# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import pickle
from pathlib import Path
import numpy as np

from ..utils.io import ImageReader
from ..components import CropByBoxes, FaissIndexer
from ..components.retrieval.faiss import FaissBuilder
from ..results import ShiTuResult
from .base import BasePipeline


class ShiTuV2Pipeline(BasePipeline):
    """ShiTuV2 Pipeline"""

    entities = "PP-ShiTuV2"

    def __init__(
        self,
        det_model,
        rec_model,
        det_batch_size=1,
        rec_batch_size=1,
        index=None,
        score_thres=None,
        hamming_radius=None,
        return_k=5,
        device=None,
        predictor_kwargs=None,
        _build_models=True,
    ):
        super().__init__(device, predictor_kwargs)
        if _build_models:
            self._build_predictor(det_model, rec_model)
            self.set_predictor(det_batch_size, rec_batch_size, device)
        self._return_k, self._score_thres, self._hamming_radius = (
            return_k,
            score_thres,
            hamming_radius,
        )
        self._indexer = self._build_indexer(index=index) if index else None

    def _build_indexer(self, index):
        return FaissIndexer(
            index=index,
            return_k=self._return_k,
            score_thres=self._score_thres,
            hamming_radius=self._hamming_radius,
        )

    def _build_predictor(self, det_model, rec_model):
        self.det_model = self._create(model=det_model)
        self.rec_model = self._create(model=rec_model)
        self._crop_by_boxes = CropByBoxes()
        self._img_reader = ImageReader(backend="opencv")

    def set_predictor(self, det_batch_size=None, rec_batch_size=None, device=None):
        if det_batch_size:
            self.det_model.set_predictor(batch_size=det_batch_size)
        if rec_batch_size:
            self.rec_model.set_predictor(batch_size=rec_batch_size)
        if device:
            self.det_model.set_predictor(device=device)
            self.rec_model.set_predictor(device=device)

    def predict(self, input, index=None, **kwargs):
        indexer = self._build_indexer(index) if index is not None else self._indexer
        assert indexer
        self.set_predictor(**kwargs)
        for det_res in self.det_model(input):
            rec_res = self.get_rec_result(det_res, indexer)
            yield self.get_final_result(det_res, rec_res)

    def get_rec_result(self, det_res, indexer):
        if len(det_res["boxes"]) == 0:
            full_img = self._img_reader.read(det_res["input_path"])
            w, h = full_img.shape[:2]
            det_res["boxes"].append(
                {
                    "cls_id": 0,
                    "label": "full_img",
                    "score": 0,
                    "coordinate": [0, 0, h, w],
                }
            )
        subs_of_img = list(self._crop_by_boxes(det_res))
        img_list = [img["img"] for img in subs_of_img]
        all_rec_res = list(self.rec_model(img_list))
        all_rec_res = next(indexer(all_rec_res))
        output = {"label": [], "score": []}
        for res in all_rec_res:
            output["label"].append(res["label"])
            output["score"].append(res["score"])
        return output

    def get_final_result(self, det_res, rec_res):
        single_img_res = {"input_path": det_res["input_path"], "boxes": []}
        for i, obj in enumerate(det_res["boxes"]):
            rec_scores = rec_res["score"][i]
            labels = rec_res["label"][i]
            single_img_res["boxes"].append(
                {
                    "labels": labels,
                    "rec_scores": rec_scores,
                    "det_score": obj["score"],
                    "coordinate": obj["coordinate"],
                }
            )
        return ShiTuResult(single_img_res)

    def build_index(
        self,
        gallery_imgs,
        gallery_label,
        metric_type="IP",
        index_type="HNSW32",
        **kwargs
    ):
        return FaissBuilder.build(
            gallery_imgs,
            gallery_label,
            self.rec_model.predict,
            metric_type=metric_type,
            index_type=index_type,
        )

    def remove_index(self, remove_ids, index):
        return FaissBuilder.remove(remove_ids, index)

    def append_index(
        self,
        gallery_imgs,
        gallery_label,
        index,
    ):
        return FaissBuilder.append(
            gallery_imgs,
            gallery_label,
            self.rec_model.predict,
            index,
        )