integrations.py

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.
"""
Integrations with other Python libraries.
"""
import functools
import importlib.util
import json
import numbers
import os
import sys
import tempfile
from pathlib import Path

from .utils import flatten_dict, is_datasets_available, logging


logger = logging.get_logger(__name__)


# comet_ml requires to be imported before any ML frameworks
_has_comet = importlib.util.find_spec("comet_ml") is not None and os.getenv("COMET_MODE", "").upper() != "DISABLED"
if _has_comet:
    try:
        import comet_ml  # noqa: F401

        if hasattr(comet_ml, "config") and comet_ml.config.get_config("comet.api_key"):
            _has_comet = True
        else:
            if os.getenv("COMET_MODE", "").upper() != "DISABLED":
                logger.warning("comet_ml is installed but `COMET_API_KEY` is not set.")
            _has_comet = False
    except (ImportError, ValueError):
        _has_comet = False

from .trainer_callback import ProgressCallback, TrainerCallback  # noqa: E402
from .trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun, IntervalStrategy  # noqa: E402
from .utils import ENV_VARS_TRUE_VALUES, is_torch_tpu_available  # noqa: E402


# Integration functions:
def is_wandb_available():
    # any value of WANDB_DISABLED disables wandb
    if os.getenv("WANDB_DISABLED", "").upper() in ENV_VARS_TRUE_VALUES:
        logger.warning(
            "Using the `WAND_DISABLED` environment variable is deprecated and will be removed in v5. Use the "
            "--report_to flag to control the integrations used for logging result (for instance --report_to none)."
        )
        return False
    return importlib.util.find_spec("wandb") is not None


def is_comet_available():
    return _has_comet


def is_tensorboard_available():
    return importlib.util.find_spec("tensorboard") is not None or importlib.util.find_spec("tensorboardX") is not None


def is_optuna_available():
    return importlib.util.find_spec("optuna") is not None


def is_ray_available():
    return importlib.util.find_spec("ray") is not None


def is_ray_tune_available():
    if not is_ray_available():
        return False
    return importlib.util.find_spec("ray.tune") is not None


def is_sigopt_available():
    return importlib.util.find_spec("sigopt") is not None


def is_azureml_available():
    if importlib.util.find_spec("azureml") is None:
        return False
    if importlib.util.find_spec("azureml.core") is None:
        return False
    return importlib.util.find_spec("azureml.core.run") is not None


def is_mlflow_available():
    if os.getenv("DISABLE_MLFLOW_INTEGRATION", "FALSE").upper() == "TRUE":
        return False
    return importlib.util.find_spec("mlflow") is not None


def is_fairscale_available():
    return importlib.util.find_spec("fairscale") is not None


def is_neptune_available():
    return importlib.util.find_spec("neptune") is not None


def is_codecarbon_available():
    return importlib.util.find_spec("codecarbon") is not None


def hp_params(trial):
    if is_optuna_available():
        import optuna

        if isinstance(trial, optuna.Trial):
            return trial.params
    if is_ray_tune_available():
        if isinstance(trial, dict):
            return trial

    if is_sigopt_available():
        if isinstance(trial, dict):
            return trial

    if is_wandb_available():
        if isinstance(trial, dict):
            return trial

    raise RuntimeError(f"Unknown type for trial {trial.__class__}")


def default_hp_search_backend():
    if is_optuna_available():
        return "optuna"
    elif is_ray_tune_available():
        return "ray"
    elif is_sigopt_available():
        return "sigopt"


def run_hp_search_optuna(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:
    import optuna

    def _objective(trial, checkpoint_dir=None):
        checkpoint = None
        if checkpoint_dir:
            for subdir in os.listdir(checkpoint_dir):
                if subdir.startswith(PREFIX_CHECKPOINT_DIR):
                    checkpoint = os.path.join(checkpoint_dir, subdir)
        trainer.objective = None
        trainer.train(resume_from_checkpoint=checkpoint, trial=trial)
        # If there hasn't been any evaluation during the training loop.
        if getattr(trainer, "objective", None) is None:
            metrics = trainer.evaluate()
            trainer.objective = trainer.compute_objective(metrics)
        return trainer.objective

    timeout = kwargs.pop("timeout", None)
    n_jobs = kwargs.pop("n_jobs", 1)
    study = optuna.create_study(direction=direction, **kwargs)
    study.optimize(_objective, n_trials=n_trials, timeout=timeout, n_jobs=n_jobs)
    best_trial = study.best_trial
    return BestRun(str(best_trial.number), best_trial.value, best_trial.params)


def run_hp_search_ray(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:
    import ray

    def _objective(trial, local_trainer, checkpoint_dir=None):
        try:
            from transformers.utils.notebook import NotebookProgressCallback

            if local_trainer.pop_callback(NotebookProgressCallback):
                local_trainer.add_callback(ProgressCallback)
        except ModuleNotFoundError:
            pass

        checkpoint = None
        if checkpoint_dir:
            for subdir in os.listdir(checkpoint_dir):
                if subdir.startswith(PREFIX_CHECKPOINT_DIR):
                    checkpoint = os.path.join(checkpoint_dir, subdir)
        local_trainer.objective = None
        local_trainer.train(resume_from_checkpoint=checkpoint, trial=trial)
        # If there hasn't been any evaluation during the training loop.
        if getattr(local_trainer, "objective", None) is None:
            metrics = local_trainer.evaluate()
            local_trainer.objective = local_trainer.compute_objective(metrics)
            local_trainer._tune_save_checkpoint()
            ray.tune.report(objective=local_trainer.objective, **metrics, done=True)

    if not trainer._memory_tracker.skip_memory_metrics:
        from .trainer_utils import TrainerMemoryTracker

        logger.warning(
            "Memory tracking for your Trainer is currently "
            "enabled. Automatically disabling the memory tracker "
            "since the memory tracker is not serializable."
        )
        trainer._memory_tracker = TrainerMemoryTracker(skip_memory_metrics=True)

    # The model and TensorBoard writer do not pickle so we have to remove them (if they exists)
    # while doing the ray hp search.
    _tb_writer = trainer.pop_callback(TensorBoardCallback)
    trainer.model = None

    # Setup default `resources_per_trial`.
    if "resources_per_trial" not in kwargs:
        # Default to 1 CPU and 1 GPU (if applicable) per trial.
        kwargs["resources_per_trial"] = {"cpu": 1}
        if trainer.args.n_gpu > 0:
            kwargs["resources_per_trial"]["gpu"] = 1
        resource_msg = "1 CPU" + (" and 1 GPU" if trainer.args.n_gpu > 0 else "")
        logger.info(
            "No `resources_per_trial` arg was passed into "
            "`hyperparameter_search`. Setting it to a default value "
            f"of {resource_msg} for each trial."
        )
    # Make sure each trainer only uses GPUs that were allocated per trial.
    gpus_per_trial = kwargs["resources_per_trial"].get("gpu", 0)
    trainer.args._n_gpu = gpus_per_trial

    # Setup default `progress_reporter`.
    if "progress_reporter" not in kwargs:
        from ray.tune import CLIReporter

        kwargs["progress_reporter"] = CLIReporter(metric_columns=["objective"])
    if "keep_checkpoints_num" in kwargs and kwargs["keep_checkpoints_num"] > 0:
        # `keep_checkpoints_num=0` would disabled checkpointing
        trainer.use_tune_checkpoints = True
        if kwargs["keep_checkpoints_num"] > 1:
            logger.warning(
                f"Currently keeping {kwargs['keep_checkpoints_num']} checkpoints for each trial. "
                "Checkpoints are usually huge, "
                "consider setting `keep_checkpoints_num=1`."
            )
    if "scheduler" in kwargs:
        from ray.tune.schedulers import ASHAScheduler, HyperBandForBOHB, MedianStoppingRule, PopulationBasedTraining

        # Check if checkpointing is enabled for PopulationBasedTraining
        if isinstance(kwargs["scheduler"], PopulationBasedTraining):
            if not trainer.use_tune_checkpoints:
                logger.warning(
                    "You are using PopulationBasedTraining but you haven't enabled checkpointing. "
                    "This means your trials will train from scratch everytime they are exploiting "
                    "new configurations. Consider enabling checkpointing by passing "
                    "`keep_checkpoints_num=1` as an additional argument to `Trainer.hyperparameter_search`."
                )

        # Check for `do_eval` and `eval_during_training` for schedulers that require intermediate reporting.
        if isinstance(
            kwargs["scheduler"], (ASHAScheduler, MedianStoppingRule, HyperBandForBOHB, PopulationBasedTraining)
        ) and (not trainer.args.do_eval or trainer.args.evaluation_strategy == IntervalStrategy.NO):
            raise RuntimeError(
                "You are using {cls} as a scheduler but you haven't enabled evaluation during training. "
                "This means your trials will not report intermediate results to Ray Tune, and "
                "can thus not be stopped early or used to exploit other trials parameters. "
                "If this is what you want, do not use {cls}. If you would like to use {cls}, "
                "make sure you pass `do_eval=True` and `evaluation_strategy='steps'` in the "
                "Trainer `args`.".format(cls=type(kwargs["scheduler"]).__name__)
            )

    trainable = ray.tune.with_parameters(_objective, local_trainer=trainer)

    @functools.wraps(trainable)
    def dynamic_modules_import_trainable(*args, **kwargs):
        """
        Wrapper around `tune.with_parameters` to ensure datasets_modules are loaded on each Actor.

        Without this, an ImportError will be thrown. See https://github.com/huggingface/transformers/issues/11565.

        Assumes that `_objective`, defined above, is a function.
        """
        if is_datasets_available():
            import datasets.load

            dynamic_modules_path = os.path.join(datasets.load.init_dynamic_modules(), "__init__.py")
            # load dynamic_modules from path
            spec = importlib.util.spec_from_file_location("datasets_modules", dynamic_modules_path)
            datasets_modules = importlib.util.module_from_spec(spec)
            sys.modules[spec.name] = datasets_modules
            spec.loader.exec_module(datasets_modules)
        return trainable(*args, **kwargs)

    # special attr set by tune.with_parameters
    if hasattr(trainable, "__mixins__"):
        dynamic_modules_import_trainable.__mixins__ = trainable.__mixins__

    analysis = ray.tune.run(
        dynamic_modules_import_trainable,
        config=trainer.hp_space(None),
        num_samples=n_trials,
        **kwargs,
    )
    best_trial = analysis.get_best_trial(metric="objective", mode=direction[:3])
    best_run = BestRun(best_trial.trial_id, best_trial.last_result["objective"], best_trial.config)
    if _tb_writer is not None:
        trainer.add_callback(_tb_writer)
    return best_run


def run_hp_search_sigopt(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:

    from sigopt import Connection

    conn = Connection()
    proxies = kwargs.pop("proxies", None)
    if proxies is not None:
        conn.set_proxies(proxies)

    experiment = conn.experiments().create(
        name="huggingface-tune",
        parameters=trainer.hp_space(None),
        metrics=[dict(name="objective", objective=direction, strategy="optimize")],
        parallel_bandwidth=1,
        observation_budget=n_trials,
        project="huggingface",
    )
    logger.info(f"created experiment: https://app.sigopt.com/experiment/{experiment.id}")

    while experiment.progress.observation_count < experiment.observation_budget:
        suggestion = conn.experiments(experiment.id).suggestions().create()
        trainer.objective = None
        trainer.train(resume_from_checkpoint=None, trial=suggestion)
        # If there hasn't been any evaluation during the training loop.
        if getattr(trainer, "objective", None) is None:
            metrics = trainer.evaluate()
            trainer.objective = trainer.compute_objective(metrics)

        values = [dict(name="objective", value=trainer.objective)]
        obs = conn.experiments(experiment.id).observations().create(suggestion=suggestion.id, values=values)
        logger.info(f"[suggestion_id, observation_id]: [{suggestion.id}, {obs.id}]")
        experiment = conn.experiments(experiment.id).fetch()

    best = list(conn.experiments(experiment.id).best_assignments().fetch().iterate_pages())[0]
    best_run = BestRun(best.id, best.value, best.assignments)

    return best_run


def run_hp_search_wandb(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:
    from .integrations import is_wandb_available

    if not is_wandb_available():
        raise ImportError("This function needs wandb installed: `pip install wandb`")
    import wandb

    # add WandbCallback if not already added in trainer callbacks
    reporting_to_wandb = False
    for callback in trainer.callback_handler.callbacks:
        if isinstance(callback, WandbCallback):
            reporting_to_wandb = True
            break
    if not reporting_to_wandb:
        trainer.add_callback(WandbCallback())
    trainer.args.report_to = "wandb"
    best_trial = {"run_id": None, "objective": None, "hyperparameters": None}
    sweep_id = kwargs.pop("sweep_id", None)
    project = kwargs.pop("project", None)
    name = kwargs.pop("name", None)
    entity = kwargs.pop("entity", None)
    metric = kwargs.pop("metric", "eval/loss")

    sweep_config = trainer.hp_space(None)
    sweep_config["metric"]["goal"] = direction
    sweep_config["metric"]["name"] = metric
    if name:
        sweep_config["name"] = name

    def _objective():

        run = wandb.run if wandb.run else wandb.init()
        trainer.state.trial_name = run.name
        run.config.update({"assignments": {}, "metric": metric})
        config = wandb.config

        trainer.objective = None

        trainer.train(resume_from_checkpoint=None, trial=vars(config)["_items"])
        # If there hasn't been any evaluation during the training loop.
        if getattr(trainer, "objective", None) is None:
            metrics = trainer.evaluate()
            trainer.objective = trainer.compute_objective(metrics)
            format_metrics = rewrite_logs(metrics)
            if metric not in format_metrics:
                logger.warning(
                    f"Provided metric {metric} not found. This might result in unexpected sweeps charts. The available metrics are {format_metrics.keys()}"
                )
        best_score = False
        if best_trial["run_id"] is not None:
            if direction == "minimize":
                best_score = trainer.objective < best_trial["objective"]
            elif direction == "maximize":
                best_score = trainer.objective > best_trial["objective"]

        if best_score or best_trial["run_id"] is None:
            best_trial["run_id"] = run.id
            best_trial["objective"] = trainer.objective
            best_trial["hyperparameters"] = dict(config)

        return trainer.objective

    sweep_id = wandb.sweep(sweep_config, project=project, entity=entity) if not sweep_id else sweep_id
    logger.info(f"wandb sweep id - {sweep_id}")
    wandb.agent(sweep_id, function=_objective, count=n_trials)

    return BestRun(best_trial["run_id"], best_trial["objective"], best_trial["hyperparameters"])


def get_available_reporting_integrations():
    integrations = []
    if is_azureml_available():
        integrations.append("azure_ml")
    if is_comet_available():
        integrations.append("comet_ml")
    if is_mlflow_available():
        integrations.append("mlflow")
    if is_tensorboard_available():
        integrations.append("tensorboard")
    if is_wandb_available():
        integrations.append("wandb")
    if is_codecarbon_available():
        integrations.append("codecarbon")
    return integrations


def rewrite_logs(d):
    new_d = {}
    eval_prefix = "eval_"
    eval_prefix_len = len(eval_prefix)
    test_prefix = "test_"
    test_prefix_len = len(test_prefix)
    for k, v in d.items():
        if k.startswith(eval_prefix):
            new_d["eval/" + k[eval_prefix_len:]] = v
        elif k.startswith(test_prefix):
            new_d["test/" + k[test_prefix_len:]] = v
        else:
            new_d["train/" + k] = v
    return new_d


class TensorBoardCallback(TrainerCallback):
    """
    A [`TrainerCallback`] that sends the logs to [TensorBoard](https://www.tensorflow.org/tensorboard).

    Args:
        tb_writer (`SummaryWriter`, *optional*):
            The writer to use. Will instantiate one if not set.
    """

    def __init__(self, tb_writer=None):
        has_tensorboard = is_tensorboard_available()
        if not has_tensorboard:
            raise RuntimeError(
                "TensorBoardCallback requires tensorboard to be installed. Either update your PyTorch version or install tensorboardX."
            )
        if has_tensorboard:
            try:
                from torch.utils.tensorboard import SummaryWriter  # noqa: F401

                self._SummaryWriter = SummaryWriter
            except ImportError:
                try:
                    from tensorboardX import SummaryWriter

                    self._SummaryWriter = SummaryWriter
                except ImportError:
                    self._SummaryWriter = None
        else:
            self._SummaryWriter = None
        self.tb_writer = tb_writer

    def _init_summary_writer(self, args, log_dir=None):
        log_dir = log_dir or args.logging_dir
        if self._SummaryWriter is not None:
            self.tb_writer = self._SummaryWriter(log_dir=log_dir)

    def on_train_begin(self, args, state, control, **kwargs):
        if not state.is_world_process_zero:
            return

        log_dir = None

        if state.is_hyper_param_search:
            trial_name = state.trial_name
            if trial_name is not None:
                log_dir = os.path.join(args.logging_dir, trial_name)

        if self.tb_writer is None:
            self._init_summary_writer(args, log_dir)

        if self.tb_writer is not None:
            self.tb_writer.add_text("args", args.to_json_string())
            if "model" in kwargs:
                model = kwargs["model"]
                if hasattr(model, "config") and model.config is not None:
                    model_config_json = model.config.to_json_string()
                    self.tb_writer.add_text("model_config", model_config_json)
            # Version of TensorBoard coming from tensorboardX does not have this method.
            if hasattr(self.tb_writer, "add_hparams"):
                self.tb_writer.add_hparams(args.to_sanitized_dict(), metric_dict={})

    def on_log(self, args, state, control, logs=None, **kwargs):
        if not state.is_world_process_zero:
            return

        if self.tb_writer is None:
            self._init_summary_writer(args)

        if self.tb_writer is not None:
            logs = rewrite_logs(logs)
            for k, v in logs.items():
                if isinstance(v, (int, float)):
                    self.tb_writer.add_scalar(k, v, state.global_step)
                else:
                    logger.warning(
                        "Trainer is attempting to log a value of "
                        f'"{v}" of type {type(v)} for key "{k}" as a scalar. '
                        "This invocation of Tensorboard's writer.add_scalar() "
                        "is incorrect so we dropped this attribute."
                    )
            self.tb_writer.flush()

    def on_train_end(self, args, state, control, **kwargs):
        if self.tb_writer:
            self.tb_writer.close()
            self.tb_writer = None


class WandbCallback(TrainerCallback):
    """
    A [`TrainerCallback`] that sends the logs to [Weight and Biases](https://www.wandb.com/).
    """

    def __init__(self):
        has_wandb = is_wandb_available()
        if not has_wandb:
            raise RuntimeError("WandbCallback requires wandb to be installed. Run `pip install wandb`.")
        if has_wandb:
            import wandb

            self._wandb = wandb
        self._initialized = False
        # log outputs
        self._log_model = os.getenv("WANDB_LOG_MODEL", "FALSE").upper() in ENV_VARS_TRUE_VALUES.union({"TRUE"})

    def setup(self, args, state, model, **kwargs):
        """
        Setup the optional Weights & Biases (*wandb*) integration.

        One can subclass and override this method to customize the setup if needed. Find more information
        [here](https://docs.wandb.ai/integrations/huggingface). You can also override the following environment
        variables:

        Environment:
            WANDB_LOG_MODEL (`bool`, *optional*, defaults to `False`):
                Whether or not to log model as artifact at the end of training. Use along with
                *TrainingArguments.load_best_model_at_end* to upload best model.
            WANDB_WATCH (`str`, *optional* defaults to `"gradients"`):
                Can be `"gradients"`, `"all"` or `"false"`. Set to `"false"` to disable gradient logging or `"all"` to
                log gradients and parameters.
            WANDB_PROJECT (`str`, *optional*, defaults to `"huggingface"`):
                Set this to a custom string to store results in a different project.
            WANDB_DISABLED (`bool`, *optional*, defaults to `False`):
                Whether or not to disable wandb entirely. Set *WANDB_DISABLED=true* to disable.
        """
        if self._wandb is None:
            return
        self._initialized = True
        if state.is_world_process_zero:
            logger.info(
                'Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"'
            )
            combined_dict = {**args.to_sanitized_dict()}

            if hasattr(model, "config") and model.config is not None:
                model_config = model.config.to_dict()
                combined_dict = {**model_config, **combined_dict}
            trial_name = state.trial_name
            init_args = {}
            if trial_name is not None:
                run_name = trial_name
                init_args["group"] = args.run_name
            else:
                run_name = args.run_name

            if self._wandb.run is None:
                self._wandb.init(
                    project=os.getenv("WANDB_PROJECT", "huggingface"),
                    name=run_name,
                    **init_args,
                )
            # add config parameters (run may have been created manually)
            self._wandb.config.update(combined_dict, allow_val_change=True)

            # define default x-axis (for latest wandb versions)
            if getattr(self._wandb, "define_metric", None):
                self._wandb.define_metric("train/global_step")
                self._wandb.define_metric("*", step_metric="train/global_step", step_sync=True)

            # keep track of model topology and gradients, unsupported on TPU
            if not is_torch_tpu_available() and os.getenv("WANDB_WATCH") != "false":
                self._wandb.watch(
                    model, log=os.getenv("WANDB_WATCH", "gradients"), log_freq=max(100, args.logging_steps)
                )

    def on_train_begin(self, args, state, control, model=None, **kwargs):
        if self._wandb is None:
            return
        hp_search = state.is_hyper_param_search
        if hp_search:
            self._wandb.finish()
            self._initialized = False
            args.run_name = None
        if not self._initialized:
            self.setup(args, state, model, **kwargs)

    def on_train_end(self, args, state, control, model=None, tokenizer=None, **kwargs):
        if self._wandb is None:
            return
        if self._log_model and self._initialized and state.is_world_process_zero:
            from .trainer import Trainer

            fake_trainer = Trainer(args=args, model=model, tokenizer=tokenizer)
            with tempfile.TemporaryDirectory() as temp_dir:
                fake_trainer.save_model(temp_dir)
                metadata = (
                    {
                        k: v
                        for k, v in dict(self._wandb.summary).items()
                        if isinstance(v, numbers.Number) and not k.startswith("_")
                    }
                    if not args.load_best_model_at_end
                    else {
                        f"eval/{args.metric_for_best_model}": state.best_metric,
                        "train/total_floss": state.total_flos,
                    }
                )
                artifact = self._wandb.Artifact(name=f"model-{self._wandb.run.id}", type="model", metadata=metadata)
                for f in Path(temp_dir).glob("*"):
                    if f.is_file():
                        with artifact.new_file(f.name, mode="wb") as fa:
                            fa.write(f.read_bytes())
                self._wandb.run.log_artifact(artifact)

    def on_log(self, args, state, control, model=None, logs=None, **kwargs):
        if self._wandb is None:
            return
        if not self._initialized:
            self.setup(args, state, model)
        if state.is_world_process_zero:
            logs = rewrite_logs(logs)
            self._wandb.log({**logs, "train/global_step": state.global_step})


class CometCallback(TrainerCallback):
    """
    A [`TrainerCallback`] that sends the logs to [Comet ML](https://www.comet.ml/site/).
    """

    def __init__(self):
        if not _has_comet:
            raise RuntimeError("CometCallback requires comet-ml to be installed. Run `pip install comet-ml`.")
        self._initialized = False
        self._log_assets = False

    def setup(self, args, state, model):
        """
        Setup the optional Comet.ml integration.

        Environment:
            COMET_MODE (`str`, *optional*):
                Whether to create an online, offline experiment or disable Comet logging. Can be "OFFLINE", "ONLINE",
                or "DISABLED". Defaults to "ONLINE".
            COMET_PROJECT_NAME (`str`, *optional*):
                Comet project name for experiments
            COMET_OFFLINE_DIRECTORY (`str`, *optional*):
                Folder to use for saving offline experiments when `COMET_MODE` is "OFFLINE"
            COMET_LOG_ASSETS (`str`, *optional*):
                Whether or not to log training assets (tf event logs, checkpoints, etc), to Comet. Can be "TRUE", or
                "FALSE". Defaults to "TRUE".

        For a number of configurable items in the environment, see
        [here](https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables).
        """
        self._initialized = True
        log_assets = os.getenv("COMET_LOG_ASSETS", "FALSE").upper()
        if log_assets in {"TRUE", "1"}:
            self._log_assets = True
        if state.is_world_process_zero:
            comet_mode = os.getenv("COMET_MODE", "ONLINE").upper()
            experiment = None
            experiment_kwargs = {"project_name": os.getenv("COMET_PROJECT_NAME", "huggingface")}
            if comet_mode == "ONLINE":
                experiment = comet_ml.Experiment(**experiment_kwargs)
                experiment.log_other("Created from", "transformers")
                logger.info("Automatic Comet.ml online logging enabled")
            elif comet_mode == "OFFLINE":
                experiment_kwargs["offline_directory"] = os.getenv("COMET_OFFLINE_DIRECTORY", "./")
                experiment = comet_ml.OfflineExperiment(**experiment_kwargs)
                experiment.log_other("Created from", "transformers")
                logger.info("Automatic Comet.ml offline logging enabled; use `comet upload` when finished")
            if experiment is not None:
                experiment._set_model_graph(model, framework="transformers")
                experiment._log_parameters(args, prefix="args/", framework="transformers")
                if hasattr(model, "config"):
                    experiment._log_parameters(model.config, prefix="config/", framework="transformers")

    def on_train_begin(self, args, state, control, model=None, **kwargs):
        if not self._initialized:
            self.setup(args, state, model)

    def on_log(self, args, state, control, model=None, logs=None, **kwargs):
        if not self._initialized:
            self.setup(args, state, model)
        if state.is_world_process_zero:
            experiment = comet_ml.config.get_global_experiment()
            if experiment is not None:
                experiment._log_metrics(logs, step=state.global_step, epoch=state.epoch, framework="transformers")

    def on_train_end(self, args, state, control, **kwargs):
        if self._initialized and state.is_world_process_zero:
            experiment = comet_ml.config.get_global_experiment()
            if (experiment is not None) and (self._log_assets is True):
                logger.info("Logging checkpoints. This may take time.")
                experiment.log_asset_folder(
                    args.output_dir, recursive=True, log_file_name=True, step=state.global_step
                )
            experiment.end()


class AzureMLCallback(TrainerCallback):
    """
    A [`TrainerCallback`] that sends the logs to [AzureML](https://pypi.org/project/azureml-sdk/).
    """

    def __init__(self, azureml_run=None):
        if not is_azureml_available():
            raise RuntimeError("AzureMLCallback requires azureml to be installed. Run `pip install azureml-sdk`.")
        self.azureml_run = azureml_run

    def on_init_end(self, args, state, control, **kwargs):
        from azureml.core.run import Run

        if self.azureml_run is None and state.is_world_process_zero:
            self.azureml_run = Run.get_context()

    def on_log(self, args, state, control, logs=None, **kwargs):
        if self.azureml_run and state.is_world_process_zero:
            for k, v in logs.items():
                if isinstance(v, (int, float)):
                    self.azureml_run.log(k, v, description=k)


class MLflowCallback(TrainerCallback):
    """
    A [`TrainerCallback`] that sends the logs to [MLflow](https://www.mlflow.org/). Can be disabled by setting
    environment variable `DISABLE_MLFLOW_INTEGRATION = TRUE`.
    """

    def __init__(self):
        if not is_mlflow_available():
            raise RuntimeError("MLflowCallback requires mlflow to be installed. Run `pip install mlflow`.")
        import mlflow

        self._MAX_PARAM_VAL_LENGTH = mlflow.utils.validation.MAX_PARAM_VAL_LENGTH
        self._MAX_PARAMS_TAGS_PER_BATCH = mlflow.utils.validation.MAX_PARAMS_TAGS_PER_BATCH

        self._initialized = False
        self._auto_end_run = False
        self._log_artifacts = False
        self._ml_flow = mlflow

    def setup(self, args, state, model):
        """
        Setup the optional MLflow integration.

        Environment:
            HF_MLFLOW_LOG_ARTIFACTS (`str`, *optional*):
                Whether to use MLflow .log_artifact() facility to log artifacts. This only makes sense if logging to a
                remote server, e.g. s3 or GCS. If set to `True` or *1*, will copy whatever is in
                [`TrainingArguments`]'s `output_dir` to the local or remote artifact storage. Using it without a remote
                storage will just copy the files to your artifact location.
            MLFLOW_EXPERIMENT_NAME (`str`, *optional*):
                Whether to use an MLflow experiment_name under which to launch the run. Default to "None" which will
                point to the "Default" experiment in MLflow. Otherwise, it is a case sensitive name of the experiment
                to be activated. If an experiment with this name does not exist, a new experiment with this name is
                created.
            MLFLOW_TAGS (`str`, *optional*):
                A string dump of a dictionary of key/value pair to be added to the MLflow run as tags. Example:
                os.environ['MLFLOW_TAGS']='{"release.candidate": "RC1", "release.version": "2.2.0"}'
            MLFLOW_NESTED_RUN (`str`, *optional*):
                Whether to use MLflow nested runs. If set to `True` or *1*, will create a nested run inside the current
                run.
            MLFLOW_RUN_ID (`str`, *optional*):
                Allow to reattach to an existing run which can be usefull when resuming training from a checkpoint.
                When MLFLOW_RUN_ID environment variable is set, start_run attempts to resume a run with the specified
                run ID and other parameters are ignored.
            MLFLOW_FLATTEN_PARAMS (`str`, *optional*):
                Whether to flatten the parameters dictionary before logging. Default to `False`.
        """
        self._log_artifacts = os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper() in ENV_VARS_TRUE_VALUES
        self._nested_run = os.getenv("MLFLOW_NESTED_RUN", "FALSE").upper() in ENV_VARS_TRUE_VALUES
        self._experiment_name = os.getenv("MLFLOW_EXPERIMENT_NAME", None)
        self._flatten_params = os.getenv("MLFLOW_FLATTEN_PARAMS", "FALSE").upper() in ENV_VARS_TRUE_VALUES
        self._run_id = os.getenv("MLFLOW_RUN_ID", None)
        logger.debug(
            f"MLflow experiment_name={self._experiment_name}, run_name={args.run_name}, nested={self._nested_run}, tags={self._nested_run}"
        )
        if state.is_world_process_zero:
            if self._ml_flow.active_run() is None or self._nested_run or self._run_id:
                if self._experiment_name:
                    # Use of set_experiment() ensure that Experiment is created if not exists
                    self._ml_flow.set_experiment(self._experiment_name)
                self._ml_flow.start_run(run_name=args.run_name, nested=self._nested_run)
                logger.debug(f"MLflow run started with run_id={self._ml_flow.active_run().info.run_id}")
                self._auto_end_run = True
            combined_dict = args.to_dict()
            if hasattr(model, "config") and model.config is not None:
                model_config = model.config.to_dict()
                combined_dict = {**model_config, **combined_dict}
            combined_dict = flatten_dict(combined_dict) if self._flatten_params else combined_dict
            # remove params that are too long for MLflow
            for name, value in list(combined_dict.items()):
                # internally, all values are converted to str in MLflow
                if len(str(value)) > self._MAX_PARAM_VAL_LENGTH:
                    logger.warning(
                        f'Trainer is attempting to log a value of "{value}" for key "{name}" as a parameter. '
                        f"MLflow's log_param() only accepts values no longer than 250 characters so we dropped this attribute. "
                        f"You can use `MLFLOW_FLATTEN_PARAMS` environment variable to flatten the parameters and avoid this message."
                    )
                    del combined_dict[name]
            # MLflow cannot log more than 100 values in one go, so we have to split it
            combined_dict_items = list(combined_dict.items())
            for i in range(0, len(combined_dict_items), self._MAX_PARAMS_TAGS_PER_BATCH):
                self._ml_flow.log_params(dict(combined_dict_items[i : i + self._MAX_PARAMS_TAGS_PER_BATCH]))
            mlflow_tags = os.getenv("MLFLOW_TAGS", None)
            if mlflow_tags:
                mlflow_tags = json.loads(mlflow_tags)
                self._ml_flow.set_tags(mlflow_tags)
        self._initialized = True

    def on_train_begin(self, args, state, control, model=None, **kwargs):
        if not self._initialized:
            self.setup(args, state, model)

    def on_log(self, args, state, control, logs, model=None, **kwargs):
        if not self._initialized:
            self.setup(args, state, model)
        if state.is_world_process_zero:
            metrics = {}
            for k, v in logs.items():
                if isinstance(v, (int, float)):
                    metrics[k] = v
                else:
                    logger.warning(
                        f'Trainer is attempting to log a value of "{v}" of type {type(v)} for key "{k}" as a metric. '
                        f"MLflow's log_metric() only accepts float and int types so we dropped this attribute."
                    )
            self._ml_flow.log_metrics(metrics=metrics, step=state.global_step)

    def on_train_end(self, args, state, control, **kwargs):
        if self._initialized and state.is_world_process_zero:
            if self._log_artifacts:
                logger.info("Logging artifacts. This may take time.")
                self._ml_flow.log_artifacts(args.output_dir)
            if self._auto_end_run and self._ml_flow.active_run():
                self._ml_flow.end_run()

    def __del__(self):
        # if the previous run is not terminated correctly, the fluent API will
        # not let you start a new run before the previous one is killed
        if self._auto_end_run and self._ml_flow and self._ml_flow.active_run() is not None:
            self._ml_flow.end_run()


class NeptuneCallback(TrainerCallback):
    """
    A [`TrainerCallback`] that sends the logs to [Neptune](https://neptune.ai).
    """

    def __init__(self):
        if not is_neptune_available():
            raise ValueError(
                "NeptuneCallback requires neptune-client to be installed. Run `pip install neptune-client`."
            )
        import neptune.new as neptune

        self._neptune = neptune
        self._initialized = False
        self._log_artifacts = False

    def setup(self, args, state, model):
        """
        Setup the Neptune integration.

        Environment:
            NEPTUNE_PROJECT (`str`, *required*):
                The project ID for neptune.ai account. Should be in format *workspace_name/project_name*
            NEPTUNE_API_TOKEN (`str`, *required*):
                API-token for neptune.ai account
            NEPTUNE_CONNECTION_MODE (`str`, *optional*):
                Neptune connection mode. *async* by default
            NEPTUNE_RUN_NAME (`str`, *optional*):
                The name of run process on Neptune dashboard
        """
        if state.is_world_process_zero:
            self._neptune_run = self._neptune.init(
                project=os.getenv("NEPTUNE_PROJECT"),
                api_token=os.getenv("NEPTUNE_API_TOKEN"),
                mode=os.getenv("NEPTUNE_CONNECTION_MODE", "async"),
                name=os.getenv("NEPTUNE_RUN_NAME", None),
                run=os.getenv("NEPTUNE_RUN_ID", None),
            )
            combined_dict = args.to_dict()
            if hasattr(model, "config") and model.config is not None:
                model_config = model.config.to_dict()
                combined_dict = {**model_config, **combined_dict}
            self._neptune_run["parameters"] = combined_dict
        self._initialized = True

    def on_train_begin(self, args, state, control, model=None, **kwargs):
        if not self._initialized:
            self.setup(args, state, model)

    def on_log(self, args, state, control, logs, model=None, **kwargs):
        if not self._initialized:
            self.setup(args, state, model)
        if state.is_world_process_zero:
            for k, v in logs.items():
                self._neptune_run[k].log(v, step=state.global_step)

    def __del__(self):
        """
        Environment:
            NEPTUNE_STOP_TIMEOUT (`int`, *optional*):
                Number of seconsds to wait for all Neptune.ai tracking calls to finish, before stopping the tracked
                run. If not set it will wait for all tracking calls to finish.
        """
        try:
            stop_timeout = os.getenv("NEPTUNE_STOP_TIMEOUT")
            stop_timeout = int(stop_timeout) if stop_timeout else None
            self._neptune_run.stop(seconds=stop_timeout)
        except AttributeError:
            pass


class CodeCarbonCallback(TrainerCallback):
    """
    A [`TrainerCallback`] that tracks the CO2 emission of training.
    """

    def __init__(self):
        if not is_codecarbon_available():
            raise RuntimeError(
                "CodeCarbonCallback requires `codecarbon` to be installed. Run `pip install codecarbon`."
            )
        import codecarbon

        self._codecarbon = codecarbon
        self.tracker = None

    def on_init_end(self, args, state, control, **kwargs):
        if self.tracker is None and state.is_local_process_zero:
            # CodeCarbon will automatically handle environment variables for configuration
            self.tracker = self._codecarbon.EmissionsTracker(output_dir=args.output_dir)

    def on_train_begin(self, args, state, control, model=None, **kwargs):
        if self.tracker and state.is_local_process_zero:
            self.tracker.start()

    def on_train_end(self, args, state, control, **kwargs):
        if self.tracker and state.is_local_process_zero:
            self.tracker.stop()


INTEGRATION_TO_CALLBACK = {
    "azure_ml": AzureMLCallback,
    "comet_ml": CometCallback,
    "mlflow": MLflowCallback,
    "neptune": NeptuneCallback,
    "tensorboard": TensorBoardCallback,
    "wandb": WandbCallback,
    "codecarbon": CodeCarbonCallback,
}


def get_reporting_integration_callbacks(report_to):
    for integration in report_to:
        if integration not in INTEGRATION_TO_CALLBACK:
            raise ValueError(
                f"{integration} is not supported, only {', '.join(INTEGRATION_TO_CALLBACK.keys())} are supported."
            )
    return [INTEGRATION_TO_CALLBACK[integration] for integration in report_to]