from collections import OrderedDict
from typing import Tuple, Union
import logging
import os

import numpy as np
import torch
import torch.nn.functional as F
from torch import nn

from timm.models.layers import DropPath, trunc_normal_

from .registry import register_lang_encoder

logger = logging.getLogger(__name__)

class LayerNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-12):
        """Construct a layernorm module in the TF style (epsilon inside the square root).
        """
        super(LayerNorm, self).__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.bias = nn.Parameter(torch.zeros(hidden_size))
        self.variance_epsilon = eps

    def forward(self, x):
        pdtype = x.dtype
        x = x.float()
        u = x.mean(-1, keepdim=True)
        s = (x - u).pow(2).mean(-1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.variance_epsilon)
        return self.weight * x.to(pdtype) + self.bias


class QuickGELU(nn.Module):
    def forward(self, x: torch.Tensor):
        return x * torch.sigmoid(1.702 * x)


class ResidualAttentionBlock(nn.Module):
    def __init__(self,
                 d_model: int,
                 n_head: int,
                 attn_mask: torch.Tensor = None,
                 drop_path: float = 0.0):
        super().__init__()

        self.attn = nn.MultiheadAttention(d_model, n_head)
        self.ln_1 = LayerNorm(d_model)
        self.mlp = nn.Sequential(OrderedDict([
            ("c_fc", nn.Linear(d_model, d_model * 4)),
            ("gelu", QuickGELU()),
            ("c_proj", nn.Linear(d_model * 4, d_model))
        ]))
        self.ln_2 = LayerNorm(d_model)
        self.attn_mask = attn_mask
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

    def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None):
        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) \
            if self.attn_mask is not None else None


        return self.attn(
            x, x, x,
            key_padding_mask=key_padding_mask,
            need_weights=False,
            attn_mask=self.attn_mask
        )[0]

    def forward(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None):
        x = x + self.drop_path(self.attention(self.ln_1(x), key_padding_mask=key_padding_mask))
        x = x + self.drop_path(self.mlp(self.ln_2(x)))
        return x


class Transformer(nn.Module):
    def __init__(self,
                 context_length: int,
                 vocab_size: int,
                 width: int,
                 layers: int,
                 heads: int,
                 drop_path: float = 0.0,
                 autogressive: bool =True):
        super().__init__()

        self.token_embedding = nn.Embedding(vocab_size, width)

        self.context_length = context_length
        self.positional_embedding = nn.Parameter(
            torch.empty(self.context_length, width)
        )

        self.width = width
        self.layers = layers
        self.autogressive = autogressive
        attn_mask = self.build_attention_mask() if autogressive else None
        dpr = [x.item() for x in torch.linspace(0, drop_path, layers)]  # stochastic depth decay rule
        self.resblocks = nn.ModuleList(
            [
                ResidualAttentionBlock(width, heads, attn_mask, dpr[i])
                for i in range(layers)
            ]
        )

        self.ln_final = LayerNorm(width)

        trunc_normal_(self.positional_embedding, std=.02)
        # nn.init.normal_(self.token_embedding, std=.02)
        trunc_normal_(self.token_embedding.weight, std=.02)
        self.apply(self._init_weights)

    @property
    def dim_out(self):
        return self.width

    def build_attention_mask(self):
        # lazily create causal attention mask, with full attention between the vision tokens
        # pytorch uses additive attention mask; fill with -inf
        mask = torch.empty(self.context_length, self.context_length)
        mask.fill_(float("-inf"))
        mask.triu_(1)  # zero out the lower diagonal
        return mask

    def _init_weights(self, m):
        if isinstance(m, (nn.Linear, nn.Conv2d)):
            logger.info('=> init weight of Linear/Conv2d from trunc norm')
            trunc_normal_(m.weight, std=0.02)
            if m.bias is not None:
                logger.info('=> init bias of Linear/Conv2d to zeros')
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
            nn.init.constant_(m.bias, 0)

    def load_pretrained(self, pretrained='', pretrained_layers=[], verbose=True):
        if os.path.isfile(pretrained):
            pretrained_dict = torch.load(pretrained, map_location='cpu')
            logging.info(f'=> loading pretrained model {pretrained}')
            model_dict = self.state_dict()
            pretrained_dict = {
                k: v for k, v in pretrained_dict.items()
                if k in model_dict.keys()
            }
            need_init_state_dict = {}
            for k, v in pretrained_dict.items():
                need_init = (
                    k.split('.')[0] in pretrained_layers
                    or pretrained_layers[0] == '*'
                )
                if need_init:
                    if verbose:
                        logging.info(f'=> init {k} from {pretrained}')

                    need_init_state_dict[k] = v
            self.load_state_dict(need_init_state_dict, strict=False)


    @torch.jit.ignore
    def no_weight_decay(self):
        return {
            'positional_embedding',
            'token_embedding',
        }

    def forward(self, input_ids, attention_mask=None):
        key_padding_mask = (input_ids == 0) if not self.autogressive else None
        x = self.token_embedding(input_ids)  # [batch_size, n_ctx, d_model]
        x = x + self.positional_embedding
        x = x.permute(1, 0, 2)  # NLD -> LND
        for block in self.resblocks:
            x = block(x, key_padding_mask)
        x = x.permute(1, 0, 2)  # LND -> NLD

        x = self.ln_final(x)

        return {'last_hidden_state': x}


@register_lang_encoder
def lang_encoder(config_encoder, tokenizer, verbose, **kwargs):
    transformer = Transformer(
        context_length=config_encoder['CONTEXT_LENGTH'],
        vocab_size=tokenizer.vocab_size,
        width=config_encoder['WIDTH'],
        layers=config_encoder['LAYERS'],
        heads=config_encoder['HEADS'],
        autogressive=config_encoder.get('AUTOGRESSIVE', True)
    )

    if config_encoder['LOAD_PRETRAINED']:
        transformer.load_pretrained()

    return transformer
