





from ..utils import DummyObject, requires_backends

class 				A    (metaclass=__UpperCAmelCase				):
      _SCREAMING_SNAKE_CASE					=							["""torch""", """transformers""", """k_diffusion"""]



      def __init__(					self     ,  *lowercase_     ,  **lowercase_					)      ->   Optional[Any]:



             '''simple docstring'''

             requires_backends(self     ,  ['''torch''', '''transformers''', '''k_diffusion''']					)



      @classmethod
      def    __a (					cls     ,  *lowercase_     ,  **lowercase_					)      ->   Tuple:



             '''simple docstring'''

             requires_backends(cls     ,  ['''torch''', '''transformers''', '''k_diffusion''']					)



      @classmethod
      def    __a (					cls     ,  *lowercase_     ,  **lowercase_					)      ->   List[Any]:



             '''simple docstring'''

             requires_backends(cls     ,  ['''torch''', '''transformers''', '''k_diffusion''']					)













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 				A    (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]     ,  )      ->   List[str]:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						size if size is not None else {'''shortest_edge''': 20}
             _snake_case      :							List[Any]     				=						parent
             _snake_case      :							Optional[int]     				=						batch_size
             _snake_case      :							str     				=						num_channels
             _snake_case      :							Union[str, Any]     				=						image_size
             _snake_case      :							Optional[Any]     				=						min_resolution
             _snake_case      :							Union[str, Any]     				=						max_resolution
             _snake_case      :							Tuple     				=						do_resize
             _snake_case      :							List[Any]     				=						size
             _snake_case      :							Optional[int]     				=						crop_pct
             _snake_case      :							Any     				=						do_normalize
             _snake_case      :							Optional[int]     				=						image_mean
             _snake_case      :							Union[str, Any]     				=						image_std



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             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 				A    (__UpperCAmelCase       ,unittest.TestCase				):
      _SCREAMING_SNAKE_CASE					=							ConvNextImageProcessor if is_vision_available() else None



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						ConvNextImageProcessingTester(self					)



      @property
      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             return self.image_processor_tester.prepare_image_processor_dict()



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							List[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    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							int     				=						self.image_processing_class.from_dict(self.image_processor_dict					)
             self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 20}					)

             _snake_case      :							Tuple     				=						self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42					)
             self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 42}					)



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             pass



      def    __a (					self					)      ->   int:



             '''simple docstring'''

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

             # Test not batched input
             _snake_case      :							List[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
             _snake_case      :							int     				=						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    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random numpy tensors
             _snake_case      :							Tuple     				=						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
             _snake_case      :							List[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
             _snake_case      :							Union[str, 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    __a (					self					)      ->   Union[str, Any]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _snake_case      :							Dict     				=						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
             _snake_case      :							Union[str, 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
             _snake_case      :							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'''],
                 )     ,  )






