


"""simple docstring"""







import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor






class    __A       ( unittest.TestCase    ):






 """simple docstring"""




 def __init__(	self				,					__A				,					__A=7				,					__A=3				,					__A=30				,					__A=400				,					__A=True				,					__A=None				,					__A=True				,					__A=[0.5, 0.5, 0.5]				,					__A=[0.5, 0.5, 0.5]				,					__A=False				,					)      ->  List[str]:
     a					=size if size is not None else {'''height''': 30, '''width''': 30}
     a					=parent
     a					=batch_size
     a					=num_channels
     a					=min_resolution
     a					=max_resolution
     a					=do_resize
     a					=size
     a					=do_normalize
     a					=image_mean
     a					=image_std
     a					=do_reduce_labels





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
     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_reduce_labels": self.do_reduce_labels,
     }

def _A		(		):







    """simple docstring"""
    a					=load_dataset('''hf-internal-testing/fixtures_ade20k''' ,					split='''test'''	)

    a					=Image.open(dataset[0]['''file''']	)
    a					=Image.open(dataset[1]['''file''']	)

    return image, map
def _A		(		):







    """simple docstring"""
    a					=load_dataset('''hf-internal-testing/fixtures_ade20k''' ,					split='''test'''	)

    a					=Image.open(dataset[0]['''file''']	)
    a					=Image.open(dataset[1]['''file''']	)
    a					=Image.open(dataset[2]['''file''']	)
    a					=Image.open(dataset[3]['''file''']	)

    return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class    __A       ( _SCREAMING_SNAKE_CASE,					unittest.TestCase    ):






 """simple docstring"""




 __lowerCAmelCase				      =						SegformerImageProcessor if is_vision_available() else None


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
     a					=SegformerImageProcessingTester(self		)


 @property
 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
     return self.image_processor_tester.prepare_image_processor_dict()


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
     a					=self.image_processing_class(**self.image_processor_dict		)
     self.assertTrue(hasattr(__A				,					'''do_resize'''		)		)
     self.assertTrue(hasattr(__A				,					'''size'''		)		)
     self.assertTrue(hasattr(__A				,					'''do_normalize'''		)		)
     self.assertTrue(hasattr(__A				,					'''image_mean'''		)		)
     self.assertTrue(hasattr(__A				,					'''image_std'''		)		)
     self.assertTrue(hasattr(__A				,					'''do_reduce_labels'''		)		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
     a					=self.image_processing_class.from_dict(self.image_processor_dict		)
     self.assertEqual(image_processor.size				,					{'''height''': 30, '''width''': 30}		)
     self.assertEqual(image_processor.do_reduce_labels				,					__A		)

     a					=self.image_processing_class.from_dict(self.image_processor_dict				,					size=42				,					reduce_labels=__A		)
     self.assertEqual(image_processor.size				,					{'''height''': 42, '''width''': 42}		)
     self.assertEqual(image_processor.do_reduce_labels				,					__A		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
     pass


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
     # Initialize image_processing
     a					=self.image_processing_class(**self.image_processor_dict		)
     # create random PIL images
     a					=prepare_image_inputs(self.image_processor_tester				,					equal_resolution=__A		)
     for image in image_inputs:
         self.assertIsInstance(__A				,					Image.Image		)

     # Test not batched input
     a					=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['''height'''],
             self.image_processor_tester.size['''width'''],
         )				,					)

     # Test batched
     a					=image_processing(__A				,					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    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
     # Initialize image_processing
     a					=self.image_processing_class(**self.image_processor_dict		)
     # create random numpy tensors
     a					=prepare_image_inputs(self.image_processor_tester				,					equal_resolution=__A				,					numpify=__A		)
     for image in image_inputs:
         self.assertIsInstance(__A				,					np.ndarray		)

     # Test not batched input
     a					=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['''height'''],
             self.image_processor_tester.size['''width'''],
         )				,					)

     # Test batched
     a					=image_processing(__A				,					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    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
     # Initialize image_processing
     a					=self.image_processing_class(**self.image_processor_dict		)
     # create random PyTorch tensors
     a					=prepare_image_inputs(self.image_processor_tester				,					equal_resolution=__A				,					torchify=__A		)
     for image in image_inputs:
         self.assertIsInstance(__A				,					torch.Tensor		)

     # Test not batched input
     a					=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['''height'''],
             self.image_processor_tester.size['''width'''],
         )				,					)

     # Test batched
     a					=image_processing(__A				,					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    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
     # Initialize image_processing
     a					=self.image_processing_class(**self.image_processor_dict		)
     # create random PyTorch tensors
     a					=prepare_image_inputs(self.image_processor_tester				,					equal_resolution=__A				,					torchify=__A		)
     a					=[]
     for image in image_inputs:
         self.assertIsInstance(__A				,					torch.Tensor		)
         maps.append(torch.zeros(image.shape[-2:]		).long()		)

     # Test not batched input
     a					=image_processing(image_inputs[0]				,					maps[0]				,					return_tensors='''pt'''		)
     self.assertEqual(
         encoding['''pixel_values'''].shape				,					(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         )				,					)
     self.assertEqual(
         encoding['''labels'''].shape				,					(
             1,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         )				,					)
     self.assertEqual(encoding['''labels'''].dtype				,					torch.long		)
     self.assertTrue(encoding['''labels'''].min().item() >= 0		)
     self.assertTrue(encoding['''labels'''].max().item() <= 255		)

     # Test batched
     a					=image_processing(__A				,					__A				,					return_tensors='''pt'''		)
     self.assertEqual(
         encoding['''pixel_values'''].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'''],
         )				,					)
     self.assertEqual(
         encoding['''labels'''].shape				,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         )				,					)
     self.assertEqual(encoding['''labels'''].dtype				,					torch.long		)
     self.assertTrue(encoding['''labels'''].min().item() >= 0		)
     self.assertTrue(encoding['''labels'''].max().item() <= 255		)

     # Test not batched input (PIL images)
     a	,   a					=prepare_semantic_single_inputs()

     a					=image_processing(__A				,					__A				,					return_tensors='''pt'''		)
     self.assertEqual(
         encoding['''pixel_values'''].shape				,					(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         )				,					)
     self.assertEqual(
         encoding['''labels'''].shape				,					(
             1,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         )				,					)
     self.assertEqual(encoding['''labels'''].dtype				,					torch.long		)
     self.assertTrue(encoding['''labels'''].min().item() >= 0		)
     self.assertTrue(encoding['''labels'''].max().item() <= 255		)

     # Test batched input (PIL images)
     a	,   a					=prepare_semantic_batch_inputs()

     a					=image_processing(__A				,					__A				,					return_tensors='''pt'''		)
     self.assertEqual(
         encoding['''pixel_values'''].shape				,					(
             2,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         )				,					)
     self.assertEqual(
         encoding['''labels'''].shape				,					(
             2,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         )				,					)
     self.assertEqual(encoding['''labels'''].dtype				,					torch.long		)
     self.assertTrue(encoding['''labels'''].min().item() >= 0		)
     self.assertTrue(encoding['''labels'''].max().item() <= 255		)





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
     # Initialize image_processing
     a					=self.image_processing_class(**self.image_processor_dict		)

     # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
     a	,   a					=prepare_semantic_single_inputs()
     a					=image_processing(__A				,					__A				,					return_tensors='''pt'''		)
     self.assertTrue(encoding['''labels'''].min().item() >= 0		)
     self.assertTrue(encoding['''labels'''].max().item() <= 150		)

     a					=True
     a					=image_processing(__A				,					__A				,					return_tensors='''pt'''		)
     self.assertTrue(encoding['''labels'''].min().item() >= 0		)
     self.assertTrue(encoding['''labels'''].max().item() <= 255		)



"""simple docstring"""







from cva import destroyAllWindows, imread, imshow, waitKey
def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    a	,   a					=img.shape[0], img.shape[1]

    def to_grayscale(lowercase ,					lowercase ,					lowercase	):
        return 0.2126 * red + 0.587 * green + 0.114 * blue

    def normalize(lowercase	):
        return min(lowercase ,					2_55	)

    for i in range(lowercase	):
        for j in range(lowercase	):
            a					=int(to_grayscale(*img[i][j]	)	)
            a					=[
                normalize(lowercase	),
                normalize(greyscale + factor	),
                normalize(greyscale + 2 * factor	),
            ]

    return img


if __name__ == "__main__":
       # read original image
       lowerCamelCase_     :     Any    		=						{
           percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
       }

       for percentage, img in images.items():
              make_sepia(img, percentage)

       for percentage, img in images.items():
              imshow(F'Original image with sepia (factor: {percentage})', img)

       waitKey(0)
       destroyAllWindows()