# Taken from Georgia Tech CS8803: Deep Learning for Robotics

from typing import Union
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Union, Optional, Tuple


class SinusoidalPosEmb(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def forward(self, x):
        device = x.device
        half_dim = self.dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
        emb = x[:, None] * emb[None, :]
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb


class ModuleAttrMixin(nn.Module):
    def __init__(self):
        super().__init__()
        self._dummy_variable = nn.Parameter()

    @property
    def device(self):
        return next(iter(self.parameters())).device

    @property
    def dtype(self):
        return next(iter(self.parameters())).dtype


class Downsample1d(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.conv = nn.Conv1d(dim, dim, 3, 2, 1)

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


class Upsample1d(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.conv = nn.ConvTranspose1d(dim, dim, 4, 2, 1)

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


class Conv1dBlock(nn.Module):
    '''
        Conv1d --> GroupNorm --> Mish
    '''

    def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8):
        super().__init__()

        self.block = nn.Sequential(
            nn.Conv1d(inp_channels, out_channels,
                      kernel_size, padding=kernel_size // 2),
            nn.GroupNorm(n_groups, out_channels),
            nn.Mish(),
        )

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


class ConditionalResidualBlock1D(nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 cond_dim,
                 kernel_size=3,
                 n_groups=8):
        super().__init__()

        self.blocks = nn.ModuleList([
            Conv1dBlock(in_channels, out_channels,
                        kernel_size, n_groups=n_groups),
            Conv1dBlock(out_channels, out_channels,
                        kernel_size, n_groups=n_groups),
        ])

        # FiLM modulation https://arxiv.org/abs/1709.07871
        # predicts per-channel scale and bias
        cond_channels = out_channels * 2
        self.out_channels = out_channels
        self.cond_encoder = nn.Sequential(
            nn.Mish(),
            nn.Linear(cond_dim, cond_channels),
            nn.Unflatten(-1, (-1, 1))
        )

        # make sure dimensions compatible
        self.residual_conv = nn.Conv1d(in_channels, out_channels, 1) \
            if in_channels != out_channels else nn.Identity()

    def forward(self, x, cond):
        '''
            x : [ batch_size x in_channels x horizon ]
            cond : [ batch_size x cond_dim]
            returns:
            out : [ batch_size x out_channels x horizon ]
        '''
        out = self.blocks[0](x)
        embed = self.cond_encoder(cond)

        embed = embed.reshape(
            embed.shape[0], 2, self.out_channels, 1)
        scale = embed[:, 0, ...]
        bias = embed[:, 1, ...]
        out = scale * out + bias

        out = self.blocks[1](out)
        out = out + self.residual_conv(x)
        return out


class ConditionalUnet1D(nn.Module):
    def __init__(self,
                 input_dim,
                 global_cond_dim,
                 diffusion_step_embed_dim=256,
                 down_dims=[256, 512, 1024],
                 kernel_size=5,
                 n_groups=8
                 ):
        """
        input_dim: Dim of actions.
        global_cond_dim: Dim of global conditioning applied with FiLM
          in addition to diffusion step embedding. This is usually obs_horizon * obs_dim
        diffusion_step_embed_dim: Size of positional encoding for diffusion iteration k
        down_dims: Channel size for each UNet level.
          The length of this array determines numebr of levels.
        kernel_size: Conv kernel size
        n_groups: Number of groups for GroupNorm
        """

        super().__init__()
        all_dims = [input_dim] + list(down_dims)
        start_dim = down_dims[0]

        dsed = diffusion_step_embed_dim
        diffusion_step_encoder = nn.Sequential(
            SinusoidalPosEmb(dsed),
            nn.Linear(dsed, dsed * 4),
            nn.Mish(),
            nn.Linear(dsed * 4, dsed),
        )
        cond_dim = dsed + global_cond_dim

        in_out = list(zip(all_dims[:-1], all_dims[1:]))
        mid_dim = all_dims[-1]
        self.mid_modules = nn.ModuleList([
            ConditionalResidualBlock1D(
                mid_dim, mid_dim, cond_dim=cond_dim,
                kernel_size=kernel_size, n_groups=n_groups
            ),
            ConditionalResidualBlock1D(
                mid_dim, mid_dim, cond_dim=cond_dim,
                kernel_size=kernel_size, n_groups=n_groups
            ),
        ])

        down_modules = nn.ModuleList([])
        for ind, (dim_in, dim_out) in enumerate(in_out):
            is_last = ind >= (len(in_out) - 1)
            down_modules.append(nn.ModuleList([
                ConditionalResidualBlock1D(
                    dim_in, dim_out, cond_dim=cond_dim,
                    kernel_size=kernel_size, n_groups=n_groups),
                ConditionalResidualBlock1D(
                    dim_out, dim_out, cond_dim=cond_dim,
                    kernel_size=kernel_size, n_groups=n_groups),
                Downsample1d(dim_out) if not is_last else nn.Identity()
            ]))

        up_modules = nn.ModuleList([])
        for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
            is_last = ind >= (len(in_out) - 1)
            up_modules.append(nn.ModuleList([
                ConditionalResidualBlock1D(
                    dim_out*2, dim_in, cond_dim=cond_dim,
                    kernel_size=kernel_size, n_groups=n_groups),
                ConditionalResidualBlock1D(
                    dim_in, dim_in, cond_dim=cond_dim,
                    kernel_size=kernel_size, n_groups=n_groups),
                Upsample1d(dim_in) if not is_last else nn.Identity()
            ]))

        final_conv = nn.Sequential(
            Conv1dBlock(start_dim, start_dim, kernel_size=kernel_size),
            nn.Conv1d(start_dim, input_dim, 1),
        )

        self.diffusion_step_encoder = diffusion_step_encoder
        self.up_modules = up_modules
        self.down_modules = down_modules
        self.final_conv = final_conv

        print("number of parameters: {:e}".format(
            sum(p.numel() for p in self.parameters()))
        )

    def forward(self,
                sample: torch.Tensor,
                timestep: Union[torch.Tensor, float, int],
                global_cond=None):
        """
        x: (B,T,input_dim)
        timestep: (B,) or int, diffusion step
        global_cond: (B,global_cond_dim)
        output: (B,T,input_dim)
        """
        # (B,T,C)
        sample = sample.moveaxis(-1, -2)
        # (B,C,T)

        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            timesteps = torch.tensor(
                [timesteps], dtype=torch.long, device=sample.device)
        elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)
        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timesteps = timesteps.expand(sample.shape[0])

        global_feature = self.diffusion_step_encoder(timesteps)

        if global_cond is not None:
            global_feature = torch.cat([
                global_feature, global_cond
            ], axis=-1)

        x = sample
        h = []
        for idx, (resnet, resnet2, downsample) in enumerate(self.down_modules):
            x = resnet(x, global_feature)
            x = resnet2(x, global_feature)
            h.append(x)
            x = downsample(x)

        for mid_module in self.mid_modules:
            x = mid_module(x, global_feature)

        for idx, (resnet, resnet2, upsample) in enumerate(self.up_modules):
            x = torch.cat((x, h.pop()), dim=1)
            x = resnet(x, global_feature)
            x = resnet2(x, global_feature)
            x = upsample(x)

        x = self.final_conv(x)

        # (B,C,T)
        x = x.moveaxis(-1, -2)
        # (B,T,C)
        return x


class TransformerForDiffusion(ModuleAttrMixin):
    def __init__(self,
                 input_dim: int,
                 output_dim: int,
                 horizon: int,
                 n_obs_steps: int = None,
                 cond_dim: int = 0,
                 n_layer: int = 8,
                 n_head: int = 8,
                 n_emb: int = 256,
                 p_drop_emb: float = 0.1,
                 p_drop_attn: float = 0.3,
                 causal_attn: bool = True,
                 time_as_cond: bool = True,
                 obs_as_cond: bool = False,
                 n_cond_layers: int = 4
                 ) -> None:
        super().__init__()

        # compute number of tokens for main trunk and condition encoder
        if n_obs_steps is None:
            n_obs_steps = horizon

        T = horizon
        T_cond = 1
        if not time_as_cond:
            T += 1
            T_cond -= 1
        obs_as_cond = cond_dim > 0
        if obs_as_cond:
            assert time_as_cond
            T_cond += n_obs_steps

        # input embedding stem
        self.input_emb = nn.Linear(input_dim, n_emb)
        self.pos_emb = nn.Parameter(torch.zeros(1, T, n_emb))
        self.drop = nn.Dropout(p_drop_emb)

        # cond encoder
        self.time_emb = SinusoidalPosEmb(n_emb)
        self.cond_obs_emb = None

        if obs_as_cond:
            self.cond_obs_emb = nn.Linear(cond_dim, n_emb)

        self.cond_pos_emb = None
        self.encoder = None
        self.decoder = None
        encoder_only = False
        if T_cond > 0:
            self.cond_pos_emb = nn.Parameter(torch.zeros(1, T_cond, n_emb))
            if n_cond_layers > 0:
                encoder_layer = nn.TransformerEncoderLayer(
                    d_model=n_emb,
                    nhead=n_head,
                    dim_feedforward=4*n_emb,
                    dropout=p_drop_attn,
                    activation='gelu',
                    batch_first=True,
                    norm_first=True
                )
                self.encoder = nn.TransformerEncoder(
                    encoder_layer=encoder_layer,
                    num_layers=n_cond_layers
                )
            else:
                self.encoder = nn.Sequential(
                    nn.Linear(n_emb, 4 * n_emb),
                    nn.Mish(),
                    nn.Linear(4 * n_emb, n_emb)
                )
            # decoder
            decoder_layer = nn.TransformerDecoderLayer(
                d_model=n_emb,
                nhead=n_head,
                dim_feedforward=4*n_emb,
                dropout=p_drop_attn,
                activation='gelu',
                batch_first=True,
                norm_first=True  # important for stability
            )
            self.decoder = nn.TransformerDecoder(
                decoder_layer=decoder_layer,
                num_layers=n_layer
            )
        else:
            # encoder only BERT
            encoder_only = True

            encoder_layer = nn.TransformerEncoderLayer(
                d_model=n_emb,
                nhead=n_head,
                dim_feedforward=4*n_emb,
                dropout=p_drop_attn,
                activation='gelu',
                batch_first=True,
                norm_first=True
            )
            self.encoder = nn.TransformerEncoder(
                encoder_layer=encoder_layer,
                num_layers=n_layer
            )

        # attention mask
        if causal_attn:
            # causal mask to ensure that attention is only applied to the left in the input sequence
            # torch.nn.Transformer uses additive mask as opposed to multiplicative mask in minGPT
            # therefore, the upper triangle should be -inf and others (including diag) should be 0.
            sz = T
            mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
            mask = mask.float().masked_fill(mask == 0, float(
                '-inf')).masked_fill(mask == 1, float(0.0))
            self.register_buffer("mask", mask)

            if time_as_cond and obs_as_cond:
                S = T_cond
                t, s = torch.meshgrid(
                    torch.arange(T),
                    torch.arange(S),
                    indexing='ij'
                )
                # add one dimension since time is the first token in cond
                mask = t >= (s-1)
                mask = mask.float().masked_fill(mask == 0, float(
                    '-inf')).masked_fill(mask == 1, float(0.0))
                self.register_buffer('memory_mask', mask)
            else:
                self.memory_mask = None
        else:
            self.mask = None
            self.memory_mask = None

        # decoder head
        self.ln_f = nn.LayerNorm(n_emb)
        self.head = nn.Linear(n_emb, output_dim)

        # constants
        self.T = T
        self.T_cond = T_cond
        self.horizon = horizon
        self.time_as_cond = time_as_cond
        self.obs_as_cond = obs_as_cond
        self.encoder_only = encoder_only

        # init
        self.apply(self._init_weights)

    def _init_weights(self, module):
        ignore_types = (nn.Dropout,
                        SinusoidalPosEmb,
                        nn.TransformerEncoderLayer,
                        nn.TransformerDecoderLayer,
                        nn.TransformerEncoder,
                        nn.TransformerDecoder,
                        nn.ModuleList,
                        nn.Mish,
                        nn.Sequential)
        if isinstance(module, (nn.Linear, nn.Embedding)):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if isinstance(module, nn.Linear) and module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.MultiheadAttention):
            weight_names = [
                'in_proj_weight', 'q_proj_weight', 'k_proj_weight', 'v_proj_weight']
            for name in weight_names:
                weight = getattr(module, name)
                if weight is not None:
                    torch.nn.init.normal_(weight, mean=0.0, std=0.02)

            bias_names = ['in_proj_bias', 'bias_k', 'bias_v']
            for name in bias_names:
                bias = getattr(module, name)
                if bias is not None:
                    torch.nn.init.zeros_(bias)
        elif isinstance(module, nn.LayerNorm):
            torch.nn.init.zeros_(module.bias)
            torch.nn.init.ones_(module.weight)
        elif isinstance(module, TransformerForDiffusion):
            torch.nn.init.normal_(module.pos_emb, mean=0.0, std=0.02)
            if module.cond_obs_emb is not None:
                torch.nn.init.normal_(module.cond_pos_emb, mean=0.0, std=0.02)
        elif isinstance(module, ignore_types):
            # no param
            pass
        else:
            raise RuntimeError("Unaccounted module {}".format(module))

    def get_optim_groups(self, weight_decay: float = 1e-3):
        """
        This long function is unfortunately doing something very simple and is being very defensive:
        We are separating out all parameters of the model into two buckets: those that will experience
        weight decay for regularization and those that won't (biases, and layernorm/embedding weights).
        We are then returning the PyTorch optimizer object.
        """

        # separate out all parameters to those that will and won't experience regularizing weight decay
        decay = set()
        no_decay = set()
        whitelist_weight_modules = (
            torch.nn.Linear, torch.nn.MultiheadAttention)
        blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)
        for mn, m in self.named_modules():
            for pn, p in m.named_parameters():
                fpn = "%s.%s" % (mn, pn) if mn else pn  # full param name

                if pn.endswith("bias"):
                    # all biases will not be decayed
                    no_decay.add(fpn)
                elif pn.startswith("bias"):
                    # MultiheadAttention bias starts with "bias"
                    no_decay.add(fpn)
                elif pn.endswith("weight") and isinstance(m, whitelist_weight_modules):
                    # weights of whitelist modules will be weight decayed
                    decay.add(fpn)
                elif pn.endswith("weight") and isinstance(m, blacklist_weight_modules):
                    # weights of blacklist modules will NOT be weight decayed
                    no_decay.add(fpn)

        # special case the position embedding parameter in the root GPT module as not decayed
        no_decay.add("pos_emb")
        no_decay.add("_dummy_variable")
        if self.cond_pos_emb is not None:
            no_decay.add("cond_pos_emb")

        # validate that we considered every parameter
        param_dict = {pn: p for pn, p in self.named_parameters()}
        inter_params = decay & no_decay
        union_params = decay | no_decay
        assert (
            len(inter_params) == 0
        ), "parameters %s made it into both decay/no_decay sets!" % (str(inter_params),)
        assert (
            len(param_dict.keys() - union_params) == 0
        ), "parameters %s were not separated into either decay/no_decay set!" % (
            str(param_dict.keys() - union_params),
        )

        # create the pytorch optimizer object
        optim_groups = [
            {
                "params": [param_dict[pn] for pn in sorted(list(decay))],
                "weight_decay": weight_decay,
            },
            {
                "params": [param_dict[pn] for pn in sorted(list(no_decay))],
                "weight_decay": 0.0,
            },
        ]
        return optim_groups

    def configure_optimizers(self,
                             learning_rate: float = 1e-4,
                             weight_decay: float = 1e-3,
                             betas: Tuple[float, float] = (0.9, 0.95)):
        optim_groups = self.get_optim_groups(weight_decay=weight_decay)
        optimizer = torch.optim.AdamW(
            optim_groups, lr=learning_rate, betas=betas
        )
        return optimizer

    def forward(self,
                sample: torch.Tensor,
                timestep: Union[torch.Tensor, float, int],
                cond: Optional[torch.Tensor] = None, **kwargs):
        """
        x: (B,T,input_dim)
        timestep: (B,) or int, diffusion step
        cond: (B,T',cond_dim)
        output: (B,T,input_dim)
        """
        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            timesteps = torch.tensor(
                [timesteps], dtype=torch.long, device=sample.device)
        elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)
        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timesteps = timesteps.expand(sample.shape[0])
        time_emb = self.time_emb(timesteps).unsqueeze(1)
        # (B,1,n_emb)

        # process input
        input_emb = self.input_emb(sample)

        if self.encoder_only:
            # BERT
            token_embeddings = torch.cat([time_emb, input_emb], dim=1)
            t = token_embeddings.shape[1]
            position_embeddings = self.pos_emb[
                :, :t, :
            ]  # each position maps to a (learnable) vector
            x = self.drop(token_embeddings + position_embeddings)
            # (B,T+1,n_emb)
            x = self.encoder(src=x, mask=self.mask)
            # (B,T+1,n_emb)
            x = x[:, 1:, :]
            # (B,T,n_emb)
        else:
            # encoder
            cond_embeddings = time_emb
            if self.obs_as_cond:
                cond_obs_emb = self.cond_obs_emb(cond)
                # (B,To,n_emb)
                cond_embeddings = torch.cat(
                    [cond_embeddings, cond_obs_emb], dim=1)
            tc = cond_embeddings.shape[1]
            position_embeddings = self.cond_pos_emb[
                :, :tc, :
            ]  # each position maps to a (learnable) vector
            x = self.drop(cond_embeddings + position_embeddings)
            x = self.encoder(x)
            memory = x
            # (B,T_cond,n_emb)

            # decoder
            token_embeddings = input_emb
            t = token_embeddings.shape[1]
            position_embeddings = self.pos_emb[
                :, :t, :
            ]  # each position maps to a (learnable) vector
            x = self.drop(token_embeddings + position_embeddings)
            # (B,T,n_emb)
            x = self.decoder(
                tgt=x,
                memory=memory,
                tgt_mask=self.mask,
                memory_mask=self.memory_mask
            )
            # (B,T,n_emb)

        # head
        x = self.ln_f(x)
        x = self.head(x)
        # (B,T,n_out)
        return x
