import string
import re
from transformers.trainer_seq2seq import Seq2SeqTrainer
from transformers.trainer import *
from datasets import load_metric
from transformers.trainer_callback import TrainerCallback
from transformers.optimization import Adafactor, AdamW


class DenserEvalCallback(TrainerCallback):

    def on_step_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):

        log_eval_steps = [1, 50, 100, 200]

        # Log
        if args.logging_strategy == IntervalStrategy.STEPS and state.global_step in log_eval_steps:
            control.should_log = True

        # Evaluate
        if args.evaluation_strategy == IntervalStrategy.STEPS and state.global_step in log_eval_steps:
            control.should_evaluate = True

        return control


class NITrainer(Seq2SeqTrainer):

    # rewrite the evaluation loop, with customized call to compute_metrics
    def evaluation_loop(
        self,
        dataloader: DataLoader,
        description: str,
        prediction_loss_only: Optional[bool] = None,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "eval",
    ) -> EvalLoopOutput:
        """
        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.

        Works both with or without labels.
        """
        args = self.args

        prediction_loss_only = prediction_loss_only if prediction_loss_only is not None else args.prediction_loss_only

        # if eval is called w/o train init deepspeed here
        if args.deepspeed and not self.deepspeed:

            # XXX: eval doesn't have `resume_from_checkpoint` arg but we should be able to do eval
            # from the checkpoint eventually
            deepspeed_engine, _, _ = deepspeed_init(
                self, num_training_steps=0, resume_from_checkpoint=None, inference=True
            )
            self.model = deepspeed_engine.module
            self.model_wrapped = deepspeed_engine
            self.deepspeed = deepspeed_engine

        model = self._wrap_model(self.model, training=False)

        # if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn't called
        # while ``train`` is running, cast it to the right dtype first and then put on device
        if not self.is_in_train:
            if args.fp16_full_eval:
                model = model.to(dtype=torch.float16, device=args.device)
            elif args.bf16_full_eval:
                model = model.to(dtype=torch.bfloat16, device=args.device)

        batch_size = dataloader.batch_size

        logger.info(f"***** Running {description} *****")
        if has_length(dataloader.dataset):
            logger.info(f"  Num examples = {self.num_examples(dataloader)}")
        else:
            logger.info("  Num examples: Unknown")
        logger.info(f"  Batch size = {batch_size}")

        model.eval()

        self.callback_handler.eval_dataloader = dataloader
        # Do this before wrapping.
        eval_dataset = dataloader.dataset

        if is_torch_tpu_available():
            dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)

        if args.past_index >= 0:
            self._past = None

        # Initialize containers
        # losses/preds/labels on GPU/TPU (accumulated for eval_accumulation_steps)
        losses_host = None
        preds_host = None
        labels_host = None
        # losses/preds/labels on CPU (final containers)
        all_losses = None
        all_preds = None
        all_labels = None
        # Will be useful when we have an iterable dataset so don't know its length.

        observed_num_examples = 0
        # Main evaluation loop
        for step, inputs in enumerate(dataloader):
            # Update the observed num examples
            observed_batch_size = find_batch_size(inputs)
            if observed_batch_size is not None:
                observed_num_examples += observed_batch_size
                # For batch samplers, batch_size is not known by the dataloader in advance.
                if batch_size is None:
                    batch_size = observed_batch_size

            # Prediction step
            loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)

            if is_torch_tpu_available():
                xm.mark_step()

            # Update containers on host
            if loss is not None:
                losses = self._nested_gather(loss.repeat(batch_size))
                losses_host = losses if losses_host is None else torch.cat((losses_host, losses), dim=0)
            if labels is not None:
                labels = self._pad_across_processes(labels)
                labels = self._nested_gather(labels)
                labels_host = labels if labels_host is None else nested_concat(labels_host, labels, padding_index=-100)
            if logits is not None:
                logits = self._pad_across_processes(logits)
                logits = self._nested_gather(logits)
                if self.preprocess_logits_for_metrics is not None:
                    logits = self.preprocess_logits_for_metrics(logits, labels)
                preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)
            self.control = self.callback_handler.on_prediction_step(args, self.state, self.control)

            # Gather all tensors and put them back on the CPU if we have done enough accumulation steps.
            if args.eval_accumulation_steps is not None and (step + 1) % args.eval_accumulation_steps == 0:
                if losses_host is not None:
                    losses = nested_numpify(losses_host)
                    all_losses = losses if all_losses is None else np.concatenate((all_losses, losses), axis=0)
                if preds_host is not None:
                    logits = nested_numpify(preds_host)
                    all_preds = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
                if labels_host is not None:
                    labels = nested_numpify(labels_host)
                    all_labels = (
                        labels if all_labels is None else nested_concat(all_labels, labels, padding_index=-100)
                    )

                # Set back to None to begin a new accumulation
                losses_host, preds_host, labels_host = None, None, None

        if args.past_index and hasattr(self, "_past"):
            # Clean the state at the end of the evaluation loop
            delattr(self, "_past")

        # Gather all remaining tensors and put them back on the CPU
        if losses_host is not None:
            losses = nested_numpify(losses_host)
            all_losses = losses if all_losses is None else np.concatenate((all_losses, losses), axis=0)
        if preds_host is not None:
            logits = nested_numpify(preds_host)
            all_preds = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
        if labels_host is not None:
            labels = nested_numpify(labels_host)
            all_labels = labels if all_labels is None else nested_concat(all_labels, labels, padding_index=-100)

        # Number of samples
        if has_length(eval_dataset):
            num_samples = len(eval_dataset)
        # The instance check is weird and does not actually check for the type, but whether the dataset has the right
        # methods. Therefore we need to make sure it also has the attribute.
        elif isinstance(eval_dataset, IterableDatasetShard) and hasattr(eval_dataset, "num_examples"):
            num_samples = eval_dataset.num_examples
        else:
            num_samples = observed_num_examples

        # Number of losses has been rounded to a multiple of batch_size and in a distributed training, the number of
        # samplers has been rounded to a multiple of batch_size, so we truncate.
        if all_losses is not None:
            all_losses = all_losses[:num_samples]
        if all_preds is not None:
            all_preds = nested_truncate(all_preds, num_samples)
        if all_labels is not None:
            all_labels = nested_truncate(all_labels, num_samples)

        # Metrics!
        if self.compute_metrics is not None and all_preds is not None and all_labels is not None:
            metrics = self.compute_metrics(dataset=eval_dataset, preds=all_preds, save_prefix=metric_key_prefix)
        else:
            metrics = {}

        metrics["global_step"] = self.state.global_step

        # To be JSON-serializable, we need to remove numpy types or zero-d tensors
        metrics = denumpify_detensorize(metrics)

        if all_losses is not None:
            metrics[f"{metric_key_prefix}_loss"] = all_losses.mean().item()

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()):
            if not key.startswith(f"{metric_key_prefix}_"):
                metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)

        return EvalLoopOutput(predictions=all_preds, label_ids=all_labels, metrics=metrics, num_samples=num_samples)

    def prediction_step(
        self,
        model: nn.Module,
        inputs: Dict[str, Union[torch.Tensor, Any]],
        prediction_loss_only: bool,
        ignore_keys: Optional[List[str]] = None,
    ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
        """
        Perform an evaluation step on `model` using `inputs`.

        Subclass and override to inject custom behavior.

        Args:
            model (`nn.Module`):
                The model to evaluate.
            inputs (`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument `labels`. Check your model's documentation for all accepted arguments.
            prediction_loss_only (`bool`):
                Whether or not to return the loss only.

        Return:
            Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, logits and
            labels (each being optional).
        """

        if not self.args.predict_with_generate or prediction_loss_only:
            return super().prediction_step(
                model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
            )

        has_labels = "labels" in inputs
        inputs = self._prepare_inputs(inputs)

        # XXX: adapt synced_gpus for fairscale as well
        gen_kwargs = {
            "max_length": self.model.config.max_length,
            "num_beams":  self.model.config.num_beams,
            "synced_gpus": True if is_deepspeed_zero3_enabled() else False,
        }

        if "attention_mask" in inputs:
            gen_kwargs["attention_mask"] = inputs.get("attention_mask", None)

        # prepare generation inputs
        # some encoder-decoder models can have varying encder's and thus
        # varying model input names
        if hasattr(self.model, "encoder") and self.model.encoder.main_input_name != self.model.main_input_name:
            generation_inputs = inputs[self.model.encoder.main_input_name]
        else:
            generation_inputs = inputs[self.model.main_input_name]

        generated_tokens = self.model.generate(
            input_ids = generation_inputs,
            **gen_kwargs,
        )
        # in case the batch is shorter than max length, the output should be padded
        if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
            generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_length"])

        with torch.no_grad():
            if has_labels:
                with self.autocast_smart_context_manager():
                    outputs = model(**inputs)
                if self.label_smoother is not None:
                    loss = self.label_smoother(outputs, inputs["labels"]).mean().detach()
                else:
                    loss = (outputs["loss"] if isinstance(outputs, dict) else outputs[0]).mean().detach()
            else:
                loss = None

        if self.args.prediction_loss_only:
            return (loss, None, None)

        if has_labels:
            labels = inputs["labels"]
            if labels.shape[-1] < gen_kwargs["max_length"]:
                labels = self._pad_tensors_to_max_len(labels, gen_kwargs["max_length"])
        else:
            labels = None

        return (loss, generated_tokens, labels)
    
    def get_train_dataloader(self) -> DataLoader:
        """
        Returns the training :class:`~torch.utils.data.DataLoader`.

        Will use no sampler if :obj:`self.train_dataset` does not implement :obj:`__len__`, a random sampler (adapted
        to distributed training if necessary) otherwise.

        Subclass and override this method if you want to inject some custom behavior.
        """
        if self.train_dataset is None:
            raise ValueError("Trainer: training requires a train_dataset.")

        train_dataset = self.train_dataset
        if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):
            train_dataset = self._remove_unused_columns(train_dataset, description="training")

        if isinstance(train_dataset, torch.utils.data.IterableDataset):
            if self.args.world_size > 1:
                train_dataset = IterableDatasetShard(
                    train_dataset,
                    batch_size=self.args.train_batch_size,
                    drop_last=self.args.dataloader_drop_last,
                    num_processes=self.args.world_size,
                    process_index=self.args.process_index,
                )

            return DataLoader(
                train_dataset,
                batch_size=self.args.train_batch_size,
                collate_fn=self.data_collator,
                num_workers=self.args.dataloader_num_workers,
                pin_memory=self.args.dataloader_pin_memory,
            )

        train_sampler = self._get_train_sampler() if not hasattr(self.args, "train_multitask") else None

        return DataLoader(
            train_dataset,
            batch_size=self.args.train_batch_size,
            sampler=train_sampler,
            collate_fn=self.data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
            pin_memory=self.args.dataloader_pin_memory,
        )

    # def create_optimizer(self):
    #     """
    #     Setup the optimizer.

    #     We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
    #     Trainer's init through :obj:`optimizers`, or subclass and override this method in a subclass.
    #     """
    #     if self.optimizer is None:
    #         decay_parameters = get_parameter_names(self.model, [nn.LayerNorm])
    #         decay_parameters = [name for name in decay_parameters if "bias" not in name]
    #         optimizer_grouped_parameters = [
    #             {
    #                 "params": [p for n, p in self.model.named_parameters() if (n in decay_parameters and "gate" not in n)],
    #                 "lr": self.args.learning_rate,
    #                 "weight_decay": self.args.weight_decay,
    #             },
    #             {
    #                 "params": [p for n, p in self.model.named_parameters() if (n not in decay_parameters and "gate" not in n)],
    #                 "lr": self.args.learning_rate,
    #                 "weight_decay": 0.0,
    #             },
    #             {
    #                 "params": [p for n, p in self.model.named_parameters() if ("gate" in n)],
    #                 "lr": 1,
    #                 "weight_decay": 0,
    #             },
    #         ]
    #         optimizer_cls = Adafactor if self.args.adafactor else AdamW
    #         if self.args.adafactor:
    #             optimizer_cls = Adafactor
    #             optimizer_kwargs = {"scale_parameter": False, "relative_step": False}
    #         else:
    #             optimizer_cls = AdamW
    #             optimizer_kwargs = {
    #                 "betas": (self.args.adam_beta1, self.args.adam_beta2),
    #                 "eps": self.args.adam_epsilon,
    #             }
    #         # optimizer_kwargs["lr"] = self.args.learning_rate
    #         if self.sharded_ddp == ShardedDDPOption.SIMPLE:
    #             self.optimizer = OSS(
    #                 params=optimizer_grouped_parameters,
    #                 optim=optimizer_cls,
    #                 **optimizer_kwargs,
    #             )
    #         else:
    #             self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)

    #     if is_sagemaker_mp_enabled():
    #         self.optimizer = smp.DistributedOptimizer(self.optimizer)

    #     return self.optimizer