



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   UpperCAmelCase_    (       _UpperCAmelCase   ,					unittest.TestCase):
							lowerCamelCase__   			=		'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'







							def    snake_case__      (   self,			__a=0):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    floats_tensor((1, 3, 128, 128),			rng=random.Random(a__))
														_lowerCAmelCase     :							str    =    np.random.RandomState(a__)
														_lowerCAmelCase     :							Optional[int]    =    {
														    "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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,			provider="CPUExecutionProvider")
														pipe.set_progress_bar_config(disable=a__)

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

														assert image.shape == (1, 128, 128, 3)
														_lowerCAmelCase     :							int    =    np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087])
														assert np.abs(image_slice - expected_slice).max() < 1E-1







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,			provider="CPUExecutionProvider")
														_lowerCAmelCase     :							Union[str, Any]    =    PNDMScheduler.from_config(pipe.scheduler.config,			skip_prk_steps=a__)
														pipe.set_progress_bar_config(disable=a__)

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

														assert image.shape == (1, 128, 128, 3)
														_lowerCAmelCase     :							int    =    np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154])

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,			provider="CPUExecutionProvider")
														_lowerCAmelCase     :							List[Any]    =    LMSDiscreteScheduler.from_config(pipe.scheduler.config)
														pipe.set_progress_bar_config(disable=a__)

														# warmup pass to apply optimizations
														_lowerCAmelCase     :							int    =    pipe(**self.get_dummy_inputs())

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

														assert image.shape == (1, 128, 128, 3)
														_lowerCAmelCase     :							Optional[int]    =    np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203])

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







							def    snake_case__      (   self):

														'''simple docstring'''

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

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

														assert image.shape == (1, 128, 128, 3)
														_lowerCAmelCase     :							List[str]    =    np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304])

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







							def    snake_case__      (   self):

														'''simple docstring'''

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

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

														assert image.shape == (1, 128, 128, 3)
														_lowerCAmelCase     :							Union[str, Any]    =    np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304])

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,			provider="CPUExecutionProvider")
														_lowerCAmelCase     :							List[str]    =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
														pipe.set_progress_bar_config(disable=a__)

														_lowerCAmelCase     :							Tuple    =    self.get_dummy_inputs()
														_lowerCAmelCase     :							List[str]    =    pipe(**a__).images
														_lowerCAmelCase     :							Dict    =    image[0, -3:, -3:, -1]

														assert image.shape == (1, 128, 128, 3)
														_lowerCAmelCase     :							Union[str, Any]    =    np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552])

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



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







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

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







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    ort.SessionOptions()
														_lowerCAmelCase     :							List[Any]    =    False
														return options







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/img2img/sketch-mountains-input.jpg")
														_lowerCAmelCase     :							List[Any]    =    init_image.resize((768, 512))
														# using the PNDM scheduler by default
														_lowerCAmelCase     :							Optional[int]    =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
														    "CompVis/stable-diffusion-v1-4",			revision="onnx",			safety_checker=a__,			feature_extractor=a__,			provider=self.gpu_provider,			sess_options=self.gpu_options,			)
														pipe.set_progress_bar_config(disable=a__)

														_lowerCAmelCase     :							Optional[int]    =    "A fantasy landscape, trending on artstation"

														_lowerCAmelCase     :							Optional[Any]    =    np.random.RandomState(0)
														_lowerCAmelCase     :							int    =    pipe(
														    prompt=a__,			image=a__,			strength=0.75,			guidance_scale=7.5,			num_inference_steps=10,			generator=a__,			output_type="np",			)
														_lowerCAmelCase     :							Dict    =    output.images
														_lowerCAmelCase     :							List[str]    =    images[0, 255:258, 383:386, -1]

														assert images.shape == (1, 512, 768, 3)
														_lowerCAmelCase     :							Union[str, Any]    =    np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019])
														# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/img2img/sketch-mountains-input.jpg")
														_lowerCAmelCase     :							str    =    init_image.resize((768, 512))
														_lowerCAmelCase     :							Optional[int]    =    LMSDiscreteScheduler.from_pretrained(
														    "runwayml/stable-diffusion-v1-5",			subfolder="scheduler",			revision="onnx")
														_lowerCAmelCase     :							Tuple    =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
														    "runwayml/stable-diffusion-v1-5",			revision="onnx",			scheduler=a__,			safety_checker=a__,			feature_extractor=a__,			provider=self.gpu_provider,			sess_options=self.gpu_options,			)
														pipe.set_progress_bar_config(disable=a__)

														_lowerCAmelCase     :							Dict    =    "A fantasy landscape, trending on artstation"

														_lowerCAmelCase     :							int    =    np.random.RandomState(0)
														_lowerCAmelCase     :							Optional[int]    =    pipe(
														    prompt=a__,			image=a__,			strength=0.75,			guidance_scale=7.5,			num_inference_steps=20,			generator=a__,			output_type="np",			)
														_lowerCAmelCase     :							int    =    output.images
														_lowerCAmelCase     :							int    =    images[0, 255:258, 383:386, -1]

														assert images.shape == (1, 512, 768, 3)
														_lowerCAmelCase     :							Tuple    =    np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431])
														# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







from math import loga






def lowerCamelCase__    (							snake_case_	:  int       )      ->				int:
   if a < 0:
      raise ValueError('''Input value must be a positive integer'''       )
   elif isinstance(snake_case_       ,      snake_case_       ):
      raise TypeError('''Input value must be a \'int\' type'''       )
   return 0 if (a == 0) else int(loga(a & -a       )       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

