import torch
import torch.nn as nn
from functools import partial
import clip
import random
from einops import rearrange, repeat
import kornia

from torch.utils.checkpoint import checkpoint
import open_clip


from ldm.modules.x_transformer import Encoder, TransformerWrapper  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
from ldm.util import instantiate_from_config_vq_diffusion
from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
from ldm.modules.attention import SpatialTransformer

from ldm.modules.diffusionmodules.util import (
    conv_nd,
    linear,
    zero_module,
    timestep_embedding,
)

from ldm.util import log_txt_as_img, exists, instantiate_from_config


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

    def encode(self, *args, **kwargs):
        raise NotImplementedError



class ClassEmbedder(nn.Module):
    def __init__(self, embed_dim, n_classes=1000, key='class'):
        super().__init__()
        self.key = key
        self.embedding = nn.Embedding(n_classes, embed_dim)

    def forward(self, batch, key=None):
        if key is None:
            key = self.key
        # this is for use in crossattn
        c = batch[key][:, None]
        c = self.embedding(c)
        return c
    
class RetouchEmbedder(nn.Module):
    def __init__(self, embed_dim=1024, n_classes = 4):
        super().__init__()

    def forward(self, labels):
        zs = []
        for label in labels:
            z = torch.Tensor(label).unsqueeze(0).unsqueeze(0).to('cuda')
            zs.append(z)
        c = torch.cat(zs, dim=0)
        return c
    
class ControlNet(nn.Module):
    def __init__(
            self,
            image_size,
            in_channels,
            model_channels,
            hint_channels,
            num_res_blocks,
            attention_resolutions,
            dropout=0,
            channel_mult=(1, 2, 4, 8),
            conv_resample=True,
            dims=2,
            use_checkpoint=False,
            use_fp16=False,
            num_heads=-1,
            num_head_channels=-1,
            num_heads_upsample=-1,
            use_scale_shift_norm=False,
            resblock_updown=False,
            use_new_attention_order=False,
            use_spatial_transformer=False,  # custom transformer support
            transformer_depth=1,  # custom transformer support
            context_dim=None,  # custom transformer support
            n_embed=None,  # custom support for prediction of discrete ids into codebook of first stage vq model
            legacy=True,
            disable_self_attentions=None,
            num_attention_blocks=None,
            disable_middle_self_attn=False,
            use_linear_in_transformer=False,
    ):
        super().__init__()
        if use_spatial_transformer:
            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'

        if context_dim is not None:
            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
            from omegaconf.listconfig import ListConfig
            if type(context_dim) == ListConfig:
                context_dim = list(context_dim)

        if num_heads_upsample == -1:
            num_heads_upsample = num_heads

        if num_heads == -1:
            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'

        if num_head_channels == -1:
            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'

        self.dims = dims
        self.image_size = image_size
        self.in_channels = in_channels
        self.model_channels = model_channels
        if isinstance(num_res_blocks, int):
            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
        else:
            if len(num_res_blocks) != len(channel_mult):
                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
                                 "as a list/tuple (per-level) with the same length as channel_mult")
            self.num_res_blocks = num_res_blocks
        if disable_self_attentions is not None:
            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
            assert len(disable_self_attentions) == len(channel_mult)
        if num_attention_blocks is not None:
            assert len(num_attention_blocks) == len(self.num_res_blocks)
            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
            print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
                  f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
                  f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
                  f"attention will still not be set.")

        self.attention_resolutions = attention_resolutions
        self.dropout = dropout
        self.channel_mult = channel_mult
        self.conv_resample = conv_resample
        self.use_checkpoint = use_checkpoint
        self.dtype = torch.float16 if use_fp16 else torch.float32
        self.num_heads = num_heads
        self.num_head_channels = num_head_channels
        self.num_heads_upsample = num_heads_upsample
        self.predict_codebook_ids = n_embed is not None

        time_embed_dim = model_channels * 4
        self.time_embed = nn.Sequential(
            linear(model_channels, time_embed_dim),
            nn.SiLU(),
            linear(time_embed_dim, time_embed_dim),
        )

        self.input_blocks = nn.ModuleList(
            [
                TimestepEmbedSequential(
                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
                )
            ]
        )
        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])

        self.input_hint_block = TimestepEmbedSequential(
            conv_nd(dims, hint_channels, 16, 3, padding=1),
            nn.SiLU(),
            conv_nd(dims, 16, 16, 3, padding=1),
            nn.SiLU(),
            conv_nd(dims, 16, 32, 3, padding=1, stride=2),
            nn.SiLU(),
            conv_nd(dims, 32, 32, 3, padding=1),
            nn.SiLU(),
            conv_nd(dims, 32, 96, 3, padding=1, stride=2),
            nn.SiLU(),
            conv_nd(dims, 96, 96, 3, padding=1),
            nn.SiLU(),
            conv_nd(dims, 96, 256, 3, padding=1, stride=2),
            nn.SiLU(),
            zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
        )

        self._feature_size = model_channels
        input_block_chans = [model_channels]
        ch = model_channels
        ds = 1
        for level, mult in enumerate(channel_mult):
            for nr in range(self.num_res_blocks[level]):
                layers = [
                    ResBlock(
                        ch,
                        time_embed_dim,
                        dropout,
                        out_channels=mult * model_channels,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = mult * model_channels
                if ds in attention_resolutions:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels
                    if legacy:
                        # num_heads = 1
                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
                    if exists(disable_self_attentions):
                        disabled_sa = disable_self_attentions[level]
                    else:
                        disabled_sa = False

                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
                        layers.append(
                            AttentionBlock(
                                ch,
                                use_checkpoint=use_checkpoint,
                                num_heads=num_heads,
                                num_head_channels=dim_head,
                                use_new_attention_order=use_new_attention_order,
                            ) if not use_spatial_transformer else SpatialTransformer(
                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
                                use_checkpoint=use_checkpoint
                            )
                        )
                self.input_blocks.append(TimestepEmbedSequential(*layers))
                self.zero_convs.append(self.make_zero_conv(ch))
                self._feature_size += ch
                input_block_chans.append(ch)
            if level != len(channel_mult) - 1:
                out_ch = ch
                self.input_blocks.append(
                    TimestepEmbedSequential(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            down=True,
                        )
                        if resblock_updown
                        else Downsample(
                            ch, conv_resample, dims=dims, out_channels=out_ch
                        )
                    )
                )
                ch = out_ch
                input_block_chans.append(ch)
                self.zero_convs.append(self.make_zero_conv(ch))
                ds *= 2
                self._feature_size += ch

        if num_head_channels == -1:
            dim_head = ch // num_heads
        else:
            num_heads = ch // num_head_channels
            dim_head = num_head_channels
        if legacy:
            # num_heads = 1
            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
        self.middle_block = TimestepEmbedSequential(
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
            AttentionBlock(
                ch,
                use_checkpoint=use_checkpoint,
                num_heads=num_heads,
                num_head_channels=dim_head,
                use_new_attention_order=use_new_attention_order,
            ) if not use_spatial_transformer else SpatialTransformer(  # always uses a self-attn
                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
                disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
                use_checkpoint=use_checkpoint
            ),
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
        )
        self.middle_block_out = self.make_zero_conv(ch)
        self._feature_size += ch

    def make_zero_conv(self, channels):
        return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))

    def forward(self, x, hint, timesteps, context, **kwargs):
        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
        emb = self.time_embed(t_emb)

        guided_hint = self.input_hint_block(hint, emb, context)

        outs = []

        h = x.type(self.dtype)
        for module, zero_conv in zip(self.input_blocks, self.zero_convs):
            if guided_hint is not None:
                h = module(h, emb, context)
                h += guided_hint
                guided_hint = None
            else:
                h = module(h, emb, context)
            outs.append(zero_conv(h, emb, context))

        h = self.middle_block(h, emb, context)
        outs.append(self.middle_block_out(h, emb, context))

        return outs


class TransformerEmbedder(AbstractEncoder):
    """Some transformer encoder layers"""
    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
        super().__init__()
        self.device = device
        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
                                              attn_layers=Encoder(dim=n_embed, depth=n_layer))

    def forward(self, tokens):
        tokens = tokens.to(self.device)  # meh
        z = self.transformer(tokens, return_embeddings=True)
        return z

    def encode(self, x):
        return self(x)


class BERTTokenizer(AbstractEncoder):
    """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
    def __init__(self, device="cuda", vq_interface=True, max_length=77):
        super().__init__()
        from transformers import BertTokenizerFast  # TODO: add to reuquirements
        self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
        self.device = device
        self.vq_interface = vq_interface
        self.max_length = max_length

    def forward(self, text):
        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
        tokens = batch_encoding["input_ids"].to(self.device)
        return tokens

    @torch.no_grad()
    def encode(self, text):
        tokens = self(text)
        if not self.vq_interface:
            return tokens
        return None, None, [None, None, tokens]

    def decode(self, text):
        return text


class BERTEmbedder(AbstractEncoder):
    """Uses the BERT tokenizr model and add some transformer encoder layers"""
    def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
                 device="cuda",use_tokenizer=True, embedding_dropout=0.0):
        super().__init__()
        self.use_tknz_fn = use_tokenizer
        if self.use_tknz_fn:
            self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
        self.device = device
        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
                                              attn_layers=Encoder(dim=n_embed, depth=n_layer),
                                              emb_dropout=embedding_dropout)

    def forward(self, text):
        if self.use_tknz_fn:
            tokens = self.tknz_fn(text)#.to(self.device)
        else:
            tokens = text
        z = self.transformer(tokens, return_embeddings=True)
        return z

    def encode(self, text):
        # output of length 77
        return self(text) # [batch_size, 77, BERTEmbedder.n_embed] # exp0023: [B, 77, 640]


class SpatialRescaler(nn.Module):
    def __init__(self,
                 n_stages=1,
                 method='bilinear',
                 multiplier=0.5,
                 in_channels=3,
                 out_channels=None,
                 bias=False):
        super().__init__()
        self.n_stages = n_stages
        assert self.n_stages >= 0
        assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
        self.multiplier = multiplier
        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
        self.remap_output = out_channels is not None
        if self.remap_output:
            print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
            self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)

    def forward(self,x):
        for stage in range(self.n_stages):
            x = self.interpolator(x, scale_factor=self.multiplier)


        if self.remap_output:
            x = self.channel_mapper(x)
        return x

    def encode(self, x):
        return self(x)


class FrozenCLIPTextEmbedder(nn.Module):
    """
    Uses the CLIP transformer encoder for text.
    """
    def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True):
        super().__init__()
        self.model, _ = clip.load(version, jit=False, device="cpu")
        self.device = device
        self.max_length = max_length
        self.n_repeat = n_repeat
        self.normalize = normalize

    def freeze(self):
        self.model = self.model.eval()
        for param in self.parameters():
            param.requires_grad = False

    def forward(self, text):
        tokens = clip.tokenize(text).to(self.device)
        z = self.model.encode_text(tokens)
        if self.normalize:
            z = z / torch.linalg.norm(z, dim=1, keepdim=True)
        return z

    def encode(self, text):
        z = self(text)
        if z.ndim==2:
            z = z[:, None, :]
        z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
        return z


class FrozenClipImageEmbedder(nn.Module):
    """
        Uses the CLIP image encoder.
        """
    def __init__(
            self,
            model,
            jit=False,
            device='cuda' if torch.cuda.is_available() else 'cpu',
            antialias=False,
        ):
        super().__init__()
        self.model, _ = clip.load(name=model, device=device, jit=jit)

        self.antialias = antialias

        self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
        self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)

    def preprocess(self, x):
        # normalize to [0,1]
        x = kornia.geometry.resize(x, (224, 224),
                                   interpolation='bicubic',align_corners=True,
                                   antialias=self.antialias)
        x = (x + 1.) / 2.
        # renormalize according to clip
        x = kornia.enhance.normalize(x, self.mean, self.std)
        return x

    def forward(self, x):
        # x is assumed to be in range [-1,1]
        return self.model.encode_image(self.preprocess(x))
    
class FrozenOpenCLIPEmbedder(AbstractEncoder):
    """
    Uses the OpenCLIP transformer encoder for text
    """
    LAYERS = [
        # "pooled",
        "last",
        "penultimate"
    ]

    def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
                 freeze=True, layer="last"):
        super().__init__()
        assert layer in self.LAYERS
        model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
        del model.visual
        self.model = model

        self.device = device
        self.max_length = max_length
        if freeze:
            self.freeze()
        self.layer = layer
        if self.layer == "last":
            self.layer_idx = 0
        elif self.layer == "penultimate":
            self.layer_idx = 1
        else:
            raise NotImplementedError()

    def freeze(self):
        self.model = self.model.eval()
        for param in self.parameters():
            param.requires_grad = False

    def forward(self, text):
        tokens = open_clip.tokenize(text)
        z = self.encode_with_transformer(tokens.to(self.device))
        return z

    def encode_with_transformer(self, text):
        x = self.model.token_embedding(text)  # [batch_size, n_ctx, d_model]
        x = x + self.model.positional_embedding
        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
        x = x.permute(1, 0, 2)  # LND -> NLD
        x = self.model.ln_final(x)
        return x

    def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
        for i, r in enumerate(self.model.transformer.resblocks):
            if i == len(self.model.transformer.resblocks) - self.layer_idx:
                break
            if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
                x = checkpoint(r, x, attn_mask)
            else:
                x = r(x, attn_mask=attn_mask)
        return x

    def encode(self, text):
        return self(text)


class PassSegMaskEncoder(nn.Module):


    def __init__(self):
        super().__init__()


    def forward(self, x):
        """convert :torch.cuda.LongTensor to torch.cuda.FloatTensor"""
        return x.to(torch.float)

    def encode(self, input):
        return self(input)


class SegMaskAndTextEncoder(nn.Module):

    def __init__(self, seg_mask_encoder_config, text_encoder_config, mask_embed_dim=1024, text_embed_dim=640):
        super().__init__()

        self.seg_mask_encoder = instantiate_from_config_vq_diffusion(seg_mask_encoder_config)
        self.text_encoder = instantiate_from_config_vq_diffusion(text_encoder_config)

        self.mask_embed_dim = mask_embed_dim
        self.text_embed_dim = text_embed_dim

        self.linear = nn.Linear(
            in_features=self.mask_embed_dim, # 1024
            out_features=self.text_embed_dim, # 640
            bias=True)


    def forward(self, input):

        seg_mask = self.seg_mask_encoder(input['seg_mask'])  # [B, 19, 1024]
        seg_mask = self.linear(seg_mask)  # [B, 19, 640]

        text = self.text_encoder(input['text']) # [B, 77, 640]

        seg_mask_and_text = torch.cat([seg_mask, text], 1) # Bx(19+77)x640

        return seg_mask_and_text

    def encode(self, input):
        return self(input)



class SegMaskEncoder(nn.Module):

    def __init__(self, seg_mask_encoder_config, mask_embed_dim=1024, context_dim=640):
        super().__init__()

        self.seg_mask_encoder = instantiate_from_config_vq_diffusion(seg_mask_encoder_config)

        self.mask_embed_dim = mask_embed_dim
        self.context_dim = context_dim

        self.linear = nn.Linear(
            in_features=self.mask_embed_dim, # 1024
            out_features=self.context_dim, # 640
            bias=True)


    def forward(self, input):


        if len(input.shape) == 4 and input.shape[1] == 1:
            # Bx1x19x1024 -> Bx19x1024
            # the '1' dimension comes from inner_function() in class DDPM
            input = input.view(input.shape[0], input.shape[2], input.shape[3])
        assert len(input.shape) == 3 # [B, 19, 1024]
        seg_mask = self.seg_mask_encoder(input)  # [B, 19, 1024]

        # match mask dimension to the desired condition dimension
        seg_mask = self.linear(seg_mask)  # [B, 19, 640]

        return seg_mask  # [B, 19, 640]

    def encode(self, input):
        return self(input)
