import torch

import open_clip

import utils


class ImageEncoder(torch.nn.Module):
    def __init__(self, args, keep_lang=False):
        super().__init__()

        print(f'Loading {args.model} pre-trained weights.')
        if '__pretrained__' in args.model:
            name, pretrained = args.model.split('__pretrained__')

        elif 'convnext_base' in args.model:
            print('convnext!!!!!!!!!!!!')
            pretrained = 'laion400m_s13b_b51k'
            name = args.model
        else:
            name = args.model
            # pretrained = 'openai'
            pretrained = args.preweight
        self.model, self.train_preprocess, self.val_preprocess = open_clip.create_model_and_transforms(
            name, pretrained=pretrained, cache_dir=args.openclip_cachedir)
        
        self.cache_dir = args.cache_dir

        if not keep_lang and hasattr(self.model, 'transformer'):
            delattr(self.model, 'transformer')

    def forward(self, images):
        assert self.model is not None
        return self.model.encode_image(images)
    
    def __call__(self, inputs):
        return self.forward(inputs)

    def save(self, filename):
        print(f'Saving image encoder to {filename}')
        utils.torch_save(self, filename)

    @classmethod
    def load(cls, model_name, filename):
        print(f'Loading image encoder from {filename}')
        state_dict = torch.load(filename)
        return cls.load(model_name, state_dict)

    @classmethod
    def load_from_state_dict(cls, model_name, state_dict):
        self.model, self.train_preprocess, self.val_preprocess = open_clip.create_model_and_transforms(
            name, pretrained=pretrained, cache_dir=args.openclip_cachedir)
        self.model.load_from_state_dict(state_dict)
        



class ClassificationHead(torch.nn.Linear):
    def __init__(self, normalize, weights, biases=None):
        output_size, input_size = weights.shape
        super().__init__(input_size, output_size)
        self.normalize = normalize
        if weights is not None:
            self.weight = torch.nn.Parameter(weights.clone())
        if biases is not None:
            self.bias = torch.nn.Parameter(biases.clone())
        else:
            self.bias = torch.nn.Parameter(torch.zeros_like(self.bias))

    def forward(self, inputs):
        if self.normalize:
            inputs = inputs / inputs.norm(dim=-1, keepdim=True)
        return super().forward(inputs)

    def __call__(self, inputs):
        return self.forward(inputs)

    def save(self, filename):
        print(f'Saving classification head to {filename}')
        utils.torch_save(self, filename)

    @classmethod
    def load(cls, filename):
        print(f'Loading classification head from {filename}')
        return utils.torch_load(filename)


class ImageClassifier(torch.nn.Module):
    def __init__(self, image_encoder, classification_head):
        super().__init__()
        self.image_encoder = image_encoder
        self.classification_head = classification_head
        if self.image_encoder is not None:
            if hasattr(self.image_encoder, 'train_preprocess'):
                self.train_preprocess = self.image_encoder.train_preprocess
                self.val_preprocess = self.image_encoder.val_preprocess
            elif hasattr(self.image_encoder.model, 'train_preprocess'):
                self.train_preprocess = self.image_encoder.model.train_preprocess
                self.val_preprocess = self.image_encoder.model.val_preprocess

    def freeze_head(self):
        self.classification_head.weight.requires_grad_(False)
        self.classification_head.bias.requires_grad_(False)

    def forward(self, inputs):
        features = self.image_encoder(inputs)
        outputs = self.classification_head(features)
        return outputs

    def __call__(self, inputs):
        return self.forward(inputs)

    def save(self, filename):
        print(f'Saving image classifier to {filename}')
        utils.torch_save(self, filename)

    @classmethod
    def load(cls, filename):
        print(f'Loading image classifier from {filename}')
        return utils.torch_load(filename)

class ImageClassifier_debug(torch.nn.Module):
    def __init__(self, image_encoder, image_encoder2, classification_head):
        super().__init__()
        self.image_encoder = image_encoder
        self.image_encoder2 = image_encoder2
        self.classification_head = classification_head
        if self.image_encoder is not None:
            self.train_preprocess = self.image_encoder.train_preprocess
            self.val_preprocess = self.image_encoder.val_preprocess

    def freeze_head(self):
        self.classification_head.weight.requires_grad_(False)
        self.classification_head.bias.requires_grad_(False)

    def forward(self, inputs):
        features = self.image_encoder(inputs)
        features2 = self.image_encoder2(inputs)
        outputs = self.classification_head(features + features2)
        return outputs

    def __call__(self, inputs):
        return self.forward(inputs)

    def save(self, filename):
        print(f'Saving image classifier to {filename}')
        utils.torch_save(self, filename)

    @classmethod
    def load(cls, filename):
        print(f'Loading image classifier from {filename}')
        return utils.torch_load(filename)

class MultiHeadImageClassifier(torch.nn.Module):
    def __init__(self, image_encoder, classification_heads):
        super().__init__()
        self.image_encoder = image_encoder
        self.classification_heads = torch.nn.ModuleList(classification_heads)
        if self.image_encoder is not None:
            self.train_preprocess = self.image_encoder.train_preprocess
            self.val_preprocess = self.image_encoder.val_preprocess

    def freeze_head(self):
        for idx in range(len(self.classification_heads)):
            self.classification_heads[idx].weight.requires_grad_(False)
            self.classification_heads[idx].bias.requires_grad_(False)

    def forward(self, inputs, head_idx):
        features = self.image_encoder(inputs)
        outputs = self.classification_heads[head_idx](features)
        return outputs

    def __call__(self, inputs, head_idx):
        return self.forward(inputs, head_idx)

    def save(self, filename):
        print(f'Saving image classifier to {filename}')
        utils.torch_save(self, filename)

    @classmethod
    def load(cls, filename):
        print(f'Loading image classifier from {filename}')
        return utils.torch_load(filename)



class ImageClassifierWithMapping(torch.nn.Module):
    def __init__(self, image_encoder, classification_head, down_proj, up_proj):
        super().__init__()
        self.image_encoder = image_encoder

        self.down_proj = down_proj
        self.up_proj = up_proj
        self.non_linear_func = torch.nn.ReLU()
        self.classification_head = classification_head
        if self.image_encoder is not None:
            if hasattr(self.image_encoder, 'train_preprocess'):
                self.train_preprocess = self.image_encoder.train_preprocess
                self.val_preprocess = self.image_encoder.val_preprocess
            elif hasattr(self.image_encoder.model, 'train_preprocess'):
                self.train_preprocess = self.image_encoder.model.train_preprocess
                self.val_preprocess = self.image_encoder.model.val_preprocess

    def freeze_head(self):
        self.down_proj.weight.requires_grad_(False)
        self.up_proj.weight.requires_grad_(False)

        self.classification_head.weight.requires_grad_(False)
        self.classification_head.bias.requires_grad_(False)


    def forward(self, inputs):
        features = self.image_encoder(inputs)
        features0 = features

        features_sub = self.down_proj(features)
        features_sub = self.non_linear_func(features_sub)
        features_sub = self.up_proj(features_sub)

        features = features0 - features_sub

        outputs = self.classification_head(features)
        return outputs

    def __call__(self, inputs):
        return self.forward(inputs)

    def save(self, filename):
        print(f'Saving image classifier to {filename}')
        utils.torch_save(self, filename)

    @classmethod
    def load(cls, filename):
        print(f'Loading image classifier from {filename}')
        return utils.torch_load(filename)

