






'''simple docstring'''
from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


a_ 		=       logging.get_logger(__name__)






class    __SCREAMING_SNAKE_CASE					(	A__ ):
 snake_case_     							=							["""pixel_values"""]




 def __init__(					self		:							Dict    ,					__lowercase		:							bool = True    ,					__lowercase		:							Union[int, float] = 1 / 2_55    ,					__lowercase		:							bool = True    ,					__lowercase		:							int = 8    ,					**__lowercase		:							int    ,					)		->  None:
       super().__init__(**_a					)

       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=do_rescale
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=rescale_factor
       SCREAMING_SNAKE_CASE__   : Any													=do_pad
       SCREAMING_SNAKE_CASE__   : List[str]													=pad_size




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




 def    __magic_name__		(					self		:							str    ,					__lowercase		:							np.ndarray    ,					__lowercase		:							int    ,					__lowercase		:							Optional[Union[str, ChannelDimension]] = None					)		->  Dict:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : Any													=get_image_size(_a					)
       SCREAMING_SNAKE_CASE__   : Tuple													=(old_height // size + 1) * size - old_height
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=(old_width // size + 1) * size - old_width

       return pad(_a    ,					((0, pad_height), (0, pad_width))    ,					mode='''symmetric'''    ,					data_format=_a					)




 def    __magic_name__		(					self		:							str    ,					__lowercase		:							ImageInput    ,					__lowercase		:							Optional[bool] = None    ,					__lowercase		:							Optional[float] = None    ,					__lowercase		:							Optional[bool] = None    ,					__lowercase		:							Optional[int] = None    ,					__lowercase		:							Optional[Union[str, TensorType]] = None    ,					__lowercase		:							Union[str, ChannelDimension] = ChannelDimension.FIRST    ,					**__lowercase		:							Dict    ,					)		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__   : List[Any]													=do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__   : List[str]													=rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__   : Tuple													=do_pad if do_pad is not None else self.do_pad
       SCREAMING_SNAKE_CASE__   : Dict													=pad_size if pad_size is not None else self.pad_size

       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=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_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

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

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

       if do_pad:
             SCREAMING_SNAKE_CASE__   : Optional[Any]													=[self.pad(_a    ,					size=_a					) for image in images]

       SCREAMING_SNAKE_CASE__   : List[str]													=[to_channel_dimension_format(_a    ,					_a					) for image in images]

       SCREAMING_SNAKE_CASE__   : List[str]													={'''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
