
'''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_	(_a				,      unittest.TestCase     ):
       # TODO: is there an appropriate internal test set?
       __lowerCAmelCase     :					Optional[int]     =   """ssube/stable-diffusion-x4-upscaler-onnx"""




       def 				__UpperCamelCase							(  self , snake_case_=0	):
         _lowerCAmelCase							:	int 					=       floats_tensor((1, 3, 1_2_8, 1_2_8) , rng=random.Random(__lowercase	)	)
         _lowerCAmelCase							:	Optional[Any] 					=       torch.manual_seed(__lowercase	)
         _lowerCAmelCase							:	int 					=       {
             """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 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Union[str, Any] 					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider"""	)
         pipe.set_progress_bar_config(disable=__lowercase	)

         _lowerCAmelCase							:	List[str] 					=       self.get_dummy_inputs()
         _lowerCAmelCase							:	Tuple 					=       pipe(**__lowercase	).images
         _lowerCAmelCase							:	Tuple 					=       image[0, -3:, -3:, -1].flatten()

         # started as 128, should now be 512
         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCAmelCase							:	Tuple 					=       np.array(
             [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]	)
         assert np.abs(image_slice - expected_slice	).max() < 1E-1




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Dict 					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider"""	)
         _lowerCAmelCase							:	Any 					=       PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__lowercase	)
         pipe.set_progress_bar_config(disable=__lowercase	)

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCAmelCase							:	Optional[int] 					=       np.array(
             [0.689_8892, 0.5924_0556, 0.5249_9527, 0.5886_6215, 0.5225_8235, 0.5257_2715, 0.6241_4473, 0.617_4387, 0.621_4964]	)
         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Optional[Any] 					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider"""	)
         _lowerCAmelCase							:	Optional[Any] 					=       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
         pipe.set_progress_bar_config(disable=__lowercase	)

         _lowerCAmelCase							:	Dict 					=       self.get_dummy_inputs()
         _lowerCAmelCase							:	List[Any] 					=       pipe(**__lowercase	).images
         _lowerCAmelCase							:	int 					=       image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCAmelCase							:	Tuple 					=       np.array(
             [0.765_9278, 0.7643_7664, 0.7557_9107, 0.769_1116, 0.7766_6986, 0.772_7672, 0.775_8664, 0.781_2226, 0.7694_2515]	)

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




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Union[str, Any] 					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider"""	)
         _lowerCAmelCase							:	Optional[Any] 					=       EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
         pipe.set_progress_bar_config(disable=__lowercase	)

         _lowerCAmelCase							:	Dict 					=       self.get_dummy_inputs()
         _lowerCAmelCase							:	Tuple 					=       pipe(**__lowercase	).images
         _lowerCAmelCase							:	Tuple 					=       image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCAmelCase							:	Dict 					=       np.array(
             [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]	)
         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1







       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Optional[Any] 					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider"""	)
         _lowerCAmelCase							:	str 					=       EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
         pipe.set_progress_bar_config(disable=__lowercase	)

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCAmelCase							:	str 					=       np.array(
             [0.7742_4496, 0.77_3601, 0.764_5288, 0.776_9598, 0.777_2739, 0.773_8688, 0.7818_7233, 0.7787_9584, 0.76_7043]	)

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



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




       @property
       def 				__UpperCamelCase							(  self	):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )




       @property
       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Dict 					=       ort.SessionOptions()
         _lowerCAmelCase							:	str 					=       False
         return options




       def 				__UpperCamelCase							(  self	):
         _lowerCAmelCase							:	Dict 					=       load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/img2img/sketch-mountains-input.jpg"""	)
         _lowerCAmelCase							:	List[str] 					=       init_image.resize((1_2_8, 1_2_8)	)
         # using the PNDM scheduler by default
         _lowerCAmelCase							:	List[str] 					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(
             """ssube/stable-diffusion-x4-upscaler-onnx""" , provider=self.gpu_provider , sess_options=self.gpu_options , )
         pipe.set_progress_bar_config(disable=__lowercase	)

         _lowerCAmelCase							:	Any 					=       """A fantasy landscape, trending on artstation"""

         _lowerCAmelCase							:	str 					=       torch.manual_seed(0	)
         _lowerCAmelCase							:	Union[str, Any] 					=       pipe(
             prompt=__lowercase , image=__lowercase , guidance_scale=7.5 , num_inference_steps=1_0 , generator=__lowercase , output_type="""np""" , )
         _lowerCAmelCase							:	List[Any] 					=       output.images
         _lowerCAmelCase							:	List[str] 					=       images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCAmelCase							:	Any 					=       np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]	)
         # 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	):
         _lowerCAmelCase							:	int 					=       load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/img2img/sketch-mountains-input.jpg"""	)
         _lowerCAmelCase							:	Any 					=       init_image.resize((1_2_8, 1_2_8)	)
         _lowerCAmelCase							:	Dict 					=       LMSDiscreteScheduler.from_pretrained(
             """ssube/stable-diffusion-x4-upscaler-onnx""" , subfolder="""scheduler"""	)
         _lowerCAmelCase							:	str 					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(
             """ssube/stable-diffusion-x4-upscaler-onnx""" , scheduler=__lowercase , provider=self.gpu_provider , sess_options=self.gpu_options , )
         pipe.set_progress_bar_config(disable=__lowercase	)

         _lowerCAmelCase							:	int 					=       """A fantasy landscape, trending on artstation"""

         _lowerCAmelCase							:	Tuple 					=       torch.manual_seed(0	)
         _lowerCAmelCase							:	List[Any] 					=       pipe(
             prompt=__lowercase , image=__lowercase , guidance_scale=7.5 , num_inference_steps=2_0 , generator=__lowercase , output_type="""np""" , )
         _lowerCAmelCase							:	Tuple 					=       output.images
         _lowerCAmelCase							:	Optional[int] 					=       images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCAmelCase							:	int 					=       np.array(
             [0.5017_3753, 0.5022_3356, 0.50_2039, 0.5023_3036, 0.502_3725, 0.502_2601, 0.501_8758, 0.5023_4085, 0.5024_1566]	)
         # 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'''

def 					__UpperCamelCase				(						lowercase__    :	list[int]			):






     '''simple docstring'''





     if not nums:  # Makes sure that the list is not empty
          raise ValueError('List is empty'			)

     __lowercase      		=sum(lowercase__			) / len(lowercase__			)  # Calculate the average
     return sum(abs(x - average			) for x in nums			) / len(lowercase__			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


