
"""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:    Tuple      =    '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      List[str]=0      )     ->   Dict:
							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     :      str      )     ->   Optional[int]:
							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     :      str      )     ->   Union[str, 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     :      Optional[Any]      )     ->   Dict:
							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     :      int      )     ->   Optional[int]:
							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     :      List[str]      )     ->   Optional[int]:
							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     :      str      )     ->   Optional[Any]:
							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     :      Any      )     ->   Union[str, Any]:
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Tuple:
							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[Any]      )     ->   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

"""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,
    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_torch_available, is_torch_tensor, logging


if is_torch_available():
		import torch


_lowercase							   =				logging.get_logger(__name__)







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    ['''pixel_values''']


						def __init__( self     :      Optional[Any]  ,A_     :      bool = True  ,A_     :      Optional[Dict[str, int]] = None  ,A_     :      PILImageResampling = PILImageResampling.BILINEAR  ,A_     :      bool = True  ,A_     :      Dict[str, int] = None  ,A_     :      bool = True  ,A_     :      Union[int, float] = 1 / 255  ,A_     :      bool = True  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,**A_     :      Optional[Any]  ,)     ->   None:
							super().__init__(**A_      )
							A						   =   size if size is not None else {'shortest_edge': 256}
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							A						   =   crop_size if crop_size is not None else {'height': 224, 'width': 224}
							A						   =   get_size_dict(A_  ,param_name='crop_size'      )
							A						   =   do_resize
							A						   =   size
							A						   =   resample
							A						   =   do_center_crop
							A						   =   crop_size
							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 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      np.ndarray  ,A_     :      Dict[str, int]  ,A_     :      PILImageResampling = PILImageResampling.BICUBIC  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      int  ,)     ->   np.ndarray:
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							if "shortest_edge" not in size:
								raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
							A						   =   get_resize_output_image_size(A_  ,size=size['shortest_edge']  ,default_to_square=A_      )
							return resize(A_  ,size=A_  ,resample=A_  ,data_format=A_  ,**A_      )


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      np.ndarray  ,A_     :      float  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      List[str]      )     ->   np.ndarray:
							return rescale(A_  ,scale=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      np.ndarray  ,A_     :      Union[float, List[float]]  ,A_     :      Union[float, List[float]]  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      Any  ,)     ->   np.ndarray:
							return normalize(A_  ,mean=A_  ,std=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      ImageInput  ,A_     :      Optional[bool] = None  ,A_     :      Dict[str, int] = None  ,A_     :      PILImageResampling = None  ,A_     :      bool = None  ,A_     :      Dict[str, int] = None  ,A_     :      Optional[bool] = None  ,A_     :      Optional[float] = None  ,A_     :      Optional[bool] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[str, TensorType]] = None  ,A_     :      Union[str, ChannelDimension] = ChannelDimension.FIRST  ,**A_     :      Tuple  ,)     ->   List[Any]:
							A						   =   do_resize if do_resize is not None else self.do_resize
							A						   =   size if size is not None else self.size
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							A						   =   resample if resample is not None else self.resample
							A						   =   do_center_crop if do_center_crop is not None else self.do_center_crop
							A						   =   crop_size if crop_size is not None else self.crop_size
							A						   =   get_size_dict(A_  ,param_name='crop_size'      )
							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						   =   make_list_of_images(A_      )

							if not valid_images(A_      ):
								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_center_crop and crop_size is None:
								raise ValueError('Crop size must be specified if do_center_crop is True.'      )

							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(A_      ) for image in images]

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

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

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

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

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

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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Union[str, Any]  ,A_     :      List[Tuple] = None      )     ->   str:
							A						   =   outputs.logits

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

								if is_torch_tensor(A_      ):
									A						   =   target_sizes.numpy()

								A						   =   []

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

							return semantic_segmentation