from __future__ import annotations

from typing import TYPE_CHECKING

import torch

if TYPE_CHECKING:
    from torch import Tensor

from .base import ModelBase, TextModel, gguf


@ModelBase.register("MiniMaxM2ForCausalLM")
class MiniMaxM2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.MINIMAXM2
    _experts_cache: dict[int, dict[str, Tensor]] = {}

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        self.gguf_writer.add_expert_feed_forward_length(self.find_hparam(["intermediate_size"]))
        self.gguf_writer.add_rope_dimension_count(self.find_hparam(["rotary_dim"]))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
        # merge expert weights
        if "block_sparse_moe.experts." in name:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            expert_cache = self._experts_cache.setdefault(bid, {})
            expert_cache[name] = data_torch
            expert_weights = ["w1", "w2", "w3"]

            # not enough expert weights to merge
            if len(expert_cache) < n_experts * len(expert_weights):
                return

            for w_name in expert_weights:
                datas: list[Tensor] = []

                for xid in range(n_experts):
                    ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
                    datas.append(expert_cache[ename])
                    del expert_cache[ename]

                data_torch = torch.stack(datas, dim=0)
                merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
                new_name = self.map_tensor_name(merged_name)
                yield from super().modify_tensors(data_torch, new_name, bid)

            del self._experts_cache[bid]
            return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("MiniMaxM3SparseForCausalLM", "MiniMaxM3SparseForConditionalGeneration")
class MiniMaxM3Model(MiniMaxM2Model):
    model_arch = gguf.MODEL_ARCH.MINIMAXM3

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        self.gguf_writer.add_expert_shared_count(self.find_hparam(["n_shared_experts"]))
        self.gguf_writer.add_expert_weights_scale(self.find_hparam(["routed_scaling_factor"]))
        self.gguf_writer.add_expert_weights_norm(True)

        sac = self.find_hparam(["sparse_attention_config"])
        self.gguf_writer.add_indexer_head_count(sac["sparse_num_index_heads"])
        self.gguf_writer.add_indexer_key_length(sac["sparse_index_dim"])
        self.gguf_writer.add_indexer_top_k(sac["sparse_topk_blocks"])
        self.gguf_writer.add_indexer_block_size(sac["sparse_block_size"])
        self.gguf_writer.add_indexer_local_blocks(sac["sparse_local_block"])

        moe_layer_freq = self.find_hparam(["moe_layer_freq"])
        n_dense = 0
        for v in moe_layer_freq:
            if v == 0:
                n_dense += 1
            else:
                break
        self.gguf_writer.add_leading_dense_block_count(n_dense)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
        # Gemma-style (1 + w) RMSNorm: bake the +1 in so llama.cpp can use plain RMSNorm
        if name.endswith("norm.weight"):
            data_torch = data_torch + 1.0

        yield from super().modify_tensors(data_torch, name, bid)