





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 ConvNextImageProcessor





class   lowercase__     (				unittest.TestCase	):







  def __init__(						self     ,    _lowercase     ,    _lowercase=7     ,    _lowercase=3     ,    _lowercase=18     ,    _lowercase=30     ,    _lowercase=400     ,    _lowercase=True     ,    _lowercase=None     ,    _lowercase=0.875     ,    _lowercase=True     ,    _lowercase=[0.5, 0.5, 0.5]     ,    _lowercase=[0.5, 0.5, 0.5]     ,    ):
       lowerCAmelCase_       :							Union[str, Any]			 =		size if size is not None else {"shortest_edge": 20}
       lowerCAmelCase_       :							Optional[int]			 =		parent
       lowerCAmelCase_       :							Tuple			 =		batch_size
       lowerCAmelCase_       :							Union[str, Any]			 =		num_channels
       lowerCAmelCase_       :							Optional[Any]			 =		image_size
       lowerCAmelCase_       :							str			 =		min_resolution
       lowerCAmelCase_       :							Dict			 =		max_resolution
       lowerCAmelCase_       :							Any			 =		do_resize
       lowerCAmelCase_       :							Tuple			 =		size
       lowerCAmelCase_       :							Optional[int]			 =		crop_pct
       lowerCAmelCase_       :							str			 =		do_normalize
       lowerCAmelCase_       :							Optional[Any]			 =		image_mean
       lowerCAmelCase_       :							Any			 =		image_std







  def 						UpperCAmelCase__		(						self    ):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_pct": self.crop_pct,
       }





@require_torch
@require_vision
class   lowercase__     (				__A  ,       unittest.TestCase	):
  __UpperCamelCase						  =	ConvNextImageProcessor if is_vision_available() else None







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Any			 =		ConvNextImageProcessingTester(self    )







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







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Any			 =		self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(_lowercase     ,    """do_resize"""    )    )
       self.assertTrue(hasattr(_lowercase     ,    """size"""    )    )
       self.assertTrue(hasattr(_lowercase     ,    """crop_pct"""    )    )
       self.assertTrue(hasattr(_lowercase     ,    """do_normalize"""    )    )
       self.assertTrue(hasattr(_lowercase     ,    """image_mean"""    )    )
       self.assertTrue(hasattr(_lowercase     ,    """image_std"""    )    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Union[str, Any]			 =		self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size     ,    {"""shortest_edge""": 20}    )

       lowerCAmelCase_       :							Any			 =		self.image_processing_class.from_dict(self.image_processor_dict     ,    size=42    )
       self.assertEqual(image_processor.size     ,    {"""shortest_edge""": 42}    )







  def 						UpperCAmelCase__		(						self    ):
       pass







  def 						UpperCAmelCase__		(						self    ):
       # Initialize image_processing
       lowerCAmelCase_       :							List[Any]			 =		self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       lowerCAmelCase_       :							Optional[Any]			 =		prepare_image_inputs(self.image_processor_tester     ,    equal_resolution=_lowercase    )
       for image in image_inputs:
            self.assertIsInstance(_lowercase     ,    Image.Image    )

       # Test not batched input
       lowerCAmelCase_       :							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.size["""shortest_edge"""],
               self.image_processor_tester.size["""shortest_edge"""],
           )     ,    )

       # Test batched
       lowerCAmelCase_       :							Dict			 =		image_processing(_lowercase     ,    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.size["""shortest_edge"""],
               self.image_processor_tester.size["""shortest_edge"""],
           )     ,    )







  def 						UpperCAmelCase__		(						self    ):
       # Initialize image_processing
       lowerCAmelCase_       :							Optional[Any]			 =		self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       lowerCAmelCase_       :							int			 =		prepare_image_inputs(self.image_processor_tester     ,    equal_resolution=_lowercase     ,    numpify=_lowercase    )
       for image in image_inputs:
            self.assertIsInstance(_lowercase     ,    np.ndarray    )

       # Test not batched input
       lowerCAmelCase_       :							str			 =		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.size["""shortest_edge"""],
               self.image_processor_tester.size["""shortest_edge"""],
           )     ,    )

       # Test batched
       lowerCAmelCase_       :							Optional[Any]			 =		image_processing(_lowercase     ,    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.size["""shortest_edge"""],
               self.image_processor_tester.size["""shortest_edge"""],
           )     ,    )







  def 						UpperCAmelCase__		(						self    ):
       # Initialize image_processing
       lowerCAmelCase_       :							Optional[int]			 =		self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       lowerCAmelCase_       :							Union[str, Any]			 =		prepare_image_inputs(self.image_processor_tester     ,    equal_resolution=_lowercase     ,    torchify=_lowercase    )
       for image in image_inputs:
            self.assertIsInstance(_lowercase     ,    torch.Tensor    )

       # Test not batched input
       lowerCAmelCase_       :							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.size["""shortest_edge"""],
               self.image_processor_tester.size["""shortest_edge"""],
           )     ,    )

       # Test batched
       lowerCAmelCase_       :							List[Any]			 =		image_processing(_lowercase     ,    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.size["""shortest_edge"""],
               self.image_processor_tester.size["""shortest_edge"""],
           )     ,    )





def 						_lowerCAmelCase    (					__lowerCamelCase :  list  ):



 """simple docstring"""





 if len(__lowerCamelCase  ) <= 1:
  return data
 else:
  return [
      *quick_sort([e for e in data[1:] if e <= data[0]]  ),
      data[0],
      *quick_sort([e for e in data[1:] if e > data[0]]  ),
  ]


if __name__ == "__main__":
      import doctest

      doctest.testmod()






