import math
from os.path import basename, dirname, join, isfile
import torch
from torch import nn
from torch.nn import functional as nnf
from torch.nn.modules.activation import ReLU


def get_prompt_list(prompt):
    if prompt == 'plain':
        return ['{}']    
    elif prompt == 'fixed':
        return ['a photo of a {}.']
    elif prompt == 'shuffle':
        return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
    elif prompt == 'shuffle+':
        return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
                            'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
                            'a bad photo of a {}.', 'a photo of the {}.']
    else:
        raise ValueError('Invalid value for prompt')        


def forward_multihead_attention(x, b, with_aff=False, attn_mask=None):
    """ 
    Simplified version of multihead attention (taken from torch source code but without tons of if clauses). 
    The mlp and layer norm come from CLIP.
    x: input.
    b: multihead attention module. 
    """

    x_ = b.ln_1(x)
    q, k, v = nnf.linear(x_, b.attn.in_proj_weight, b.attn.in_proj_bias).chunk(3, dim=-1)
    tgt_len, bsz, embed_dim = q.size()

    head_dim = embed_dim // b.attn.num_heads
    scaling = float(head_dim) ** -0.5

    q = q.contiguous().view(tgt_len, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
    k = k.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
    v = v.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)

    q = q * scaling

    attn_output_weights = torch.bmm(q, k.transpose(1, 2)) #  n_heads * batch_size, tokens^2, tokens^2
    if attn_mask is not None:


        attn_mask_type, attn_mask = attn_mask
        n_heads = attn_output_weights.size(0) // attn_mask.size(0)
        attn_mask = attn_mask.repeat(n_heads, 1)
        
        if attn_mask_type == 'cls_token':
            # the mask only affects similarities compared to the readout-token.
            attn_output_weights[:, 0, 1:] = attn_output_weights[:, 0, 1:] * attn_mask[None,...]
            # attn_output_weights[:, 0, 0] = 0*attn_output_weights[:, 0, 0]

        if attn_mask_type == 'all':
            # print(attn_output_weights.shape, attn_mask[:, None].shape)
            attn_output_weights[:, 1:, 1:] = attn_output_weights[:, 1:, 1:] * attn_mask[:, None]
        
    
    attn_output_weights = torch.softmax(attn_output_weights, dim=-1)

    attn_output = torch.bmm(attn_output_weights, v)
    attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
    attn_output = b.attn.out_proj(attn_output)

    x = x + attn_output
    x = x + b.mlp(b.ln_2(x))

    if with_aff:
        return x, attn_output_weights
    else:
        return x


class CLIPDenseBase(nn.Module):

    def __init__(self, version, reduce_cond, reduce_dim, prompt, n_tokens):
        super().__init__()

        from rope.external.cliplib import clip

        # prec = torch.FloatTensor
        self.clip_model, _ = clip.load(version, device='cpu', jit=False)
        self.model = self.clip_model.visual

        # if not None, scale conv weights such that we obtain n_tokens.
        self.n_tokens = n_tokens

        for p in self.clip_model.parameters():
            p.requires_grad_(False)

        # conditional
        if reduce_cond is not None:
            self.reduce_cond = nn.Linear(512, reduce_cond)
            for p in self.reduce_cond.parameters():
                p.requires_grad_(False)
        else:
            self.reduce_cond = None        

        self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
        self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
        
        self.reduce = nn.Linear(768, reduce_dim)

        self.prompt_list = get_prompt_list(prompt)     

        # precomputed prompts
        import pickle
        if isfile('precomputed_prompt_vectors.pickle'):
            precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
            self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}        
        else:
            self.precomputed_prompts = dict()
    
    def rescaled_pos_emb(self, new_size):
        assert len(new_size) == 2

        a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
        b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
        return torch.cat([self.model.positional_embedding[:1], b])

    def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
        

        with torch.no_grad():

            inp_size = x_inp.shape[2:]

            if self.n_tokens is not None:
                stride2 = x_inp.shape[2] // self.n_tokens
                conv_weight2 = nnf.interpolate(self.model.conv1.weight, (stride2, stride2), mode='bilinear', align_corners=True)
                x = nnf.conv2d(x_inp, conv_weight2, bias=self.model.conv1.bias, stride=stride2, dilation=self.model.conv1.dilation)
            else:
                x = self.model.conv1(x_inp)  # shape = [*, width, grid, grid]

            x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
            x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]

            x = torch.cat([self.model.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]

            standard_n_tokens = 50 if self.model.conv1.kernel_size[0] == 32 else 197

            if x.shape[1] != standard_n_tokens:
                new_shape = int(math.sqrt(x.shape[1]-1))
                x = x + self.rescaled_pos_emb((new_shape, new_shape)).to(x.dtype)[None,:,:]
            else:
                x = x + self.model.positional_embedding.to(x.dtype)

            x = self.model.ln_pre(x)

            x = x.permute(1, 0, 2)  # NLD -> LND

            activations, affinities = [], []
            for i, res_block in enumerate(self.model.transformer.resblocks):
                
                if mask is not None:
                    mask_layer, mask_type, mask_tensor = mask
                    if mask_layer == i or mask_layer == 'all':
                        # import ipdb; ipdb.set_trace()
                        size = int(math.sqrt(x.shape[0] - 1))
                        
                        attn_mask = (mask_type, nnf.interpolate(mask_tensor.unsqueeze(1).float(), (size, size)).view(mask_tensor.shape[0], size * size))
                        
                    else:
                        attn_mask = None
                else:
                    attn_mask = None

                x, aff_per_head = forward_multihead_attention(x, res_block, with_aff=True, attn_mask=attn_mask)

                if i in extract_layers:
                    affinities += [aff_per_head]

                    #if self.n_tokens is not None:
                    #    activations += [nnf.interpolate(x, inp_size, mode='bilinear', align_corners=True)]
                    #else:
                    activations += [x]

                if len(extract_layers) > 0 and i == max(extract_layers) and skip:
                    print('early skip')
                    break
                
            x = x.permute(1, 0, 2)  # LND -> NLD
            x = self.model.ln_post(x[:, 0, :])

            if self.model.proj is not None:
                x = x @ self.model.proj

            return x, activations, affinities

    def sample_prompts(self, words, prompt_list=None):

        prompt_list = prompt_list if prompt_list is not None else self.prompt_list

        prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
        prompts = [prompt_list[i] for i in prompt_indices]
        return [promt.format(w) for promt, w in zip(prompts, words)]

    def get_cond_vec(self, conditional, batch_size):
        # compute conditional from a single string
        if conditional is not None and type(conditional) == str:
            cond = self.compute_conditional(conditional)
            cond = cond.repeat(batch_size, 1)

        # compute conditional from string list/tuple
        elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
            assert len(conditional) == batch_size
            cond = self.compute_conditional(conditional)

        # use conditional directly
        elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
            cond = conditional

        # compute conditional from image
        elif conditional is not None and type(conditional) == torch.Tensor:
            with torch.no_grad():
                cond, _, _ = self.visual_forward(conditional)
        else:
            raise ValueError('invalid conditional')
        return cond   

    def compute_conditional(self, conditional):
        from rope.external.cliplib import clip

        dev = next(self.parameters()).device

        if type(conditional) in {list, tuple}:
            text_tokens = clip.tokenize(conditional).to(dev)
            cond = self.clip_model.encode_text(text_tokens)
        else:
            if conditional in self.precomputed_prompts:
                cond = self.precomputed_prompts[conditional].float().to(dev)
            else:
                text_tokens = clip.tokenize([conditional]).to(dev)
                cond = self.clip_model.encode_text(text_tokens)[0]
        
        if self.shift_vector is not None:
            return cond + self.shift_vector
        else:
            return cond


def clip_load_untrained(version):
    assert version == 'ViT-B/16'
    from clip.model import CLIP
    from clip.clip import _MODELS, _download
    model = torch.jit.load(_download(_MODELS['ViT-B/16'])).eval()
    state_dict = model.state_dict()

    vision_width = state_dict["visual.conv1.weight"].shape[0]
    vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
    vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
    grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
    image_resolution = vision_patch_size * grid_size
    embed_dim = state_dict["text_projection"].shape[1]
    context_length = state_dict["positional_embedding"].shape[0]
    vocab_size = state_dict["token_embedding.weight"].shape[0]
    transformer_width = state_dict["ln_final.weight"].shape[0]
    transformer_heads = transformer_width // 64
    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))

    return CLIP(embed_dim, image_resolution, vision_layers, vision_width, vision_patch_size, 
        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)    


class CLIPDensePredT(CLIPDenseBase):

    def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed', 
                 extra_blocks=0, reduce_cond=None, fix_shift=False,
                 learn_trans_conv_only=False,  limit_to_clip_only=False, upsample=False, 
                 add_calibration=False, rev_activations=False, trans_conv=None, n_tokens=None, complex_trans_conv=False):
        
        super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
        # device = 'cpu'

        self.extract_layers = extract_layers
        self.cond_layer = cond_layer
        self.limit_to_clip_only = limit_to_clip_only
        self.process_cond = None
        self.rev_activations = rev_activations
        
        depth = len(extract_layers)

        if add_calibration:
            self.calibration_conds = 1

        self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None

        self.add_activation1 = True

        self.version = version
        
        self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]

        if fix_shift:
            # self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'clip_text_shift_vector.pth')), requires_grad=False)
            self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'shift_text_to_vis.pth')), requires_grad=False)
            # self.shift_vector = nn.Parameter(-1*torch.load(join(dirname(basename(__file__)), 'shift2.pth')), requires_grad=False)
        else:
            self.shift_vector = None

        if trans_conv is None:
            trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
        else:
            # explicitly define transposed conv kernel size
            trans_conv_ks = (trans_conv, trans_conv)

        if not complex_trans_conv:
            self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
        else:
            assert trans_conv_ks[0] == trans_conv_ks[1]

            tp_kernels = (trans_conv_ks[0] // 4, trans_conv_ks[0] // 4)

            self.trans_conv = nn.Sequential(
                nn.Conv2d(reduce_dim, reduce_dim, kernel_size=3, padding=1),
                nn.ReLU(),
                nn.ConvTranspose2d(reduce_dim, reduce_dim // 2, kernel_size=tp_kernels[0], stride=tp_kernels[0]),
                nn.ReLU(),
                nn.ConvTranspose2d(reduce_dim // 2, 1, kernel_size=tp_kernels[1], stride=tp_kernels[1]),               
            )

#        self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
        
        assert len(self.extract_layers) == depth

        self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
        self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
        self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
        
        # refinement and trans conv

        if learn_trans_conv_only:
            for p in self.parameters():
                p.requires_grad_(False)
            
            for p in self.trans_conv.parameters():
                p.requires_grad_(True)

        self.prompt_list = get_prompt_list(prompt)


    def forward(self, inp_image, conditional=None, return_features=False, mask=None):

        assert type(return_features) == bool

        inp_image = inp_image.to(self.model.positional_embedding.device)

        if mask is not None:
            raise ValueError('mask not supported')

        # x_inp = normalize(inp_image)
        x_inp = inp_image

        bs, dev = inp_image.shape[0], x_inp.device

        cond = self.get_cond_vec(conditional, bs)

        visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))

        activation1 = activations[0]
        activations = activations[1:]

        _activations = activations[::-1] if not self.rev_activations else activations

        a = None
        for i, (activation, block, reduce) in enumerate(zip(_activations, self.blocks, self.reduces)):
            
            if a is not None:
                a = reduce(activation) + a
            else:
                a = reduce(activation)

            if i == self.cond_layer:
                if self.reduce_cond is not None:
                    cond = self.reduce_cond(cond)
                
                a = self.film_mul(cond) * a + self.film_add(cond)

            a = block(a)

        for block in self.extra_blocks:
            a = a + block(a)

        a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens

        size = int(math.sqrt(a.shape[2]))

        a = a.view(bs, a.shape[1], size, size)

        a = self.trans_conv(a)

        if self.n_tokens is not None:
            a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear', align_corners=True) 

        if self.upsample_proj is not None:
            a = self.upsample_proj(a)
            a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')

        if return_features:
            return a, visual_q, cond, [activation1] + activations
        else:
            return a,



class CLIPDensePredTMasked(CLIPDensePredT):

    def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, 
                 prompt='fixed', extra_blocks=0, reduce_cond=None, fix_shift=False, learn_trans_conv_only=False, 
                 refine=None, limit_to_clip_only=False, upsample=False, add_calibration=False, n_tokens=None):

        super().__init__(version=version, extract_layers=extract_layers, cond_layer=cond_layer, reduce_dim=reduce_dim, 
                         n_heads=n_heads, prompt=prompt, extra_blocks=extra_blocks, reduce_cond=reduce_cond, 
                         fix_shift=fix_shift, learn_trans_conv_only=learn_trans_conv_only,
                         limit_to_clip_only=limit_to_clip_only, upsample=upsample, add_calibration=add_calibration,
                         n_tokens=n_tokens)

    def visual_forward_masked(self, img_s, seg_s):
        return super().visual_forward(img_s, mask=('all', 'cls_token', seg_s))

    def forward(self, img_q, cond_or_img_s, seg_s=None, return_features=False):

        if seg_s is None:
            cond = cond_or_img_s
        else:
            img_s = cond_or_img_s

            with torch.no_grad():
                cond, _, _ = self.visual_forward_masked(img_s, seg_s)

        return super().forward(img_q, cond, return_features=return_features)



class CLIPDenseBaseline(CLIPDenseBase):

    def __init__(self, version='ViT-B/32', cond_layer=0, 
                extract_layer=9, reduce_dim=128, reduce2_dim=None, prompt='fixed', 
                 reduce_cond=None, limit_to_clip_only=False, n_tokens=None):
        
        super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
        device = 'cpu'

        # self.cond_layer = cond_layer
        self.extract_layer = extract_layer
        self.limit_to_clip_only = limit_to_clip_only
        self.shift_vector = None

        self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
        
        assert reduce2_dim is not None

        self.reduce2 = nn.Sequential(
            nn.Linear(reduce_dim, reduce2_dim),
            nn.ReLU(),
            nn.Linear(reduce2_dim, reduce_dim)
        )
        
        trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
        self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)


    def forward(self, inp_image, conditional=None, return_features=False):

        inp_image = inp_image.to(self.model.positional_embedding.device)

        # x_inp = normalize(inp_image)
        x_inp = inp_image

        bs, dev = inp_image.shape[0], x_inp.device

        cond = self.get_cond_vec(conditional, bs)

        visual_q, activations, affinities = self.visual_forward(x_inp, extract_layers=[self.extract_layer])

        a = activations[0]
        a = self.reduce(a)
        a = self.film_mul(cond) * a + self.film_add(cond)

        if self.reduce2 is not None:
            a = self.reduce2(a)

        # the original model would execute a transformer block here

        a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens

        size = int(math.sqrt(a.shape[2]))

        a = a.view(bs, a.shape[1], size, size)
        a = self.trans_conv(a)

        if return_features:
            return a, visual_q, cond, activations
        else:
            return a,


class CLIPSegMultiLabel(nn.Module):

    def __init__(self, model) -> None:
        super().__init__()

        from third_party.JoEm.data_loader import get_seen_idx, get_unseen_idx, VOC

        self.pascal_classes = VOC

        from models.clipseg import CLIPDensePredT
        from general_utils import load_model
        # self.clipseg = load_model('rd64-vit16-neg0.2-phrasecut', strict=False)
        self.clipseg = load_model(model, strict=False)
        
        self.clipseg.eval()

    def forward(self, x):

        bs = x.shape[0]
        out = torch.ones(21, bs, 352, 352).to(x.device) * -10

        for class_id, class_name in enumerate(self.pascal_classes):
        
            fac = 3 if class_name == 'background' else 1

            with torch.no_grad():
                pred = torch.sigmoid(self.clipseg(x, class_name)[0][:,0]) * fac

            out[class_id] += pred


        out = out.permute(1, 0, 2, 3)

        return out

        # construct output tensor