



'''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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


lowercase_	 =						logging.get_logger(__name__)




def lowerCAmelCase						(__A):



     """simple docstring"""



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

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

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

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






class __A    (						lowercase__		):



 '''simple docstring'''







 __lowerCamelCase					: List[Any]    = ['pixel_values']



 def __init__(self				,     A = True				,     A = None				,     A = PILImageResampling.BILINEAR				,     A = True				,     A = None				,     A = True				,     A = 1 / 255				,     A = True				,     A = None				,     A = None				,     **A				,     )					->  str:


      """simple docstring"""



      super().__init__(**A )
      _a											=						size if size is not None else {'''shortest_edge''': 224}
      _a											=						get_size_dict(A				,     default_to_square=A )
      _a											=						crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
      _a											=						get_size_dict(A				,     param_name='''crop_size''' )

      _a											=						do_resize
      _a											=						size
      _a											=						do_center_crop
      _a											=						crop_size
      _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       a__						(self				,     A				,     A				,     A = PILImageResampling.BILINEAR				,     A = None				,     **A				,     )					->  List[Any]:


      """simple docstring"""



      _a											=						get_size_dict(A				,     default_to_square=A )
      if "shortest_edge" in size:
           _a											=						get_resize_output_image_size(A				,     size['''shortest_edge''']				,     default_to_square=A )
      elif "height" in size and "width" in size:
           _a											=						(size['''height'''], size['''width'''])
      else:
           raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
      return resize(A				,     size=A				,     resample=A				,     data_format=A				,     **A )



 def       a__						(self				,     A				,     A				,     A = None				,     **A				,     )					->  Optional[int]:


      """simple docstring"""



      _a											=						get_size_dict(A )
      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(A				,     size=(size['''height'''], size['''width'''])				,     data_format=A				,     **A )



 def       a__						(self				,     A				,     A				,     A = None				,     **A				,     )					->  Union[str, Any]:


      """simple docstring"""



      return rescale(A				,     scale=A				,     data_format=A				,     **A )



 def       a__						(self				,     A				,     A				,     A				,     A = None				,     **A				,     )					->  int:


      """simple docstring"""



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



 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 = ChannelDimension.FIRST				,     )					->  str:


      """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.''' )

      # All transformations expect numpy arrays.
      _a											=						to_numpy_array(A )

      if do_resize:
           _a											=						self.resize(image=A				,     size=A				,     resample=A )

      if do_center_crop:
           _a											=						self.center_crop(A				,     size=A )

      if do_rescale:
           _a											=						self.rescale(image=A				,     scale=A )

      if do_normalize:
           _a											=						self.normalize(image=A				,     mean=A				,     std=A )

      _a											=						to_channel_dimension_format(A				,     A )
      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 = ChannelDimension.FIRST				,     **A				,     )					->  List[str]:


      """simple docstring"""



      _a											=						do_resize if do_resize is not None else self.do_resize
      _a											=						resample if resample is not None else self.resample
      _a											=						do_center_crop if do_center_crop is not None else self.do_center_crop
      _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(A				,     default_to_square=A )
      _a											=						crop_size if crop_size is not None else self.crop_size
      _a											=						get_size_dict(A				,     param_name='''crop_size''' )

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

      _a											=						make_batched(A )

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

      _a											=						{'''pixel_values''': videos}
      return BatchFeature(data=A				,     tensor_type=A )








'''simple docstring'''





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"):
  lowercase_	 =						{
      "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:
  lowercase_	 =						{
      "linear": PIL.Image.LINEAR,
      "bilinear": PIL.Image.BILINEAR,
      "bicubic": PIL.Image.BICUBIC,
      "lanczos": PIL.Image.LANCZOS,
      "nearest": PIL.Image.NEAREST,
  }




def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						(images / 2 + 0.5).clamp(0						,			1)
     _a											=						images.cpu().permute(0						,			2						,			3						,			1).float().numpy()
     _a											=						numpy_to_pil(__A)
     return images




def lowerCAmelCase						(__A):



     """simple docstring"""



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

     return pil_images



