# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import json
from collections.abc import Sequence
from typing import Union

import partial_json_parser
from partial_json_parser.core.options import Allow

from vllm.entrypoints.chat_utils import make_tool_call_id
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.entrypoints.openai.tool_parsers.utils import (
    consume_space,
    find_common_prefix,
    is_complete_json,
    partial_json_loads,
)
from vllm.logger import init_logger
from vllm.transformers_utils.tokenizer import AnyTokenizer

logger = init_logger(__name__)


@ToolParserManager.register_module("granite")
class GraniteToolParser(ToolParser):
    """
    Tool call parser for the granite 3.0 models. Intended
    for use with the examples/tool_chat_template_granite.jinja
    template.

    Used when --enable-auto-tool-choice --tool-call-parser granite
    are all set
    """

    def __init__(self, tokenizer: AnyTokenizer):
        super().__init__(tokenizer)
        # for granite 3.0, the token `<|tool_call|>`
        self.bot_token = "<|tool_call|>"
        # for granite 3.1, the string `<tool_call>`
        self.bot_string = "<tool_call>"

    def extract_tool_calls(
        self, model_output: str, request: ChatCompletionRequest
    ) -> ExtractedToolCallInformation:
        stripped = (
            model_output.strip()
            .removeprefix(self.bot_token)
            .removeprefix(self.bot_string)
            .lstrip()
        )
        if not stripped or stripped[0] != "[":
            return ExtractedToolCallInformation(
                tools_called=False, tool_calls=[], content=model_output
            )
        try:
            raw_function_calls = json.loads(stripped)
            if not isinstance(raw_function_calls, list):
                raise Exception(
                    f"Expected dict or list, got {type(raw_function_calls)}"
                )

            logger.debug("Extracted %d tool calls", len(raw_function_calls))
            tool_calls = [
                ToolCall(
                    type="function",
                    function=FunctionCall(
                        name=function_call["name"],
                        # function call args are JSON but as a string
                        arguments=json.dumps(
                            function_call["arguments"], ensure_ascii=False
                        ),
                    ),
                )
                for function_call in raw_function_calls
            ]

            return ExtractedToolCallInformation(
                tools_called=True,
                tool_calls=tool_calls,
                content=None,
            )

        except Exception as e:
            logger.error("Error in extracting tool call from response %s", e)
            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]:
        start_idx = consume_space(0, current_text)
        if current_text[start_idx:].startswith(self.bot_token):
            start_idx = consume_space(start_idx + len(self.bot_token), current_text)
        if current_text[start_idx:].startswith(self.bot_string):
            start_idx = consume_space(start_idx + len(self.bot_string), current_text)
        if (
            not current_text
            or start_idx >= len(current_text)
            or current_text[start_idx] != "["
        ):
            return DeltaMessage(content=delta_text)

        # bit mask flags for partial JSON parsing. If the name hasn't been
        # sent yet, don't allow sending
        # an incomplete string since OpenAI only ever (as far as I have
        # seen) allows sending the entire tool/ function name at once.
        flags = Allow.ALL if self.current_tool_name_sent else Allow.ALL & ~Allow.STR
        try:
            tool_call_arr = None
            is_complete = None
            try:
                tool_calls, end_idx = partial_json_loads(
                    current_text[start_idx:], flags
                )
                if type(tool_calls) is list:
                    tool_call_arr = tool_calls
                else:
                    return DeltaMessage(content=delta_text)

                is_complete = [True] * len(tool_calls)
                if not is_complete_json(current_text[start_idx : start_idx + end_idx]):
                    is_complete[-1] = False
            except partial_json_parser.core.exceptions.MalformedJSON:
                logger.debug("not enough tokens to parse into JSON yet")
                return None

            # case -- if no tokens have been streamed for the tool, e.g.
            #   only the array brackets, stream nothing
            if not tool_call_arr:
                return None

            # select as the current tool call the one we're on the state at
            current_tool_call: dict = tool_call_arr[self.current_tool_id]

            delta = None
            # case: we are starting a new tool in the array
            #   -> array has > 0 length AND length has moved past cursor
            if len(tool_call_arr) > self.current_tool_id + 1:
                # if we're moving on to a new call, first make sure we
                # haven't missed anything in the previous one that was
                # auto-generated due to JSON completions, but wasn't
                # streamed to the client yet.
                if self.current_tool_id >= 0:
                    cur_arguments = current_tool_call.get("arguments")
                    if cur_arguments:
                        cur_args_json = json.dumps(cur_arguments, ensure_ascii=False)
                        sent = len(self.streamed_args_for_tool[self.current_tool_id])
                        argument_diff = cur_args_json[sent:]

                        logger.debug("got arguments diff: %s", argument_diff)
                        delta = DeltaMessage(
                            tool_calls=[
                                DeltaToolCall(
                                    index=self.current_tool_id,
                                    function=DeltaFunctionCall(
                                        arguments=argument_diff
                                    ).model_dump(exclude_none=True),
                                )
                            ]
                        )
                        self.streamed_args_for_tool[self.current_tool_id] += (
                            argument_diff
                        )

                # re-set stuff pertaining to progress in the current tool
                self.current_tool_id = len(tool_call_arr) - 1
                self.current_tool_name_sent = False
                self.streamed_args_for_tool.append("")
                logger.debug("starting on new tool %d", self.current_tool_id)
                return delta

            # if the current tool name hasn't been sent, send if available
            # - otherwise send nothing
            elif not self.current_tool_name_sent:
                function_name = current_tool_call.get("name")
                if function_name:
                    delta = DeltaMessage(
                        tool_calls=[
                            DeltaToolCall(
                                index=self.current_tool_id,
                                type="function",
                                id=make_tool_call_id(),
                                function=DeltaFunctionCall(
                                    name=function_name
                                ).model_dump(exclude_none=True),
                            )
                        ]
                    )
                    self.current_tool_name_sent = True

            # now we know we're on the same tool call and we're streaming
            # arguments
            else:
                cur_arguments = current_tool_call.get("arguments")

                if cur_arguments:
                    sent = len(self.streamed_args_for_tool[self.current_tool_id])
                    cur_args_json = json.dumps(cur_arguments, ensure_ascii=False)
                    prev_arguments = self.prev_tool_call_arr[self.current_tool_id].get(
                        "arguments"
                    )

                    argument_diff = None
                    if is_complete[self.current_tool_id]:
                        argument_diff = cur_args_json[sent:]
                    elif prev_arguments:
                        prev_args_json = json.dumps(prev_arguments, ensure_ascii=False)
                        if cur_args_json != prev_args_json:
                            prefix = find_common_prefix(prev_args_json, cur_args_json)
                            argument_diff = prefix[sent:]

                    if argument_diff is not None:
                        delta = DeltaMessage(
                            tool_calls=[
                                DeltaToolCall(
                                    index=self.current_tool_id,
                                    function=DeltaFunctionCall(
                                        arguments=argument_diff
                                    ).model_dump(exclude_none=True),
                                )
                            ]
                        )
                        self.streamed_args_for_tool[self.current_tool_id] += (
                            argument_diff
                        )

            self.prev_tool_call_arr = tool_call_arr
            return delta

        except Exception as e:
            logger.error("Error trying to handle streaming tool call: %s", e)
            logger.debug(
                "Skipping chunk as a result of tool streaming extraction error"
            )
            return None
