import typing
import torch
import diffusers
from compel import Compel, ReturnedEmbeddingsType
import modules.shared as shared
import modules.prompt_parser as prompt_parser


def convert_to_compel(prompt: str):
    if prompt is None:
        return None
    all_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(
        prompt, 100
    )[
        0
    ]
    output_list = prompt_parser.parse_prompt_attention(all_schedules[0][1])
    converted_prompt = []
    for subprompt, weight in output_list:
        if subprompt != " ":
            if weight == 1:
                converted_prompt.append(subprompt)
            else:
                converted_prompt.append(f"({subprompt}){weight}")
    converted_prompt = " ".join(converted_prompt)
    return converted_prompt


def compel_encode_prompt(
    pipeline: diffusers.StableDiffusionXLPipeline,
    prompt: str,
    negative_prompt: str,
    prompt_2: typing.Optional[str] = None,
    negative_prompt_2: typing.Optional[str] = None,
    is_refiner: bool = None,
):
    if shared.sd_model_type not in {"sd", "sdxl"}:
        shared.log.warning(
            f"Compel encoding not yet supported for {type(pipeline).__name__}."
        )
        return (None,) * 4
    if shared.opts.data["prompt_attention"] != "Compel parser":
        prompt = convert_to_compel(prompt)
        negative_prompt = convert_to_compel(negative_prompt)
        prompt_2 = convert_to_compel(prompt_2)
        negative_prompt_2 = convert_to_compel(negative_prompt_2)

    compel_te1 = Compel(
        tokenizer=pipeline.tokenizer,
        text_encoder=pipeline.text_encoder,
        returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
        requires_pooled=False,
    )

    compel_te2 = Compel(
        tokenizer=pipeline.tokenizer_2,
        text_encoder=pipeline.text_encoder_2,
        returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
        requires_pooled=True,
    )
    if not is_refiner:
        positive_te1 = compel_te1(prompt)
        positive_te2, positive_pooled = compel_te2(prompt_2)
        positive = torch.cat((positive_te1, positive_te2), dim=-1)

        negative_te1 = compel_te1(negative_prompt)
        negative_te2, negative_pooled = compel_te2(negative_prompt_2)
        negative = torch.cat((negative_te1, negative_te2), dim=-1)
    else:
        positive, positive_pooled = compel_te2(prompt)
        negative, negative_pooled = compel_te2(negative_prompt)

    shared.log.debug(compel_te1.parse_prompt_string(prompt))
    [prompt_embed, negative_embed] = compel_te2.pad_conditioning_tensors_to_same_length(
        [positive, negative]
    )
    return prompt_embed, positive_pooled, negative_embed, negative_pooled