

'''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?
  A__					=    'ssube/stable-diffusion-x4-upscaler-onnx'





  def 						A  (		self				:     str							, _a				:     Any=0			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =floats_tensor((1, 3, 128, 128)							, rng=random.Random(_a			)			)
       _SCREAMING_SNAKE_CASE							   =torch.manual_seed(_a			)
       _SCREAMING_SNAKE_CASE							   ={
           '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 						A  (		self				:     int			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							, provider='CPUExecutionProvider'			)
       pipe.set_progress_bar_config(disable=_a			)

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

       # started as 128, should now be 512
       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE							   =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 						A  (		self				:     str			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							, provider='CPUExecutionProvider'			)
       _SCREAMING_SNAKE_CASE							   =PNDMScheduler.from_config(pipe.scheduler.config							, skip_prk_steps=_a			)
       pipe.set_progress_bar_config(disable=_a			)

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

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE							   =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 						A  (		self				:     Union[str, Any]			)     ->   Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							, provider='CPUExecutionProvider'			)
       _SCREAMING_SNAKE_CASE							   =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
       pipe.set_progress_bar_config(disable=_a			)

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

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE							   =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 						A  (		self				:     Optional[int]			)     ->   List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							, provider='CPUExecutionProvider'			)
       _SCREAMING_SNAKE_CASE							   =EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
       pipe.set_progress_bar_config(disable=_a			)

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

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE							   =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 						A  (		self				:     List[str]			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							, provider='CPUExecutionProvider'			)
       _SCREAMING_SNAKE_CASE							   =EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
       pipe.set_progress_bar_config(disable=_a			)

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

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE							   =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 							A__   (	unittest.TestCase  ):





  @property
  def 						A  (		self				:     Tuple			)     ->   Optional[Any]:





       '''simple docstring'''






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





  @property
  def 						A  (		self				:     Tuple			)     ->   Any:





       '''simple docstring'''






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





  def 						A  (		self				:     List[Any]			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/img2img/sketch-mountains-input.jpg'			)
       _SCREAMING_SNAKE_CASE							   =init_image.resize((128, 128)			)
       # using the PNDM scheduler by default
       _SCREAMING_SNAKE_CASE							   =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			)

       _SCREAMING_SNAKE_CASE							   ='A fantasy landscape, trending on artstation'

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

       assert images.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE							   =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 						A  (		self				:     Any			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/img2img/sketch-mountains-input.jpg'			)
       _SCREAMING_SNAKE_CASE							   =init_image.resize((128, 128)			)
       _SCREAMING_SNAKE_CASE							   =LMSDiscreteScheduler.from_pretrained(
           'ssube/stable-diffusion-x4-upscaler-onnx'							, subfolder='scheduler'			)
       _SCREAMING_SNAKE_CASE							   =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			)

       _SCREAMING_SNAKE_CASE							   ='A fantasy landscape, trending on artstation'

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

       assert images.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE							   =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



'''simple docstring'''
def 	_lowerCAmelCase (						_UpperCamelCase						:					int = 50    )				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[[0] * 3 for _ in range(length + 1    )]

     for row_length in range(length + 1    ):
          for tile_length in range(2							,			5    ):
               for tile_start in range(row_length - tile_length + 1    ):
                    different_colour_ways_number[row_length][tile_length - 2] += (
                        different_colour_ways_number[row_length - tile_start - tile_length][
                            tile_length - 2
                        ]
                        + 1
                    )

     return sum(different_colour_ways_number[length]    )


if __name__ == "__main__":
     print(f'''{solution() = }''')
