# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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
import warnings

import torch
import torch.distributed
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp import ShardedOptimStateDictConfig, ShardedStateDictConfig, StateDictType
from transformers import PreTrainedTokenizer, ProcessorMixin

from .checkpoint_manager import BaseCheckpointManager


class FSDPCheckpointManager(BaseCheckpointManager):
    """
    A checkpoint manager that saves and loads
    - model
    - optimizer
    - lr_scheduler
    - extra_states
    in a SPMD way.

    We save
    - sharded model states and optimizer states
    - full lr_scheduler states
    - huggingface tokenizer and config for ckpt merge
    """

    def __init__(
        self,
        model: FSDP,
        optimizer: torch.optim.Optimizer,
        lr_scheduler: torch.optim.lr_scheduler.LRScheduler,
        tokenizer: PreTrainedTokenizer,
        processor: ProcessorMixin,
        *args,
        **kwargs,
    ):
        super().__init__(model, optimizer, lr_scheduler, tokenizer, processor)

    def load_checkpoint(self, path=None, *args, **kwargs):
        if path is None:
            return

        # every rank download its own checkpoint
        local_model_path = os.path.join(path, f"model_world_size_{self.world_size}_rank_{self.rank}.pt")
        local_optim_path = os.path.join(path, f"optim_world_size_{self.world_size}_rank_{self.rank}.pt")
        local_extra_state_path = os.path.join(path, f"extra_state_world_size_{self.world_size}_rank_{self.rank}.pt")
        print(
            f"[rank-{self.rank}]: Loading from {local_model_path} and {local_optim_path} and {local_extra_state_path}"
        )
        model_state_dict = torch.load(local_model_path)
        optimizer_state_dict = torch.load(local_optim_path)
        extra_state_dict = torch.load(local_extra_state_path)
        lr_scheduler_state_dict = extra_state_dict["lr_scheduler"]

        state_dict_cfg = ShardedStateDictConfig(offload_to_cpu=True)
        optim_cfg = ShardedOptimStateDictConfig(offload_to_cpu=True)
        with FSDP.state_dict_type(self.model, StateDictType.SHARDED_STATE_DICT, state_dict_cfg, optim_cfg):
            self.model.load_state_dict(model_state_dict)
            if self.optimizer is not None:
                self.optimizer.load_state_dict(optimizer_state_dict)
        # recover random state
        if "rng" in extra_state_dict:
            # 'rng' may not exist for backward compatibility
            self.load_rng_state(extra_state_dict["rng"])

        if self.lr_scheduler is not None:
            self.lr_scheduler.load_state_dict(lr_scheduler_state_dict)

    def save_checkpoint(self, local_path: str, global_step: int, remove_previous_ckpt=False, *args, **kwargs):
        # record the previous global step
        self.previous_global_step = global_step

        # remove previous local_path
        # TODO: shall we remove previous ckpt every save?
        if remove_previous_ckpt:
            self.remove_previous_save_local_path()
        local_path = self.local_mkdir(local_path)
        torch.distributed.barrier()

        # every rank will save its own model and optim shard
        state_dict_cfg = ShardedStateDictConfig(offload_to_cpu=True)
        optim_cfg = ShardedOptimStateDictConfig(offload_to_cpu=True)
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            with FSDP.state_dict_type(self.model, StateDictType.SHARDED_STATE_DICT, state_dict_cfg, optim_cfg):
                model_state_dict = self.model.state_dict()
                if self.optimizer is not None:
                    optimizer_state_dict = self.optimizer.state_dict()
                else:
                    optimizer_state_dict = None
                if self.lr_scheduler is not None:
                    lr_scheduler_state_dict = self.lr_scheduler.state_dict()
                else:
                    lr_scheduler_state_dict = None

                extra_state_dict = {
                    "lr_scheduler": lr_scheduler_state_dict,
                    "rng": self.get_rng_state(),
                }
                model_path = os.path.join(local_path, f"model_world_size_{self.world_size}_rank_{self.rank}.pt")
                optim_path = os.path.join(local_path, f"optim_world_size_{self.world_size}_rank_{self.rank}.pt")
                extra_path = os.path.join(local_path, f"extra_state_world_size_{self.world_size}_rank_{self.rank}.pt")

                print(f"[rank-{self.rank}]: Saving model to {os.path.abspath(model_path)}")
                print(f"[rank-{self.rank}]: Saving checkpoint to {os.path.abspath(model_path)}")
                print(f"[rank-{self.rank}]: Saving extra_state to {os.path.abspath(extra_path)}")
                torch.save(model_state_dict, model_path)
                torch.save(optimizer_state_dict, optim_path)  # TODO: address optimizer is None
                torch.save(extra_state_dict, extra_path)

        # wait for everyone to dump to local
        torch.distributed.barrier()

        if self.rank == 0:
            hf_local_path = os.path.join(local_path, "huggingface")
            os.makedirs(hf_local_path, exist_ok=True)
            self.model._fsdp_wrapped_module.config.save_pretrained(hf_local_path)
            if self.processor:
                self.processor.save_pretrained(hf_local_path)
            else:
                self.tokenizer.save_pretrained(hf_local_path)

        torch.distributed.barrier()

        self.previous_save_local_path = local_path