






'''simple docstring'''
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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase__			=      logging.get_logger(__name__)


if is_vision_available():
 import PIL


class 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: Optional[int]									=     ['pixel_values']




    def __init__(      self	:							List[Any]	,  a	:							bool = True	,  a	:							Dict[str, int] = None	,  a	:							PILImageResampling = PILImageResampling.BICUBIC	,  a	:							bool = True	,  a	:							Dict[str, int] = None	,  a	:							bool = True	,  a	:							Union[int, float] = 1 / 2_5_5	,  a	:							bool = True	,  a	:							Optional[Union[float, List[float]]] = None	,  a	:							Optional[Union[float, List[float]]] = None	,  a	:							bool = True	,  **a	:							List[str]	,  ):






       '''simple docstring'''






       super().__init__(**a      )
       lowercase_						:     Union[str, Any]							     =  size if size is not None else {"shortest_edge": 2_2_4}
       lowercase_						:     int							     =  get_size_dict(a	,  default_to_square=a      )
       lowercase_						:     str							     =  crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
       lowercase_						:     Optional[int]							     =  get_size_dict(a	,  default_to_square=a	,  param_name="crop_size"      )

       lowercase_						:     int							     =  do_resize
       lowercase_						:     str							     =  size
       lowercase_						:     int							     =  resample
       lowercase_						:     Dict							     =  do_center_crop
       lowercase_						:     List[str]							     =  crop_size
       lowercase_						:     int							     =  do_rescale
       lowercase_						:     int							     =  rescale_factor
       lowercase_						:     Optional[Any]							     =  do_normalize
       lowercase_						:     Optional[Any]							     =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       lowercase_						:     Union[str, Any]							     =  image_std if image_std is not None else OPENAI_CLIP_STD
       lowercase_						:     Optional[int]							     =  do_convert_rgb




    def 					lowerCAmelCase__					(      self	:							Dict	,  a	:							np.ndarray	,  a	:							Dict[str, int]	,  a	:							PILImageResampling = PILImageResampling.BICUBIC	,  a	:							Optional[Union[str, ChannelDimension]] = None	,  **a	:							Optional[int]	,  ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  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()}"""      )
       lowercase_						:     Any							     =  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 					lowerCAmelCase__					(      self	:							Optional[int]	,  a	:							np.ndarray	,  a	:							Dict[str, int]	,  a	:							Optional[Union[str, ChannelDimension]] = None	,  **a	:							Any	,  ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  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, width). Got {size.keys()}"""      )
       return center_crop(a	,  size=(size["height"], size["width"])	,  data_format=a	,  **a      )




    def 					lowerCAmelCase__					(      self	:							List[str]	,  a	:							np.ndarray	,  a	:							Union[int, float]	,  a	:							Optional[Union[str, ChannelDimension]] = None	,  **a	:							List[Any]	,  ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Dict	,  a	:							np.ndarray	,  a	:							Union[float, List[float]]	,  a	:							Union[float, List[float]]	,  a	:							Optional[Union[str, ChannelDimension]] = None	,  **a	:							Optional[Any]	,  ):






       '''simple docstring'''






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





    def 					lowerCAmelCase__					(      self	:							int	,  a	:							ImageInput	,  a	:							bool = None	,  a	:							Dict[str, int] = None	,  a	:							PILImageResampling = None	,  a	:							bool = None	,  a	:							int = None	,  a	:							bool = None	,  a	:							float = None	,  a	:							bool = None	,  a	:							Optional[Union[float, List[float]]] = None	,  a	:							Optional[Union[float, List[float]]] = None	,  a	:							bool = None	,  a	:							Optional[Union[str, TensorType]] = None	,  a	:							Optional[ChannelDimension] = ChannelDimension.FIRST	,  **a	:							Union[str, Any]	,  ):






       '''simple docstring'''






       lowercase_						:     Any							     =  do_resize if do_resize is not None else self.do_resize
       lowercase_						:     Union[str, Any]							     =  size if size is not None else self.size
       lowercase_						:     str							     =  get_size_dict(a	,  param_name="size"	,  default_to_square=a      )
       lowercase_						:     Dict							     =  resample if resample is not None else self.resample
       lowercase_						:     Dict							     =  do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase_						:     List[str]							     =  crop_size if crop_size is not None else self.crop_size
       lowercase_						:     Tuple							     =  get_size_dict(a	,  param_name="crop_size"	,  default_to_square=a      )
       lowercase_						:     List[str]							     =  do_rescale if do_rescale is not None else self.do_rescale
       lowercase_						:     Tuple							     =  rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase_						:     List[Any]							     =  do_normalize if do_normalize is not None else self.do_normalize
       lowercase_						:     int							     =  image_mean if image_mean is not None else self.image_mean
       lowercase_						:     Optional[Any]							     =  image_std if image_std is not None else self.image_std
       lowercase_						:     str							     =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowercase_						:     List[str]							     =  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."      )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
          lowercase_						:     List[Any]							     =  [convert_to_rgb(a      ) for image in images]

       # All transformations expect numpy arrays.
       lowercase_						:     Optional[Any]							     =  [to_numpy_array(a      ) for image in images]

       if do_resize:
          lowercase_						:     Tuple							     =  [self.resize(image=a	,  size=a	,  resample=a      ) for image in images]

       if do_center_crop:
          lowercase_						:     List[Any]							     =  [self.center_crop(image=a	,  size=a      ) for image in images]

       if do_rescale:
          lowercase_						:     Any							     =  [self.rescale(image=a	,  scale=a      ) for image in images]

       if do_normalize:
          lowercase_						:     Union[str, Any]							     =  [self.normalize(image=a	,  mean=a	,  std=a      ) for image in images]

       lowercase_						:     Tuple							     =  [to_channel_dimension_format(a	,  a      ) for image in images]

       lowercase_						:     Optional[Any]							     =  {"pixel_values": images}
       return BatchFeature(data=a	,  tensor_type=a      )












'''simple docstring'''
import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
 import onnxruntime as ort


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




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






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




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  ort.SessionOptions()
       lowercase_						:     Optional[int]							     =  False
       return options





    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo.png"      )
       lowercase_						:     Tuple							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"      )
       lowercase_						:     List[str]							     =  load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"      )

       # using the PNDM scheduler by default
       lowercase_						:     List[Any]							     =  OnnxStableDiffusionInpaintPipelineLegacy.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      )

       lowercase_						:     List[str]							     =  "A red cat sitting on a park bench"

       lowercase_						:     int							     =  np.random.RandomState(0      )
       lowercase_						:     int							     =  pipe(
           prompt=a	,  image=a	,  mask_image=a	,  strength=0.75	,  guidance_scale=7.5	,  num_inference_steps=1_5	,  generator=a	,  output_type="np"	,  )

       lowercase_						:     Tuple							     =  output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)
       assert np.abs(expected_image - image      ).max() < 1e-2




