

'''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    snake_case     (      lowercase_,						unittest.TestCase		):






				"""simple docstring"""
				# TODO: is there an appropriate internal test set?
				_a    	=     """ssube/stable-diffusion-x4-upscaler-onnx"""



				def   a__					(    self,     _lowercase=0       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				floats_tensor((1, 3, 128, 128),     rng=random.Random(_lowercase       )       )
											SCREAMING_SNAKE_CASE_				=				torch.manual_seed(_lowercase       )
											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       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,     provider='CPUExecutionProvider'       )
											pipe.set_progress_bar_config(disable=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				self.get_dummy_inputs()
											SCREAMING_SNAKE_CASE_				=				pipe(**_lowercase       ).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_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   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,     provider='CPUExecutionProvider'       )
											SCREAMING_SNAKE_CASE_				=				PNDMScheduler.from_config(pipe.scheduler.config,     skip_prk_steps=_lowercase       )
											pipe.set_progress_bar_config(disable=_lowercase       )

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

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE_				=				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   a__					(    self       )     ->							str:
											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=_lowercase       )

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

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

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

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE_				=				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   a__					(    self       )     ->							List[Any]:
											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=_lowercase       )

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

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE_				=				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    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



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



				@property
				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				ort.SessionOptions()
											SCREAMING_SNAKE_CASE_				=				False
											return options



				def   a__					(    self       )     ->							Dict:
											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=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				'A fantasy landscape, trending on artstation'

											SCREAMING_SNAKE_CASE_				=				torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				pipe(
											    prompt=_lowercase,     image=_lowercase,     guidance_scale=7.5,     num_inference_steps=10,     generator=_lowercase,     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([0.4_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   a__					(    self       )     ->							Optional[int]:
											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=_lowercase,     provider=self.gpu_provider,     sess_options=self.gpu_options,     )
											pipe.set_progress_bar_config(disable=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				'A fantasy landscape, trending on artstation'

											SCREAMING_SNAKE_CASE_				=				torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				pipe(
											    prompt=_lowercase,     image=_lowercase,     guidance_scale=7.5,     num_inference_steps=20,     generator=_lowercase,     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_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 os

# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE							:				List[Any]     	=			[int(0.5 * n * (n + 1)) for n in range(1, 101)]




def 							_UpperCamelCase				(   )      -> List[Any]:
							SCREAMING_SNAKE_CASE_				=				os.path.dirname(os.path.realpath(lowerCAmelCase__   )   )
							SCREAMING_SNAKE_CASE_				=				os.path.join(lowerCAmelCase__		,'words.txt'   )

							SCREAMING_SNAKE_CASE_				=				''
							with open(lowerCAmelCase__   ) as f:
														SCREAMING_SNAKE_CASE_				=				f.readline()

							SCREAMING_SNAKE_CASE_				=				[word.strip('"'   ) for word in words.strip('\r\n'   ).split(','   )]
							SCREAMING_SNAKE_CASE_				=				[
							    word
							    for word in [sum(ord(lowerCAmelCase__   ) - 64 for x in word   ) for word in words]
							    if word in TRIANGULAR_NUMBERS
							]
							return len(lowerCAmelCase__   )


if __name__ == "__main__":
							print(solution())





