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  ):



 '''simple docstring'''

 def __init__(     self    :						List[Any]    ,     lowerCamelCase    :						Optional[Any]    ,     lowerCamelCase    :						Any=7    ,     lowerCamelCase    :						Union[str, Any]=3    ,     lowerCamelCase    :						Dict=1_8    ,     lowerCamelCase    :						Any=3_0    ,     lowerCamelCase    :						Any=4_0_0    ,     lowerCamelCase    :						Optional[Any]=True    ,     lowerCamelCase    :						List[str]=1 / 2_5_5    ,     lowerCamelCase    :						Any=True    ,     lowerCamelCase    :						Tuple=8    ,     )	->							int:
        _UpperCamelCase       :    Optional[int]					  =					parent
        _UpperCamelCase       :    Optional[int]					  =					batch_size
        _UpperCamelCase       :    List[str]					  =					num_channels
        _UpperCamelCase       :    Optional[int]					  =					image_size
        _UpperCamelCase       :    Tuple					  =					min_resolution
        _UpperCamelCase       :    List[Any]					  =					max_resolution
        _UpperCamelCase       :    List[str]					  =					do_rescale
        _UpperCamelCase       :    Dict					  =					rescale_factor
        _UpperCamelCase       :    List[str]					  =					do_pad
        _UpperCamelCase       :    str					  =					pad_size

 def    __snake_case   (     self    :						Any					)	->							List[str]:
        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    :						Optional[int]    ,     lowerCamelCase    :						int=False    ,     lowerCamelCase    :						Optional[int]=False    ,     lowerCamelCase    :						Union[str, Any]=False					)	->							List[str]:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
               _UpperCamelCase       :    Tuple					  =					[]
               for i in range(self.batch_size					):
                      image_inputs.append(
                          np.random.randint(
                              2_5_5    ,     size=(self.num_channels, self.max_resolution, self.max_resolution)    ,     dtype=np.uinta					)					)
        else:
               _UpperCamelCase       :    Tuple					  =					[]
               for i in range(self.batch_size					):
                      _UpperCamelCase  , _UpperCamelCase       :    Union[str, Any]					  =					np.random.choice(np.arange(self.min_resolution    ,     self.max_resolution					)    ,     2					)
                      image_inputs.append(np.random.randint(2_5_5    ,     size=(self.num_channels, width, height)    ,     dtype=np.uinta					)					)

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

        if torchify:
               _UpperCamelCase       :    Dict					  =					[torch.from_numpy(lowerCamelCase					) for x in image_inputs]

        return image_inputs







@require_torch
@require_vision
class 				__lowerCAmelCase					(      lowercase_,		unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		Tuple			      =	SwinaSRImageProcessor if is_vision_available() else None

 def    __snake_case   (     self    :						Dict					)	->							str:
        _UpperCamelCase       :    Optional[Any]					  =					SwinaSRImageProcessingTester(self					)

 @property
 def    __snake_case   (     self    :						Dict					)	->							Optional[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()

 def    __snake_case   (     self    :						Any					)	->							Dict:
        _UpperCamelCase       :    Union[str, 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    __snake_case   (     self    :						Optional[int]					)	->							str:
        pass

 def    __snake_case   (     self    :						List[Any]    ,     lowerCamelCase    :						str					)	->							Optional[int]:
        _UpperCamelCase  , _UpperCamelCase       :    str					  =					get_image_size(lowerCamelCase					)
        _UpperCamelCase       :    Any					  =					self.image_processor_tester.pad_size

        _UpperCamelCase       :    str					  =					(old_height // size + 1) * size - old_height
        _UpperCamelCase       :    Union[str, Any]					  =					(old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width

 def    __snake_case   (     self    :						Optional[Any]					)	->							Optional[Any]:
        _UpperCamelCase       :    Union[str, Any]					  =					self.image_processing_class(**self.image_processor_dict					)
        # create random PIL images
        _UpperCamelCase       :    Any					  =					self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase					)
        for image in image_inputs:
               self.assertIsInstance(lowerCamelCase    ,     Image.Image					)

        # Test not batched input
        _UpperCamelCase       :    int					  =					image_processor(image_inputs[0]    ,     return_tensors='pt'					).pixel_values
        _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					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    :						str					)	->							Tuple:
        _UpperCamelCase       :    Union[str, Any]					  =					self.image_processing_class(**self.image_processor_dict					)
        # create random numpy tensors
        _UpperCamelCase       :    Tuple					  =					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
        _UpperCamelCase       :    Dict					  =					image_processor(image_inputs[0]    ,     return_tensors='pt'					).pixel_values
        _UpperCamelCase  , _UpperCamelCase       :    int					  =					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    :						Optional[Any]					)	->							Union[str, Any]:
        _UpperCamelCase       :    str					  =					self.image_processing_class(**self.image_processor_dict					)
        # create random PyTorch tensors
        _UpperCamelCase       :    Tuple					  =					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
        _UpperCamelCase       :    Dict					  =					image_processor(image_inputs[0]    ,     return_tensors='pt'					).pixel_values
        _UpperCamelCase  , _UpperCamelCase       :    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 SCREAMING_SNAKE_CASE			(       lowercase_:		int = 4000000			):
       _UpperCamelCase       :    str					  =					1
       _UpperCamelCase       :    str					  =					2
       _UpperCamelCase       :    Tuple					  =					0
       while j <= n:
              if j % 2 == 0:
                     total += j
              _UpperCamelCase  , _UpperCamelCase       :    int					  =					j, i + j

       return total


if __name__ == "__main__":
 print(f'''{solution() = }''')



