
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       a__	( lowerCamelCase_       , unittest.TestCase				):
     a		:      Dict		 =     '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''







     def       lowerCAmelCase_    (     self  ,		A=0			)  ->   List[Any]:



          '''simple docstring'''



          a     =				floats_tensor((1, 3, 128, 128)  ,		rng=random.Random(__snake_case			)			)
          a     =				np.random.RandomState(__snake_case			)
          a     =				{
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 3,
              "strength": 0.7_5,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,		provider="CPUExecutionProvider"			)
          pipe.set_progress_bar_config(disable=__snake_case			)

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

          assert image.shape == (1, 128, 128, 3)
          a     =				np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]			)
          assert np.abs(image_slice - expected_slice			).max() < 1e-1







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,		provider="CPUExecutionProvider"			)
          a     =				PNDMScheduler.from_config(pipe.scheduler.config  ,		skip_prk_steps=__snake_case			)
          pipe.set_progress_bar_config(disable=__snake_case			)

          a     =				self.get_dummy_inputs()
          a     =				pipe(**__snake_case			).images
          a     =				image[0, -3:, -3:, -1]

          assert image.shape == (1, 128, 128, 3)
          a     =				np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]			)

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







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,		provider="CPUExecutionProvider"			)
          a     =				LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
          pipe.set_progress_bar_config(disable=__snake_case			)

          # warmup pass to apply optimizations
          a     =				pipe(**self.get_dummy_inputs()			)

          a     =				self.get_dummy_inputs()
          a     =				pipe(**__snake_case			).images
          a     =				image[0, -3:, -3:, -1]

          assert image.shape == (1, 128, 128, 3)
          a     =				np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]			)

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







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,		provider="CPUExecutionProvider"			)
          a     =				EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
          pipe.set_progress_bar_config(disable=__snake_case			)

          a     =				self.get_dummy_inputs()
          a     =				pipe(**__snake_case			).images
          a     =				image[0, -3:, -3:, -1]

          assert image.shape == (1, 128, 128, 3)
          a     =				np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]			)

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







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,		provider="CPUExecutionProvider"			)
          a     =				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
          pipe.set_progress_bar_config(disable=__snake_case			)

          a     =				self.get_dummy_inputs()
          a     =				pipe(**__snake_case			).images
          a     =				image[0, -3:, -3:, -1]

          assert image.shape == (1, 128, 128, 3)
          a     =				np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]			)

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







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,		provider="CPUExecutionProvider"			)
          a     =				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
          pipe.set_progress_bar_config(disable=__snake_case			)

          a     =				self.get_dummy_inputs()
          a     =				pipe(**__snake_case			).images
          a     =				image[0, -3:, -3:, -1]

          assert image.shape == (1, 128, 128, 3)
          a     =				np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]			)

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







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







     @property
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



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







     @property
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				ort.SessionOptions()
          a     =				False
          return options







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/img2img/sketch-mountains-input.jpg"			)
          a     =				init_image.resize((768, 512)			)
          # using the PNDM scheduler by default
          a     =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(
              "CompVis/stable-diffusion-v1-4"  ,		revision="onnx"  ,		safety_checker=__snake_case  ,		feature_extractor=__snake_case  ,		provider=self.gpu_provider  ,		sess_options=self.gpu_options  ,		)
          pipe.set_progress_bar_config(disable=__snake_case			)

          a     =				"A fantasy landscape, trending on artstation"

          a     =				np.random.RandomState(0			)
          a     =				pipe(
              prompt=__snake_case  ,		image=__snake_case  ,		strength=0.7_5  ,		guidance_scale=7.5  ,		num_inference_steps=10  ,		generator=__snake_case  ,		output_type="np"  ,		)
          a     =				output.images
          a     =				images[0, 255:258, 383:386, -1]

          assert images.shape == (1, 512, 768, 3)
          a     =				np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]			)
          # 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			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/img2img/sketch-mountains-input.jpg"			)
          a     =				init_image.resize((768, 512)			)
          a     =				LMSDiscreteScheduler.from_pretrained(
              "runwayml/stable-diffusion-v1-5"  ,		subfolder="scheduler"  ,		revision="onnx"			)
          a     =				OnnxStableDiffusionImgaImgPipeline.from_pretrained(
              "runwayml/stable-diffusion-v1-5"  ,		revision="onnx"  ,		scheduler=__snake_case  ,		safety_checker=__snake_case  ,		feature_extractor=__snake_case  ,		provider=self.gpu_provider  ,		sess_options=self.gpu_options  ,		)
          pipe.set_progress_bar_config(disable=__snake_case			)

          a     =				"A fantasy landscape, trending on artstation"

          a     =				np.random.RandomState(0			)
          a     =				pipe(
              prompt=__snake_case  ,		image=__snake_case  ,		strength=0.7_5  ,		guidance_scale=7.5  ,		num_inference_steps=20  ,		generator=__snake_case  ,		output_type="np"  ,		)
          a     =				output.images
          a     =				images[0, 255:258, 383:386, -1]

          assert images.shape == (1, 512, 768, 3)
          a     =				np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]			)
          # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







import math

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5		, int(math.sqrt(__UpperCamelCase) + 1)		, 6):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase = 0.1)      ->					int:
     a     =				3
     a     =				3

     while primes / (2 * j - 1) >= ratio:
          for i in range(j * j + j + 1		, (j + 2) * (j + 2)		, j + 1):
               primes += is_prime(__UpperCamelCase)
          j += 2
     return j


if __name__ == "__main__":
   import doctest

   doctest.testmod()

