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from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...utils import auto_docstring
from ..auto import CONFIG_MAPPING, AutoConfig


@auto_docstring(checkpoint="nvidia/nemotron-3.5-asr-streaming-0.6b")
@strict
class Nemotron3_5AsrConfig(PreTrainedConfig):
    r"""
    vocab_size (`int`, *optional*, defaults to 13088):
        Vocabulary size of the joint network output (including the blank token).
    decoder_hidden_size (`int`, *optional*, defaults to 640):
        Hidden size of the LSTM prediction network (NeMo's `pred_hidden`).
    num_decoder_layers (`int`, *optional*, defaults to 2):
        Number of LSTM layers in the prediction network.
    hidden_act (`str`, *optional*, defaults to `"relu"`):
        Activation in the joint network.
    max_symbols_per_step (`int`, *optional*, defaults to 10):
        Maximum number of non-blank symbols emitted per encoder time step during greedy decoding.
    encoder_config (`Union[dict, NemotronAsrStreamingEncoderConfig]`, *optional*):
        The config object or dictionary of the encoder. Reuses [`NemotronAsrStreamingEncoderConfig`] directly,
        since the encoder is identical to [`NemotronAsrStreaming`]'s.
    blank_token_id (`int`, *optional*, defaults to 13087):
        Blank token id for RNN-T decoding.
    num_prompts (`int`, *optional*, defaults to 128):
        Number of language-prompt slots. The target language is encoded as a one-hot vector of this
        size, broadcast across the encoder time axis and concatenated with the encoder output before
        the `prompt_kernel` fusion MLP.
    prompt_intermediate_size (`int`, *optional*, defaults to 2048):
        Hidden size of the `prompt_kernel` fusion MLP (`Linear(hidden + num_prompts -> intermediate)
        -> ReLU -> Linear(intermediate -> hidden)`).
    default_prompt_id (`int`, *optional*, defaults to 101):
        Prompt index used to condition the model when `prompt_ids` is not provided. Defaults to the
        `auto` language-detection slot (index 101 in the NeMo prompt dictionary), matching NeMo's default
        of `target_lang="auto"`: the model detects the language itself and emits an `<xx-XX>` tag.

    Example:
    ```python
    >>> from transformers import Nemotron3_5AsrForRNNT, Nemotron3_5AsrConfig

    >>> configuration = Nemotron3_5AsrConfig()
    >>> model = Nemotron3_5AsrForRNNT(configuration)
    >>> configuration = model.config
    ```
    """

    model_type = "nemotron3_5_asr"
    sub_configs = {"encoder_config": AutoConfig}

    vocab_size: int = 13088
    decoder_hidden_size: int = 640
    num_decoder_layers: int = 2
    hidden_act: str = "relu"
    max_symbols_per_step: int = 10
    encoder_config: dict | PreTrainedConfig | None = None
    pad_token_id: int = 0
    blank_token_id: int = 13087
    is_encoder_decoder: bool = True
    num_prompts: int = 128
    prompt_intermediate_size: int = 2048
    default_prompt_id: int = 101

    def __post_init__(self, **kwargs):
        if isinstance(self.encoder_config, dict):
            self.encoder_config["model_type"] = self.encoder_config.get("model_type", "nemotron_asr_streaming_encoder")
            self.encoder_config = CONFIG_MAPPING[self.encoder_config["model_type"]](**self.encoder_config)
        elif self.encoder_config is None:
            self.encoder_config = CONFIG_MAPPING["nemotron_asr_streaming_encoder"]()

        super().__post_init__(**kwargs)


__all__ = ["Nemotron3_5AsrConfig"]