import importlib
import importlib.resources
import inspect
import logging
import pkgutil
from dataclasses import dataclass
from functools import lru_cache
from typing import List

import numpy as np
import torch
from vllm.model_executor.layers.quantization.awq import AWQConfig
from vllm.model_executor.layers.quantization.gptq import GPTQConfig
from vllm.model_executor.layers.quantization.marlin import MarlinConfig
from vllm.model_executor.model_loader.utils import set_default_torch_dtype
from vllm.distributed import initialize_model_parallel

from sglang.srt.managers.router.infer_batch import Batch, ForwardMode
from sglang.srt.memory_pool import ReqToTokenPool, TokenToKVPool
from sglang.srt.utils import is_multimodal_model, get_available_gpu_memory


QUANTIZATION_CONFIG_MAPPING = {
    "awq": AWQConfig,
    "gptq": GPTQConfig,
    "marlin": MarlinConfig,
}

logger = logging.getLogger("model_runner")

# for server args in model endpoints
global_server_args_dict = {}


@lru_cache()
def import_model_classes():
    model_arch_name_to_cls = {}
    package_name = "sglang.srt.models"
    package = importlib.import_module(package_name)
    for _, name, ispkg in pkgutil.iter_modules(package.__path__, package_name + "."):
        if not ispkg:
            module = importlib.import_module(name)
            if hasattr(module, "EntryClass"):
                model_arch_name_to_cls[module.EntryClass.__name__] = module.EntryClass
    return model_arch_name_to_cls


def get_model_cls_by_arch_name(model_arch_names):
    model_arch_name_to_cls = import_model_classes()

    model_class = None
    for arch in model_arch_names:
        if arch in model_arch_name_to_cls:
            model_class = model_arch_name_to_cls[arch]
            break
    else:
        raise ValueError(
            f"Unsupported architectures: {arch}. "
            f"Supported list: {list(model_arch_name_to_cls.keys())}"
        )
    return model_class


@dataclass
class InputMetadata:
    model_runner: "ModelRunner"
    forward_mode: ForwardMode
    batch_size: int
    total_num_tokens: int
    max_seq_len: int
    req_pool_indices: torch.Tensor
    start_loc: torch.Tensor
    seq_lens: torch.Tensor
    prefix_lens: torch.Tensor
    positions: torch.Tensor
    req_to_token_pool: ReqToTokenPool
    token_to_kv_pool: TokenToKVPool

    # for extend
    extend_seq_lens: torch.Tensor = None
    extend_start_loc: torch.Tensor = None
    max_extend_len: int = 0

    out_cache_loc: torch.Tensor = None
    out_cache_cont_start: torch.Tensor = None
    out_cache_cont_end: torch.Tensor = None

    other_kv_index: torch.Tensor = None
    return_logprob: bool = False
    top_logprobs_nums: List[int] = None

    # for flashinfer
    qo_indptr: torch.Tensor = None
    kv_indptr: torch.Tensor = None
    kv_indices: torch.Tensor = None
    kv_last_page_len: torch.Tensor = None
    prefill_wrapper = None
    decode_wrapper = None

    def init_flashinfer_args(self, tp_size):
        from flashinfer import (
            BatchDecodeWithPagedKVCacheWrapper,
            BatchPrefillWithPagedKVCacheWrapper,
        )

        self.kv_indptr = torch.zeros(
            (self.batch_size + 1,), dtype=torch.int32, device="cuda"
        )
        self.kv_indptr[1:] = torch.cumsum(self.seq_lens, dim=0)
        self.kv_last_page_len = torch.ones(
            (self.batch_size,), dtype=torch.int32, device="cuda"
        )
        req_pool_indices_cpu = self.req_pool_indices.cpu().numpy()
        seq_lens_cpu = self.seq_lens.cpu().numpy()
        self.kv_indices = torch.cat(
            [
                self.req_to_token_pool.req_to_token[
                    req_pool_indices_cpu[i], : seq_lens_cpu[i]
                ]
                for i in range(self.batch_size)
            ],
            dim=0,
        ).contiguous()

        workspace_buffer = torch.empty(
            32 * 1024 * 1024, dtype=torch.int8, device="cuda"
        )
        if (
            self.forward_mode == ForwardMode.PREFILL
            or self.forward_mode == ForwardMode.EXTEND
        ):
            self.qo_indptr = torch.zeros(
                (self.batch_size + 1,), dtype=torch.int32, device="cuda"
            )
            self.qo_indptr[1:] = torch.cumsum(self.extend_seq_lens, dim=0)
            self.prefill_wrapper = BatchPrefillWithPagedKVCacheWrapper(
                workspace_buffer, "NHD"
            )
            args = [
                self.qo_indptr,
                self.kv_indptr,
                self.kv_indices,
                self.kv_last_page_len,
                self.model_runner.model_config.num_attention_heads // tp_size,
                self.model_runner.model_config.num_key_value_heads // tp_size,
                self.model_runner.model_config.head_dim
            ]

            self.prefill_wrapper.begin_forward(*args)
        else:
            self.decode_wrapper = BatchDecodeWithPagedKVCacheWrapper(
                workspace_buffer, "NHD"
            )
            self.decode_wrapper.begin_forward(
                self.kv_indptr,
                self.kv_indices,
                self.kv_last_page_len,
                self.model_runner.model_config.num_attention_heads // tp_size,
                self.model_runner.model_config.num_key_value_heads // tp_size,
                self.model_runner.model_config.head_dim,
                1,
                "NONE",
                "float16",
            )

    def init_extend_args(self):
        self.extend_seq_lens = self.seq_lens - self.prefix_lens
        self.extend_start_loc = torch.zeros_like(self.seq_lens)
        self.extend_start_loc[1:] = torch.cumsum(self.extend_seq_lens[:-1], dim=0)
        self.max_extend_len = int(torch.max(self.extend_seq_lens))

    @classmethod
    def create(
        cls,
        model_runner,
        tp_size,
        forward_mode,
        req_pool_indices,
        seq_lens,
        prefix_lens,
        position_ids_offsets,
        out_cache_loc,
        out_cache_cont_start=None,
        out_cache_cont_end=None,
        top_logprobs_nums=None,
        return_logprob=False,
    ):
        batch_size = len(req_pool_indices)
        start_loc = torch.zeros((batch_size,), dtype=torch.int32, device="cuda")
        start_loc[1:] = torch.cumsum(seq_lens[:-1], dim=0)
        total_num_tokens = int(torch.sum(seq_lens))
        max_seq_len = int(torch.max(seq_lens))

        if forward_mode == ForwardMode.DECODE:
            positions = ((seq_lens - 1) + position_ids_offsets).to(torch.int64)
            other_kv_index = model_runner.req_to_token_pool.req_to_token[
                req_pool_indices[0], seq_lens[0] - 1
            ].item()
        else:
            seq_lens_cpu = seq_lens.cpu().numpy()
            prefix_lens_cpu = prefix_lens.cpu().numpy()
            position_ids_offsets_cpu = position_ids_offsets.cpu().numpy()
            positions = torch.tensor(
                np.concatenate(
                    [
                        np.arange(
                            prefix_lens_cpu[i] + position_ids_offsets_cpu[i],
                            seq_lens_cpu[i] + position_ids_offsets_cpu[i],
                        )
                        for i in range(batch_size)
                    ],
                    axis=0,
                ),
                device="cuda",
            )
            other_kv_index = None

        ret = cls(
            model_runner=model_runner,
            forward_mode=forward_mode,
            batch_size=batch_size,
            total_num_tokens=total_num_tokens,
            max_seq_len=max_seq_len,
            req_pool_indices=req_pool_indices,
            start_loc=start_loc,
            seq_lens=seq_lens,
            prefix_lens=prefix_lens,
            positions=positions,
            req_to_token_pool=model_runner.req_to_token_pool,
            token_to_kv_pool=model_runner.token_to_kv_pool,
            out_cache_loc=out_cache_loc,
            out_cache_cont_start=out_cache_cont_start,
            out_cache_cont_end=out_cache_cont_end,
            other_kv_index=other_kv_index,
            return_logprob=return_logprob,
            top_logprobs_nums=top_logprobs_nums,
        )

        if forward_mode == ForwardMode.EXTEND:
            ret.init_extend_args()

        if global_server_args_dict.get("enable_flashinfer", False):
            ret.init_flashinfer_args(tp_size)

        return ret


class ModelRunner:
    def __init__(
        self,
        model_config,
        mem_fraction_static,
        tp_rank,
        tp_size,
        nccl_port,
        load_format="auto",
        trust_remote_code=True,
        server_args_dict: dict = {},
    ):
        self.model_config = model_config
        self.mem_fraction_static = mem_fraction_static
        self.tp_rank = tp_rank
        self.tp_size = tp_size
        self.nccl_port = nccl_port
        self.load_format = load_format
        self.trust_remote_code = trust_remote_code

        global global_server_args_dict
        global_server_args_dict = server_args_dict

        # Init torch distributed
        torch.cuda.set_device(self.tp_rank)
        torch.distributed.init_process_group(
            backend="nccl",
            world_size=self.tp_size,
            rank=self.tp_rank,
            init_method=f"tcp://127.0.0.1:{self.nccl_port}",
        )

        initialize_model_parallel(tensor_model_parallel_size=self.tp_size)

        total_gpu_memory = get_available_gpu_memory(
            self.tp_rank, distributed=self.tp_size > 1
        ) * (1 << 30)
        self.load_model()
        self.init_memory_pool(total_gpu_memory)

        self.is_multimodal_model = is_multimodal_model(self.model_config)

    def load_model(self):
        """See also vllm/model_executor/model_loader.py::get_model"""
        # Select model class
        architectures = getattr(self.model_config.hf_config, "architectures", [])
        model_class = get_model_cls_by_arch_name(architectures)
        logger.info(f"Rank {self.tp_rank}: load weight begin.")

        # Load weights
        quant_config = None

        quant_cfg = getattr(self.model_config.hf_config, "quantization_config", None)
        if quant_cfg is not None:
            quant_method = quant_cfg.get("quant_method", "").lower()
            # compat: autogptq >=0.8.0 use checkpoint_format: str
            # compat: autogptq <=0.7.1 is_marlin_format: bool
            is_format_marlin = quant_cfg.get(
                "checkpoint_format"
            ) == "marlin" or quant_cfg.get("is_marlin_format", False)

            # Use marlin if the GPTQ model is serialized in marlin format.
            if quant_method == "gptq" and is_format_marlin:
                quant_method = "marlin"

            quant_config_class = QUANTIZATION_CONFIG_MAPPING.get(quant_method)

            if quant_config_class is None:
                raise ValueError(f"Unsupported quantization method: {quant_method}")

            quant_config = quant_config_class.from_config(quant_cfg)
            logger.info(f"quant_config: {quant_config}")

        with set_default_torch_dtype(torch.float16):
            with torch.device("cuda"):
                model = model_class(
                    config=self.model_config.hf_config, quant_config=quant_config
                )
            model.load_weights(
                self.model_config.path,
                cache_dir=None,
                load_format=self.load_format,
                revision=None,
            )
        self.model = model.eval()

        logger.info(f"Rank {self.tp_rank}: load weight end.")

    def profile_max_num_token(self, total_gpu_memory):
        available_gpu_memory = get_available_gpu_memory(
            self.tp_rank, distributed=self.tp_size > 1
        ) * (1 << 30)
        head_dim = self.model_config.head_dim
        head_num = self.model_config.num_key_value_heads // self.tp_size
        cell_size = head_num * head_dim * self.model_config.num_hidden_layers * 2 * 2
        rest_memory = available_gpu_memory - total_gpu_memory * (
            1 - self.mem_fraction_static
        )
        max_num_token = int(rest_memory // cell_size)
        return max_num_token

    def init_memory_pool(self, total_gpu_memory):
        self.max_total_num_token = self.profile_max_num_token(total_gpu_memory)

        if self.max_total_num_token <= 0:
            raise RuntimeError(
                "Not enought memory. " "Please try to increase --mem-fraction-static."
            )

        self.req_to_token_pool = ReqToTokenPool(
            int(self.max_total_num_token / self.model_config.context_len * 256),
            self.model_config.context_len + 8,
        )
        self.token_to_kv_pool = TokenToKVPool(
            self.max_total_num_token,
            dtype=torch.float16,
            head_num=self.model_config.num_key_value_heads // self.tp_size,
            head_dim=self.model_config.head_dim,
            layer_num=self.model_config.num_hidden_layers,
        )

    @torch.inference_mode()
    def forward_prefill(self, batch: Batch):
        input_metadata = InputMetadata.create(
            self,
            forward_mode=ForwardMode.PREFILL,
            tp_size=self.tp_size,
            req_pool_indices=batch.req_pool_indices,
            seq_lens=batch.seq_lens,
            prefix_lens=batch.prefix_lens,
            position_ids_offsets=batch.position_ids_offsets,
            out_cache_loc=batch.out_cache_loc,
            top_logprobs_nums=batch.top_logprobs_nums,
            return_logprob=batch.return_logprob,
        )
        return self.model.forward(
            batch.input_ids, input_metadata.positions, input_metadata
        )

    @torch.inference_mode()
    def forward_extend(self, batch: Batch):
        input_metadata = InputMetadata.create(
            self,
            forward_mode=ForwardMode.EXTEND,
            tp_size=self.tp_size,
            req_pool_indices=batch.req_pool_indices,
            seq_lens=batch.seq_lens,
            prefix_lens=batch.prefix_lens,
            position_ids_offsets=batch.position_ids_offsets,
            out_cache_loc=batch.out_cache_loc,
            top_logprobs_nums=batch.top_logprobs_nums,
            return_logprob=batch.return_logprob,
        )
        return self.model.forward(
            batch.input_ids, input_metadata.positions, input_metadata
        )

    @torch.inference_mode()
    def forward_decode(self, batch: Batch):
        input_metadata = InputMetadata.create(
            self,
            forward_mode=ForwardMode.DECODE,
            tp_size=self.tp_size,
            req_pool_indices=batch.req_pool_indices,
            seq_lens=batch.seq_lens,
            prefix_lens=batch.prefix_lens,
            position_ids_offsets=batch.position_ids_offsets,
            out_cache_loc=batch.out_cache_loc,
            out_cache_cont_start=batch.out_cache_cont_start,
            out_cache_cont_end=batch.out_cache_cont_end,
            top_logprobs_nums=batch.top_logprobs_nums,
            return_logprob=batch.return_logprob,
        )
        return self.model.forward(
            batch.input_ids, input_metadata.positions, input_metadata
        )

    @torch.inference_mode()
    def forward_extend_multi_modal(self, batch: Batch):
        input_metadata = InputMetadata.create(
            self,
            forward_mode=ForwardMode.EXTEND,
            tp_size=self.tp_size,
            req_pool_indices=batch.req_pool_indices,
            seq_lens=batch.seq_lens,
            prefix_lens=batch.prefix_lens,
            position_ids_offsets=batch.position_ids_offsets,
            out_cache_loc=batch.out_cache_loc,
            top_logprobs_nums=batch.top_logprobs_nums,
            return_logprob=batch.return_logprob,
        )
        return self.model.forward(
            batch.input_ids,
            input_metadata.positions,
            input_metadata,
            batch.pixel_values,
            batch.image_sizes,
            batch.image_offsets,
        )

    def forward(self, batch: Batch, forward_mode: ForwardMode):
        if self.is_multimodal_model and forward_mode == ForwardMode.EXTEND:
            return self.forward_extend_multi_modal(batch)
        elif forward_mode == ForwardMode.DECODE:
            return self.forward_decode(batch)
        elif forward_mode == ForwardMode.EXTEND:
            return self.forward_extend(batch)
        elif forward_mode == ForwardMode.PREFILL:
            return self.forward_prefill(batch)
        else:
            raise ValueError(f"Invaid forward mode: {forward_mode}")
