# 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.

import math
from typing import Optional

import torch
import torch.nn as nn
import torch.nn.functional as F

from .multihead_attention import MultiheadAttention  # noqa
from .axial_attention import ColumnSelfAttention, RowSelfAttention


def gelu(x):
    """Implementation of the gelu activation function.
    For information: OpenAI GPT's gelu is slightly different
    (and gives slightly different results):
    0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
    """
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))


def symmetrize(x):
    "Make layer symmetric in final two dimensions, used for contact prediction."
    return x + x.transpose(-1, -2)


def apc(x):
    "Perform average product correct, used for contact prediction."
    a1 = x.sum(-1, keepdims=True)
    a2 = x.sum(-2, keepdims=True)
    a12 = x.sum((-1, -2), keepdims=True)

    avg = a1 * a2
    avg.div_(a12)  # in-place to reduce memory
    normalized = x - avg
    return normalized


class ESM1LayerNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-12, affine=True):
        """Construct a layernorm layer in the TF style (eps inside the sqrt)."""
        super().__init__()
        self.hidden_size = (hidden_size,) if isinstance(hidden_size, int) else tuple(hidden_size)
        self.eps = eps
        self.affine = bool(affine)
        if self.affine:
            self.weight = nn.Parameter(torch.ones(hidden_size))
            self.bias = nn.Parameter(torch.zeros(hidden_size))
        else:
            self.weight, self.bias = None, None

    def forward(self, x):
        dims = tuple(-(i + 1) for i in range(len(self.hidden_size)))
        means = x.mean(dims, keepdim=True)
        x_zeromean = x - means
        variances = x_zeromean.pow(2).mean(dims, keepdim=True)
        x = x_zeromean / torch.sqrt(variances + self.eps)
        if self.affine:
            x = (self.weight * x) + self.bias
        return x


try:
    from apex.normalization import FusedLayerNorm as _FusedLayerNorm

    class ESM1bLayerNorm(_FusedLayerNorm):
        @torch.jit.unused
        def forward(self, x):
            if not x.is_cuda:
                return super().forward(x)
            else:
                with torch.cuda.device(x.device):
                    return super().forward(x)

except ImportError:
    from torch.nn import LayerNorm as ESM1bLayerNorm


class TransformerLayer(nn.Module):
    """Transformer layer block."""

    def __init__(
        self,
        embed_dim,
        ffn_embed_dim,
        attention_heads,
        add_bias_kv=True,
        use_esm1b_layer_norm=False,
        use_rotary_embeddings: bool = False,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.ffn_embed_dim = ffn_embed_dim
        self.attention_heads = attention_heads
        self.use_rotary_embeddings = use_rotary_embeddings
        self._init_submodules(add_bias_kv, use_esm1b_layer_norm)

    def _init_submodules(self, add_bias_kv, use_esm1b_layer_norm):
        BertLayerNorm = ESM1bLayerNorm if use_esm1b_layer_norm else ESM1LayerNorm

        self.self_attn = MultiheadAttention(
            self.embed_dim,
            self.attention_heads,
            add_bias_kv=add_bias_kv,
            add_zero_attn=False,
            use_rotary_embeddings=self.use_rotary_embeddings,
        )
        self.self_attn_layer_norm = BertLayerNorm(self.embed_dim)

        self.fc1 = nn.Linear(self.embed_dim, self.ffn_embed_dim)
        self.fc2 = nn.Linear(self.ffn_embed_dim, self.embed_dim)

        self.final_layer_norm = BertLayerNorm(self.embed_dim)

    def forward(
        self, x, self_attn_mask=None, self_attn_padding_mask=None, need_head_weights=False
    ):
        residual = x
        x = self.self_attn_layer_norm(x)
        x, attn = self.self_attn(
            query=x,
            key=x,
            value=x,
            key_padding_mask=self_attn_padding_mask,
            need_weights=True,
            need_head_weights=need_head_weights,
            attn_mask=self_attn_mask,
        )
        x = residual + x

        residual = x
        x = self.final_layer_norm(x)
        x = gelu(self.fc1(x))
        x = self.fc2(x)
        x = residual + x
        #print(f'------{attn.half().dtype}-----')

        return x, attn#.half() ###


class AxialTransformerLayer(nn.Module):
    """Implements an Axial MSA Transformer block."""

    def __init__(
        self,
        embedding_dim: int = 768,
        ffn_embedding_dim: int = 3072,
        num_attention_heads: int = 8,
        dropout: float = 0.1,
        attention_dropout: float = 0.1,
        activation_dropout: float = 0.1,
        max_tokens_per_msa: int = 2**14,
    ) -> None:
        super().__init__()

        # Initialize parameters
        self.embedding_dim = embedding_dim
        self.dropout_prob = dropout

        row_self_attention = RowSelfAttention(
            embedding_dim,
            num_attention_heads,
            dropout=dropout,
            max_tokens_per_msa=max_tokens_per_msa,
        )

        column_self_attention = ColumnSelfAttention(
            embedding_dim,
            num_attention_heads,
            dropout=dropout,
            max_tokens_per_msa=max_tokens_per_msa,
        )

        feed_forward_layer = FeedForwardNetwork(
            embedding_dim,
            ffn_embedding_dim,
            activation_dropout=activation_dropout,
            max_tokens_per_msa=max_tokens_per_msa,
        )

        self.row_self_attention = self.build_residual(row_self_attention)
        self.column_self_attention = self.build_residual(column_self_attention)
        self.feed_forward_layer = self.build_residual(feed_forward_layer)

    def build_residual(self, layer: nn.Module):
        return NormalizedResidualBlock(
            layer,
            self.embedding_dim,
            self.dropout_prob,
        )

    def forward(
        self,
        x: torch.Tensor,
        self_attn_mask: Optional[torch.Tensor] = None,
        self_attn_padding_mask: Optional[torch.Tensor] = None,
        need_head_weights: bool = False,
    ):
        """
        LayerNorm is applied either before or after the self-attention/ffn
        modules similar to the original Transformer implementation.
        """
        x, row_attn = self.row_self_attention(
            x,
            self_attn_mask=self_attn_mask,
            self_attn_padding_mask=self_attn_padding_mask,
        )
        x, column_attn = self.column_self_attention(
            x,
            self_attn_mask=self_attn_mask,
            self_attn_padding_mask=self_attn_padding_mask,
        )
        x = self.feed_forward_layer(x)
        if need_head_weights:
            return x, column_attn, row_attn
        else:
            return x


class LearnedPositionalEmbedding(nn.Embedding):
    """
    This module learns positional embeddings up to a fixed maximum size.
    Padding ids are ignored by either offsetting based on padding_idx
    or by setting padding_idx to None and ensuring that the appropriate
    position ids are passed to the forward function.
    """

    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int):
        if padding_idx is not None:
            num_embeddings_ = num_embeddings + padding_idx + 1
        else:
            num_embeddings_ = num_embeddings
        super().__init__(num_embeddings_, embedding_dim, padding_idx)
        self.max_positions = num_embeddings

    def forward(self, input: torch.Tensor):
        """Input is expected to be of size [bsz x seqlen]."""
        if input.size(1) > self.max_positions:
            raise ValueError(
                f"Sequence length {input.size(1)} above maximum "
                f" sequence length of {self.max_positions}"
            )
        mask = input.ne(self.padding_idx).int()
        positions = (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + self.padding_idx
        return F.embedding(
            positions,
            self.weight,
            self.padding_idx,
            self.max_norm,
            self.norm_type,
            self.scale_grad_by_freq,
            self.sparse,
        )


class SinusoidalPositionalEmbedding(nn.Module):
    def __init__(self, embed_dim, padding_idx, learned=False):
        super().__init__()
        self.embed_dim = embed_dim
        self.padding_idx = padding_idx
        self.register_buffer("_float_tensor", torch.FloatTensor(1))
        self.weights = None

    def forward(self, x):
        bsz, seq_len = x.shape
        max_pos = self.padding_idx + 1 + seq_len
        if self.weights is None or max_pos > self.weights.size(0):
            self.weights = self.get_embedding(max_pos)
        self.weights = self.weights.type_as(self._float_tensor)

        positions = self.make_positions(x)
        return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach()

    def make_positions(self, x):
        mask = x.ne(self.padding_idx)
        range_buf = torch.arange(x.size(1), device=x.device).expand_as(x) + self.padding_idx + 1
        positions = range_buf.expand_as(x)
        return positions * mask.long() + self.padding_idx * (1 - mask.long())

    def get_embedding(self, num_embeddings):
        half_dim = self.embed_dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
        if self.embed_dim % 2 == 1:
            # zero pad
            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
        if self.padding_idx is not None:
            emb[self.padding_idx, :] = 0
        return emb


class RobertaLMHead(nn.Module):
    """Head for masked language modeling."""

    def __init__(self, embed_dim, output_dim, weight):
        super().__init__()
        self.dense = nn.Linear(embed_dim, embed_dim)
        self.layer_norm = ESM1bLayerNorm(embed_dim)
        self.weight = weight
        self.bias = nn.Parameter(torch.zeros(output_dim))

    def forward(self, features):
        x = self.dense(features)
        x = gelu(x)
        x = self.layer_norm(x)
        # project back to size of vocabulary with bias
        x = F.linear(x, self.weight) + self.bias
        return x


class ContactPredictionHead(nn.Module):
    """Performs symmetrization, apc, and computes a logistic regression on the output features"""

    def __init__(
        self,
        in_features: int,
        prepend_bos: bool,
        append_eos: bool,
        bias=True,
        eos_idx: Optional[int] = None,
    ):
        super().__init__()
        self.in_features = in_features
        self.prepend_bos = prepend_bos
        self.append_eos = append_eos
        if append_eos and eos_idx is None:
            raise ValueError("Using an alphabet with eos token, but no eos token was passed in.")
        self.eos_idx = eos_idx
        self.regression = nn.Linear(in_features, 1, bias)
        self.activation = nn.Sigmoid()

    def forward(self, tokens, attentions):
        # remove eos token attentions
        if self.append_eos:
            eos_mask = tokens.ne(self.eos_idx).to(attentions)
            eos_mask = eos_mask.unsqueeze(1) * eos_mask.unsqueeze(2)
            attentions = attentions * eos_mask[:, None, None, :, :]
            attentions = attentions[..., :-1, :-1]
        # remove cls token attentions
        if self.prepend_bos:
            attentions = attentions[..., 1:, 1:]
        batch_size, layers, heads, seqlen, _ = attentions.size()
        attentions = attentions.view(batch_size, layers * heads, seqlen, seqlen)

        # features: B x C x T x T
        attentions = attentions.to(
            self.regression.weight.device
        ) # attentions always float32, may need to convert to float16
        attentions = apc(symmetrize(attentions))
        attentions = attentions.permute(0, 2, 3, 1)
        #print(f'----------{attentions.dtype, attentions.float().dtype}----') 
        return attentions.sum(dim=-1), self.activation(self.regression(attentions).squeeze(3))#float().to(self.regression.weight.device)).squeeze(3))


class NormalizedResidualBlock(nn.Module):
    def __init__(
        self,
        layer: nn.Module,
        embedding_dim: int,
        dropout: float = 0.1,
    ):
        super().__init__()
        self.embedding_dim = embedding_dim

        self.layer = layer
        self.dropout_module = nn.Dropout(
            dropout,
        )
        self.layer_norm = ESM1bLayerNorm(self.embedding_dim)

    def forward(self, x, *args, **kwargs):
        residual = x
        x = self.layer_norm(x)
        outputs = self.layer(x, *args, **kwargs)
        if isinstance(outputs, tuple):
            x, *out = outputs
        else:
            x = outputs
            out = None

        x = self.dropout_module(x)
        x = residual + x

        if out is not None:
            return (x,) + tuple(out)
        else:
            return x


class FeedForwardNetwork(nn.Module):
    def __init__(
        self,
        embedding_dim: int,
        ffn_embedding_dim: int,
        activation_dropout: float = 0.1,
        max_tokens_per_msa: int = 2**14,
    ):
        super().__init__()
        self.embedding_dim = embedding_dim
        self.ffn_embedding_dim = ffn_embedding_dim
        self.max_tokens_per_msa = max_tokens_per_msa
        self.activation_fn = nn.GELU()
        self.activation_dropout_module = nn.Dropout(
            activation_dropout,
        )
        self.fc1 = nn.Linear(embedding_dim, ffn_embedding_dim)
        self.fc2 = nn.Linear(ffn_embedding_dim, embedding_dim)

    def forward(self, x):
        x = self.activation_fn(self.fc1(x))
        x = self.activation_dropout_module(x)
        x = self.fc2(x)
        return x