import re
import math
from torch import nn
from functools import partial
from timm.models.layers.norm_act import LayerNormAct2d
from torchvision.ops.misc import SqueezeExcitation as SELayer
from torchvision.models.mobilenetv3 import InvertedResidual, InvertedResidualConfig


class IdentityMap(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x, *args, **kwargs):
        return x

    @property
    def config(self):
        return {"mm_projector_type": 'identity'}


class Minigpt(nn.Module):
    def __init__(self, config=None):
        super(Minigpt, self).__init__()
        # c*4 is the input size, and c is the output size for the linear layer
        inc, ouc = config.mm_hidden_size, config.hidden_size
        self.linear = nn.Linear(inc * 4, ouc)

    def forward(self, x):
        # x is the input tensor with shape [b, num_tokens, c]
        b, num_tokens, c = x.shape

        # Check if num_tokens is divisible by 4
        if num_tokens % 4 != 0:
            raise ValueError("num_tokens must be divisible by 4")

        # Reshape x to [b, num_tokens/4, c*4]
        x = x.view(b, num_tokens // 4, c * 4)

        # Apply the linear transformation
        x = self.linear(x)
        return x


class Vanilla(nn.Module):
    def __init__(self, config=None):
        super(Vanilla, self).__init__()
        # c*4 is the input size, and c is the output size for the linear layer
        inc, ouc = config.mm_hidden_size, config.hidden_size
        self.linear = nn.Linear(inc * 4, ouc)

    def forward(self, x):
        b, num_tokens, c = x.shape

        # Check if num_tokens is divisible by 4
        if num_tokens % 4 != 0:
            raise ValueError("num_tokens must be divisible by 4")

        # First, reshape to [b, num_tokens//4, 4, c]
        x = x.view(b, num_tokens // 4, 4, c)

        # Then, permute to interleave the tokens
        x = x.permute(0, 1, 3, 2).contiguous()

        # Finally, reshape to [b, num_tokens//4, c*4] to interleave features of 4 tokens
        x = x.view(b, num_tokens // 4, c * 4)

        # Apply the linear transformation
        x = self.linear(x)
        return x


class LDPBlock(nn.Module):
    # Lightweight Downsample Projector Block

    def __init__(self, config=None):
        super().__init__()

        inc, ouc = config.mm_hidden_size, config.hidden_size
        layer_norm = partial(LayerNormAct2d, act_layer=None)
        se_layer = partial(SELayer, scale_activation=nn.Hardsigmoid)
        self.mlp = nn.Sequential(
            nn.Identity(), nn.Linear(inc, ouc), nn.GELU(), nn.Linear(ouc, ouc)
        )
        self.mb_block = nn.Sequential(
            nn.Identity(),
            InvertedResidual(InvertedResidualConfig(ouc, 3, ouc, ouc, True, "HS", 1, 1, 1), layer_norm, se_layer),
            InvertedResidual(InvertedResidualConfig(ouc, 3, ouc, ouc, True, "HS", 2, 1, 1), layer_norm, se_layer)
        )

    def forward(self, x):
        b, num_tokens, c = x.shape
        h = int(math.sqrt(num_tokens))
        x = self.mlp(x)
        x = x.permute(0, 2, 1).reshape(b, -1, h, h)
        x = self.mb_block(x)
        x = x.flatten(2).permute(0, 2, 1)
        return x


class LDPNetProjector(nn.Module):

    def __init__(self, config=None):
        super().__init__()
        self.model = LDPBlock(config)

    def forward(self, x):
        return self.model(x)


class SPP(nn.Module):

    def __init__(self, config=None, projector_type='v1'):
        super().__init__()

        self.projector_type = projector_type

        inc, ouc = config.mm_hidden_size, config.hidden_size
        self.linear_0 = nn.Linear(inc, inc)

        self.linear_1 = nn.Linear(inc, ouc)

        self.pooling = nn.AvgPool2d(kernel_size=2)

        self.linear_2 = nn.Linear(ouc, ouc)

    def forward(self, x):
        b, num_tokens, c = x.shape
        h = int(math.sqrt(num_tokens))
        if 'v1' in self.projector_type:
            x = self.linear_1(x)
            x = x.permute(0, 2, 1).reshape(b, -1, h, h)
            x = self.pooling(x)
            x = x.flatten(2).permute(0, 2, 1)
            x = self.linear_2(x)
        elif 'v2' in self.projector_type:
            x = self.linear_1(x)
            x = self.linear_2(x)
            x = x.permute(0, 2, 1).reshape(b, -1, h, h)
            x = self.pooling(x)
            x = x.flatten(2).permute(0, 2, 1)
        elif 'v3' in self.projector_type:
            x = self.linear_0(x)
            x = x.permute(0, 2, 1).reshape(b, -1, h, h)
            x = self.pooling(x)
            x = x.flatten(2).permute(0, 2, 1)
            x = self.linear_1(x)
            x = self.linear_2(x)
        return x


def build_vision_projector(config, delay_load=False, **kwargs):
    projector_type = getattr(config, 'mm_projector_type', 'mlp2x_gelu')

    if projector_type == 'linear':
        return nn.Linear(config.mm_hidden_size, config.hidden_size)

    elif projector_type.startswith('mlp'):
        mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
        if mlp_gelu_match:
            mlp_depth = int(mlp_gelu_match.group(1))
            modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
            for _ in range(1, mlp_depth):
                modules.append(nn.GELU())
                modules.append(nn.Linear(config.hidden_size, config.hidden_size))
            return nn.Sequential(*modules)

    elif projector_type.startswith('spp'):
        return SPP(config, projector_type)

    elif projector_type == 'ldp':
        return LDPNetProjector(config)

    elif projector_type == 'vanilla':
        return Vanilla(config)

    elif projector_type == 'minigpt':
        return Minigpt(config)

    elif projector_type == 'identity':
        return IdentityMap()

    raise ValueError(f'Unknown projector type: {projector_type}')
