import torch
from .. import shared

class Embedding:
    def __init__(self, vec, name, step=None):
        self.vec = vec
        self.name = name
        self.step = step
        self.shape = None
        self.vectors = 0
        self.cached_checksum = None
        self.sd_checkpoint = None
        self.sd_checkpoint_name = None
        self.optimizer_state_dict = None
        self.filename = None

        self.shape = vec.shape[-1]
        self.vectors = vec.shape[0]

    def save(self, filename):
        embedding_data = {
            "string_to_token": {"*": 265},
            "string_to_param": {"*": self.vec},
            "name": self.name,
            "step": self.step,
            "sd_checkpoint": self.sd_checkpoint,
            "sd_checkpoint_name": self.sd_checkpoint_name,
        }

        torch.save(embedding_data, filename)

        if shared.opts.save_optimizer_state and self.optimizer_state_dict is not None:
            optimizer_saved_dict = {
                'hash': self.checksum(),
                'optimizer_state_dict': self.optimizer_state_dict,
            }
            torch.save(optimizer_saved_dict, f"{filename}.optim")

    def checksum(self):
        if self.cached_checksum is not None:
            return self.cached_checksum

        def const_hash(a):
            r = 0
            for v in a:
                r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
            return r

        self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
        return self.cached_checksum

class EmbeddingDatabase:
    def __init__(self):
        self.ids_lookup = {}
        self.word_embeddings = {}
        self.skipped_embeddings = {}
        self.expected_shape = -1
        self.embedding_dirs = {}
        self.previously_displayed_embeddings = ()

    def register_embedding(self, embedding, model):
        self.word_embeddings[embedding.name] = embedding

        ids = model.tokenize([embedding.name])[0]

        first_id = ids[0]
        if first_id not in self.ids_lookup:
            self.ids_lookup[first_id] = []

        self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True)

        return embedding

    def find_embedding_at_position(self, tokens, offset):
        token = tokens[offset]
        possible_matches = self.ids_lookup.get(token, None)

        if possible_matches is None:
            return None, None

        for ids, embedding in possible_matches:
            if tokens[offset:offset + len(ids)] == ids:
                return embedding, len(ids)

        return None, None