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"""NemotronH model configuration"""

import regex as re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)


class NemotronHConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a
    [`NemotronHModel`]. It is used to instantiate a NemotronH model according
    to the specified arguments, defining the model architecture. Instantiating
    a configuration with the defaults will yield a similar configuration to
    that of the NemotronH-v0.1 model.
    Args:
        vocab_size (`int`, *optional*, defaults to 131072):
            Vocabulary size of the NemotronH model. Defines the number of
            different tokens that can be represented by the `inputs_ids`
            passed when calling [`NemotronHModel`]
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be
            tied. Note that this is only relevant if the model has an output
            word embedding layer.
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 21504):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 52):
            Number of hidden layers in the Transformer encoder.
        hybrid_override_pattern (`str`, *optional*, defaults to
            `"M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-"`):
            The pattern of the hybrid model. The pattern is a string of
            characters where each character represents
            M: Mamba2, *: Attention, -: MLP
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the
            Transformer encoder.
        attention_head_dim (`int`, *optional*, defaults to 128):
            Dimension of each attention head.
        num_key_value_heads (`int`, *optional*, defaults to 8):
            This is the number of key_value heads that should be used to
            implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use
            Multi Head Attention (MHA), if `num_key_value_heads=1` the model
            will use Multi Query Attention (MQA) otherwise GQA is used.
        mlp_hidden_act (`str`, *optional*, defaults to "relu2"):
            The non-linear activation function in the MLP layers.
        attention_bias (`bool`, *optional*, defaults to `False`):
            Whether to use bias in attention layers.
        mlp_bias (`bool`, *optional*, defaults to `False`):
            Whether to use bias in MLP layers.
        use_bias (`bool`, *optional*, defaults to `False`):
            Whether to use bias in the model.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for
            initializing all weight matrices.
        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
            The epsilon used by the layer normalization layers.
        residual_in_fp32 (`bool`, *optional*, defaults to `False`):
            Whether or not residuals should be in `float32`. If set to `False`
            residuals will keep the same `dtype` as the rest of the model.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values
            attentions (not used by all models). Only relevant if
            `config.is_decoder=True`.
        num_logits_to_keep (`int` or `None`, *optional*, defaults to 1):
            Number of prompt logits to calculate during generation. If `None`,
            all logits will be calculated. If an integer value, only last
            `num_logits_to_keep` logits will be calculated.
        pad_token_id (`int`, *optional*, defaults to 0):
            The id of the padding token.
        bos_token_id (`int`, *optional*, defaults to 1):
            The id of the "beginning-of-sequence" token.
        eos_token_id (`int`, *optional*, defaults to 2):
            The id of the "end-of-sequence" token.
        sliding_window (`int`, *optional*, defaults to None):
            Sliding window attention window size.
        max_position_embeddings (`int`, *optional*, defaults to 4096):
            The maximum sequence length that this model might ever be used
            with.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        hidden_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the hidden states.
        use_mamba_kernels (`bool`, *optional*, defaults to `True`):
            Flag indicating whether or not to use the fast mamba kernels.
            These are available only if `mamba-ssm` and `causal-conv1d`
            are installed, and the mamba modules are running on a CUDA device.
        ssm_state_size (`int`, *optional*, defaults to 128):
            The dimension of the mamba state space latents.
        mamba_num_heads (`int`, *optional*, defaults to 128):
            Number of heads in Mamba layers.
        mamba_n_groups (`int`, *optional*, defaults to 8):
            Number of groups in Mamba layers.
        mamba_head_dim (`int`, *optional*, defaults to 64):
            Dimension of each Mamba head.
        mamba_d_conv (`int`, *optional*, defaults to 4):
            The size of the mamba convolution kernel.
        mamba_expand (`int`, *optional*, defaults to 2):
            Expanding factor used to determine the mamba intermediate size.
        mamba_hidden_act (`str`, *optional*, defaults to "silu"):
            The non-linear activation function in the Mamba layers.
        mamba_dt_min (`float`, *optional*, defaults to 0.001):
            Minimum value for the time step in Mamba.
        mamba_dt_max (`float`, *optional*, defaults to 0.1):
            Maximum value for the time step in Mamba.
        mamba_dt_limit (`tuple`, *optional*, defaults to (0.0, float("inf"))):
            Limits for the time step in Mamba.
        mamba_dt_init_floor (`float`, *optional*, defaults to 1e-4):
            Floor value for time step initialization in Mamba.
        mamba_conv_bias (`bool`, *optional*, defaults to `True`):
            Whether to use bias in the convolution layer of the mamba mixer
            block.
        mamba_proj_bias (`bool`, *optional*, defaults to `False`):
            Whether to use bias in the input and output projections of the
            mamba mixer block.
        mamba_chunk_size (`int`, *optional*, defaults to 256):
            Size of chunks for Mamba processing.
        rescale_prenorm_residual (`bool`, *optional*, defaults to `True`):
            Whether to rescale the pre-normalization residual connections.
    """

    model_type = "nemotron_h"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=131072,
        tie_word_embeddings=False,
        hidden_size=4096,
        intermediate_size=21504,
        num_hidden_layers=52,
        hybrid_override_pattern="M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-",
        num_attention_heads=32,
        head_dim=128,
        num_key_value_heads=8,  # nemo: num_query_groups
        mlp_hidden_act="relu2",
        attention_bias=False,
        mlp_bias=False,
        use_bias=False,
        initializer_range=0.02,  # nemo: init_method_std
        layer_norm_epsilon=1e-5,  # nemo: layernorm_epsilon
        residual_in_fp32=False,  #  Megatron Core default value
        use_cache=True,
        num_logits_to_keep=1,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        sliding_window=None,
        max_position_embeddings=4096,
        attention_dropout=0.0,
        hidden_dropout=0.0,  # * ADDED
        use_mamba_kernels=True,
        ssm_state_size=128,  # mamba_state_size
        mamba_num_heads=128,
        mamba_n_groups=8,  # nemo: mamba_ssm_ngroups = num_heads
        mamba_head_dim=64,
        mamba_d_conv=4,
        mamba_expand=2,
        mamba_hidden_act="silu",
        mamba_dt_min=0.001,
        mamba_dt_max=0.1,
        mamba_dt_limit=(0.0, float("inf")),
        mamba_dt_init_floor=1e-4,
        mamba_conv_bias=True,
        mamba_proj_bias=False,
        mamba_chunk_size=256,
        rescale_prenorm_residual=True,
        n_routed_experts=8,
        n_shared_experts=1,
        moe_intermediate_size=7688,
        moe_shared_expert_intermediate_size=7688,
        moe_latent_size=None,
        num_experts_per_tok=2,
        routed_scaling_factor=1.0,
        n_group=1,
        topk_group=1,
        norm_topk_prob=True,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.tie_word_embeddings = tie_word_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.hybrid_override_pattern = hybrid_override_pattern
        self.num_attention_heads = num_attention_heads
        self.head_dim = head_dim
        self.sliding_window = sliding_window
        self.max_position_embeddings = max_position_embeddings
        self.attention_dropout = attention_dropout
        self.hidden_dropout = hidden_dropout

        # Validate hybrid_override_pattern
        # M: Mamba2, *: Attention, -: MLP
        assert len(self.hybrid_override_pattern) == self.num_hidden_layers, (
            "hybrid_override_pattern must have same length as num_hidden_layers"
        )
        assert re.match(r"^[*-M]+$", self.hybrid_override_pattern), (
            "hybrid_override_pattern must only contain characters 'M', '*', or '-'"
        )

        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.mlp_hidden_act = mlp_hidden_act
        self.attention_bias = attention_bias
        self.mlp_bias = mlp_bias
        self.use_bias = use_bias
        self.initializer_range = initializer_range
        self.layer_norm_epsilon = layer_norm_epsilon
        self.residual_in_fp32 = residual_in_fp32

        self.use_cache = use_cache
        self.num_logits_to_keep = num_logits_to_keep

        self.use_mamba_kernels = use_mamba_kernels
        self.n_groups = mamba_n_groups
        self.mamba_head_dim = mamba_head_dim
        self.ssm_state_size = ssm_state_size
        self.mamba_num_heads = mamba_num_heads
        self.conv_kernel = mamba_d_conv
        self.expand = mamba_expand
        self.mamba_hidden_act = mamba_hidden_act
        self.time_step_min = mamba_dt_min
        self.time_step_max = mamba_dt_max
        self.time_step_limit = mamba_dt_limit
        self.time_step_floor = mamba_dt_init_floor
        self.use_conv_bias = mamba_conv_bias
        self.mamba_proj_bias = mamba_proj_bias
        self.chunk_size = mamba_chunk_size
        self.rescale_prenorm_residual = rescale_prenorm_residual
        self.n_routed_experts = n_routed_experts
        self.n_shared_experts = n_shared_experts
        self.moe_intermediate_size = moe_intermediate_size
        self.moe_shared_expert_intermediate_size = moe_shared_expert_intermediate_size  # noqa: E501
        self.moe_latent_size = moe_latent_size
        self.num_experts_per_tok = num_experts_per_tok
        self.routed_scaling_factor = routed_scaling_factor
        self.n_group = n_group
        self.topk_group = topk_group
        self.norm_topk_prob = norm_topk_prob

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )

    @property
    def layers_block_type(self):
        return [
            "mamba"
            if self.hybrid_override_pattern[i] == "M"
            else "attention"
            if self.hybrid_override_pattern[i] == "*"
            else "mlp"
            if self.hybrid_override_pattern[i] == "-"
            else "moe"
            for i in range(self.num_hidden_layers)
        ]
