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# Copyright 2023 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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
Audio/Text processor class for CLAP
"""

from ..processing_utils import ProcessorMixin
from ..tokenizer_utils_base import BatchEncoding

__all__ = [
    "ClapProcessor",
]


class ClapProcessor(ProcessorMixin):
    r"""
    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.
    """
    feature_extractor_class = "ClapFeatureExtractor"
    tokenizer_class = "RobertaTokenizer"

    pretrained_init_configuration = {"laion/clap-htsat-unfused": {"do_lower_case": True}}

    def __init__(self, feature_extractor, tokenizer):
        super().__init__(feature_extractor, tokenizer)

    def __call__(self, text=None, audios=None, return_tensors=None, **kwargs):
        """
        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`, `paddle.Tensor`, `List[np.ndarray]`, `List[paddle.Tensor]`):
                The audio or batch of audios to be prepared. Each audio can be NumPy array or PaddlePaddle tensor. In case
                of a NumPy array/PaddlePaddle 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:
                - `'pd'`: Return PaddlePaddle `paddle.Tensor` objects.
                - `'np'`: Return NumPy `np.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_rate = kwargs.pop("sampling_rate", None)

        if text is None and audios is None:
            raise ValueError("You have to specify either text or audios. Both cannot be none.")

        if text is not None:
            encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)

        if audios is not None:
            audio_features = self.feature_extractor(
                audios, sampling_rate=sampling_rate, return_tensors=return_tensors, **kwargs
            )

        if text is not None and audios is not None:
            encoding["input_features"] = audio_features.input_features
            return encoding
        elif text is not None:
            return encoding
        else:
            return BatchEncoding(data=dict(**audio_features), tensor_type=return_tensors)

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer
        to the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    @property
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        feature_extractor_input_names = self.feature_extractor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))
