import collections
import math
import os
import sys
import time
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
import torch.distributed as dist

# from packaging import version
from torch import nn
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.dataset import Dataset, IterableDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm.auto import tqdm
from transformers import Trainer
from transformers.configuration_utils import PretrainedConfig
from transformers.debug_utils import DebugOption, DebugUnderflowOverflow
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME

# Integrations must be imported before ML frameworks:
from transformers.integrations import hp_params
from transformers.trainer import TRAINER_STATE_NAME
from transformers.trainer_callback import TrainerState
from transformers.trainer_pt_utils import IterableDatasetShard
from transformers.trainer_utils import (
    HPSearchBackend,
    ShardedDDPOption,
    TrainOutput,
    get_last_checkpoint,
    set_seed,
    speed_metrics,
)
from transformers.utils import logging

from neural_compressor.experimental import Pruning


if TYPE_CHECKING:
    import optuna


__version__ = "4.9.2"

logger = logging.get_logger(__name__)


class IncTrainer(Trainer):
    def train(
        self,
        pruner: Optional[Pruning] = None,
        resume_from_checkpoint: Optional[Union[str, bool]] = None,
        trial: Union["optuna.Trial", Dict[str, Any]] = None,
        ignore_keys_for_eval: Optional[List[str]] = None,
        **kwargs,
    ):
        """
        Main training entry point.

        Args:
            pruner (:obj:`Pruning`, `optional`):
                Pruning object handling the pruning process.
            resume_from_checkpoint (:obj:`str` or :obj:`bool`, `optional`):
                If a :obj:`str`, local path to a saved checkpoint as saved by a previous instance of
                :class:`~transformers.Trainer`. If a :obj:`bool` and equals `True`, load the last checkpoint in
                `args.output_dir` as saved by a previous instance of :class:`~transformers.Trainer`. If present,
                training will resume from the model/optimizer/scheduler states loaded here.
            trial (:obj:`optuna.Trial` or :obj:`Dict[str, Any]`, `optional`):
                The trial run or the hyperparameter dictionary for hyperparameter search.
            ignore_keys_for_eval (:obj:`List[str]`, `optional`)
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions for evaluation during the training.
            kwargs:
                Additional keyword arguments used to hide deprecated arguments
        """
        resume_from_checkpoint = None if not resume_from_checkpoint else resume_from_checkpoint

        # memory metrics - must set up as early as possible
        self._memory_tracker.start()

        args = self.args

        self.is_in_train = True

        # do_train is not a reliable argument, as it might not be set and .train() still called, so
        # the following is a workaround:
        if args.fp16_full_eval and not args.do_train:
            self._move_model_to_device(self.model, args.device)

        if "model_path" in kwargs:
            resume_from_checkpoint = kwargs.pop("model_path")
            warnings.warn(
                "`model_path` is deprecated and will be removed in a future version. Use `resume_from_checkpoint` "
                "instead.",
                FutureWarning,
            )
        if len(kwargs) > 0:
            raise TypeError(f"train() received got unexpected keyword arguments: {', '.join(list(kwargs.keys()))}.")
        # This might change the seed so needs to run first.
        self._hp_search_setup(trial)

        # Model re-init
        model_reloaded = False
        if self.model_init is not None:
            # Seed must be set before instantiating the model when using model_init.
            set_seed(args.seed)
            self.model = self.call_model_init(trial)
            model_reloaded = True
            # Reinitializes optimizer and scheduler
            self.optimizer, self.lr_scheduler = None, None

        # Load potential model checkpoint
        if isinstance(resume_from_checkpoint, bool) and resume_from_checkpoint:
            resume_from_checkpoint = get_last_checkpoint(args.output_dir)
            if resume_from_checkpoint is None:
                raise ValueError(f"No valid checkpoint found in output directory ({args.output_dir})")

        if resume_from_checkpoint is not None:
            if not os.path.isfile(os.path.join(resume_from_checkpoint, WEIGHTS_NAME)):
                raise ValueError(f"Can't find a valid checkpoint at {resume_from_checkpoint}")

            logger.info(f"Loading model from {resume_from_checkpoint}).")

            if os.path.isfile(os.path.join(resume_from_checkpoint, CONFIG_NAME)):
                config = PretrainedConfig.from_json_file(os.path.join(resume_from_checkpoint, CONFIG_NAME))
                checkpoint_version = config.transformers_version
                if checkpoint_version is not None and checkpoint_version != __version__:
                    logger.warn(
                        f"You are resuming training from a checkpoint trained with {checkpoint_version} of "
                        f"Transformers but your current version is {__version__}. This is not recommended and could "
                        "yield to errors or unwanted behaviors."
                    )

            # We load the model state dict on the CPU to avoid an OOM error.
            state_dict = torch.load(os.path.join(resume_from_checkpoint, WEIGHTS_NAME), map_location="cpu")
            # If the model is on the GPU, it still works!
            self._load_state_dict_in_model(state_dict)

            # release memory
            del state_dict

        # If model was re-initialized, put it on the right device and update self.model_wrapped
        if model_reloaded:
            if self.place_model_on_device:
                self._move_model_to_device(self.model, args.device)
            self.model_wrapped = self.model

        # Keeping track whether we can can len() on the dataset or not
        train_dataset_is_sized = isinstance(self.train_dataset, collections.abc.Sized)

        # Data loader and number of training steps
        train_dataloader = self.get_train_dataloader()

        # Setting up training control variables:
        # number of training epochs: num_train_epochs
        # number of training steps per epoch: num_update_steps_per_epoch
        # total number of training steps to execute: max_steps
        total_train_batch_size = args.train_batch_size * args.gradient_accumulation_steps * args.world_size
        if train_dataset_is_sized:
            num_update_steps_per_epoch = len(train_dataloader) // args.gradient_accumulation_steps
            num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)
            if args.max_steps > 0:
                max_steps = args.max_steps
                num_train_epochs = args.max_steps // num_update_steps_per_epoch + int(
                    args.max_steps % num_update_steps_per_epoch > 0
                )
                # May be slightly incorrect if the last batch in the training datalaoder has a smaller size but it's
                # the best we can do.
                num_train_samples = args.max_steps * total_train_batch_size
            else:
                max_steps = math.ceil(args.num_train_epochs * num_update_steps_per_epoch)
                num_train_epochs = math.ceil(args.num_train_epochs)
                num_train_samples = len(self.train_dataset) * args.num_train_epochs
        else:
            # see __init__. max_steps is set when the dataset has no __len__
            max_steps = args.max_steps
            # Setting a very large number of epochs so we go as many times as necessary over the iterator.
            num_train_epochs = sys.maxsize
            num_update_steps_per_epoch = max_steps
            num_train_samples = args.max_steps * total_train_batch_size

        if DebugOption.UNDERFLOW_OVERFLOW in self.args.debug:
            if self.args.n_gpu > 1:
                # nn.DataParallel(model) replicates the model, creating new variables and module
                # references registered here no longer work on other gpus, breaking the module
                raise ValueError(
                    "Currently --debug underflow_overflow is not supported under DP. Please use DDP (torch.distributed.launch)."
                )
            else:
                debug_overflow = DebugUnderflowOverflow(self.model)  # noqa

        delay_optimizer_creation = self.sharded_ddp is not None and self.sharded_ddp != ShardedDDPOption.SIMPLE

        if not delay_optimizer_creation:
            self.create_optimizer_and_scheduler(num_training_steps=max_steps)

        self.state = TrainerState()
        self.state.is_hyper_param_search = trial is not None

        # Activate gradient checkpointing if needed
        if args.gradient_checkpointing:
            self.model.gradient_checkpointing_enable()

        model = self._wrap_model(self.model_wrapped)

        # for the rest of this function `model` is the outside model, whether it was wrapped or not
        if model is not self.model:
            self.model_wrapped = model

        if delay_optimizer_creation:
            self.create_optimizer_and_scheduler(num_training_steps=max_steps)

        # Check if saved optimizer or scheduler states exist
        self._load_optimizer_and_scheduler(resume_from_checkpoint)

        # important: at this point:
        # self.model         is the Transformers Model
        # self.model_wrapped is DDP(Transformers Model), Deepspeed(Transformers Model), etc.

        # Train!
        num_examples = (
            self.num_examples(train_dataloader) if train_dataset_is_sized else total_train_batch_size * args.max_steps
        )

        logger.info("***** Running training *****")
        logger.info(f"  Num examples = {num_examples}")
        logger.info(f"  Num Epochs = {num_train_epochs}")
        logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
        logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}")
        logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
        logger.info(f"  Total optimization steps = {max_steps}")

        self.state.epoch = 0
        start_time = time.time()
        epochs_trained = 0
        steps_trained_in_current_epoch = 0
        steps_trained_progress_bar = None

        # Check if continuing training from a checkpoint
        if resume_from_checkpoint is not None and os.path.isfile(
            os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME)
        ):
            self.state = TrainerState.load_from_json(os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME))
            epochs_trained = self.state.global_step // num_update_steps_per_epoch
            if not args.ignore_data_skip:
                steps_trained_in_current_epoch = self.state.global_step % (num_update_steps_per_epoch)
                steps_trained_in_current_epoch *= args.gradient_accumulation_steps
            else:
                steps_trained_in_current_epoch = 0

            logger.info("  Continuing training from checkpoint, will skip to saved global_step")
            logger.info(f"  Continuing training from epoch {epochs_trained}")
            logger.info(f"  Continuing training from global step {self.state.global_step}")
            if not args.ignore_data_skip:
                logger.info(
                    f"  Will skip the first {epochs_trained} epochs then the first {steps_trained_in_current_epoch} "
                    "batches in the first epoch. If this takes a lot of time, you can add the `--ignore_data_skip` "
                    "flag to your launch command, but you will resume the training on data already seen by your model."
                )
                if self.is_local_process_zero() and not args.disable_tqdm:
                    steps_trained_progress_bar = tqdm(total=steps_trained_in_current_epoch)
                    steps_trained_progress_bar.set_description("Skipping the first batches")

        # Update the references
        self.callback_handler.model = self.model
        self.callback_handler.optimizer = self.optimizer
        self.callback_handler.lr_scheduler = self.lr_scheduler
        self.callback_handler.train_dataloader = train_dataloader
        self.state.trial_name = self.hp_name(trial) if self.hp_name is not None else None
        if trial is not None:
            assignments = trial.assignments if self.hp_search_backend == HPSearchBackend.SIGOPT else trial
            self.state.trial_params = hp_params(assignments)
        else:
            self.state.trial_params = None
        # This should be the same if the state has been saved but in case the training arguments changed, it's safer
        # to set this after the load.
        self.state.max_steps = max_steps
        self.state.num_train_epochs = num_train_epochs
        self.state.is_local_process_zero = self.is_local_process_zero()
        self.state.is_world_process_zero = self.is_world_process_zero()

        tr_loss = torch.tensor(0.0).to(args.device)
        # _total_loss_scalar is updated everytime .item() has to be called on tr_loss and stores the sum of all losses
        self._total_loss_scalar = 0.0
        self._globalstep_last_logged = self.state.global_step
        model.zero_grad()

        self.control = self.callback_handler.on_train_begin(args, self.state, self.control)

        # Skip the first epochs_trained epochs to get the random state of the dataloader at the right point.
        if not args.ignore_data_skip:
            for epoch in range(epochs_trained):
                # We just need to begin an iteration to create the randomization of the sampler.
                for _ in train_dataloader:
                    break

        for epoch in range(epochs_trained, num_train_epochs):
            if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):
                train_dataloader.sampler.set_epoch(epoch)
            elif isinstance(train_dataloader.dataset, IterableDatasetShard):
                train_dataloader.dataset.set_epoch(epoch)

            epoch_iterator = train_dataloader

            # Reset the past mems state at the beginning of each epoch if necessary.
            if args.past_index >= 0:
                self._past = None

            steps_in_epoch = (
                len(epoch_iterator) if train_dataset_is_sized else args.max_steps * args.gradient_accumulation_steps
            )
            self.control = self.callback_handler.on_epoch_begin(args, self.state, self.control)
            if isinstance(pruner, Pruning):
                pruner.on_epoch_begin(epoch)

            for step, inputs in enumerate(epoch_iterator):

                # Skip past any already trained steps if resuming training
                if steps_trained_in_current_epoch > 0:
                    steps_trained_in_current_epoch -= 1
                    if steps_trained_progress_bar is not None:
                        steps_trained_progress_bar.update(1)
                    if steps_trained_in_current_epoch == 0:
                        self._load_rng_state(resume_from_checkpoint)
                    continue
                elif steps_trained_progress_bar is not None:
                    steps_trained_progress_bar.close()
                    steps_trained_progress_bar = None

                if step % args.gradient_accumulation_steps == 0:
                    self.control = self.callback_handler.on_step_begin(args, self.state, self.control)
                    if isinstance(pruner, Pruning):
                        pruner.on_batch_begin(step)
                if (
                    ((step + 1) % args.gradient_accumulation_steps != 0)
                    and args.local_rank != -1
                    and args._no_sync_in_gradient_accumulation
                ):
                    # Avoid unnecessary DDP synchronization since there will be no backward pass on this example.
                    with model.no_sync():
                        tr_loss_step = self.training_step(model, inputs)
                else:
                    tr_loss_step = self.training_step(model, inputs)

                if args.logging_nan_inf_filter and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step)):
                    # if loss is nan or inf simply add the average of previous logged losses
                    tr_loss += tr_loss / (1 + self.state.global_step - self._globalstep_last_logged)
                else:
                    tr_loss += tr_loss_step

                self.current_flos += float(self.floating_point_ops(inputs))

                if (step + 1) % args.gradient_accumulation_steps == 0 or (
                    # last step in epoch but step is always smaller than gradient_accumulation_steps
                    steps_in_epoch <= args.gradient_accumulation_steps
                    and (step + 1) == steps_in_epoch
                ):
                    # Gradient clipping
                    if args.max_grad_norm is not None and args.max_grad_norm > 0:

                        if hasattr(self.optimizer, "clip_grad_norm"):
                            # Some optimizers (like the sharded optimizer) have a specific way to do gradient clipping
                            self.optimizer.clip_grad_norm(args.max_grad_norm)
                        elif hasattr(model, "clip_grad_norm_"):
                            # Some models (like FullyShardedDDP) have a specific way to do gradient clipping
                            model.clip_grad_norm_(args.max_grad_norm)
                        else:
                            # Revert to normal clipping otherwise, handling Apex or full precision
                            nn.utils.clip_grad_norm_(
                                model.parameters(),
                                args.max_grad_norm,
                            )

                    # Optimizer step
                    optimizer_was_run = True
                    self.optimizer.step()

                    if optimizer_was_run:
                        self.lr_scheduler.step()

                    model.zero_grad()
                    self.state.global_step += 1
                    self.state.epoch = epoch + (step + 1) / steps_in_epoch
                    self.control = self.callback_handler.on_step_end(args, self.state, self.control)
                    if isinstance(pruner, Pruning):
                        pruner.on_batch_end()
                    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
                else:
                    self.control = self.callback_handler.on_substep_end(args, self.state, self.control)

                if self.control.should_epoch_stop or self.control.should_training_stop:
                    break

            self.control = self.callback_handler.on_epoch_end(args, self.state, self.control)
            if isinstance(pruner, Pruning):
                pruner.on_epoch_end()
            self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)

            if DebugOption.TPU_METRICS_DEBUG in self.args.debug:
                logger.warning(
                    "You enabled PyTorch/XLA debug metrics but you don't have a TPU "
                    "configured. Check your training configuration if this is unexpected."
                )

            if self.control.should_training_stop:
                break

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

        logger.info("\n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n")
        if args.load_best_model_at_end and self.state.best_model_checkpoint is not None:
            # Wait for everyone to get here so we are sur the model has been saved by process 0.
            if args.local_rank != -1:
                dist.barrier()

            logger.info(
                f"Loading best model from {self.state.best_model_checkpoint} (score: {self.state.best_metric})."
            )

            best_model_path = os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME)
            if os.path.exists(best_model_path):
                # We load the model state dict on the CPU to avoid an OOM error.
                state_dict = torch.load(best_model_path, map_location="cpu")
                # If the model is on the GPU, it still works!
                self._load_state_dict_in_model(state_dict)
            else:
                logger.warn(
                    f"Could not locate the best model at {best_model_path}, if you are running a distributed training "
                    "on multiple nodes, you should activate `--save_on_each_node`."
                )

        # add remaining tr_loss
        self._total_loss_scalar += tr_loss.item()
        train_loss = self._total_loss_scalar / self.state.global_step

        metrics = speed_metrics("train", start_time, num_samples=num_train_samples, num_steps=self.state.max_steps)
        self.store_flos()
        metrics["total_flos"] = self.state.total_flos
        metrics["train_loss"] = train_loss

        self.is_in_train = False

        self._memory_tracker.stop_and_update_metrics(metrics)

        self.log(metrics)

        self.control = self.callback_handler.on_train_end(args, self.state, self.control)

        return TrainOutput(self.state.global_step, train_loss, metrics)
