





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 SwinaSRImageProcessor
      from transformers.image_transforms import get_image_size







class        __snake_case			(				unittest.TestCase     ):





       '''simple docstring'''







       def __init__(    self					:       Optional[Any]				,							A					:       Optional[Any]				,							A					:       Dict=7				,							A					:       Tuple=3				,							A					:       int=18				,							A					:       Optional[int]=30				,							A					:       Dict=400				,							A					:       List[Any]=True				,							A					:       Optional[Any]=1 / 255				,							A					:       List[Any]=True				,							A					:       Any=8				,							):
         __snake_case:		int					    =		parent
         __snake_case:		Dict					    =		batch_size
         __snake_case:		Any					    =		num_channels
         __snake_case:		Tuple					    =		image_size
         __snake_case:		Tuple					    =		min_resolution
         __snake_case:		Optional[Any]					    =		max_resolution
         __snake_case:		int					    =		do_rescale
         __snake_case:		List[Any]					    =		rescale_factor
         __snake_case:		Optional[int]					    =		do_pad
         __snake_case:		Any					    =		pad_size




       def 				UpperCAmelCase__					(    self					:       str						):
         return {
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
             "pad_size": self.pad_size,
         }






       def 				UpperCAmelCase__					(    self					:       int				,							A					:       Optional[int]=False				,							A					:       Optional[Any]=False				,							A					:       Optional[Any]=False						):
         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

         if equal_resolution:
           __snake_case:		int					    =		[]
           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:
           __snake_case:		Tuple					    =		[]
           for i in range(self.batch_size						):
             __snake_case:		Dict					    =		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
           __snake_case:		List[Any]					    =		[Image.fromarray(np.moveaxis(_lowerCAmelCase				,							0				,							-1						)						) for x in image_inputs]

         if torchify:
           __snake_case:		int					    =		[torch.from_numpy(_lowerCAmelCase						) for x in image_inputs]

         return image_inputs







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





       '''simple docstring'''







       lowerCAmelCase__ 						=	SwinaSRImageProcessor if is_vision_available() else None




       def 				UpperCAmelCase__					(    self					:       Tuple						):
         __snake_case:		int					    =		SwinaSRImageProcessingTester(self						)




       @property
       def 				UpperCAmelCase__					(    self					:       str						):
         return self.image_processor_tester.prepare_image_processor_dict()




       def 				UpperCAmelCase__					(    self					:       Any						):
         __snake_case:		List[Any]					    =		self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(_lowerCAmelCase				,							"""do_rescale"""						)						)
         self.assertTrue(hasattr(_lowerCAmelCase				,							"""rescale_factor"""						)						)
         self.assertTrue(hasattr(_lowerCAmelCase				,							"""do_pad"""						)						)
         self.assertTrue(hasattr(_lowerCAmelCase				,							"""pad_size"""						)						)




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         pass




       def 				UpperCAmelCase__					(    self					:       List[str]				,							A					:       List[str]						):
         __snake_case:		Optional[Any]					    =		get_image_size(_lowerCAmelCase						)
         __snake_case:		int					    =		self.image_processor_tester.pad_size

         __snake_case:		Optional[int]					    =		(old_height // size + 1) * size - old_height
         __snake_case:		str					    =		(old_width // size + 1) * size - old_width
         return old_height + pad_height, old_width + pad_width




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         # Initialize image_processor
         __snake_case:		Union[str, Any]					    =		self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         __snake_case:		List[str]					    =		self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase						)
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase				,							Image.Image						)

         # Test not batched input
         __snake_case:		Any					    =		image_processor(image_inputs[0]				,							return_tensors="""pt"""						).pixel_values
         __snake_case:		int					    =		self.calculate_expected_size(np.array(image_inputs[0]						)						)
         self.assertEqual(
             encoded_images.shape				,							(
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )				,							)




       def 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         # Initialize image_processor
         __snake_case:		int					    =		self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         __snake_case:		Dict					    =		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
         __snake_case:		List[str]					    =		image_processor(image_inputs[0]				,							return_tensors="""pt"""						).pixel_values
         __snake_case:		Optional[Any]					    =		self.calculate_expected_size(image_inputs[0]						)
         self.assertEqual(
             encoded_images.shape				,							(
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )				,							)






       def 				UpperCAmelCase__					(    self					:       Tuple						):
         # Initialize image_processor
         __snake_case:		List[Any]					    =		self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         __snake_case:		Union[str, Any]					    =		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
         __snake_case:		Optional[Any]					    =		image_processor(image_inputs[0]				,							return_tensors="""pt"""						).pixel_values
         __snake_case:		List[Any]					    =		self.calculate_expected_size(image_inputs[0]						)
         self.assertEqual(
             encoded_images.shape				,							(
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )				,							)












import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image




class  __lowerCAmelCase							(				unittest.TestCase   ,			lowerCAmelCase):






       def        SCREAMING_SNAKE_CASE     ( self:       Dict					):
         lowercase     :Any   						=    load_tool("image-segmentation"					)
         self.tool.setup()
         lowercase     :int   						=    load_tool("image-segmentation"							,       remote=_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       str					):
         lowercase     :Any   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :Dict   						=    self.tool(_lowerCAmelCase							,       "cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]					):
         lowercase     :Dict   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :List[Any]   						=    self.remote_tool(_lowerCAmelCase							,       "cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]					):
         lowercase     :Optional[Any]   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :Dict   						=    self.tool(image=_lowerCAmelCase							,       label="cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       Tuple					):
         lowercase     :int   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :List[str]   						=    self.remote_tool(image=_lowerCAmelCase							,       label="cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)



