





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, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor


class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



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





        '''simple docstring'''




        A_       :					Union[str, Any]        =      size if size is not None else {"shortest_edge": 20}
        A_       :					Union[str, Any]        =      crop_size if crop_size is not None else {"height": 18, "width": 18}
        A_       :					Optional[Any]        =      parent
        A_       :					str        =      batch_size
        A_       :					Any        =      num_channels
        A_       :					Dict        =      image_size
        A_       :					Dict        =      min_resolution
        A_       :					List[str]        =      max_resolution
        A_       :					str        =      do_resize
        A_       :					Tuple        =      size
        A_       :					List[str]        =      do_center_crop
        A_       :					List[str]        =      crop_size
        A_       :					Optional[Any]        =      do_flip_channel_order





     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_flip_channel_order": self.do_flip_channel_order,
        }




@require_torch
@require_vision
class        __magic_name__	(				lowerCamelCase__     ,					unittest.TestCase				):





     """simple docstring"""



     __UpperCamelCase            =  MobileViTImageProcessor if is_vision_available() else None



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





        '''simple docstring'''




        A_       :					List[Any]        =      MobileViTImageProcessingTester(self	)



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        A_       :					Tuple        =      self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(snake_case      ,   "do_resize"	)	)
        self.assertTrue(hasattr(snake_case      ,   "size"	)	)
        self.assertTrue(hasattr(snake_case      ,   "do_center_crop"	)	)
        self.assertTrue(hasattr(snake_case      ,   "center_crop"	)	)
        self.assertTrue(hasattr(snake_case      ,   "do_flip_channel_order"	)	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        A_       :					int        =      self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size      ,   {"shortest_edge": 20}	)
        self.assertEqual(image_processor.crop_size      ,   {"height": 18, "width": 18}	)

        A_       :					Optional[Any]        =      self.image_processing_class.from_dict(self.image_processor_dict      ,   size=42      ,   crop_size=84	)
        self.assertEqual(image_processor.size      ,   {"shortest_edge": 42}	)
        self.assertEqual(image_processor.crop_size      ,   {"height": 84, "width": 84}	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        pass



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




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

        # Test not batched input
        A_       :					Optional[int]        =      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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )      ,   )

        # Test batched
        A_       :					List[str]        =      image_processing(snake_case      ,   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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )      ,   )



     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




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

        # Test not batched input
        A_       :					Union[str, Any]        =      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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )      ,   )

        # Test batched
        A_       :					Union[str, Any]        =      image_processing(snake_case      ,   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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )      ,   )





     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




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

        # Test not batched input
        A_       :					Optional[Any]        =      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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )      ,   )

        # Test batched
        A_       :					Union[str, Any]        =      image_processing(snake_case      ,   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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )      ,   )







from __future__ import annotations

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int						)			->    list[list[int]]:
   A_       :					list[list[int]]        =      []
   A_       :					list[int]        =      []
   A_       :					Dict        =      0
   A_       :					str        =      sum(_lowerCAmelCase						)
   create_state_space_tree(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)
   return result

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





print(*result)
