
    sg.                     6    d Z ddlmZ ddlmZ  G d de      Zy)z%
Audio/Text processor class for CLAP
   )ProcessorMixin)BatchEncodingc                   N     e Zd ZdZdZdZ fdZd	dZd Zd Z	e
d        Z xZS )
ClapProcessora  
    Constructs a CLAP processor which wraps a CLAP feature extractor and a RoBerta tokenizer into a single processor.

    [`ClapProcessor`] offers all the functionalities of [`ClapFeatureExtractor`] and [`RobertaTokenizerFast`]. See the
    [`~ClapProcessor.__call__`] and [`~ClapProcessor.decode`] for more information.

    Args:
        feature_extractor ([`ClapFeatureExtractor`]):
            The audio processor is a required input.
        tokenizer ([`RobertaTokenizerFast`]):
            The tokenizer is a required input.
    ClapFeatureExtractor)RobertaTokenizerRobertaTokenizerFastc                 &    t         |   ||       y N)super__init__)selffeature_extractor	tokenizer	__class__s      [/var/www/html/venv/lib/python3.12/site-packages/transformers/models/clap/processing_clap.pyr   zClapProcessor.__init__(   s    *I6    c                    |j                  dd      }||t        d      | | j                  |fd|i|}| | j                  |f||d|}||j	                         |S |S t        t        di |      S )a	  
        Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `text`
        and `kwargs` arguments to RobertaTokenizerFast's [`~RobertaTokenizerFast.__call__`] if `text` is not `None` to
        encode the text. To prepare the audio(s), this method forwards the `audios` and `kwrags` arguments to
        ClapFeatureExtractor's [`~ClapFeatureExtractor.__call__`] if `audios` 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).
            audios (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The audio or batch of audios to be prepared. Each audio can be NumPy array or PyTorch tensor. In case
                of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels,
                and T the sample length of the audio.

            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:
            [`BatchEncoding`]: A [`BatchEncoding`] 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`).
            - **audio_features** -- Audio features to be fed to a model. Returned when `audios` is not `None`.
        sampling_rateNz?You have to specify either text or audios. Both cannot be none.return_tensors)r   r   )datatensor_type )pop
ValueErrorr   r   updater   dict)r   textaudiosr   kwargsr   encodingaudio_featuress           r   __call__zClapProcessor.__call__+   s    F 

?D9<FN^__%t~~dT>TVTH3T33&3NV\N  2OON+OO d&<^&<.YYr   c                 :     | j                   j                  |i |S )z
        This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        )r   batch_decoder   argsr    s      r   r%   zClapProcessor.batch_decodec   s     
 +t~~**D;F;;r   c                 :     | j                   j                  |i |S )z
        This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer
        to the docstring of this method for more information.
        )r   decoder&   s      r   r)   zClapProcessor.decodej   s     
 %t~~$$d5f55r   c                     | j                   j                  }| j                  j                  }t        t        j                  ||z               S r   )r   model_input_namesr   listr   fromkeys)r   tokenizer_input_namesfeature_extractor_input_namess      r   r+   zClapProcessor.model_input_namesq   s?     $ @ @(,(>(>(P(P%DMM"7:W"WXYYr   )NNN)__name__
__module____qualname____doc__feature_extractor_classtokenizer_classr   r#   r%   r)   propertyr+   __classcell__)r   s   @r   r   r      sA     5BO76Zp<6 Z Zr   r   N)r3   processing_utilsr   tokenization_utils_baser   r   r   r   r   <module>r:      s"    / 4^ZN ^Zr   