






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      lowercase__     (			_UpperCAmelCase,					unittest.TestCase	):
	# TODO: is there an appropriate internal test set?
	a_         ="""ssube/stable-diffusion-x4-upscaler-onnx"""

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase=0       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						floats_tensor((1, 3, 128, 128)		,    rng=random.Random(__UpperCAmelCase       )       )
							lowerCAmelCase__       =						torch.manual_seed(__UpperCAmelCase       )
							lowerCAmelCase__       =						{
							    "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       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,    provider="CPUExecutionProvider"       )
							pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

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

							# started as 128, should now be 512
							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__       =						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 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,    provider="CPUExecutionProvider"       )
							lowerCAmelCase__       =						PNDMScheduler.from_config(pipe.scheduler.config		,    skip_prk_steps=__UpperCAmelCase       )
							pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

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

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__       =						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 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,    provider="CPUExecutionProvider"       )
							lowerCAmelCase__       =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
							pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

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

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







							'''simple docstring'''





							lowerCAmelCase__       =						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,    provider="CPUExecutionProvider"       )
							lowerCAmelCase__       =						EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
							pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

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

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__       =						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 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,    provider="CPUExecutionProvider"       )
							lowerCAmelCase__       =						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
							pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

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

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__       =						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      lowercase__     (			unittest.TestCase	):

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







							'''simple docstring'''





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

	@property
	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/img2img/sketch-mountains-input.jpg"       )
							lowerCAmelCase__       =						init_image.resize((128, 128)       )
							# using the PNDM scheduler by default
							lowerCAmelCase__       =						OnnxStableDiffusionUpscalePipeline.from_pretrained(
							    "ssube/stable-diffusion-x4-upscaler-onnx"		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
							pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

							lowerCAmelCase__       =						"A fantasy landscape, trending on artstation"

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

							assert images.shape == (1, 512, 512, 3)
							lowerCAmelCase__       =						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 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							lowerCAmelCase__       =						load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/img2img/sketch-mountains-input.jpg"       )
							lowerCAmelCase__       =						init_image.resize((128, 128)       )
							lowerCAmelCase__       =						LMSDiscreteScheduler.from_pretrained(
							    "ssube/stable-diffusion-x4-upscaler-onnx"		,    subfolder="scheduler"       )
							lowerCAmelCase__       =						OnnxStableDiffusionUpscalePipeline.from_pretrained(
							    "ssube/stable-diffusion-x4-upscaler-onnx"		,    scheduler=__UpperCAmelCase		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
							pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

							lowerCAmelCase__       =						"A fantasy landscape, trending on artstation"

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

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














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_							=    {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''IBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''IBertForMaskedLM''',
	    '''IBertForMultipleChoice''',
	    '''IBertForQuestionAnswering''',
	    '''IBertForSequenceClassification''',
	    '''IBertForTokenClassification''',
	    '''IBertModel''',
	    '''IBertPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ibert import (
		    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    IBertForMaskedLM,
		    IBertForMultipleChoice,
		    IBertForQuestionAnswering,
		    IBertForSequenceClassification,
		    IBertForTokenClassification,
		    IBertModel,
		    IBertPreTrainedModel,
		)

else:
	import sys

	a_							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






