


"""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


__snake_case		=       False


class     __lowerCamelCase  (					unittest.TestCase				):
     pass







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


     def 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
          pipe.to(lowerCamelCase_							)
          pipe.set_progress_bar_config(disable=lowerCamelCase_							)

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

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

          assert image.shape == (1, 512, 512, 3)
          _a    	=				np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945]							)

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



"""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,
    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


__snake_case		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Any    =		['pixel_values']


     def __init__(      self    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = PILImageResampling.BILINEAR    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = 1 / 255    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							None:
          super().__init__(**__UpperCAmelCase							)
          _a    	=				size if size is not None else {'''shortest_edge''': 384}
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)

          _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 224 / 256
          _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    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = PILImageResampling.BICUBIC    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							np.ndarray:
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
          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 < 384:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               _a    	=				int(shortest_edge / crop_pct							)
               _a    	=				get_resize_output_image_size(__UpperCAmelCase    ,				size=__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
               _a    	=				resize(image=__UpperCAmelCase    ,				size=__UpperCAmelCase    ,				resample=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=__UpperCAmelCase    ,				size=(shortest_edge, shortest_edge)    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)
          else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   __UpperCAmelCase    ,				size=(shortest_edge, shortest_edge)    ,				resample=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							Optional[int]:
          return rescale(__UpperCAmelCase    ,				scale=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							np.ndarray:
          return normalize(__UpperCAmelCase    ,				mean=__UpperCAmelCase    ,				std=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = ChannelDimension.FIRST    ,				**__UpperCAmelCase    ,				)       ->							PIL.Image.Image:
          _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(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)

          _a    	=				make_list_of_images(__UpperCAmelCase							)

          if not valid_images(__UpperCAmelCase							):
               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"] < 384 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(__UpperCAmelCase							) for image in images]

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

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

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

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

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