from typing import Union

import torch

from transformers import CLIPTokenizer, CLIPTextModel


class PromptProcessor():
    def __init__(self, tokenizer: CLIPTokenizer, text_encoder: CLIPTextModel):
        self.tokenizer = tokenizer
        self.text_encoder = text_encoder

    def get_input_ids(self, prompt: Union[str, list[str]]):
        return self.tokenizer(
            prompt,
            padding="do_not_pad",
        ).input_ids

    def unify_input_ids(self, input_ids: list[int]):
        return self.tokenizer.pad(
            {"input_ids": input_ids},
            padding=True,
            pad_to_multiple_of=self.tokenizer.model_max_length,
            return_tensors="pt"
        )

    def get_embeddings(self, input_ids: torch.IntTensor, attention_mask=None):
        prompts = input_ids.shape[0]

        input_ids = input_ids.view((-1, self.tokenizer.model_max_length)).to(self.text_encoder.device)
        if attention_mask is not None:
            attention_mask = attention_mask.view((-1, self.tokenizer.model_max_length)).to(self.text_encoder.device)

        text_embeddings = self.text_encoder(input_ids, attention_mask=attention_mask)[0]
        text_embeddings = text_embeddings.view((prompts, -1, text_embeddings.shape[2]))
        return text_embeddings