


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   __lowerCamelCase (unittest.TestCase	):







       def __init__( self:   Tuple,A_:   Union[str, Any],A_:   Optional[int]=7,A_:   Tuple=3,A_:   Any=18,A_:   str=30,A_:   Tuple=400,A_:   Optional[Any]=True,A_:   Union[str, Any]=1 / 255,A_:   int=True,A_:   List[str]=8,):






            '''simple docstring'''




            __UpperCamelCase   = parent
            __UpperCamelCase   = batch_size
            __UpperCamelCase   = num_channels
            __UpperCamelCase   = image_size
            __UpperCamelCase   = min_resolution
            __UpperCamelCase   = max_resolution
            __UpperCamelCase   = do_rescale
            __UpperCamelCase   = rescale_factor
            __UpperCamelCase   = do_pad
            __UpperCamelCase   = pad_size







       def 		snake_case_  ( self:   Tuple				):






            '''simple docstring'''




            return {
                "do_rescale": self.do_rescale,
                "rescale_factor": self.rescale_factor,
                "do_pad": self.do_pad,
                "pad_size": self.pad_size,
            }







       def 		snake_case_  ( self:   int,A_:   Tuple=False,A_:   str=False,A_:   Dict=False				):






            '''simple docstring'''




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

            if equal_resolution:
                 __UpperCamelCase   = []
                 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:
                 __UpperCamelCase   = []
                 for i in range(self.batch_size				):
                      __UpperCamelCase,						__UpperCamelCase   = 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
                 __UpperCamelCase   = [Image.fromarray(np.moveaxis(A_,0,-1				)				) for x in image_inputs]

            if torchify:
                 __UpperCamelCase   = [torch.from_numpy(A_				) for x in image_inputs]

            return image_inputs



@require_torch
@require_vision
class   __lowerCamelCase (_a							,			unittest.TestCase	):
       _lowercase       							=    SwinaSRImageProcessor if is_vision_available() else None







       def 		snake_case_  ( self:   List[Any]				):






            '''simple docstring'''




            __UpperCamelCase   = SwinaSRImageProcessingTester(self				)







       @property
       def 		snake_case_  ( self:   Union[str, Any]				):






            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()







       def 		snake_case_  ( self:   Tuple				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            self.assertTrue(hasattr(A_,'do_rescale'				)				)
            self.assertTrue(hasattr(A_,'rescale_factor'				)				)
            self.assertTrue(hasattr(A_,'do_pad'				)				)
            self.assertTrue(hasattr(A_,'pad_size'				)				)







       def 		snake_case_  ( self:   str				):






            '''simple docstring'''




            pass







       def 		snake_case_  ( self:   Optional[int],A_:   List[Any]				):






            '''simple docstring'''




            __UpperCamelCase,						__UpperCamelCase   = get_image_size(A_				)
            __UpperCamelCase   = self.image_processor_tester.pad_size

            __UpperCamelCase   = (old_height // size + 1) * size - old_height
            __UpperCamelCase   = (old_width // size + 1) * size - old_width
            return old_height + pad_height, old_width + pad_width







       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




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

            # Test not batched input
            __UpperCamelCase   = image_processor(image_inputs[0],return_tensors='pt'				).pixel_values
            __UpperCamelCase,						__UpperCamelCase   = 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 		snake_case_  ( self:   Tuple				):






            '''simple docstring'''




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

            # Test not batched input
            __UpperCamelCase   = image_processor(image_inputs[0],return_tensors='pt'				).pixel_values
            __UpperCamelCase,						__UpperCamelCase   = self.calculate_expected_size(image_inputs[0]				)
            self.assertEqual(
                encoded_images.shape,(
                    1,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),)







       def 		snake_case_  ( self:   Union[str, Any]				):






            '''simple docstring'''




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

            # Test not batched input
            __UpperCamelCase   = image_processor(image_inputs[0],return_tensors='pt'				).pixel_values
            __UpperCamelCase,						__UpperCamelCase   = self.calculate_expected_size(image_inputs[0]				)
            self.assertEqual(
                encoded_images.shape,(
                    1,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),)






from math import cos, sin






def 		_A		(		_lowercase   )				->   list[list[float]]:


     """simple docstring"""


     __UpperCamelCase   = float(_lowercase   )
     return [[scaling_factor * int(x == y   ) for x in range(2   )] for y in range(2   )]






def 		_A		(		_lowercase   )				->   list[list[float]]:


     """simple docstring"""


     __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
     return [[c, -s], [s, c]]






def 		_A		(		_lowercase   )				->   list[list[float]]:


     """simple docstring"""


     __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
     __UpperCamelCase   = c * s
     return [[c * c, cs], [cs, s * s]]






def 		_A		(		_lowercase   )				->   list[list[float]]:


     """simple docstring"""


     __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
     __UpperCamelCase   = c * s
     return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(f"""    {scaling(5) = }""")
print(f"""  {rotation(4_5) = }""")
print(f"""{projection(4_5) = }""")


print(f"""{reflection(4_5) = }""")


