





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor

class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def __init__(self       :      Tuple				,			a__       :      int				,			a__       :      Any=7				,			a__       :      Any=3				,			a__       :      Union[str, Any]=18				,			a__       :      str=30				,			a__       :      Optional[int]=400				,			a__       :      Any=True				,			a__       :      Tuple=None				,			a__       :      str=True				,			a__       :      str=[0.5, 0.5, 0.5]				,			a__       :      Any=[0.5, 0.5, 0.5]				,			):





         """simple docstring"""



         __snake_case							       =   size if size is not None else {'''height''': 18, '''width''': 18}
         __snake_case							       =   parent
         __snake_case							       =   batch_size
         __snake_case							       =   num_channels
         __snake_case							       =   image_size
         __snake_case							       =   min_resolution
         __snake_case							       =   max_resolution
         __snake_case							       =   do_resize
         __snake_case							       =   size
         __snake_case							       =   do_normalize
         __snake_case							       =   image_mean
         __snake_case							       =   image_std


      def    a (self       :      str      ):





         """simple docstring"""



         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }






@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[str]							=    DPTImageProcessor if is_vision_available() else None


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   DPTImageProcessingTester(self      )


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(a__				,			'''image_mean'''      )      )
         self.assertTrue(hasattr(a__				,			'''image_std'''      )      )
         self.assertTrue(hasattr(a__				,			'''do_normalize'''      )      )
         self.assertTrue(hasattr(a__				,			'''do_resize'''      )      )
         self.assertTrue(hasattr(a__				,			'''size'''      )      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   self.image_processing_class.from_dict(self.image_processor_dict      )
         self.assertEqual(image_processor.size				,			{'''height''': 18, '''width''': 18}      )

         __snake_case							       =   self.image_processing_class.from_dict(self.image_processor_dict				,			size=42      )
         self.assertEqual(image_processor.size				,			{'''height''': 42, '''width''': 42}      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



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

         # Test not batched input
         __snake_case							       =   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
         __snake_case							       =   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    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   self.image_processing_class(**self.image_processor_dict      )
         # create random numpy tensors
         __snake_case							       =   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
         __snake_case							       =   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
         __snake_case							       =   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    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   self.image_processing_class(**self.image_processor_dict      )
         # create random PyTorch tensors
         __snake_case							       =   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
         __snake_case							       =   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
         __snake_case							       =   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'''],
             )				,			)








from __future__ import annotations






def lowerCamelCase__    (							snake_case_	:  list[int]       ,      snake_case_	:  int       )      ->				list[list[int]]:
   __snake_case							       =   []
   __snake_case							       =   []
   __snake_case							       =   0
   __snake_case							       =   sum(snake_case_       )
   create_state_space_tree(snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       )
   return result






def lowerCamelCase__    (							snake_case_	:  list[int]       ,      snake_case_	:  int       ,      snake_case_	:  int       ,      snake_case_	:  list[int]       ,      snake_case_	:  list[list[int]]       ,      snake_case_	:  int       ,      )      ->				None:
   if sum(snake_case_       ) > max_sum or (remaining_nums_sum + sum(snake_case_       )) < max_sum:
      return
   if sum(snake_case_       ) == max_sum:
      result.append(snake_case_       )
      return
   for index in range(snake_case_       ,      len(snake_case_       )       ):
      create_state_space_tree(
          snake_case_       ,      snake_case_       ,      index + 1       ,      [*path, nums[index]]       ,      snake_case_       ,      remaining_nums_sum - nums[index]       ,      )
snake_case_				=			[3, 34, 4, 12, 5, 2]
snake_case_				=			9
snake_case_				=			generate_sum_of_subsets_soln(nums, max_sum)







print(*result)

