

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_					(       UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    pass


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






    '''simple docstring'''






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







    @property
    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:Optional[Any]        =		ort.SessionOptions()
        __UpperCamelCase					:Optional[int]        =		False
        return options







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

        __UpperCamelCase					:List[str]        =		'''A red cat sitting on a park bench'''

        __UpperCamelCase					:Any        =		np.random.RandomState(0)
        __UpperCamelCase					:Union[str, Any]        =		pipe(
            prompt=__lowercase			,			image=__lowercase			,			mask_image=__lowercase			,			guidance_scale=7.5			,			num_inference_steps=10			,			generator=__lowercase			,			output_type='''np'''			,			)
        __UpperCamelCase					:Optional[Any]        =		output.images
        __UpperCamelCase					:List[str]        =		images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        __UpperCamelCase					:Tuple        =		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       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:Union[str, Any]        =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png''')
        __UpperCamelCase					:Union[str, Any]        =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''')
        __UpperCamelCase					:Dict        =		LMSDiscreteScheduler.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''			,			subfolder='''scheduler'''			,			revision='''onnx''')
        __UpperCamelCase					:List[str]        =		OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''			,			revision='''onnx'''			,			scheduler=__lowercase			,			safety_checker=__lowercase			,			feature_extractor=__lowercase			,			provider=self.gpu_provider			,			sess_options=self.gpu_options			,			)
        pipe.set_progress_bar_config(disable=__lowercase)

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

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

        assert images.shape == (1, 512, 512, 3)
        __UpperCamelCase					:List[Any]        =		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






def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if not grid or not grid[0]:
        raise TypeError('''The grid does not contain the appropriate information'''     )

    for cell_n in range(1  ,      len(grid[0]     )     ):
        grid[0][cell_n] += grid[0][cell_n - 1]
    __UpperCamelCase					:Union[str, Any]        =		grid[0]

    for row_n in range(1  ,      len(SCREAMING_SNAKE_CASE     )     ):
        __UpperCamelCase					:Optional[int]        =		grid[row_n]
        __UpperCamelCase					:Dict        =		fill_row(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
        __UpperCamelCase					:Any        =		grid[row_n]

    return grid[-1][-1]



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    current_row[0] += row_above[0]
    for cell_n in range(1  ,      len(SCREAMING_SNAKE_CASE     )     ):
        current_row[cell_n] += min(current_row[cell_n - 1]  ,      row_above[cell_n]     )

    return current_row


if __name__ == "__main__":
    import doctest

    doctest.testmod()





