

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       a__	( unittest.TestCase				):







     def __init__(     self  ,		A  ,		A=7  ,		A=3  ,		A=10  ,		A=18  ,		A=30  ,		A=400  ,		A=True  ,		A=None  ,		A=True  ,		A=[0.5, 0.5, 0.5]  ,		A=[0.5, 0.5, 0.5]  ,		A=None  ,		)  ->   Union[str, Any]:



          '''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     =				crop_size







     def       lowerCAmelCase_    (     self			)  ->   Union[str, 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,
              "crop_size": self.crop_size,
          }







@require_torch
@require_vision
class       a__	( __SCREAMING_SNAKE_CASE       , unittest.TestCase				):
     a		:      Tuple		 =     VivitImageProcessor if is_vision_available() else None







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				VivitImageProcessingTester(self			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          return self.image_processor_tester.prepare_image_processor_dict()







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,		"image_mean"			)			)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,		"image_std"			)			)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,		"do_normalize"			)			)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,		"do_resize"			)			)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,		"do_center_crop"			)			)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,		"size"			)			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				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}			)

          a     =				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       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''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=_SCREAMING_SNAKE_CASE			)
          for video in video_inputs:
               self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,		_SCREAMING_SNAKE_CASE			)
               self.assertIsInstance(video[0]  ,		Image.Image			)

          # Test not batched input
          a     =				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
          a     =				image_processing(_SCREAMING_SNAKE_CASE  ,		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       lowerCAmelCase_    (     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=_SCREAMING_SNAKE_CASE  ,		numpify=_SCREAMING_SNAKE_CASE			)
          for video in video_inputs:
               self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,		_SCREAMING_SNAKE_CASE			)
               self.assertIsInstance(video[0]  ,		np.ndarray			)

          # Test not batched input
          a     =				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
          a     =				image_processing(_SCREAMING_SNAKE_CASE  ,		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       lowerCAmelCase_    (     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=_SCREAMING_SNAKE_CASE  ,		torchify=_SCREAMING_SNAKE_CASE			)
          for video in video_inputs:
               self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,		_SCREAMING_SNAKE_CASE			)
               self.assertIsInstance(video[0]  ,		torch.Tensor			)

          # Test not batched input
          a     =				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
          a     =				image_processing(_SCREAMING_SNAKE_CASE  ,		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"],
              )  ,		)







import math
import sys

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					int:
     if number != int(__UpperCamelCase):
          raise ValueError("the value of input must be a natural number")
     if number < 0:
          raise ValueError("the value of input must not be a negative number")
     if number == 0:
          return 1
     a     =				[-1] * (number + 1)
     a     =				0
     for i in range(1		, number + 1):
          a     =				sys.maxsize
          a     =				int(math.sqrt(__UpperCamelCase))
          for j in range(1		, root + 1):
               a     =				1 + answers[i - (j**2)]
               a     =				min(__UpperCamelCase		, __UpperCamelCase)
          a     =				answer
     return answers[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()

