# Copyright 2026 Google LLC
#
# 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
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# 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 ast
import logging
from pathlib import Path
import queue
import time
import jupyter_client

logger = logging.getLogger(__name__)


def validate_coder_script(code_str: str):
    """Validates that the generated python code does not violate constraints.

    Specifically checks for usage of cv2.resize and numpy slicing which could
    mess up the transformation tracking.
    """
    try:
        tree = ast.parse(code_str)
    except SyntaxError as e:
        raise ValueError(f"Syntax error in code: {e}")

    for node in ast.walk(tree):
        # Prevent cv2.resize
        if isinstance(node, ast.Attribute):
            if node.attr == "resize":
                # Quick check if it might be cv2.resize
                raise ValueError(
                    "SECURITY/VALIDATION ERROR: Direct usage of resize() is"
                    " forbidden. You MUST use canvas.resize_by_factor(factor)."
                )

        # Prevent slicing (very aggressive, but forces them to use canvas.crop)
        # Note: We cannot easily block all slicing because they might slice other arrays.
        # But we can look for specific variable names or just warn.
        # Let's be lenient on slicing but strict in the prompt.

    return True


class PythonExecutor:
    def __init__(self, session_dir: Path | None = None):
        self.session_dir = session_dir
        try:
            self.km = jupyter_client.KernelManager(kernel_name="python3")
            self.km.start_kernel()
            self.kc = self.km.client()
            self.kc.start_channels()
            self.kc.wait_for_ready(timeout=10)
        except Exception as e:
            logger.error(f"Failed to start Jupyter kernel: {e}")
            raise RuntimeError(f"Kernel startup failed: {e}")

    def execute(self, code: str) -> str:
        # First validate code
        try:
            validate_coder_script(code)
        except ValueError as ve:
            return str(ve)

        start_time = time.time()
        self.kc.execute(code)
        output_text = []

        while True:
            try:
                msg = self.kc.get_iopub_msg(timeout=15)
                msg_type = msg["header"]["msg_type"]

                if msg_type == "stream":
                    output_text.append(msg["content"]["text"])
                elif msg_type == "error":
                    traceback = "\n".join(msg["content"]["traceback"])
                    output_text.append(f"Traceback:\n{traceback}")
                elif msg_type in ("display_data", "execute_result"):
                    data = msg["content"]["data"]
                    if "text/plain" in data:
                        output_text.append(data["text/plain"])
                elif msg_type == "status" and msg["content"]["execution_state"] == "idle":
                    break
            except queue.Empty:
                output_text.append("[Execution Timeout Error]")
                break

        return "".join(output_text)

    def close(self):
        try:
            self.kc.stop_channels()
            self.km.shutdown_kernel()
        except Exception as e:
            logger.error(f"Failed to cleanly shutdown kernel: {e}")