import logging
from typing import List, Tuple

from dbgpt.core.interface.output_parser import BaseOutputParser

logger = logging.getLogger(__name__)


class ExtractRefineSummaryParser(BaseOutputParser):
    def __init__(self, is_stream_out: bool = True, **kwargs):
        super().__init__(is_stream_out=is_stream_out, **kwargs)

    def parse_prompt_response(
        self, response, max_length: int = 128
    ) -> List[Tuple[str, str, str]]:
        # clean_str = super().parse_prompt_response(response)
        print("clean prompt response:", response)

        # if response.startswith("Triplets:"):
        #     response = response[len("Triplets:") :]
        #     pattern = r"\([^()]+\)"
        #     response = re.findall(pattern, response)
        # # response = response.strip().split("\n")
        # print("parse prompt response:", response)
        # results = []
        # for text in response:
        #     if not text or text[0] != "(" or text[-1] != ")":
        #         # skip empty lines and non-triplets
        #         continue
        #     tokens = text[1:-1].split(",")
        #     if len(tokens) != 3:
        #         continue
        #
        #     if any(len(s.encode("utf-8")) > max_length for s in tokens):
        #         # We count byte-length instead of len() for UTF-8 chars,
        #         # will skip if any of the tokens are too long.
        #         # This is normally due to a poorly formatted triplet
        #         # extraction, in more serious KG building cases
        #         # we'll need NLP models to better extract triplets.
        #         continue
        #
        #     subject, predicate, obj = map(str.strip, tokens)
        #     if not subject or not predicate or not obj:
        #         # skip partial triplets
        #         continue
        #     results.append((subject.lower(), predicate.lower(), obj.lower()))
        return response

    def parse_view_response(self, speak, data) -> str:
        ### tool out data to table view
        return data
