


'''simple docstring'''

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      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase=7			,  UpperCamelCase=3			,  UpperCamelCase=10			,  UpperCamelCase=18			,  UpperCamelCase=30			,  UpperCamelCase=400			,  UpperCamelCase=True			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=[0.5, 0.5, 0.5]			,  UpperCamelCase=[0.5, 0.5, 0.5]			,  UpperCamelCase=None			,  ):







   """simple docstring"""
   lowerCamelCase_      =  size if size is not None else {"shortest_edge": 18}
   lowerCamelCase_      =  crop_size if crop_size is not None else {"height": 18, "width": 18}

   lowerCamelCase_      =  parent
   lowerCamelCase_      =  batch_size
   lowerCamelCase_      =  num_channels
   lowerCamelCase_      =  num_frames
   lowerCamelCase_      =  image_size
   lowerCamelCase_      =  min_resolution
   lowerCamelCase_      =  max_resolution
   lowerCamelCase_      =  do_resize
   lowerCamelCase_      =  size
   lowerCamelCase_      =  do_normalize
   lowerCamelCase_      =  image_mean
   lowerCamelCase_      =  image_std
   lowerCamelCase_      =  crop_size







  def   snake_case		(    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,
       "crop_size": self.crop_size,
   }





@require_torch
@require_vision
class      snake_case    (    lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  VivitImageProcessor if is_vision_available() else None






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  VivitImageProcessingTester(self     )






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   return self.image_processor_tester.prepare_image_processor_dict()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  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_      =  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   snake_case		(    self     ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_      =  self.image_processing_class(**self.image_processor_dict     )
   # create random PIL videos
   lowerCamelCase_      =  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_      =  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_      =  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   snake_case		(    self     ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_      =  self.image_processing_class(**self.image_processor_dict     )
   # create random numpy tensors
   lowerCamelCase_      =  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_      =  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_      =  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   snake_case		(    self     ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_      =  self.image_processing_class(**self.image_processor_dict     )
   # create random PyTorch tensors
   lowerCamelCase_      =  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_      =  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_      =  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"],
       )			,  )





"""simple docstring"""





def 						_lowerCAmelCase				(				UpperCamelCase_ = 100				):
		__SCREAMING_SNAKE_CASE            = 0
		__SCREAMING_SNAKE_CASE            = 0
		for i in range(1  ,				n + 1				):
				sum_of_squares += i**2
				sum_of_ints += i
		return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
							print(F"""{solution() = }""")




