

"""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	(				A_,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      Any         =  "ssube/stable-diffusion-x4-upscaler-onnx"

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				Any=0)->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			floats_tensor((1, 3, 1_2_8, 1_2_8)     ,  rng=random.Random(_snake_case))
						__lowerCAmelCase:       Union[str, Any]          =			torch.manual_seed(_snake_case)
						__lowerCAmelCase:       List[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 							lowercase_     (	self							:				Optional[Any])->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,  provider="CPUExecutionProvider")
						pipe.set_progress_bar_config(disable=_snake_case)

						__lowerCAmelCase:       List[Any]          =			self.get_dummy_inputs()
						__lowerCAmelCase:       Union[str, Any]          =			pipe(**_snake_case).images
						__lowerCAmelCase:       Optional[int]          =			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:       List[str]          =			np.array(
						    [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223])
						assert np.abs(image_slice - expected_slice).max() < 1e-1

				def 							lowercase_     (	self							:				List[str])->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,  provider="CPUExecutionProvider")
						__lowerCAmelCase:       List[str]          =			PNDMScheduler.from_config(pipe.scheduler.config     ,  skip_prk_steps=_snake_case)
						pipe.set_progress_bar_config(disable=_snake_case)

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__lowerCAmelCase:       Any          =			np.array(
						    [0.6898892, 0.59240556, 0.52499527, 0.58866215, 0.52258235, 0.52572715, 0.62414473, 0.6174387, 0.6214964])
						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1

				def 							lowercase_     (	self							:				Tuple)->   Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,  provider="CPUExecutionProvider")
						__lowerCAmelCase:       int          =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
						pipe.set_progress_bar_config(disable=_snake_case)

						__lowerCAmelCase:       Union[str, Any]          =			self.get_dummy_inputs()
						__lowerCAmelCase:       str          =			pipe(**_snake_case).images
						__lowerCAmelCase:       Dict          =			image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__lowerCAmelCase:       Tuple          =			np.array(
						    [0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515])

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

				def 							lowercase_     (	self							:				Optional[int])->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,  provider="CPUExecutionProvider")
						__lowerCAmelCase:       Union[str, Any]          =			EulerDiscreteScheduler.from_config(pipe.scheduler.config)
						pipe.set_progress_bar_config(disable=_snake_case)

						__lowerCAmelCase:       Optional[Any]          =			self.get_dummy_inputs()
						__lowerCAmelCase:       Any          =			pipe(**_snake_case).images
						__lowerCAmelCase:       Union[str, Any]          =			image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__lowerCAmelCase:       List[str]          =			np.array(
						    [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223])
						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1




				def 							lowercase_     (	self							:				Dict)->   Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,  provider="CPUExecutionProvider")
						__lowerCAmelCase:       int          =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
						pipe.set_progress_bar_config(disable=_snake_case)

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

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__lowerCAmelCase:       Union[str, Any]          =			np.array(
						    [0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043])

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





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

				@property
				def 							lowercase_     (	self							:				int)->   List[str]:



						'''simple docstring'''


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

				@property
				def 							lowercase_     (	self							:				Union[str, Any])->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			ort.SessionOptions()
						__lowerCAmelCase:       Optional[Any]          =			False
						return options

				def 							lowercase_     (	self							:				Optional[Any])->   str:



						'''simple docstring'''


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

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

						__lowerCAmelCase:       Union[str, Any]          =			torch.manual_seed(0)
						__lowerCAmelCase:       Tuple          =			pipe(
						    prompt=_snake_case     ,  image=_snake_case     ,  guidance_scale=7.5     ,  num_inference_steps=1_0     ,  generator=_snake_case     ,  output_type="np"     ,  )
						__lowerCAmelCase:       Tuple          =			output.images
						__lowerCAmelCase:       List[Any]          =			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 							lowercase_     (	self							:				List[str])->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			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))
						__lowerCAmelCase:       Any          =			LMSDiscreteScheduler.from_pretrained(
						    "ssube/stable-diffusion-x4-upscaler-onnx"     ,  subfolder="scheduler")
						__lowerCAmelCase:       List[str]          =			OnnxStableDiffusionUpscalePipeline.from_pretrained(
						    "ssube/stable-diffusion-x4-upscaler-onnx"     ,  scheduler=_snake_case     ,  provider=self.gpu_provider     ,  sess_options=self.gpu_options     ,  )
						pipe.set_progress_bar_config(disable=_snake_case)

						__lowerCAmelCase:       List[Any]          =			'''A fantasy landscape, trending on artstation'''

						__lowerCAmelCase:       Optional[int]          =			torch.manual_seed(0)
						__lowerCAmelCase:       Union[str, Any]          =			pipe(
						    prompt=_snake_case     ,  image=_snake_case     ,  guidance_scale=7.5     ,  num_inference_steps=2_0     ,  generator=_snake_case     ,  output_type="np"     ,  )
						__lowerCAmelCase:       List[str]          =			output.images
						__lowerCAmelCase:       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:       str          =			np.array(
						    [0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566])
						# 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



def      __UpperCAmelCase      (    )							->      int:
		with open(os.path.dirname(__lowerCamelCase	) + '''/p022_names.txt'''	) as file:
				lowercase__   :    List[Any]				=			str(file.readlines()[0]	)
				lowercase__   :    Dict				=			names.replace('''"'''						,     ''''''	).split(''','''	)

		names.sort()

		lowercase__   :    int				=			0
		lowercase__   :    Optional[Any]				=			0

		for i, name in enumerate(__lowerCamelCase	):
				for letter in name:
						name_score += ord(__lowerCamelCase	) - 64

				total_score += (i + 1) * name_score
				lowercase__   :    List[str]				=			0
		return total_score


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