



"""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       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   List[str]						=						'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=0						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	floats_tensor((1, 3, 128, 128)      ,     rng=random.Random(_SCREAMING_SNAKE_CASE						)						)
    UpperCAmelCase	:					Optional[int]	=	np.random.RandomState(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Union[str, 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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,     provider="""CPUExecutionProvider"""						)
    pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

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

    assert image.shape == (1, 128, 128, 3)
    UpperCAmelCase	:					Union[str, Any]	=	np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087]						)
    assert np.abs(image_slice - expected_slice						).max() < 1E-1

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,     provider="""CPUExecutionProvider"""						)
    UpperCAmelCase	:					Optional[Any]	=	PNDMScheduler.from_config(pipe.scheduler.config      ,     skip_prk_steps=_SCREAMING_SNAKE_CASE						)
    pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

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

    assert image.shape == (1, 128, 128, 3)
    UpperCAmelCase	:					List[Any]	=	np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154]						)

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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,     provider="""CPUExecutionProvider"""						)
    UpperCAmelCase	:					str	=	LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
    pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

    # warmup pass to apply optimizations
    UpperCAmelCase	:					Union[str, Any]	=	pipe(**self.get_dummy_inputs()						)

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

    assert image.shape == (1, 128, 128, 3)
    UpperCAmelCase	:					Optional[int]	=	np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203]						)

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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




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

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

    assert image.shape == (1, 128, 128, 3)
    UpperCAmelCase	:					int	=	np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]						)

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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




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

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

    assert image.shape == (1, 128, 128, 3)
    UpperCAmelCase	:					List[Any]	=	np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]						)

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




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





    '''simple docstring'''




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

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

    assert image.shape == (1, 128, 128, 3)
    UpperCAmelCase	:					Dict	=	np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552]						)

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



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

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




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

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	ort.SessionOptions()
    UpperCAmelCase	:					str	=	False
    return options

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





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/img2img/sketch-mountains-input.jpg"""						)
    UpperCAmelCase	:					Tuple	=	init_image.resize((768, 512)						)
    # using the PNDM scheduler by default
    UpperCAmelCase	:					str	=	OnnxStableDiffusionImgaImgPipeline.from_pretrained(
        """CompVis/stable-diffusion-v1-4"""      ,     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						)

    UpperCAmelCase	:					Any	=	"""A fantasy landscape, trending on artstation"""

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

    assert images.shape == (1, 512, 768, 3)
    UpperCAmelCase	:					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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	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	:					int	=	LMSDiscreteScheduler.from_pretrained(
        """runwayml/stable-diffusion-v1-5"""      ,     subfolder="""scheduler"""      ,     revision="""onnx"""						)
    UpperCAmelCase	:					Any	=	OnnxStableDiffusionImgaImgPipeline.from_pretrained(
        """runwayml/stable-diffusion-v1-5"""      ,     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						)

    UpperCAmelCase	:					Union[str, Any]	=	"""A fantasy landscape, trending on artstation"""

    UpperCAmelCase	:					List[str]	=	np.random.RandomState(0						)
    UpperCAmelCase	:					Any	=	pipe(
        prompt=_SCREAMING_SNAKE_CASE      ,     image=_SCREAMING_SNAKE_CASE      ,     strength=0.75      ,     guidance_scale=7.5      ,     num_inference_steps=20      ,     generator=_SCREAMING_SNAKE_CASE      ,     output_type="""np"""      ,     )
    UpperCAmelCase	:					List[str]	=	output.images
    UpperCAmelCase	:					Dict	=	images[0, 255:258, 383:386, -1]

    assert images.shape == (1, 512, 768, 3)
    UpperCAmelCase	:					Tuple	=	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 inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def       _snake_case						(						*UpperCamelCase						:		str			,		UpperCamelCase						:		Optional[Union[Dict, Any]] = None			,		UpperCamelCase						:		Tuple=True			,		UpperCamelCase						:		Optional[int]=2						):
 from .. import __version__

 UpperCAmelCase	:					Tuple	=	take_from
 UpperCAmelCase	:					Optional[Any]	=	()
 if not isinstance(args[0]			,		UpperCamelCase						):
  UpperCAmelCase	:					List[str]	=	(args,)

 for attribute, version_name, message in args:
  if version.parse(version.parse(UpperCamelCase						).base_version						) >= version.parse(UpperCamelCase						):
   raise ValueError(
       F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
       F" version {__version__} is >= {version_name}"						)

  UpperCAmelCase	:					Optional[int]	=	None
  if isinstance(UpperCamelCase			,		UpperCamelCase						) and attribute in deprecated_kwargs:
   values += (deprecated_kwargs.pop(UpperCamelCase						),)
   UpperCAmelCase	:					List[str]	=	F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
  elif hasattr(UpperCamelCase			,		UpperCamelCase						):
   values += (getattr(UpperCamelCase			,		UpperCamelCase						),)
   UpperCAmelCase	:					List[Any]	=	F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
  elif deprecated_kwargs is None:
   UpperCAmelCase	:					int	=	F"`{attribute}` is deprecated and will be removed in version {version_name}."

  if warning is not None:
   UpperCAmelCase	:					Optional[Any]	=	warning + """ """ if standard_warn else """"""
   warnings.warn(warning + message			,		UpperCamelCase			,		stacklevel=UpperCamelCase						)

 if isinstance(UpperCamelCase			,		UpperCamelCase						) and len(UpperCamelCase						) > 0:
  UpperCAmelCase	:					Optional[int]	=	inspect.getouterframes(inspect.currentframe()						)[1]
  UpperCAmelCase	:					Union[str, Any]	=	call_frame.filename
  UpperCAmelCase	:					List[Any]	=	call_frame.lineno
  UpperCAmelCase	:					List[str]	=	call_frame.function
  UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	next(iter(deprecated_kwargs.items()						)						)
  raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"						)

 if len(UpperCamelCase						) == 0:
  return
 elif len(UpperCamelCase						) == 1:
  return values[0]
 return values
