


"""simple docstring"""







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 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=0						) ->					Dict:
     a					=floats_tensor((1, 3, 128, 128)			,   rng=random.Random(__A						)						)
     a					=torch.manual_seed(__A						)
     a					={
         '''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 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     a					=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''						)
     pipe.set_progress_bar_config(disable=__A						)

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

     # started as 128, should now be 512
     assert image.shape == (1, 512, 512, 3)
     a					=np.array(
         [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]						)
     assert np.abs(image_slice - expected_slice						).max() < 1E-1


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a					=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''						)
     a					=PNDMScheduler.from_config(pipe.scheduler.config			,   skip_prk_steps=__A						)
     pipe.set_progress_bar_config(disable=__A						)

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

     assert image.shape == (1, 512, 512, 3)
     a					=np.array(
         [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]						)
     assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a					=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''						)
     a					=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
     pipe.set_progress_bar_config(disable=__A						)

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

     assert image.shape == (1, 512, 512, 3)
     a					=np.array(
         [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]						)

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''						)
     a					=EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
     pipe.set_progress_bar_config(disable=__A						)

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

     assert image.shape == (1, 512, 512, 3)
     a					=np.array(
         [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]						)
     assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''						)
     a					=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
     pipe.set_progress_bar_config(disable=__A						)

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

     assert image.shape == (1, 512, 512, 3)
     a					=np.array(
         [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]						)

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






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






 """simple docstring"""




 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )


 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=ort.SessionOptions()
     a					=False
     return options


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

     a					='''A fantasy landscape, trending on artstation'''

     a					=torch.manual_seed(0						)
     a					=pipe(
         prompt=__A			,   image=__A			,   guidance_scale=7.5			,   num_inference_steps=10			,   generator=__A			,   output_type='''np'''			,   )
     a					=output.images
     a					=images[0, 255:258, 383:386, -1]

     assert images.shape == (1, 512, 512, 3)
     a					=np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]						)
     # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





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

     a					='''A fantasy landscape, trending on artstation'''

     a					=torch.manual_seed(0						)
     a					=pipe(
         prompt=__A			,   image=__A			,   guidance_scale=7.5			,   num_inference_steps=20			,   generator=__A			,   output_type='''np'''			,   )
     a					=output.images
     a					=images[0, 255:258, 383:386, -1]

     assert images.shape == (1, 512, 512, 3)
     a					=np.array(
         [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]						)
     # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



"""simple docstring"""







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


lowerCamelCase_       :       int      						=  logging.get_logger(__name__)






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "upernet"


 def __init__(		self			,   __A=None			,   __A=512			,   __A=0.02			,   __A=[1, 2, 3, 6]			,   __A=True			,   __A=0.4			,   __A=384			,   __A=256			,   __A=1			,   __A=False			,   __A=255			,   **__A			,   ) ->					Tuple:
     super().__init__(**__A						)

     if backbone_config is None:
         logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''						)
         a					=CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']						)
     elif isinstance(__A			,   __A						):
         a					=backbone_config.get('''model_type'''						)
         a					=CONFIG_MAPPING[backbone_model_type]
         a					=config_class.from_dict(__A						)

     a					=backbone_config
     a					=hidden_size
     a					=initializer_range
     a					=pool_scales
     a					=use_auxiliary_head
     a					=auxiliary_loss_weight
     a					=auxiliary_in_channels
     a					=auxiliary_channels
     a					=auxiliary_num_convs
     a					=auxiliary_concat_input
     a					=loss_ignore_index





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=copy.deepcopy(self.__dict__						)
     a					=self.backbone_config.to_dict()
     a					=self.__class__.model_type
     return output