
'''simple docstring'''
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


A__ :		Any					=False







@nightly
@require_torch_gpu
class 			__A					(			unittest.TestCase      ):
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  Any       				=		VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							)
   # remove text_unet
   pipe.remove_unused_weights()
   pipe.to(lowerCamelCase							)
   pipe.set_progress_bar_config(disable=lowerCamelCase							)

   __A		:  Optional[int]       				=		load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)

   __A		:  str       				=		torch.manual_seed(0							)
   __A		:  int       				=		pipe(
       prompt="""first prompt"""					, image=lowerCamelCase					, text_to_image_strength=0.75					, generator=lowerCamelCase					, guidance_scale=7.5					, num_inference_steps=2					, output_type="""numpy"""					, ).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(lowerCamelCase							)
     __A		:  Union[str, Any]       				=		VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCamelCase							)

   pipe.to(lowerCamelCase							)
   pipe.set_progress_bar_config(disable=lowerCamelCase							)

   __A		:  List[Any]       				=		generator.manual_seed(0							)
   __A		:  List[Any]       				=		pipe(
       prompt="""first prompt"""					, image=lowerCamelCase					, text_to_image_strength=0.75					, generator=lowerCamelCase					, guidance_scale=7.5					, num_inference_steps=2					, output_type="""numpy"""					, ).images

   assert np.abs(image - new_image							).sum() < 1e-5, "Models don't have the same forward pass"






 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   __A		:  Tuple       				=		VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							)
   pipe.remove_unused_weights()
   pipe.to(lowerCamelCase							)
   pipe.set_progress_bar_config(disable=lowerCamelCase							)

   __A		:  List[str]       				=		"""cyberpunk 2077"""
   __A		:  List[str]       				=		load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)
   __A		:  str       				=		torch.manual_seed(0							)
   __A		:  Optional[Any]       				=		pipe(
       prompt=lowerCamelCase					, image=lowerCamelCase					, text_to_image_strength=0.75					, generator=lowerCamelCase					, guidance_scale=7.5					, num_inference_steps=50					, output_type="""numpy"""					, ).images

   __A		:  Tuple       				=		image[0, 2_53:2_56, 2_53:2_56, -1]

   assert image.shape == (1, 5_12, 5_12, 3)
   __A		:  str       				=		np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2





'''simple docstring'''
A__ :		Optional[Any]					=[chr(i) for i in range(32, 126)]
A__ :		Tuple					=list(range(len(alphabets)))
A__ :		int					=list(range(len(alphabets)))
A__ :		Optional[Any]					=list(range(len(alphabets)))
A__ :		Any					=list(reversed(range(len(alphabets))))
A__ :		Optional[int]					=[]
A__ :		Tuple					=0







def 							A_  (    )					->							Any:





  """simple docstring"""

  global gear_one_pos
  global gear_two_pos
  global gear_three_pos
  __A		:  Any       				=		gear_one[0]
  gear_one.append(__SCREAMING_SNAKE_CASE			)
  del gear_one[0]
  gear_one_pos += 1
  if gear_one_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
    __A		:  Any       				=		gear_two[0]
    gear_two.append(__SCREAMING_SNAKE_CASE			)
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
      __A		:  str       				=		gear_three[0]
      gear_three.append(__SCREAMING_SNAKE_CASE			)
      del gear_three[0]
      gear_three_pos += 1







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str			)					->							List[str]:





  """simple docstring"""

  __A		:  Union[str, Any]       				=		alphabets.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Dict       				=		gear_one[target]
  __A		:  int       				=		gear_two[target]
  __A		:  List[str]       				=		gear_three[target]
  __A		:  List[Any]       				=		reflector[target]
  __A		:  List[Any]       				=		gear_three.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Tuple       				=		gear_two.index(__SCREAMING_SNAKE_CASE			)
  __A		:  List[str]       				=		gear_one.index(__SCREAMING_SNAKE_CASE			)
  code.append(alphabets[target]			)
  rotator()


if __name__ == "__main__":
     A__ :		int					=list(input('Type your message:\n'))
     while True:
          try:
               A__ :		Any					=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!'
     )



