





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:
    _UpperCAmelCase				:       Optional[Any]	   =				False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor

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







 def      A_	(      self  :      Union[str, Any]	)			->	Tuple:
     lowerCamelCase__    :      Dict					 =		'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
     lowerCamelCase__    :      Union[str, Any]					 =		Image.open(requests.get(UpperCAmelCase			,     stream=UpperCAmelCase	).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    lowerCAmelCase							(					__SCREAMING_SNAKE_CASE,  unittest.TestCase			):
 UpperCAmelCase__													=		PixaStructImageProcessor if is_vision_available() else None
 def      A_	(      self  :      str	)			->	Optional[Any]:
     lowerCamelCase__    :      List[str]					 =		PixaStructImageProcessingTester(self	)
 @property
 def      A_	(      self  :      str	)			->	List[Any]:
     return self.image_processor_tester.prepare_image_processor_dict()
 def      A_	(      self  :      Tuple	)			->	Optional[int]:
     lowerCamelCase__    :      Union[str, Any]					 =		self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'do_normalize'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'do_convert_rgb'	)	)
 def      A_	(      self  :      Any	)			->	Optional[int]:
     lowerCamelCase__    :      List[str]					 =		self.image_processor_tester.prepare_dummy_image()

     lowerCamelCase__    :      Optional[int]					 =		self.image_processing_class(**self.image_processor_dict	)
     lowerCamelCase__    :      str					 =		2048

     lowerCamelCase__    :      List[Any]					 =		image_processor(UpperCAmelCase			,     return_tensors='pt'			,     max_patches=UpperCAmelCase	)
     self.assertTrue(torch.allclose(inputs.flattened_patches.mean()			,     torch.tensor(0.0_6_0_6	)			,     atol=1e-3			,     rtol=1e-3	)	)
 def      A_	(      self  :      Optional[int]	)			->	str:
     # Initialize image_processor
     lowerCamelCase__    :      List[Any]					 =		self.image_processing_class(**self.image_processor_dict	)
     # create random PIL images
     lowerCamelCase__    :      Dict					 =		prepare_image_inputs(self.image_processor_tester			,     equal_resolution=UpperCAmelCase	)
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase			,     Image.Image	)

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

         # Test batched
         lowerCamelCase__    :      Optional[int]					 =		image_processor(
             UpperCAmelCase			,     return_tensors='pt'			,     max_patches=UpperCAmelCase	).flattened_patches
         self.assertEqual(
             encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )
 def      A_	(      self  :      Tuple	)			->	Dict:
     # Initialize image_processor
     lowerCamelCase__    :      Any					 =		self.image_processing_class(**self.image_processor_dict	)
     # create random PIL images
     lowerCamelCase__    :      List[str]					 =		prepare_image_inputs(self.image_processor_tester			,     equal_resolution=UpperCAmelCase	)
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase			,     Image.Image	)

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

     lowerCamelCase__    :      Dict					 =		True

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

         lowerCamelCase__    :      int					 =		'Hello'

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

         # Test batched
         lowerCamelCase__    :      str					 =		image_processor(
             UpperCAmelCase			,     return_tensors='pt'			,     max_patches=UpperCAmelCase			,     header_text=UpperCAmelCase	).flattened_patches
         self.assertEqual(
             encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )
 def      A_	(      self  :      Tuple	)			->	List[str]:
     # Initialize image_processor
     lowerCamelCase__    :      Optional[Any]					 =		self.image_processing_class(**self.image_processor_dict	)
     # create random numpy tensors
     lowerCamelCase__    :      List[Any]					 =		prepare_image_inputs(self.image_processor_tester			,     equal_resolution=UpperCAmelCase			,     numpify=UpperCAmelCase	)
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase			,     np.ndarray	)

     lowerCamelCase__    :      Optional[int]					 =		(
         (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
         lowerCamelCase__    :      Optional[Any]					 =		image_processor(
             image_inputs[0]			,     return_tensors='pt'			,     max_patches=UpperCAmelCase	).flattened_patches
         self.assertEqual(
             encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

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







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

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

         # Test batched
         lowerCamelCase__    :      Any					 =		image_processor(
             UpperCAmelCase			,     return_tensors='pt'			,     max_patches=UpperCAmelCase	).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    lowerCAmelCase							(					__SCREAMING_SNAKE_CASE,  unittest.TestCase			):
 UpperCAmelCase__													=		PixaStructImageProcessor if is_vision_available() else None
 def      A_	(      self  :      Union[str, Any]	)			->	str:
     lowerCamelCase__    :      List[Any]					 =		PixaStructImageProcessingTester(self			,     num_channels=4	)
     lowerCamelCase__    :      List[str]					 =		3
 @property
 def      A_	(      self  :      Dict	)			->	Optional[int]:
     return self.image_processor_tester.prepare_image_processor_dict()
 def      A_	(      self  :      Tuple	)			->	Tuple:
     lowerCamelCase__    :      str					 =		self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'do_normalize'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'do_convert_rgb'	)	)







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

     # Test not batched input
     lowerCamelCase__    :      Any					 =		(
         (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
         lowerCamelCase__    :      List[str]					 =		image_processor(
             image_inputs[0]			,     return_tensors='pt'			,     max_patches=UpperCAmelCase	).flattened_patches
         self.assertEqual(
             encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

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







def 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				if len(a_						) <= 1:
								return [tuple(a_						)]

				__A   			= []

				def generate(a_		,   a_						):
								if k == 1:
												res.append(tuple(arr[:]						)						)
												return

								generate(k - 1		,   a_						)

								for i in range(k - 1						):
												if k % 2 == 0:  # k is even
																__A			,							__A   			= arr[k - 1], arr[i]
												else:  # k is odd
																__A			,							__A   			= arr[k - 1], arr[0]
												generate(k - 1		,   a_						)

				generate(len(a_						)		,   a_						)
				return res


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:int							 =				input('Enter numbers separated by a comma:\n').strip()
					SCREAMING_SNAKE_CASE							:Dict							 =				[int(item) for item in user_input.split(',')]
					print(heaps(arr))
