"""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     UpperCAmelCase						(_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    pass



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



    """simple docstring"""

    @property
    def _snake_case			(   self							):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )






    @property
    def _snake_case			(   self							):
       lowercase__:   Any					=      ort.SessionOptions()
       lowercase__:   Optional[Any]					=      False
       return options






    def _snake_case			(   self							):
       lowercase__:   int					=      load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png'''							)
       lowercase__:   Tuple					=      load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''							)
       lowercase__:   Tuple					=      OnnxStableDiffusionInpaintPipeline.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''    ,			revision='''onnx'''    ,			safety_checker=_UpperCAmelCase    ,			feature_extractor=_UpperCAmelCase    ,			provider=self.gpu_provider    ,			sess_options=self.gpu_options    ,			)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   List[Any]					=      '''A red cat sitting on a park bench'''

       lowercase__:   int					=      np.random.RandomState(0							)
       lowercase__:   List[str]					=      pipe(
           prompt=_UpperCAmelCase    ,			image=_UpperCAmelCase    ,			mask_image=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=10    ,			generator=_UpperCAmelCase    ,			output_type='''np'''    ,			)
       lowercase__:   Tuple					=      output.images
       lowercase__:   int					=      images[0, 255:258, 255:258, -1]

       assert images.shape == (1, 512, 512, 3)
       lowercase__:   Union[str, Any]					=      np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]							)

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






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

       lowercase__:   Any					=      '''A red cat sitting on a park bench'''

       lowercase__:   List[str]					=      np.random.RandomState(0							)
       lowercase__:   Dict					=      pipe(
           prompt=_UpperCAmelCase    ,			image=_UpperCAmelCase    ,			mask_image=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=20    ,			generator=_UpperCAmelCase    ,			output_type='''np'''    ,			)
       lowercase__:   Union[str, Any]					=      output.images
       lowercase__:   Any					=      images[0, 255:258, 255:258, -1]

       assert images.shape == (1, 512, 512, 3)
       lowercase__:   Union[str, Any]					=      np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]							)

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

"""simple docstring"""






__A			   = [
    (1_0_0_0, "M"),
    (9_0_0, "CM"),
    (5_0_0, "D"),
    (4_0_0, "CD"),
    (1_0_0, "C"),
    (9_0, "XC"),
    (5_0, "L"),
    (4_0, "XL"),
    (1_0, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   lowercase__:   Dict					=      {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}
   lowercase__:   List[str]					=      0
   lowercase__:   List[Any]					=      0
   while place < len(__UpperCAmelCase ):
      if (place + 1 < len(__UpperCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  str:
   lowercase__:   Optional[Any]					=      []
   for arabic, roman in ROMAN:
      ((lowercase__), (lowercase__)):   Tuple					=      divmod(__UpperCAmelCase       ,    __UpperCAmelCase )
      result.append(roman * factor )
      if number == 0:
         break
   return "".join(__UpperCAmelCase )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
