import torch
import torch.nn as nn
from functools import partial
import clip
from einops import rearrange, repeat
from transformers import CLIPTokenizer, CLIPTextModel
import kornia

from ldm.modules.x_transformer import Encoder, TransformerWrapper  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test


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 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)


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 FrozenCLIPEmbedder(AbstractEncoder):
    """Uses the CLIP transformer encoder for text (from Hugging Face)"""
    def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
        super().__init__()
        self.tokenizer = CLIPTokenizer.from_pretrained(version)
        self.transformer = CLIPTextModel.from_pretrained(version)
        self.device = device
        self.max_length = max_length
        self.freeze()

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

    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)
        outputs = self.transformer(input_ids=tokens)

        z = outputs.last_hidden_state
        return z

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

import hashlib
import os
import urllib
import warnings
from typing import Any, Union, List
from pkg_resources import packaging

import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm

from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer

try:
    from torchvision.transforms import InterpolationMode
    BICUBIC = InterpolationMode.BICUBIC
except ImportError:
    BICUBIC = Image.BICUBIC


if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
    warnings.warn("PyTorch version 1.7.1 or higher is recommended")


__all__ = ["available_models", "load", "tokenize"]
_tokenizer = _Tokenizer()

def tokenize_with_truncation(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> torch.LongTensor:
    """
    Returns the tokenized representation of given input string(s)

    Parameters
    ----------
    texts : Union[str, List[str]]
        An input string or a list of input strings to tokenize

    context_length : int
        The context length to use; all CLIP models use 77 as the context length

    truncate: bool
        Whether to truncate the text in case its encoding is longer than the context length

    Returns
    -------
    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
    """
    if isinstance(texts, str):
        texts = [texts]

    sot_token = _tokenizer.encoder["<|startoftext|>"]
    eot_token = _tokenizer.encoder["<|endoftext|>"]
    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
    result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)

    for i, tokens in enumerate(all_tokens):
        if len(tokens) > context_length:
            if truncate:
                tokens = tokens[:context_length]
                tokens[-1] = eot_token
            else:
                raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
        result[i, :len(tokens)] = torch.tensor(tokens)

    return result

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)
        tokens = tokenize_with_truncation(text, truncate=True).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='ViT-L/14',
            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.model, _ = clip.load(name=model, device=device)

        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]
        z = self.model.encode_image(self.preprocess(x))
        if z.ndim==2:
            z = z[:, None, :]
        return z


############### OPENCLIP #################
import open_clip

class OpenClipTextEmbedder(nn.Module):
    def __init__(
        self,
        model='ViT-bigG-14',
        pretrained='laion2b_s39b_b160k',
        device='cuda' if torch.cuda.is_available() else 'cpu',
        normalize=True,):
        super().__init__()
        self.model, _, _ = open_clip.create_model_and_transforms(model, pretrained=pretrained, device='cpu')
        self.tokenizer = open_clip.get_tokenizer(model)
        self.normalize = normalize
        self.device = device

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

    def forward(self, text):
        tok_text = self.tokenizer(text).to(self.device)
        z = self.model.encode_text(tok_text)
        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=1)
        return z

class OpenClipImageEmbedder(nn.Module):
    def __init__(
            self,
            model='ViT-bigG-14',
            pretrained='laion2b_s39b_b160k',
            device='cuda' if torch.cuda.is_available() else 'cpu',
        ):
        super().__init__()
        self.model, _, _ = open_clip.create_model_and_transforms(model, pretrained=pretrained, device=device)
        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):
        x = kornia.geometry.resize(x, (224, 224),
                                   interpolation='bicubic',align_corners=True,
                                   antialias=self.antialias)
        x = kornia.enhance.normalize(x, self.mean, self.std)
        return x

    def forward(self, x):
        z = self.model.encode_image(self.preprocess(x))
        if z.ndim==2:
            z = z[:, None, :]
        return z

class DinoV2(nn.Module):
    def __init__(self, model='dinov2_vitb14', ckpt='dino_ckpt/dinov2_vitb14_pretrain.pth'):
        super().__init__()
        # device='cuda' if torch.cuda.is_available() else 'cpu'
        # self.model = torch.hub.load('facebookresearch/dinov2', model)
        self.model = torch.hub.load('dinov2', model, source='local', pretrained=False)
        self.model.load_state_dict(torch.load(ckpt))
        # self.model = self.model.to(device)
        self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]), persistent=False)
        self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]), persistent=False)

    def preprocess(self, x):
        x = kornia.geometry.resize(x, (224, 224),
                                   interpolation='bicubic',align_corners=True,
                                   antialias=False)
        x = kornia.enhance.normalize(x, self.mean, self.std)
        return x

    def forward(self, x):
        return self.model.forward_features(self.preprocess(x))['x_norm_patchtokens']

if __name__ == "__main__":
    from ldm.util import count_params
    model = FrozenCLIPEmbedder()
    count_params(model, verbose=True)