





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

SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						logging.get_logger(__name__)






def       _A  (      lowerCamelCase   ):
			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   __lowerCAmelCase			(					_UpperCamelCase  ):
			_UpperCamelCase :						List[str]     		=				["""pixel_values"""]





			def __init__( self					,			snake_case = True					,			snake_case = None					,			snake_case = PILImageResampling.BILINEAR					,			snake_case = True					,			snake_case = None					,			snake_case = True					,			snake_case = 1 / 255					,			snake_case = True					,			snake_case = True					,			snake_case = None					,			snake_case = None					,			**snake_case					,			)				->  None:
						"""simple docstring"""





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

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





			def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case = PILImageResampling.BILINEAR					,			snake_case = None					,			**snake_case					,			)				->  np.ndarray:
						"""simple docstring"""





						a__	:						Tuple      		=  get_size_dict(snake_case					,			default_to_square=snake_case      )
						if "shortest_edge" in size:
									a__	:						str      		=  get_resize_output_image_size(snake_case					,			size["shortest_edge"]					,			default_to_square=snake_case      )
						elif "height" in size and "width" in size:
									a__	:						Dict      		=  (size["height"], size["width"])
						else:
									raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
						return resize(snake_case					,			size=snake_case					,			resample=snake_case					,			data_format=snake_case					,			**snake_case      )





			def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case = None					,			**snake_case					,			)				->  np.ndarray:
						"""simple docstring"""





						a__	:						Union[str, Any]      		=  get_size_dict(snake_case      )
						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(snake_case					,			size=(size["height"], size["width"])					,			data_format=snake_case					,			**snake_case      )





			def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case = True					,			snake_case = None					,			**snake_case					,			)				->  List[str]:
						"""simple docstring"""





						a__	:						Tuple      		=  image.astype(np.floataa      )
						if offset:
									a__	:						Tuple      		=  image - (scale / 2)
						return rescale(snake_case					,			scale=snake_case					,			data_format=snake_case					,			**snake_case      )





			def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case = None					,			**snake_case					,			)				->  np.ndarray:
						"""simple docstring"""





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





			def 	_snake_case ( self					,			snake_case					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = ChannelDimension.FIRST					,			)				->  np.ndarray:
						"""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.
						a__	:						Any      		=  to_numpy_array(snake_case      )

						if do_resize:
									a__	:						Optional[int]      		=  self.resize(image=snake_case					,			size=snake_case					,			resample=snake_case      )

						if do_center_crop:
									a__	:						Optional[Any]      		=  self.center_crop(snake_case					,			size=snake_case      )

						if do_rescale:
									a__	:						Union[str, Any]      		=  self.rescale(image=snake_case					,			scale=snake_case					,			offset=snake_case      )

						if do_normalize:
									a__	:						Optional[int]      		=  self.normalize(image=snake_case					,			mean=snake_case					,			std=snake_case      )

						a__	:						int      		=  to_channel_dimension_format(snake_case					,			snake_case      )
						return image





			def 	_snake_case ( self					,			snake_case					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = None					,			snake_case = ChannelDimension.FIRST					,			**snake_case					,			)				->  PIL.Image.Image:
						"""simple docstring"""





						a__	:						Union[str, Any]      		=  do_resize if do_resize is not None else self.do_resize
						a__	:						Union[str, Any]      		=  resample if resample is not None else self.resample
						a__	:						List[Any]      		=  do_center_crop if do_center_crop is not None else self.do_center_crop
						a__	:						str      		=  do_rescale if do_rescale is not None else self.do_rescale
						a__	:						Tuple      		=  rescale_factor if rescale_factor is not None else self.rescale_factor
						a__	:						int      		=  offset if offset is not None else self.offset
						a__	:						int      		=  do_normalize if do_normalize is not None else self.do_normalize
						a__	:						List[str]      		=  image_mean if image_mean is not None else self.image_mean
						a__	:						Tuple      		=  image_std if image_std is not None else self.image_std

						a__	:						Dict      		=  size if size is not None else self.size
						a__	:						Tuple      		=  get_size_dict(snake_case					,			default_to_square=snake_case      )
						a__	:						Any      		=  crop_size if crop_size is not None else self.crop_size
						a__	:						List[Any]      		=  get_size_dict(snake_case					,			param_name="crop_size"      )

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

						a__	:						Dict      		=  make_batched(snake_case      )

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

						a__	:						int      		=  {"pixel_values": videos}
						return BatchFeature(data=snake_case					,			tensor_type=snake_case      )








import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  SCREAMING_SNAKE_CASE__       :			int			      =						{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  SCREAMING_SNAKE_CASE__       :			Dict			      =						{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }






def       _A  (      lowerCamelCase   ):
   a__	:						Optional[int]      		=  (images / 2 + 0.5).clamp(0							, 1   )
   a__	:						List[Any]      		=  images.cpu().permute(0							, 2							, 3							, 1   ).float().numpy()
   a__	:						int      		=  numpy_to_pil(lowerCamelCase   )
   return images






def       _A  (      lowerCamelCase   ):
   if images.ndim == 3:
      a__	:						Tuple      		=  images[None, ...]
   a__	:						Dict      		=  (images * 255).round().astype("uint8"   )
   if images.shape[-1] == 1:
      # special case for grayscale (single channel) images
      a__	:						str      		=  [Image.fromarray(image.squeeze()							, mode="L"   ) for image in images]
   else:
      a__	:						List[Any]      		=  [Image.fromarray(lowerCamelCase   ) for image in images]

   return pil_images

