


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







    @property
    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	int								=		ort.SessionOptions()
         __lowerCamelCase   :	Optional[Any]								=		False
         return options







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

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

         __lowerCamelCase   :	Union[str, Any]								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Union[str, Any]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	Optional[Any]								=		output.images
         __lowerCamelCase   :	Tuple								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Union[str, Any]								=		np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]	)

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







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

         __lowerCamelCase   :	str								=		'A red cat sitting on a park bench'

         __lowerCamelCase   :	Tuple								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Optional[int]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=20			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	List[str]								=		output.images
         __lowerCamelCase   :	Dict								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Tuple								=		np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]	)

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










'''simple docstring'''



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     int:
         if dst_width < 0 or dst_height < 0:
              raise ValueError('Destination width/height should be > 0'	)

         __lowerCamelCase   :	Dict								=		img
         __lowerCamelCase   :	Any								=		img.shape[1]
         __lowerCamelCase   :	Optional[int]								=		img.shape[0]
         __lowerCamelCase   :	Dict								=		dst_width
         __lowerCamelCase   :	str								=		dst_height

         __lowerCamelCase   :	Dict								=		self.src_w / self.dst_w
         __lowerCamelCase   :	List[Any]								=		self.src_h / self.dst_h

         __lowerCamelCase   :	Optional[int]								=		(
             np.ones((self.dst_h, self.dst_w, 3)			,     np.uinta	) * 2_55
         )







    def       lowercase_				(							self	)				->     List[Any]:
         for i in range(self.dst_h	):
              for j in range(self.dst_w	):
                   __lowerCamelCase   :	Union[str, Any]								=		self.img[self.get_y(SCREAMING_SNAKE_CASE_	)][self.get_x(SCREAMING_SNAKE_CASE_	)]







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_x * x	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_y * y	)


if __name__ == "__main__":
      A__						,					A__		:					Optional[Any]												=  800, 600
      A__		:					List[str]												=  imread("""image_data/lena.jpg""", 1)
      A__		:					List[Any]												=  NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
      )
      waitKey(0)
      destroyAllWindows()






