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 	lowerCamelCase_     (   UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						int						=      """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""







    def 			UpperCamelCase__   (	self , __lowercase=0)       ->	Optional[int]:
        __UpperCamelCase     :Dict			  =		floats_tensor((1, 3, 128, 128) , rng=random.Random(__lowercase))
        __UpperCamelCase     :List[Any]			  =		np.random.RandomState(__lowercase)
        __UpperCamelCase     :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 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Tuple			  =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''')
        pipe.set_progress_bar_config(disable=__lowercase)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCamelCase     :List[str]			  =		np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87])
        assert np.abs(image_slice - expected_slice).max() < 1E-1







    def 			UpperCamelCase__   (	self)       ->	int:
        __UpperCamelCase     :Optional[int]			  =		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     :List[Any]			  =		pipe(**__lowercase).images
        __UpperCamelCase     :List[str]			  =		image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        __UpperCamelCase     :List[Any]			  =		np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54])

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







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Optional[int]			  =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''')
        __UpperCamelCase     :List[str]			  =		LMSDiscreteScheduler.from_config(pipe.scheduler.config)
        pipe.set_progress_bar_config(disable=__lowercase)

        # warmup pass to apply optimizations
        __UpperCamelCase     :str			  =		pipe(**self.get_dummy_inputs())

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCamelCase     :Any			  =		np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03])

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







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        __UpperCamelCase     :Optional[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=__lowercase)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCamelCase     :Union[str, Any]			  =		np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04])

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







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Tuple			  =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''')
        __UpperCamelCase     :Union[str, Any]			  =		EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
        pipe.set_progress_bar_config(disable=__lowercase)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCamelCase     :Dict			  =		np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04])

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







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :List[str]			  =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''')
        __UpperCamelCase     :List[str]			  =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
        pipe.set_progress_bar_config(disable=__lowercase)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCamelCase     :Any			  =		np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52])

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


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






    '''simple docstring'''






    @property
    def 			UpperCamelCase__   (	self)       ->	str:
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )







    @property
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :List[Any]			  =		ort.SessionOptions()
        __UpperCamelCase     :str			  =		False
        return options







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :Dict			  =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/img2img/sketch-mountains-input.jpg''')
        __UpperCamelCase     :Optional[Any]			  =		init_image.resize((768, 512))
        # using the PNDM scheduler by default
        __UpperCamelCase     :List[str]			  =		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     :Optional[Any]			  =		'''A fantasy landscape, trending on artstation'''

        __UpperCamelCase     :Any			  =		np.random.RandomState(0)
        __UpperCamelCase     :int			  =		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     :Union[str, Any]			  =		images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 768, 3)
        __UpperCamelCase     :str			  =		np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19])
        # 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)       ->	Dict:
        __UpperCamelCase     :str			  =		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))
        __UpperCamelCase     :Optional[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=__lowercase , safety_checker=__lowercase , feature_extractor=__lowercase , provider=self.gpu_provider , sess_options=self.gpu_options , )
        pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase     :List[Any]			  =		'''A fantasy landscape, trending on artstation'''

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

        assert images.shape == (1, 512, 768, 3)
        __UpperCamelCase     :Dict			  =		np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31])
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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









import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version


def   _SCREAMING_SNAKE_CASE	( *SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE = None ,			SCREAMING_SNAKE_CASE=True ,			SCREAMING_SNAKE_CASE=2       ):
  from .. import __version__

  A_      :				Union[str, Any]		     =					take_from
  A_      :				Optional[Any]		     =					()
  if not isinstance(args[0] ,			SCREAMING_SNAKE_CASE       ):
    A_      :				List[str]		     =					(args,)

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

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

    if warning is not None:
      A_      :				Union[str, Any]		     =					warning + ''' ''' if standard_warn else ''''''
      warnings.warn(warning + message ,			SCREAMING_SNAKE_CASE ,			stacklevel=SCREAMING_SNAKE_CASE       )

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

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




