


'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
  import PIL

a__  :					str        =       logging.get_logger(__name__)







def _UpperCamelCase       (     __A    )							->		List[List[ImageInput]]:


      '''simple docstring'''



      if isinstance(lowerCAmelCase__      ,			(list, tuple)    ) and isinstance(videos[0]      ,			(list, tuple)    ) and is_valid_image(videos[0][0]    ):
            return videos

      elif isinstance(lowerCAmelCase__      ,			(list, tuple)    ) and is_valid_image(videos[0]    ):
            return [videos]

      elif is_valid_image(lowerCAmelCase__    ):
            return [[videos]]

      raise ValueError(F'''Could not make batched video from {videos}'''    )



class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	['pixel_values']


    def __init__(     self  ,					a = True  ,					a = None  ,					a = PILImageResampling.BILINEAR  ,					a = True  ,					a = None  ,					a = True  ,					a = 1 / 2_55  ,					a = True  ,					a = True  ,					a = None  ,					a = None  ,					**a  ,					):
          super().__init__(**__lowerCAmelCase    )
          UpperCamelCase__										=				size if size is not None else {"shortest_edge": 2_56}
          UpperCamelCase__										=				get_size_dict(__lowerCAmelCase  ,					default_to_square=__lowerCAmelCase    )
          UpperCamelCase__										=				crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          UpperCamelCase__										=				get_size_dict(__lowerCAmelCase  ,					param_name="crop_size"    )

          UpperCamelCase__										=				do_resize
          UpperCamelCase__										=				size
          UpperCamelCase__										=				do_center_crop
          UpperCamelCase__										=				crop_size
          UpperCamelCase__										=				resample
          UpperCamelCase__										=				do_rescale
          UpperCamelCase__										=				rescale_factor
          UpperCamelCase__										=				offset
          UpperCamelCase__										=				do_normalize
          UpperCamelCase__										=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCamelCase__										=				image_std if image_std is not None else IMAGENET_STANDARD_STD


    def    __a							(     self  ,					a  ,					a  ,					a = PILImageResampling.BILINEAR  ,					a = None  ,					**a  ,					):
          UpperCamelCase__										=				get_size_dict(__lowerCAmelCase  ,					default_to_square=__lowerCAmelCase    )
          if "shortest_edge" in size:
                UpperCamelCase__										=				get_resize_output_image_size(__lowerCAmelCase  ,					size["shortest_edge"]  ,					default_to_square=__lowerCAmelCase    )
          elif "height" in size and "width" in size:
                UpperCamelCase__										=				(size["height"], size["width"])
          else:
                raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''    )
          return resize(__lowerCAmelCase  ,					size=__lowerCAmelCase  ,					resample=__lowerCAmelCase  ,					data_format=__lowerCAmelCase  ,					**__lowerCAmelCase    )


    def    __a							(     self  ,					a  ,					a  ,					a = None  ,					**a  ,					):
          UpperCamelCase__										=				get_size_dict(__lowerCAmelCase    )
          if "height" not in size or "width" not in size:
                raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''    )
          return center_crop(__lowerCAmelCase  ,					size=(size["height"], size["width"])  ,					data_format=__lowerCAmelCase  ,					**__lowerCAmelCase    )


    def    __a							(     self  ,					a  ,					a  ,					a = True  ,					a = None  ,					**a  ,					):
          UpperCamelCase__										=				image.astype(np.floataa    )
          if offset:
                UpperCamelCase__										=				image - (scale / 2)
          return rescale(__lowerCAmelCase  ,					scale=__lowerCAmelCase  ,					data_format=__lowerCAmelCase  ,					**__lowerCAmelCase    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a = None  ,					**a  ,					):
          return normalize(__lowerCAmelCase  ,					mean=__lowerCAmelCase  ,					std=__lowerCAmelCase  ,					data_format=__lowerCAmelCase  ,					**__lowerCAmelCase    )


    def    __a							(     self  ,					a  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = ChannelDimension.FIRST  ,					):



          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_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."    )

          if offset and not do_rescale:
                raise ValueError("For offset, do_rescale must also be set to True."    )

          # All transformations expect numpy arrays.
          UpperCamelCase__										=				to_numpy_array(__lowerCAmelCase    )

          if do_resize:
                UpperCamelCase__										=				self.resize(image=__lowerCAmelCase  ,					size=__lowerCAmelCase  ,					resample=__lowerCAmelCase    )

          if do_center_crop:
                UpperCamelCase__										=				self.center_crop(__lowerCAmelCase  ,					size=__lowerCAmelCase    )

          if do_rescale:
                UpperCamelCase__										=				self.rescale(image=__lowerCAmelCase  ,					scale=__lowerCAmelCase  ,					offset=__lowerCAmelCase    )

          if do_normalize:
                UpperCamelCase__										=				self.normalize(image=__lowerCAmelCase  ,					mean=__lowerCAmelCase  ,					std=__lowerCAmelCase    )

          UpperCamelCase__										=				to_channel_dimension_format(__lowerCAmelCase  ,					__lowerCAmelCase    )
          return image


    def    __a							(     self  ,					a  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = ChannelDimension.FIRST  ,					**a  ,					):
          UpperCamelCase__										=				do_resize if do_resize is not None else self.do_resize
          UpperCamelCase__										=				resample if resample is not None else self.resample
          UpperCamelCase__										=				do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCamelCase__										=				do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase__										=				rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase__										=				offset if offset is not None else self.offset
          UpperCamelCase__										=				do_normalize if do_normalize is not None else self.do_normalize
          UpperCamelCase__										=				image_mean if image_mean is not None else self.image_mean
          UpperCamelCase__										=				image_std if image_std is not None else self.image_std

          UpperCamelCase__										=				size if size is not None else self.size
          UpperCamelCase__										=				get_size_dict(__lowerCAmelCase  ,					default_to_square=__lowerCAmelCase    )
          UpperCamelCase__										=				crop_size if crop_size is not None else self.crop_size
          UpperCamelCase__										=				get_size_dict(__lowerCAmelCase  ,					param_name="crop_size"    )

          if not valid_images(__lowerCAmelCase    ):
                raise ValueError(
                    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                    "torch.Tensor, tf.Tensor or jax.ndarray."    )

          UpperCamelCase__										=				make_batched(__lowerCAmelCase    )

          UpperCamelCase__										=				[
              [
                  self._preprocess_image(
                      image=__lowerCAmelCase  ,					do_resize=__lowerCAmelCase  ,					size=__lowerCAmelCase  ,					resample=__lowerCAmelCase  ,					do_center_crop=__lowerCAmelCase  ,					crop_size=__lowerCAmelCase  ,					do_rescale=__lowerCAmelCase  ,					rescale_factor=__lowerCAmelCase  ,					offset=__lowerCAmelCase  ,					do_normalize=__lowerCAmelCase  ,					image_mean=__lowerCAmelCase  ,					image_std=__lowerCAmelCase  ,					data_format=__lowerCAmelCase  ,					)
                  for img in video
              ]
              for video in videos
          ]

          UpperCamelCase__										=				{"pixel_values": videos}
          return BatchFeature(data=__lowerCAmelCase  ,					tensor_type=__lowerCAmelCase    )






"""simple docstring"""






import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__lowerCAmelCase    :       Union[str, Any]					     =0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__lowerCAmelCase    :       Optional[int]					     =[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	WATERMARK_BITS
            lowercase														=	WatermarkEncoder()

            self.encoder.set_watermark("""bits"""		,       self.watermark						)







      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if images.shape[-1] < 256:
                  return images

            lowercase														=	(255 * (images / 2 + 0.5)).cpu().permute(0		,       2		,       3		,       1						).float().numpy()

            lowercase														=	[self.encoder.encode(__lowerCAmelCase		,       """dwtDct"""						) for image in images]

            lowercase														=	torch.from_numpy(np.array(__lowerCAmelCase						)						).permute(0		,       3		,       1		,       2						)

            lowercase														=	torch.clamp(2 * (images / 255 - 0.5)		,       min=-1.0		,       max=1.0						)
            return images



