import os
import gc
import pandas as pd
import numpy as np

from typing import Tuple, List, Dict
from io import BytesIO
from PIL import Image

from pathlib import Path
from huggingface_hub import hf_hub_download
import re

# from tagger.deepbooru import re_special as tag_escape_pattern

tag_escape_pattern = re.compile(r'([\\()])')

# i'm not sure if it's okay to add this file to the repository
import tagger.dbimutils as dbimutils

# select a device to process
use_cpu = True

if use_cpu:
    tf_device_name = '/cpu:0'
else:
    tf_device_name = '/gpu:0'

class Interrogator:
    @staticmethod
    def postprocess_tags(
        tags: Dict[str, float],

        threshold=0.35,
        additional_tags: List[str] = [],
        exclude_tags: List[str] = [],
        sort_by_alphabetical_order=False,
        add_confident_as_weight=False,
        replace_underscore=False,
        replace_underscore_excludes: List[str] = [],
        escape_tag=False
    ) -> Dict[str, float]:
        for t in additional_tags:
            tags[t] = 1.0

        # those lines are totally not "pythonic" but looks better to me
        tags = {
            t: c

            # sort by tag name or confident
            for t, c in sorted(
                tags.items(),
                key=lambda i: i[0 if sort_by_alphabetical_order else 1],
                reverse=not sort_by_alphabetical_order
            )

            # filter tags
            if (
                c >= threshold
                and t not in exclude_tags
            )
        }

        new_tags = []
        for tag in list(tags):
            new_tag = tag

            if replace_underscore and tag not in replace_underscore_excludes:
                new_tag = new_tag.replace('_', ' ')

            if escape_tag:
                new_tag = tag_escape_pattern.sub(r'\\\1', new_tag)

            if add_confident_as_weight:
                new_tag = f'({new_tag}:{tags[tag]})'

            new_tags.append((new_tag, tags[tag]))
        tags = dict(new_tags)

        return tags

    def __init__(self, name: str) -> None:
        self.name = name

    def load(self):
        raise NotImplementedError()

    def unload(self) -> bool:
        unloaded = False

        if hasattr(self, 'model') and self.model is not None:
            del self.model
            unloaded = True
            print(f'Unloaded {self.name}')

        if hasattr(self, 'tags'):
            del self.tags

        return unloaded

    def interrogate(
        self,
        image: Image
    ) -> Tuple[
        Dict[str, float],  # rating confidents
        Dict[str, float]  # tag confidents
    ]:
        raise NotImplementedError()


class DeepDanbooruInterrogator(Interrogator):
    def __init__(self, name: str, project_path: os.PathLike) -> None:
        super().__init__(name)
        self.project_path = project_path

    def load(self) -> None:
        print(f'Loading {self.name} from {str(self.project_path)}')

        import tensorflow as tf

        # tensorflow maps nearly all vram by default, so we limit this
        # https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth
        # TODO: only run on the first run
        for device in tf.config.experimental.list_physical_devices('GPU'):
            tf.config.experimental.set_memory_growth(device, True)

        with tf.device(tf_device_name):
            import deepdanbooru.project as ddp

            self.model = ddp.load_model_from_project(
                project_path=self.project_path,
                compile_model=False
            )

            print(f'Loaded {self.name} model from {str(self.project_path)}')

            self.tags = ddp.load_tags_from_project(
                project_path=self.project_path
            )

    def unload(self) -> bool:
        # unloaded = super().unload()

        # if unloaded:
        #     # tensorflow suck
        #     # https://github.com/keras-team/keras/issues/2102
        #     import tensorflow as tf
        #     tf.keras.backend.clear_session()
        #     gc.collect()

        # return unloaded

        # There is a bug in Keras where it is not possible to release a model that has been loaded into memory.
        # Downgrading to keras==2.1.6 may solve the issue, but it may cause compatibility issues with other packages.
        # Using subprocess to create a new process may also solve the problem, but it can be too complex (like Automatic1111 did).
        # It seems that for now, the best option is to keep the model in memory, as most users use the Waifu Diffusion model with onnx.

        return False

    def interrogate(
        self,
        image: Image
    ) -> Tuple[
        Dict[str, float],  # rating confidents
        Dict[str, float]  # tag confidents
    ]:
        # init model
        if not hasattr(self, 'model') or self.model is None:
            self.load()

        import deepdanbooru.data as ddd

        # convert an image to fit the model
        image_bufs = BytesIO()
        image.save(image_bufs, format='PNG')
        image = ddd.load_image_for_evaluate(
            image_bufs,
            self.model.input_shape[2],
            self.model.input_shape[1]
        )

        image = image.reshape((1, *image.shape[0:3]))

        # evaluate model
        result = self.model.predict(image)

        confidents = result[0].tolist()
        ratings = {}
        tags = {}

        for i, tag in enumerate(self.tags):
            tags[tag] = confidents[i]

        return ratings, tags


class WaifuDiffusionInterrogator(Interrogator):
    def __init__(
        self,
        name: str,
        model_path='model.onnx',
        tags_path='selected_tags.csv',
        **kwargs
    ) -> None:
        super().__init__(name)
        self.model_path = model_path
        self.tags_path = tags_path
        self.kwargs = kwargs

    def download(self) -> Tuple[os.PathLike, os.PathLike]:
        print(f"Loading {self.name} model file from {self.kwargs['repo_id']}")

        model_path = Path(hf_hub_download(
            **self.kwargs, filename=self.model_path))
        tags_path = Path(hf_hub_download(
            **self.kwargs, filename=self.tags_path))
        return model_path, tags_path

    def load(self) -> None:
        model_path, tags_path = self.download()

        from onnxruntime import InferenceSession

        # https://onnxruntime.ai/docs/execution-providers/
        # https://github.com/toriato/stable-diffusion-webui-wd14-tagger/commit/e4ec460122cf674bbf984df30cdb10b4370c1224#r92654958
        providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
        if use_cpu:
            providers.pop(0)

        self.model = InferenceSession(str(model_path), providers=providers)

        print(f'Loaded {self.name} model from {model_path}')

        self.tags = pd.read_csv(tags_path)

    def interrogate(
        self,
        image: Image
    ) -> Tuple[
        Dict[str, float],  # rating confidents
        Dict[str, float]  # tag confidents
    ]:
        # init model
        if not hasattr(self, 'model') or self.model is None:
            self.load()

        # code for converting the image and running the model is taken from the link below
        # thanks, SmilingWolf!
        # https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags/blob/main/app.py

        # convert an image to fit the model
        _, height, _, _ = self.model.get_inputs()[0].shape

        # alpha to white
        image = image.convert('RGBA')
        new_image = Image.new('RGBA', image.size, 'WHITE')
        new_image.paste(image, mask=image)
        image = new_image.convert('RGB')
        image = np.asarray(image)

        # PIL RGB to OpenCV BGR
        image = image[:, :, ::-1]

        image = dbimutils.make_square(image, height)
        image = dbimutils.smart_resize(image, height)
        image = image.astype(np.float32)
        image = np.expand_dims(image, 0)

        # evaluate model
        input_name = self.model.get_inputs()[0].name
        label_name = self.model.get_outputs()[0].name
        confidents = self.model.run([label_name], {input_name: image})[0]

        tags = self.tags[:][['name']]
        tags['confidents'] = confidents[0]

        # first 4 items are for rating (general, sensitive, questionable, explicit)
        ratings = dict(tags[:4].values)

        # rest are regular tags
        tags = dict(tags[4:].values)

        return ratings, tags