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      :						Union[str, Any]    ,					lowercase__      :						Any    ,					lowercase__      :						List[Any]=7    ,					lowercase__      :						List[str]=3    ,					lowercase__      :						str=30    ,					lowercase__      :						Optional[Any]=400    ,					lowercase__      :						Union[str, Any]=True    ,					lowercase__      :						Optional[int]=None    ,					lowercase__      :						int=True    ,					lowercase__      :						str=[0.5, 0.5, 0.5]    ,					lowercase__      :						List[Any]=[0.5, 0.5, 0.5]    ,					lowercase__      :						Tuple=False    ,					):







        '''simple docstring'''



        lowerCAmelCase__  			=      size if size is not None else {'height': 30, 'width': 30}
        lowerCAmelCase__  			=      parent
        lowerCAmelCase__  			=      batch_size
        lowerCAmelCase__  			=      num_channels
        lowerCAmelCase__  			=      min_resolution
        lowerCAmelCase__  			=      max_resolution
        lowerCAmelCase__  			=      do_resize
        lowerCAmelCase__  			=      size
        lowerCAmelCase__  			=      do_normalize
        lowerCAmelCase__  			=      image_mean
        lowerCAmelCase__  			=      image_std
        lowerCAmelCase__  			=      do_reduce_labels




 def 	__snake_case		(    self      :						Tuple):







        '''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_reduce_labels": self.do_reduce_labels,
        }






def    __lowerCamelCase	(		):
       lowerCAmelCase__  			=      load_dataset('hf-internal-testing/fixtures_ade20k'			,						split='test'			)

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

       return image, map






def    __lowerCamelCase	(		):
       lowerCAmelCase__  			=      load_dataset('hf-internal-testing/fixtures_ade20k'			,						split='test'			)

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

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



@require_torch
@require_vision
class        a_				(		SCREAMING_SNAKE_CASE ,  unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_							=      SegformerImageProcessor if is_vision_available() else None




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







        '''simple docstring'''



        lowerCAmelCase__  			=      SegformerImageProcessingTester(self)




 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()




 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase__    ,					'do_resize'))
        self.assertTrue(hasattr(lowercase__    ,					'size'))
        self.assertTrue(hasattr(lowercase__    ,					'do_normalize'))
        self.assertTrue(hasattr(lowercase__    ,					'image_mean'))
        self.assertTrue(hasattr(lowercase__    ,					'image_std'))
        self.assertTrue(hasattr(lowercase__    ,					'do_reduce_labels'))




 def 	__snake_case		(    self      :						Dict):







        '''simple docstring'''



        lowerCAmelCase__  			=      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    ,					lowercase__)

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




 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        pass




 def 	__snake_case		(    self      :						int):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        lowerCAmelCase__  			=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=lowercase__)
        for image in image_inputs:
               self.assertIsInstance(lowercase__    ,					Image.Image)

        # Test not batched input
        lowerCAmelCase__  			=      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
        lowerCAmelCase__  			=      image_processing(lowercase__    ,					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      :						Dict):







        '''simple docstring'''



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

        # Test not batched input
        lowerCAmelCase__  			=      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
        lowerCAmelCase__  			=      image_processing(lowercase__    ,					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'''



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

        # Test not batched input
        lowerCAmelCase__  			=      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
        lowerCAmelCase__  			=      image_processing(lowercase__    ,					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      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        lowerCAmelCase__  			=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=lowercase__    ,					torchify=lowercase__)
        lowerCAmelCase__  			=      []
        for image in image_inputs:
               self.assertIsInstance(lowercase__    ,					torch.Tensor)
               maps.append(torch.zeros(image.shape[-2:]).long())

        # Test not batched input
        lowerCAmelCase__  			=      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
        lowerCAmelCase__  			=      image_processing(lowercase__    ,					lowercase__    ,					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)
        lowerCAmelCase__			,      lowerCAmelCase__  			=      prepare_semantic_single_inputs()

        lowerCAmelCase__  			=      image_processing(lowercase__    ,					lowercase__    ,					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)
        lowerCAmelCase__			,      lowerCAmelCase__  			=      prepare_semantic_batch_inputs()

        lowerCAmelCase__  			=      image_processing(lowercase__    ,					lowercase__    ,					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 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



        lowerCAmelCase__  			=      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
        lowerCAmelCase__			,      lowerCAmelCase__  			=      prepare_semantic_single_inputs()
        lowerCAmelCase__  			=      image_processing(lowercase__    ,					lowercase__    ,					return_tensors='pt')
        self.assertTrue(encoding['labels'].min().item() >= 0)
        self.assertTrue(encoding['labels'].max().item() <= 150)

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

def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       if target == 0:
              answer.append(path.copy()			)
       else:
              for index in range(lowerCAmelCase__			,						len(lowerCAmelCase__			)			):
                     if target >= candidates[index]:
                            path.append(candidates[index]			)
                            backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						target - candidates[index]			,						lowerCAmelCase__			)
                            path.pop(len(lowerCAmelCase__			) - 1			)






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      []  # type: list[int]
       lowerCAmelCase__  			=      []  # type: list[int]
       backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						0			)
       return answer






def    __lowerCamelCase	(		):
       print(combination_sum([-8, 2.3, 0]			,						1			)			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
