'''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 		_lowerCamelCase		(   UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
    def 						UpperCamelCase_				(  self , lowerCAmelCase=0   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   str=      floats_tensor((1, 3, 128, 128) , rng=random.Random(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      np.random.RandomState(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   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   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider'''   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

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

     assert image.shape == (1, 128, 128, 3)
     SCREAMING_SNAKE_CASE__:   Dict=      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 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[str]=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider'''   )
     SCREAMING_SNAKE_CASE__:   int=      PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=lowerCAmelCase   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

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

     assert image.shape == (1, 128, 128, 3)
     SCREAMING_SNAKE_CASE__:   Optional[int]=      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 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   str=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider'''   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

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

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

     assert image.shape == (1, 128, 128, 3)
     SCREAMING_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 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider'''   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

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

     assert image.shape == (1, 128, 128, 3)
     SCREAMING_SNAKE_CASE__:   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 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     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=lowerCAmelCase   )

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

     assert image.shape == (1, 128, 128, 3)
     SCREAMING_SNAKE_CASE__:   List[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 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   str=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider'''   )
     SCREAMING_SNAKE_CASE__:   List[str]=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

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

     assert image.shape == (1, 128, 128, 3)
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      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    ):
    @property
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )
    @property
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   int=      ort.SessionOptions()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      False
     return options
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   int=      load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/img2img/sketch-mountains-input.jpg'''   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      init_image.resize((768, 512)   )
     # using the PNDM scheduler by default
     SCREAMING_SNAKE_CASE__:   List[str]=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(
         '''CompVis/stable-diffusion-v1-4''' , revision='''onnx''' , safety_checker=lowerCAmelCase , feature_extractor=lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[str]=      '''A fantasy landscape, trending on artstation'''

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

     assert images.shape == (1, 512, 768, 3)
     SCREAMING_SNAKE_CASE__:   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 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[str]=      load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/img2img/sketch-mountains-input.jpg'''   )
     SCREAMING_SNAKE_CASE__:   Any=      init_image.resize((768, 512)   )
     SCREAMING_SNAKE_CASE__:   Any=      LMSDiscreteScheduler.from_pretrained(
         '''runwayml/stable-diffusion-v1-5''' , subfolder='''scheduler''' , revision='''onnx'''   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(
         '''runwayml/stable-diffusion-v1-5''' , revision='''onnx''' , scheduler=lowerCAmelCase , safety_checker=lowerCAmelCase , feature_extractor=lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   str=      '''A fantasy landscape, trending on artstation'''

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

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


import re







def 			A__      (	snake_case_ :		str      ):

 if len(re.findall('''[ATCG]'''       ,				snake_case_      )      ) != len(snake_case_      ):
  raise ValueError('''Invalid Strand'''      )

 return dna.translate(dna.maketrans('''ATCG'''       ,				'''TAGC'''      )      )


if __name__ == "__main__":
      import doctest

      doctest.testmod()

