def __init__(
        self,
        renormalize_logits=True,
        output_scores=False,
        return_dict_in_generate=False,
        output_hidden_states=False,
        output_attentions=False,
        temperature=1.0,
        do_sample=False,
        coarse_semantic_pad_token=12_048,
        coarse_rate_hz=75,
        n_coarse_codebooks=2,
        coarse_infer_token=12_050,
        max_coarse_input_length=256,
        max_coarse_history: int = 630,
        sliding_window_len: int = 60,
        **kwargs,
    ):
        """Class that holds a generation configuration for [`BarkCoarseModel`].

        This configuration inherit from [`GenerationConfig`] and can be used to control the model generation. Read the
        documentation from [`GenerationConfig`] for more information.

        Args:
            renormalize_logits (`bool`, *optional*, defaults to `True`):
                Whether to renormalize the logits after applying all the logits processors or warpers (including the
                custom ones). It's highly recommended to set this flag to `True` as the search algorithms suppose the
                score logits are normalized but some logit processors or warpers break the normalization.
            output_scores (`bool`, *optional*, defaults to `False`):
                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            output_hidden_states (`bool`, *optional*, defaults to `False`):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more details.
            output_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more details.
            temperature (`float`, *optional*, defaults to 1.0):
                The value used to modulate the next token probabilities.
            do_sample (`bool`, *optional*, defaults to `False`):
                Whether or not to use sampling ; use greedy decoding otherwise.
            coarse_semantic_pad_token (`int`, *optional*, defaults to 12_048):
                Coarse semantic pad token.
            coarse_rate_hz (`int`, *optional*, defaults to 75):
                Coarse rate in Hertz.
            n_coarse_codebooks (`int`, *optional*, defaults to 2):
                Number of coarse codebooks.
            coarse_infer_token (`int`, *optional*, defaults to 12_050):
                Coarse infer token.
            max_coarse_input_length (`int`, *optional*, defaults to 256):
                Max length of input coarse vector.
            max_coarse_history (`int`, *optional*, defaults to 630):
                Max length of the output of the coarse acoustics model used in the fine generation step.
            sliding_window_len (`int`, *optional*, defaults to 60):
                The coarse generation step uses a sliding window to generate raw audio.
        """
        super().__init__(
            temperature=temperature,
            do_sample=do_sample,
            renormalize_logits=renormalize_logits,
            output_scores=output_scores,
            return_dict_in_generate=return_dict_in_generate,
            output_hidden_states=output_hidden_states,
            output_attentions=output_attentions,
            **kwargs,
        )

        self.coarse_semantic_pad_token = coarse_semantic_pad_token
        self.coarse_rate_hz = coarse_rate_hz
        self.n_coarse_codebooks = n_coarse_codebooks
        self.coarse_infer_token = coarse_infer_token
        self.max_coarse_input_length = max_coarse_input_length
        self.max_coarse_history = max_coarse_history
        self.sliding_window_len = sliding_window_len
Class that holds a generation configuration for [`BarkCoarseModel`].

        This configuration inherit from [`GenerationConfig`] and can be used to control the model generation. Read the
        documentation from [`GenerationConfig`] for more information.

        Args:
            renormalize_logits (`bool`, *optional*, defaults to `True`):
                Whether to renormalize the logits after applying all the logits processors or warpers (including the
                custom ones). It's highly recommended to set this flag to `True` as the search algorithms suppose the
                score logits are normalized but some logit processors or warpers break the normalization.
            output_scores (`bool`, *optional*, defaults to `False`):
                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            output_hidden_states (`bool`, *optional*, defaults to `False`):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more details.
            output_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more details.
            temperature (`float`, *optional*, defaults to 1.0):
                The value used to modulate the next token probabilities.
            do_sample (`bool`, *optional*, defaults to `False`):
                Whether or not to use sampling ; use greedy decoding otherwise.
            coarse_semantic_pad_token (`int`, *optional*, defaults to 12_048):
                Coarse semantic pad token.
            coarse_rate_hz (`int`, *optional*, defaults to 75):
                Coarse rate in Hertz.
            n_coarse_codebooks (`int`, *optional*, defaults to 2):
                Number of coarse codebooks.
            coarse_infer_token (`int`, *optional*, defaults to 12_050):
                Coarse infer token.
            max_coarse_input_length (`int`, *optional*, defaults to 256):
                Max length of input coarse vector.
            max_coarse_history (`int`, *optional*, defaults to 630):
                Max length of the output of the coarse acoustics model used in the fine generation step.
            sliding_window_len (`int`, *optional*, defaults to 60):
                The coarse generation step uses a sliding window to generate raw audio.
        
dpkd/transformers/src/transformers/models/bark/generation_configuration_bark.py