


'''simple docstring'''


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class 				a_       (    _lowerCAmelCase							):
     __A								=      42
     __A								=      42


if is_transformers_available() and is_torch_available():
 from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline









'''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

lowerCAmelCase :					Any 							=logging.get_logger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]			):
   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 				a_       (    _lowerCAmelCase							):
     __A								=      ["pixel_values"]





     def __init__(			self		: List[str]     ,				lowercase		: bool = True     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: PILImageResampling = PILImageResampling.BILINEAR     ,				lowercase		: bool = True     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: bool = True     ,				lowercase		: Union[int, float] = 1 / 255     ,				lowercase		: bool = True     ,				lowercase		: bool = True     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				**lowercase		: Tuple     ,				):





        """simple docstring"""




        super().__init__(**lowercase						)
        lowercase_      :Any         =			size if size is not None else {"shortest_edge": 256}
        lowercase_      :int         =			get_size_dict(lowercase     ,				default_to_square=lowercase						)
        lowercase_      :str         =			crop_size if crop_size is not None else {"height": 224, "width": 224}
        lowercase_      :List[str]         =			get_size_dict(lowercase     ,				param_name="crop_size"						)

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





     def 							lowercase__				(			self		: Union[str, Any]     ,				lowercase		: np.ndarray     ,				lowercase		: Dict[str, int]     ,				lowercase		: PILImageResampling = PILImageResampling.BILINEAR     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: Optional[Any]     ,				):





        """simple docstring"""




        lowercase_      :List[Any]         =			get_size_dict(lowercase     ,				default_to_square=lowercase						)
        if "shortest_edge" in size:
           lowercase_      :int         =			get_resize_output_image_size(lowercase     ,				size["shortest_edge"]     ,				default_to_square=lowercase						)
        elif "height" in size and "width" in size:
           lowercase_      :Union[str, Any]         =			(size["height"], size["width"])
        else:
           raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'						)
        return resize(lowercase     ,				size=lowercase     ,				resample=lowercase     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: str     ,				lowercase		: np.ndarray     ,				lowercase		: Dict[str, int]     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: str     ,				):





        """simple docstring"""




        lowercase_      :Any         =			get_size_dict(lowercase						)
        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(lowercase     ,				size=(size["height"], size["width"])     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: np.ndarray     ,				lowercase		: Union[int, float]     ,				lowercase		: bool = True     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: List[str]     ,				):





        """simple docstring"""




        lowercase_      :List[str]         =			image.astype(np.floataa						)
        if offset:
           lowercase_      :List[str]         =			image - (scale / 2)
        return rescale(lowercase     ,				scale=lowercase     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: Tuple     ,				lowercase		: np.ndarray     ,				lowercase		: Union[float, List[float]]     ,				lowercase		: Union[float, List[float]]     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: Dict     ,				):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Tuple     ,				lowercase		: ImageInput     ,				lowercase		: bool = None     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: PILImageResampling = None     ,				lowercase		: bool = None     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: bool = None     ,				lowercase		: float = None     ,				lowercase		: bool = None     ,				lowercase		: bool = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[ChannelDimension] = ChannelDimension.FIRST     ,				):





        """simple docstring"""




        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.
        lowercase_      :Optional[int]         =			to_numpy_array(lowercase						)

        if do_resize:
           lowercase_      :Tuple         =			self.resize(image=lowercase     ,				size=lowercase     ,				resample=lowercase						)

        if do_center_crop:
           lowercase_      :Any         =			self.center_crop(lowercase     ,				size=lowercase						)

        if do_rescale:
           lowercase_      :Optional[Any]         =			self.rescale(image=lowercase     ,				scale=lowercase     ,				offset=lowercase						)

        if do_normalize:
           lowercase_      :Tuple         =			self.normalize(image=lowercase     ,				mean=lowercase     ,				std=lowercase						)

        lowercase_      :Optional[Any]         =			to_channel_dimension_format(lowercase     ,				lowercase						)
        return image







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





        """simple docstring"""




        lowercase_      :str         =			do_resize if do_resize is not None else self.do_resize
        lowercase_      :Optional[Any]         =			resample if resample is not None else self.resample
        lowercase_      :Tuple         =			do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_      :Dict         =			do_rescale if do_rescale is not None else self.do_rescale
        lowercase_      :Optional[int]         =			rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_      :Dict         =			offset if offset is not None else self.offset
        lowercase_      :Tuple         =			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[int]         =			image_std if image_std is not None else self.image_std

        lowercase_      :int         =			size if size is not None else self.size
        lowercase_      :Optional[int]         =			get_size_dict(lowercase     ,				default_to_square=lowercase						)
        lowercase_      :List[Any]         =			crop_size if crop_size is not None else self.crop_size
        lowercase_      :List[str]         =			get_size_dict(lowercase     ,				param_name="crop_size"						)

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

        lowercase_      :List[str]         =			make_batched(lowercase						)

        lowercase_      :List[Any]         =			[
            [
                self._preprocess_image(
                    image=lowercase     ,				do_resize=lowercase     ,				size=lowercase     ,				resample=lowercase     ,				do_center_crop=lowercase     ,				crop_size=lowercase     ,				do_rescale=lowercase     ,				rescale_factor=lowercase     ,				offset=lowercase     ,				do_normalize=lowercase     ,				image_mean=lowercase     ,				image_std=lowercase     ,				data_format=lowercase     ,				)
                for img in video
            ]
            for video in videos
        ]

        lowercase_      :Optional[int]         =			{"pixel_values": videos}
        return BatchFeature(data=lowercase     ,				tensor_type=lowercase						)


