# for god  knows why it cannot "see" performer_pytorch
import os
import re
import sys

import torch
from torch import nn

from dlas.models.lucidrains.performer.autoregressive_wrapper import \
    AutoregressiveWrapper
from dlas.models.lucidrains.performer.performer_pytorch import PerformerLM

sys.path.insert(0, os.path.dirname(os.path.realpath(__file__)))


ENC_PREFIX = 'enc_'
DEC_PREFIX = 'dec_'


def group_dict_by_key(cond, d):
    return_val = [dict(), dict()]
    for key in d.keys():
        match = bool(cond(key))
        ind = int(not match)
        return_val[ind][key] = d[key]
    return (*return_val,)


def string_begins_with(prefix, str):
    return bool(re.match(f'^{prefix}', str))


def group_by_key_prefix(prefix, d):
    return group_dict_by_key(lambda x: string_begins_with(prefix, x), d)


def group_by_key_prefix_and_remove_prefix(prefix, d):
    kwargs_with_prefix, kwargs = group_dict_by_key(
        lambda x: string_begins_with(prefix, x), d)
    kwargs_without_prefix = dict(
        map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
    return kwargs_without_prefix, kwargs


def extract_enc_dec_kwargs(kwargs):
    enc_kwargs, kwargs = group_by_key_prefix_and_remove_prefix(
        ENC_PREFIX, kwargs)
    dec_kwargs, kwargs = group_by_key_prefix_and_remove_prefix(
        DEC_PREFIX, kwargs)
    return enc_kwargs, dec_kwargs, kwargs


def extract_and_set_enc_dec_kwargs(kwargs):
    enc_kwargs, dec_kwargs, kwargs = extract_enc_dec_kwargs(kwargs)
    if 'mask' in enc_kwargs:
        dec_kwargs.setdefault('context_mask', enc_kwargs['mask'])
    return enc_kwargs, dec_kwargs, kwargs


class PerformerEncDec(nn.Module):
    def __init__(
        self,
        dim,
        ignore_index=0,
        pad_value=0,
        tie_token_embeds=False,
        no_projection=False,
        **kwargs
    ):
        super().__init__()
        enc_kwargs, dec_kwargs, _ = extract_enc_dec_kwargs(kwargs)

        assert 'dim' not in dec_kwargs and 'dim' not in enc_kwargs, 'you must set the dim for both encoder and decoder'

        enc_kwargs['dim'] = dec_kwargs['dim'] = dim
        enc_kwargs['no_projection'] = dec_kwargs['no_projection'] = no_projection

        dec_kwargs['causal'] = True
        dec_kwargs['cross_attend'] = True

        enc = PerformerLM(**enc_kwargs)
        dec = PerformerLM(**dec_kwargs)

        if tie_token_embeds:
            enc.token_emb = dec.token_emb

        self.enc = enc
        self.dec = AutoregressiveWrapper(
            dec, ignore_index=ignore_index, pad_value=pad_value)

    @torch.no_grad()
    def generate(self, seq_in, seq_out_start, seq_len, **kwargs):
        enc_kwargs, dec_kwargs, kwargs = extract_and_set_enc_dec_kwargs(kwargs)
        encodings = self.enc(seq_in, return_encodings=True, **enc_kwargs)
        return self.dec.generate(seq_out_start, seq_len, context=encodings, **{**dec_kwargs, **kwargs})

    def forward(self, seq_in, seq_out, enc_mask=None, **kwargs):
        enc_kwargs, dec_kwargs, kwargs = extract_and_set_enc_dec_kwargs(kwargs)
        encodings = self.enc(seq_in, mask=enc_mask,
                             return_encodings=True, **enc_kwargs)
        return self.dec(seq_out, context=encodings, context_mask=enc_mask, **dec_kwargs)