"""simple docstring"""
import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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        __snake_case	(					__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



      def   lowerCamelCase_       (						self   ,      lowercase=0)     ->      Tuple:





        '''simple docstring'''





        a__:					Dict      							=     floats_tensor((1, 3, 1_28, 1_28)   ,      rng=random.Random(lowercase))
        a__:					Tuple      							=     np.random.RandomState(lowercase)
        a__:					Tuple      							=     {
            'prompt': 'A painting of a squirrel eating a burger',
            'image': image,
            'generator': generator,
            'num_inference_steps': 3,
            'strength': 0.75,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					Optional[Any]      							=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,      provider='CPUExecutionProvider')
        pipe.set_progress_bar_config(disable=lowercase)

        a__:					Any      							=     self.get_dummy_inputs()
        a__:					List[Any]      							=     pipe(**lowercase).images
        a__:					Optional[Any]      							=     image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 1_28, 1_28, 3)
        a__:					Dict      							=     np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087])
        assert np.abs(image_slice - expected_slice).max() < 1e-1



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					Optional[int]      							=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,      provider='CPUExecutionProvider')
        a__:					Tuple      							=     PNDMScheduler.from_config(pipe.scheduler.config   ,      skip_prk_steps=lowercase)
        pipe.set_progress_bar_config(disable=lowercase)

        a__:					int      							=     self.get_dummy_inputs()
        a__:					Optional[Any]      							=     pipe(**lowercase).images
        a__:					Tuple      							=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 1_28, 1_28, 3)
        a__:					str      							=     np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154])

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



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					List[str]      							=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,      provider='CPUExecutionProvider')
        a__:					List[Any]      							=     LMSDiscreteScheduler.from_config(pipe.scheduler.config)
        pipe.set_progress_bar_config(disable=lowercase)

        # warmup pass to apply optimizations
        a__:					List[Any]      							=     pipe(**self.get_dummy_inputs())

        a__:					Tuple      							=     self.get_dummy_inputs()
        a__:					Optional[Any]      							=     pipe(**lowercase).images
        a__:					List[Any]      							=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 1_28, 1_28, 3)
        a__:					Optional[Any]      							=     np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203])

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



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					Any      							=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,      provider='CPUExecutionProvider')
        a__:					List[Any]      							=     EulerDiscreteScheduler.from_config(pipe.scheduler.config)
        pipe.set_progress_bar_config(disable=lowercase)

        a__:					Optional[Any]      							=     self.get_dummy_inputs()
        a__:					Any      							=     pipe(**lowercase).images
        a__:					Optional[int]      							=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 1_28, 1_28, 3)
        a__:					Tuple      							=     np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304])

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



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[int]      							=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,      provider='CPUExecutionProvider')
        a__:					Optional[int]      							=     EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
        pipe.set_progress_bar_config(disable=lowercase)

        a__:					List[str]      							=     self.get_dummy_inputs()
        a__:					Union[str, Any]      							=     pipe(**lowercase).images
        a__:					List[str]      							=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 1_28, 1_28, 3)
        a__:					Any      							=     np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304])

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



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,      provider='CPUExecutionProvider')
        a__:					str      							=     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
        pipe.set_progress_bar_config(disable=lowercase)

        a__:					List[Any]      							=     self.get_dummy_inputs()
        a__:					Optional[Any]      							=     pipe(**lowercase).images
        a__:					Dict      							=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 1_28, 1_28, 3)
        a__:					List[Any]      							=     np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552])

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







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



      @property
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					Optional[int]      							=     ort.SessionOptions()
        a__:					Union[str, Any]      							=     False
        return options



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					str      							=     load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/img2img/sketch-mountains-input.jpg')
        a__:					Any      							=     init_image.resize((7_68, 5_12))
        # using the PNDM scheduler by default
        a__:					List[Any]      							=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'   ,      revision='onnx'   ,      safety_checker=lowercase   ,      feature_extractor=lowercase   ,      provider=self.gpu_provider   ,      sess_options=self.gpu_options   ,      )
        pipe.set_progress_bar_config(disable=lowercase)

        a__:					Dict      							=     'A fantasy landscape, trending on artstation'

        a__:					Dict      							=     np.random.RandomState(0)
        a__:					int      							=     pipe(
            prompt=lowercase   ,      image=lowercase   ,      strength=0.75   ,      guidance_scale=7.5   ,      num_inference_steps=10   ,      generator=lowercase   ,      output_type='np'   ,      )
        a__:					List[str]      							=     output.images
        a__:					Dict      							=     images[0, 2_55:2_58, 3_83:3_86, -1]

        assert images.shape == (1, 5_12, 7_68, 3)
        a__:					List[Any]      							=     np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019])
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

        assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					str      							=     load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/img2img/sketch-mountains-input.jpg')
        a__:					str      							=     init_image.resize((7_68, 5_12))
        a__:					List[str]      							=     LMSDiscreteScheduler.from_pretrained(
            'runwayml/stable-diffusion-v1-5'   ,      subfolder='scheduler'   ,      revision='onnx')
        a__:					Optional[int]      							=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(
            'runwayml/stable-diffusion-v1-5'   ,      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)

        a__:					Optional[Any]      							=     'A fantasy landscape, trending on artstation'

        a__:					List[Any]      							=     np.random.RandomState(0)
        a__:					Dict      							=     pipe(
            prompt=lowercase   ,      image=lowercase   ,      strength=0.75   ,      guidance_scale=7.5   ,      num_inference_steps=20   ,      generator=lowercase   ,      output_type='np'   ,      )
        a__:					str      							=     output.images
        a__:					List[Any]      							=     images[0, 2_55:2_58, 3_83:3_86, -1]

        assert images.shape == (1, 5_12, 7_68, 3)
        a__:					Union[str, Any]      							=     np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431])
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

        assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2



"""simple docstring"""
import unittest

from knapsack import knapsack as k




class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					List[Any]      							=     0
        a__:					Dict      							=     [0]
        a__:					int      							=     [0]
        a__:					Optional[Any]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      0)

        a__:					str      							=     [60]
        a__:					Dict      							=     [10]
        a__:					List[str]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      0)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					int      							=     3
        a__:					str      							=     [1, 2, 3]
        a__:					Dict      							=     [3, 2, 1]
        a__:					Optional[int]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      5)



      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					Any      							=     50
        a__:					Optional[int]      							=     [60, 1_00, 1_20]
        a__:					str      							=     [10, 20, 30]
        a__:					int      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      2_20)


if __name__ == "__main__":
 unittest.main()


