
"""simple docstring"""


import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    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 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Dict   =     '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   List[str]=0   )  -> str:
							A     = floats_tensor((1, 3, 128, 128)	,rng=random.Random(A_   )   )
							A     = np.random.RandomState(A_   )
							A     = {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': image,
							    'generator': generator,
							    'num_inference_steps': 3,
							    'strength': 0.75,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Tuple:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							pipe.set_progress_bar_config(disable=A_   )

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

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]   )
							assert np.abs(image_slice - expected_slice   ).max() < 1e-1


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = PNDMScheduler.from_config(pipe.scheduler.config	,skip_prk_steps=A_   )
							pipe.set_progress_bar_config(disable=A_   )

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

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> int:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

							# warmup pass to apply optimizations
							A     = pipe(**self.get_dummy_inputs()   )

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

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Tuple:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

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

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> str:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

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

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]   )

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




						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Tuple:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

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

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]   )

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







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



						'''simple docstring'''


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


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> List[Any]:
							A     = ort.SessionOptions()
							A     = False
							return options


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> str:
							A     = load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'   )
							A     = init_image.resize((768, 512)   )
							# using the PNDM scheduler by default
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'	,revision='onnx'	,safety_checker=A_	,feature_extractor=A_	,provider=self.gpu_provider	,sess_options=self.gpu_options	,)
							pipe.set_progress_bar_config(disable=A_   )

							A     = 'A fantasy landscape, trending on artstation'

							A     = np.random.RandomState(0   )
							A     = pipe(
							    prompt=A_	,image=A_	,strength=0.75	,guidance_scale=7.5	,num_inference_steps=10	,generator=A_	,output_type='np'	,)
							A     = output.images
							A     = images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A     = np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]   )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> 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((768, 512)   )
							A     = LMSDiscreteScheduler.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'	,subfolder='scheduler'	,revision='onnx'   )
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'	,revision='onnx'	,scheduler=A_	,safety_checker=A_	,feature_extractor=A_	,provider=self.gpu_provider	,sess_options=self.gpu_options	,)
							pipe.set_progress_bar_config(disable=A_   )

							A     = 'A fantasy landscape, trending on artstation'

							A     = np.random.RandomState(0   )
							A     = pipe(
							    prompt=A_	,image=A_	,strength=0.75	,guidance_scale=7.5	,num_inference_steps=20	,generator=A_	,output_type='np'	,)
							A     = output.images
							A     = images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A     = np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]   )
							# 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 Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
      import PIL


a__      :     Dict         =		logging.get_logger(__name__)





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						Optional[int]							=   ["""pixel_values"""]


  def __init__(							self ,__snake_case = True ,__snake_case = None ,__snake_case = None ,__snake_case = PILImageResampling.BILINEAR ,__snake_case = True ,__snake_case = 1 / 2_5_5 ,__snake_case = True ,__snake_case = None ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       super().__init__(**__snake_case )
       A_          =       size if size is not None else {'''shortest_edge''': 3_8_4}
       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )

       A_          =       do_resize
       A_          =       size
       # Default value set here for backwards compatibility where the value in config is None
       A_          =       crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
       A_          =       resample
       A_          =       do_rescale
       A_          =       rescale_factor
       A_          =       do_normalize
       A_          =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       A_          =       image_std if image_std is not None else IMAGENET_STANDARD_STD


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case = PILImageResampling.BICUBIC ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )
       if "shortest_edge" not in size:
            raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}' )
       A_          =       size['''shortest_edge''']

       if shortest_edge < 3_8_4:
            # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
            A_          =       int(shortest_edge / crop_pct )
            A_          =       get_resize_output_image_size(__snake_case ,size=__snake_case ,default_to_square=__snake_case )
            A_          =       resize(image=__snake_case ,size=__snake_case ,resample=__snake_case ,data_format=__snake_case ,**__snake_case )
            # then crop to (shortest_edge, shortest_edge)
            return center_crop(image=__snake_case ,size=(shortest_edge, shortest_edge) ,data_format=__snake_case ,**__snake_case )
       else:
            # warping (no cropping) when evaluated at 384 or larger
            return resize(
                __snake_case ,size=(shortest_edge, shortest_edge) ,resample=__snake_case ,data_format=__snake_case ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       return rescale(__snake_case ,scale=__snake_case ,data_format=__snake_case ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       return normalize(__snake_case ,mean=__snake_case ,std=__snake_case ,data_format=__snake_case ,**__snake_case )




  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = None ,__snake_case = ChannelDimension.FIRST ,**__snake_case ,):



       """simple docstring"""





       A_          =       do_resize if do_resize is not None else self.do_resize
       A_          =       crop_pct if crop_pct is not None else self.crop_pct
       A_          =       resample if resample is not None else self.resample
       A_          =       do_rescale if do_rescale is not None else self.do_rescale
       A_          =       rescale_factor if rescale_factor is not None else self.rescale_factor
       A_          =       do_normalize if do_normalize is not None else self.do_normalize
       A_          =       image_mean if image_mean is not None else self.image_mean
       A_          =       image_std if image_std is not None else self.image_std

       A_          =       size if size is not None else self.size
       A_          =       get_size_dict(__snake_case ,default_to_square=__snake_case )

       A_          =       make_list_of_images(__snake_case )

       if not valid_images(__snake_case ):
            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 or resample is None:
            raise ValueError('''Size and resample must be specified if do_resize is True.''' )

       if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
            raise ValueError('''crop_pct must be specified if size < 384.''' )

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

       if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

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

       if do_resize:
            A_          =       [self.resize(image=__snake_case ,size=__snake_case ,crop_pct=__snake_case ,resample=__snake_case ) for image in images]

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

       if do_normalize:
            A_          =       [self.normalize(image=__snake_case ,mean=__snake_case ,std=__snake_case ) for image in images]

       A_          =       [to_channel_dimension_format(__snake_case ,__snake_case ) for image in images]

       A_          =       {'''pixel_values''': images}
       return BatchFeature(data=__snake_case ,tensor_type=__snake_case )



