# Copyright (c) FULIUCANSHENG.
# Licensed under the MIT License.

import re
import json
import logging
import torch
import hashlib
import pandas as pd
from PIL import Image
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
from diffusers.utils import numpy_to_pil
from diffusers.models import ControlNetModel, T2IAdapter, MultiAdapter
from diffusers.pipelines import (
    StableDiffusionXLPipeline,
    StableDiffusionXLImg2ImgPipeline,
    StableDiffusionXLInpaintPipeline,
    StableDiffusionXLControlNetPipeline,
    StableDiffusionXLControlNetImg2ImgPipeline,
    StableDiffusionXLControlNetInpaintPipeline,
    StableDiffusionXLAdapterPipeline,
)
from unitorch import is_xformers_available
from unitorch.utils import is_remote_url
from unitorch.models.diffusers import GenericStableXLModel
from unitorch.models.diffusers import StableXLProcessor

from unitorch.utils import pop_value, nested_dict_value
from unitorch.cli import (
    cached_path,
    add_default_section_for_init,
    add_default_section_for_function,
)
from unitorch.cli import CoreConfigureParser, GenericScript
from unitorch.cli import register_script
from unitorch.cli.models.diffusers import (
    pretrained_stable_infos,
    pretrained_stable_extensions_infos,
    load_weight,
)
from unitorch.cli.pipelines import Schedulers


class StableXLForText2ImageFastAPIPipeline(GenericStableXLModel):
    def __init__(
        self,
        config_path: str,
        text_config_path: str,
        text2_config_path: str,
        vae_config_path: str,
        scheduler_config_path: str,
        vocab_path: str,
        merge_path: str,
        vocab2_path: str,
        merge2_path: str,
        quant_config_path: Optional[str] = None,
        max_seq_length: Optional[int] = 77,
        pad_token: Optional[str] = "<|endoftext|>",
        pad_token2: Optional[str] = "!",
        weight_path: Optional[Union[str, List[str]]] = None,
        state_dict: Optional[Dict[str, Any]] = None,
        device: Optional[Union[str, int]] = "cpu",
        enable_cpu_offload: Optional[bool] = False,
        enable_xformers: Optional[bool] = False,
    ):
        super().__init__(
            config_path=config_path,
            text_config_path=text_config_path,
            text2_config_path=text2_config_path,
            vae_config_path=vae_config_path,
            scheduler_config_path=scheduler_config_path,
            quant_config_path=quant_config_path,
        )
        self.processor = StableXLProcessor(
            vocab_path=vocab_path,
            merge_path=merge_path,
            vocab2_path=vocab2_path,
            merge2_path=merge2_path,
            vae_config_path=vae_config_path,
            max_seq_length=max_seq_length,
            pad_token=pad_token,
            pad_token2=pad_token2,
        )
        self._device = "cpu" if device == "cpu" else int(device)

        self.from_pretrained(weight_path, state_dict=state_dict)
        self.eval()

        self._enable_cpu_offload = enable_cpu_offload
        self._enable_xformers = enable_xformers

        self.pipeline = StableDiffusionXLPipeline(
            vae=self.vae,
            text_encoder=self.text,
            text_encoder_2=self.text2,
            unet=self.unet,
            scheduler=self.scheduler,
            tokenizer=None,
            tokenizer_2=None,
        )
        self.pipeline.set_progress_bar_config(disable=True)
        if self._enable_cpu_offload and self._device != "cpu":
            self.pipeline.enable_model_cpu_offload(self._device)
        else:
            self.to(device=self._device)

        if self._enable_xformers and self._device != "cpu":
            assert is_xformers_available(), "Please install xformers first."
            self.pipeline.enable_xformers_memory_efficient_attention()

    @classmethod
    @add_default_section_for_init("core/fastapi/pipeline/stable_xl/text2image")
    def from_core_configure(
        cls,
        config,
        pretrained_name: Optional[str] = "stable-xl-base",
        config_path: Optional[str] = None,
        text_config_path: Optional[str] = None,
        text2_config_path: Optional[str] = None,
        vae_config_path: Optional[str] = None,
        scheduler_config_path: Optional[str] = None,
        vocab_path: Optional[str] = None,
        merge_path: Optional[str] = None,
        vocab2_path: Optional[str] = None,
        merge2_path: Optional[str] = None,
        quant_config_path: Optional[str] = None,
        pretrained_weight_path: Optional[str] = None,
        device: Optional[str] = "cpu",
        **kwargs,
    ):
        config.set_default_section("core/fastapi/pipeline/stable_xl/text2image")
        pretrained_name = config.getoption("pretrained_name", pretrained_name)
        pretrained_infos = nested_dict_value(pretrained_stable_infos, pretrained_name)

        config_path = config.getoption("config_path", config_path)
        config_path = pop_value(
            config_path,
            nested_dict_value(pretrained_infos, "unet", "config"),
        )
        config_path = cached_path(config_path)

        text_config_path = config.getoption("text_config_path", text_config_path)
        text_config_path = pop_value(
            text_config_path,
            nested_dict_value(pretrained_infos, "text", "config"),
        )
        text_config_path = cached_path(text_config_path)

        text2_config_path = config.getoption("text2_config_path", text2_config_path)
        text2_config_path = pop_value(
            text2_config_path,
            nested_dict_value(pretrained_infos, "text2", "config"),
        )
        text2_config_path = cached_path(text2_config_path)

        vae_config_path = config.getoption("vae_config_path", vae_config_path)
        vae_config_path = pop_value(
            vae_config_path,
            nested_dict_value(pretrained_infos, "vae", "config"),
        )
        vae_config_path = cached_path(vae_config_path)

        scheduler_config_path = config.getoption(
            "scheduler_config_path", scheduler_config_path
        )
        scheduler_config_path = pop_value(
            scheduler_config_path,
            nested_dict_value(pretrained_infos, "scheduler"),
        )
        scheduler_config_path = cached_path(scheduler_config_path)

        vocab_path = config.getoption("vocab_path", vocab_path)
        vocab_path = pop_value(
            vocab_path,
            nested_dict_value(pretrained_infos, "text", "vocab"),
        )
        vocab_path = cached_path(vocab_path)

        merge_path = config.getoption("merge_path", merge_path)
        merge_path = pop_value(
            merge_path,
            nested_dict_value(pretrained_infos, "text", "merge"),
        )
        merge_path = cached_path(merge_path)

        vocab2_path = config.getoption("vocab2_path", vocab2_path)
        vocab2_path = pop_value(
            vocab2_path,
            nested_dict_value(pretrained_infos, "text2", "vocab"),
        )
        vocab2_path = cached_path(vocab2_path)

        merge2_path = config.getoption("merge2_path", merge2_path)
        merge2_path = pop_value(
            merge2_path,
            nested_dict_value(pretrained_infos, "text2", "merge"),
        )
        merge2_path = cached_path(merge2_path)

        quant_config_path = config.getoption("quant_config_path", quant_config_path)
        if quant_config_path is not None:
            quant_config_path = cached_path(quant_config_path)

        max_seq_length = config.getoption("max_seq_length", 77)
        pad_token = config.getoption("pad_token", "<|endoftext|>")
        pad_token2 = config.getoption("pad_token2", "!")
        weight_path = config.getoption("pretrained_weight_path", pretrained_weight_path)
        device = config.getoption("device", device)
        enable_cpu_offload = config.getoption("enable_cpu_offload", True)
        enable_xformers = config.getoption("enable_xformers", True)

        state_dict = None
        if weight_path is None and pretrained_infos is not None:
            state_dict = [
                load_weight(
                    nested_dict_value(pretrained_infos, "unet", "weight"),
                    prefix_keys={"": "unet."},
                ),
                load_weight(
                    nested_dict_value(pretrained_infos, "text", "weight"),
                    prefix_keys={"": "text."},
                ),
                load_weight(
                    nested_dict_value(pretrained_infos, "text2", "weight"),
                    prefix_keys={"": "text2."},
                ),
                load_weight(
                    nested_dict_value(pretrained_infos, "vae", "weight"),
                    prefix_keys={"": "vae."},
                ),
            ]

        inst = cls(
            config_path=config_path,
            text_config_path=text_config_path,
            text2_config_path=text2_config_path,
            vae_config_path=vae_config_path,
            scheduler_config_path=scheduler_config_path,
            vocab_path=vocab_path,
            merge_path=merge_path,
            vocab2_path=vocab2_path,
            merge2_path=merge2_path,
            quant_config_path=quant_config_path,
            max_seq_length=max_seq_length,
            pad_token=pad_token,
            pad_token2=pad_token2,
            weight_path=weight_path,
            state_dict=state_dict,
            device=device,
            enable_cpu_offload=enable_cpu_offload,
            enable_xformers=enable_xformers,
        )
        return inst

    @torch.no_grad()
    @add_default_section_for_function("core/fastapi/pipeline/stable_xl/text2image")
    def __call__(
        self,
        text: str,
        neg_text: Optional[str] = "",
        height: Optional[int] = 1024,
        width: Optional[int] = 1024,
        guidance_scale: Optional[float] = 7.5,
        num_timesteps: Optional[int] = 50,
        seed: Optional[int] = 1123,
        freeu_params: Optional[Tuple[float, float, float, float]] = (
            0.9,
            0.2,
            1.2,
            1.4,
        ),
    ):
        text_inputs = self.processor.text2image_inputs(
            text,
            negative_prompt=neg_text,
        )
        self.scheduler.set_timesteps(num_inference_steps=num_timesteps)

        inputs = text_inputs
        self.pipeline.enable_freeu(*freeu_params)
        self.seed = seed

        inputs = {k: v.unsqueeze(0) if v is not None else v for k, v in inputs.items()}
        inputs = {
            k: v.to(device=self.device) if v is not None else v
            for k, v in inputs.items()
        }

        prompt_embeds_results = self.get_prompt_outputs(
            inputs["input_ids"],
            input2_ids=inputs["input2_ids"],
            negative_input_ids=inputs["negative_input_ids"],
            negative_input2_ids=inputs["negative_input2_ids"],
        )
        prompt_embeds = prompt_embeds_results.prompt_embeds
        negative_prompt_embeds = prompt_embeds_results.negative_prompt_embeds
        pooled_prompt_embeds = prompt_embeds_results.pooled_prompt_embeds
        negative_pooled_prompt_embeds = (
            prompt_embeds_results.negative_pooled_prompt_embeds
        )

        outputs = self.pipeline(
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            generator=torch.Generator(device=self.pipeline.device).manual_seed(
                self.seed
            ),
            height=height,
            width=width,
            guidance_scale=guidance_scale,
            output_type="np.array",
        )

        images = torch.from_numpy(outputs.images)
        images = numpy_to_pil(images.cpu().numpy())
        return images[0]
