




'''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   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





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



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

								A_									= self.get_dummy_inputs()
								A_									= pipe(**_lowercase).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_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     __snake_case						(	self     :						List[str])    ->							Union[str, Any]:
								A_									= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint      ,       provider='CPUExecutionProvider')
								A_									= PNDMScheduler.from_config(pipe.scheduler.config      ,       skip_prk_steps=_lowercase)
								pipe.set_progress_bar_config(disable=_lowercase)

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

								assert image.shape == (1, 512, 512, 3)
								A_									= 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     __snake_case						(	self     :						Optional[int])    ->							str:
								A_									= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint      ,       provider='CPUExecutionProvider')
								A_									= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
								pipe.set_progress_bar_config(disable=_lowercase)

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

								assert image.shape == (1, 512, 512, 3)
								A_									= 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     __snake_case						(	self     :						Optional[Any])    ->							Optional[int]:
								A_									= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint      ,       provider='CPUExecutionProvider')
								A_									= EulerDiscreteScheduler.from_config(pipe.scheduler.config)
								pipe.set_progress_bar_config(disable=_lowercase)

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

								assert image.shape == (1, 512, 512, 3)
								A_									= 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     __snake_case						(	self     :						List[str])    ->							Optional[int]:
								A_									= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint      ,       provider='CPUExecutionProvider')
								A_									= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
								pipe.set_progress_bar_config(disable=_lowercase)

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

								assert image.shape == (1, 512, 512, 3)
								A_									= 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   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





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



					@property
					def     __snake_case						(	self     :						Dict)    ->							str:
								A_									= ort.SessionOptions()
								A_									= False
								return options



					def     __snake_case						(	self     :						Any)    ->							Optional[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=_lowercase)

								A_									= 'A fantasy landscape, trending on artstation'

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

								assert images.shape == (1, 512, 512, 3)
								A_									= np.array([0.48_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     __snake_case						(	self     :						List[Any])    ->							int:
								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=_lowercase      ,       provider=self.gpu_provider      ,       sess_options=self.gpu_options      ,       )
								pipe.set_progress_bar_config(disable=_lowercase)

								A_									= 'A fantasy landscape, trending on artstation'

								A_									= torch.manual_seed(0)
								A_									= pipe(
								    prompt=_lowercase      ,       image=_lowercase      ,       guidance_scale=7.5      ,       num_inference_steps=20      ,       generator=_lowercase      ,       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_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'''
from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands





def 			lowerCamelCase(							)		->		int:
			A_									= ArgumentParser('Transformers CLI tool'       ,usage='transformers-cli <command> [<args>]'     )
			A_									= parser.add_subparsers(help='transformers-cli command helpers'     )

			# Register commands
			ConvertCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			DownloadCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			EnvironmentCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			RunCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			ServeCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			UserCommands.register_subcommand(SCREAMING_SNAKE_CASE_     )
			AddNewModelCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			AddNewModelLikeCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			LfsCommands.register_subcommand(SCREAMING_SNAKE_CASE_     )
			PTtoTFCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )

			# Let's go
			A_									= parser.parse_args()

			if not hasattr(SCREAMING_SNAKE_CASE_       ,'func'     ):
						parser.print_help()
						exit(1     )

			# Run
			A_									= args.func(SCREAMING_SNAKE_CASE_     )
			service.run()


if __name__ == "__main__":
							main()




