src/transformers/training_args.py/TrainingArguments/__post_init__
class TrainingArguments:    def __post_init__(self):
        # Handle --use_env option in torch.distributed.launch (local_rank not passed as an arg then).
        # This needs to happen before any call to self.device or self.n_gpu.
        env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
        if env_local_rank != -1 and env_local_rank != self.local_rank:
            self.local_rank = env_local_rank

        # convert to int
        self.log_level = trainer_log_levels[self.log_level]
        self.log_level_replica = trainer_log_levels[self.log_level_replica]

        # expand paths, if not os.makedirs("~/bar") will make directory
        # in the current directory instead of the actual home
        #  see https://github.com/huggingface/transformers/issues/10628
        if self.output_dir is not None:
            self.output_dir = os.path.expanduser(self.output_dir)
        if self.logging_dir is None and self.output_dir is not None:
            self.logging_dir = os.path.join(self.output_dir, default_logdir())
        if self.logging_dir is not None:
            self.logging_dir = os.path.expanduser(self.logging_dir)

        if self.disable_tqdm is None:
            self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN

        if isinstance(self.evaluation_strategy, EvaluationStrategy):
            warnings.warn(
                "using `EvaluationStrategy` for `evaluation_strategy` is deprecated and will be removed in version 5 of 🤗 Transformers. Use `IntervalStrategy` instead",
                FutureWarning,
            )
            # Go back to the underlying string or we won't be able to instantiate `IntervalStrategy` on it.
            self.evaluation_strategy = self.evaluation_strategy.value

        self.evaluation_strategy = IntervalStrategy(self.evaluation_strategy)
        self.logging_strategy = IntervalStrategy(self.logging_strategy)
        self.save_strategy = IntervalStrategy(self.save_strategy)
        self.hub_strategy = HubStrategy(self.hub_strategy)

        self.lr_scheduler_type = SchedulerType(self.lr_scheduler_type)
        if self.do_eval is False and self.evaluation_strategy != IntervalStrategy.NO:
            self.do_eval = True

        # eval_steps has to be defined and non-zero, fallbacks to logging_steps if the latter is non-zero
        if self.evaluation_strategy == IntervalStrategy.STEPS and (self.eval_steps is None or self.eval_steps == 0):
            if self.logging_steps > 0:
                logger.info(f"using `logging_steps` to initialize `eval_steps` to {self.logging_steps}")
                self.eval_steps = self.logging_steps
            else:
                raise ValueError(
                    f"evaluation strategy {self.evaluation_strategy} requires either non-zero --eval_steps or --logging_steps"
                )

        # logging_steps must be non-zero for logging_strategy that is other than 'no'
        if self.logging_strategy == IntervalStrategy.STEPS and self.logging_steps == 0:
            raise ValueError(f"logging strategy {self.logging_strategy} requires non-zero --logging_steps")

        # Sanity checks for load_best_model_at_end: we require save and eval strategies to be compatible.
        if self.load_best_model_at_end:
            if self.evaluation_strategy != self.save_strategy:
                raise ValueError(
                    "--load_best_model_at_end requires the save and eval strategy to match, but found\n- Evaluation "
                    f"strategy: {self.evaluation_strategy}\n- Save strategy: {self.save_strategy}"
                )
            if self.evaluation_strategy == IntervalStrategy.STEPS and self.save_steps % self.eval_steps != 0:
                raise ValueError(
                    "--load_best_model_at_end requires the saving steps to be a round multiple of the evaluation "
                    f"steps, but found {self.save_steps}, which is not a round multiple of {self.eval_steps}."
                )

        if self.load_best_model_at_end and self.metric_for_best_model is None:
            self.metric_for_best_model = "loss"
        if self.greater_is_better is None and self.metric_for_best_model is not None:
            self.greater_is_better = self.metric_for_best_model not in ["loss", "eval_loss"]
        if self.run_name is None:
            self.run_name = self.output_dir

        if is_torch_available() and self.device.type != "cuda" and (self.fp16 or self.fp16_full_eval):
            raise ValueError(
                "Mixed precision training with AMP or APEX (`--fp16`) and FP16 evaluation can only be used on CUDA devices."
            )
        if self.report_to is None:
            logger.info(
                "The default value for the training argument `--report_to` will change in v5 (from all installed "
                "integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as "
                "now. You should start updating your code and make this info disappear :-)."
            )
            self.report_to = "all"
        if self.report_to == "all" or self.report_to == ["all"]:
            # Import at runtime to avoid a circular import.
            from .integrations import get_available_reporting_integrations

            self.report_to = get_available_reporting_integrations()
        elif self.report_to == "none" or self.report_to == ["none"]:
            self.report_to = []
        elif not isinstance(self.report_to, list):
            self.report_to = [self.report_to]

        if self.warmup_ratio < 0 or self.warmup_ratio > 1:
            raise ValueError("warmup_ratio must lie in range [0,1]")
        elif self.warmup_ratio > 0 and self.warmup_steps > 0:
            logger.info(
                "Both warmup_ratio and warmup_steps given, warmup_steps will override any effect of warmup_ratio during training"
            )

        if isinstance(self.sharded_ddp, bool):
            self.sharded_ddp = "simple" if self.sharded_ddp else ""
        if isinstance(self.sharded_ddp, str):
            self.sharded_ddp = [ShardedDDPOption(s) for s in self.sharded_ddp.split()]
        if self.sharded_ddp == [ShardedDDPOption.OFFLOAD]:
            raise ValueError(
                "`--sharded_ddp offload` can't work on its own. It needs to be added to `--sharded_ddp zero_dp_2` or "
                '`--sharded_ddp zero_dp_3`. For example, `--sharded_ddp "zero_dp_2 offload"`.'
            )
        elif len(self.sharded_ddp) > 1 and ShardedDDPOption.SIMPLE in self.sharded_ddp:
            raise ValueError("`--sharded_ddp simple` is not compatible with any other option.")
        elif ShardedDDPOption.ZERO_DP_2 in self.sharded_ddp and ShardedDDPOption.ZERO_DP_3 in self.sharded_ddp:
            raise ValueError("`--sharded_ddp zero_dp_2` is not compatible with `--sharded_ddp zero_dp_3`.")

        if self.tpu_metrics_debug:
            warnings.warn(
                "using `--tpu_metrics_debug` is deprecated and will be removed in version 5 of 🤗 Transformers. Use `--debug tpu_metrics_debug` instead",
                FutureWarning,
            )
            self.debug += " tpu_metrics_debug"
            self.tpu_metrics_debug = False
        if isinstance(self.debug, str):
            self.debug = [DebugOption(s) for s in self.debug.split()]

        if self.deepspeed:
            # - must be run very last in arg parsing, since it will use a lot of these settings.
            # - must be run before the model is created.
            from transformers.deepspeed import HfTrainerDeepSpeedConfig

            # will be used later by the Trainer
            # note: leave self.deepspeed unmodified in case a user relies on it not to be modified)
            self.hf_deepspeed_config = HfTrainerDeepSpeedConfig(self.deepspeed)
            self.hf_deepspeed_config.trainer_config_process(self)

        if self.push_to_hub_token is not None:
            warnings.warn(
                "`--push_to_hub_token` is deprecated and will be removed in version 5 of 🤗 Transformers. Use "
                "`--hub_token` instead.",
                FutureWarning,
            )
            self.hub_token = self.push_to_hub_token

        if self.push_to_hub_model_id is not None:
            self.hub_model_id = get_full_repo_name(
                self.push_to_hub_model_id, organization=self.push_to_hub_organization, token=self.hub_token
            )
            if self.push_to_hub_organization is not None:
                warnings.warn(
                    "`--push_to_hub_model_id` and `--push_to_hub_organization` are deprecated and will be removed in "
                    "version 5 of 🤗 Transformers. Use `--hub_model_id` instead and pass the full repo name to this "
                    f"argument (in this case {self.hub_model_id}).",
                    FutureWarning,
                )
            else:
                warnings.warn(
                    "`--push_to_hub_model_id` is deprecated and will be removed in version 5 of 🤗 Transformers. Use "
                    "`--hub_model_id` instead and pass the full repo name to this argument (in this case "
                    f"{self.hub_model_id}).",
                    FutureWarning,
                )
        elif self.push_to_hub_organization is not None:
            self.hub_model_id = f"{self.push_to_hub_organization}/{Path(self.output_dir).name}"
            warnings.warn(
                "`--push_to_hub_organization` is deprecated and will be removed in version 5 of 🤗 Transformers. Use "
                "`--hub_model_id` instead and pass the full repo name to this argument (in this case "
                f"{self.hub_model_id}).",
                FutureWarning,
            )