content: # Copyright 2024 The HuggingFace Inc. team.
  # SPDX-License-Identifier: Apache-2.0
  
  """
  Image/Text processor class for SigLIP.
  """
  
  from typing import List, Optional, Union
  
  from transformers.feature_extraction_utils import BatchFeature
  from transformers.image_utils import ImageInput
  from transformers.processing_utils import ProcessorMixin
  from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
  from transformers.utils import TensorType
  
  
  class SiglipProcessor(ProcessorMixin):
      r"""
      Constructs a Siglip processor which wraps a Siglip image processor and a Siglip tokenizer into a single processor.
  
      [`SiglipProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`SiglipTokenizer`]. See the
      [`~SiglipProcessor.__call__`] and [`~SiglipProcessor.decode`] for more information.
  
      Args:
          image_processor ([`SiglipImageProcessor`]):
              The image processor is a required input.
          tokenizer ([`SiglipTokenizer`]):
              The tokenizer is a required input.
      """
  
      attributes = ["image_processor", "tokenizer"]
      image_processor_class = "SiglipImageProcessor"
      tokenizer_class = "SiglipTokenizer"
  
      def __init__(self, image_processor, tokenizer):
          super().__init__(image_processor, tokenizer)
  
      def __call__(
          self,
          text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
          images: ImageInput = None,
          padding: Union[bool, str, PaddingStrategy] = False,
          truncation: Union[bool, str, TruncationStrategy] = None,
          max_length: int = None,
          return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
      ) -> BatchFeature:
          """
          Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
          and `kwargs` arguments to SiglipTokenizer's [`~SiglipTokenizer.__call__`] if `text` is not `None` to encode
          the text. To prepare the image(s), this method forwards the `images` argument to
          SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
          of the above two methods for more information.
  
          Args:
              text (`str`, `List[str]`, `List[List[str]]`):
                  The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                  (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                  `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
              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. Both channels-first and channels-last formats are supported.
              padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
                  Select a strategy to pad the returned sequences (according to the model's padding side and padding
                  index) among:
                  - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
                    sequence if provided).
                  - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                    acceptable input length for the model if that argument is not provided.
                  - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
                    lengths).
              max_length (`int`, *optional*):
                  Maximum length of the returned list and optionally padding length (see above).
              truncation (`bool`, *optional*):
                  Activates truncation to cut input sequences longer than `max_length` to `max_length`.
              return_tensors (`str` or [`~utils.TensorType`], *optional*):
                  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:
  
              - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
              - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
                `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
                `None`).
              - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
          """
  
          if text is None and images is None:
              raise ValueError("You have to specify either text or images. Both cannot be none.")
  
          if text is not None:
              encoding = self.tokenizer(
                  text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
              )
  
          if images is not None:
              image_features = self.image_processor(images, return_tensors=return_tensors)
  
          if text is not None and images is not None:
              encoding["pixel_values"] = image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          else:
              return BatchFeature(data=dict(**image_features), tensor_type=return_tensors)
  
      def decode(self, *args, **kwargs):
          """
          This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
          the docstring of this method for more information.
          """
          return self.tokenizer.decode(*args, **kwargs)
  
      def batch_decode(self, *args, **kwargs):
          """
          This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
          refer to the docstring of this method for more information.
          """
          return self.tokenizer.batch_decode(*args, **kwargs)
  
      @property
      # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->Siglip, T5->Siglip
      def model_input_names(self):
          tokenizer_input_names = self.tokenizer.model_input_names
          image_processor_input_names = self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
  