





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 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   @property
   def 							__lowercase		( self)      ->						str:







          '''simple docstring'''

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

   @property
   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          a__						:     List[str]			=    ort.SessionOptions()
          a__						:     Any			=    False
          return options

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Optional[int]			=    load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/in_paint/overture-creations-5sI6fQgYIuo.png')
          a__						:     Any			=    load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/in_paint/overture-creations-5sI6fQgYIuo_mask.png')
          a__						:     Dict			=    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
          a__						:     List[Any]			=    OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
              'CompVis/stable-diffusion-v1-4' ,  revision='onnx' ,  safety_checker=lowercase ,  feature_extractor=lowercase ,  provider=self.gpu_provider ,  sess_options=self.gpu_options ,  )
          pipe.set_progress_bar_config(disable=lowercase)

          a__						:     Tuple			=    'A red cat sitting on a park bench'

          a__						:     Union[str, Any]			=    np.random.RandomState(0)
          a__						:     str			=    pipe(
              prompt=lowercase ,  image=lowercase ,  mask_image=lowercase ,  strength=0.75 ,  guidance_scale=7.5 ,  num_inference_steps=15 ,  generator=lowercase ,  output_type='np' ,  )

          a__						:     Optional[int]			=    output.images[0]

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









import enum
import shutil
import sys


lowercase						, lowercase				:	List[Any]      						=  shutil.get_terminal_size()

lowercase				:	Union[str, Any]      						=  {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}





class 			A__   (							enum.Enum     ):




   """simple docstring"""



   __A       :    List[str]	  =  0
   __A       :    str	  =  1



def A_     (		A__		,				A__=""    )  ->		int:
       sys.stdout.write(str(A__    ) + end    )
       sys.stdout.flush()



def A_     (		A__		,				A__		,				A__=""    )  ->		int:
       forceWrite(F'\u001b[{color}m{content}\u001b[0m'		,				A__    )



def A_     (		)  ->		Any:
       forceWrite('\r'    )



def A_     (		A__		,				A__    )  ->		List[str]:
       forceWrite(F'\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'    )



def A_     (		)  ->		Any:
       forceWrite(' ' * TERMINAL_WIDTH    )
       reset_cursor()



def A_     (		)  ->		Any:
       reset_cursor()
       forceWrite('-' * TERMINAL_WIDTH    )


