


"""simple docstring"""


import unittest

import numpy as np
import requests

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

   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   __snake_case    					=					False

if is_vision_available():
   from PIL import Image

   from transformers import PixaStructImageProcessor

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=7 ,     lowerCamelCase__=3 ,     lowerCamelCase__=18 ,     lowerCamelCase__=30 ,     lowerCamelCase__=400 ,     lowerCamelCase__=None ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=None ,     )      ->							Union[str, Any]:
    lowercase__       :       Union[str, Any]				=						size if size is not None else {"""height""": 20, """width""": 20}
    lowercase__       :       List[Any]				=						parent
    lowercase__       :       Union[str, Any]				=						batch_size
    lowercase__       :       Union[str, Any]				=						num_channels
    lowercase__       :       Dict				=						image_size
    lowercase__       :       Tuple				=						min_resolution
    lowercase__       :       Union[str, Any]				=						max_resolution
    lowercase__       :       Optional[int]				=						size
    lowercase__       :       Union[str, Any]				=						do_normalize
    lowercase__       :       Optional[Any]				=						do_convert_rgb
    lowercase__       :       int				=						[512, 1024, 2048, 4096]
    lowercase__       :       List[Any]				=						patch_size if patch_size is not None else {"""height""": 16, """width""": 16}


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    lowercase__       :       int				=						"""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
    lowercase__       :       List[Any]				=						Image.open(requests.get(lowerCamelCase__ ,     stream=lowerCamelCase__						).raw						).convert("""RGB"""						)
    return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,   reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''     ,   )
@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Optional[Any]									=    PixaStructImageProcessor if is_vision_available() else None


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Any				=						PixaStructImageProcessingTester(self						)


   @property
   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    return self.image_processor_tester.prepare_image_processor_dict()


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    lowercase__       :       str				=						self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_normalize"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_convert_rgb"""						)						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       int				=						self.image_processor_tester.prepare_dummy_image()

    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    lowercase__       :       List[Any]				=						2048

    lowercase__       :       Dict				=						image_processor(lowerCamelCase__ ,     return_tensors="""pt""" ,     max_patches=lowerCamelCase__						)
    self.assertTrue(torch.allclose(inputs.flattened_patches.mean() ,     torch.tensor(0.0606						) ,     atol=1E-3 ,     rtol=1E-3						)						)


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    # Initialize image_processor
    lowercase__       :       Dict				=						self.image_processing_class(**self.image_processor_dict						)
    # create random PIL images
    lowercase__       :       int				=						prepare_image_inputs(self.image_processor_tester ,     equal_resolution=lowerCamelCase__						)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase__ ,     Image.Image						)

    # Test not batched input
    lowercase__       :       List[Any]				=						(
        (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
        * self.image_processor_tester.num_channels
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
     # Test not batched input
     lowercase__       :       Union[str, Any]				=						image_processor(
         image_inputs[0] ,     return_tensors="""pt""" ,     max_patches=lowerCamelCase__						).flattened_patches
     self.assertEqual(
         encoded_images.shape ,     (1, max_patch, expected_hidden_dim) ,     )

     # Test batched
     lowercase__       :       Tuple				=						image_processor(
         lowerCamelCase__ ,     return_tensors="""pt""" ,     max_patches=lowerCamelCase__						).flattened_patches
     self.assertEqual(
         encoded_images.shape ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,     )


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    # Initialize image_processor
    lowercase__       :       Any				=						self.image_processing_class(**self.image_processor_dict						)
    # create random PIL images
    lowercase__       :       Dict				=						prepare_image_inputs(self.image_processor_tester ,     equal_resolution=lowerCamelCase__						)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase__ ,     Image.Image						)

    # Test not batched input
    lowercase__       :       Optional[int]				=						(
        (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
        * self.image_processor_tester.num_channels
    ) + 2

    lowercase__       :       Optional[int]				=						True

    for max_patch in self.image_processor_tester.max_patches:
     # Test not batched input
     with self.assertRaises(lowerCamelCase__						):
      lowercase__       :       List[Any]				=						image_processor(
          image_inputs[0] ,     return_tensors="""pt""" ,     max_patches=lowerCamelCase__						).flattened_patches

     lowercase__       :       List[Any]				=						"""Hello"""

     lowercase__       :       Any				=						image_processor(
         image_inputs[0] ,     return_tensors="""pt""" ,     max_patches=lowerCamelCase__ ,     header_text=lowerCamelCase__						).flattened_patches
     self.assertEqual(
         encoded_images.shape ,     (1, max_patch, expected_hidden_dim) ,     )

     # Test batched
     lowercase__       :       Union[str, Any]				=						image_processor(
         lowerCamelCase__ ,     return_tensors="""pt""" ,     max_patches=lowerCamelCase__ ,     header_text=lowerCamelCase__						).flattened_patches
     self.assertEqual(
         encoded_images.shape ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,     )


   def 					UpperCAmelCase__(						self						)      ->							Any:
    # Initialize image_processor
    lowercase__       :       str				=						self.image_processing_class(**self.image_processor_dict						)
    # create random numpy tensors
    lowercase__       :       Any				=						prepare_image_inputs(self.image_processor_tester ,     equal_resolution=lowerCamelCase__ ,     numpify=lowerCamelCase__						)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase__ ,     np.ndarray						)

    lowercase__       :       List[str]				=						(
        (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
        * self.image_processor_tester.num_channels
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
     # Test not batched input
     lowercase__       :       Optional[Any]				=						image_processor(
         image_inputs[0] ,     return_tensors="""pt""" ,     max_patches=lowerCamelCase__						).flattened_patches
     self.assertEqual(
         encoded_images.shape ,     (1, max_patch, expected_hidden_dim) ,     )

     # Test batched
     lowercase__       :       Any				=						image_processor(
         lowerCamelCase__ ,     return_tensors="""pt""" ,     max_patches=lowerCamelCase__						).flattened_patches
     self.assertEqual(
         encoded_images.shape ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,     )







   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    # Initialize image_processor
    lowercase__       :       Any				=						self.image_processing_class(**self.image_processor_dict						)
    # create random PyTorch tensors
    lowercase__       :       str				=						prepare_image_inputs(self.image_processor_tester ,     equal_resolution=lowerCamelCase__ ,     torchify=lowerCamelCase__						)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase__ ,     torch.Tensor						)

    # Test not batched input
    lowercase__       :       Optional[Any]				=						(
        (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
        * self.image_processor_tester.num_channels
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
     # Test not batched input
     lowercase__       :       Optional[int]				=						image_processor(
         image_inputs[0] ,     return_tensors="""pt""" ,     max_patches=lowerCamelCase__						).flattened_patches
     self.assertEqual(
         encoded_images.shape ,     (1, max_patch, expected_hidden_dim) ,     )

     # Test batched
     lowercase__       :       Union[str, Any]				=						image_processor(
         lowerCamelCase__ ,     return_tensors="""pt""" ,     max_patches=lowerCamelCase__						).flattened_patches
     self.assertEqual(
         encoded_images.shape ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,     )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,   reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''     ,   )
@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Any									=    PixaStructImageProcessor if is_vision_available() else None


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						PixaStructImageProcessingTester(self ,     num_channels=4						)
    lowercase__       :       List[str]				=						3


   @property
   def 					UpperCAmelCase__(						self						)      ->							int:
    return self.image_processor_tester.prepare_image_processor_dict()


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    lowercase__       :       Optional[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_normalize"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_convert_rgb"""						)						)







   def 					UpperCAmelCase__(						self						)      ->							Dict:
    # Initialize image_processor
    lowercase__       :       Optional[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    # create random PIL images
    lowercase__       :       str				=						prepare_image_inputs(self.image_processor_tester ,     equal_resolution=lowerCamelCase__						)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase__ ,     Image.Image						)

    # Test not batched input
    lowercase__       :       Dict				=						(
        (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
        * (self.image_processor_tester.num_channels - 1)
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
     # Test not batched input
     lowercase__       :       str				=						image_processor(
         image_inputs[0] ,     return_tensors="""pt""" ,     max_patches=lowerCamelCase__						).flattened_patches
     self.assertEqual(
         encoded_images.shape ,     (1, max_patch, expected_hidden_dim) ,     )

     # Test batched
     lowercase__       :       str				=						image_processor(
         lowerCamelCase__ ,     return_tensors="""pt""" ,     max_patches=lowerCamelCase__						).flattened_patches
     self.assertEqual(
         encoded_images.shape ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,     )



"""simple docstring"""


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[Any]  ):
 lowercase__       :       List[str]				=						len(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						sum(lowerCamelCase__  )

 lowercase__       :       Optional[int]				=						[[False for x in range(s + 1  )] for y in range(n + 1  )]

 for i in range(1       ,		n + 1  ):
  lowercase__       :       int				=						True

 for i in range(1       ,		s + 1  ):
  lowercase__       :       int				=						False

 for i in range(1       ,		n + 1  ):
  for j in range(1       ,		s + 1  ):
   lowercase__       :       Optional[Any]				=						dp[i][j - 1]

   if arr[i - 1] <= j:
    lowercase__       :       int				=						dp[i][j] or dp[i - 1][j - arr[i - 1]]

 for j in range(int(s / 2  )       ,		-1       ,		-1  ):
  if dp[n][j] is True:
   lowercase__       :       List[Any]				=						s - 2 * j
   break

 return diff