# Copyright 2024 the LlamaFactory 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 json
import logging
import os
import signal
import sys
import time
from concurrent.futures import ThreadPoolExecutor
from datetime import timedelta
from typing import TYPE_CHECKING, Any, Dict, Optional

import torch
import transformers
from peft import PeftModel
from transformers import PreTrainedModel, ProcessorMixin, TrainerCallback
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, has_length
from transformers.utils import (
    SAFE_WEIGHTS_NAME,
    WEIGHTS_NAME,
    is_safetensors_available,
)

from ..extras.constants import TRAINER_LOG, V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from ..extras.logging import LoggerHandler, get_logger


if is_safetensors_available():
    from safetensors import safe_open
    from safetensors.torch import save_file

if TYPE_CHECKING:
    from transformers import TrainerControl, TrainerState, TrainingArguments
    from trl import AutoModelForCausalLMWithValueHead


logger = get_logger(__name__)


def fix_valuehead_checkpoint(
    model: "AutoModelForCausalLMWithValueHead", output_dir: str, safe_serialization: bool
) -> None:
    r"""
    The model is already unwrapped.

    There are three cases:
    1. full tuning without ds_zero3: state_dict = {"model.layers.*": ..., "v_head.summary.*": ...}
    2. lora tuning without ds_zero3: state_dict = {"v_head.summary.*": ...}
    3. under deepspeed zero3: state_dict = {"pretrained_model.model.layers.*": ..., "v_head.summary.*": ...}

    We assume `stage3_gather_16bit_weights_on_model_save=true`.
    """
    if not isinstance(model.pretrained_model, (PreTrainedModel, PeftModel)):
        return

    if safe_serialization:
        path_to_checkpoint = os.path.join(output_dir, SAFE_WEIGHTS_NAME)
        with safe_open(path_to_checkpoint, framework="pt", device="cpu") as f:
            state_dict: Dict[str, torch.Tensor] = {key: f.get_tensor(key) for key in f.keys()}
    else:
        path_to_checkpoint = os.path.join(output_dir, WEIGHTS_NAME)
        state_dict: Dict[str, torch.Tensor] = torch.load(path_to_checkpoint, map_location="cpu")

    decoder_state_dict = {}
    v_head_state_dict = {}
    for name, param in state_dict.items():
        if name.startswith("v_head."):
            v_head_state_dict[name] = param
        else:
            decoder_state_dict[name.replace("pretrained_model.", "", 1)] = param

    model.pretrained_model.save_pretrained(
        output_dir, state_dict=decoder_state_dict or None, safe_serialization=safe_serialization
    )

    if safe_serialization:
        save_file(v_head_state_dict, os.path.join(output_dir, V_HEAD_SAFE_WEIGHTS_NAME), metadata={"format": "pt"})
    else:
        torch.save(v_head_state_dict, os.path.join(output_dir, V_HEAD_WEIGHTS_NAME))

    os.remove(path_to_checkpoint)
    logger.info("Value head model saved at: {}".format(output_dir))


class FixValueHeadModelCallback(TrainerCallback):
    def on_save(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called after a checkpoint save.
        """
        if args.should_save:
            fix_valuehead_checkpoint(
                model=kwargs.pop("model"),
                output_dir=os.path.join(args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, state.global_step)),
                safe_serialization=args.save_safetensors,
            )


class SaveProcessorCallback(TrainerCallback):
    def __init__(self, processor: "ProcessorMixin") -> None:
        r"""
        Initializes a callback for saving the processor.
        """
        self.processor = processor

    def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the end of training.
        """
        if args.should_save:
            getattr(self.processor, "image_processor").save_pretrained(args.output_dir)


class PissaConvertCallback(TrainerCallback):
    r"""
    Initializes a callback for converting the PiSSA adapter to a normal one.
    """

    def on_train_begin(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the beginning of training.
        """
        if args.should_save:
            model = kwargs.pop("model")
            pissa_init_dir = os.path.join(args.output_dir, "pissa_init")
            logger.info("Initial PiSSA adapter will be saved at: {}.".format(pissa_init_dir))
            if isinstance(model, PeftModel):
                init_lora_weights = getattr(model.peft_config["default"], "init_lora_weights")
                setattr(model.peft_config["default"], "init_lora_weights", True)
                model.save_pretrained(pissa_init_dir, safe_serialization=args.save_safetensors)
                setattr(model.peft_config["default"], "init_lora_weights", init_lora_weights)

    def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the end of training.
        """
        if args.should_save:
            model = kwargs.pop("model")
            pissa_init_dir = os.path.join(args.output_dir, "pissa_init")
            pissa_backup_dir = os.path.join(args.output_dir, "pissa_backup")
            pissa_convert_dir = os.path.join(args.output_dir, "pissa_converted")
            logger.info("Converted PiSSA adapter will be saved at: {}.".format(pissa_convert_dir))
            # 1. save a pissa backup with init_lora_weights: True
            # 2. save a converted lora with init_lora_weights: pissa
            # 3. load the pissa backup with init_lora_weights: True
            # 4. delete the initial adapter and change init_lora_weights to pissa
            if isinstance(model, PeftModel):
                init_lora_weights = getattr(model.peft_config["default"], "init_lora_weights")
                setattr(model.peft_config["default"], "init_lora_weights", True)
                model.save_pretrained(pissa_backup_dir, safe_serialization=args.save_safetensors)
                setattr(model.peft_config["default"], "init_lora_weights", init_lora_weights)
                model.save_pretrained(
                    pissa_convert_dir, safe_serialization=args.save_safetensors, convert_pissa_to_lora=pissa_init_dir
                )
                model.load_adapter(pissa_backup_dir, "default", is_trainable=True)
                model.set_adapter("default")
                model.delete_adapter("pissa_init")
                setattr(model.peft_config["default"], "init_lora_weights", init_lora_weights)


class LogCallback(TrainerCallback):
    def __init__(self) -> None:
        r"""
        Initializes a callback for logging training and evaluation status.
        """
        """ Progress """
        self.start_time = 0
        self.cur_steps = 0
        self.max_steps = 0
        self.elapsed_time = ""
        self.remaining_time = ""
        self.thread_pool: Optional["ThreadPoolExecutor"] = None
        """ Status """
        self.aborted = False
        self.do_train = False
        """ Web UI """
        self.webui_mode = os.environ.get("LLAMABOARD_ENABLED", "0").lower() in ["true", "1"]
        if self.webui_mode:
            signal.signal(signal.SIGABRT, self._set_abort)
            self.logger_handler = LoggerHandler(os.environ.get("LLAMABOARD_WORKDIR"))
            logging.root.addHandler(self.logger_handler)
            transformers.logging.add_handler(self.logger_handler)

    def _set_abort(self, signum, frame) -> None:
        self.aborted = True

    def _reset(self, max_steps: int = 0) -> None:
        self.start_time = time.time()
        self.cur_steps = 0
        self.max_steps = max_steps
        self.elapsed_time = ""
        self.remaining_time = ""

    def _timing(self, cur_steps: int) -> None:
        cur_time = time.time()
        elapsed_time = cur_time - self.start_time
        avg_time_per_step = elapsed_time / cur_steps if cur_steps != 0 else 0
        remaining_time = (self.max_steps - cur_steps) * avg_time_per_step
        self.cur_steps = cur_steps
        self.elapsed_time = str(timedelta(seconds=int(elapsed_time)))
        self.remaining_time = str(timedelta(seconds=int(remaining_time)))

    def _write_log(self, output_dir: str, logs: Dict[str, Any]) -> None:
        with open(os.path.join(output_dir, TRAINER_LOG), "a", encoding="utf-8") as f:
            f.write(json.dumps(logs) + "\n")

    def _create_thread_pool(self, output_dir: str) -> None:
        os.makedirs(output_dir, exist_ok=True)
        self.thread_pool = ThreadPoolExecutor(max_workers=1)

    def _close_thread_pool(self) -> None:
        if self.thread_pool is not None:
            self.thread_pool.shutdown(wait=True)
            self.thread_pool = None

    def on_init_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the end of the initialization of the `Trainer`.
        """
        if (
            args.should_save
            and os.path.exists(os.path.join(args.output_dir, TRAINER_LOG))
            and args.overwrite_output_dir
        ):
            logger.warning("Previous trainer log in this folder will be deleted.")
            os.remove(os.path.join(args.output_dir, TRAINER_LOG))

    def on_train_begin(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the beginning of training.
        """
        if args.should_save:
            self.do_train = True
            self._reset(max_steps=state.max_steps)
            self._create_thread_pool(output_dir=args.output_dir)

    def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the end of training.
        """
        self._close_thread_pool()

    def on_substep_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the end of an substep during gradient accumulation.
        """
        if self.aborted:
            control.should_epoch_stop = True
            control.should_training_stop = True

    def on_step_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called at the end of a training step.
        """
        if self.aborted:
            control.should_epoch_stop = True
            control.should_training_stop = True

    def on_evaluate(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called after an evaluation phase.
        """
        if not self.do_train:
            self._close_thread_pool()

    def on_predict(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called after a successful prediction.
        """
        if not self.do_train:
            self._close_thread_pool()

    def on_log(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
        r"""
        Event called after logging the last logs.
        """
        if not args.should_save:
            return

        self._timing(cur_steps=state.global_step)
        logs = dict(
            current_steps=self.cur_steps,
            total_steps=self.max_steps,
            loss=state.log_history[-1].get("loss", None),
            eval_loss=state.log_history[-1].get("eval_loss", None),
            predict_loss=state.log_history[-1].get("predict_loss", None),
            reward=state.log_history[-1].get("reward", None),
            accuracy=state.log_history[-1].get("rewards/accuracies", None),
            learning_rate=state.log_history[-1].get("learning_rate", None),
            epoch=state.log_history[-1].get("epoch", None),
            percentage=round(self.cur_steps / self.max_steps * 100, 2) if self.max_steps != 0 else 100,
            elapsed_time=self.elapsed_time,
            remaining_time=self.remaining_time,
            throughput="{:.2f}".format(state.num_input_tokens_seen / (time.time() - self.start_time)),
            total_tokens=state.num_input_tokens_seen,
        )
        logs = {k: v for k, v in logs.items() if v is not None}
        if self.webui_mode and all(key in logs for key in ["loss", "learning_rate", "epoch"]):
            logger.info(
                "{{'loss': {:.4f}, 'learning_rate': {:2.4e}, 'epoch': {:.2f}, 'throughput': {}}}".format(
                    logs["loss"], logs["learning_rate"], logs["epoch"], logs["throughput"]
                )
            )

        if self.thread_pool is not None:
            self.thread_pool.submit(self._write_log, args.output_dir, logs)

    def on_prediction_step(
        self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs
    ):
        r"""
        Event called after a prediction step.
        """
        if self.do_train:
            return

        if self.aborted:
            sys.exit(0)

        if not args.should_save:
            return

        eval_dataloader = kwargs.pop("eval_dataloader", None)
        if has_length(eval_dataloader):
            if self.max_steps == 0:
                self._reset(max_steps=len(eval_dataloader))
                self._create_thread_pool(output_dir=args.output_dir)

            self._timing(cur_steps=self.cur_steps + 1)
            if self.cur_steps % 5 == 0 and self.thread_pool is not None:
                logs = dict(
                    current_steps=self.cur_steps,
                    total_steps=self.max_steps,
                    percentage=round(self.cur_steps / self.max_steps * 100, 2) if self.max_steps != 0 else 100,
                    elapsed_time=self.elapsed_time,
                    remaining_time=self.remaining_time,
                )
                self.thread_pool.submit(self._write_log, args.output_dir, logs)
