





'''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__							:							Any =False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor

class      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 def __init__(       self       :							Optional[int]							,	__A       :							List[str]							,	__A       :							Tuple=7							,	__A       :							Optional[int]=3							,	__A       :							Optional[Any]=1_8							,	__A       :							Any=3_0							,	__A       :							Dict=4_0_0							,	__A       :							List[Any]=None							,	__A       :							List[str]=True							,	__A       :							Tuple=True							,	__A       :							int=None							,	):
        __UpperCamelCase       =  size if size is not None else {'height': 2_0, 'width': 2_0}
        __UpperCamelCase       =  parent
        __UpperCamelCase       =  batch_size
        __UpperCamelCase       =  num_channels
        __UpperCamelCase       =  image_size
        __UpperCamelCase       =  min_resolution
        __UpperCamelCase       =  max_resolution
        __UpperCamelCase       =  size
        __UpperCamelCase       =  do_normalize
        __UpperCamelCase       =  do_convert_rgb
        __UpperCamelCase       =  [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
        __UpperCamelCase       =  patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
        __UpperCamelCase       =  Image.open(requests.get(__A							,	stream=__A	).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     (    __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Dict					=PixaStructImageProcessor if is_vision_available() else None






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  PixaStructImageProcessingTester(self	)






 @property
 def        _lowerCamelCase					(       self       :							str	):
        return self.image_processor_tester.prepare_image_processor_dict()






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(__A							,	'do_normalize'	)	)
        self.assertTrue(hasattr(__A							,	'do_convert_rgb'	)	)






 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  self.image_processor_tester.prepare_dummy_image()

        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        __UpperCamelCase       =  2_0_4_8

        __UpperCamelCase       =  image_processor(__A							,	return_tensors='pt'							,	max_patches=__A	)
        self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,	torch.tensor(0.0606	)							,	atol=1e-3							,	rtol=1e-3	)	)






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

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

               # Test batched
               __UpperCamelCase       =  image_processor(
                   __A							,	return_tensors='pt'							,	max_patches=__A	).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,	)






 def        _lowerCamelCase					(       self       :							int	):
        # Initialize image_processor
        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        __UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A	)
        for image in image_inputs:
               self.assertIsInstance(__A							,	Image.Image	)

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

        __UpperCamelCase       =  True

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

               __UpperCamelCase       =  'Hello'

               __UpperCamelCase       =  image_processor(
                   image_inputs[0]							,	return_tensors='pt'							,	max_patches=__A							,	header_text=__A	).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,	(1, max_patch, expected_hidden_dim)							,	)

               # Test batched
               __UpperCamelCase       =  image_processor(
                   __A							,	return_tensors='pt'							,	max_patches=__A							,	header_text=__A	).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,	)






 def        _lowerCamelCase					(       self       :							Tuple	):
        # Initialize image_processor
        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        __UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	numpify=__A	)
        for image in image_inputs:
               self.assertIsInstance(__A							,	np.ndarray	)

        __UpperCamelCase       =  (
            (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
               __UpperCamelCase       =  image_processor(
                   image_inputs[0]							,	return_tensors='pt'							,	max_patches=__A	).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,	(1, max_patch, expected_hidden_dim)							,	)

               # Test batched
               __UpperCamelCase       =  image_processor(
                   __A							,	return_tensors='pt'							,	max_patches=__A	).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        # Initialize image_processor
        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        __UpperCamelCase       =  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
        __UpperCamelCase       =  (
            (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
               __UpperCamelCase       =  image_processor(
                   image_inputs[0]							,	return_tensors='pt'							,	max_patches=__A	).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,	(1, max_patch, expected_hidden_dim)							,	)

               # Test batched
               __UpperCamelCase       =  image_processor(
                   __A							,	return_tensors='pt'							,	max_patches=__A	).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     (    __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[str]					=PixaStructImageProcessor if is_vision_available() else None






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  PixaStructImageProcessingTester(self							,	num_channels=4	)
        __UpperCamelCase       =  3






 @property
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        return self.image_processor_tester.prepare_image_processor_dict()






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(__A							,	'do_normalize'	)	)
        self.assertTrue(hasattr(__A							,	'do_convert_rgb'	)	)






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

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

               # Test batched
               __UpperCamelCase       =  image_processor(
                   __A							,	return_tensors='pt'							,	max_patches=__A	).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,	)







'''simple docstring'''
import random
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  Optional[Any]     ) ->      tuple:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  [], [], []
       for element in data:
              if element < pivot:
                     less.append(__lowercase     )
              elif element > pivot:
                     greater.append(__lowercase     )
              else:
                     equal.append(__lowercase     )
       return less, equal, greater


def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int     ) ->      Dict:



       """simple docstring"""






       if index >= len(__lowercase     ) or index < 0:
              return None

       __UpperCamelCase       =  items[random.randint(0				,  len(__lowercase     ) - 1     )]
       __UpperCamelCase       =  0
       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  _partition(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(__lowercase				,  __lowercase     )
       # must be in larger
       else:
              return quick_select(__lowercase				,  index - (m + count)     )
