"""
# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""

from abc import ABC, abstractmethod
from copy import deepcopy
from typing import TYPE_CHECKING, Any

import paddle.distributed as dist

from fastdeploy import envs
from fastdeploy.utils import spec_logger

if TYPE_CHECKING:
    from fastdeploy.config import FDConfig


class Proposer(ABC):
    """
    Proposer Base Class.

    Used to provide an extensible interface for draft tokens within
    the speculative decoding framework
    """

    def __init__(self, fd_config: "FDConfig"):
        """
        Init Speculative proposer
        """
        fd_config.parallel_config.tp_group = None
        fd_config.parallel_config.ep_group = None
        self.fd_config = deepcopy(fd_config)
        fd_config.parallel_config.tp_group = dist.get_group(
            fd_config.parallel_config.data_parallel_rank + envs.FD_TP_GROUP_GID_OFFSET
        )
        fd_config.parallel_config.ep_group = dist.get_group(
            fd_config.parallel_config.data_parallel_size + envs.FD_TP_GROUP_GID_OFFSET
        )
        self.fd_config.parallel_config.tp_group = dist.get_group(
            fd_config.parallel_config.data_parallel_rank + envs.FD_TP_GROUP_GID_OFFSET
        )
        self.fd_config.parallel_config.ep_group = dist.get_group(
            fd_config.parallel_config.data_parallel_size + envs.FD_TP_GROUP_GID_OFFSET
        )
        self.parallel_config = self.fd_config.parallel_config
        self.model_config = self.fd_config.model_config
        self.speculative_config = self.fd_config.speculative_config
        self.cache_config = self.fd_config.cache_config
        self.quant_config = self.fd_config.quant_config
        self.graph_opt_config = self.fd_config.graph_opt_config
        self.scheduler_config = self.fd_config.scheduler_config

        self.max_num_seqs = self.scheduler_config.max_num_seqs
        self.max_model_len = self.model_config.max_model_len
        self.speculative_method = self.speculative_config.method
        self.max_draft_token_num = self.speculative_config.num_speculative_tokens
        self.num_model_steps = self.speculative_config.num_model_steps

        self.max_ngram_size = self.speculative_config.max_ngram_size
        self.min_ngram_size = self.speculative_config.min_ngram_size

        self.enable_mm = self.model_config.enable_mm

        spec_logger.info(f"Speculate config: {self.speculative_config}")

    def run(self, *args, **kwargs) -> Any:
        """
        Unified entry point for all proposer types.
        Dispatches to subclass-specific logic via `_run_impl`.
        """
        return self._run_impl(*args, **kwargs)

    @abstractmethod
    def _run_impl(self, *args, **kwargs) -> Any:
        """
        Implementation for different method
        """
        raise NotImplementedError

    def is_chunk_prefill_enabled(self) -> bool:
        """
        Check whether chunk-based prefill is enabled.
        Default is False.

        Returns:
            bool: True if chunk prefill is enabled; False otherwise.
        """
        return False

    def prepare_dummy_speculative_drafts(
        self,
        share_inputs,
        batch_size: int,
    ) -> None:
        """
        Construct a set of dummy draft tokens for CUDAGraph capture scenarios,
        used only to stabilize shape/step count, with no requirement for semantic correctness.

        Args:
            share_inputs: share_inputs dict maintained by GPUModelRunner
            batch_size: current batch_size for dummy_run
            expected_decode_len: expected number of decode steps (must match what's passed to _dummy_run)
        """

        max_fake_drafts = self.max_draft_token_num

        stop = share_inputs["stop_flags"][0].item()
        if not stop:
            share_inputs["draft_tokens"][:batch_size, :max_fake_drafts] = 5
            share_inputs["seq_lens_this_time"][:batch_size] = max_fake_drafts + 1
        else:
            share_inputs["seq_lens_this_time"][:batch_size] = 0