# 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(object):
    supported_backend = ["wandb", "mlflow", "swanlab", "vemlp_wandb", "tensorboard", "console"]

    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)
            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
            wandb.init(project=project_name, name=experiment_name, config=config)
            self.logger['wandb'] = wandb

        if 'mlflow' in default_backend:
            import mlflow
            import os

            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 swanlab
            import os

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

        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

    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()


class _TensorboardAdapter:

    def __init__(self):
        from torch.utils.tensorboard import SummaryWriter
        import os
        tensorboard_dir = os.environ.get("TENSORBOARD_DIR", "tensorboard_log")
        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)


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


@dataclasses.dataclass
class ValidationGenerationsLogger:

    def log(self, loggers, samples, step):
        if 'wandb' in loggers:
            self.log_generations_to_wandb(samples, step)
        if 'swanlab' in loggers:
            self.log_generations_to_swanlab(samples, step)
        if 'mlflow' in loggers:
            self.log_generations_to_mlflow(samples, step)

    def log_generations_to_wandb(self, samples, step):
        """Log samples to wandb as a table"""
        import wandb

        # Create column names for all samples
        columns = ["step"] + sum([[f"input_{i+1}", f"output_{i+1}", f"score_{i+1}"] for i in range(len(samples))], [])

        if not hasattr(self, 'validation_table'):
            # Initialize the table on first call
            self.validation_table = wandb.Table(columns=columns)

        # Create a new table with same columns and existing data
        # Workaround for https://github.com/wandb/wandb/issues/2981#issuecomment-1997445737
        new_table = wandb.Table(columns=columns, data=self.validation_table.data)

        # Add new row with all data
        row_data = []
        row_data.append(step)
        for sample in samples:
            row_data.extend(sample)

        new_table.add_data(*row_data)

        # Update reference and log
        wandb.log({"val/generations": new_table}, step=step)
        self.validation_table = new_table

    def log_generations_to_swanlab(self, samples, step):
        """Log samples to swanlab as text"""
        import swanlab

        swanlab_text_list = []
        for i, sample in enumerate(samples):
            row_text = f"""
            input: {sample[0]}
            
            ---
            
            output: {sample[1]}
            
            ---
            
            score: {sample[2]}
            """
            swanlab_text_list.append(swanlab.Text(row_text, caption=f"sample {i+1}"))

        # Log to swanlab
        swanlab.log({"val/generations": swanlab_text_list}, step=step)

    def log_generations_to_mlflow(self, samples, step):
        """Log validation generation to mlflow as artifacts"""
        #https://mlflow.org/docs/latest/api_reference/python_api/mlflow.html?highlight=log_artifact#mlflow.log_artifact

        import mlflow
        import tempfile
        import json
        try:
            with tempfile.TemporaryDirectory() as tmp_dir:
                validation_gen_step_file = Path(tmp_dir, f"val_step{step}.json")
                row_data = []
                for sample in samples:
                    data = {"input": sample[0], "output": sample[1], "score": sample[2]}
                    row_data.append(data)
                with open(validation_gen_step_file, "w") as file:
                    json.dump(row_data, file)
                mlflow.log_artifact(validation_gen_step_file)
        except Exception as e:
            print(f"WARNING: save validation generation file to mlflow failed with error {e}")
