# This code is borrowed from Automatic1111's webui with modifications
# AGPL v3.0 (c) 2023 AUTOMATIC1111, read the full license here
# https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/master/LICENSE.txt

# Modified by kabachuha and incorporated into the AGPL v3.0 license of the project
# Copyright (C) 2023 by Artem Khrapov (kabachuha)
# Read LICENSE for usage terms.

from collections import namedtuple

import math
import torch

import open_clip
from typing import Optional

from modules import prompt_parser, devices, sd_hijack
from modules.shared import opts

import os
from ldm.util import instantiate_from_config

tokenizer = open_clip.tokenizer._tokenizer
from modules import textual_inversion

class PromptChunk:
    """
    This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt.
    If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary.
    Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token,
    so just 75 tokens from prompt.
    """

    def __init__(self):
        self.tokens = []
        self.multipliers = []
        self.fixes = []

class HijackDummy:
    fixes = None
    comments = []
    layers = None
    circular_enabled = False
    clip = None
    optimization_method = None

    embedding_db = textual_inversion.textual_inversion.EmbeddingDatabase()

class Invoke(object):
    KEY = 'invoked_by'
    PRETRAINED = 'from_pretrained'
    PIPELINE = 'pipeline'
    TRAINER = 'trainer'
    LOCAL_TRAINER = 'local_trainer'
    PREPROCESSOR = 'preprocessor'

PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])

class FrozenOpenCLIPEmbedder(torch.nn.Module):
    """
    Uses the OpenCLIP transformer encoder for text
    """
    LAYERS = ['last', 'penultimate']

    def __init__(self,
                 arch='ViT-H-14',
                 version='open_clip_pytorch_model.bin',
                 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()
        
        # ^ vanilla

        self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
        self.id_start = tokenizer.encoder["<start_of_text>"]
        self.id_end = tokenizer.encoder["<end_of_text>"]
        self.id_pad = 0

        # ^ with custom words

        self.hijack = HijackDummy()
        self.chunk_length = 75
    
    def tokenize(self, texts):
        if not (hasattr(opts, 'use_old_emphasis_implementation') and opts.use_old_emphasis_implementation):
            tokenized = [tokenizer.encode(text) for text in texts]
        else:
            assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
        return tokenized
    
    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 encode_with_transformers(self, tokens):
        # set self.wrapped.layer_idx here according to opts.CLIP_stop_at_last_layers
        z = self.encode_with_transformer(tokens)

        return z
    
    def encode_embedding_init_text(self, init_text, nvpt):
        ids = tokenizer.encode(init_text)
        ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
        embedded = self.model.token_embedding.wrapped(ids).squeeze(0)

        return embedded
    
    def empty_chunk(self):
        """creates an empty PromptChunk and returns it"""

        chunk = PromptChunk()
        chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)
        chunk.multipliers = [1.0] * (self.chunk_length + 2)
        return chunk
    
    def get_target_prompt_token_count(self, token_count):
        """returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented"""

        return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length


    def tokenize_line(self, line):
        """
        this transforms a single prompt into a list of PromptChunk objects - as many as needed to
        represent the prompt.
        Returns the list and the total number of tokens in the prompt.
        """

        if opts.enable_emphasis:
            parsed = prompt_parser.parse_prompt_attention(line)
        else:
            parsed = [[line, 1.0]]

        tokenized = self.tokenize([text for text, _ in parsed])

        chunks = []
        chunk = PromptChunk()
        token_count = 0
        last_comma = -1

        def next_chunk(is_last=False):
            """puts current chunk into the list of results and produces the next one - empty;
            if is_last is true, tokens <end-of-text> tokens at the end won't add to token_count"""
            nonlocal token_count
            nonlocal last_comma
            nonlocal chunk

            if is_last:
                token_count += len(chunk.tokens)
            else:
                token_count += self.chunk_length

            to_add = self.chunk_length - len(chunk.tokens)
            if to_add > 0:
                chunk.tokens += [self.id_end] * to_add
                chunk.multipliers += [1.0] * to_add

            chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end]
            chunk.multipliers = [1.0] + chunk.multipliers + [1.0]

            last_comma = -1
            chunks.append(chunk)
            chunk = PromptChunk()

        for tokens, (text, weight) in zip(tokenized, parsed):
            if text == 'BREAK' and weight == -1:
                next_chunk()
                continue

            position = 0
            while position < len(tokens):
                token = tokens[position]

                if token == self.comma_token:
                    last_comma = len(chunk.tokens)

                # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
                # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
                elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack:
                    break_location = last_comma + 1

                    reloc_tokens = chunk.tokens[break_location:]
                    reloc_mults = chunk.multipliers[break_location:]

                    chunk.tokens = chunk.tokens[:break_location]
                    chunk.multipliers = chunk.multipliers[:break_location]

                    next_chunk()
                    chunk.tokens = reloc_tokens
                    chunk.multipliers = reloc_mults

                if len(chunk.tokens) == self.chunk_length:
                    next_chunk()

                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, position)
                if embedding is None:
                    chunk.tokens.append(token)
                    chunk.multipliers.append(weight)
                    position += 1
                    continue

                emb_len = int(embedding.vec.shape[0])
                if len(chunk.tokens) + emb_len > self.chunk_length:
                    next_chunk()

                chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding))

                chunk.tokens += [0] * emb_len
                chunk.multipliers += [weight] * emb_len
                position += embedding_length_in_tokens

        if len(chunk.tokens) > 0 or len(chunks) == 0:
            next_chunk(is_last=True)

        return chunks, token_count
    
    def process_texts(self, texts):
        """
        Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum
        length, in tokens, of all texts.
        """

        token_count = 0

        cache = {}
        batch_chunks = []
        for line in texts:
            if line in cache:
                chunks = cache[line]
            else:
                chunks, current_token_count = self.tokenize_line(line)
                token_count = max(current_token_count, token_count)

                cache[line] = chunks

            batch_chunks.append(chunks)

        return batch_chunks, token_count

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

    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
            x = r(x, attn_mask=attn_mask)
        return x

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

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

    def from_pretrained(cls,
                        model_name_or_path: str,
                        revision: Optional[str] = None,
                        cfg_dict=None,
                        device: str = None,
                        **kwargs):
        """Instantiate a model from local directory or remote model repo. Note
        that when loading from remote, the model revision can be specified.

        Args:
            model_name_or_path(str): A model dir or a model id to be loaded
            revision(str, `optional`): The revision used when the model_name_or_path is
                a model id of the remote hub. default `master`.
            cfg_dict(Config, `optional`): An optional model config. If provided, it will replace
                the config read out of the `model_name_or_path`
            device(str, `optional`): The device to load the model.
            **kwargs:
                task(str, `optional`): The `Tasks` enumeration value to replace the task value
                read out of config in the `model_name_or_path`. This is useful when the model to be loaded is not
                equal to the model saved.
                For example, load a `backbone` into a `text-classification` model.
                Other kwargs will be directly fed into the `model` key, to replace the default configs.
        Returns:
            A model instance.

        """
        prefetched = kwargs.get('model_prefetched')
        if prefetched is not None:
            kwargs.pop('model_prefetched')
        invoked_by = kwargs.get(Invoke.KEY)
        if invoked_by is not None:
            kwargs.pop(Invoke.KEY)
        else:
            invoked_by = Invoke.PRETRAINED

        if os.path.exists(model_name_or_path):
            local_model_dir = model_name_or_path
        if cfg_dict is not None:
            cfg = cfg_dict
            """else:
            cfg = Config.from_file(
                osp.join(local_model_dir, ModelFile.CONFIGURATION))"""
        task_name = cfg.task
        if 'task' in kwargs:
            task_name = kwargs.pop('task')
        model_cfg = cfg.model
        if hasattr(model_cfg, 'model_type') and not hasattr(model_cfg, 'type'):
            model_cfg.type = model_cfg.model_type
        model_cfg.model_dir = local_model_dir

        print("plugins", cfg.safe_get('plugins'))

        # install and import remote repos before build
        # register_plugins_repo(cfg.safe_get('plugins'))
        # register_modelhub_repo(local_model_dir, cfg.get('allow_remote', False))

        for k, v in kwargs.items():
            model_cfg[k] = v
        if device is not None:
            model_cfg.device = device
        """if task_name is Tasks.backbone:
            model_cfg.init_backbone = True
            model = build_backbone(model_cfg)
        else:"""
        model = instantiate_from_config(model_cfg)
        # model = build_model(model_cfg, task_name=task_name)

        # dynamically add pipeline info to model for pipeline inference
        if hasattr(cfg, 'pipeline'):
            model.pipeline = cfg.pipeline

        if not hasattr(model, 'cfg'):
            model.cfg = cfg

        model_cfg.pop('model_dir', None)
        model.name = model_name_or_path
        model.model_dir = local_model_dir
        return model

    def forward(self, texts):
        """
        Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
        Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
        be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024.
        An example shape returned by this function can be: (2, 77, 768).
        Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet
        is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
        """

        batch_chunks, token_count = self.process_texts(texts)

        used_embeddings = {}
        chunk_count = max([len(x) for x in batch_chunks])

        zs = []
        for i in range(chunk_count):
            batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks]

            tokens = [x.tokens for x in batch_chunk]
            multipliers = [x.multipliers for x in batch_chunk]
            self.hijack.fixes = [x.fixes for x in batch_chunk]

            for fixes in self.hijack.fixes:
                for position, embedding in fixes:
                    used_embeddings[embedding.name] = embedding

            z = self.process_tokens(tokens, multipliers)
            zs.append(z)

        if len(used_embeddings) > 0:
            embeddings_list = ", ".join([f'{name} [{embedding.checksum()}]' for name, embedding in used_embeddings.items()])
            self.hijack.comments.append(f"Used embeddings: {embeddings_list}")

        return torch.hstack(zs)

    def process_tokens(self, remade_batch_tokens, batch_multipliers):
        """
        sends one single prompt chunk to be encoded by transformers neural network.
        remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually
        there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens.
        Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier
        corresponds to one token.
        """
        tokens = torch.asarray(remade_batch_tokens).to(devices.device)

        # this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones.
        if self.id_end != self.id_pad:
            for batch_pos in range(len(remade_batch_tokens)):
                index = remade_batch_tokens[batch_pos].index(self.id_end)
                tokens[batch_pos, index+1:tokens.shape[1]] = self.id_pad

        z = self.encode_with_transformers(tokens)

        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
        batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
        original_mean = z.mean()
        z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
        new_mean = z.mean()
        z = z * (original_mean / new_mean)

        return z