code: 
  
  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
  
  
  if is_torch_available():
      import torch
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import TvltImageProcessor
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase=10 ,  lowerCAmelCase=10 ,  lowerCAmelCase=False ,  lowerCAmelCase=False						)  ->   Optional[int]:
        UpperCAmelCase__ :       int						=						[]
        for i in range(image_processor_tester.num_frames						):
              video.append(np.random.randint(2_55 ,  size=(image_processor_tester.num_channels, width, height) ,  dtype=np.uinta						)						)
  
        if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              UpperCAmelCase__ :       Optional[int]						=						[Image.fromarray(np.moveaxis(lowerCAmelCase ,  0 ,  -1						)						) for frame in video]
  
        if torchify:
              UpperCAmelCase__ :       Any						=						[torch.from_numpy(lowerCAmelCase						) for frame in video]
  
        return video
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase=False ,  lowerCAmelCase=False ,  lowerCAmelCase=False						)  ->   Dict:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
        UpperCAmelCase__ :       Optional[Any]						=						[]
        for i in range(image_processor_tester.batch_size						):
              if equal_resolution:
                    UpperCAmelCase__ :       List[Any]						=						image_processor_tester.max_resolution
              else:
                    UpperCAmelCase__ :       Dict						=						np.random.choice(
                        np.arange(image_processor_tester.min_resolution ,  image_processor_tester.max_resolution						) ,  2						)
                    UpperCAmelCase__ :       int						=						prepare_video(
                        image_processor_tester=lowerCAmelCase ,  width=lowerCAmelCase ,  height=lowerCAmelCase ,  numpify=lowerCAmelCase ,  torchify=lowerCAmelCase ,  )
              video_inputs.append(lowerCAmelCase						)
  
        return video_inputs
  
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase=7       ,					_lowerCamelCase=3       ,					_lowerCamelCase=4       ,					_lowerCamelCase=18       ,					_lowerCamelCase=30       ,					_lowerCamelCase=400       ,					_lowerCamelCase=True       ,					_lowerCamelCase=None       ,					_lowerCamelCase=True       ,					_lowerCamelCase=[0.5, 0.5, 0.5]       ,					_lowerCamelCase=[0.5, 0.5, 0.5]       ,					_lowerCamelCase=True       ,					_lowerCamelCase=None       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						size if size is not None else {"""shortest_edge""": 18}
          UpperCAmelCase__ :       str						=						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
  
          UpperCAmelCase__ :       Optional[int]						=						parent
          UpperCAmelCase__ :       List[str]						=						batch_size
          UpperCAmelCase__ :       List[str]						=						num_channels
          UpperCAmelCase__ :       Optional[int]						=						num_frames
          UpperCAmelCase__ :       Union[str, Any]						=						image_size
          UpperCAmelCase__ :       int						=						min_resolution
          UpperCAmelCase__ :       Dict						=						max_resolution
          UpperCAmelCase__ :       Any						=						do_resize
          UpperCAmelCase__ :       List[Any]						=						size
          UpperCAmelCase__ :       List[Any]						=						do_normalize
          UpperCAmelCase__ :       List[str]						=						image_mean
          UpperCAmelCase__ :       Any						=						image_std
          UpperCAmelCase__ :       Union[str, Any]						=						do_center_crop
          UpperCAmelCase__ :       str						=						crop_size
  
  
  
  
    def    _a			(self  ):
  
  
          """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,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }
  
  
  
  
  @require_torch
  @require_vision
  class 			lowerCamelCase  (					lowerCAmelCase__				,				unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     TvltImageProcessor if is_vision_available() else None
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						TvltImageProcessorTester(self  )
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						self.image_processing_class(**self.image_processor_dict  )
          self.assertTrue(hasattr(_lowerCamelCase       ,					"""image_mean"""  )  )
          self.assertTrue(hasattr(_lowerCamelCase       ,					"""image_std"""  )  )
          self.assertTrue(hasattr(_lowerCamelCase       ,					"""do_normalize"""  )  )
          self.assertTrue(hasattr(_lowerCamelCase       ,					"""do_resize"""  )  )
          self.assertTrue(hasattr(_lowerCamelCase       ,					"""do_center_crop"""  )  )
          self.assertTrue(hasattr(_lowerCamelCase       ,					"""size"""  )  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						self.image_processing_class(**self.image_processor_dict  )
          # create random PIL videos
          UpperCAmelCase__ :       str						=						prepare_video_inputs(self.image_processor_tester       ,					equal_resolution=_lowerCamelCase  )
          for video in video_inputs:
                self.assertIsInstance(_lowerCamelCase       ,					_lowerCamelCase  )
                self.assertIsInstance(video[0]       ,					Image.Image  )
  
          # Test not batched input
          UpperCAmelCase__ :       str						=						image_processor(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
          UpperCAmelCase__ :       Any						=						image_processor(_lowerCamelCase       ,					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  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						self.image_processing_class(**self.image_processor_dict  )
          # create random numpy tensors
          UpperCAmelCase__ :       Optional[Any]						=						prepare_video_inputs(self.image_processor_tester       ,					equal_resolution=_lowerCamelCase       ,					numpify=_lowerCamelCase  )
          for video in video_inputs:
                self.assertIsInstance(_lowerCamelCase       ,					_lowerCamelCase  )
                self.assertIsInstance(video[0]       ,					np.ndarray  )
  
          # Test not batched input
          UpperCAmelCase__ :       Union[str, Any]						=						image_processor(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
          UpperCAmelCase__ :       Optional[Any]						=						image_processor(_lowerCamelCase       ,					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  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						self.image_processing_class(**self.image_processor_dict  )
          # create random PyTorch tensors
          UpperCAmelCase__ :       Optional[int]						=						prepare_video_inputs(self.image_processor_tester       ,					equal_resolution=_lowerCamelCase       ,					torchify=_lowerCamelCase  )
          for video in video_inputs:
                self.assertIsInstance(_lowerCamelCase       ,					_lowerCamelCase  )
                self.assertIsInstance(video[0]       ,					torch.Tensor  )
  
          # Test not batched input
          UpperCAmelCase__ :       str						=						image_processor(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
          UpperCAmelCase__ :       str						=						image_processor(_lowerCamelCase       ,					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"""],
              )       ,					)
  
  
style_context: 
  
  """simple docstring"""
  from __future__ import annotations
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   set[int]:
        UpperCAmelCase__ :       List[Any]						=						{1}
        UpperCAmelCase__ :       Optional[int]						=						[1]
        for _ in range(2 ,  depth + 1						):
              UpperCAmelCase__ :       Tuple						=						[*previous_coefficients, 0]
              UpperCAmelCase__ :       List[str]						=						[0, *previous_coefficients]
              UpperCAmelCase__ :       Dict						=						[]
              for x, y in zip(lowerCAmelCase ,  lowerCAmelCase						):
                    coefficients.add(x + y						)
                    previous_coefficients.append(x + y						)
        return coefficients
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   set[int]:
        UpperCAmelCase__ :       List[str]						=						set()
        for number in unique_coefficients:
              UpperCAmelCase__ :       int						=						2
              UpperCAmelCase__ :       List[Any]						=						number
              while divisor**2 <= copy_number:
                    UpperCAmelCase__ :       Tuple						=						0
                    while copy_number % divisor == 0:
                          copy_number //= divisor
                          multiplicity += 1
                    if multiplicity >= 2:
                          non_squarefrees.add(lowerCAmelCase						)
                          break
                    divisor += 1
  
        return unique_coefficients.difference(lowerCAmelCase						)
  
  
  
  
  def 				a__	(	lowerCAmelCase = 51						)  ->   int:
        UpperCAmelCase__ :       Any						=						get_pascal_triangle_unique_coefficients(lowerCAmelCase						)
        UpperCAmelCase__ :       Dict						=						get_squarefrees(lowerCAmelCase						)
        return sum(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      print(f'''{solution() = }''')
  
  