


"""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_     (			UpperCamelCase      ,  unittest.TestCase):

	"""simple docstring"""





	snake_case__    :  List[Any]    =      "ssube/stable-diffusion-x4-upscaler-onnx"




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    List[Any]=0       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    floats_tensor((1, 3, 1_2_8, 1_2_8)      ,      rng=random.Random(UpperCAmelCase__       )       )
						__SCREAMING_SNAKE_CASE						       =    torch.manual_seed(UpperCAmelCase__       )
						__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   UpperCAmelCase_				(			self      :    int       )	->     List[str]:
						__SCREAMING_SNAKE_CASE						       =    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint      ,      provider="CPUExecutionProvider"       )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__       )

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

						# started as 128, should now be 512
						assert image.shape == (1, 5_1_2, 5_1_2, 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   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     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=UpperCAmelCase__       )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__       )

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

						assert image.shape == (1, 5_1_2, 5_1_2, 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   UpperCAmelCase_				(			self      :    Dict       )	->     Dict:
						__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=UpperCAmelCase__       )

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

						assert image.shape == (1, 5_1_2, 5_1_2, 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   UpperCAmelCase_				(			self      :    List[str]       )	->     Optional[int]:
						__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=UpperCAmelCase__       )

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

						assert image.shape == (1, 5_1_2, 5_1_2, 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   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Union[str, 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=UpperCAmelCase__       )

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

						assert image.shape == (1, 5_1_2, 5_1_2, 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 			UpperCamelCase_     (			unittest.TestCase):

	"""simple docstring"""





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




	@property
	def   UpperCAmelCase_				(			self      :    Any       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    ort.SessionOptions()
						__SCREAMING_SNAKE_CASE						       =    False
						return options




	def   UpperCAmelCase_				(			self      :    int       )	->     Any:
						__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((1_2_8, 1_2_8)       )
						# 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=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    "A fantasy landscape, trending on artstation"

						__SCREAMING_SNAKE_CASE						       =    torch.manual_seed(0       )
						__SCREAMING_SNAKE_CASE						       =    pipe(
						    prompt=UpperCAmelCase__      ,      image=UpperCAmelCase__      ,      guidance_scale=7.5      ,      num_inference_steps=1_0      ,      generator=UpperCAmelCase__      ,      output_type="np"      ,      )
						__SCREAMING_SNAKE_CASE						       =    output.images
						__SCREAMING_SNAKE_CASE						       =    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)
						__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   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     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((1_2_8, 1_2_8)       )
						__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=UpperCAmelCase__      ,      provider=self.gpu_provider      ,      sess_options=self.gpu_options      ,      )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    "A fantasy landscape, trending on artstation"

						__SCREAMING_SNAKE_CASE						       =    torch.manual_seed(0       )
						__SCREAMING_SNAKE_CASE						       =    pipe(
						    prompt=UpperCAmelCase__      ,      image=UpperCAmelCase__      ,      guidance_scale=7.5      ,      num_inference_steps=2_0      ,      generator=UpperCAmelCase__      ,      output_type="np"      ,      )
						__SCREAMING_SNAKE_CASE						       =    output.images
						__SCREAMING_SNAKE_CASE						       =    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)
						__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"""







from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
		import PIL

if is_torch_available():
		import torch


a__    :							Optional[int]            =					logging.get_logger(__name__)




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Optional[Any]    =      ["pixel_values"]




	def __init__(			self      :    Dict      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    PILImageResampling = PILImageResampling.BILINEAR      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Union[int, float] = 1 / 2_5_5      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    bool = True      ,      **UpperCAmelCase__      :    Dict      ,      )	->     None:
						super().__init__(**UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    size if size is not None else {"shortest_edge": 2_2_4}
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      default_to_square=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      param_name="crop_size"       )

						__SCREAMING_SNAKE_CASE						       =    do_resize
						__SCREAMING_SNAKE_CASE						       =    size
						__SCREAMING_SNAKE_CASE						       =    resample
						__SCREAMING_SNAKE_CASE						       =    do_rescale
						__SCREAMING_SNAKE_CASE						       =    rescale_factor
						__SCREAMING_SNAKE_CASE						       =    do_center_crop
						__SCREAMING_SNAKE_CASE						       =    crop_size
						__SCREAMING_SNAKE_CASE						       =    do_flip_channel_order




	def   UpperCAmelCase_				(			self      :    Any      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Dict[str, int]      ,      UpperCAmelCase__      :    PILImageResampling = PIL.Image.BILINEAR      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    Optional[Any]      ,      )	->     np.ndarray:
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      default_to_square=UpperCAmelCase__       )
						if "shortest_edge" not in size:
											raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""       )
						__SCREAMING_SNAKE_CASE						       =    get_resize_output_image_size(UpperCAmelCase__      ,      size=size["shortest_edge"]      ,      default_to_square=UpperCAmelCase__       )
						return resize(UpperCAmelCase__      ,      size=UpperCAmelCase__      ,      resample=UpperCAmelCase__      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Dict[str, int]      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    Any      ,      )	->     np.ndarray:
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__       )
						if "height" not in size or "width" not in size:
											raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""       )
						return center_crop(UpperCAmelCase__      ,      size=(size["height"], size["width"])      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[int]      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Union[int, float]      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    Dict      ,      )	->     Dict:
						return rescale(UpperCAmelCase__      ,      scale=UpperCAmelCase__      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[int]      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None       )	->     np.ndarray:
						return flip_channel_order(UpperCAmelCase__      ,      data_format=UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[int]      ,      UpperCAmelCase__      :    ImageInput      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    PILImageResampling = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    float = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Optional[Union[str, TensorType]] = None      ,      UpperCAmelCase__      :    ChannelDimension = ChannelDimension.FIRST      ,      **UpperCAmelCase__      :    str      ,      )	->     PIL.Image.Image:
						__SCREAMING_SNAKE_CASE						       =    do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE						       =    resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE						       =    do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE						       =    rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE						       =    do_center_crop if do_center_crop is not None else self.do_center_crop
						__SCREAMING_SNAKE_CASE						       =    (
						    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
						)

						__SCREAMING_SNAKE_CASE						       =    size if size is not None else self.size
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      default_to_square=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    crop_size if crop_size is not None else self.crop_size
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      param_name="crop_size"       )

						__SCREAMING_SNAKE_CASE						       =    make_list_of_images(UpperCAmelCase__       )

						if not valid_images(UpperCAmelCase__       ):
											raise ValueError(
											    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
											    "torch.Tensor, tf.Tensor or jax.ndarray."       )

						if do_resize and size is None:
											raise ValueError("Size must be specified if do_resize is True."       )

						if do_rescale and rescale_factor is None:
											raise ValueError("Rescale factor must be specified if do_rescale is True."       )

						if do_center_crop and crop_size is None:
											raise ValueError("Crop size must be specified if do_center_crop is True."       )

						# All transformations expect numpy arrays.
						__SCREAMING_SNAKE_CASE						       =    [to_numpy_array(UpperCAmelCase__       ) for image in images]

						if do_resize:
											__SCREAMING_SNAKE_CASE						       =    [self.resize(image=UpperCAmelCase__      ,      size=UpperCAmelCase__      ,      resample=UpperCAmelCase__       ) for image in images]

						if do_center_crop:
											__SCREAMING_SNAKE_CASE						       =    [self.center_crop(image=UpperCAmelCase__      ,      size=UpperCAmelCase__       ) for image in images]

						if do_rescale:
											__SCREAMING_SNAKE_CASE						       =    [self.rescale(image=UpperCAmelCase__      ,      scale=UpperCAmelCase__       ) for image in images]

						# the pretrained checkpoints assume images are BGR, not RGB
						if do_flip_channel_order:
											__SCREAMING_SNAKE_CASE						       =    [self.flip_channel_order(image=UpperCAmelCase__       ) for image in images]

						__SCREAMING_SNAKE_CASE						       =    [to_channel_dimension_format(UpperCAmelCase__      ,      UpperCAmelCase__       ) for image in images]

						__SCREAMING_SNAKE_CASE						       =    {"pixel_values": images}
						return BatchFeature(data=UpperCAmelCase__      ,      tensor_type=UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    List[Tuple] = None       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
											if len(UpperCAmelCase__       ) != len(UpperCAmelCase__       ):
																raise ValueError(
																    "Make sure that you pass in as many target sizes as the batch dimension of the logits"       )

											if is_torch_tensor(UpperCAmelCase__       ):
																__SCREAMING_SNAKE_CASE						       =    target_sizes.numpy()

											__SCREAMING_SNAKE_CASE						       =    []

											for idx in range(len(UpperCAmelCase__       )       ):
																__SCREAMING_SNAKE_CASE						       =    torch.nn.functional.interpolate(
																    logits[idx].unsqueeze(dim=0       )      ,      size=target_sizes[idx]      ,      mode="bilinear"      ,      align_corners=UpperCAmelCase__       )
																__SCREAMING_SNAKE_CASE						       =    resized_logits[0].argmax(dim=0       )
																semantic_segmentation.append(UpperCAmelCase__       )
						else:
											__SCREAMING_SNAKE_CASE						       =    logits.argmax(dim=1       )
											__SCREAMING_SNAKE_CASE						       =    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

						return semantic_segmentation
