






"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_   : str   			= logging.get_logger(__name__)




class 							lowerCAmelCase__				(			__a ):






      '''simple docstring'''

      def __init__(						self     :  Dict						,	*lowercase_     :  Dict						,	**lowercase_     :  Tuple):




          '''simple docstring'''


          warnings.warn(
              '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
              ''' Please use PerceiverImageProcessor instead.'''						,	__a						,	)
          super().__init__(*__a						,	**__a)










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   (       __a					,				unittest.TestCase	):

					'''simple docstring'''

					_A  :	List[Any]     =					'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''



					def 					lowerCAmelCase (     self :		int						,   __a :		List[Any]=0							)	->     Optional[int]:


												"""simple docstring"""

												__lowercase					:							Any			=   floats_tensor((1, 3, 128, 128)						,   rng=random.Random(__a							)							)
												__lowercase					:							Any			=   np.random.RandomState(__a							)
												__lowercase					:							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 					lowerCAmelCase (     self :		Union[str, Any]							)	->     Any:


												"""simple docstring"""

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

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

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							List[Any]			=   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 :		Any							)	->     Any:


												"""simple docstring"""

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

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

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Any			=   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 :		int							)	->     str:


												"""simple docstring"""

												__lowercase					:							int			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Union[str, Any]			=   LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

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

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

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							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 :		Optional[int]							)	->     Optional[Any]:


												"""simple docstring"""

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

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

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Optional[int]			=   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[Any]							)	->     str:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Dict			=   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

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

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Union[str, 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 :		Dict							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							Optional[Any]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Tuple			=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

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

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Dict			=   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 						lowerCAmelCase   (       unittest.TestCase	):

					'''simple docstring'''

					@property
					def 					lowerCAmelCase (     self :		Tuple							)	->     Optional[int]:


												"""simple docstring"""

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



					@property
					def 					lowerCAmelCase (     self :		str							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   ort.SessionOptions()
												__lowercase					:							Dict			=   False
												return options



					def 					lowerCAmelCase (     self :		int							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg"""							)
												__lowercase					:							Union[str, Any]			=   init_image.resize((768, 512)							)
												# using the PNDM scheduler by default
												__lowercase					:							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							)

												__lowercase					:							int			=   """A fantasy landscape, trending on artstation"""

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

												assert images.shape == (1, 512, 768, 3)
												__lowercase					:							Optional[int]			=   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]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg"""							)
												__lowercase					:							Any			=   init_image.resize((768, 512)							)
												__lowercase					:							int			=   LMSDiscreteScheduler.from_pretrained(
												    """runwayml/stable-diffusion-v1-5"""						,   subfolder="""scheduler"""						,   revision="""onnx"""							)
												__lowercase					:							str			=   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							)

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

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

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