import torch
from vllm.config import CUDAGraphMode
from vllm.v1.spec_decode.ngram_proposer import \
    NgramProposer as VllmNgramProposer

from vllm_ascend.spec_decode.interface import Proposer, SpecDcodeType


class NgramProposer(VllmNgramProposer, Proposer):

    def __init__(self, vllm_config, device, runner):
        super().__init__(vllm_config)
        self.name = SpecDcodeType.NGRAM
        self.device = device
        self.runner = runner

    def load_model(self, *args, **kwargs):
        # No model to load.
        pass

    @torch.inference_mode()
    def dummy_run(self,
                  num_tokens,
                  with_prefill=None,
                  skip_attn=None,
                  num_reqs=None,
                  num_tokens_across_dp=None,
                  aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
                  batch_descriptor=None):
        pass

    def generate_token_ids(self,
                           valid_sampled_token_ids,
                           sampling_metadata=None,
                           scheduler_output=None,
                           spec_decode_metadata=None,
                           positions=None,
                           num_scheduled_tokens=None,
                           hidden_states=None,
                           attn_metadata=None,
                           aux_hidden_states=None) -> list[list[int]]:
        # TODO(woosuk): Optimize.
        draft_token_ids: list[list[int]] = []
        for i, sampled_ids in enumerate(valid_sampled_token_ids):
            num_sampled_ids = len(sampled_ids)
            if not num_sampled_ids:
                # Skip speculative decoding.
                draft_token_ids.append([])
                continue

            # Skip requests that require top-p, top-k, etc.
            req_id = self.runner.input_batch.req_ids[i]
            if req_id in self.runner.input_batch.spec_decode_unsupported_reqs:
                draft_token_ids.append([])
                continue

            # Add sampled_token_ids to token_ids_cpu.
            start_idx = self.runner.input_batch.num_tokens_no_spec[i]
            end_idx = start_idx + num_sampled_ids
            self.runner.input_batch.token_ids_cpu[
                i, start_idx:end_idx] = sampled_ids
            drafter_output = self.propose(
                self.runner.input_batch.token_ids_cpu[i, :end_idx])
            if drafter_output is None or len(drafter_output) == 0:
                draft_token_ids.append([])
            else:
                draft_token_ids.append(drafter_output.tolist())
        return draft_token_ids