



'''simple docstring'''






from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_pt_objects import *  # noqa F403
else:
     from .scheduling_consistency_models import CMStochasticIterativeScheduler
     from .scheduling_ddim import DDIMScheduler
     from .scheduling_ddim_inverse import DDIMInverseScheduler
     from .scheduling_ddim_parallel import DDIMParallelScheduler
     from .scheduling_ddpm import DDPMScheduler
     from .scheduling_ddpm_parallel import DDPMParallelScheduler
     from .scheduling_deis_multistep import DEISMultistepScheduler
     from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
     from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
     from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
     from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
     from .scheduling_euler_discrete import EulerDiscreteScheduler
     from .scheduling_heun_discrete import HeunDiscreteScheduler
     from .scheduling_ipndm import IPNDMScheduler
     from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
     from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
     from .scheduling_karras_ve import KarrasVeScheduler
     from .scheduling_pndm import PNDMScheduler
     from .scheduling_repaint import RePaintScheduler
     from .scheduling_sde_ve import ScoreSdeVeScheduler
     from .scheduling_sde_vp import ScoreSdeVpScheduler
     from .scheduling_unclip import UnCLIPScheduler
     from .scheduling_unipc_multistep import UniPCMultistepScheduler
     from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
     from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_flax_objects import *  # noqa F403
else:
     from .scheduling_ddim_flax import FlaxDDIMScheduler
     from .scheduling_ddpm_flax import FlaxDDPMScheduler
     from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
     from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
     from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
     from .scheduling_pndm_flax import FlaxPNDMScheduler
     from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
     from .scheduling_utils_flax import (
         FlaxKarrasDiffusionSchedulers,
         FlaxSchedulerMixin,
         FlaxSchedulerOutput,
         broadcast_to_shape_from_left,
     )


try:
     if not (is_torch_available() and is_scipy_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
     from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
     if not (is_torch_available() and is_torchsde_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
     from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler







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     a    ( lowercase__							,       unittest.TestCase   ):



      """simple docstring"""


      a  : int							       =	'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
      def 	UpperCAmelCase      (      self					:			List[str]		,      __lowercase					:			Optional[Any]=0					)							->      Any:
        __UpperCAmelCase							:	Any           =  floats_tensor((1, 3, 128, 128)		,      rng=random.Random(__lowercase					)					)
        __UpperCAmelCase							:	int           =  np.random.RandomState(__lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  {
            """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 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Optional[Any]:
        __UpperCAmelCase							:	List[Any]           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        pipe.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	int           =  self.get_dummy_inputs()
        __UpperCAmelCase							:	Optional[Any]           =  pipe(**__lowercase					).images
        __UpperCAmelCase							:	Union[str, Any]           =  image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	List[str]           =  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 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      List[str]:
        __UpperCAmelCase							:	Dict           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	int           =  PNDMScheduler.from_config(pipe.scheduler.config		,      skip_prk_steps=__lowercase					)
        pipe.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	Any           =  self.get_dummy_inputs()
        __UpperCAmelCase							:	Tuple           =  pipe(**__lowercase					).images
        __UpperCAmelCase							:	Dict           =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	str           =  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 	UpperCAmelCase      (      self					:			str					)							->      Tuple:
        __UpperCAmelCase							:	Tuple           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	int           =  LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
        pipe.set_progress_bar_config(disable=__lowercase					)

        # warmup pass to apply optimizations
        __UpperCAmelCase							:	Optional[int]           =  pipe(**self.get_dummy_inputs()					)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	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 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      str:
        __UpperCAmelCase							:	int           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	Union[str, Any]           =  EulerDiscreteScheduler.from_config(pipe.scheduler.config					)
        pipe.set_progress_bar_config(disable=__lowercase					)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	Tuple           =  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 	UpperCAmelCase      (      self					:			int					)							->      Any:
        __UpperCAmelCase							:	Any           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	List[Any]           =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config					)
        pipe.set_progress_bar_config(disable=__lowercase					)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	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 	UpperCAmelCase      (      self					:			Tuple					)							->      str:
        __UpperCAmelCase							:	Dict           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	str           =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
        pipe.set_progress_bar_config(disable=__lowercase					)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	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     a    ( unittest.TestCase   ):



      """simple docstring"""


      @property
      def 	UpperCAmelCase      (      self					:			Dict					)							->      List[Any]:
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )
      @property
      def 	UpperCAmelCase      (      self					:			Tuple					)							->      Tuple:
        __UpperCAmelCase							:	Optional[int]           =  ort.SessionOptions()
        __UpperCAmelCase							:	List[Any]           =  False
        return options
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Tuple:
        __UpperCAmelCase							:	List[Any]           =  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/img2img/sketch-mountains-input.jpg"""					)
        __UpperCAmelCase							:	Dict           =  init_image.resize((768, 512)					)
        # using the PNDM scheduler by default
        __UpperCAmelCase							:	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					)

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

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

        assert images.shape == (1, 512, 768, 3)
        __UpperCAmelCase							:	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 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      str:
        __UpperCAmelCase							:	Tuple           =  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/img2img/sketch-mountains-input.jpg"""					)
        __UpperCAmelCase							:	int           =  init_image.resize((768, 512)					)
        __UpperCAmelCase							:	Tuple           =  LMSDiscreteScheduler.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""		,      subfolder="""scheduler"""		,      revision="""onnx"""					)
        __UpperCAmelCase							:	Tuple           =  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					)

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

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

        assert images.shape == (1, 512, 768, 3)
        __UpperCAmelCase							:	str           =  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


