text: # coding=utf-8
  # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """Feature extractor class for CLIP."""
  
  from typing import List, Optional, Union
  
  import numpy as np
  from PIL import Image
  
  from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
  from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
  from ...utils import TensorType, logging
  
  
  logger = logging.get_logger(__name__)
  
  
  class CLIPFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
      r"""
      Constructs a CLIP feature extractor.
  
      This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
      should refer to this superclass for more information regarding those methods.
  
      Args:
          do_resize (`bool`, *optional*, defaults to `True`):
              Whether to resize the input to a certain `size`.
          size (`int`, *optional*, defaults to 224):
              Resize the input to the given size. Only has an effect if `do_resize` is set to `True`.
          resample (`int`, *optional*, defaults to `PIL.Image.BICUBIC`):
              An optional resampling filter. This can be one of `PIL.Image.NEAREST`, `PIL.Image.BOX`,
              `PIL.Image.BILINEAR`, `PIL.Image.HAMMING`, `PIL.Image.BICUBIC` or `PIL.Image.LANCZOS`. Only has an effect
              if `do_resize` is set to `True`.
          do_center_crop (`bool`, *optional*, defaults to `True`):
              Whether to crop the input at the center. If the input size is smaller than `crop_size` along any edge, the
              image is padded with 0's and then center cropped.
          crop_size (`int`, *optional*, defaults to 224):
              Desired output size when applying center-cropping. Only has an effect if `do_center_crop` is set to `True`.
          do_normalize (`bool`, *optional*, defaults to `True`):
              Whether or not to normalize the input with `image_mean` and `image_std`.
          image_mean (`List[int]`, defaults to `[0.485, 0.456, 0.406]`):
              The sequence of means for each channel, to be used when normalizing images.
          image_std (`List[int]`, defaults to `[0.229, 0.224, 0.225]`):
              The sequence of standard deviations for each channel, to be used when normalizing images.
      """
  
      model_input_names = ["pixel_values"]
  
      def __init__(
          self,
          do_resize=True,
          size=224,
          resample=Image.BICUBIC,
          do_center_crop=True,
          crop_size=224,
          do_normalize=True,
          image_mean=None,
          image_std=None,
          **kwargs
      ):
          super().__init__(**kwargs)
          self.do_resize = do_resize
          self.size = size
          self.resample = resample
          self.do_center_crop = do_center_crop
          self.crop_size = crop_size
          self.do_normalize = do_normalize
          self.image_mean = image_mean if image_mean is not None else [0.48145466, 0.4578275, 0.40821073]
          self.image_std = image_std if image_std is not None else [0.26862954, 0.26130258, 0.27577711]
  
      def __call__(
          self,
          images: Union[
              Image.Image, np.ndarray, "torch.Tensor", List[Image.Image], List[np.ndarray], List["torch.Tensor"]  # noqa
          ],
          return_tensors: Optional[Union[str, TensorType]] = None,
          **kwargs
      ) -> BatchFeature:
          """
          Main method to prepare for the model one or several image(s).
  
          <Tip warning={true}>
  
          NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
          PIL images.
  
          </Tip>
  
          Args:
              images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                  The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                  tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
                  number of channels, H and W are image height and width.
  
              return_tensors (`str` or [`~file_utils.TensorType`], *optional*, defaults to `'np'`):
                  If set, will return tensors of a particular framework. Acceptable values are:
  
                  - `'tf'`: Return TensorFlow `tf.constant` objects.
                  - `'pt'`: Return PyTorch `torch.Tensor` objects.
                  - `'np'`: Return NumPy `np.ndarray` objects.
                  - `'jax'`: Return JAX `jnp.ndarray` objects.
  
          Returns:
              [`BatchFeature`]: A [`BatchFeature`] with the following fields:
  
              - **pixel_values** -- Pixel values to be fed to a model.
          """
          # Input type checking for clearer error
          valid_images = False
  
          # Check that images has a valid type
          if isinstance(images, (Image.Image, np.ndarray)) or is_torch_tensor(images):
              valid_images = True
          elif isinstance(images, (list, tuple)):
              if len(images) == 0 or isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]):
                  valid_images = True
  
          if not valid_images:
              raise ValueError(
                  "Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example), "
                  "`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
              )
  
          is_batched = bool(
              isinstance(images, (list, tuple))
              and (isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]))
          )
  
          if not is_batched:
              images = [images]
  
          # transformations (resizing + center cropping + normalization)
          if self.do_resize and self.size is not None and self.resample is not None:
              images = [self.resize(image=image, size=self.size, resample=self.resample) for image in images]
          if self.do_center_crop and self.crop_size is not None:
              images = [self.center_crop(image, self.crop_size) for image in images]
          if self.do_normalize:
              images = [self.normalize(image=image, mean=self.image_mean, std=self.image_std) for image in images]
  
          # return as BatchFeature
          data = {"pixel_values": images}
          encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
  
          return encoded_inputs
  
      def center_crop(self, image, size):
          """
          Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the
          size is given, it will be padded (so the returned result has the size asked).
  
          Args:
              image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
                  The image to resize.
              size (`int` or `Tuple[int, int]`):
                  The size to which crop the image.
          """
          self._ensure_format_supported(image)
          if not isinstance(size, tuple):
              size = (size, size)
  
          if not isinstance(image, Image.Image):
              image = self.to_pil_image(image)
  
          image_width, image_height = image.size
          crop_height, crop_width = size
  
          crop_top = int((image_height - crop_height + 1) * 0.5)
          crop_left = int((image_width - crop_width + 1) * 0.5)
  
          return image.crop((crop_left, crop_top, crop_left + crop_width, crop_top + crop_height))
  
      def resize(self, image, size, resample=Image.BICUBIC):
          """
          Resizes `image`. Note that this will trigger a conversion of `image` to a PIL Image.
  
          Args:
              image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
                  The image to resize.
              size (`int` or `Tuple[int, int]`):
                  The size to use for resizing the image. If `int` it will be resized to match the shorter side
              resample (`int`, *optional*, defaults to `PIL.Image.BILINEAR`):
                  The filter to user for resampling.
          """
          self._ensure_format_supported(image)
  
          if not isinstance(image, Image.Image):
              image = self.to_pil_image(image)
          if isinstance(size, tuple):
              new_w, new_h = size
          else:
              width, height = image.size
              short, long = (width, height) if width <= height else (height, width)
              if short == size:
                  return image
              new_short, new_long = size, int(size * long / short)
              new_w, new_h = (new_short, new_long) if width <= height else (new_long, new_short)
          return image.resize((new_w, new_h), resample)
  