
"""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       						=       1000







class 		lowerCamelCase__					:





			'''simple docstring'''



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

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

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



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

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



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ) ->     List[str]:
				_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase								=box
				_lowerCAmelCase								=(xa, ya)
				_lowerCAmelCase								=(ya - ya) * (xa - xa)
				_lowerCAmelCase								=_SMALL_OBJ * self.scale
				if instance_area < small or ya - ya < 40 * self.scale:
					if ya >= self.height - 5:
						_lowerCAmelCase								=(xa, ya)
					else:
						_lowerCAmelCase								=(xa, ya)

				_lowerCAmelCase								=(ya - ya) / np.sqrt(self.height * self.width  )
				_lowerCAmelCase								=self._change_color_brightness(__UpperCAmelCase     ,     brightness_factor=0.7  )
				_lowerCAmelCase								=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 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase="g"     ,     __UpperCAmelCase="left"     ,     ) ->     Union[str, Any]:
				_lowerCAmelCase								=0
				_lowerCAmelCase								=self.font_size
				_lowerCAmelCase								=np.maximum(list(mplc.to_rgb(__UpperCAmelCase  )  )     ,     0.2  )
				_lowerCAmelCase								=max(0.8     ,     np.max(__UpperCAmelCase  )  )
				_lowerCAmelCase								={
				    """facecolor""": """black""",
				    """alpha""": self.alpha,
				    """pad""": self.pad,
				    """edgecolor""": """none""",
				}
				_lowerCAmelCase						,							_lowerCAmelCase								=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 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase=None  ) ->     List[Any]:
				if saveas is None:
					_lowerCAmelCase								=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     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ) ->     Optional[Any]:
				_lowerCAmelCase								=[self.idaobj[i] for i in classes]
				_lowerCAmelCase								=[self.idaattr[i] for i in attr_classes]
				_lowerCAmelCase								=[
				    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     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ) ->     str:
				_lowerCAmelCase								=[self.idaobj[i] for i in classes]
				if scores is not None:
					if labels is None:
						_lowerCAmelCase								=["""{:.0f}%""".format(s * 1_00  ) for s in scores]
					else:
						_lowerCAmelCase								=["""{} {:.0f}%""".format(__UpperCAmelCase     ,     s * 1_00  ) for li, s in zip(__UpperCAmelCase     ,     __UpperCAmelCase  )]
				return labels



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase=2_55  ) ->     int:
				_lowerCAmelCase								=np.random.randint(0     ,     len(_COLORS  )  )
				_lowerCAmelCase								=_COLORS[idx] * maximum
				if not self.rgb:
					_lowerCAmelCase								=ret[::-1]
				return ret



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

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

				try:
					import numexpr as ne  # fuse them with numexpr

					_lowerCAmelCase								=ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""  )
				except ImportError:
					_lowerCAmelCase								=alpha.astype("""float32"""  ) / 2_5_5.0
					_lowerCAmelCase								=img * (1 - alpha) + rgb * alpha

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



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ) ->     Tuple:
				assert brightness_factor >= -1.0 and brightness_factor <= 1.0
				_lowerCAmelCase								=mplc.to_rgb(__UpperCAmelCase  )
				_lowerCAmelCase								=colorsys.rgb_to_hls(*mplc.to_rgb(__UpperCAmelCase  )  )
				_lowerCAmelCase								=polygon_color[1] + (brightness_factor * polygon_color[1])
				_lowerCAmelCase								=0.0 if modified_lightness < 0.0 else modified_lightness
				_lowerCAmelCase								=1.0 if modified_lightness > 1.0 else modified_lightness
				_lowerCAmelCase								=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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BlipImageProcessor






class        SCREAMING_SNAKE_CASE(				unittest.TestCase     ):

       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__=7					,		lowerCamelCase__=3					,		lowerCamelCase__=18					,		lowerCamelCase__=30					,		lowerCamelCase__=400					,		lowerCamelCase__=True					,		lowerCamelCase__=None					,		lowerCamelCase__=True					,		lowerCamelCase__=False					,		lowerCamelCase__=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]					,		lowerCamelCase__=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]					,		lowerCamelCase__=True					,		)    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						size if size is not None else {"""height""": 20, """width""": 20}
           __lowercase							=						parent
           __lowercase							=						batch_size
           __lowercase							=						num_channels
           __lowercase							=						image_size
           __lowercase							=						min_resolution
           __lowercase							=						max_resolution
           __lowercase							=						do_resize
           __lowercase							=						size
           __lowercase							=						do_normalize
           __lowercase							=						image_mean
           __lowercase							=						image_std
           __lowercase							=						do_pad
           __lowercase							=						do_convert_rgb

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

           """simple docstring"""



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




       def     snake_case__			(    self					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False     )    ->					Tuple:

           """simple docstring"""



           assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

           if equal_resolution:
               __lowercase							=						[]
               for i in range(self.batch_size     ):
                   image_inputs.append(
                       np.random.randint(
                           255					,		size=(self.num_channels, self.max_resolution, self.max_resolution)					,		dtype=np.uinta     )     )
           else:
               __lowercase							=						[]
               for i in range(self.batch_size     ):
                   __lowercase				,__lowercase							=						np.random.choice(np.arange(self.min_resolution					,		self.max_resolution     )					,		2     )
                   image_inputs.append(np.random.randint(255					,		size=(self.num_channels, width, height)					,		dtype=np.uinta     )     )

           if not numpify and not torchify:
               # PIL expects the channel dimension as last dimension
               __lowercase							=						[Image.fromarray(np.moveaxis(lowerCamelCase__					,		0					,		-1     )     ) for x in image_inputs]

           if torchify:
               __lowercase							=						[torch.from_numpy(lowerCamelCase__     ) for x in image_inputs]

           return image_inputs






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE(				__A   ,      unittest.TestCase     ):
       snake_case_ :     Optional[int]    =					BlipImageProcessor if is_vision_available() else None

       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           __lowercase							=						BlipImageProcessingTester(self     )

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

           """simple docstring"""



           return self.image_processor_tester.prepare_image_processor_dict()

       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_resize"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""size"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_normalize"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""image_mean"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""image_std"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_convert_rgb"""     )     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           pass

       def     snake_case__			(    self     )    ->					List[str]:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           # create random PIL images
           __lowercase							=						self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__     )
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__					,		Image.Image     )

           # Test not batched input
           __lowercase							=						image_processor(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["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)

           # Test batched
           __lowercase							=						image_processor(lowerCamelCase__					,		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["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)

       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



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

           # Test not batched input
           __lowercase							=						image_processor(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["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)

           # Test batched
           __lowercase							=						image_processor(lowerCamelCase__					,		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["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)




       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



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

           # Test not batched input
           __lowercase							=						image_processor(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["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)

           # Test batched
           __lowercase							=						image_processor(lowerCamelCase__					,		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["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE(				__A   ,      unittest.TestCase     ):
       snake_case_ :     Union[str, Any]    =					BlipImageProcessor if is_vision_available() else None

       def     snake_case__			(    self     )    ->					List[str]:

           """simple docstring"""



           __lowercase							=						BlipImageProcessingTester(self					,		num_channels=4     )
           __lowercase							=						3

       @property
       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           return self.image_processor_tester.prepare_image_processor_dict()

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_resize"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""size"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_normalize"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""image_mean"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""image_std"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_convert_rgb"""     )     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           pass




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

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           # create random PIL images
           __lowercase							=						self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__     )
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__					,		Image.Image     )

           # Test not batched input
           __lowercase							=						image_processor(image_inputs[0]					,		return_tensors="""pt"""     ).pixel_values
           self.assertEqual(
               encoded_images.shape					,		(
                   1,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)

           # Test batched
           __lowercase							=						image_processor(lowerCamelCase__					,		return_tensors="""pt"""     ).pixel_values
           self.assertEqual(
               encoded_images.shape					,		(
                   self.image_processor_tester.batch_size,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)




