import logging
from typing import Any, Dict, List, Literal, Optional, Set, Tuple, Type, Union

from sglang.srt.entrypoints.openai.protocol import (
    StructuralTagResponseFormat,
    StructuresResponseFormat,
    Tool,
    ToolChoice,
)
from sglang.srt.function_call.base_format_detector import BaseFormatDetector
from sglang.srt.function_call.core_types import ToolCallItem
from sglang.srt.function_call.deepseekv3_detector import DeepSeekV3Detector
from sglang.srt.function_call.deepseekv31_detector import DeepSeekV31Detector
from sglang.srt.function_call.glm4_moe_detector import Glm4MoeDetector
from sglang.srt.function_call.gpt_oss_detector import GptOssDetector
from sglang.srt.function_call.kimik2_detector import KimiK2Detector
from sglang.srt.function_call.llama32_detector import Llama32Detector
from sglang.srt.function_call.mistral_detector import MistralDetector
from sglang.srt.function_call.pythonic_detector import PythonicDetector
from sglang.srt.function_call.qwen3_coder_detector import Qwen3CoderDetector
from sglang.srt.function_call.qwen25_detector import Qwen25Detector
from sglang.srt.function_call.step3_detector import Step3Detector
from sglang.srt.function_call.utils import get_json_schema_constraint

logger = logging.getLogger(__name__)


class FunctionCallParser:
    """
    Parser for function/tool calls in model outputs.

    This class handles both streaming and non-streaming parsing of function calls using a detector.
    In streaming scenarios, each time new_text is received, it calls detector.parse_streaming_increment
    and returns the resulting normal_text and calls to the upper layer (or SSE).
    """

    ToolCallParserEnum: Dict[str, Type[BaseFormatDetector]] = {
        "llama3": Llama32Detector,
        "qwen25": Qwen25Detector,
        "mistral": MistralDetector,
        "deepseekv3": DeepSeekV3Detector,
        "deepseekv31": DeepSeekV31Detector,
        "pythonic": PythonicDetector,
        "kimi_k2": KimiK2Detector,
        "qwen3_coder": Qwen3CoderDetector,
        "glm45": Glm4MoeDetector,
        "step3": Step3Detector,
        "gpt-oss": GptOssDetector,
    }

    def __init__(self, tools: List[Tool], tool_call_parser: str):
        detector: Type[BaseFormatDetector] = None
        detector_class = self.ToolCallParserEnum.get(tool_call_parser)
        if detector_class:
            detector = detector_class()
        else:
            raise ValueError(f"Unsupported tool_call_parser: {tool_call_parser}")

        self.detector = detector
        self.tools = tools

    def has_tool_call(self, text: str) -> bool:
        """
        Check if the given text contains a tool call in the format supported by this parser.
        This delegates to the detector's implementation.

        Args:
            text: The text to check for tool calls

        Returns:
            True if the text contains a tool call, False otherwise
        """
        if not self.tools:
            return False
        return self.detector.has_tool_call(text)

    def parse_non_stream(self, full_text: str) -> Tuple[str, list[ToolCallItem]]:
        """
        One-time parsing of the full text to extract tool calls.

        Args:
            full_text: The complete text to parse

        Returns:
            A tuple containing:
            - The remaining text after parsing that was not consumed by the detector (can be treated as normal text)
            - A list of tool calls parsed from the text
        """
        if not self.tools:
            return full_text, []
        parsed_result = self.detector.detect_and_parse(full_text, self.tools)
        tool_call_list = parsed_result.calls
        if tool_call_list:
            return parsed_result.normal_text, tool_call_list
        else:
            return full_text, []

    def parse_stream_chunk(self, chunk_text: str) -> Tuple[str, list[ToolCallItem]]:
        """
        Streaming incremental parsing of chunks of text as they arrive.

        Args:
            chunk_text: The new chunk of text to parse

        Returns:
            A tuple containing:
            - The normal text that should be displayed to the user
            - A list of tool calls parsed from the chunk
        """
        if not self.tools:
            return chunk_text, []
        final_normal_text = ""
        final_calls = []

        sp_result = self.detector.parse_streaming_increment(chunk_text, self.tools)
        if sp_result.normal_text:
            final_normal_text = sp_result.normal_text
        if sp_result.calls:
            final_calls.extend(sp_result.calls)
            final_normal_text = sp_result.normal_text

        return final_normal_text, final_calls

    def get_structure_tag(self) -> StructuralTagResponseFormat:
        """
        Generate a structural tag response format for all available tools.

        This creates the necessary structural tags that guide the model's output format.
        """
        tool_structures: List[StructuresResponseFormat] = list()
        tool_trigger_set: Set[str] = set()

        get_structure_info = self.detector.structure_info()
        for tool in self.tools:
            function = tool.function
            name = function.name
            assert name is not None
            info = get_structure_info(name)

            # accept all if not strict, otherwise only accept the schema
            schema = function.parameters if function.strict else {}

            tool_structures.append(
                StructuresResponseFormat(
                    begin=info.begin,
                    schema=schema,  # type: ignore
                    end=info.end,
                )
            )
            tool_trigger_set.add(info.trigger)

        return StructuralTagResponseFormat(
            type="structural_tag",
            structures=tool_structures,
            triggers=list(tool_trigger_set),
        )

    def get_structure_constraint(
        self, tool_choice: Union[ToolChoice, Literal["auto", "required"]]
    ) -> Optional[Tuple[str, Any]]:
        """
        Returns the appropriate structure constraint for tool calls based on the tool_choice.
        The constraint is used to guide the model's output format.

        Args:
            tool_choice: The tool choice setting from the request

        Returns:
            A tuple of (constraint_type, constraint_value) to be added to sampling parameters,
            or None if no constraint applies.
        """
        # NOTE: structural_tag only supports JSON-compatible content between the begin and end.
        # It cannot parse or validate function call Pythonic or XML-ish syntax.
        if (
            self.detector.supports_structural_tag()
            and tool_choice == "auto"
            and any(tool.function.strict for tool in self.tools)
        ):
            strict_tag = self.get_structure_tag()
            return ("structural_tag", strict_tag)
        elif tool_choice == "required" or isinstance(tool_choice, ToolChoice):
            json_schema = get_json_schema_constraint(self.tools, tool_choice)
            return ("json_schema", json_schema)

    def get_ebnf(
        self, tool_choice: Union[ToolChoice, Literal["required"]]
    ) -> Optional[str]:
        """
        Get the EBNF grammar for the specified tool choice.

        Args:
            tool_choice: The tool choice specification

        Returns:
            EBNF grammar string, or None if no valid tools found

        Note:
            If a specific function is requested but not found in available tools,
            logs a warning and falls back to using all available tools for backward compatibility.
        """
        filtered_tools = []
        if isinstance(tool_choice, ToolChoice):
            fn_name = tool_choice.function.name
            filtered_tools = [t for t in self.tools if t.function.name == fn_name]

            # Check if the requested function exists in available tools
            if not filtered_tools:
                available_functions = [t.function.name for t in self.tools]
                logger.warning(
                    f"Function '{fn_name}' not found in available tools. "
                    f"Available functions: {available_functions}. "
                    f"Skipping tool choice."
                )

                # TODO: Return a 400 error instead of warning when adapter supports proper error handling
                # For now, fall back to return None
                return None
        else:
            filtered_tools = self.tools

        return self.detector.build_ebnf(filtered_tools)
