





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 				lowercase		(				self :		int     )							->						int:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )






  @property
  def 				lowercase		(				self :		List[Any]     )							->						Tuple:
     __lowerCAmelCase								=	ort.SessionOptions()
     __lowerCAmelCase								=	False
     return options






  def 				lowercase		(				self :		Optional[Any]     )							->						List[str]:
     __lowerCAmelCase								=	load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'     )
     __lowerCAmelCase								=	load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'     )
     __lowerCAmelCase								=	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_     )

     __lowerCAmelCase								=	'A red cat sitting on a park bench'

     __lowerCAmelCase								=	np.random.RandomState(0     )
     __lowerCAmelCase								=	pipe(
         prompt=lowerCAmelCase_					,  image=lowerCAmelCase_					,  mask_image=lowerCAmelCase_					,  guidance_scale=7.5					,  num_inference_steps=1_0					,  generator=lowerCAmelCase_					,  output_type='np'					,  )
     __lowerCAmelCase								=	output.images
     __lowerCAmelCase								=	images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

     assert images.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase								=	np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64]     )

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






  def 				lowercase		(				self :		List[str]     )							->						int:
     __lowerCAmelCase								=	load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'     )
     __lowerCAmelCase								=	load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'     )
     __lowerCAmelCase								=	LMSDiscreteScheduler.from_pretrained(
         'runwayml/stable-diffusion-inpainting'					,  subfolder='scheduler'					,  revision='onnx'     )
     __lowerCAmelCase								=	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_     )

     __lowerCAmelCase								=	'A red cat sitting on a park bench'

     __lowerCAmelCase								=	np.random.RandomState(0     )
     __lowerCAmelCase								=	pipe(
         prompt=lowerCAmelCase_					,  image=lowerCAmelCase_					,  mask_image=lowerCAmelCase_					,  guidance_scale=7.5					,  num_inference_steps=2_0					,  generator=lowerCAmelCase_					,  output_type='np'					,  )
     __lowerCAmelCase								=	output.images
     __lowerCAmelCase								=	images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

     assert images.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase								=	np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25]     )

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








import re

from filelock import FileLock


try:
     import nltk

     _snake_case  :    Any						    =  True
except (ImportError, ModuleNotFoundError):
     _snake_case  :    Union[str, Any]						    =  False

if NLTK_AVAILABLE:
     with FileLock('.lock') as lock:
          nltk.download('punkt', quiet=True)

def   a_    ( lowerCAmelCase_      :			str	):
   re.sub('<n>',	'',	lowerCAmelCase_	)  # remove pegasus newline char
   assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
   return "\n".join(nltk.sent_tokenize(lowerCAmelCase_	)	)

