# 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.
"""
A unified tracking interface that supports logging data to different backend
"""
import dataclasses
from enum import Enum
from functools import partial
from pathlib import Path
from typing import List, Union, Dict, Any


class Tracking:
    """A unified tracking interface for logging experiment data to multiple backends.

    This class provides a centralized way to log experiment metrics, parameters, and artifacts
    to various tracking backends including WandB, MLflow, SwanLab, TensorBoard, and console.

    Attributes:
        supported_backend: List of supported tracking backends.
        logger: Dictionary of initialized logger instances for each backend.
    """

    supported_backend = ["wandb", "mlflow", "swanlab", "vemlp_wandb", "tensorboard", "console", "clearml"]

    def __init__(self, project_name, experiment_name, default_backend: Union[str, List[str]] = "console", config=None):
        if isinstance(default_backend, str):
            default_backend = [default_backend]
        for backend in default_backend:
            if backend == "tracking":
                import warnings

                warnings.warn("`tracking` logger is deprecated. use `wandb` instead.", DeprecationWarning, stacklevel=2)
            else:
                assert backend in self.supported_backend, f"{backend} is not supported"

        self.logger = {}

        if "tracking" in default_backend or "wandb" in default_backend:
            import wandb

            settings = None
            if config and config["trainer"].get("wandb_proxy", None):
                settings = wandb.Settings(https_proxy=config["trainer"]["wandb_proxy"])
            wandb.init(project=project_name, name=experiment_name, config=config, settings=settings)
            self.logger["wandb"] = wandb

        if "mlflow" in default_backend:
            import os

            import mlflow

            MLFLOW_TRACKING_URI = os.environ.get("MLFLOW_TRACKING_URI", None)
            if MLFLOW_TRACKING_URI:
                mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)

            # Project_name is actually experiment_name in MLFlow
            # If experiment does not exist, will create a new experiment
            experiment = mlflow.set_experiment(project_name)
            mlflow.start_run(experiment_id=experiment.experiment_id, run_name=experiment_name)
            mlflow.log_params(_compute_mlflow_params_from_objects(config))
            self.logger["mlflow"] = _MlflowLoggingAdapter()

        if "swanlab" in default_backend:
            import os

            import swanlab

            SWANLAB_API_KEY = os.environ.get("SWANLAB_API_KEY", None)
            SWANLAB_LOG_DIR = os.environ.get("SWANLAB_LOG_DIR", "swanlab_log")
            SWANLAB_MODE = os.environ.get("SWANLAB_MODE", "cloud")
            if SWANLAB_API_KEY:
                swanlab.login(SWANLAB_API_KEY)  # NOTE: previous login information will be overwritten

            if config is None:
                config = {}  # make sure config is not None, otherwise **config will raise error
            swanlab.init(
                project=project_name,
                experiment_name=experiment_name,
                config={"FRAMEWORK": "verl", **config},
                logdir=SWANLAB_LOG_DIR,
                mode=SWANLAB_MODE,
            )
            self.logger["swanlab"] = swanlab

        if "vemlp_wandb" in default_backend:
            import os

            import volcengine_ml_platform
            from volcengine_ml_platform import wandb as vemlp_wandb

            volcengine_ml_platform.init(
                ak=os.environ["VOLC_ACCESS_KEY_ID"],
                sk=os.environ["VOLC_SECRET_ACCESS_KEY"],
                region=os.environ["MLP_TRACKING_REGION"],
            )

            vemlp_wandb.init(
                project=project_name,
                name=experiment_name,
                config=config,
                sync_tensorboard=True,
            )
            self.logger["vemlp_wandb"] = vemlp_wandb

        if "tensorboard" in default_backend:
            self.logger["tensorboard"] = _TensorboardAdapter(project_name, experiment_name)

        if "console" in default_backend:
            from verl.utils.logger.aggregate_logger import LocalLogger
            self.console_logger = LocalLogger(print_to_console=True)
            self.logger['console'] = self.console_logger

        if "clearml" in default_backend:
            self.logger["clearml"] = ClearMLLogger(project_name, experiment_name, config)

        if "ml_tracker" in default_backend:
            pass

    def log(self, data, step, backend=None):
        for default_backend, logger_instance in self.logger.items():
            if backend is None or default_backend in backend:
                logger_instance.log(data=data, step=step)

    def __del__(self):
        if "wandb" in self.logger:
            self.logger["wandb"].finish(exit_code=0)
        if "swanlab" in self.logger:
            self.logger["swanlab"].finish()
        if "vemlp_wandb" in self.logger:
            self.logger["vemlp_wandb"].finish(exit_code=0)
        if "tensorboard" in self.logger:
            self.logger["tensorboard"].finish()

        if "clearnml" in self.logger:
            self.logger["clearnml"].finish()

class ClearMLLogger:
    def __init__(self, project_name: str, experiment_name: str, config):
        self.project_name = project_name
        self.experiment_name = experiment_name

        import clearml

        self._task: clearml.Task = clearml.Task.init(
            task_name=experiment_name,
            project_name=project_name,
            continue_last_task=True,
            output_uri=False,
        )

        self._task.connect_configuration(config, name="Hyperparameters")

    def _get_logger(self):
        return self._task.get_logger()

    def log(self, data, step):
        import numpy as np
        import pandas as pd

        # logs = self._rewrite_logs(data)
        logger = self._get_logger()
        for k, v in data.items():
            title, series = k.split("/", 1)

            if isinstance(v, (int, float, np.floating, np.integer)):
                logger.report_scalar(
                    title=title,
                    series=series,
                    value=v,
                    iteration=step,
                )
            elif isinstance(v, pd.DataFrame):
                logger.report_table(
                    title=title,
                    series=series,
                    table_plot=v,
                    iteration=step,
                )
            else:
                logger.warning(f'Trainer is attempting to log a value of "{v}" of type {type(v)} for key "{k}". This invocation of ClearML logger\'s function is incorrect so this attribute was dropped. ')

    def finish(self):
        self._task.mark_completed()


class _TensorboardAdapter:
    def __init__(self, project_name, experiment_name):
        import os

        from torch.utils.tensorboard import SummaryWriter

        tensorboard_dir = os.environ.get("TENSORBOARD_DIR", "./log/tensorboard_log")
        tensorboard_dir = os.path.join(tensorboard_dir, f"{project_name}/{experiment_name}")
        os.makedirs(tensorboard_dir, exist_ok=True)
        print(f"Saving tensorboard log to {tensorboard_dir}.")
        self.writer = SummaryWriter(tensorboard_dir)

    def log(self, data, step):
        for key in data:
            self.writer.add_scalar(key, data[key], step)

    def finish(self):
        self.writer.close()


class _MlflowLoggingAdapter:
    def log(self, data, step):
        import mlflow

        results = {k.replace("@", "_at_"): v for k, v in data.items()}
        mlflow.log_metrics(metrics=results, step=step)


# class _MLTrackerAdapter:
#     def log(self, data, step):
#         for key in data:
#             ml_tracker.log()


def _compute_mlflow_params_from_objects(params) -> Dict[str, Any]:
    if params is None:
        return {}

    return _flatten_dict(_transform_params_to_json_serializable(params, convert_list_to_dict=True), sep="/")


def _transform_params_to_json_serializable(x, convert_list_to_dict: bool):
    _transform = partial(_transform_params_to_json_serializable, convert_list_to_dict=convert_list_to_dict)

    if dataclasses.is_dataclass(x):
        return _transform(dataclasses.asdict(x))
    if isinstance(x, dict):
        return {k: _transform(v) for k, v in x.items()}
    if isinstance(x, list):
        if convert_list_to_dict:
            return {"list_len": len(x)} | {f"{i}": _transform(v) for i, v in enumerate(x)}
        else:
            return [_transform(v) for v in x]
    if isinstance(x, Path):
        return str(x)
    if isinstance(x, Enum):
        return x.value

    return x


def _flatten_dict(raw: Dict[str, Any], *, sep: str) -> Dict[str, Any]:
    import pandas as pd

    ans = pd.json_normalize(raw, sep=sep).to_dict(orient="records")[0]
    assert isinstance(ans, dict)
    return ans


def _compute_mlflow_params_from_objects(params) -> Dict[str, Any]:
    if params is None:
        return {}

    return _flatten_dict(_transform_params_to_json_serializable(params, convert_list_to_dict=True), sep='/')


def _transform_params_to_json_serializable(x, convert_list_to_dict: bool):
    _transform = partial(_transform_params_to_json_serializable, convert_list_to_dict=convert_list_to_dict)

    if dataclasses.is_dataclass(x):
        return _transform(dataclasses.asdict(x))
    if isinstance(x, dict):
        return {k: _transform(v) for k, v in x.items()}
    if isinstance(x, list):
        if convert_list_to_dict:
            return {'list_len': len(x)} | {f'{i}': _transform(v) for i, v in enumerate(x)}
        else:
            return [_transform(v) for v in x]
    if isinstance(x, Path):
        return str(x)
    if isinstance(x, Enum):
        return x.value

    return x


def _flatten_dict(raw: Dict[str, Any], *, sep: str) -> Dict[str, Any]:
    import pandas as pd
    ans = pd.json_normalize(raw, sep=sep).to_dict(orient='records')[0]
    assert isinstance(ans, dict)
    return ans
