


import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    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				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    "ssube/stable-diffusion-x4-upscaler-onnx"





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    Dict=0 ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((1, 3, 128, 128)      ,   rng=random.Random(__lowerCamelCase ) )
             lowerCamelCase__	:       str					=						torch.manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       str					=						{
                 "prompt": "A painting of a squirrel eating a burger",
                 "image": image,
                 "generator": generator,
                 "num_inference_steps": 3,
                 "guidance_scale": 7.5,
                 "output_type": "numpy",
             }
             return inputs





       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint      ,   provider="CPUExecutionProvider" )
             pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       List[Any]					=						self.get_dummy_inputs()
             lowerCamelCase__	:       List[Any]					=						pipe(**__lowerCamelCase ).images
             lowerCamelCase__	:       Any					=						image[0, -3:, -3:, -1].flatten()

             # started as 128, should now be 512
             assert image.shape == (1, 512, 512, 3)
             lowerCamelCase__	:       str					=						np.array(
                 [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
             assert np.abs(image_slice - expected_slice ).max() < 1E-1





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint      ,   provider="CPUExecutionProvider" )
             lowerCamelCase__	:       Dict					=						PNDMScheduler.from_config(pipe.scheduler.config      ,   skip_prk_steps=__lowerCamelCase )
             pipe.set_progress_bar_config(disable=__lowerCamelCase )

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

             assert image.shape == (1, 512, 512, 3)
             lowerCamelCase__	:       Dict					=						np.array(
                 [0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4] )
             assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint      ,   provider="CPUExecutionProvider" )
             lowerCamelCase__	:       Any					=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
             pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						self.get_dummy_inputs()
             lowerCamelCase__	:       Dict					=						pipe(**__lowerCamelCase ).images
             lowerCamelCase__	:       List[str]					=						image[0, -3:, -3:, -1]

             assert image.shape == (1, 512, 512, 3)
             lowerCamelCase__	:       List[Any]					=						np.array(
                 [0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5] )

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





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint      ,   provider="CPUExecutionProvider" )
             lowerCamelCase__	:       Optional[Any]					=						EulerDiscreteScheduler.from_config(pipe.scheduler.config )
             pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Any					=						self.get_dummy_inputs()
             lowerCamelCase__	:       Tuple					=						pipe(**__lowerCamelCase ).images
             lowerCamelCase__	:       str					=						image[0, -3:, -3:, -1]

             assert image.shape == (1, 512, 512, 3)
             lowerCamelCase__	:       Optional[Any]					=						np.array(
                 [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
             assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint      ,   provider="CPUExecutionProvider" )
             lowerCamelCase__	:       Optional[int]					=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
             pipe.set_progress_bar_config(disable=__lowerCamelCase )

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

             assert image.shape == (1, 512, 512, 3)
             lowerCamelCase__	:       Optional[int]					=						np.array(
                 [0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3] )

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







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


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



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





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


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						ort.SessionOptions()
             lowerCamelCase__	:       List[Any]					=						False
             return options





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/img2img/sketch-mountains-input.jpg" )
             lowerCamelCase__	:       List[str]					=						init_image.resize((128, 128) )
             # using the PNDM scheduler by default
             lowerCamelCase__	:       List[Any]					=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
                 "ssube/stable-diffusion-x4-upscaler-onnx"      ,   provider=self.gpu_provider      ,   sess_options=self.gpu_options      ,   )
             pipe.set_progress_bar_config(disable=__lowerCamelCase )

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

             lowerCamelCase__	:       Any					=						torch.manual_seed(0 )
             lowerCamelCase__	:       Union[str, Any]					=						pipe(
                 prompt=__lowerCamelCase      ,   image=__lowerCamelCase      ,   guidance_scale=7.5      ,   num_inference_steps=10      ,   generator=__lowerCamelCase      ,   output_type="np"      ,   )
             lowerCamelCase__	:       List[Any]					=						output.images
             lowerCamelCase__	:       Optional[int]					=						images[0, 255:258, 383:386, -1]

             assert images.shape == (1, 512, 512, 3)
             lowerCamelCase__	:       Optional[int]					=						np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2] )
             # 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							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/img2img/sketch-mountains-input.jpg" )
             lowerCamelCase__	:       Tuple					=						init_image.resize((128, 128) )
             lowerCamelCase__	:       str					=						LMSDiscreteScheduler.from_pretrained(
                 "ssube/stable-diffusion-x4-upscaler-onnx"      ,   subfolder="scheduler" )
             lowerCamelCase__	:       Optional[int]					=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
                 "ssube/stable-diffusion-x4-upscaler-onnx"      ,   scheduler=__lowerCamelCase      ,   provider=self.gpu_provider      ,   sess_options=self.gpu_options      ,   )
             pipe.set_progress_bar_config(disable=__lowerCamelCase )

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

             lowerCamelCase__	:       Union[str, Any]					=						torch.manual_seed(0 )
             lowerCamelCase__	:       List[str]					=						pipe(
                 prompt=__lowerCamelCase      ,   image=__lowerCamelCase      ,   guidance_scale=7.5      ,   num_inference_steps=20      ,   generator=__lowerCamelCase      ,   output_type="np"      ,   )
             lowerCamelCase__	:       Optional[Any]					=						output.images
             lowerCamelCase__	:       Optional[int]					=						images[0, 255:258, 383:386, -1]

             assert images.shape == (1, 512, 512, 3)
             lowerCamelCase__	:       str					=						np.array(
                 [0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6] )
             # 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_	(	_A    :					int					,      _A    :					int		):

      """simple docstring"""




      if a < 0 or b < 0:
            raise ValueError("the value of both inputs must be positive"		)

      lowerCamelCase__	:       List[str]					=						str(bin(_A		)		)[2:]  # remove the leading "0b"
      lowerCamelCase__	:       List[Any]					=						str(bin(_A		)		)[2:]  # remove the leading "0b"

      lowerCamelCase__	:       List[Any]					=						max(len(_A		)					,      len(_A		)		)

      return "0b" + "".join(
          str(int(char_a != char_b		)		)
          for char_a, char_b in zip(a_binary.zfill(_A		)					,      b_binary.zfill(_A		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


