





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 _a							(      lowerCamelCase:  str   ,							lowerCamelCase:  Optional[Any]=10   ,							lowerCamelCase:  List[str]=10   ,							lowerCamelCase:  Optional[Any]=False   ,							lowerCamelCase:  List[Any]=False					)      ->		Dict:







      '''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(UpperCAmelCase__   ,							0   ,							-1					)					) for frame in video]

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

      return video



def _a							(      lowerCamelCase:  List[str]   ,							lowerCamelCase:  List[str]=False   ,							lowerCamelCase:  Any=False   ,							lowerCamelCase:  List[str]=False					)      ->		Any:







      '''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			  =							image_processor_tester.max_resolution
            else:
                  __A			  =							np.random.choice(
                      np.arange(image_processor_tester.min_resolution   ,							image_processor_tester.max_resolution					)   ,							2					)
                  __A			  =							prepare_video(
                      image_processor_tester=UpperCAmelCase__   ,							width=UpperCAmelCase__   ,							height=UpperCAmelCase__   ,							numpify=UpperCAmelCase__   ,							torchify=UpperCAmelCase__   ,							)
            video_inputs.append(UpperCAmelCase__					)

      return video_inputs





class A_   (     unittest.TestCase  ):




   def __init__(self      :Dict       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :str=7       ,  _UpperCamelCase      :str=3       ,  _UpperCamelCase      :Any=4       ,  _UpperCamelCase      :int=18       ,  _UpperCamelCase      :Union[str, Any]=30       ,  _UpperCamelCase      :Tuple=400       ,  _UpperCamelCase      :Tuple=True       ,  _UpperCamelCase      :Dict=None       ,  _UpperCamelCase      :Dict=True       ,  _UpperCamelCase      :List[Any]=[0.5, 0.5, 0.5]       ,  _UpperCamelCase      :Any=[0.5, 0.5, 0.5]       ,  _UpperCamelCase      :Tuple=True       ,  _UpperCamelCase      :Tuple=None       ,  )->   Any:
         __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 _lowerCAmelCase (self      :Optional[Any]					)->   List[Any]:
         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 A_   (     _a					, unittest.TestCase  ):
   lowerCAmelCase__  	=					TvltImageProcessor if is_vision_available() else None




   def _lowerCAmelCase (self      :int					)->   Union[str, Any]:
         __A			  =							TvltImageProcessorTester(self					)




   @property
   def _lowerCAmelCase (self      :int					)->   Any:
         return self.image_processor_tester.prepare_image_processor_dict()




   def _lowerCAmelCase (self      :Dict					)->   List[Any]:
         __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      :List[Any]					)->   Tuple:
         __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_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(_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      :Tuple					)->   Optional[Any]:
         __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_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(_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      :List[Any]					)->   List[str]:
         __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_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(_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'''],
             )       ,  )







'''simple docstring'''


__SCREAMING_SNAKE_CASE						=      [chr(i) for i in range(32, 126)]
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(reversed(range(len(alphabets))))
__SCREAMING_SNAKE_CASE						=      []
__SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      0
def 					__magic_name__(       ):
						global gear_one_pos
						global gear_two_pos
						global gear_three_pos
						UpperCamelCase_     :	List[Any]					     =     gear_one[0]
						gear_one.append(UpperCAmelCase__	)
						del gear_one[0]
						gear_one_pos += 1
						if gear_one_pos % int(len(UpperCAmelCase__	)	) == 0:
												UpperCamelCase_     :	List[str]					     =     gear_two[0]
												gear_two.append(UpperCAmelCase__	)
												del gear_two[0]
												gear_two_pos += 1
												if gear_two_pos % int(len(UpperCAmelCase__	)	) == 0:
																		UpperCamelCase_     :	List[Any]					     =     gear_three[0]
																		gear_three.append(UpperCAmelCase__	)
																		del gear_three[0]
																		gear_three_pos += 1
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Any					     =     alphabets.index(UpperCAmelCase__	)
						UpperCamelCase_     :	List[str]					     =     gear_one[target]
						UpperCamelCase_     :	str					     =     gear_two[target]
						UpperCamelCase_     :	int					     =     gear_three[target]
						UpperCamelCase_     :	Union[str, Any]					     =     reflector[target]
						UpperCamelCase_     :	Dict					     =     gear_three.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_two.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_one.index(UpperCAmelCase__	)
						code.append(alphabets[target]	)
						rotator()


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE						=      list(input("Type your message:\n"))
	while True:
		try:
			__SCREAMING_SNAKE_CASE						=      int(input("Please set token:(must be only digits)\n"))
			break
		except Exception as error:
			print(error)
	for _ in range(token):
		rotator()
	for j in decode:
		engine(j)
	print("\n" + "".join(code))
	print(
	    f"\nYour Token is {token} please write it down.\nIf you want to decode "
	    "this message again you should input same digits as token!"
	)




