





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_video_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import VivitImageProcessor

class    lowerCAmelCase							(					unittest.TestCase			):
 def __init__(      self  :      List[Any]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      Any=7			,     UpperCAmelCase  :      Dict=3			,     UpperCAmelCase  :      Optional[int]=10			,     UpperCAmelCase  :      List[Any]=18			,     UpperCAmelCase  :      Dict=30			,     UpperCAmelCase  :      List[str]=400			,     UpperCAmelCase  :      Optional[int]=True			,     UpperCAmelCase  :      str=None			,     UpperCAmelCase  :      List[Any]=True			,     UpperCAmelCase  :      List[str]=[0.5, 0.5, 0.5]			,     UpperCAmelCase  :      Optional[int]=[0.5, 0.5, 0.5]			,     UpperCAmelCase  :      List[str]=None			,     )			->	Any:
     lowerCamelCase__    :      Any					 =		size if size is not None else {'shortest_edge': 18}
     lowerCamelCase__    :      List[Any]					 =		crop_size if crop_size is not None else {'height': 18, 'width': 18}

     lowerCamelCase__    :      Tuple					 =		parent
     lowerCamelCase__    :      List[Any]					 =		batch_size
     lowerCamelCase__    :      Dict					 =		num_channels
     lowerCamelCase__    :      Union[str, Any]					 =		num_frames
     lowerCamelCase__    :      Any					 =		image_size
     lowerCamelCase__    :      int					 =		min_resolution
     lowerCamelCase__    :      Tuple					 =		max_resolution
     lowerCamelCase__    :      List[str]					 =		do_resize
     lowerCamelCase__    :      List[str]					 =		size
     lowerCamelCase__    :      Union[str, Any]					 =		do_normalize
     lowerCamelCase__    :      List[str]					 =		image_mean
     lowerCamelCase__    :      Tuple					 =		image_std
     lowerCamelCase__    :      Tuple					 =		crop_size







 def      A_	(      self  :      Tuple	)			->	List[Any]:
     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_size": self.crop_size,
     }


@require_torch
@require_vision
class    lowerCAmelCase							(					__UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		VivitImageProcessor if is_vision_available() else None
 def      A_	(      self  :      Optional[Any]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[int]					 =		VivitImageProcessingTester(self	)
 @property
 def      A_	(      self  :      int	)			->	Union[str, Any]:
     return self.image_processor_tester.prepare_image_processor_dict()
 def      A_	(      self  :      Dict	)			->	Any:
     lowerCamelCase__    :      Optional[int]					 =		self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'image_mean'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'image_std'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'do_normalize'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'do_resize'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'do_center_crop'	)	)
     self.assertTrue(hasattr(UpperCAmelCase			,     'size'	)	)
 def      A_	(      self  :      Dict	)			->	Optional[Any]:
     lowerCamelCase__    :      Optional[int]					 =		self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size			,     {'shortest_edge': 18}	)
     self.assertEqual(image_processor.crop_size			,     {'height': 18, 'width': 18}	)

     lowerCamelCase__    :      Union[str, Any]					 =		self.image_processing_class.from_dict(self.image_processor_dict			,     size=42			,     crop_size=84	)
     self.assertEqual(image_processor.size			,     {'shortest_edge': 42}	)
     self.assertEqual(image_processor.crop_size			,     {'height': 84, 'width': 84}	)
 def      A_	(      self  :      List[Any]	)			->	Optional[int]:
     # Initialize image_processing
     lowerCamelCase__    :      Tuple					 =		self.image_processing_class(**self.image_processor_dict	)
     # create random PIL videos
     lowerCamelCase__    :      List[Any]					 =		prepare_video_inputs(self.image_processor_tester			,     equal_resolution=UpperCAmelCase	)
     for video in video_inputs:
         self.assertIsInstance(UpperCAmelCase			,     UpperCAmelCase	)
         self.assertIsInstance(video[0]			,     Image.Image	)

     # Test not batched input
     lowerCamelCase__    :      str					 =		image_processing(video_inputs[0]			,     return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_videos.shape			,     (
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )			,     )

     # Test batched
     lowerCamelCase__    :      Optional[int]					 =		image_processing(UpperCAmelCase			,     return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_videos.shape			,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )			,     )
 def      A_	(      self  :      int	)			->	Union[str, Any]:
     # Initialize image_processing
     lowerCamelCase__    :      List[Any]					 =		self.image_processing_class(**self.image_processor_dict	)
     # create random numpy tensors
     lowerCamelCase__    :      List[str]					 =		prepare_video_inputs(self.image_processor_tester			,     equal_resolution=UpperCAmelCase			,     numpify=UpperCAmelCase	)
     for video in video_inputs:
         self.assertIsInstance(UpperCAmelCase			,     UpperCAmelCase	)
         self.assertIsInstance(video[0]			,     np.ndarray	)

     # Test not batched input
     lowerCamelCase__    :      Any					 =		image_processing(video_inputs[0]			,     return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_videos.shape			,     (
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )			,     )

     # Test batched
     lowerCamelCase__    :      Tuple					 =		image_processing(UpperCAmelCase			,     return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_videos.shape			,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )			,     )







 def      A_	(      self  :      Dict	)			->	Optional[int]:
     # Initialize image_processing
     lowerCamelCase__    :      Optional[Any]					 =		self.image_processing_class(**self.image_processor_dict	)
     # create random PyTorch tensors
     lowerCamelCase__    :      Union[str, Any]					 =		prepare_video_inputs(self.image_processor_tester			,     equal_resolution=UpperCAmelCase			,     torchify=UpperCAmelCase	)
     for video in video_inputs:
         self.assertIsInstance(UpperCAmelCase			,     UpperCAmelCase	)
         self.assertIsInstance(video[0]			,     torch.Tensor	)

     # Test not batched input
     lowerCamelCase__    :      Union[str, Any]					 =		image_processing(video_inputs[0]			,     return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_videos.shape			,     (
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )			,     )

     # Test batched
     lowerCamelCase__    :      Tuple					 =		image_processing(UpperCAmelCase			,     return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_videos.shape			,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )			,     )












def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     bool:
    lowerCamelCase__    :      List[str]					 =		len(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		[[False] * (required_sum + 1) for _ in range(arr_len + 1 )]

    # for each arr value, a sum of zero(0) can be formed by not taking any element
    # hence True/1
    for i in range(arr_len + 1 ):
        lowerCamelCase__    :      Tuple					 =		True

    # sum is not zero and set is empty then false
    for i in range(1						,	required_sum + 1 ):
        lowerCamelCase__    :      Dict					 =		False

    for i in range(1						,	arr_len + 1 ):
        for j in range(1						,	required_sum + 1 ):
            if arr[i - 1] > j:
                lowerCamelCase__    :      str					 =		subset[i - 1][j]
            if arr[i - 1] <= j:
                lowerCamelCase__    :      Dict					 =		subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

    return subset[arr_len][required_sum]


if __name__ == "__main__":
    import doctest

    doctest.testmod()





