

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


SCREAMING_SNAKE_CASE						:Union[str, Any]      =			1000






class 		__magic_name__ :






     def __init__(							self						,		_lowercase						,		_lowercase=1.2						,		_lowercase="g"						,		_lowercase=0.5						,		_lowercase="-"						,		_lowercase="test_out.jpg"						,		_lowercase=True						,		_lowercase=False						,		_lowercase=None						,		_lowercase=None						,		_lowercase=0.7						,		)->			Dict:
         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						,		_lowercase						,		_lowercase=None     )->			List[str]:
         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						,		_lowercase						,		_lowercase=None						,		_lowercase=None						,		_lowercase=None						,		_lowercase=None     )->			Any:
         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						,		_lowercase						,		_lowercase						,		_lowercase     )->			Union[str, Any]:
         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						,		_lowercase						,		_lowercase						,		_lowercase="g"						,		_lowercase="left"						,		)->			str:
         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						,		_lowercase=None     )->			int:
         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						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )->			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						,		_lowercase						,		_lowercase     )->			int:
         UpperCamelCase_          =		[self.idaobj[i] for i in classes]
         if scores is not None:
             if labels is None:
                 UpperCamelCase_          =		["{:.0f}%".format(s * 100     ) for s in scores]
             else:
                 UpperCamelCase_          =		["{} {:.0f}%".format(_lowercase						,		s * 100     ) for li, s in zip(_lowercase						,		_lowercase     )]
         return labels






     def      UpperCAmelCase_							(							self						,		_lowercase=255     )->			Optional[Any]:
         UpperCamelCase_          =		np.random.randint(0						,		len(_COLORS     )     )
         UpperCamelCase_          =		_COLORS[idx] * maximum
         if not self.rgb:
             UpperCamelCase_          =		ret[::-1]
         return ret






     def      UpperCAmelCase_							(							self     )->			int:
         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						,		_lowercase						,		_lowercase     )->			List[str]:
         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
SCREAMING_SNAKE_CASE						:str      =			(
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)






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 ConvNextImageProcessor
class       __a					( unittest.TestCase     ):


     """simple docstring"""


     def __init__(	self			: List[str]				,_UpperCamelCase			: int				,_UpperCamelCase			: int=7				,_UpperCamelCase			: int=3				,_UpperCamelCase			: List[str]=1_8				,_UpperCamelCase			: Dict=3_0				,_UpperCamelCase			: Tuple=4_0_0				,_UpperCamelCase			: Optional[int]=True				,_UpperCamelCase			: int=None				,_UpperCamelCase			: str=0.875				,_UpperCamelCase			: Tuple=True				,_UpperCamelCase			: Dict=[0.5, 0.5, 0.5]				,_UpperCamelCase			: str=[0.5, 0.5, 0.5]				,)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=size if size is not None else {"""shortest_edge""": 2_0}
         SCREAMING_SNAKE_CASE__								=parent
         SCREAMING_SNAKE_CASE__								=batch_size
         SCREAMING_SNAKE_CASE__								=num_channels
         SCREAMING_SNAKE_CASE__								=image_size
         SCREAMING_SNAKE_CASE__								=min_resolution
         SCREAMING_SNAKE_CASE__								=max_resolution
         SCREAMING_SNAKE_CASE__								=do_resize
         SCREAMING_SNAKE_CASE__								=size
         SCREAMING_SNAKE_CASE__								=crop_pct
         SCREAMING_SNAKE_CASE__								=do_normalize
         SCREAMING_SNAKE_CASE__								=image_mean
         SCREAMING_SNAKE_CASE__								=image_std




     def   __A				(	self			: Dict						)		->   str:


         '''simple docstring'''





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






@require_torch
@require_vision
class       __a					( __lowerCamelCase							,     unittest.TestCase     ):


     """simple docstring"""


     _A				:    Dict									=  ConvNextImageProcessor if is_vision_available() else None




     def   __A				(	self			: List[str]						)		->   Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=ConvNextImageProcessingTester(self						)




     @property
     def   __A				(	self			: str						)		->   Optional[int]:


         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""do_resize"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""size"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""crop_pct"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""do_normalize"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""image_mean"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""image_std"""						)						)




     def   __A				(	self			: Tuple						)		->   Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class.from_dict(self.image_processor_dict						)
         self.assertEqual(image_processor.size				,{"""shortest_edge""": 2_0}						)

         SCREAMING_SNAKE_CASE__								=self.image_processing_class.from_dict(self.image_processor_dict				,size=4_2						)
         self.assertEqual(image_processor.size				,{"""shortest_edge""": 4_2}						)




     def   __A				(	self			: Any						)		->   List[str]:


         '''simple docstring'''





         pass




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


         '''simple docstring'''





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

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)




     def   __A				(	self			: str						)		->   str:


         '''simple docstring'''





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

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)




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


         '''simple docstring'''





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

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)






