# 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 logging
import os
import warnings
from typing import Optional, Union

import torch
import torch.distributed
from accelerate import init_empty_weights
from omegaconf import DictConfig
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp import ShardedOptimStateDictConfig, ShardedStateDictConfig, StateDictType
from transformers import GenerationConfig, PreTrainedTokenizer, ProcessorMixin

from verl.utils.device import is_cuda_available
from verl.utils.fs import copy_to_local, is_non_local
from verl.utils.fsdp_utils import fsdp_version, get_fsdp_full_state_dict, get_fsdp_state_ctx
from verl.utils.logger import log_with_rank

from .checkpoint_manager import BaseCheckpointManager

# Setup logging
logger = logging.getLogger(__file__)
logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "INFO"))


class FSDPCheckpointManager(BaseCheckpointManager):
    """
    Manage FSDP checkpointing in SPMD training.

    - Saves/loads per-rank sharded model & optimizer states
    - Persists full lr_scheduler and RNG state
    - Stores HF tokenizer/processor and model/config for unified restore

    Args:
        model (FSDP): Wrapped model instance.
        optimizer (Optimizer): Training optimizer.
        lr_scheduler (LRScheduler): Learning-rate scheduler.
        processing_class (PreTrainedTokenizer or ProcessorMixin, optional):
            Pre-/post-processing artifact handler.
        checkpoint_contents DictConfig: Configuration for checkpoint contents.
            - 'load': Components to load; must contain 'model'. Defaults to ['model', 'optimizer', 'extra'].
            - 'save': Components to save; must contain 'model'. Defaults to ['model', 'optimizer', 'extra'].
    """

    def __init__(
        self,
        model: FSDP,
        optimizer: Optional[torch.optim.Optimizer] = None,
        lr_scheduler: Optional[torch.optim.lr_scheduler.LRScheduler] = None,
        processing_class: Union[PreTrainedTokenizer, ProcessorMixin] = None,
        checkpoint_contents: DictConfig = None,
        **kwargs,
    ):
        if processing_class is None:
            assert "tokenizer" in kwargs, "tokenizer or processor must be provided"
            warnings.warn("`tokenizer` is deprecated. use `processing_class` instead.", DeprecationWarning, stacklevel=2)
            processing_class = kwargs.pop("tokenizer")

        super().__init__(
            model,
            optimizer,
            lr_scheduler=lr_scheduler,
            processing_class=processing_class,
            checkpoint_contents=checkpoint_contents,
        )

    def load_checkpoint(self, local_path: str, hdfs_path: str = None, del_local_after_load=False):
        """
        Load an FSDP checkpoint for this rank.

        Downloads and loads:
          - model and optimizer shards
          - extra state dict (scheduler + RNG)

        Args:
            local_path: Directory with per-rank checkpoint files.
            hdfs_path: Unused (for API compatibility).
            del_local_after_load: Remove local files after loading.
        """
        if local_path is None:
            return

        # check if the checkpoint_load_contents is valid
        if self.should_load_model:
            assert self.model is not None, "model must be provided when checkpoint_contents.load includes ['model']"
        if self.should_load_optimizer:
            assert self.optimizer is not None, "optimizer must be provided when checkpoint_contents.load includes ['optimizer']"

        # every rank download its own checkpoint
        state_dict_cfg = ShardedStateDictConfig(offload_to_cpu=True if is_cuda_available else False) if self.should_load_model else None
        optim_cfg = ShardedOptimStateDictConfig(offload_to_cpu=True if is_cuda_available else False) if self.should_load_optimizer else None
        with get_fsdp_state_ctx(self.model, StateDictType.SHARDED_STATE_DICT, state_dict_cfg, optim_cfg):
            if self.should_load_model:
                remote_model_path = os.path.join(local_path, f"model_world_size_{self.world_size}_rank_{self.rank}.pt")
                local_model_path = copy_to_local(remote_model_path)
                model_state_dict = torch.load(local_model_path, weights_only=False)
                self.model.load_state_dict(model_state_dict)
                log_with_rank(f"Loaded model from {remote_model_path}", rank=self.rank, logger=logger)

            if self.should_load_optimizer:
                remote_optim_path = os.path.join(local_path, f"optim_world_size_{self.world_size}_rank_{self.rank}.pt")
                local_optim_path = copy_to_local(remote_optim_path)
                optimizer_state_dict = torch.load(local_optim_path, weights_only=False)
                self.optimizer.load_state_dict(optimizer_state_dict)
                log_with_rank(f"Loaded optimizer from {remote_optim_path}", rank=self.rank, logger=logger)

        if self.should_load_extra:
            remote_extra_state_path = os.path.join(local_path, f"extra_state_world_size_{self.world_size}_rank_{self.rank}.pt")
            local_extra_state_path = copy_to_local(remote_extra_state_path)
            extra_state_dict = torch.load(local_extra_state_path, weights_only=False)
            # recover random state
            if "rng" in extra_state_dict:
                # 'rng' may not exist for backward compatibility
                self.load_rng_state(extra_state_dict["rng"])
                log_with_rank(f"Loaded rng from {remote_extra_state_path}", rank=self.rank, logger=logger)

            lr_scheduler_state_dict = extra_state_dict["lr_scheduler"]
            if lr_scheduler_state_dict is not None and self.lr_scheduler is not None:
                self.lr_scheduler.load_state_dict(lr_scheduler_state_dict)
                log_with_rank(f"Loaded lr_scheduler from {remote_extra_state_path}", rank=self.rank, logger=logger)

        if self.rank == 0 and del_local_after_load:
            try:
                os.remove(local_model_path) if is_non_local(local_model_path) else None
                os.remove(local_optim_path) if is_non_local(local_optim_path) else None
                os.remove(local_extra_state_path) if is_non_local(local_extra_state_path) else None
            except Exception as e:
                log_with_rank(f"remove local resume ckpt file after loading failed, exception {e} will be ignored", rank=self.rank, logger=logger)

        # wait for everyone to load checkpoints
        torch.distributed.barrier()

    def save_checkpoint(self, local_path: str, hdfs_path: str = None, global_step: int = 0, max_ckpt_to_keep=None):
        """
        Save an FSDP checkpoint for this rank.

        Writes:
          - model & optimizer shard files
          - extra state dict (scheduler + RNG)
          - HF tokenizer/processor and model/config on rank 0
          - optional full HF model under 'huggingface/' if requested

        Rotates old checkpoints, keeping at most `max_ckpt_to_keep`.

        Args:
            local_path: Target directory for checkpoint files.
            hdfs_path: Unused (for API compatibility).
            global_step: Current training step (used for bookkeeping).
            max_ckpt_to_keep: Number of recent checkpoints to retain.
        """
        if local_path is None:
            return

        # record the previous global step
        self.previous_global_step = global_step

        # remove previous local_path, only rank 0 should do this
        if self.rank == 0 and max_ckpt_to_keep and isinstance(max_ckpt_to_keep, int) and max_ckpt_to_keep > 0 and len(self.previous_saved_paths) >= max_ckpt_to_keep:
            keep_start = len(self.previous_saved_paths) - max_ckpt_to_keep + 1
            self.remove_previous_save_local_path(self.previous_saved_paths[:keep_start])
            self.previous_saved_paths = self.previous_saved_paths[keep_start:]

        local_path = self.local_mkdir(local_path)
        torch.distributed.barrier()

        # check if the checkpoint_save_contents is valid
        if self.should_save_model:
            assert self.model is not None, "model must be provided when checkpoint_contents.save includes ['model']"
        if self.should_save_optimizer:
            assert self.optimizer is not None, "optimizer must be provided when checkpoint_contents.save includes ['optimizer']"

        # every rank will save its own model and optim shard
        state_dict_cfg = ShardedStateDictConfig(offload_to_cpu=True if is_cuda_available else False)
        optim_cfg = ShardedOptimStateDictConfig(offload_to_cpu=True if is_cuda_available else False)
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            with get_fsdp_state_ctx(self.model, StateDictType.SHARDED_STATE_DICT, state_dict_cfg, optim_cfg):
                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")

                if self.should_save_model:
                    model_state_dict = self.model.state_dict()
                    torch.save(model_state_dict, model_path)
                    log_with_rank(f"Saved model to {os.path.abspath(model_path)}", rank=self.rank, logger=logger)

                if self.should_save_optimizer:
                    optimizer_state_dict = self.optimizer.state_dict()
                    torch.save(optimizer_state_dict, optim_path)
                    log_with_rank(f"Saved optim to {os.path.abspath(optim_path)}", rank=self.rank, logger=logger)

                if self.should_save_extra:
                    lr_scheduler_state_dict = self.lr_scheduler.state_dict() if self.lr_scheduler is not None else None
                    extra_state_dict = {
                        "lr_scheduler": lr_scheduler_state_dict,
                        "rng": self.get_rng_state(),
                    }
                    torch.save(extra_state_dict, extra_path)
                    log_with_rank(f"Saved extra_state to {os.path.abspath(extra_path)}", rank=self.rank, logger=logger)

        if self.rank == 0:
            if fsdp_version(self.model) == 1:
                unwrap_model = self.model._fsdp_wrapped_module
            else:
                unwrap_model = self.model

            model_config = unwrap_model.config
            if unwrap_model.can_generate() and hasattr(model_config, "name_or_path") and model_config.name_or_path:
                # Some model's name_or_path is empty if not initialized from pretrained,
                # in this cases, we don't save generation config.
                generation_config = GenerationConfig.from_pretrained(model_config.name_or_path)
                generation_config.save_pretrained(local_path)
            else:
                generation_config = None

            model_config.save_pretrained(local_path)
            self.processing_class.save_pretrained(local_path)
            log_with_rank(f"Saved model config and tokenizer class to {os.path.abspath(local_path)}", rank=self.rank, logger=logger, log_only_rank_0=True)

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

        if self.should_save_hf_model:
            # Only rank 0 will save hf model and,
            # offload to cpu to save LLMs which may be too large to fit in one GPU
            state_dict = get_fsdp_full_state_dict(self.model, offload_to_cpu=True, rank0_only=True)

            if self.rank == 0:
                hf_local_path = os.path.join(local_path, "huggingface")
                os.makedirs(hf_local_path, exist_ok=True)

                if "ForTokenClassification" in model_config.architectures[0]:
                    from transformers import AutoModelForTokenClassification

                    auto_model_cls = AutoModelForTokenClassification
                elif "ForCausalLM" in model_config.architectures[0]:
                    from transformers import AutoModelForCausalLM

                    auto_model_cls = AutoModelForCausalLM
                elif "ForConditionalGeneration" in model_config.architectures[0]:
                    from transformers import AutoModelForVision2Seq

                    auto_model_cls = AutoModelForVision2Seq
                else:
                    raise NotImplementedError(f"Unknown architecture {model_config['architectures']}")

                with init_empty_weights():
                    save_model = auto_model_cls.from_config(model_config, torch_dtype=torch.bfloat16)
                save_model.to_empty(device="cpu")

                if save_model.can_generate():
                    if generation_config is not None:
                        save_model.generation_config = generation_config
                    else:
                        print(f"Warning: {self.__class__.__name__}.save_checkpoint: Generation config file not found in, using a generation config created from the model config when saving hf_model.")

                save_model.save_pretrained(hf_local_path, state_dict=state_dict)
                self.processing_class.save_pretrained(hf_local_path)
                log_with_rank(f"Saved hf_model to {os.path.abspath(hf_local_path)}", rank=self.rank, logger=logger, log_only_rank_0=True)
                del state_dict
                del save_model

            # wait for rank0 to dump hf_model to local
            torch.distributed.barrier()

        self.previous_saved_paths.append(local_path)
