




import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Tuple     =       CONFIG_NAME







      @register_to_config
      def __init__(  self       :			int			,							snake_case       :			bool = True			,							snake_case       :			int = 8			,							snake_case       :			str = "lanczos"			,							snake_case       :			bool = True			,							snake_case       :			bool = False			,							):
          super().__init__()







      @staticmethod
      def    snake_case						(  snake_case       :			np.ndarray					):
          if images.ndim == 3:
              __UpperCamelCase			=    images[None, ...]
          __UpperCamelCase			=    (images * 255).round().astype('''uint8'''					)
          if images.shape[-1] == 1:
              # special case for grayscale (single channel) images
              __UpperCamelCase			=    [Image.fromarray(image.squeeze()			,							mode='''L'''					) for image in images]
          else:
              __UpperCamelCase			=    [Image.fromarray(snake_case					) for image in images]

          return pil_images







      @staticmethod
      def    snake_case						(  snake_case       :			Union[List[PIL.Image.Image], PIL.Image.Image]					):
          if not isinstance(snake_case			,							snake_case					):
              __UpperCamelCase			=    [images]
          __UpperCamelCase			=    [np.array(snake_case					).astype(np.floataa					) / 2_55.0 for image in images]
          __UpperCamelCase			=    np.stack(snake_case			,							axis=0					)

          return images







      @staticmethod
      def    snake_case						(  snake_case       :			np.ndarray					):
          if images.ndim == 3:
              __UpperCamelCase			=    images[..., None]

          __UpperCamelCase			=    torch.from_numpy(images.transpose(0			,							3			,							1			,							2					)					)
          return images







      @staticmethod
      def    snake_case						(  snake_case       :			torch.FloatTensor					):
          __UpperCamelCase			=    images.cpu().permute(0			,							2			,							3			,							1					).float().numpy()
          return images







      @staticmethod
      def    snake_case						(  snake_case       :			List[Any]					):
          return 2.0 * images - 1.0







      @staticmethod
      def    snake_case						(  snake_case       :			str					):
          return (images / 2 + 0.5).clamp(0			,							1					)







      @staticmethod
      def    snake_case						(  snake_case       :			PIL.Image.Image					):
          __UpperCamelCase			=    image.convert('''RGB'''					)
          return image







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			PIL.Image.Image			,							snake_case       :			Optional[int] = None			,							snake_case       :			Optional[int] = None			,							):
          if height is None:
              __UpperCamelCase			=    image.height
          if width is None:
              __UpperCamelCase			=    image.width

          __UpperCamelCase		,   __UpperCamelCase			=    (
              x - x % self.config.vae_scale_factor for x in (width, height)
          )  # resize to integer multiple of vae_scale_factor
          __UpperCamelCase			=    image.resize((width, height)			,							resample=PIL_INTERPOLATION[self.config.resample]					)
          return image







      def    snake_case						(  self       :			Optional[int]			,							snake_case       :			Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]			,							snake_case       :			Optional[int] = None			,							snake_case       :			Optional[int] = None			,							):
          __UpperCamelCase			=    (PIL.Image.Image, np.ndarray, torch.Tensor)
          if isinstance(snake_case			,							snake_case					):
              __UpperCamelCase			=    [image]
          elif not (isinstance(snake_case			,							snake_case					) and all(isinstance(snake_case			,							snake_case					) for i in image					)):
              raise ValueError(
                  F"Input is in incorrect format: {[type(snake_case					) for i in image]}. Currently, we only support {', '.join(snake_case					)}"					)

          if isinstance(image[0]			,							PIL.Image.Image					):
              if self.config.do_convert_rgb:
                  __UpperCamelCase			=    [self.convert_to_rgb(snake_case					) for i in image]
              if self.config.do_resize:
                  __UpperCamelCase			=    [self.resize(snake_case			,							snake_case			,							snake_case					) for i in image]
              __UpperCamelCase			=    self.pil_to_numpy(snake_case					)  # to np
              __UpperCamelCase			=    self.numpy_to_pt(snake_case					)  # to pt

          elif isinstance(image[0]			,							np.ndarray					):
              __UpperCamelCase			=    np.concatenate(snake_case			,							axis=0					) if image[0].ndim == 4 else np.stack(snake_case			,							axis=0					)
              __UpperCamelCase			=    self.numpy_to_pt(snake_case					)
              __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    image.shape
              if self.config.do_resize and (
                  height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
              ):
                  raise ValueError(
                      F"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
                      F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"					)

          elif isinstance(image[0]			,							torch.Tensor					):
              __UpperCamelCase			=    torch.cat(snake_case			,							axis=0					) if image[0].ndim == 4 else torch.stack(snake_case			,							axis=0					)
              __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    image.shape

              # don't need any preprocess if the image is latents
              if channel == 4:
                  return image

              if self.config.do_resize and (
                  height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
              ):
                  raise ValueError(
                      F"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
                      F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"					)

        # expected range [0,1], normalize to [-1,1]
          __UpperCamelCase			=    self.config.do_normalize
          if image.min() < 0:
              warnings.warn(
                  '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
                  F"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"			,							snake_case			,							)
              __UpperCamelCase			=    False

          if do_normalize:
              __UpperCamelCase			=    self.normalize(snake_case					)

          return image







      def    snake_case						(  self       :			Union[str, Any]			,							snake_case       :			torch.FloatTensor			,							snake_case       :			str = "pil"			,							snake_case       :			Optional[List[bool]] = None			,							):
          if not isinstance(snake_case			,							torch.Tensor					):
              raise ValueError(
                  F"Input for postprocessing is in incorrect format: {type(snake_case					)}. We only support pytorch tensor"					)
          if output_type not in ["latent", "pt", "np", "pil"]:
              __UpperCamelCase			=    (
                  F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
                  '''`pil`, `np`, `pt`, `latent`'''
              )
              deprecate('''Unsupported output_type'''			,							'''1.0.0'''			,							snake_case			,							standard_warn=snake_case					)
              __UpperCamelCase			=    '''np'''

          if output_type == "latent":
              return image

          if do_denormalize is None:
              __UpperCamelCase			=    [self.config.do_normalize] * image.shape[0]

          __UpperCamelCase			=    torch.stack(
              [self.denormalize(image[i]					) if do_denormalize[i] else image[i] for i in range(image.shape[0]					)]					)

          if output_type == "pt":
              return image

          __UpperCamelCase			=    self.pt_to_numpy(snake_case					)

          if output_type == "np":
              return image

          if output_type == "pil":
              return self.numpy_to_pil(snake_case					)







class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Optional[Any]     =       CONFIG_NAME







      @register_to_config
      def __init__(  self       :			Any			,							snake_case       :			bool = True			,							snake_case       :			int = 8			,							snake_case       :			str = "lanczos"			,							snake_case       :			bool = True			,							):
          super().__init__()







      @staticmethod
      def    snake_case						(  snake_case       :			str					):
          if images.ndim == 3:
              __UpperCamelCase			=    images[None, ...]
          __UpperCamelCase			=    (images * 255).round().astype('''uint8'''					)
          if images.shape[-1] == 1:
              # special case for grayscale (single channel) images
              __UpperCamelCase			=    [Image.fromarray(image.squeeze()			,							mode='''L'''					) for image in images]
          else:
              __UpperCamelCase			=    [Image.fromarray(image[:, :, :3]					) for image in images]

          return pil_images







      @staticmethod
      def    snake_case						(  snake_case       :			str					):
          return image[:, :, 1] * 2**8 + image[:, :, 2]







      def    snake_case						(  self       :			Dict			,							snake_case       :			List[str]					):
          if images.ndim == 3:
              __UpperCamelCase			=    images[None, ...]
          __UpperCamelCase			=    images[:, :, :, 3:]
          if images.shape[-1] == 6:
              __UpperCamelCase			=    (images_depth * 255).round().astype('''uint8'''					)
              __UpperCamelCase			=    [
                  Image.fromarray(self.rgblike_to_depthmap(snake_case					)			,							mode='''I;16'''					) for image_depth in images_depth
              ]
          elif images.shape[-1] == 4:
              __UpperCamelCase			=    (images_depth * 6_55_35.0).astype(np.uintaa					)
              __UpperCamelCase			=    [Image.fromarray(snake_case			,							mode='''I;16'''					) for image_depth in images_depth]
          else:
              raise Exception('''Not supported'''					)

          return pil_images







      def    snake_case						(  self       :			Dict			,							snake_case       :			torch.FloatTensor			,							snake_case       :			str = "pil"			,							snake_case       :			Optional[List[bool]] = None			,							):
          if not isinstance(snake_case			,							torch.Tensor					):
              raise ValueError(
                  F"Input for postprocessing is in incorrect format: {type(snake_case					)}. We only support pytorch tensor"					)
          if output_type not in ["latent", "pt", "np", "pil"]:
              __UpperCamelCase			=    (
                  F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
                  '''`pil`, `np`, `pt`, `latent`'''
              )
              deprecate('''Unsupported output_type'''			,							'''1.0.0'''			,							snake_case			,							standard_warn=snake_case					)
              __UpperCamelCase			=    '''np'''

          if do_denormalize is None:
              __UpperCamelCase			=    [self.config.do_normalize] * image.shape[0]

          __UpperCamelCase			=    torch.stack(
              [self.denormalize(image[i]					) if do_denormalize[i] else image[i] for i in range(image.shape[0]					)]					)

          __UpperCamelCase			=    self.pt_to_numpy(snake_case					)

          if output_type == "np":
              if image.shape[-1] == 6:
                  __UpperCamelCase			=    np.stack([self.rgblike_to_depthmap(im[:, :, 3:]					) for im in image]			,							axis=0					)
              else:
                  __UpperCamelCase			=    image[:, :, :, 3:]
              return image[:, :, :, :3], image_depth

          if output_type == "pil":
              return self.numpy_to_pil(snake_case					), self.numpy_to_depth(snake_case					)
          else:
              raise Exception(F"This type {output_type} is not supported"					)













'''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


snake_case_  :							Any            =	logging.get_logger(__name__)


if is_vision_available():
					import PIL


class        __a   (lowerCamelCase  ):
							__a       : Any          =							["pixel_values"]

							def __init__(				self				:  Optional[int]							,  __magic_name__				:  bool = True							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  PILImageResampling = PILImageResampling.BICUBIC							,  __magic_name__				:  bool = True							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  bool = True							,  __magic_name__				:  Union[int, float] = 1 / 2_55							,  __magic_name__				:  bool = True							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  bool = True							,  **__magic_name__				:  Union[str, Any]							,  )       ->							None:







													"""simple docstring"""
													super().__init__(**__magic_name__ )
													UpperCAmelCase_      :   List[Any]      =	size if size is not None else {'''shortest_edge''': 2_24}
													UpperCAmelCase_      :   List[str]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   int      =	crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
													UpperCAmelCase_      :   Union[str, Any]      =	get_size_dict(__magic_name__ )

													UpperCAmelCase_      :   str      =	do_resize
													UpperCAmelCase_      :   Optional[Any]      =	size
													UpperCAmelCase_      :   int      =	resample
													UpperCAmelCase_      :   List[str]      =	do_center_crop
													UpperCAmelCase_      :   Optional[Any]      =	crop_size
													UpperCAmelCase_      :   List[str]      =	do_rescale
													UpperCAmelCase_      :   Union[str, Any]      =	rescale_factor
													UpperCAmelCase_      :   int      =	do_normalize
													UpperCAmelCase_      :   Any      =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													UpperCAmelCase_      :   Optional[Any]      =	image_std if image_std is not None else OPENAI_CLIP_STD
													UpperCAmelCase_      :   int      =	do_convert_rgb

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







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   Any      =	get_resize_output_image_size(
													    __magic_name__							,  size=(size['''height'''], size['''width'''])							,  default_to_square=__magic_name__ )
													return resize(__magic_name__							,  size=__magic_name__							,  resample=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  List[Any]							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Dict[str, int]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Any							,  )       ->							np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	get_size_dict(__magic_name__ )
													return center_crop(__magic_name__							,  size=(size['''height'''], size['''width'''])							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Union[int, float]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Any							,  )       ->							Union[str, Any]:







													"""simple docstring"""
													return rescale(__magic_name__							,  scale=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Union[float, List[float]]							,  __magic_name__				:  Union[float, List[float]]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Optional[int]							,  )       ->							np.ndarray:







													"""simple docstring"""
													return normalize(__magic_name__							,  mean=__magic_name__							,  std=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )



							def      UpperCAmelCase__						(				self				:  Dict							,  __magic_name__				:  ImageInput							,  __magic_name__				:  bool = None							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  PILImageResampling = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  int = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  float = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  Optional[Union[str, TensorType]] = None							,  __magic_name__				:  Optional[ChannelDimension] = ChannelDimension.FIRST							,  **__magic_name__				:  Union[str, Any]							,  )       ->							PIL.Image.Image:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_      :   Dict      =	size if size is not None else self.size
													UpperCAmelCase_      :   Union[str, Any]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   str      =	resample if resample is not None else self.resample
													UpperCAmelCase_      :   Tuple      =	do_center_crop if do_center_crop is not None else self.do_center_crop
													UpperCAmelCase_      :   List[str]      =	crop_size if crop_size is not None else self.crop_size
													UpperCAmelCase_      :   Optional[Any]      =	get_size_dict(__magic_name__ )
													UpperCAmelCase_      :   str      =	do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_      :   Tuple      =	rescale_factor if rescale_factor is not None else self.rescale_factor
													UpperCAmelCase_      :   List[Any]      =	do_normalize if do_normalize is not None else self.do_normalize
													UpperCAmelCase_      :   Tuple      =	image_mean if image_mean is not None else self.image_mean
													UpperCAmelCase_      :   int      =	image_std if image_std is not None else self.image_std
													UpperCAmelCase_      :   Optional[Any]      =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													UpperCAmelCase_      :   Any      =	make_list_of_images(__magic_name__ )

													if not valid_images(__magic_name__ ):
																			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:
																			UpperCAmelCase_      :   Optional[int]      =	[convert_to_rgb(__magic_name__ ) for image in images]

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

													if do_resize:
																			UpperCAmelCase_      :   Dict      =	[self.resize(image=__magic_name__							,  size=__magic_name__							,  resample=__magic_name__ ) for image in images]

													if do_center_crop:
																			UpperCAmelCase_      :   Union[str, Any]      =	[self.center_crop(image=__magic_name__							,  size=__magic_name__ ) for image in images]

													if do_rescale:
																			UpperCAmelCase_      :   Dict      =	[self.rescale(image=__magic_name__							,  scale=__magic_name__ ) for image in images]

													if do_normalize:
																			UpperCAmelCase_      :   Optional[int]      =	[self.normalize(image=__magic_name__							,  mean=__magic_name__							,  std=__magic_name__ ) for image in images]

													UpperCAmelCase_      :   Optional[Any]      =	[to_channel_dimension_format(__magic_name__							,  __magic_name__ ) for image in images]

													UpperCAmelCase_      :   int      =	{'''pixel_values''': images}
													return BatchFeature(data=__magic_name__							,  tensor_type=__magic_name__ )





