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 ddlmZmZmZmZ ddlmZ  G d	 d
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      Zy)z(
Image/Text processor class for SigLIP.
    )ListOptionalUnion   )BatchFeature)
ImageInput)ProcessorMixin)PaddingStrategyPreTokenizedInput	TextInputTruncationStrategy)
TensorTypec                        e Zd ZdZddgZdZdZ fdZdddddej                  fd	e
eeee   ee   f   d
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eeef   de
eeef   dedee
eef      defdZd Zd Zed        Z xZS )SiglipProcessora  
    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.
    image_processor	tokenizerSiglipImageProcessorSiglipTokenizerc                 &    t         |   ||       y N)super__init__)selfr   r   	__class__s      _/var/www/html/venv/lib/python3.12/site-packages/transformers/models/siglip/processing_siglip.pyr   zSiglipProcessor.__init__.   s    )4    NFtextimagespadding
truncation
max_lengthreturn_tensorsreturnc                     ||t        d      || j                  |||||      }|| j                  ||      }||j                  d<   |S |S t	        t        di |      S )a  
        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`.
        z?You have to specify either text or images. Both cannot be none.)r"   r   r    r!   )r"   pixel_values)datatensor_type )
ValueErrorr   r   r%   r   dict)	r   r   r   r   r    r!   r"   encodingimage_featuress	            r   __call__zSiglipProcessor.__call__1   s    n <FN^__~~^WQ[hr & H !11&1XN 2'5'B'BH^$OOT%;N%;XXr   c                 :     | j                   j                  |i |S )z
        This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        )r   decoder   argskwargss      r   r/   zSiglipProcessor.decode{   s     
 %t~~$$d5f55r   c                 :     | j                   j                  |i |S )z
        This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        )r   batch_decoder0   s      r   r4   zSiglipProcessor.batch_decode   s     
 +t~~**D;F;;r   c                     | j                   j                  }| j                  j                  }t        t        j                  ||z               S r   )r   model_input_namesr   listr*   fromkeys)r   tokenizer_input_namesimage_processor_input_namess      r   r6   z!SiglipProcessor.model_input_names   sA     !% @ @&*&:&:&L&L#DMM"7:U"UVWWr   )__name__
__module____qualname____doc__
attributesimage_processor_classtokenizer_classr   r   PYTORCHr   r   r   r   r   boolstrr
   r   intr   r   r-   r/   r4   propertyr6   __classcell__)r   s   @r   r   r      s     $[1J2'O5
 _c!5:;?;E;M;MHYI0$y/4HYCZZ[HY HY tS/12	HY
 $%778HY HY !sJ!78HY 
HYT6< X Xr   r   N)r>   typingr   r   r   feature_extraction_utilsr   image_utilsr   processing_utilsr	   tokenization_utils_baser
   r   r   r   utilsr   r   r(   r   r   <module>rN      s4    ) ( 4 % . h h rXn rXr   