

'''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


lowerCamelCase :      List[Any]					 =     logging.get_logger(__name__)


if is_vision_available():
     import PIL







class 							A__    (       A__  ):
  A__		 =				['pixel_values']





  def __init__(		self      :	int							,					_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 / 255							,					_a      :	bool = True							,					_a      :	Optional[Union[float, List[float]]] = None							,					_a      :	Optional[Union[float, List[float]]] = None							,					_a      :	bool = True							,					**_a      :	Any							,					)	->    None:





       '''simple docstring'''






       super().__init__(**_a )
       _SCREAMING_SNAKE_CASE							    =size if size is not None else {'shortest_edge': 224}
       _SCREAMING_SNAKE_CASE							    =get_size_dict(_a							,					default_to_square=_a )
       _SCREAMING_SNAKE_CASE							    =crop_size if crop_size is not None else {'height': 224, 'width': 224}
       _SCREAMING_SNAKE_CASE							    =get_size_dict(_a							,					default_to_square=_a							,					param_name='crop_size' )

       _SCREAMING_SNAKE_CASE							    =do_resize
       _SCREAMING_SNAKE_CASE							    =size
       _SCREAMING_SNAKE_CASE							    =resample
       _SCREAMING_SNAKE_CASE							    =do_center_crop
       _SCREAMING_SNAKE_CASE							    =crop_size
       _SCREAMING_SNAKE_CASE							    =do_rescale
       _SCREAMING_SNAKE_CASE							    =rescale_factor
       _SCREAMING_SNAKE_CASE							    =do_normalize
       _SCREAMING_SNAKE_CASE							    =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       _SCREAMING_SNAKE_CASE							    =image_std if image_std is not None else OPENAI_CLIP_STD
       _SCREAMING_SNAKE_CASE							    =do_convert_rgb





  def 						A  (		self      :	int							,					_a      :	np.ndarray							,					_a      :	Dict[str, int]							,					_a      :	PILImageResampling = PILImageResampling.BICUBIC							,					_a      :	Optional[Union[str, ChannelDimension]] = None							,					**_a      :	str							,					)	->    np.ndarray:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =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()}" )
       _SCREAMING_SNAKE_CASE							    =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 						A  (		self      :	Optional[Any]							,					_a      :	np.ndarray							,					_a      :	Dict[str, int]							,					_a      :	Optional[Union[str, ChannelDimension]] = None							,					**_a      :	Optional[Any]							,					)	->    np.ndarray:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =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 						A  (		self      :	List[Any]							,					_a      :	np.ndarray							,					_a      :	Union[int, float]							,					_a      :	Optional[Union[str, ChannelDimension]] = None							,					**_a      :	str							,					)	->    str:





       '''simple docstring'''






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





  def 						A  (		self      :	List[Any]							,					_a      :	np.ndarray							,					_a      :	Union[float, List[float]]							,					_a      :	Union[float, List[float]]							,					_a      :	Optional[Union[str, ChannelDimension]] = None							,					**_a      :	Optional[Any]							,					)	->    np.ndarray:





       '''simple docstring'''






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





  def 						A  (		self      :	Tuple							,					_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      :	Optional[Any]							,					)	->    PIL.Image.Image:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =do_resize if do_resize is not None else self.do_resize
       _SCREAMING_SNAKE_CASE							    =size if size is not None else self.size
       _SCREAMING_SNAKE_CASE							    =get_size_dict(_a							,					param_name='size'							,					default_to_square=_a )
       _SCREAMING_SNAKE_CASE							    =resample if resample is not None else self.resample
       _SCREAMING_SNAKE_CASE							    =do_center_crop if do_center_crop is not None else self.do_center_crop
       _SCREAMING_SNAKE_CASE							    =crop_size if crop_size is not None else self.crop_size
       _SCREAMING_SNAKE_CASE							    =get_size_dict(_a							,					param_name='crop_size'							,					default_to_square=_a )
       _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_normalize if do_normalize is not None else self.do_normalize
       _SCREAMING_SNAKE_CASE							    =image_mean if image_mean is not None else self.image_mean
       _SCREAMING_SNAKE_CASE							    =image_std if image_std is not None else self.image_std
       _SCREAMING_SNAKE_CASE							    =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       _SCREAMING_SNAKE_CASE							    =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:
            _SCREAMING_SNAKE_CASE							    =[convert_to_rgb(_a ) for image in images]

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

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

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

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

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

       _SCREAMING_SNAKE_CASE							    =[to_channel_dimension_format(_a							,					_a ) for image in images]

       _SCREAMING_SNAKE_CASE							    ={'pixel_values': images}
       return BatchFeature(data=_a							,					tensor_type=_a )



'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCamelCase :      Optional[int]					 =     False







class 							A__    (       unittest.TestCase  ):
  pass







@slow
@require_torch_gpu
class 							A__    (       unittest.TestCase  ):





  def 						A  (		self      :	Tuple )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion' )
       pipe.to(_a )
       pipe.set_progress_bar_config(disable=_a )

       _SCREAMING_SNAKE_CASE							    =load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
       _SCREAMING_SNAKE_CASE							    =torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =pipe(
           image=_a							,					generator=_a							,					guidance_scale=7.5							,					num_inference_steps=50							,					output_type='numpy'							,					).images

       _SCREAMING_SNAKE_CASE							    =image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE							    =np.array([0.04_41, 0.04_69, 0.05_07, 0.05_75, 0.06_32, 0.06_50, 0.08_65, 0.09_09, 0.09_45] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
