

"""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  _lowerCAmelCase  (     lowercase					,unittest.TestCase       ):







 """simple docstring"""







 pass







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







 """simple docstring"""







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


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


 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/in_paint/overture-creations-5sI6fQgYIuo.png"     )
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"     )
    __lowercase								=						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								=						"A red cat sitting on a park bench"

    __lowercase								=						np.random.RandomState(0     )
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__, image=UpperCAmelCase__, mask_image=UpperCAmelCase__, guidance_scale=7.5, num_inference_steps=1_0, generator=UpperCAmelCase__, output_type="np", )
    __lowercase								=						output.images
    __lowercase								=						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)
    __lowercase								=						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  _lowercase			(      self							:      List[Any]     ):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/in_paint/overture-creations-5sI6fQgYIuo.png"     )
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"     )
    __lowercase								=						LMSDiscreteScheduler.from_pretrained(
        "runwayml/stable-diffusion-inpainting", subfolder="scheduler", revision="onnx"     )
    __lowercase								=						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								=						"A red cat sitting on a park bench"

    __lowercase								=						np.random.RandomState(0     )
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__, image=UpperCAmelCase__, mask_image=UpperCAmelCase__, guidance_scale=7.5, num_inference_steps=2_0, generator=UpperCAmelCase__, output_type="np", )
    __lowercase								=						output.images
    __lowercase								=						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)
    __lowercase								=						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"""





import re

from filelock import FileLock


try:
   import nltk

   _a							=							True
except (ImportError, ModuleNotFoundError):
   _a							=							False

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




def   _A  (  UpperCamelCase_					:	str)     ->						str:
   '''simple docstring'''


   re.sub("<n>",      "",      UpperCamelCase_)  # 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(UpperCamelCase_))




