





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  (		snake_case    ,	snake_case=1_0    ,	snake_case=1_0    ,	snake_case=False    ,	snake_case=False     )  ->						Any:


   '''simple docstring'''







   __A				 =       []
   for i in range(image_processor_tester.num_frames     ):
      video.append(np.random.randint(2_5_5    ,	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(snake_case    ,	0    ,	-1     )     ) for frame in video]

   if torchify:
      __A				 =       [torch.from_numpy(snake_case     ) for frame in video]

   return video
def 				__UpperCamelCase  (		snake_case    ,	snake_case=False    ,	snake_case=False    ,	snake_case=False     )  ->						Tuple:


   '''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=snake_case    ,	width=snake_case    ,	height=snake_case    ,	numpify=snake_case    ,	torchify=snake_case    ,	)
      video_inputs.append(snake_case     )

   return video_inputs







class 						_lowerCAmelCase(						unittest.TestCase):

  """simple docstring"""







  def __init__( self  ,    UpperCAmelCase  ,    UpperCAmelCase=7  ,    UpperCAmelCase=3  ,    UpperCAmelCase=4  ,    UpperCAmelCase=18  ,    UpperCAmelCase=30  ,    UpperCAmelCase=4_00  ,    UpperCAmelCase=True  ,    UpperCAmelCase=None  ,    UpperCAmelCase=True  ,    UpperCAmelCase=[0.5, 0.5, 0.5]  ,    UpperCAmelCase=[0.5, 0.5, 0.5]  ,    UpperCAmelCase=True  ,    UpperCAmelCase=None  ,    )-> List[str]:
     __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 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     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(						_a		,   unittest.TestCase):

  """simple docstring"""







  lowerCamelCase__      					=   TvltImageProcessor if is_vision_available() else None

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A				 =       TvltImageProcessorTester(self     )

  @property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     return self.image_processor_tester.prepare_image_processor_dict()

  def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     __A				 =       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 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     # Initialize image_processor
     __A				 =       self.image_processing_class(**self.image_processor_dict     )
     # create random PIL videos
     __A				 =       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
     __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(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__	( self     )-> int:
     # Initialize image_processor
     __A				 =       self.image_processing_class(**self.image_processor_dict     )
     # create random numpy tensors
     __A				 =       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
     __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(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__	( self     )-> List[str]:
     # Initialize image_processor
     __A				 =       self.image_processing_class(**self.image_processor_dict     )
     # create random PyTorch tensors
     __A				 =       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
     __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(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'''],
         )  ,    )








from __future__ import annotations
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						list:


   '''simple docstring'''







   __A				 =       [None] * (len(snake_case     ) + len(snake_case     ))

   __A				 =       0  # pointer to current index for left Half
   __A				 =       0  # pointer to current index for the right Half
   __A				 =       0  # pointer to current index for the sorted array Half

   while pointera < len(snake_case     ) and pointera < len(snake_case     ):
      if left_half[pointera] < right_half[pointera]:
         __A				 =       left_half[pointera]
         pointera += 1
         index += 1
      else:
         __A				 =       right_half[pointera]
         pointera += 1
         index += 1
   while pointera < len(snake_case     ):
      __A				 =       left_half[pointera]
      pointera += 1
      index += 1

   while pointera < len(snake_case     ):
      __A				 =       right_half[pointera]
      pointera += 1
      index += 1

   return sorted_array
def 				__UpperCamelCase  (		snake_case     )  ->						list:


   '''simple docstring'''







   if len(snake_case     ) <= 1:
      return array
   # the actual formula to calculate the middle element = left + (right - left) // 2
   # this avoids integer overflow in case of large N
   __A				 =       0 + (len(snake_case     ) - 0) // 2

   # Split the array into halves till the array length becomes equal to One
   # merge the arrays of single length returned by mergeSort function and
   # pass them into the merge arrays function which merges the array
   __A				 =       array[:middle]
   __A				 =       array[middle:]

   return merge(merge_sort(snake_case     )    ,	merge_sort(snake_case     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

