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






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Dict=0    ):
													__lowerCamelCase					:		int		   =     floats_tensor((1, 3, 128, 128)							,					rng=random.Random(UpperCAmelCase    )    )
													__lowerCamelCase					:		Any		   =     torch.manual_seed(UpperCAmelCase    )
													__lowerCamelCase					:		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       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		Optional[int]		   =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							,					provider="CPUExecutionProvider"    )
													pipe.set_progress_bar_config(disable=UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     self.get_dummy_inputs()
													__lowerCamelCase					:		Optional[Any]		   =     pipe(**UpperCAmelCase    ).images
													__lowerCamelCase					:		str		   =     image[0, -3:, -3:, -1].flatten()

													# started as 128, should now be 512
													assert image.shape == (1, 512, 512, 3)
													__lowerCamelCase					:		Any		   =     np.array(
													    [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]    )
													assert np.abs(image_slice - expected_slice    ).max() < 1E-1






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Union[str, Any]		   =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							,					provider="CPUExecutionProvider"    )
													__lowerCamelCase					:		Optional[int]		   =     PNDMScheduler.from_config(pipe.scheduler.config							,					skip_prk_steps=UpperCAmelCase    )
													pipe.set_progress_bar_config(disable=UpperCAmelCase    )

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

													assert image.shape == (1, 512, 512, 3)
													__lowerCamelCase					:		int		   =     np.array(
													    [0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4]    )
													assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-1






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Optional[Any]		   =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							,					provider="CPUExecutionProvider"    )
													__lowerCamelCase					:		Dict		   =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
													pipe.set_progress_bar_config(disable=UpperCAmelCase    )

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

													assert image.shape == (1, 512, 512, 3)
													__lowerCamelCase					:		Optional[Any]		   =     np.array(
													    [0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5]    )

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






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Dict		   =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							,					provider="CPUExecutionProvider"    )
													__lowerCamelCase					:		Any		   =     EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
													pipe.set_progress_bar_config(disable=UpperCAmelCase    )

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

													assert image.shape == (1, 512, 512, 3)
													__lowerCamelCase					:		Any		   =     np.array(
													    [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]    )
													assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-1







						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Optional[Any]		   =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint							,					provider="CPUExecutionProvider"    )
													__lowerCamelCase					:		Optional[int]		   =     EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
													pipe.set_progress_bar_config(disable=UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     self.get_dummy_inputs()
													__lowerCamelCase					:		Any		   =     pipe(**UpperCAmelCase    ).images
													__lowerCamelCase					:		List[str]		   =     image[0, -3:, -3:, -1]

													assert image.shape == (1, 512, 512, 3)
													__lowerCamelCase					:		Union[str, Any]		   =     np.array(
													    [0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3]    )

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

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






						@property
						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													return (
													    "CUDAExecutionProvider",
													    {
													        "gpu_mem_limit": "15000000000",  # 15GB
													        "arena_extend_strategy": "kSameAsRequested",
													    },
													)






						@property
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		Optional[Any]		   =     ort.SessionOptions()
													__lowerCamelCase					:		int		   =     False
													return options






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[Any]		   =     load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/img2img/sketch-mountains-input.jpg"    )
													__lowerCamelCase					:		Union[str, Any]		   =     init_image.resize((128, 128)    )
													# using the PNDM scheduler by default
													__lowerCamelCase					:		List[Any]		   =     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					:		Tuple		   =     "A fantasy landscape, trending on artstation"

													__lowerCamelCase					:		Union[str, Any]		   =     torch.manual_seed(0    )
													__lowerCamelCase					:		Union[str, Any]		   =     pipe(
													    prompt=UpperCAmelCase							,					image=UpperCAmelCase							,					guidance_scale=7.5							,					num_inference_steps=10							,					generator=UpperCAmelCase							,					output_type="np"							,					)
													__lowerCamelCase					:		Dict		   =     output.images
													__lowerCamelCase					:		Optional[int]		   =     images[0, 255:258, 383:386, -1]

													assert images.shape == (1, 512, 512, 3)
													__lowerCamelCase					:		Any		   =     np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2]    )
													# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

													assert np.abs(image_slice.flatten() - expected_slice    ).max() < 2E-2







						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Optional[int]		   =     load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/img2img/sketch-mountains-input.jpg"    )
													__lowerCamelCase					:		int		   =     init_image.resize((128, 128)    )
													__lowerCamelCase					:		Tuple		   =     LMSDiscreteScheduler.from_pretrained(
													    "ssube/stable-diffusion-x4-upscaler-onnx"							,					subfolder="scheduler"    )
													__lowerCamelCase					:		int		   =     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					:		Any		   =     "A fantasy landscape, trending on artstation"

													__lowerCamelCase					:		str		   =     torch.manual_seed(0    )
													__lowerCamelCase					:		str		   =     pipe(
													    prompt=UpperCAmelCase							,					image=UpperCAmelCase							,					guidance_scale=7.5							,					num_inference_steps=20							,					generator=UpperCAmelCase							,					output_type="np"							,					)
													__lowerCamelCase					:		List[Any]		   =     output.images
													__lowerCamelCase					:		Dict		   =     images[0, 255:258, 383:386, -1]

													assert images.shape == (1, 512, 512, 3)
													__lowerCamelCase					:		Optional[Any]		   =     np.array(
													    [0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6]    )
													# 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 typing import TYPE_CHECKING

from ...utils import _LazyModule


__A  =				{'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}


if TYPE_CHECKING:
				from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
				import sys

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