import os
import json
from argparse import ArgumentParser
from glob import glob
from tqdm import tqdm

import torch
from safetensors.torch import load_file, save_file


def weight_dequant_fp8(weight_fp8, scale_inv):
    """
    Dequantize FP8 weights to BF16 using scale_inv.

    Args:
        weight_fp8: FP8 tensor
        scale_inv: Inverse scale tensor (F32)

    Returns:
        BF16 tensor
    """
    # Convert FP8 to float32 first
    weight_f32 = weight_fp8.to(torch.float32)

    # Apply inverse scaling
    # scale_inv shape is typically [out_features_blocks, in_features_blocks]
    # We need to broadcast it properly to match weight dimensions
    if scale_inv.dim() == 2:
        # Expand scale_inv to match weight dimensions
        out_blocks, in_blocks = scale_inv.shape
        weight_blocks_out = weight_fp8.shape[0] // out_blocks
        weight_blocks_in = weight_fp8.shape[1] // in_blocks

        # Repeat scale_inv to match weight shape
        scale_inv_expanded = scale_inv.repeat_interleave(weight_blocks_out, dim=0)
        scale_inv_expanded = scale_inv_expanded.repeat_interleave(weight_blocks_in, dim=1)

        weight_f32 = weight_f32 * scale_inv_expanded
    else:
        weight_f32 = weight_f32 * scale_inv

    # Convert to BF16
    return weight_f32.to(torch.bfloat16)


def main(fp8_path, bf16_path):
    torch.set_default_dtype(torch.bfloat16)
    os.makedirs(bf16_path, exist_ok=True)

    model_index_file = os.path.join(fp8_path, "model.safetensors.index.json")
    with open(model_index_file, "r") as f:
        model_index = json.load(f)

    weight_map = model_index["weight_map"]

    # Cache for loaded safetensor files
    loaded_files = {}
    fp8_weight_names = []

    # Helper function to get tensor from the correct file
    def get_tensor(tensor_name):
        if tensor_name not in weight_map:
            return None
        file_name = weight_map[tensor_name]
        if file_name not in loaded_files:
            file_path = os.path.join(fp8_path, file_name)
            loaded_files[file_name] = load_file(file_path, device="cuda")
        return loaded_files[file_name][tensor_name]

    safetensor_files = list(glob(os.path.join(fp8_path, "*.safetensors")))
    safetensor_files = [f for f in safetensor_files if not f.endswith(".index.json")]
    safetensor_files.sort()

    print(f"Found {len(safetensor_files)} safetensor files to convert")

    for safetensor_file in tqdm(safetensor_files, desc="Converting files"):
        file_name = os.path.basename(safetensor_file)
        current_state_dict = load_file(safetensor_file, device="cuda")
        loaded_files[file_name] = current_state_dict

        new_state_dict = {}

        for weight_name, weight in current_state_dict.items():
            # Skip scale_inv tensors
            if weight_name.endswith("_scale_inv"):
                continue

            # Check if this is an FP8 weight (F8_E4M3 has element_size of 1)
            if weight.dtype == torch.float8_e4m3fn or weight.element_size() == 1:
                scale_inv_name = f"{weight_name}_scale_inv"
                scale_inv = get_tensor(scale_inv_name)

                if scale_inv is not None:
                    fp8_weight_names.append(weight_name)
                    new_state_dict[weight_name] = weight_dequant_fp8(weight, scale_inv)
                else:
                    print(f"Warning: Missing scale_inv tensor for {weight_name}, keeping as-is")
                    new_state_dict[weight_name] = weight
            else:
                # Already BF16 or F32, keep as-is
                new_state_dict[weight_name] = weight

        # Save converted weights
        new_safetensor_file = os.path.join(bf16_path, file_name)
        save_file(new_state_dict, new_safetensor_file)

        # Memory management: keep only the 2 most recently used files
        if len(loaded_files) > 2:
            oldest_file = next(iter(loaded_files))
            del loaded_files[oldest_file]
            torch.cuda.empty_cache()

    # Update model index - remove all _scale_inv entries
    print("Updating model index...")
    new_weight_map = {}
    for weight_name, file_name in weight_map.items():
        if not weight_name.endswith("_scale_inv"):
            new_weight_map[weight_name] = file_name

    new_model_index = {
        "metadata": model_index.get("metadata", {}),
        "weight_map": new_weight_map
    }

    new_model_index_file = os.path.join(bf16_path, "model.safetensors.index.json")
    with open(new_model_index_file, "w") as f:
        json.dump(new_model_index, f, indent=2)

    print(f"Conversion complete! Converted {len(fp8_weight_names)} FP8 weights to BF16")
    print(f"Output saved to: {bf16_path}")


if __name__ == "__main__":
    parser = ArgumentParser(description="Convert MiniMax-M2 from FP8 to BF16")
    parser.add_argument("--input-fp8-hf-path", type=str, required=True,
                        help="Path to the FP8 model directory")
    parser.add_argument("--output-bf16-hf-path", type=str, required=True,
                        help="Path to save the BF16 model")
    args = parser.parse_args()

    main(args.input_fp8_hf_path, args.output_bf16_hf_path)