






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  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						Optional[Any]										=							'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
      def 						lowercase__					(     self				,			a=0   ):
       snake_case__  :    Any							       =floats_tensor((1, 3, 1_2_8, 1_2_8)				,			rng=random.Random(a   )   )
       snake_case__  :    List[Any]							       =np.random.RandomState(a   )
       snake_case__  :    Tuple							       ={
           """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 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider="""CPUExecutionProvider"""   )
       pipe.set_progress_bar_config(disable=a   )

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

       assert image.shape == (1, 1_2_8, 1_2_8, 3)
       snake_case__  :    int							       =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 						lowercase__					(     self   ):
       snake_case__  :    str							       =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider="""CPUExecutionProvider"""   )
       snake_case__  :    Tuple							       =PNDMScheduler.from_config(pipe.scheduler.config				,			skip_prk_steps=a   )
       pipe.set_progress_bar_config(disable=a   )

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

       assert image.shape == (1, 1_2_8, 1_2_8, 3)
       snake_case__  :    List[str]							       =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 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider="""CPUExecutionProvider"""   )
       snake_case__  :    Dict							       =LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
       pipe.set_progress_bar_config(disable=a   )

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

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

       assert image.shape == (1, 1_2_8, 1_2_8, 3)
       snake_case__  :    List[str]							       =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 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider="""CPUExecutionProvider"""   )
       snake_case__  :    List[str]							       =EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
       pipe.set_progress_bar_config(disable=a   )

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

       assert image.shape == (1, 1_2_8, 1_2_8, 3)
       snake_case__  :    List[str]							       =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 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider="""CPUExecutionProvider"""   )
       snake_case__  :    List[str]							       =EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
       pipe.set_progress_bar_config(disable=a   )

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

       assert image.shape == (1, 1_2_8, 1_2_8, 3)
       snake_case__  :    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 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider="""CPUExecutionProvider"""   )
       snake_case__  :    Any							       =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
       pipe.set_progress_bar_config(disable=a   )

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

       assert image.shape == (1, 1_2_8, 1_2_8, 3)
       snake_case__  :    int							       =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  _lowercase   (   unittest.TestCase						):
      @property
      def 						lowercase__					(     self   ):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )
      @property
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =ort.SessionOptions()
       snake_case__  :    Tuple							       =False
       return options
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/img2img/sketch-mountains-input.jpg"""   )
       snake_case__  :    int							       =init_image.resize((7_6_8, 5_1_2)   )
       # using the PNDM scheduler by default
       snake_case__  :    str							       =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   )

       snake_case__  :    List[str]							       ="""A fantasy landscape, trending on artstation"""

       snake_case__  :    Optional[Any]							       =np.random.RandomState(0   )
       snake_case__  :    Dict							       =pipe(
           prompt=a				,			image=a				,			strength=0.75				,			guidance_scale=7.5				,			num_inference_steps=1_0				,			generator=a				,			output_type="""np"""				,			)
       snake_case__  :    Optional[int]							       =output.images
       snake_case__  :    Dict							       =images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

       assert images.shape == (1, 5_1_2, 7_6_8, 3)
       snake_case__  :    Tuple							       =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 						lowercase__					(     self   ):
       snake_case__  :    Any							       =load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/img2img/sketch-mountains-input.jpg"""   )
       snake_case__  :    List[str]							       =init_image.resize((7_6_8, 5_1_2)   )
       snake_case__  :    Optional[int]							       =LMSDiscreteScheduler.from_pretrained(
           """runwayml/stable-diffusion-v1-5"""				,			subfolder="""scheduler"""				,			revision="""onnx"""   )
       snake_case__  :    Optional[Any]							       =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   )

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

       snake_case__  :    Dict							       =np.random.RandomState(0   )
       snake_case__  :    Optional[Any]							       =pipe(
           prompt=a				,			image=a				,			strength=0.75				,			guidance_scale=7.5				,			num_inference_steps=2_0				,			generator=a				,			output_type="""np"""				,			)
       snake_case__  :    Optional[int]							       =output.images
       snake_case__  :    List[str]							       =images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

       assert images.shape == (1, 5_1_2, 7_6_8, 3)
       snake_case__  :    List[str]							       =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













def 			A__							(      _a  :					Optional[Any]		,  _a  :					Tuple		,  _a  :					List[str]=False	):





 '''simple docstring'''







 if isinstance(_a		,  _a	) and isinstance(_a		,  _a	):
  snake_case__  :    int							       =len(set_a.intersection(_a	)	)

  if alternative_union:
   snake_case__  :    int							       =len(_a	) + len(_a	)
  else:
   snake_case__  :    Tuple							       =len(set_a.union(_a	)	)

  return intersection / union

 if isinstance(_a		,  (list, tuple)	) and isinstance(_a		,  (list, tuple)	):
  snake_case__  :    Optional[int]							       =[element for element in set_a if element in set_b]

  if alternative_union:
   snake_case__  :    Optional[Any]							       =len(_a	) + len(_a	)
   return len(_a	) / union
  else:
   snake_case__  :    Dict							       =set_a + [element for element in set_b if element not in set_a]
   return len(_a	) / len(_a	)

  return len(_a	) / len(_a	)
 return None


if __name__ == "__main__":
       __lowerCamelCase	:		List[Any]										=   {"""a""", """b""", """c""", """d""", """e"""}
       __lowerCamelCase	:		Optional[int]										=   {"""c""", """d""", """e""", """f""", """h""", """i"""}
       print(jaccard_similarity(set_a, set_b))





