





import gc
import tempfile
import unittest

import numpy as np
import torch

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


__A             =       False



class      _SCREAMING_SNAKE_CASE		(			unittest.TestCase					):







 '''simple docstring'''
 pass



@nightly
@require_torch_gpu
class      _SCREAMING_SNAKE_CASE		(			unittest.TestCase					):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->Optional[Any]:



   '''simple docstring'''



   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()

 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:   str    						=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" ,				torch_dtype=torch.floataa)
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)

   lowerCamelCase__:   str    						=load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg")

   lowerCamelCase__:   Dict    						=torch.manual_seed(0)
   lowerCamelCase__:   int    						=pipe.dual_guided(
       prompt="first prompt" ,				image=lowercase_ ,				text_to_image_strength=0.75 ,				generator=lowercase_ ,				guidance_scale=7.5 ,				num_inference_steps=2 ,				output_type="numpy" ,				).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(lowercase_)
     lowerCamelCase__:   Dict    						=VersatileDiffusionPipeline.from_pretrained(lowercase_ ,				torch_dtype=torch.floataa)
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)

   lowerCamelCase__:   List[Any]    						=generator.manual_seed(0)
   lowerCamelCase__:   Any    						=pipe.dual_guided(
       prompt="first prompt" ,				image=lowercase_ ,				text_to_image_strength=0.75 ,				generator=lowercase_ ,				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 							SCREAMING_SNAKE_CASE_      (self       :     int) ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:   Dict    						=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" ,				torch_dtype=torch.floataa)
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)

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

   lowerCamelCase__:   List[Any]    						=image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   lowerCamelCase__:   int    						=np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001])

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

   lowerCamelCase__:   Tuple    						="A painting of a squirrel eating a burger "
   lowerCamelCase__:   Dict    						=torch.manual_seed(0)
   lowerCamelCase__:   str    						=pipe.text_to_image(
       prompt=lowercase_ ,				generator=lowercase_ ,				guidance_scale=7.5 ,				num_inference_steps=50 ,				output_type="numpy").images

   lowerCamelCase__:   Optional[Any]    						=image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   lowerCamelCase__:   Dict    						=np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778])

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

   lowerCamelCase__:   Tuple    						=pipe.image_variation(lowercase_ ,				generator=lowercase_ ,				output_type="numpy").images

   lowerCamelCase__:   List[Any]    						=image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   lowerCamelCase__:   Optional[Any]    						=np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456])

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












def   lowerCAmelCase_				( __a	)  ->	str:







  """simple docstring"""





  lowerCamelCase__:   List[str]    						=""
  for ch in key:
    if ch == " " or ch not in key_no_dups and ch.isalpha():
      key_no_dups += ch
  return key_no_dups





def   lowerCAmelCase_				( __a	)  ->	dict[str, str]:







  """simple docstring"""





  lowerCamelCase__:   Dict    						=[chr(i + 65	) for i in range(26	)]
  # Remove duplicate characters from key
  lowerCamelCase__:   Union[str, Any]    						=remove_duplicates(key.upper()	)
  lowerCamelCase__:   List[Any]    						=len(__a	)
  # First fill cipher with key characters
  lowerCamelCase__:   Optional[Any]    						={alphabet[i]: char for i, char in enumerate(__a	)}
  # Then map remaining characters in alphabet to
  # the alphabet from the beginning
  for i in range(len(__a	)			,					26	):
    lowerCamelCase__:   Optional[int]    						=alphabet[i - offset]
    # Ensure we are not mapping letters to letters previously mapped
    while char in key:
      offset -= 1
      lowerCamelCase__:   List[Any]    						=alphabet[i - offset]
    lowerCamelCase__:   str    						=char
  return cipher_alphabet





def   lowerCAmelCase_				( __a			,					__a	)  ->	str:







  """simple docstring"""





  return "".join(cipher_map.get(__a			,					__a	) for ch in message.upper()	)





def   lowerCAmelCase_				( __a			,					__a	)  ->	str:







  """simple docstring"""





  lowerCamelCase__:   List[Any]    						={v: k for k, v in cipher_map.items()}
  return "".join(rev_cipher_map.get(__a			,					__a	) for ch in message.upper()	)





def   lowerCAmelCase_				( )  ->	None:







  """simple docstring"""





  lowerCamelCase__:   Optional[Any]    						=input("Enter message to encode or decode: "	).strip()
  lowerCamelCase__:   Dict    						=input("Enter keyword: "	).strip()
  lowerCamelCase__:   Any    						=input("Encipher or decipher? E/D:"	).strip()[0].lower()
  try:
    lowerCamelCase__:   Union[str, Any]    						={"e": encipher, "d": decipher}[option]
  except KeyError:
    raise KeyError("invalid input option"	)
  lowerCamelCase__:   List[Any]    						=create_cipher_map(__a	)
  print(func(__a			,					__a	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

