




"""simple docstring"""






def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			List[str]    =    [1]
				for i in range(2							,    snake_case_		):
								factorials.append(factorials[-1] * i		)
				assert 0 <= k < factorials[-1] * n, "k out of bounds"

				_lowerCamelCase     :			str    =    []
				_lowerCamelCase     :			Dict    =    list(range(snake_case_		)		)

				# Find permutation
				while factorials:
								_lowerCamelCase     :			Optional[Any]    =    factorials.pop()
								_lowerCamelCase     :			Dict    =    divmod(snake_case_							,    snake_case_		)
								permutation.append(elements[number]		)
								elements.remove(elements[number]		)
				permutation.append(elements[0]		)

				return permutation


if __name__ == "__main__":
			import doctest

			doctest.testmod()






"""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:
       a_ =			False

if is_vision_available():
       from PIL import Image

       from transformers import PixaStructImageProcessor






class 		__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					,						):



     '''simple docstring'''




     __A    :						str						 =     size if size is not None else {'''height''': 20, '''width''': 20}
     __A    :						Dict						 =     parent
     __A    :						Union[str, Any]						 =     batch_size
     __A    :						List[Any]						 =     num_channels
     __A    :						Union[str, Any]						 =     image_size
     __A    :						Any						 =     min_resolution
     __A    :						str						 =     max_resolution
     __A    :						Any						 =     size
     __A    :						Dict						 =     do_normalize
     __A    :						str						 =     do_convert_rgb
     __A    :						List[Any]						 =     [512, 1024, 2048, 4096]
     __A    :						Dict						 =     patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[str]						 =     '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
     __A    :						Optional[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 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





 """simple docstring"""


 _lowerCamelCase        =     PixaStructImageProcessor if is_vision_available() else None

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     PixaStructImageProcessingTester(self  )

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						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  ):



     '''simple docstring'''




     __A    :						Tuple						 =     self.image_processor_tester.prepare_dummy_image()

     __A    :						Optional[int]						 =     self.image_processing_class(**self.image_processor_dict  )
     __A    :						Dict						 =     2048

     __A    :						Any						 =     image_processor(__lowerCamelCase					,						return_tensors='''pt'''					,						max_patches=__lowerCamelCase  )
     self.assertTrue(torch.allclose(inputs.flattened_patches.mean()					,						torch.tensor(0.0_6_0_6  )					,						atol=1e-3					,						rtol=1e-3  )  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A    :						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
     __A    :						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
         __A    :						Dict						 =     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
         __A    :						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  ):



     '''simple docstring'''




     __A    :						List[str]						 =     self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A    :						Union[str, Any]						 =     prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__lowerCamelCase  )
     for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase					,						Image.Image  )

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

     __A    :						str						 =     True

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

         __A    :						Any						 =     '''Hello'''

         __A    :						Tuple						 =     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
         __A    :						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  ):



     '''simple docstring'''




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

     __A    :						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
         __A    :						List[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
         __A    :						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)					,						)







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A    :						Optional[Any]						 =     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
     __A    :						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
         __A    :						List[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
         __A    :						Dict						 =     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 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





 """simple docstring"""


 _lowerCamelCase        =     PixaStructImageProcessor if is_vision_available() else None

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     PixaStructImageProcessingTester(self					,						num_channels=4  )
     __A    :						Any						 =     3

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Union[str, 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  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A    :						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
     __A    :						List[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
         __A    :						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
         __A    :						int						 =     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)					,						)




