# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 faulthandler
import json
import platform

# to run the solution files we're using a timing based approach
import signal
import sys
import time
import traceback

# used for debugging to time steps
from datetime import datetime
from decimal import Decimal
from enum import Enum
from io import StringIO

# from pyext import RuntimeModule
from types import ModuleType

# used for testing the code that reads from input
from unittest.mock import mock_open, patch

import numpy as np


import_string = "from string import *\nfrom re import *\nfrom datetime import *\nfrom collections import *\nfrom heapq import *\nfrom bisect import *\nfrom copy import *\nfrom math import *\nfrom random import *\nfrom statistics import *\nfrom itertools import *\nfrom functools import *\nfrom operator import *\nfrom io import *\nfrom sys import *\nfrom json import *\nfrom builtins import *\nfrom typing import *\nimport string\nimport re\nimport datetime\nimport collections\nimport heapq\nimport bisect\nimport copy\nimport math\nimport random\nimport statistics\nimport itertools\nimport functools\nimport operator\nimport io\nimport sys\nimport json\nsys.setrecursionlimit(50000)\n"


def truncatefn(s, length=300):
    if isinstance(s, str):
        pass
    else:
        s = str(s)
    if len(s) <= length:
        return s

    return s[: length // 2] + "...(truncated) ..." + s[-length // 2 :]


class CODE_TYPE(Enum):
    call_based = 0
    standard_input = 1


# stuff for setting up signal timer
class TimeoutException(Exception):
    pass


def timeout_handler_factory(debug: bool):
    def timeout_handler(signum, frame):
        if debug:
            print("timeout occured: alarm went off")
        raise TimeoutException

    return timeout_handler


# used to capture stdout as a list
# from https://stackoverflow.com/a/16571630/6416660
# alternative use redirect_stdout() from contextlib
class Capturing(list):
    def __enter__(self):
        self._stdout = sys.stdout
        sys.stdout = self._stringio = StringIO()
        # Make closing the StringIO a no-op
        self._stringio.close = lambda x: 1
        return self

    def __exit__(self, *args):
        self.append(self._stringio.getvalue())
        del self._stringio  # free up some memory
        sys.stdout = self._stdout


# Custom mock for sys.stdin that supports buffer attribute
class MockStdinWithBuffer:
    def __init__(self, inputs: str):
        self.inputs = inputs
        self._stringio = StringIO(inputs)
        self.buffer = MockBuffer(inputs)

    def read(self, *args):
        return self.inputs

    def readline(self, *args):
        return self._stringio.readline(*args)

    def readlines(self, *args):
        return self.inputs.split("\n")

    def __getattr__(self, name):
        # Delegate other attributes to StringIO
        return getattr(self._stringio, name)


class MockBuffer:
    def __init__(self, inputs: str):
        self.inputs = inputs.encode("utf-8")  # Convert to bytes

    def read(self, *args):
        # Return as byte strings that can be split
        return self.inputs

    def readline(self, *args):
        return self.inputs.split(b"\n")[0] + b"\n"


def clean_if_name(code: str) -> str:
    try:
        astree = ast.parse(code)
        last_block = astree.body[-1]
        if isinstance(last_block, ast.If):
            condition = last_block.test
            if ast.unparse(condition).strip() == "__name__ == '__main__'":
                code = (
                    ast.unparse(astree.body[:-1]) + "\n" + ast.unparse(last_block.body)  # type: ignore
                )
    except:
        pass

    return code


def make_function(code: str) -> str:
    try:
        import_stmts = []
        all_other_stmts = []
        astree = ast.parse(code)
        for stmt in astree.body:
            if isinstance(stmt, (ast.Import, ast.ImportFrom)):
                import_stmts.append(stmt)
            else:
                all_other_stmts.append(stmt)

        function_ast = ast.FunctionDef(
            name="wrapped_function",
            args=ast.arguments(posonlyargs=[], args=[], kwonlyargs=[], kw_defaults=[], defaults=[]),
            body=all_other_stmts,
            decorator_list=[],
            lineno=-1,
        )
        main_code = (
            import_string
            + "\n"
            + ast.unparse(import_stmts)  # type: ignore
            + "\n"
            + ast.unparse(function_ast)  # type: ignore
        )
        return main_code
    except Exception:
        return code


def call_method(method, inputs):
    if isinstance(inputs, list):
        inputs = "\n".join(inputs)

    inputs_line_iterator = iter(inputs.split("\n"))

    # Create custom stdin mock with buffer support
    mock_stdin = MockStdinWithBuffer(inputs)

    # sys.setrecursionlimit(10000)

    # @patch('builtins.input', side_effect=inputs.split("\n"))
    @patch("builtins.open", mock_open(read_data=inputs))
    @patch("sys.stdin", mock_stdin)  # Use our custom mock instead of StringIO
    @patch("sys.stdin.readline", lambda *args: next(inputs_line_iterator))
    @patch("sys.stdin.readlines", lambda *args: inputs.split("\n"))
    @patch("sys.stdin.read", lambda *args: inputs)
    # @patch('sys.stdout.write', print)
    def _inner_call_method(_method):
        try:
            return _method()
        except SystemExit:
            pass
        finally:
            pass

    return _inner_call_method(method)


def get_function(compiled_sol, fn_name: str):  # type: ignore
    try:
        assert hasattr(compiled_sol, fn_name)
        return getattr(compiled_sol, fn_name)
    except Exception:
        return


def compile_code(code: str, timeout: int):
    signal.alarm(timeout)
    try:
        tmp_sol = ModuleType("tmp_sol", "")
        exec(code, tmp_sol.__dict__)
        if "class Solution" in code:
            # leetcode wraps solutions in `Solution`
            # this is a hack to check if it is leetcode solution or not
            # currently livecodebench only supports LeetCode but
            # else condition allows future extensibility to other platforms
            compiled_sol = tmp_sol.Solution()
        else:
            # do nothing in the other case since function is accesible
            compiled_sol = tmp_sol

        assert compiled_sol is not None
    finally:
        signal.alarm(0)

    return compiled_sol


def convert_line_to_decimals(line: str) -> tuple[bool, list[Decimal]]:
    try:
        decimal_line = [Decimal(elem) for elem in line.split()]
    except:
        return False, []
    return True, decimal_line


def get_stripped_lines(val: str):
    ## you don't want empty lines to add empty list after splitlines!
    val = val.strip()

    return [val_line.strip() for val_line in val.split("\n")]


def grade_call_based(code: str, all_inputs: list, all_outputs: list, fn_name: str, timeout: int):
    # call-based clean up logic
    # need to wrap in try-catch logic after to catch the correct errors, but for now this is fine.
    code = import_string + "\n\n" + code
    compiled_sol = compile_code(code, timeout)

    if compiled_sol is None:
        return

    method = get_function(compiled_sol, fn_name)

    if method is None:
        return

    all_inputs = [[json.loads(line) for line in inputs.split("\n")] for inputs in all_inputs]

    all_outputs = [json.loads(output) for output in all_outputs]

    total_execution = 0
    all_results = []
    for idx, (gt_inp, gt_out) in enumerate(zip(all_inputs, all_outputs)):
        signal.alarm(timeout)
        faulthandler.enable()
        try:
            # can lock here so time is useful
            start = time.time()
            prediction = method(*gt_inp)
            total_execution += time.time() - start
            signal.alarm(0)

            # don't penalize model if it produces tuples instead of lists
            # ground truth sequences are not tuples
            if isinstance(prediction, tuple):
                prediction = list(prediction)

            tmp_result = prediction == gt_out

            # handle floating point comparisons
            # Adopted improved floating point comparison from https://huggingface.co/nvidia/Nemotron-Cascade-8B/blob/main/evaluation/eval/tools/code_verifier_utils.py#L326-L335
            try:
                tmp_result = tmp_result or (json.dumps(prediction) == json.dumps(gt_out))
            except Exception:
                pass

            try:
                tmp_result = tmp_result or (np.allclose(float(prediction), float(gt_out)))
            except Exception:
                pass

            all_results.append(tmp_result)

            if not tmp_result:
                return all_results, {
                    "output": truncatefn(prediction),
                    "inputs": truncatefn(gt_inp),
                    "expected": truncatefn(gt_out),
                    "error_code": -2,
                    "error_message": "Wrong Answer",
                }
        except Exception as e:
            signal.alarm(0)
            if "timeoutexception" in repr(e).lower():
                all_results.append(-3)
                return all_results, {
                    "error": repr(e),
                    "error_code": -3,
                    "error_message": "Time Limit Exceeded",
                    "inputs": truncatefn(gt_inp),
                    "expected": truncatefn(gt_out),
                }
            else:
                all_results.append(-4)
                return all_results, {
                    "error": repr(e),
                    "error_code": -4,
                    "error_message": "Runtime Error",
                    "inputs": truncatefn(gt_inp),
                    "expected": truncatefn(gt_out),
                    "traceback": traceback.format_exc(),
                }

        finally:
            signal.alarm(0)
            faulthandler.disable()

    return all_results, {"execution time": total_execution}


def grade_stdio(
    code: str,
    all_inputs: list,
    all_outputs: list,
    timeout: int,
):
    ## runtime doesn't interact well with __name__ == '__main__'
    code = clean_if_name(code)

    ## we wrap the given code inside another function
    code = make_function(code)

    compiled_sol = compile_code(code, timeout)
    if compiled_sol is None:
        return

    method = get_function(compiled_sol, "wrapped_function")

    if method is None:
        return

    all_results = []
    total_execution_time = 0
    for idx, (gt_inp, gt_out) in enumerate(zip(all_inputs, all_outputs)):
        signal.alarm(timeout)
        faulthandler.enable()

        signal.alarm(timeout)
        with Capturing() as captured_output:
            try:
                start = time.time()
                call_method(method, gt_inp)
                total_execution_time += time.time() - start
                # reset the alarm
                signal.alarm(0)
            except Exception as e:
                signal.alarm(0)
                if "timeoutexception" in repr(e).lower():
                    all_results.append(-3)
                    return all_results, {
                        "error": repr(e),
                        "error_code": -3,
                        "error_message": "Time Limit Exceeded",
                        "inputs": truncatefn(gt_inp),
                        "expected": truncatefn(gt_out),
                    }
                else:
                    all_results.append(-4)
                    return all_results, {
                        "error": repr(e),
                        "error_code": -4,
                        "error_message": "Runtime Error",
                        "inputs": truncatefn(gt_inp),
                        "expected": truncatefn(gt_out),
                        "traceback": traceback.format_exc(),
                    }

            finally:
                signal.alarm(0)
                faulthandler.disable()

        prediction = captured_output[0]

        stripped_prediction_lines = get_stripped_lines(prediction)
        stripped_gt_out_lines = get_stripped_lines(gt_out)

        ## WA happens in multiple circumstances
        ## so cache the return to make it clean!
        WA_send_args = {
            "output": truncatefn(prediction),
            "inputs": truncatefn(gt_inp),
            "expected": truncatefn(gt_out),
            "error_code": -2,
        }

        if len(stripped_prediction_lines) != len(stripped_gt_out_lines):
            all_results.append(-2)
            WA_send_args["error_message"] = "Wrong answer: mismatched output length"
            return all_results, WA_send_args

        for output_line_idx, (
            stripped_prediction_line,
            stripped_gt_out_line,
        ) in enumerate(zip(stripped_prediction_lines, stripped_gt_out_lines)):
            WA_send_args["error_message"] = (
                f"Wrong answer at {output_line_idx=}: {truncatefn(stripped_prediction_line)} != {truncatefn(stripped_gt_out_line)}"
            )

            ## CASE 1: exact match
            if stripped_prediction_line == stripped_gt_out_line:
                continue

            ## CASE 2: element-wise comparision
            ## if there are floating elements
            ## use `decimal` library for good floating point comparision
            ## otherwise gotcha: np.isclose(50000000000000000, 50000000000000001) = True
            ## note that we should always be able to convert to decimals

            success, decimal_prediction_line = convert_line_to_decimals(stripped_prediction_line)
            if not success:
                all_results.append(-2)
                return all_results, WA_send_args
            success, decimal_gtout_line = convert_line_to_decimals(stripped_gt_out_line)
            if not success:
                all_results.append(-2)
                return all_results, WA_send_args

            if decimal_prediction_line == decimal_gtout_line:
                continue

            all_results.append(-2)
            return all_results, WA_send_args
        all_results.append(True)

    return all_results, {"execution time": total_execution_time}


def run_test(in_outs, test=None, debug=False, timeout=6):
    """
    if test(generated_code) is not None it'll try to run the code.
    otherwise it'll just return an input and output pair.
    """
    signal.signal(signal.SIGALRM, timeout_handler_factory(debug))

    # Disable functionalities that can make destructive changes to the test.
    # max memory is set to 4GB
    reliability_guard(maximum_memory_bytes=4 * 1024**3)

    if debug:
        print(f"start = {datetime.now().time()}")

    # The in_outs is already loaded from the sample from the parent process
    # try:
    #     in_outs = json.loads(sample["input_output"])
    # except ValueError as e:
    #     raise e
    #     in_outs = None

    if in_outs:
        if in_outs.get("fn_name") is None:
            which_type = CODE_TYPE.standard_input  # Standard input
            method_name = None

        else:
            which_type = CODE_TYPE.call_based  # Call-based
            method_name = in_outs["fn_name"]

    if debug:
        print(f"loaded input_output = {datetime.now().time()}")

    if test is None:
        assert False, "should not happen: test code is none"
        return in_outs, {"error": "no test code provided"}
    elif test is not None:
        results = []
        if debug:
            print(f"loading test code = {datetime.now().time()}")

        if which_type == CODE_TYPE.call_based:
            signal.alarm(timeout)
            try:
                results, metadata = grade_call_based(
                    code=test,
                    all_inputs=in_outs["inputs"],
                    all_outputs=in_outs["outputs"],
                    fn_name=method_name,
                    timeout=timeout,
                )
                return results, metadata
            except Exception as e:
                return [-4], {
                    "error_code": -4,
                    "error_message": f"Error during testing: {e}",
                }
            finally:
                signal.alarm(0)
        elif which_type == CODE_TYPE.standard_input:
            # sol
            # if code has if __name__ == "__main__": then remove it

            signal.alarm(timeout)
            try:
                results, metadata = grade_stdio(
                    code=test,
                    all_inputs=in_outs["inputs"],
                    all_outputs=in_outs["outputs"],
                    timeout=timeout,
                )
                return results, metadata
            except Exception as e:
                return [-4], {
                    "error_code": -4,
                    "error_message": f"Error during testing: {e}",
                }
            finally:
                signal.alarm(0)


def _set_resource_limit(resource_type: str, target_soft_limit):
    import resource

    current_soft, current_hard = resource.getrlimit(resource_type)
    if current_soft < target_soft_limit:
        try:
            resource.setrlimit(resource_type, (target_soft_limit, current_hard))
        except ValueError as e:
            print(repr(e))


def reliability_guard(maximum_memory_bytes=None):
    """
    This disables various destructive functions and prevents the generated code
    from interfering with the test (e.g. fork bomb, killing other processes,
    removing filesystem files, etc.)
    WARNING
    This function is NOT a security sandbox. Untrusted code, including, model-
    generated code, should not be blindly executed outside of one. See the
    Codex paper for more information about OpenAI's code sandbox, and proceed
    with caution.
    """

    if maximum_memory_bytes is not None:
        import resource

        # The resource limit on RLIMIT_AS has been disabled because setting it caused additional out-of-memory (OOM) issues with Ray.
        # This happens since Ray and its subprocesses share the same virtual address space.
        # _set_resource_limit(resource.RLIMIT_AS, maximum_memory_bytes)
        _set_resource_limit(resource.RLIMIT_DATA, maximum_memory_bytes)

        if not platform.uname().system == "Darwin":
            _set_resource_limit(resource.RLIMIT_STACK, maximum_memory_bytes)

    faulthandler.disable()

    import builtins

    # builtins.exit = None
    builtins.quit = None

    import os

    os.environ["OMP_NUM_THREADS"] = "1"

    os.kill = None
    os.system = None
    os.putenv = None
    os.remove = None
    os.removedirs = None
    os.rmdir = None
    os.fchdir = None
    os.setuid = None
    os.fork = None
    os.forkpty = None
    os.killpg = None
    os.rename = None
    os.renames = None
    os.truncate = None
    os.replace = None
    os.unlink = None
    os.fchmod = None
    os.fchown = None
    os.chmod = None
    os.chown = None
    os.chroot = None
    os.fchdir = None
    os.lchflags = None
    os.lchmod = None
    os.lchown = None
    os.getcwd = None
    os.chdir = None

    import shutil

    shutil.rmtree = None
    shutil.move = None
    shutil.chown = None

    import subprocess

    subprocess.Popen = None  # type: ignore

    __builtins__["help"] = None

    import sys

    sys.modules["ipdb"] = None
    sys.modules["joblib"] = None
    sys.modules["resource"] = None
    sys.modules["psutil"] = None
    sys.modules["tkinter"] = None
