from typing import Union, Optional
from pathlib import Path

import torch
import torch.nn as nn

from safetensors import safe_open
from safetensors.torch import save_file

from transformers import CLIPTextModel
from transformers.models.clip import CLIPTextConfig
from transformers.models.clip.modeling_clip import CLIPTextEmbeddings

from models.sparse import PseudoSparseEmbedding


def resize_embedding(old_embedding: nn.Embedding, new_num_embeddings: int, initializer_factor: Optional[float] = None) -> nn.Embedding:
    old_num_embeddings, old_embedding_dim = old_embedding.weight.shape

    if old_num_embeddings == new_num_embeddings:
        return old_embedding

    n = min(old_num_embeddings, new_num_embeddings)

    new_embedding = nn.Embedding(
        new_num_embeddings,
        old_embedding_dim,
        device=old_embedding.weight.device,
        dtype=old_embedding.weight.dtype
    )
    if initializer_factor is not None:
        new_embedding.weight.data.normal_(mean=0.0, std=initializer_factor * 0.02)
    else:
        nn.init.zeros_(new_embedding.weight.data)
    new_embedding.weight.data[:n, :] = old_embedding.weight.data[:n, :]
    return new_embedding


class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
    def __init__(self, config: CLIPTextConfig, embeddings: CLIPTextEmbeddings, dropout_p: float = 0.0):
        super().__init__(config)

        self.token_embedding = embeddings.token_embedding
        self.position_embedding = embeddings.position_embedding
        self.initializer_factor = config.initializer_factor

        self.token_override_embedding = PseudoSparseEmbedding(
            self.token_embedding.embedding_dim,
            dropout_p=dropout_p,
            device=self.token_embedding.weight.device,
            dtype=self.token_embedding.weight.dtype,
        )

    def resize(self, size: int):
        self.token_override_embedding.resize(size)
        self.token_embedding = resize_embedding(self.token_embedding, size, self.initializer_factor)

    def add_embed(
        self,
        token_ids: Union[int, list[int]],
        initializer: Optional[Union[int, list[int], torch.FloatTensor]] = None,
        initializer_noise: float = 0.0,
    ):
        if isinstance(token_ids, int):
            token_ids = [token_ids]

        if initializer is None:
            initializer = token_ids

        if isinstance(initializer, int):
            initializer = [initializer]

        if isinstance(initializer, list):
            initializer = (initializer * len(token_ids))[:len(token_ids)]

            with torch.no_grad():
                initializer = self.get_embed(initializer)

        initializer = initializer.to(
            device=self.token_embedding.weight.device,
            dtype=self.token_embedding.weight.dtype,
        )

        if initializer_noise != 0:
            initializer += torch.randn_like(initializer) * initializer_noise

        token_ids = torch.tensor(token_ids, dtype=torch.long)

        self.token_embedding.weight.data[token_ids] = initializer
        self.token_override_embedding.set(token_ids, initializer)

    def load_embed(self, input_ids: list[int], filename: Path):
        with safe_open(filename, framework="pt", device="cpu") as file:
            self.add_embed(input_ids, file.get_tensor("embed"))

    def save_embed(self, input_ids: list[int], filename: Path):
        save_file({"embed": self.get_embed(input_ids)}, filename)

    def persist(self):
        input_ids = torch.arange(
            self.token_embedding.num_embeddings,
            device=self.token_override_embedding.mapping.device
        )
        embs, mask = self.token_override_embedding(input_ids)
        if embs is not None:
            input_ids = input_ids[mask]
            self.token_embedding.weight.data[input_ids] = embs
            self.token_override_embedding.unset(input_ids)

    def get_embed(self, input_ids: Union[list[int], torch.LongTensor]):
        if isinstance(input_ids, list):
            input_ids = torch.tensor(input_ids, device=self.token_embedding.weight.device, dtype=torch.long)

        embs = self.token_embedding(input_ids)
        embs_override, mask = self.token_override_embedding(input_ids)
        if embs_override is not None:
            embs[mask] = embs_override

        return embs

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
    ) -> torch.Tensor:
        seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]

        if position_ids is None:
            position_ids = self.position_ids[:, :seq_length]

        if inputs_embeds is None:
            inputs_embeds = self.get_embed(input_ids)

        position_embeddings = self.position_embedding(position_ids)
        embeddings = inputs_embeds + position_embeddings

        return embeddings


def patch_managed_embeddings(text_encoder: CLIPTextModel, dropout_p: float = 0.0) -> ManagedCLIPTextEmbeddings:
    text_embeddings = ManagedCLIPTextEmbeddings(text_encoder.config, text_encoder.text_model.embeddings, dropout_p)
    text_encoder.text_model.embeddings = text_embeddings
    return text_embeddings