





import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
     import onnxruntime as ort



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







 '''simple docstring'''

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any]) ->Any:



   '''simple docstring'''



   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->Dict:



   '''simple docstring'''



   lowerCamelCase__:   str    						=ort.SessionOptions()
   lowerCamelCase__:   str    						=False
   return options







 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->str:



   '''simple docstring'''



   lowerCamelCase__:   Any    						=load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/in_paint/overture-creations-5sI6fQgYIuo.png")
   lowerCamelCase__:   Optional[int]    						=load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/in_paint/overture-creations-5sI6fQgYIuo_mask.png")
   lowerCamelCase__:   int    						=load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy")

   # using the PNDM scheduler by default
   lowerCamelCase__:   str    						=OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
       "CompVis/stable-diffusion-v1-4" ,				revision="onnx" ,				safety_checker=UpperCAmelCase_ ,				feature_extractor=UpperCAmelCase_ ,				provider=self.gpu_provider ,				sess_options=self.gpu_options ,				)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:   Optional[Any]    						="A red cat sitting on a park bench"

   lowerCamelCase__:   List[str]    						=np.random.RandomState(0)
   lowerCamelCase__:   Tuple    						=pipe(
       prompt=UpperCAmelCase_ ,				image=UpperCAmelCase_ ,				mask_image=UpperCAmelCase_ ,				strength=0.75 ,				guidance_scale=7.5 ,				num_inference_steps=15 ,				generator=UpperCAmelCase_ ,				output_type="np" ,				)

   lowerCamelCase__:   List[str]    						=output.images[0]

   assert image.shape == (512, 512, 3)
   assert np.abs(expected_image - image).max() < 1E-2












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()

