



"""simple docstring"""
import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort



class       SCREAMING_SNAKE_CASE__  (					_UpperCamelCase      ,			unittest.TestCase ):
   pass



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

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




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

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	ort.SessionOptions()
    UpperCAmelCase	:					int	=	False
    return options

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/in_paint/overture-creations-5sI6fQgYIuo.png"""						)
    UpperCAmelCase	:					List[str]	=	load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""						)
    UpperCAmelCase	:					Union[str, Any]	=	OnnxStableDiffusionInpaintPipeline.from_pretrained(
        """runwayml/stable-diffusion-inpainting"""      ,     revision="""onnx"""      ,     safety_checker=lowerCAmelCase_      ,     feature_extractor=lowerCAmelCase_      ,     provider=self.gpu_provider      ,     sess_options=self.gpu_options      ,     )
    pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

    UpperCAmelCase	:					List[Any]	=	"""A red cat sitting on a park bench"""

    UpperCAmelCase	:					List[Any]	=	np.random.RandomState(0						)
    UpperCAmelCase	:					Dict	=	pipe(
        prompt=lowerCAmelCase_      ,     image=lowerCAmelCase_      ,     mask_image=lowerCAmelCase_      ,     guidance_scale=7.5      ,     num_inference_steps=10      ,     generator=lowerCAmelCase_      ,     output_type="""np"""      ,     )
    UpperCAmelCase	:					List[str]	=	output.images
    UpperCAmelCase	:					Dict	=	images[0, 255:258, 255:258, -1]

    assert images.shape == (1, 512, 512, 3)
    UpperCAmelCase	:					Union[str, Any]	=	np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]						)

    assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/in_paint/overture-creations-5sI6fQgYIuo.png"""						)
    UpperCAmelCase	:					Tuple	=	load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""						)
    UpperCAmelCase	:					List[Any]	=	LMSDiscreteScheduler.from_pretrained(
        """runwayml/stable-diffusion-inpainting"""      ,     subfolder="""scheduler"""      ,     revision="""onnx"""						)
    UpperCAmelCase	:					List[Any]	=	OnnxStableDiffusionInpaintPipeline.from_pretrained(
        """runwayml/stable-diffusion-inpainting"""      ,     revision="""onnx"""      ,     scheduler=lowerCAmelCase_      ,     safety_checker=lowerCAmelCase_      ,     feature_extractor=lowerCAmelCase_      ,     provider=self.gpu_provider      ,     sess_options=self.gpu_options      ,     )
    pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

    UpperCAmelCase	:					Union[str, Any]	=	"""A red cat sitting on a park bench"""

    UpperCAmelCase	:					Optional[Any]	=	np.random.RandomState(0						)
    UpperCAmelCase	:					List[str]	=	pipe(
        prompt=lowerCAmelCase_      ,     image=lowerCAmelCase_      ,     mask_image=lowerCAmelCase_      ,     guidance_scale=7.5      ,     num_inference_steps=20      ,     generator=lowerCAmelCase_      ,     output_type="""np"""      ,     )
    UpperCAmelCase	:					List[str]	=	output.images
    UpperCAmelCase	:					Tuple	=	images[0, 255:258, 255:258, -1]

    assert images.shape == (1, 512, 512, 3)
    UpperCAmelCase	:					Tuple	=	np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]						)

    assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3







from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

_snake_case  :    str						    =  (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_snake_case  :    list[int]						    =  [ord(letter) for letter in string.ascii_lowercase]
_snake_case  :    set[int]						    =  {ord(char) for char in VALID_CHARS}

_snake_case  :    list[str]						    =  ["the", "be", "to", "of", "and", "in", "that", "have"]

def   a_    ( lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			tuple[int, ...]	):
   __lowerCAmelCase								=	""
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42

   for keychar, cipherchar in zip(cycle(lowerCAmelCase_	),	lowerCAmelCase_	):
      __lowerCAmelCase								=	cipherchar ^ keychar
      if decodedchar not in VALID_INTS:
         return None
      decoded += chr(lowerCAmelCase_	)

   return decoded

def   a_    ( lowerCAmelCase_      :			list[int]	):
   __lowerCAmelCase								=	[]
   for key in product(lowerCAmelCase_,	repeat=3	):
      __lowerCAmelCase								=	try_key(lowerCAmelCase_,	lowerCAmelCase_	)
      if encoded is not None:
         possibles.append(lowerCAmelCase_	)
   return possibles

def   a_    ( lowerCAmelCase_      :			list[str],	lowerCAmelCase_      :			str	):
   return [possible for possible in possibles if common_word in possible.lower()]

def   a_    ( lowerCAmelCase_      :			str = "p059_cipher.txt"	):
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	Path(lowerCAmelCase_	).parent.joinpath(lowerCAmelCase_	).read_text(encoding='utf-8'	)

   __lowerCAmelCase								=	[int(lowerCAmelCase_	) for number in data.strip().split(','	)]

   __lowerCAmelCase								=	filter_valid_chars(lowerCAmelCase_	)
   for common_word in COMMON_WORDS:
      __lowerCAmelCase								=	filter_common_word(lowerCAmelCase_,	lowerCAmelCase_	)
      if len(lowerCAmelCase_	) == 1:
         break

   __lowerCAmelCase								=	possibles[0]
   return sum(ord(lowerCAmelCase_	) for char in decoded_text	)


if __name__ == "__main__":
     print(F"""{solution() = }""")

