# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from dataclasses import dataclass, field
from math import log
import torch
from fairseq import utils
from fairseq.data import LanguagePairDataset
from fairseq.dataclass import ChoiceEnum
from fairseq.tasks import register_task
from fairseq.tasks.translation import TranslationConfig, TranslationTask, load_langpair_dataset
from fairseq.utils import new_arange
import logging
from omegaconf import II
import numpy as np

NOISE_CHOICES = ChoiceEnum(["random_delete", "random_mask", "no_noise", "full_mask", "block_mask"])


@dataclass
class TranslationLevenshteinConfig(TranslationConfig):
    noise: NOISE_CHOICES = field(
        default="random_delete",
        metadata={
            "help": "type of noise"
        },
    )
    start_p: float = field(
        default=0.5, metadata={"help": "minus prob"}
    )
    minus_p: float = field(
        default=0.2, metadata={"help": "minus prob"}
    )
    total_up: int = field(
        default=300000, metadata={"help": "total updates"}
    )
    block_size: int = field(
        default=5, metadata={"help": "block size"}
    )


logger = logging.getLogger(__name__)


@register_task("translation_lev_modified", dataclass=TranslationLevenshteinConfig)
class TranslationLevenshteinModifiedTask(TranslationTask):
    """
    Translation (Sequence Generation) task for Levenshtein Transformer
    See `"Levenshtein Transformer" <https://arxiv.org/abs/1905.11006>`_.
    """

    cfg: TranslationLevenshteinConfig

    def load_dataset(self, split, epoch=1, combine=False, **kwargs):
        """Load a given dataset split.

        Args:
            split (str): name of the split (e.g., train, valid, test)
        """
        paths = utils.split_paths(self.cfg.data)
        assert len(paths) > 0
        data_path = paths[(epoch - 1) % len(paths)]

        # infer langcode
        src, tgt = self.cfg.source_lang, self.cfg.target_lang

        self.datasets[split] = load_langpair_dataset(
            data_path,
            split,
            src,
            self.src_dict,
            tgt,
            self.tgt_dict,
            combine=combine,
            dataset_impl=self.cfg.dataset_impl,
            upsample_primary=self.cfg.upsample_primary,
            left_pad_source=self.cfg.left_pad_source,
            left_pad_target=self.cfg.left_pad_target,
            max_source_positions=self.cfg.max_source_positions,
            max_target_positions=self.cfg.max_target_positions,
            truncate_source=self.cfg.truncate_source,
        )

    def inject_noise(self, target_tokens):
        def _random_delete(target_tokens):
            pad = self.tgt_dict.pad()
            bos = self.tgt_dict.bos()
            eos = self.tgt_dict.eos()

            max_len = target_tokens.size(1)
            target_mask = target_tokens.eq(pad)
            target_score = target_tokens.clone().float().uniform_()
            target_score.masked_fill_(
                target_tokens.eq(bos) | target_tokens.eq(eos), 0.0
            )
            target_score.masked_fill_(target_mask, 1)
            target_score, target_rank = target_score.sort(1)
            target_length = target_mask.size(1) - target_mask.float().sum(
                1, keepdim=True
            )

            # do not delete <bos> and <eos> (we assign 0 score for them)
            target_cutoff = (
                    2
                    + (
                            (target_length - 2)
                            * target_score.new_zeros(target_score.size(0), 1).uniform_()
                    ).long()
            )
            target_cutoff = target_score.sort(1)[1] >= target_cutoff

            prev_target_tokens = (
                target_tokens.gather(1, target_rank)
                    .masked_fill_(target_cutoff, pad)
                    .gather(1, target_rank.masked_fill_(target_cutoff, max_len).sort(1)[1])
            )
            prev_target_tokens = prev_target_tokens[
                                 :, : prev_target_tokens.ne(pad).sum(1).max()
                                 ]

            return prev_target_tokens

        def _random_mask(target_tokens):
            pad = self.tgt_dict.pad()
            bos = self.tgt_dict.bos()
            eos = self.tgt_dict.eos()
            unk = self.tgt_dict.unk()

            target_masks = (
                    target_tokens.ne(pad) & target_tokens.ne(bos) & target_tokens.ne(eos)
            )
            target_score = target_tokens.clone().float().uniform_()
            target_score.masked_fill_(~target_masks, 2.0)
            target_length = target_masks.sum(1).float()
            target_length = target_length * target_length.clone().uniform_()
            target_length = target_length + 1  # make sure to mask at least one token.

            _, target_rank = target_score.sort(1)
            target_cutoff = new_arange(target_rank) < target_length[:, None].long()
            prev_target_tokens = target_tokens.masked_fill(
                target_cutoff.scatter(1, target_rank, target_cutoff), unk
            )
            return prev_target_tokens

        def _full_mask(target_tokens):
            pad = self.tgt_dict.pad()
            bos = self.tgt_dict.bos()
            eos = self.tgt_dict.eos()
            unk = self.tgt_dict.unk()

            target_mask = (
                    target_tokens.eq(bos) | target_tokens.eq(eos) | target_tokens.eq(pad)
            )
            return target_tokens.masked_fill(~target_mask, unk)

        def _block_mask(target_tokens):
            block_size = self.cfg.block_size
            pad = self.tgt_dict.pad()
            unk = self.tgt_dict.unk()
            target_masks = target_tokens.ne(pad)
            target_length = target_masks.sum(1).float()
            cutoff_length = target_length * target_length.clone().uniform_()
            cutoff_length = cutoff_length.int() + 1  # make sure to mask at least one token.
            prev_target_tokens = torch.ones((target_tokens.size(0),
                                             target_tokens.size(1) + block_size)).to(target_tokens)
            padded_target_tokens = torch.ones((target_tokens.size(0),
                                             target_tokens.size(1) + block_size)).to(target_tokens)
            for i in range(target_tokens.size(0)):
                remain_length = target_length[i].int() - cutoff_length[i]
                prev_target_tokens[i][:remain_length] = target_tokens[i][:remain_length]
                prev_target_tokens[i][remain_length:block_size + remain_length] = unk
                padded_target_tokens[i][:target_tokens.size(1)] = target_tokens[i]
            prev_target_tokens = prev_target_tokens[
                                 :, : prev_target_tokens.ne(pad).sum(1).max()
                                 ]
            padded_target_tokens = padded_target_tokens[
                                   :, : prev_target_tokens.ne(pad).sum(1).max()
                                   ]
            return prev_target_tokens, padded_target_tokens

        if self.cfg.noise == "random_delete":
            return _random_delete(target_tokens)
        elif self.cfg.noise == "random_mask":
            return _random_mask(target_tokens)
        elif self.cfg.noise == "block_mask":
            return _block_mask(target_tokens)
        elif self.cfg.noise == "full_mask":
            return _full_mask(target_tokens)
        elif self.cfg.noise == "no_noise":
            return target_tokens
        else:
            raise NotImplementedError

    def build_generator(self, models, args, **unused):
        # add models input to match the API for SequenceGenerator
        from fairseq.iterative_refinement_generator import IterativeRefinementGenerator

        return IterativeRefinementGenerator(
            self.target_dictionary,
            eos_penalty=getattr(args, "iter_decode_eos_penalty", 0.0),
            max_iter=getattr(args, "iter_decode_max_iter", 10),
            beam_size=getattr(args, "iter_decode_with_beam", 1),
            reranking=getattr(args, "iter_decode_with_external_reranker", False),
            decoding_format=getattr(args, "decoding_format", None),
            adaptive=not getattr(args, "iter_decode_force_max_iter", False),
            retain_history=getattr(args, "retain_iter_history", False),
        )

    def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None):
        if constraints is not None:
            # Though see Susanto et al. (ACL 2020): https://www.aclweb.org/anthology/2020.acl-main.325/
            raise NotImplementedError(
                "Constrained decoding with the translation_lev task is not supported"
            )

        return LanguagePairDataset(
            src_tokens, src_lengths, self.source_dictionary, append_bos=False
        )

    def train_step(
            self, sample, model, criterion, optimizer, update_num, ignore_grad=False
    ):
        model.train()
        train_ratio = max(0, min(1, update_num / self.cfg.total_up))
        sample["glat"] = {"context_p": self.cfg.start_p - self.cfg.minus_p * train_ratio}
        sample["prev_target"], sample["target"] = self.inject_noise(sample["target"])
        with torch.autograd.profiler.record_function("forward"):
            loss, sample_size, logging_output = criterion(model, sample)
        if ignore_grad:
            loss *= 0
        with torch.autograd.profiler.record_function("backward"):
            optimizer.backward(loss)
        return loss, sample_size, logging_output

    def valid_step(self, sample, model, criterion):
        model.eval()
        with torch.no_grad():
            sample["prev_target"], sample["target"] = self.inject_noise(sample["target"])
            loss, sample_size, logging_output = criterion(model, sample)
            EVAL_BLEU_ORDER = 4
            if self.cfg.eval_bleu:
                bleu = self._inference_with_bleu(self.sequence_generator, sample, model)
                logging_output["_bleu_sys_len"] = bleu.sys_len
                logging_output["_bleu_ref_len"] = bleu.ref_len
                # we split counts into separate entries so that they can be
                # summed efficiently across workers using fast-stat-sync
                assert len(bleu.counts) == EVAL_BLEU_ORDER
                for i in range(EVAL_BLEU_ORDER):
                    logging_output["_bleu_counts_" + str(i)] = bleu.counts[i]
                    logging_output["_bleu_totals_" + str(i)] = bleu.totals[i]
        return loss, sample_size, logging_output

    def _inference_with_bleu(self, generator, sample, model):
        import sacrebleu

        def decode(toks, escape_unk=False):
            s = self.tgt_dict.string(
                toks.int().cpu(),
                self.cfg.eval_bleu_remove_bpe,
                # The default unknown string in fairseq is `<unk>`, but
                # this is tokenized by sacrebleu as `< unk >`, inflating
                # BLEU scores. Instead, we use a somewhat more verbose
                # alternative that is unlikely to appear in the real
                # reference, but doesn't get split into multiple tokens.
                unk_string=("UNKNOWNTOKENINREF" if escape_unk else "UNKNOWNTOKENINHYP"),
            )
            if self.tokenizer:
                s = self.tokenizer.decode(s)
            return s

        gen_out = self.inference_step(generator, [model], sample, prefix_tokens=None)
        hyps, refs = [], []
        for i in range(len(gen_out)):
            hyps.append(decode(gen_out[i][0]["tokens"]))
            refs.append(
                decode(
                    utils.strip_pad(sample["target"][i], self.tgt_dict.pad()),
                    escape_unk=True,  # don't count <unk> as matches to the hypo
                )
            )
        if self.cfg.eval_bleu_print_samples:
            logger.info("example hypothesis: " + hyps[0])
            logger.info("example reference: " + refs[0])
        if self.cfg.eval_tokenized_bleu:
            return sacrebleu.corpus_bleu(hyps, [refs], tokenize="none")
        else:
            return sacrebleu.corpus_bleu(hyps, [refs])
