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     UpperCAmelCase__       (    _A   :				Optional[int]			,   _A   :				List[Any]=10			,   _A   :				Optional[int]=10			,   _A   :				Dict=False			,   _A   :				Any=False							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=[]
    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
      a__						=[Image.fromarray(np.moveaxis(_A			,   0			,   -1							)							) for frame in video]
  
    if torchify:
      a__						=[torch.from_numpy(_A							) for frame in video]
  
    return video
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[str]			,   _A   :				Optional[int]=False			,   _A   :				Dict=False			,   _A   :				Optional[Any]=False							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
    a__						=[]
    for i in range(image_processor_tester.batch_size							):
      if equal_resolution:
        a__						=a__						=image_processor_tester.max_resolution
      else:
        a__,   a__						=np.random.choice(
            np.arange(image_processor_tester.min_resolution			,   image_processor_tester.max_resolution							)			,   2							)
        a__						=prepare_video(
            image_processor_tester=_A			,   width=_A			,   height=_A			,   numpify=_A			,   torchify=_A			,   )
      video_inputs.append(_A							)
  
    return video_inputs
  
  
  
  
  
  
  
  class 		__magic_name__    (							unittest.TestCase							):
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self,					lowercase_,					lowercase_=7,					lowercase_=3,					lowercase_=4,					lowercase_=18,					lowercase_=30,					lowercase_=400,					lowercase_=True,					lowercase_=None,					lowercase_=True,					lowercase_=[0.5, 0.5, 0.5],					lowercase_=[0.5, 0.5, 0.5],					lowercase_=True,					lowercase_=None,					)							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=size if size is not None else {'''shortest_edge''': 18}
       a__						=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
  
       a__						=parent
       a__						=batch_size
       a__						=num_channels
       a__						=num_frames
       a__						=image_size
       a__						=min_resolution
       a__						=max_resolution
       a__						=do_resize
       a__						=size
       a__						=do_normalize
       a__						=image_mean
       a__						=image_std
       a__						=do_center_crop
       a__						=crop_size
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """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 		__magic_name__    (							lowerCamelCase__	, unittest.TestCase							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			str												=     TvltImageProcessor if is_vision_available() else None
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=TvltImageProcessorTester(self )
  
  
  
     @property
     def 				_UpperCAmelCase				(       self )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(lowercase_,					'''image_mean''' ) )
       self.assertTrue(hasattr(lowercase_,					'''image_std''' ) )
       self.assertTrue(hasattr(lowercase_,					'''do_normalize''' ) )
       self.assertTrue(hasattr(lowercase_,					'''do_resize''' ) )
       self.assertTrue(hasattr(lowercase_,					'''do_center_crop''' ) )
       self.assertTrue(hasattr(lowercase_,					'''size''' ) )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.image_processing_class(**self.image_processor_dict )
       # create random PIL videos
       a__						=prepare_video_inputs(self.image_processor_tester,					equal_resolution=lowercase_ )
       for video in video_inputs:
         self.assertIsInstance(lowercase_,					lowercase_ )
         self.assertIsInstance(video[0],					Image.Image )
  
       # Test not batched input
       a__						=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
       a__						=image_processor(lowercase_,					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 				_UpperCAmelCase				(       self )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       a__						=prepare_video_inputs(self.image_processor_tester,					equal_resolution=lowercase_,					numpify=lowercase_ )
       for video in video_inputs:
         self.assertIsInstance(lowercase_,					lowercase_ )
         self.assertIsInstance(video[0],					np.ndarray )
  
       # Test not batched input
       a__						=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
       a__						=image_processor(lowercase_,					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 				_UpperCAmelCase				(       self )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       a__						=prepare_video_inputs(self.image_processor_tester,					equal_resolution=lowercase_,					torchify=lowercase_ )
       for video in video_inputs:
         self.assertIsInstance(lowercase_,					lowercase_ )
         self.assertIsInstance(video[0],					torch.Tensor )
  
       # Test not batched input
       a__						=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
       a__						=image_processor(lowercase_,					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: 
  
  import math
  from decimal import Decimal, getcontext
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				int = 4_00_00_00							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    try:
      a__						=int(_A							)
    except (TypeError, ValueError):
      raise TypeError('''Parameter n must be int or castable to int.'''							)
    if n <= 0:
      raise ValueError('''Parameter n must be greater than or equal to one.'''							)
    a__						=1_00
    a__						=(Decimal(5							) ** Decimal(0.5							) + 1) / Decimal(2							)
  
    a__						=(math.floor(math.log(n * (phi + 2)			,   _A							) - 1							) // 3) * 3 + 2
    a__						=Decimal(round(phi ** Decimal(index + 1							)							)							) / (phi + 2)
    a__						=num // 2
    return int(_A							)
  
  
  if __name__ == "__main__":
         print(f"""{solution() = }""")
  