





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        _lowerCAmelCase    (			unittest.TestCase							):


		'''simple docstring'''







		def __init__(      self      :					Tuple      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					str=7      ,				UpperCamelCase      :					Union[str, Any]=3      ,				UpperCamelCase      :					int=10      ,				UpperCamelCase      :					Dict=18      ,				UpperCamelCase      :					Any=30      ,				UpperCamelCase      :					Optional[Any]=4_00      ,				UpperCamelCase      :					Optional[int]=True      ,				UpperCamelCase      :					Dict=None      ,				UpperCamelCase      :					List[str]=True      ,				UpperCamelCase      :					List[Any]=[0.5, 0.5, 0.5]      ,				UpperCamelCase      :					Optional[int]=[0.5, 0.5, 0.5]      ,				UpperCamelCase      :					str=None      ,				):


				'''simple docstring'''





				_snake_case   :				Optional[int]														=	size if size is not None else {'shortest_edge': 18}
				_snake_case   :				Optional[int]														=	crop_size if crop_size is not None else {'height': 18, 'width': 18}

				_snake_case   :				Tuple														=	parent
				_snake_case   :				Union[str, Any]														=	batch_size
				_snake_case   :				Tuple														=	num_channels
				_snake_case   :				Optional[int]														=	num_frames
				_snake_case   :				Union[str, Any]														=	image_size
				_snake_case   :				List[str]														=	min_resolution
				_snake_case   :				int														=	max_resolution
				_snake_case   :				Optional[int]														=	do_resize
				_snake_case   :				Dict														=	size
				_snake_case   :				List[str]														=	do_normalize
				_snake_case   :				List[Any]														=	image_mean
				_snake_case   :				int														=	image_std
				_snake_case   :				List[Any]														=	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,
				    "crop_size": self.crop_size,
				}




@require_torch
@require_vision
class        _lowerCAmelCase    (			UpperCAmelCase_							,    unittest.TestCase							):


		'''simple docstring'''
		a_							:       List[str]      					=VivitImageProcessor if is_vision_available() else None







		def UpperCamelCase_ (      self      :					str							):


				'''simple docstring'''





				_snake_case   :				Any														=	VivitImageProcessingTester(self							)







		@property
		def UpperCamelCase_ (      self      :					Union[str, Any]							):


				'''simple docstring'''





				return self.image_processor_tester.prepare_image_processor_dict()







		def UpperCamelCase_ (      self      :					Union[str, Any]							):


				'''simple docstring'''





				_snake_case   :				str														=	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 UpperCamelCase_ (      self      :					Tuple							):


				'''simple docstring'''





				_snake_case   :				Optional[Any]														=	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}							)

				_snake_case   :				Union[str, Any]														=	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 UpperCamelCase_ (      self      :					str							):


				'''simple docstring'''





				_snake_case   :				Any														=	self.image_processing_class(**self.image_processor_dict							)
				# create random PIL videos
				_snake_case   :				Optional[int]														=	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
				_snake_case   :				Optional[int]														=	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
				_snake_case   :				Union[str, Any]														=	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 UpperCamelCase_ (      self      :					Any							):


				'''simple docstring'''





				_snake_case   :				int														=	self.image_processing_class(**self.image_processor_dict							)
				# create random numpy tensors
				_snake_case   :				int														=	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
				_snake_case   :				Any														=	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
				_snake_case   :				int														=	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 UpperCamelCase_ (      self      :					int							):


				'''simple docstring'''





				_snake_case   :				List[str]														=	self.image_processing_class(**self.image_processor_dict							)
				# create random PyTorch tensors
				_snake_case   :				int														=	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
				_snake_case   :				Tuple														=	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
				_snake_case   :				Dict														=	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'],
				    )      ,				)











# Function to print upper half of diamond (pyramid)


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]				)->		List[str]:
  for i in range(0    ,							lowerCAmelCase				):
    for _ in range(0    ,							n - i - 1				):  # printing spaces
      print(' '    ,							end=''				)
    for _ in range(0    ,							i + 1				):  # printing stars
      print('* '    ,							end=''				)
    print()


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]				)->		List[Any]:
  for i in range(lowerCAmelCase    ,							0    ,							-1				):
    for _ in range(lowerCAmelCase    ,							0    ,							-1				):  # printing stars
      print('* '    ,							end=''				)
    print()
    for _ in range(n - i + 1    ,							0    ,							-1				):  # printing spaces
      print(' '    ,							end=''				)


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple				)->		int:
  if n <= 0:
    print('       ...       ....        nothing printing :('				)
    return
  floyd(lowerCAmelCase				)  # upper half
  reverse_floyd(lowerCAmelCase				)  # lower half


if __name__ == "__main__":
      print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
      print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
      lowerCAmelCase_						   =    1
      while K:
            lowerCAmelCase_						   =    int(input("""enter the number and , and see the magic : """))
            print()
            pretty_print(user_number)
            lowerCAmelCase_						   =    int(input("""press 0 to exit... and 1 to continue..."""))

      print("""Good Bye...""")




