    self.image_processing_class(**self.image_processor_dict					)
									# create random PyTorch tensors
									__A     :						Dict          =    prepare_video_inputs(self.image_processor_tester					, equal_resolution=_A					, torchify=_A					)
									for video in video_inputs:
														self.assertIsInstance(_A					, _A					)
														self.assertIsInstance(video[0]					, torch.Tensor					)

									# Test not batched input
									__A     :						Optional[Any]          =    image_processor(video_inputs[0]					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )

									# Test batched
									__A     :						int          =    image_processor(_A					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )




