# Copyright 2022 The OFA-Sys Team. 
# All rights reserved.
# This source code is licensed under the Apache 2.0 license 
# found in the LICENSE file in the root directory.

from dataclasses import dataclass, field
import logging
import os
import math
import torch
from typing import Dict, Optional

from fairseq import search
from fairseq.data import FairseqDataset, iterators
from fairseq.optim.amp_optimizer import AMPOptimizer
from fairseq.dataclass import FairseqDataclass
from fairseq.tasks import FairseqTask, register_task
from omegaconf import DictConfig


logger = logging.getLogger(__name__)


@dataclass
class OFAConfig(FairseqDataclass):
    data: Optional[str] = field(
        default=None,
        metadata={
            "help": "comma separated path to data list, will be iterated upon during epochs "
                    "in round-robin manner; valid data are always in the last"
        },
    )
    selected_cols: Optional[str] = field(
        default=None,
        metadata={"help": "selected cols"},
    )
    bpe_dir: Optional[str] = field(
        default=None,
        metadata={"help": "bpe dir"},
    )
    max_source_positions: int = field(
        default=1024, metadata={"help": "max number of tokens in the source sequence"}
    )
    max_target_positions: int = field(
        default=1024, metadata={"help": "max number of tokens in the target sequence"}
    )
    max_src_length: int = field(
        default=128, metadata={"help": "the maximum src sequence length"}
    )
    max_tgt_length: int = field(
        default=30, metadata={"help": "the maximum target sequence length"}
    )

    code_dict_size: int = field(
        default=8192, metadata={"help": "code dict size"}
    )
    patch_image_size: int = field(
        default=480, metadata={"help": "patch image size"}
    )
    num_bins: int = field(
        default=1000, metadata={"help": "number of quantization bins"}
    )

    imagenet_default_mean_and_std: bool = field(
        default=False,
        metadata={"help": "imagenet normalize"},
    )
    constraint_range: Optional[str] = field(
        default=None,
        metadata={"help": "constraint range"}
    )


@register_task("ofa", dataclass=OFAConfig)
class OFATask(FairseqTask):
    def __init__(self, cfg: OFAConfig, src_dict, tgt_dict):
        super().__init__(cfg)
        self.src_dict = src_dict
        self.tgt_dict = tgt_dict

    @classmethod
    def setup_task(cls, cfg: DictConfig, **kwargs):
        """Setup the task."""

        # load dictionaries
        src_dict = cls.load_dictionary(
            os.path.join(cfg.bpe_dir, "dict.txt")
        )
        tgt_dict = cls.load_dictionary(
            os.path.join(cfg.bpe_dir, "dict.txt")
        )
        src_dict.add_symbol("<mask>")
        tgt_dict.add_symbol("<mask>")
        print(src_dict)
        for i in range(cfg.code_dict_size):
            src_dict.add_symbol("<code_{}>".format(i))
            tgt_dict.add_symbol("<code_{}>".format(i))
        # quantization
        for i in range(cfg.num_bins):
            src_dict.add_symbol("<bin_{}>".format(i))
            tgt_dict.add_symbol("<bin_{}>".format(i))

        logger.info("source dictionary: {} types".format(len(src_dict)))
        logger.info("target dictionary: {} types".format(len(tgt_dict)))
        return cls(cfg, src_dict, tgt_dict)

    def get_batch_iterator(
        self,
        dataset,
        max_tokens=None,
        max_sentences=None,
        max_positions=None,
        ignore_invalid_inputs=False,
        required_batch_size_multiple=1,
        seed=1,
        num_shards=1,
        shard_id=0,
        num_workers=0,
        epoch=1,
        data_buffer_size=0,
        disable_iterator_cache=False,
    ):
        assert isinstance(dataset, FairseqDataset)

        # initialize the dataset with the correct starting epoch
        dataset.set_epoch(epoch)

        # create mini-batches with given size constraints
        batch_sampler = [
            [j for j in range(i, min(i + max_sentences, len(dataset)))]
            for i in range(0, len(dataset), max_sentences)
        ]
        total_row_count = dataset.dataset.get_total_row_count()
        num_batches = math.ceil(math.ceil(total_row_count / num_shards) / max_sentences)
        if len(batch_sampler) < num_batches:
            batch_sampler.append([])

        # return a reusable, sharded iterator
        epoch_iter = iterators.EpochBatchIterator(
            dataset=dataset,
            collate_fn=dataset.collater,
            batch_sampler=batch_sampler,
            seed=seed,
            num_shards=1,
            shard_id=0,
            num_workers=num_workers,
            epoch=epoch,
            buffer_size=data_buffer_size
        )

        return epoch_iter

    def build_model(self, cfg: FairseqDataclass):
        model = super().build_model(cfg)
        bpe_dict = {
            "_name": "gpt2",
            "gpt2_encoder_json": os.path.join(self.cfg.bpe_dir, "encoder.json"),
            "gpt2_vocab_bpe": os.path.join(self.cfg.bpe_dir, "vocab.bpe")
        }
        bpe_dict = DictConfig(bpe_dict)
        self.bpe = self.build_bpe(bpe_dict)
        return model

    def build_generator(
        self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None, prefix_allowed_tokens_fn=None,
    ):
        """
        Build a :class:`~fairseq.SequenceGenerator` instance for this
        task.

        Args:
            models (List[~fairseq.models.FairseqModel]): ensemble of models
            args (fairseq.dataclass.configs.GenerationConfig):
                configuration object (dataclass) for generation
            extra_gen_cls_kwargs (Dict[str, Any]): extra options to pass
                through to SequenceGenerator
            prefix_allowed_tokens_fn (Callable[[int, torch.Tensor], List[int]]):
                If provided, this function constrains the beam search to
                allowed tokens only at each step. The provided function
                should take 2 arguments: the batch ID (`batch_id: int`)
                and a unidimensional tensor of token ids (`inputs_ids:
                torch.Tensor`). It has to return a `List[int]` with the
                allowed tokens for the next generation step conditioned
                on the previously generated tokens (`inputs_ids`) and
                the batch ID (`batch_id`). This argument is useful for
                constrained generation conditioned on the prefix, as
                described in "Autoregressive Entity Retrieval"
                (https://arxiv.org/abs/2010.00904) and
                https://github.com/facebookresearch/GENRE.
        """
        if getattr(args, "score_reference", False):
            from fairseq.sequence_scorer import SequenceScorer

            return SequenceScorer(
                self.target_dictionary,
                compute_alignment=getattr(args, "print_alignment", False),
            )

        from fairseq.sequence_generator import (
            # SequenceGenerator,
            SequenceGeneratorWithAlignment,
        )
        from models.sequence_generator import SequenceGenerator

        # Choose search strategy. Defaults to Beam Search.
        sampling = getattr(args, "sampling", False)
        sampling_topk = getattr(args, "sampling_topk", -1)
        sampling_topp = getattr(args, "sampling_topp", -1.0)
        diverse_beam_groups = getattr(args, "diverse_beam_groups", -1)
        diverse_beam_strength = getattr(args, "diverse_beam_strength", 0.5)
        match_source_len = getattr(args, "match_source_len", False)
        diversity_rate = getattr(args, "diversity_rate", -1)
        constrained = getattr(args, "constraints", False)
        if prefix_allowed_tokens_fn is None:
            prefix_allowed_tokens_fn = getattr(args, "prefix_allowed_tokens_fn", None)
        if (
            sum(
                int(cond)
                for cond in [
                    sampling,
                    diverse_beam_groups > 0,
                    match_source_len,
                    diversity_rate > 0,
                ]
            )
            > 1
        ):
            raise ValueError("Provided Search parameters are mutually exclusive.")
        assert sampling_topk < 0 or sampling, "--sampling-topk requires --sampling"
        assert sampling_topp < 0 or sampling, "--sampling-topp requires --sampling"

        if sampling:
            search_strategy = search.Sampling(
                self.target_dictionary, sampling_topk, sampling_topp
            )
        elif diverse_beam_groups > 0:
            search_strategy = search.DiverseBeamSearch(
                self.target_dictionary, diverse_beam_groups, diverse_beam_strength
            )
        elif match_source_len:
            # this is useful for tagging applications where the output
            # length should match the input length, so we hardcode the
            # length constraints for simplicity
            search_strategy = search.LengthConstrainedBeamSearch(
                self.target_dictionary,
                min_len_a=1,
                min_len_b=0,
                max_len_a=1,
                max_len_b=0,
            )
        elif diversity_rate > -1:
            search_strategy = search.DiverseSiblingsSearch(
                self.target_dictionary, diversity_rate
            )
        elif constrained:
            search_strategy = search.LexicallyConstrainedBeamSearch(
                self.target_dictionary, args.constraints
            )
        elif prefix_allowed_tokens_fn:
            search_strategy = search.PrefixConstrainedBeamSearch(
                self.target_dictionary, prefix_allowed_tokens_fn
            )
        else:
            search_strategy = search.BeamSearch(self.target_dictionary)

        extra_gen_cls_kwargs = extra_gen_cls_kwargs or {}
        if seq_gen_cls is None:
            if getattr(args, "print_alignment", False):
                seq_gen_cls = SequenceGeneratorWithAlignment
                extra_gen_cls_kwargs["print_alignment"] = args.print_alignment
            else:
                seq_gen_cls = SequenceGenerator

        
        return seq_gen_cls(
            models,
            self.target_dictionary,
            beam_size=getattr(args, "beam", 5),
            max_len_a=getattr(args, "max_len_a", 0),
            max_len_b=getattr(args, "max_len_b", 200),
            min_len=getattr(args, "min_len", 1),
            normalize_scores=(not getattr(args, "unnormalized", False)),
            len_penalty=getattr(args, "lenpen", 1),
            unk_penalty=getattr(args, "unkpen", 0),
            temperature=getattr(args, "temperature", 1.0),
            match_source_len=getattr(args, "match_source_len", False),
            no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0),
            search_strategy=search_strategy,
            constraint_range=self.cfg.constraint_range,
            **extra_gen_cls_kwargs,
        )

    def train_step(
        self, sample, model, criterion, optimizer, update_num, ignore_grad=False, **extra_kwargs
    ):
        """
        Do forward and backward, and return the loss as computed by *criterion*
        for the given *model* and *sample*.

        Args:
            sample (dict): the mini-batch. The format is defined by the
                :class:`~fairseq.data.FairseqDataset`.
            model (~fairseq.models.BaseFairseqModel): the model
            criterion (~fairseq.criterions.FairseqCriterion): the criterion
            optimizer (~fairseq.optim.FairseqOptimizer): the optimizer
            update_num (int): the current update
            ignore_grad (bool): multiply loss by 0 if this is set to True

        Returns:
            tuple:
                - the loss
                - the sample size, which is used as the denominator for the
                  gradient
                - logging outputs to display while training
        """
        model.train()
        model.set_num_updates(update_num)
        with torch.autograd.profiler.record_function("forward"):
            with torch.cuda.amp.autocast(enabled=(isinstance(optimizer, AMPOptimizer))):
                loss, sample_size, logging_output = criterion(model, sample, update_num=update_num)
        if ignore_grad:
            loss *= 0
        with torch.autograd.profiler.record_function("backward"):
            optimizer.backward(loss)
        return loss, sample_size, logging_output

    def max_positions(self):
        """Return the max sentence length allowed by the task."""
        return (self.cfg.max_source_positions, self.cfg.max_target_positions)

    @property
    def source_dictionary(self):
        """Return the source :class:`~fairseq.data.Dictionary`."""
        return self.src_dict

    @property
    def target_dictionary(self):
        """Return the target :class:`~fairseq.data.Dictionary`."""
        return self.tgt_dict
