# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# code modified from deepseekv3_tool_parser.py

from collections.abc import Sequence
from typing import Union

import regex as re

from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                              DeltaFunctionCall, DeltaMessage,
                                              DeltaToolCall,
                                              ExtractedToolCallInformation,
                                              FunctionCall, ToolCall)
from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
    ToolParser, ToolParserManager)
from vllm.logger import init_logger
from vllm.transformers_utils.tokenizer import AnyTokenizer

logger = init_logger(__name__)


@ToolParserManager.register_module("glm45")
class Glm4MoeModelToolParser(ToolParser):

    def __init__(self, tokenizer: AnyTokenizer):
        super().__init__(tokenizer)
        self.current_tool_name_sent = False
        self.prev_tool_call_arr: list[dict] = []
        self.current_tool_id = -1
        self.streamed_args_for_tool: list[str] = []
        self.tool_call_start_token = "<tool_call>"
        self.tool_call_end_token = "</tool_call>"

        self.tool_calls_start_token = self.tool_call_start_token

        # Updated regex for the XML-based format
        self.tool_call_regex = re.compile(
            r"<tool_call>\s*"
            r"(?P<function_name>[^\n<]+)\s*"  # 函数名（到换行或 <）
            r"(?P<arguments>(?:\s*<arg_key>[^<]+</arg_key>\s*"
            r"<arg_value>[^<]*</arg_value>\s*)*)\s*"
            r"</tool_call>",
            re.DOTALL,
        )

        # Regex for parsing individual arguments
        self.arg_regex = re.compile(
            r"<arg_key>(?P<key>[^<]+)</arg_key>\s*<arg_value>(?P<value>[^<]*)</arg_value>",
            re.DOTALL,
        )

        # Streaming regex
        self.stream_tool_call_portion_regex = re.compile(
            r"(?P<function_name>[^\n<]+)\s*"
            r"(?P<arguments>(?:\s*<arg_key>[^<]+</arg_key>\s*"
            r"<arg_value>[^<]*</arg_value>\s*)*)",
            re.DOTALL,
        )

        # For streaming, we also need a regex to match just the function name
        self.stream_tool_call_name_regex = re.compile(
            r"(?P<function_name>[^\n<]+)",
            re.DOTALL,
        )

        if not self.model_tokenizer:
            raise ValueError(
                "The model tokenizer must be passed to the ToolParser "
                "constructor during construction.")

        self.tool_call_start_token_id = self.vocab.get(
            self.tool_call_start_token)
        self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)

    def _parse_arguments(self, args_text: str) -> str:
        """Parse XML-based arguments into JSON format."""
        if not args_text or not args_text.strip():
            return "{}"

        args_dict = {}
        matches = self.arg_regex.findall(args_text)

        for key, value in matches:
            args_dict[key.strip()] = value.strip()

        import json
        return json.dumps(args_dict, ensure_ascii=False)

    def extract_tool_calls(
        self,
        model_output: str,
        request: ChatCompletionRequest,
    ) -> ExtractedToolCallInformation:

        # sanity check; avoid unnecessary processing
        if self.tool_calls_start_token not in model_output:
            return ExtractedToolCallInformation(tools_called=False,
                                                tool_calls=[],
                                                content=model_output)

        try:
            # Find all tool calls in the output
            function_call_matches = self.tool_call_regex.findall(model_output)

            logger.debug("function_call_matches: %s", function_call_matches)

            if not function_call_matches:
                return ExtractedToolCallInformation(
                    tools_called=False,
                    tool_calls=[],
                    content=model_output,
                )

            tool_calls = []
            for i, match in enumerate(function_call_matches):
                function_name, function_args_xml = match
                function_name = function_name.strip()

                # Parse XML arguments to JSON
                function_args_json = self._parse_arguments(function_args_xml)

                tool_calls.append(
                    ToolCall(
                        id=f"call_{i}",
                        type='function',
                        function=FunctionCall(name=function_name,
                                              arguments=function_args_json),
                    ))

            # Extract content before the first tool call
            content = model_output[:model_output.find(self.
                                                      tool_calls_start_token)]
            return ExtractedToolCallInformation(
                tools_called=bool(tool_calls),
                tool_calls=tool_calls,
                content=content.strip() if content.strip() else None,
            )

        except Exception:
            logger.exception("Error in extracting tool call from response.")
            return ExtractedToolCallInformation(tools_called=False,
                                                tool_calls=[],
                                                content=model_output)

    def extract_tool_calls_streaming(
        self,
        previous_text: str,
        current_text: str,
        delta_text: str,
        previous_token_ids: Sequence[int],
        current_token_ids: Sequence[int],
        delta_token_ids: Sequence[int],
        request: ChatCompletionRequest,
    ) -> Union[DeltaMessage, None]:

        logger.debug("delta_text: %s", delta_text)
        logger.debug("delta_token_ids: %s", delta_token_ids)
        # check to see if we should be streaming a tool call - is there a
        if self.tool_call_start_token_id not in current_token_ids:
            logger.debug("No tool call tokens found!")
            return DeltaMessage(content=delta_text)
        delta_text = delta_text.replace(self.tool_calls_start_token,
                                        "").replace(self.tool_call_end_token,
                                                    "")
        try:

            # figure out where we are in the parsing by counting tool call
            # start & end tags
            prev_tool_start_count = previous_token_ids.count(
                self.tool_call_start_token_id)
            prev_tool_end_count = previous_token_ids.count(
                self.tool_call_end_token_id)
            cur_tool_start_count = current_token_ids.count(
                self.tool_call_start_token_id)
            cur_tool_end_count = current_token_ids.count(
                self.tool_call_end_token_id)
            tool_call_portion = None
            text_portion = None

            # case: if we're generating text, OR rounding out a tool call
            if (cur_tool_start_count == cur_tool_end_count
                    and prev_tool_end_count == cur_tool_end_count
                    and self.tool_call_end_token not in delta_text):
                logger.debug("Generating text content! skipping tool parsing.")
                return DeltaMessage(content=delta_text)

            if self.tool_call_end_token in delta_text:
                logger.debug("tool_call_end_token in delta_text")
                full_text = current_text + delta_text
                tool_call_portion = full_text.split(
                    self.tool_call_start_token)[-1].split(
                        self.tool_call_end_token)[0].rstrip()
                delta_text = delta_text.split(
                    self.tool_call_end_token)[0].rstrip()
                text_portion = delta_text.split(
                    self.tool_call_end_token)[-1].lstrip()

            # case -- we're starting a new tool call
            if (cur_tool_start_count > cur_tool_end_count
                    and cur_tool_start_count > prev_tool_start_count):
                if len(delta_token_ids) > 1:
                    tool_call_portion = current_text.split(
                        self.tool_call_start_token)[-1]
                else:
                    tool_call_portion = None
                    delta = None

                text_portion = None

                # set cursors and state appropriately
                self.current_tool_id += 1
                self.current_tool_name_sent = False
                self.streamed_args_for_tool.append("")
                logger.debug("Starting on a new tool %s", self.current_tool_id)

            # case -- we're updating an existing tool call
            elif (cur_tool_start_count > cur_tool_end_count
                  and cur_tool_start_count == prev_tool_start_count):

                # get the portion of the text that's the tool call
                tool_call_portion = current_text.split(
                    self.tool_call_start_token)[-1]
                text_portion = None

            # case -- the current tool call is being closed.
            elif (cur_tool_start_count == cur_tool_end_count
                  and cur_tool_end_count >= prev_tool_end_count):
                if self.prev_tool_call_arr is None or len(
                        self.prev_tool_call_arr) == 0:
                    logger.debug(
                        "attempting to close tool call, but no tool call")
                    return None
                diff = self.prev_tool_call_arr[self.current_tool_id].get(
                    "arguments")
                if diff:
                    diff = (diff.encode("utf-8").decode("unicode_escape")
                            if diff is str else diff)
                    if '"}' not in delta_text:
                        return None
                    end_loc = delta_text.rindex('"}')
                    diff = delta_text[:end_loc] + '"}'
                    logger.debug(
                        "Finishing tool and found diff that had not "
                        "been streamed yet: %s",
                        diff,
                    )
                    self.streamed_args_for_tool[self.current_tool_id] += diff
                    return DeltaMessage(tool_calls=[
                        DeltaToolCall(
                            index=self.current_tool_id,
                            function=DeltaFunctionCall(
                                arguments=diff).model_dump(exclude_none=True),
                        )
                    ])

            # case -- otherwise we're just generating text
            else:
                text = delta_text.replace(self.tool_call_start_token, "")
                text = text.replace(self.tool_call_end_token, "")
                delta = DeltaMessage(tool_calls=[], content=text)
                return delta

            current_tool_call = dict()
            if tool_call_portion:
                current_tool_call_matches = (
                    self.stream_tool_call_portion_regex.match(
                        tool_call_portion))
                if current_tool_call_matches:
                    tool_id, tool_args = (current_tool_call_matches.groups())
                    tool_name = tool_id.split('.')[1].split(':')[0]
                    current_tool_call['id'] = tool_id
                    current_tool_call["name"] = tool_name
                    current_tool_call["arguments"] = tool_args
                else:
                    current_tool_call_name_matches = (
                        self.stream_tool_call_name_regex.match(
                            tool_call_portion))
                    if current_tool_call_name_matches:
                        tool_id_str, = current_tool_call_name_matches.groups()
                        tool_name = tool_id_str.split('.')[1].split(':')[0]
                        current_tool_call['id'] = tool_id_str
                        current_tool_call["name"] = tool_name
                        current_tool_call["arguments"] = ""
                    else:
                        logger.debug("Not enough token")
                        return None

            # case - we haven't sent the tool name yet. If it's available, send
            #   it. otherwise, wait until it's available.
            if not self.current_tool_name_sent:
                if current_tool_call is None:
                    return None
                function_name: Union[str, None] = current_tool_call.get("name")
                tool_id = current_tool_call.get("id")
                if function_name:
                    self.current_tool_name_sent = True
                    return DeltaMessage(tool_calls=[
                        DeltaToolCall(
                            index=self.current_tool_id,
                            type="function",
                            id=tool_id,
                            function=DeltaFunctionCall(
                                name=function_name).model_dump(
                                    exclude_none=True),
                        )
                    ])
                else:
                    return None

            # case -- otherwise, send the tool call delta

            # if the tool call portion is None, send the delta as text
            if tool_call_portion is None:
                # if there's text but not tool calls, send that -
                # otherwise None to skip chunk
                delta = (DeltaMessage(
                    content=delta_text) if text_portion is not None else None)
                return delta

            # now, the nitty-gritty of tool calls
            # now we have the portion to parse as tool call.

            logger.debug("Trying to parse current tool call with ID %s",
                         self.current_tool_id)

            # if we're starting a new tool call, push an empty object in as
            #   a placeholder for the arguments
            if len(self.prev_tool_call_arr) <= self.current_tool_id:
                self.prev_tool_call_arr.append({})

            # main logic for tool parsing here - compare prev. partially-parsed
            #   JSON to the current partially-parsed JSON
            prev_arguments = self.prev_tool_call_arr[self.current_tool_id].get(
                "arguments")
            cur_arguments = current_tool_call.get("arguments")

            logger.debug("diffing old arguments: %s", prev_arguments)
            logger.debug("against new ones: %s", cur_arguments)

            # case -- no arguments have been created yet. skip sending a delta.
            if not cur_arguments and not prev_arguments:
                logger.debug("Skipping text %s - no arguments", delta_text)
                delta = None

            # case -- prev arguments are defined, but non are now.
            #   probably impossible, but not a fatal error - just keep going
            elif not cur_arguments and prev_arguments:
                logger.error("should be impossible to have arguments reset "
                             "mid-call. skipping streaming anything.")
                delta = None

            # case -- we now have the first info about arguments available from
            #   autocompleting the JSON
            elif cur_arguments and not prev_arguments:

                delta = DeltaMessage(tool_calls=[
                    DeltaToolCall(
                        index=self.current_tool_id,
                        function=DeltaFunctionCall(
                            arguments=cur_arguments).model_dump(
                                exclude_none=True),
                    )
                ])
                self.streamed_args_for_tool[
                    self.current_tool_id] = cur_arguments

            # last case -- we have an update to existing arguments.
            elif cur_arguments and prev_arguments:
                if (isinstance(delta_text, str)
                        and cur_arguments != prev_arguments
                        and len(cur_arguments) > len(prev_arguments)
                        and cur_arguments.startswith(prev_arguments)):
                    delta_arguments = cur_arguments[len(prev_arguments):]
                    logger.debug("got diff %s", delta_text)

                    delta = DeltaMessage(tool_calls=[
                        DeltaToolCall(
                            index=self.current_tool_id,
                            function=DeltaFunctionCall(
                                arguments=delta_arguments).model_dump(
                                    exclude_none=True),
                        )
                    ])
                    self.streamed_args_for_tool[
                        self.current_tool_id] = cur_arguments
                else:
                    delta = None

            # handle saving the state for the current tool into
            # the "prev" list for use in diffing for the next iteration
            if self.current_tool_id == len(self.prev_tool_call_arr) - 1:
                self.prev_tool_call_arr[
                    self.current_tool_id] = current_tool_call
            else:
                self.prev_tool_call_arr.append(current_tool_call)

            return delta

        except Exception:
            logger.exception("Error trying to handle streaming tool call.")
            return None  # do not stream a delta. skip this token ID.
