code: import random
  import unittest
  
  import torch
  
  from diffusers import IFImgaImgPipeline
  from diffusers.utils import floats_tensor
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import skip_mps, torch_device
  
  from ..pipeline_params import (
      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
      TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
  )
  from ..test_pipelines_common import PipelineTesterMixin
  from . import IFPipelineTesterMixin
  
  
  
  
  @skip_mps
  class    __lowerCAmelCase  (       a_,	a_,	unittest.TestCase						):
  
  
  
   '''simple docstring'''
   _lowerCamelCase						:	Union[str, Any]				    = IFImgaImgPipeline
   _lowerCamelCase						:	Any				    = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
   _lowerCamelCase						:	Tuple				    = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _lowerCamelCase						:	int				    = PipelineTesterMixin.required_optional_params - {'latents'}
  
   def 					__snake_case					(			self       :		Optional[int]  ) ->							Dict:
          return self._get_dummy_components()
  
   def 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		Optional[int]       ,			lowerCamelCase       :		str=0  ) ->							str:
          if str(lowerCamelCase  ).startswith('mps'  ):
                 _UpperCamelCase     :       str  						=  torch.manual_seed(lowerCamelCase  )
          else:
                 _UpperCamelCase     :       Any  						=  torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )
  
          _UpperCamelCase     :       Tuple  						=  floats_tensor((1, 3, 3_2, 3_2)       ,			rng=random.Random(lowerCamelCase  )  ).to(lowerCamelCase  )
  
          _UpperCamelCase     :       List[str]  						=  {
              'prompt': 'A painting of a squirrel eating a burger',
              'image': image,
              'generator': generator,
              'num_inference_steps': 2,
              'output_type': 'numpy',
          }
  
          return inputs
  
   def 					__snake_case					(			self       :		Optional[Any]  ) ->							Dict:
          self._test_save_load_optional_components()
  
   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()       ,			reason='XFormers attention is only available with CUDA and `xformers` installed'       ,			)
   def 					__snake_case					(			self       :		Any  ) ->							Union[str, Any]:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )
  
   @unittest.skipIf(torch_device != 'cuda'       ,			reason='float16 requires CUDA'  )
   def 					__snake_case					(			self       :		int  ) ->							Dict:
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1e-1  )
  
   @unittest.skipIf(torch_device != 'cuda'       ,			reason='float16 requires CUDA'  )
   def 					__snake_case					(			self       :		str  ) ->							List[Any]:
          super().test_floataa_inference(expected_max_diff=1e-1  )
  
   def 					__snake_case					(			self       :		Any  ) ->							int:
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2  )
  
   def 					__snake_case					(			self       :		List[str]  ) ->							Any:
          self._test_save_load_local()
  
   def 					__snake_case					(			self       :		Optional[Any]  ) ->							Optional[Any]:
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2       ,			)
  
  
  
  
style_context: UpperCAmelCase					=				[chr(i) for i in range(32, 1_26)]
  UpperCAmelCase					=				list(range(len(alphabets)))
  UpperCAmelCase					=				list(range(len(alphabets)))
  UpperCAmelCase					=				list(range(len(alphabets)))
  UpperCAmelCase					=				list(reversed(range(len(alphabets))))
  UpperCAmelCase					=				[]
  UpperCAmelCase					=				UpperCAmelCase					=				UpperCAmelCase					=				0
  
  
  def SCREAMING_SNAKE_CASE				(     ):
         global gear_one_pos
         global gear_two_pos
         global gear_three_pos
         _UpperCamelCase     :       Any  						=  gear_one[0]
         gear_one.append(lowercase_				)
         del gear_one[0]
         gear_one_pos += 1
         if gear_one_pos % int(len(lowercase_				)				) == 0:
                _UpperCamelCase     :       str  						=  gear_two[0]
                gear_two.append(lowercase_				)
                del gear_two[0]
                gear_two_pos += 1
                if gear_two_pos % int(len(lowercase_				)				) == 0:
                       _UpperCamelCase     :       int  						=  gear_three[0]
                       gear_three.append(lowercase_				)
                       del gear_three[0]
                       gear_three_pos += 1
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
         _UpperCamelCase     :       Dict  						=  alphabets.index(lowercase_				)
         _UpperCamelCase     :       Union[str, Any]  						=  gear_one[target]
         _UpperCamelCase     :       Any  						=  gear_two[target]
         _UpperCamelCase     :       Tuple  						=  gear_three[target]
         _UpperCamelCase     :       Tuple  						=  reflector[target]
         _UpperCamelCase     :       Any  						=  gear_three.index(lowercase_				)
         _UpperCamelCase     :       Any  						=  gear_two.index(lowercase_				)
         _UpperCamelCase     :       str  						=  gear_one.index(lowercase_				)
         code.append(alphabets[target]				)
         rotator()
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				list(input('''Type your message:\n'''))
   while True:
    try:
     UpperCAmelCase					=				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!'''
   )
  
  
  
  