# Copyright 2025 Bytedance Ltd. and/or its affiliates
#
# 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
#
#     XXXX
#
# 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.

import logging
import threading
import time

from mcp import Tool

logger = logging.getLogger(__file__)


class TokenBucket:
    def __init__(self, rate_limit: float):
        self.rate_limit = rate_limit  # tokens per second
        self.tokens = rate_limit
        self.last_update = time.time()
        self.lock = threading.Lock()

    def acquire(self) -> bool:
        with self.lock:
            now = time.time()
            # Add new tokens based on time elapsed
            new_tokens = (now - self.last_update) * self.rate_limit
            self.tokens = min(self.rate_limit, self.tokens + new_tokens)
            self.last_update = now

            if self.tokens >= 1:
                self.tokens -= 1
                return True
            return False


def mcp2openai(mcp_tool: Tool) -> dict:
    """Convert a MCP Tool to an OpenAI ChatCompletionTool."""
    openai_format = {
        "type": "function",
        "function": {
            "name": mcp_tool.name,
            "description": mcp_tool.description,
            "parameters": mcp_tool.inputSchema,
            "strict": False,
        },
    }
    if not openai_format["function"]["parameters"].get("required", None):
        openai_format["function"]["parameters"]["required"] = []
    return openai_format
