#  ------------------------------------------------------------------------------------------
#  Copyright (c) Microsoft Corporation. All rights reserved.
#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
#  ------------------------------------------------------------------------------------------
import math
import torch
import torch.nn as nn
from typing import Optional, List
from typing import Dict
import torch.nn.functional as F




def mark_only_lora_as_trainable(model: nn.Module, bias: str = 'none') -> None:
    for n, p in model.named_parameters():
        if 'lora_' not in n:
            p.requires_grad = False
    if bias == 'none':
        return
    elif bias == 'all':
        for n, p in model.named_parameters():
            if 'bias' in n:
                p.requires_grad = True
    elif bias == 'lora_only':
        for m in model.modules():
            if isinstance(m, LoRALayer) and \
                    hasattr(m, 'bias') and \
                    m.bias is not None:
                m.bias.requires_grad = True
    else:
        raise NotImplementedError


def lora_state_dict(model: nn.Module, bias: str = 'none') -> Dict[str, torch.Tensor]:
    my_state_dict = model.state_dict()
    if bias == 'none':
        return {k: my_state_dict[k] for k in my_state_dict if 'lora_' in k}
    elif bias == 'all':
        return {k: my_state_dict[k] for k in my_state_dict if 'lora_' in k or 'bias' in k}
    elif bias == 'lora_only':
        to_return = {}
        for k in my_state_dict:
            if 'lora_' in k:
                to_return[k] = my_state_dict[k]
                bias_name = k.split('lora_')[0]+'bias'
                if bias_name in my_state_dict:
                    to_return[bias_name] = my_state_dict[bias_name]
        return to_return
    else:
        raise NotImplementedError


class LoRALayer():
    def __init__(
            self,
            r: int,
            lora_alpha: int,
            lora_dropout: float,
            merge_weights: bool,
    ):
        self.r = r
        self.lora_alpha = lora_alpha
        # Optional dropout
        if lora_dropout > 0.:
            self.lora_dropout = nn.Dropout(p=lora_dropout)
        else:
            self.lora_dropout = lambda x: x
        # Mark the weight as unmerged
        self.merged = False
        self.merge_weights = merge_weights


class LoraEmbedding(nn.Embedding, LoRALayer):
    # LoRA implemented in a dense layer
    def __init__(
            self,
            num_embeddings: int,
            embedding_dim: int,
            r: int = 0,
            lora_alpha: int = 1,
            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=0,
                           merge_weights=merge_weights)
        # Actual trainable parameters
        if r > 0:
            self.lora_A = nn.Parameter(self.weight.new_zeros((r, num_embeddings)))
            self.lora_B = nn.Parameter(self.weight.new_zeros((embedding_dim, r)))
            self.scaling = self.lora_alpha / self.r
            # Freezing the pre-trained weight matrix
            self.weight.requires_grad = False
        self.reset_parameters()

    def reset_parameters(self):
        nn.Embedding.reset_parameters(self)
        if hasattr(self, 'lora_A'):
            # initialize A the same way as the default for nn.Linear and B to zero
            nn.init.zeros_(self.lora_A)
            nn.init.normal_(self.lora_B)

    def train(self, mode: bool = True):
        nn.Embedding.train(self, mode)
        if self.merge_weights and self.merged:
            # Make sure that the weights are not merged
            if self.r > 0:
                self.weight.data -= (self.lora_B @ self.lora_A).T * self.scaling
            self.merged = False

    def eval(self):
        nn.Embedding.eval(self)
        if self.merge_weights and not self.merged:
            # Merge the weights and mark it
            if self.r > 0:
                self.weight.data += (self.lora_B @ self.lora_A) * self.scaling
            self.merged = True

    def forward(self, x: torch.Tensor):
        if self.r > 0 and not self.merged:
            result = nn.Embedding.forward(self, x)
            if self.r > 0:
                after_A = F.embedding(
                    x, self.lora_A.T, self.padding_idx, self.max_norm,
                    self.norm_type, self.scale_grad_by_freq, self.sparse
                )
                result += (after_A @ self.lora_B.T) * self.scaling
            return result
        else:
            return nn.Embedding.forward(self, x)


class LoraLinear(nn.Linear, LoRALayer):
    # LoRA implemented in a dense layer
    def __init__(
            self,
            in_features: int,
            out_features: int,
            r: int = 0,
            lora_alpha: int = 1,
            lora_dropout: float = 0.,
            fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out)
            merge_weights: bool = True,
            **kwargs
    ):
        nn.Linear.__init__(self, in_features, out_features, **kwargs)
        LoRALayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout,
                           merge_weights=merge_weights)

        self.fan_in_fan_out = fan_in_fan_out
        # Actual trainable parameters
        if r > 0:
            self.lora_A = nn.Parameter(self.weight.new_zeros((r, in_features)))
            self.lora_B = nn.Parameter(self.weight.new_zeros((out_features, r)))
            self.scaling = self.lora_alpha / self.r
            # Freezing the pre-trained weight matrix
            self.weight.requires_grad = False
        self.reset_parameters()
        if fan_in_fan_out:
            self.weight.data = self.weight.data.T

    def reset_parameters(self):
        nn.Linear.reset_parameters(self)
        if hasattr(self, 'lora_A'):
            # initialize A the same way as the default for nn.Linear and B to zero
            nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
            nn.init.zeros_(self.lora_B)

    def train(self, mode: bool = True):
        def T(w):
            return w.T if self.fan_in_fan_out else w
        nn.Linear.train(self, mode)
        if self.merge_weights and self.merged:
            # Make sure that the weights are not merged
            if self.r > 0:
                self.weight.data -= T(self.lora_B @ self.lora_A) * self.scaling
            self.merged = False

    def eval(self):
        def T(w):
            return w.T if self.fan_in_fan_out else w
        nn.Linear.eval(self)
        if self.merge_weights and not self.merged:
            # Merge the weights and mark it
            if self.r > 0:
                self.weight.data += T(self.lora_B @ self.lora_A) * self.scaling
            self.merged = True

    def forward(self, x: torch.Tensor):
        def T(w):
            return w.T if self.fan_in_fan_out else w
        if self.r > 0 and not self.merged:
            result = x @ T(self.weight.T)
            if self.bias is not None:
                result += self.bias
            if self.r > 0:
                result += (self.lora_dropout(x) @ self.lora_A.T @ self.lora_B.T) * self.scaling
            return result
        else:
            return F.linear(x, T(self.weight), bias=self.bias)

class MergedLinear(nn.Linear, LoRALayer):
    # LoRA implemented in a dense layer
    def __init__(
            self,
            in_features: int,
            out_features: int,
            r: int = 0,
            lora_alpha: int = 1,
            lora_dropout: float = 0.,
            enable_lora: List[bool] = [False],
            fan_in_fan_out: bool = False,
            merge_weights: bool = True,
            **kwargs
    ):
        nn.Linear.__init__(self, in_features, out_features, **kwargs)
        LoRALayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout,
                           merge_weights=merge_weights)
        assert out_features % len(enable_lora) == 0, \
            'The length of enable_lora must divide out_features'
        self.enable_lora = enable_lora
        self.fan_in_fan_out = fan_in_fan_out
        # Actual trainable parameters
        if r > 0 and any(enable_lora):
            self.lora_A = nn.Parameter(
                self.weight.new_zeros((r * sum(enable_lora), in_features)))
            self.lora_B = nn.Parameter(
                self.weight.new_zeros((out_features // len(enable_lora) * sum(enable_lora), r))
            ) # weights for Conv1D with groups=sum(enable_lora)
            self.scaling = self.lora_alpha / self.r
            # Freezing the pre-trained weight matrix
            self.weight.requires_grad = False
            # Compute the indices
            self.lora_ind = self.weight.new_zeros(
                (out_features, ), dtype=torch.bool
            ).view(len(enable_lora), -1)
            self.lora_ind[enable_lora, :] = True
            self.lora_ind = self.lora_ind.view(-1)
        self.reset_parameters()
        if fan_in_fan_out:
            self.weight.data = self.weight.data.T

    def reset_parameters(self):
        nn.Linear.reset_parameters(self)
        if hasattr(self, 'lora_A'):
            # initialize A the same way as the default for nn.Linear and B to zero
            nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
            nn.init.zeros_(self.lora_B)

    def zero_pad(self, x):
        result = x.new_zeros((*x.shape[:-1], self.out_features))
        result = result.view(-1, self.out_features)
        result[:, self.lora_ind] = x.reshape(
            -1, self.out_features // len(self.enable_lora) * sum(self.enable_lora)
        )
        return result.view((*x.shape[:-1], self.out_features))

    def train(self, mode: bool = True):
        def T(w):
            return w.T if self.fan_in_fan_out else w
        nn.Linear.train(self, mode)
        if self.merge_weights and self.merged:
            # Make sure that the weights are not merged
            if self.r > 0 and any(self.enable_lora):
                delta_w = F.conv1d(
                    self.lora_A.data.unsqueeze(0),
                    self.lora_B.data.unsqueeze(-1),
                    groups=sum(self.enable_lora)
                ).squeeze(0)
                self.weight.data -= self.zero_pad(T(delta_w * self.scaling))
            self.merged = False

    def eval(self):
        def T(w):
            return w.T if self.fan_in_fan_out else w
        nn.Linear.eval(self)
        if self.merge_weights and not self.merged:
            # Merge the weights and mark it
            if self.r > 0 and any(self.enable_lora):
                delta_w = F.conv1d(
                    self.lora_A.data.unsqueeze(0),
                    self.lora_B.data.unsqueeze(-1),
                    groups=sum(self.enable_lora)
                ).squeeze(0)
                self.weight.data += self.zero_pad(T(delta_w * self.scaling))
            self.merged = True

    def forward(self, x: torch.Tensor):
        def T(w):
            return w.T if self.fan_in_fan_out else w
        if self.merged:
            return F.linear(x, T(self.weight), bias=self.bias)
        else:
            result = F.linear(x, T(self.weight), bias=self.bias)
            if self.r > 0:
                after_A = F.linear(self.lora_dropout(x), self.lora_A)
                after_B = F.conv1d(
                    after_A.transpose(-2, -1),
                    self.lora_B.unsqueeze(-1),
                    groups=sum(self.enable_lora)
                ).transpose(-2, -1)
                result += self.zero_pad(after_B) * self.scaling
            return result


class Conv2d(nn.Conv2d, LoRALayer):
    # LoRA implemented in a dense layer
    def __init__(
            self,
            in_channels: int,
            out_channels: int,
            kernel_size: int,
            r: int = 0,
            lora_alpha: int = 1,
            lora_dropout: float = 0.,
            merge_weights: bool = True,
            **kwargs
    ):
        nn.Conv2d.__init__(self, in_channels, out_channels, kernel_size, **kwargs)
        LoRALayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout,
                           merge_weights=merge_weights)
        assert type(kernel_size) is int
        # Actual trainable parameters
        if r > 0:
            self.lora_A = nn.Parameter(
                self.weight.new_zeros((r*kernel_size, in_channels*kernel_size))
            )
            self.lora_B = nn.Parameter(
                self.weight.new_zeros((out_channels*kernel_size, r*kernel_size))
            )
            self.scaling = self.lora_alpha / self.r
            # Freezing the pre-trained weight matrix
            self.weight.requires_grad = False
        self.reset_parameters()

    def reset_parameters(self):
        nn.Conv2d.reset_parameters(self)
        if hasattr(self, 'lora_A'):
            # initialize A the same way as the default for nn.Linear and B to zero
            nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
            nn.init.zeros_(self.lora_B)

    def train(self, mode: bool = True):
        nn.Conv2d.train(self, mode)
        if self.merge_weights and self.merged:
            # Make sure that the weights are not merged
            self.weight.data -= (self.lora_B @ self.lora_A).view(self.weight.shape) * self.scaling
            self.merged = False

    def eval(self):
        nn.Conv2d.eval(self)
        if self.merge_weights and not self.merged:
            # Merge the weights and mark it
            self.weight.data += (self.lora_B @ self.lora_A).view(self.weight.shape) * self.scaling
            self.merged = True

    def forward(self, x: torch.Tensor):
        if self.r > 0 and not self.merged:
            return F.conv2d(
                x,
                self.weight + (self.lora_B @ self.lora_A).view(self.weight.shape) * self.scaling,
                self.bias, self.stride, self.padding, self.dilation, self.groups
            )
        return nn.Conv2d.forward(self, x)
