# 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 random
import shutil
import tempfile

import numpy as np
import torch
import torch.distributed
from filelock import FileLock
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from transformers import PreTrainedTokenizer, ProcessorMixin


class 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
    ):
        self.previous_global_step = None
        self.previous_save_local_path = None

        self.model = model
        self.optimizer = optimizer
        self.lr_scheduler = lr_scheduler
        self.tokenizer = tokenizer
        self.processor = processor

        assert isinstance(self.model, FSDP)
        self.rank = torch.distributed.get_rank()
        self.world_size = torch.distributed.get_world_size()

    def load_checkpoint(self, *args, **kwargs):
        raise NotImplementedError

    def save_checkpoint(self, *args, **kwargs):
        raise NotImplementedError

    def remove_previous_save_local_path(self):
        if not self.previous_save_local_path:
            return

        abs_path = os.path.abspath(self.previous_save_local_path)
        print(f"Checkpoint manager remove previous save local path: {abs_path}")
        if not os.path.exists(abs_path):
            return

        # remove previous local_path
        shutil.rmtree(abs_path, ignore_errors=True)

    @staticmethod
    def local_mkdir(path):
        if not os.path.isabs(path):
            working_dir = os.getcwd()
            path = os.path.join(working_dir, path)

        # Using hash value of path as lock file name to avoid long file name
        lock_filename = f"ckpt_{hash(path) & 0xFFFFFFFF:08x}.lock"
        lock_path = os.path.join(tempfile.gettempdir(), lock_filename)

        try:
            with FileLock(lock_path, timeout=60):  # Add timeout
                # make a new dir
                os.makedirs(path, exist_ok=True)
        except Exception as e:
            print(f"Warning: Failed to acquire lock for {path}: {e}")
            # Even if the lock is not acquired, try to create the directory
            os.makedirs(path, exist_ok=True)

        return path

    @staticmethod
    def get_rng_state():
        rng_state = {
            "cpu": torch.get_rng_state(),
            "cuda": torch.cuda.get_rng_state(),
            "numpy": np.random.get_state(),
            "random": random.getstate(),
        }
        return rng_state

    @staticmethod
    def load_rng_state(rng_state):
        torch.set_rng_state(rng_state["cpu"])
        torch.cuda.set_rng_state(rng_state["cuda"])
        np.random.set_state(rng_state["numpy"])
        random.setstate(rng_state["random"])


def find_latest_ckpt_path(path, directory_format="global_step_{}"):
    if path is None:
        return None

    tracker_file = get_checkpoint_tracker_filename(path)
    if not os.path.exists(tracker_file):
        print("Checkpoint tracker file does not exist: %s", tracker_file)
        return None

    with open(tracker_file, "rb") as f:
        iteration = int(f.read().decode())
    ckpt_path = os.path.join(path, directory_format.format(iteration))
    if not os.path.exists(ckpt_path):
        print("Checkpoint does not exist: %s", ckpt_path)
        return None

    print("Found checkpoint: %s", ckpt_path)
    return ckpt_path


def get_checkpoint_tracker_filename(root_path: str):
    """
    Tracker file rescords the latest chckpoint during training to restart from.
    """
    return os.path.join(root_path, "latest_checkpointed_iteration.txt")