

'''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 							_snake_case (       _a	,  unittest.TestCase						):
   _A       :     int       =					'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      List[Any]=0							):
     SCREAMING_SNAKE_CASE:str						      =					floats_tensor((1, 3, 128, 128)     ,rng=random.Random(SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:List[str]						      =					np.random.RandomState(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE: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			:      List[Any]							):
     SCREAMING_SNAKE_CASE:str						      =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,provider="CPUExecutionProvider"							)
     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

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

     assert image.shape == (1, 128, 128, 3)
     SCREAMING_SNAKE_CASE:Dict						      =					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			:      List[str]							):
     SCREAMING_SNAKE_CASE:Any						      =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,provider="CPUExecutionProvider"							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					PNDMScheduler.from_config(pipe.scheduler.config     ,skip_prk_steps=SCREAMING_SNAKE_CASE__							)
     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

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

     assert image.shape == (1, 128, 128, 3)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					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			:      int							):
     SCREAMING_SNAKE_CASE:Dict						      =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,provider="CPUExecutionProvider"							)
     SCREAMING_SNAKE_CASE:List[str]						      =					LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     # warmup pass to apply optimizations
     SCREAMING_SNAKE_CASE:Tuple						      =					pipe(**self.get_dummy_inputs()							)

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

     assert image.shape == (1, 128, 128, 3)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					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			:      Dict							):
     SCREAMING_SNAKE_CASE:int						      =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,provider="CPUExecutionProvider"							)
     SCREAMING_SNAKE_CASE:str						      =					EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

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

     assert image.shape == (1, 128, 128, 3)
     SCREAMING_SNAKE_CASE: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			:      Dict							):
     SCREAMING_SNAKE_CASE:List[str]						      =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,provider="CPUExecutionProvider"							)
     SCREAMING_SNAKE_CASE:int						      =					EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

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

     assert image.shape == (1, 128, 128, 3)
     SCREAMING_SNAKE_CASE:Dict						      =					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							):
     SCREAMING_SNAKE_CASE:Any						      =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,provider="CPUExecutionProvider"							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

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

     assert image.shape == (1, 128, 128, 3)
     SCREAMING_SNAKE_CASE:List[str]						      =					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 							_snake_case (       unittest.TestCase						):
   @property
   def     __UpperCamelCase						( self			:      Dict							):
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )
   @property
   def     __UpperCamelCase						( self			:      Dict							):
     SCREAMING_SNAKE_CASE:Dict						      =					ort.SessionOptions()
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					False
     return options
   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/img2img/sketch-mountains-input.jpg"							)
     SCREAMING_SNAKE_CASE:int						      =					init_image.resize((768, 512)							)
     # using the PNDM scheduler by default
     SCREAMING_SNAKE_CASE:Dict						      =					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__							)

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

     SCREAMING_SNAKE_CASE:List[Any]						      =					np.random.RandomState(0							)
     SCREAMING_SNAKE_CASE:int						      =					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"     ,)
     SCREAMING_SNAKE_CASE:Tuple						      =					output.images
     SCREAMING_SNAKE_CASE:Tuple						      =					images[0, 255:258, 383:386, -1]

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

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

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

     assert images.shape == (1, 512, 768, 3)
     SCREAMING_SNAKE_CASE:List[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









'''simple docstring'''

import warnings
from functools import wraps
from typing import Callable





def    A_  ( snake_case ):

  @wraps(snake_case )
  def _inner_fn(*snake_case      ,       **snake_case ):
    warnings.warn(
        (F'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')      ,       snake_case      ,       )
    return fn(*snake_case      ,       **snake_case )

  return _inner_fn






