from typing import Optional
import math

import torch
import torch.nn as nn


class LoraLayer():
    def __init__(
        self,
        r: int,
        lora_alpha: int,
        lora_dropout: float,
        merge_weights: bool,
    ):
        self.r = r
        self.lora_alpha = lora_alpha
        self.lora_dropout_p = lora_dropout

        if lora_dropout > 0.:
            self.lora_dropout = nn.Dropout(p=lora_dropout)
        else:
            self.lora_dropout = nn.Identity()

        self.merged = False
        self.merge_weights = merge_weights


class LoraEmbedding(nn.Embedding, LoraLayer):
    def __init__(
        self,
        num_embeddings: int,
        embedding_dim: int,
        r: int = 0,
        lora_alpha: int = 1,
        lora_dropout: float = 0.0,
        merge_weights: bool = True,
        **kwargs
    ):
        nn.Embedding.__init__(self, num_embeddings, embedding_dim, **kwargs)
        LoraLayer.__init__(
            self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights
        )

        self.register_buffer('trainable_ids', self.weight.new_zeros(num_embeddings, dtype=torch.long) - 1)

        self.lora_A = nn.ParameterList()
        self.lora_B = nn.Linear(r, embedding_dim, bias=False)
        self.scaling = self.lora_alpha / self.r
        self.weight.requires_grad = False

        self.reset_parameters()

    def new_resized(self, new_num_embeddings: int, initializer_factor: Optional[float] = None):
        n = min(self.num_embeddings, new_num_embeddings)

        new_emb = LoraEmbedding(
            new_num_embeddings,
            self.embedding_dim,
            self.r,
            self.lora_alpha,
            self.lora_dropout_p,
            device=self.weight.device,
            dtype=self.weight.dtype
        )
        if initializer_factor is not None:
            new_emb.weight.data.normal_(mean=0.0, std=initializer_factor * 0.02)
        else:
            nn.init.zeros_(new_emb.weight.data)
        new_emb.weight.data[:n, :] = self.weight.data[:n, :]
        for param in self.lora_A:
            new_emb.lora_A.append(param)
        new_emb.lora_B.weight[:].data = self.lora_B.weight[:].data
        new_emb.trainable_ids[:n] = self.trainable_ids[:n]

        return new_emb

    def mark_trainable(self, input_ids: torch.LongTensor):
        trainable_ids = self.trainable_ids[input_ids]
        new_ids = input_ids[trainable_ids == -1]

        if new_ids.shape[0] == 0:
            return

        n1 = len(self.lora_A)
        n2 = n1 + new_ids.shape[0]
        self.trainable_ids[new_ids] = torch.arange(n1, n2)
        for _ in new_ids:
            w = self.weight.new_zeros(self.r)
            self.lora_A.append(w)

        if len(self.lora_A) > 1:
            elems = torch.stack([param for param in self.lora_A])
            nn.init.kaiming_uniform_(elems, a=math.sqrt(5))

    def get_weights(self, input_ids: torch.Tensor):
        if len(input_ids.shape) != 1:
            return torch.stack([self.get_weights(batch) for batch in input_ids])

        weights = self.weight.new_zeros((input_ids.shape[0], self.embedding_dim))

        if not self.merged:
            trainable_ids = self.trainable_ids[input_ids]
            mask = ~(trainable_ids == -1)
            elems = [self.lora_A[id] for id in trainable_ids[mask]]

            if len(elems) != 0:
                w = self.lora_B(self.lora_dropout(torch.stack(elems))) * self.scaling
                weights[mask] = w.to(dtype=weights.dtype)

        return weights

    def persist(self):
        self.weight.data += self.get_weights(torch.arange(self.trainable_ids.shape[0]))
        self.trainable_ids[:] = -1
        self.lora_A = nn.ParameterList()
        nn.init.zeros_(self.lora_B.weight)

    def reset_parameters(self):
        nn.Embedding.reset_parameters(self)
        if hasattr(self, "lora_A"):
            self.trainable_ids[:] = -1
            self.lora_A = nn.ParameterList()
            nn.init.zeros_(self.lora_B.weight)

    def train(self, mode: bool = True):
        nn.Embedding.train(self, mode)
        self.lora_A.train(mode)
        self.lora_B.train(mode)
        if not mode and self.merge_weights and not self.merged:
            self.weight.data += self.get_weights(torch.arange(self.trainable_ids.shape[0]))
            self.merged = True
        elif self.merge_weights and self.merged:
            self.weight.data -= self.get_weights(torch.arange(self.trainable_ids.shape[0]))
            self.merged = False

    def eval(self):
        nn.Embedding.eval(self)
        self.lora_A.eval()
        self.lora_B.eval()

    def forward(self, input_ids: torch.LongTensor):
        result = nn.Embedding.forward(self, input_ids)
        result += self.get_weights(input_ids)
        return result