#
#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
#  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.
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import asyncio
import logging
import collections
import re
from typing import Any
from dataclasses import dataclass

from rag.graphrag.general.extractor import Extractor
from rag.graphrag.general.mind_map_prompt import MIND_MAP_EXTRACTION_PROMPT
from rag.graphrag.utils import ErrorHandlerFn, perform_variable_replacements, chat_limiter
from rag.llm.chat_model import Base as CompletionLLM
import markdown_to_json
from functools import reduce
from common.token_utils import num_tokens_from_string


@dataclass
class MindMapResult:
    """Unipartite Mind Graph result class definition."""

    output: dict


class MindMapExtractor(Extractor):
    _input_text_key: str
    _mind_map_prompt: str
    _on_error: ErrorHandlerFn

    def __init__(
        self,
        llm_invoker: CompletionLLM,
        prompt: str | None = None,
        input_text_key: str | None = None,
        on_error: ErrorHandlerFn | None = None,
    ):
        self._llm = llm_invoker
        self._input_text_key = input_text_key or "input_text"
        self._mind_map_prompt = prompt or MIND_MAP_EXTRACTION_PROMPT
        self._on_error = on_error or (lambda _e, _s, _d: None)

    def _key(self, k):
        return re.sub(r"\*+", "", k)

    def _be_children(self, obj: dict, keyset: set):
        if isinstance(obj, str):
            obj = [obj]
        if isinstance(obj, list):
            keyset.update(obj)
            obj = [re.sub(r"\*+", "", i) for i in obj]
            return [{"id": i, "children": []} for i in obj if i]
        arr = []
        for k, v in obj.items():
            k = self._key(k)
            if k and k not in keyset:
                keyset.add(k)
                arr.append({"id": k, "children": self._be_children(v, keyset)})
        return arr

    async def __call__(self, sections: list[str], prompt_variables: dict[str, Any] | None = None) -> MindMapResult:
        """Call method definition."""
        if prompt_variables is None:
            prompt_variables = {}

        res = []
        token_count = max(self._llm.max_length * 0.8, self._llm.max_length - 512)
        texts = []
        cnt = 0
        tasks = []
        for i in range(len(sections)):
            section_cnt = num_tokens_from_string(sections[i])
            if cnt + section_cnt >= token_count and texts:
                tasks.append(asyncio.create_task(self._process_document("".join(texts), prompt_variables, res)))
                texts = []
                cnt = 0

            texts.append(sections[i])
            cnt += section_cnt
        if texts:
            tasks.append(asyncio.create_task(self._process_document("".join(texts), prompt_variables, res)))
        try:
            await asyncio.gather(*tasks, return_exceptions=False)
        except Exception as e:
            logging.error(f"Error processing document: {e}")
            for t in tasks:
                t.cancel()
            await asyncio.gather(*tasks, return_exceptions=True)
            raise
        if not res:
            return MindMapResult(output={"id": "root", "children": []})
        merge_json = reduce(self._merge, res)
        if len(merge_json) > 1:
            keys = [re.sub(r"\*+", "", k) for k, v in merge_json.items() if isinstance(v, dict)]
            keyset = set(i for i in keys if i)
            merge_json = {"id": "root", "children": [{"id": self._key(k), "children": self._be_children(v, keyset)} for k, v in merge_json.items() if isinstance(v, dict) and self._key(k)]}
        else:
            k = self._key(list(merge_json.keys())[0])
            merge_json = {"id": k, "children": self._be_children(list(merge_json.items())[0][1], {k})}

        return MindMapResult(output=merge_json)

    def _merge(self, d1, d2):
        for k in d1:
            if k in d2:
                if isinstance(d1[k], dict) and isinstance(d2[k], dict):
                    self._merge(d1[k], d2[k])
                elif isinstance(d1[k], list) and isinstance(d2[k], list):
                    d2[k].extend(d1[k])
                else:
                    d2[k] = d1[k]
            else:
                d2[k] = d1[k]

        return d2

    def _list_to_kv(self, data):
        for key, value in data.items():
            if isinstance(value, dict):
                self._list_to_kv(value)
            elif isinstance(value, list):
                new_value = {}
                for i in range(len(value)):
                    if isinstance(value[i], list) and i > 0:
                        new_value[value[i - 1]] = value[i][0]
                data[key] = new_value
            else:
                continue
        return data

    def _todict(self, layer: collections.OrderedDict):
        to_ret = layer
        if isinstance(layer, collections.OrderedDict):
            to_ret = dict(layer)

        try:
            for key, value in to_ret.items():
                to_ret[key] = self._todict(value)
        except AttributeError:
            pass

        return self._list_to_kv(to_ret)

    async def _process_document(self, text: str, prompt_variables: dict[str, str], out_res) -> str:
        variables = {
            **prompt_variables,
            self._input_text_key: text,
        }
        text = perform_variable_replacements(self._mind_map_prompt, variables=variables)
        async with chat_limiter:
            response = await self._async_chat(text, [{"role": "user", "content": "Output:"}], {})
        response = re.sub(r"```[^\n]*", "", response)
        logging.debug(response)
        logging.debug(self._todict(markdown_to_json.dictify(response)))
        out_res.append(self._todict(markdown_to_json.dictify(response)))
