



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     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       # TODO: is there an appropriate internal test set?
       UpperCAmelCase__    : Tuple       					=  "ssube/stable-diffusion-x4-upscaler-onnx"
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_=0	)					->		int:
          UpperCamelCase   :  Tuple						     =     floats_tensor((1, 3, 128, 128),      rng=random.Random(SCREAMING_SNAKE_CASE_	)	)
          UpperCamelCase   :  str						     =     torch.manual_seed(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[Any]						     =     {
              '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  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Tuple						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

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

          # started as 128, should now be 512
          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Optional[int]						     =     np.array(
              [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]	)
          assert np.abs(image_slice - expected_slice	).max() < 1e-1
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  List[Any]						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          UpperCamelCase   :  Any						     =     PNDMScheduler.from_config(pipe.scheduler.config,      skip_prk_steps=SCREAMING_SNAKE_CASE_	)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

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

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Tuple						     =     np.array(
              [0.6_89_88_92, 0.59_24_05_56, 0.52_49_95_27, 0.58_86_62_15, 0.52_25_82_35, 0.52_57_27_15, 0.62_41_44_73, 0.6_17_43_87, 0.6_21_49_64]	)
          assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Union[str, Any]						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          UpperCamelCase   :  int						     =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

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

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  List[Any]						     =     np.array(
              [0.7_65_92_78, 0.76_43_76_64, 0.75_57_91_07, 0.7_69_11_16, 0.77_66_69_86, 0.7_72_76_72, 0.7_75_86_64, 0.7_81_22_26, 0.76_94_25_15]	)

          assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Dict						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          UpperCamelCase   :  str						     =     EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

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

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Optional[int]						     =     np.array(
              [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]	)
          assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1






       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Union[str, Any]						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          UpperCamelCase   :  Optional[int]						     =     EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

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

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Optional[int]						     =     np.array(
              [0.77_42_44_96, 0.77_36_01, 0.7_64_52_88, 0.7_76_95_98, 0.7_77_27_39, 0.7_73_86_88, 0.78_18_72_33, 0.77_87_95_84, 0.76_70_43]	)

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






@nightly
@require_onnxruntime
@require_torch_gpu
class     lowerCAmelCase_		(  unittest.TestCase							):
       @property
       def  snake_case_	(      self	)					->		Tuple:
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )
       @property
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  str						     =     ort.SessionOptions()
          UpperCamelCase   :  str						     =     False
          return options
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  List[str]						     =     load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/img2img/sketch-mountains-input.jpg'	)
          UpperCamelCase   :  Any						     =     init_image.resize((128, 128)	)
          # using the PNDM scheduler by default
          UpperCamelCase   :  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=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Any						     =     'A fantasy landscape, trending on artstation'

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

          assert images.shape == (1, 512, 512, 3)
          UpperCamelCase   :  str						     =     np.array([0.48_83, 0.49_47, 0.49_80, 0.49_75, 0.49_82, 0.49_80, 0.50_00, 0.50_06, 0.49_72]	)
          # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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






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

          UpperCamelCase   :  Optional[int]						     =     'A fantasy landscape, trending on artstation'

          UpperCamelCase   :  List[Any]						     =     torch.manual_seed(0	)
          UpperCamelCase   :  Optional[Any]						     =     pipe(
              prompt=SCREAMING_SNAKE_CASE_,      image=SCREAMING_SNAKE_CASE_,      guidance_scale=7.5,      num_inference_steps=20,      generator=SCREAMING_SNAKE_CASE_,      output_type='np',      )
          UpperCamelCase   :  Dict						     =     output.images
          UpperCamelCase   :  Dict						     =     images[0, 255:258, 383:386, -1]

          assert images.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Optional[int]						     =     np.array(
              [0.50_17_37_53, 0.50_22_33_56, 0.50_20_39, 0.50_23_30_36, 0.5_02_37_25, 0.5_02_26_01, 0.5_01_87_58, 0.50_23_40_85, 0.50_24_15_66]	)
          # 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 		UpperCamelCase     (       snake_case__	:   str     )     ->					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()



