# Copyright 2023-2024 SGLang Team
# 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.
# ==============================================================================
import os
from typing import Dict, List, Optional, Tuple, Union

import torch
import torch.distributed as dist
from PIL.Image import Image
from torch.distributed.tensor import DeviceMesh, DTensor

from sglang.srt.model_executor.model_runner import LocalSerializedTensor
from sglang.srt.patch_torch import monkey_patch_torch_reductions
from sglang.srt.server import Engine
from sglang.srt.utils import MultiprocessingSerializer, broadcast_pyobj


class VerlEngine:
    def __init__(
        self,
        device_mesh_cpu: DeviceMesh,
        nnodes: int = 1,
        **kwargs,
    ):
        monkey_patch_torch_reductions()
        self._device_mesh_cpu = device_mesh_cpu
        self._tp_rank = device_mesh_cpu.get_local_rank()
        self._tp_size = device_mesh_cpu.size()
        tp_size_per_node = self._tp_size // nnodes
        node_rank = self._tp_rank // tp_size_per_node
        first_rank_in_node = self._tp_rank % tp_size_per_node == 0

        if first_rank_in_node:
            os.environ["SGLANG_BLOCK_NONZERO_RANK_CHILDREN"] = "0"
            self._engine = Engine(
                **kwargs, tp_size=self._tp_size, node_rank=node_rank, nnodes=nnodes
            )
        else:
            self._engine = None

        dist.barrier(group=self._device_mesh_cpu.get_group())

    def generate(
        self,
        # The input prompt. It can be a single prompt or a batch of prompts.
        prompt: Optional[Union[List[str], str]] = None,
        sampling_params: Optional[Union[List[Dict], Dict]] = None,
        # The token ids for text; one can either specify text or input_ids.
        input_ids: Optional[Union[List[List[int]], List[int]]] = None,
        # The image input. It can be an image instance, file name, URL, or base64 encoded string.
        # Can be formatted as:
        # - Single image for a single request
        # - List of images (one per request in a batch)
        # - List of lists of images (multiple images per request)
        # See also python/sglang/srt/utils.py:load_image for more details.
        image_data: Optional[
            Union[
                List[List[Union[Image, str]]],
                List[Union[Image, str]],
                Union[Image, str],
            ]
        ] = None,
        return_logprob: Optional[Union[List[bool], bool]] = False,
        logprob_start_len: Optional[Union[List[int], int]] = None,
        top_logprobs_num: Optional[Union[List[int], int]] = None,
        token_ids_logprob: Optional[Union[List[List[int]], List[int]]] = None,
        lora_path: Optional[List[Optional[str]]] = None,
        custom_logit_processor: Optional[Union[List[str], str]] = None,
    ) -> Dict:
        """
        The arguments of this function is the same as `sglang/srt/managers/io_struct.py::GenerateReqInput`.
        Please refer to `GenerateReqInput` for the documentation.
        """
        if self._tp_rank == 0:
            output = self._engine.generate(
                prompt=prompt,
                sampling_params=sampling_params,
                input_ids=input_ids,
                image_data=image_data,
                return_logprob=return_logprob,
                logprob_start_len=logprob_start_len,
                top_logprobs_num=top_logprobs_num,
                token_ids_logprob=token_ids_logprob,
                lora_path=lora_path,
                custom_logit_processor=custom_logit_processor,
            )
        else:
            output = None

        # Most naive implementation, can extract tensor and send via gloo if too slow
        [output] = broadcast_pyobj(
            data=[output],
            rank=self._tp_rank,
            dist_group=self._device_mesh_cpu.get_group(),
            src=self._device_mesh_cpu.mesh[0].item(),
        )

        return output

    def update_weights_from_tensor(
        self,
        named_tensors: List[Tuple[str, torch.Tensor]],
        load_format: Optional[str] = None,
    ):
        # Most naive implementation, can optimize a lot if it is bottleneck
        for tensor_index, (name, tensor) in enumerate(named_tensors):
            serialized_tensor = MultiprocessingSerializer.serialize(
                _preprocess_tensor_for_update_weights(tensor)
            )

            if self._tp_rank == 0:
                gathered_serialized_tensors = [None for _ in range(self._tp_size)]
            else:
                gathered_serialized_tensors = None
            dist.gather_object(
                obj=serialized_tensor,
                object_gather_list=gathered_serialized_tensors,
                dst=self._device_mesh_cpu.mesh.tolist()[0],
                group=self._device_mesh_cpu.get_group(),
            )

            if self._tp_rank == 0:
                self._engine.update_weights_from_tensor(
                    named_tensors=[
                        (
                            name,
                            LocalSerializedTensor(values=gathered_serialized_tensors),
                        )
                    ],
                    load_format=load_format,
                    flush_cache=tensor_index == len(named_tensors) - 1,
                )

    def release_memory_occupation(self):
        if self._tp_rank == 0:
            self._engine.release_memory_occupation()

    def resume_memory_occupation(self):
        if self._tp_rank == 0:
            self._engine.resume_memory_occupation()

    def shutdown(self):
        if self._engine is not None:
            self._engine.shutdown()


def _preprocess_tensor_for_update_weights(tensor: torch.Tensor):
    if isinstance(tensor, DTensor):
        return tensor.full_tensor()
    return tensor
