"""
This logic is largely copied from the Hendrycks' MATH release (math_equivalence), and borrowed from:
- https://github.com/microsoft/ProphetNet/tree/master/CRITIC
- https://github.com/openai/prm800k
- https://github.com/microsoft/ToRA/blob/main/src/eval/grader.py
- https://github.com/deepseek-ai/DeepSeek-Math/blob/main/evaluation/eval/eval_utils.py
"""

import re
import regex
import multiprocessing
from math import isclose
from typing import Union

from sympy import simplify, N
from sympy.parsing.sympy_parser import parse_expr
from sympy.parsing.latex import parse_latex

try:
    from latex2sympy2 import latex2sympy
except ModuleNotFoundError:
    def latex2sympy(value):
        raise ValueError("latex2sympy2 is not installed")


def choice_answer_clean(pred: str):
    pred = pred.strip("\n").rstrip(".").rstrip("/").strip(" ").lstrip(":")
    # Clean the answer based on the dataset
    tmp = re.findall(r"\b(A|B|C|D|E)\b", pred.upper())
    if tmp:
        pred = tmp
    else:
        pred = [pred.strip().strip(".")]
    pred = pred[-1]
    # Remove the period at the end, again!
    pred = pred.rstrip(".").rstrip("/")
    return pred


def parse_digits(num):
    num = regex.sub(",", "", str(num))
    try:
        return float(num)
    except:
        if num.endswith("%"):
            num = num[:-1]
            if num.endswith("\\"):
                num = num[:-1]
            try:
                return float(num) / 100
            except:
                pass
    return None


def is_digit(num):
    # paired with parse_digits
    return parse_digits(num) is not None


def str_to_pmatrix(input_str):
    input_str = input_str.strip()
    matrix_str = re.findall(r"\{.*,.*\}", input_str)
    pmatrix_list = []

    for m in matrix_str:
        m = m.strip("{}")
        pmatrix = r"\begin{pmatrix}" + m.replace(",", "\\") + r"\end{pmatrix}"
        pmatrix_list.append(pmatrix)

    return ", ".join(pmatrix_list)


def _is_ordered_sequence(value: str) -> bool:
    value = str(value).strip()
    return (
        (value.startswith("(") and value.endswith(")"))
        or (value.startswith("[") and value.endswith("]"))
        or value.startswith("\\begin{pmatrix}")
        or value.startswith("\\begin{bmatrix}")
    )


def _strip_set_delimiters(value: str) -> str:
    value = str(value).strip()
    if value.startswith("\\{") and value.endswith("\\}"):
        return value[2:-2].strip()
    if value.startswith("{") and value.endswith("}"):
        return value[1:-1].strip()
    return value


def _split_top_level_commas(value: str):
    parts = []
    start = 0
    depth = 0
    escaped = False
    for idx, ch in enumerate(value):
        if escaped:
            escaped = False
            continue
        if ch == "\\":
            escaped = True
            continue
        if ch in "{[(":
            depth += 1
        elif ch in "}])":
            depth = max(0, depth - 1)
        elif ch == "," and depth == 0:
            parts.append(value[start:idx].strip())
            start = idx + 1
    parts.append(value[start:].strip())
    return parts


def _unordered_comma_list_equal(prediction: str, reference: str, include_percentage: bool, is_close: bool) -> bool:
    if _is_ordered_sequence(prediction) or _is_ordered_sequence(reference):
        return False
    pred_parts = _split_top_level_commas(_strip_set_delimiters(prediction))
    ref_parts = _split_top_level_commas(_strip_set_delimiters(reference))
    if len(pred_parts) < 2 or len(pred_parts) != len(ref_parts):
        return False
    if any(not part for part in pred_parts) or any(not part for part in ref_parts):
        return False

    unmatched = list(ref_parts)
    for pred_part in pred_parts:
        matched_idx = None
        for idx, ref_part in enumerate(unmatched):
            if math_equal(pred_part, ref_part, include_percentage=include_percentage, is_close=is_close):
                matched_idx = idx
                break
        if matched_idx is None:
            return False
        unmatched.pop(matched_idx)
    return True


def math_equal(
    prediction: Union[bool, float, str],
    reference: Union[float, str],
    include_percentage: bool = True,
    is_close: bool = True,
    timeout: bool = False,
) -> bool:
    """
    Exact match of math if and only if:
    1. numerical equal: both can convert to float and are equal
    2. symbolic equal: both can convert to sympy expression and are equal
    """
    # print("Judge:", prediction, reference)
    if prediction is None or reference is None:
        return False
    if str(prediction.strip().lower()) == str(reference.strip().lower()):
        return True
    if (
        reference in ["A", "B", "C", "D", "E"]
        and choice_answer_clean(prediction) == reference
    ):
        return True

    # MATH often asks for roots/values "separated by commas"; those are sets,
    # so order should not matter. Run this before numeric parsing, otherwise
    # short lists such as "3,2" can be misread as the number 32.
    if _unordered_comma_list_equal(str(prediction), str(reference), include_percentage, is_close):
        return True

    try:  # 1. numerical equal
        if is_digit(prediction) and is_digit(reference):
            prediction = parse_digits(prediction)
            reference = parse_digits(reference)
            # number questions
            if include_percentage:
                gt_result = [reference / 100, reference, reference * 100]
            else:
                gt_result = [reference]
            for item in gt_result:
                try:
                    if is_close:
                        if numeric_equal(prediction, item):
                            return True
                    else:
                        if item == prediction:
                            return True
                except Exception:
                    continue
            return False
    except:
        pass

    if not prediction and prediction not in [0, False]:
        return False

    # 2. symbolic equal
    reference = str(reference).strip()
    prediction = str(prediction).strip()

    ## pmatrix (amps)
    if "pmatrix" in prediction and not "pmatrix" in reference:
        reference = str_to_pmatrix(reference)

    ## deal with [], (), {}
    pred_str, ref_str = prediction, reference
    if (
        prediction.startswith("[")
        and prediction.endswith("]")
        and not reference.startswith("(")
    ) or (
        prediction.startswith("(")
        and prediction.endswith(")")
        and not reference.startswith("[")
    ):
        pred_str = pred_str.strip("[]()")
        ref_str = ref_str.strip("[]()")
    for s in ["{", "}", "(", ")"]:
        ref_str = ref_str.replace(s, "")
        pred_str = pred_str.replace(s, "")
    if pred_str.lower() == ref_str.lower():
        return True

    if _unordered_comma_list_equal(prediction, reference, include_percentage, is_close):
        return True

    ## [a, b] vs. [c, d], return a==c and b==d
    if (
        regex.match(r"(\(|\[).+(\)|\])", prediction) is not None
        and regex.match(r"(\(|\[).+(\)|\])", reference) is not None
    ):
        pred_parts = prediction[1:-1].split(",")
        ref_parts = reference[1:-1].split(",")
        if len(pred_parts) == len(ref_parts):
            if all(
                [
                    math_equal(
                        pred_parts[i], ref_parts[i], include_percentage, is_close
                    )
                    for i in range(len(pred_parts))
                ]
            ):
                return True
    if (
        (
            prediction.startswith("\\begin{pmatrix}")
            or prediction.startswith("\\begin{bmatrix}")
        )
        and (
            prediction.endswith("\\end{pmatrix}")
            or prediction.endswith("\\end{bmatrix}")
        )
        and (
            reference.startswith("\\begin{pmatrix}")
            or reference.startswith("\\begin{bmatrix}")
        )
        and (
            reference.endswith("\\end{pmatrix}") or reference.endswith("\\end{bmatrix}")
        )
    ):
        pred_lines = [
            line.strip()
            for line in prediction[
                len("\\begin{pmatrix}") : -len("\\end{pmatrix}")
            ].split("\\\\")
            if line.strip()
        ]
        ref_lines = [
            line.strip()
            for line in reference[
                len("\\begin{pmatrix}") : -len("\\end{pmatrix}")
            ].split("\\\\")
            if line.strip()
        ]
        matched = True
        if len(pred_lines) == len(ref_lines):
            for pred_line, ref_line in zip(pred_lines, ref_lines):
                pred_parts = pred_line.split("&")
                ref_parts = ref_line.split("&")
                if len(pred_parts) == len(ref_parts):
                    if not all(
                        [
                            math_equal(
                                pred_parts[i],
                                ref_parts[i],
                                include_percentage,
                                is_close,
                            )
                            for i in range(len(pred_parts))
                        ]
                    ):
                        matched = False
                        break
                else:
                    matched = False
                if not matched:
                    break
        else:
            matched = False
        if matched:
            return True

    if prediction.count("=") == 1 and reference.count("=") == 1:
        pred = prediction.split("=")
        pred = f"{pred[0].strip()} - ({pred[1].strip()})"
        ref = reference.split("=")
        ref = f"{ref[0].strip()} - ({ref[1].strip()})"
        if symbolic_equal(pred, ref) or symbolic_equal(f"-({pred})", ref):
            return True
    elif (
        prediction.count("=") == 1
        and len(prediction.split("=")[0].strip()) <= 2
        and "=" not in reference
    ):
        if math_equal(
            prediction.split("=")[1], reference, include_percentage, is_close
        ):
            return True
    elif (
        reference.count("=") == 1
        and len(reference.split("=")[0].strip()) <= 2
        and "=" not in prediction
    ):
        if math_equal(
            prediction, reference.split("=")[1], include_percentage, is_close
        ):
            return True

    # symbolic equal with sympy
    if timeout:
        if call_with_timeout(symbolic_equal_process, prediction, reference):
            return True
    else:
        if symbolic_equal(prediction, reference):
            return True

    return False


def math_equal_process(param):
    return math_equal(param[-2], param[-1])


def numeric_equal(prediction: float, reference: float):
    # Note that relative tolerance has significant impact
    # on the result of the synthesized GSM-Hard dataset
    # if reference.is_integer():
    #     return isclose(reference, round(prediction), abs_tol=1e-4)
    # else:
    # prediction = round(prediction, len(str(reference).split(".")[-1]))
    return isclose(reference, prediction, rel_tol=1e-4)


def symbolic_equal(a, b):
    def _parse(s):
        for f in [parse_latex, parse_expr, latex2sympy]:
            try:
                return f(s.replace("\\\\", "\\"))
            except:
                try:
                    return f(s)
                except:
                    pass
        return s

    a = _parse(a)
    b = _parse(b)

    # direct equal
    try:
        if str(a) == str(b) or a == b:
            return True
    except:
        pass

    # simplify equal
    try:
        if a.equals(b) or simplify(a - b) == 0:
            return True
    except:
        pass

    # equation equal
    try:
        if (abs(a.lhs - a.rhs)).equals(abs(b.lhs - b.rhs)):
            return True
    except:
        pass

    try:
        if numeric_equal(float(N(a)), float(N(b))):
            return True
    except:
        pass

    # matrix
    try:
        # if a and b are matrix
        if a.shape == b.shape:
            _a = a.applyfunc(lambda x: round(x, 3))
            _b = b.applyfunc(lambda x: round(x, 3))
            if _a.equals(_b):
                return True
    except:
        pass

    return False


def symbolic_equal_process(a, b, output_queue):
    result = symbolic_equal(a, b)
    output_queue.put(result)


def call_with_timeout(func, *args, timeout=1, **kwargs):
    output_queue = multiprocessing.Queue()
    process_args = args + (output_queue,)
    process = multiprocessing.Process(target=func, args=process_args, kwargs=kwargs)
    process.start()
    process.join(timeout)

    if process.is_alive():
        process.terminate()
        process.join()
        return False

    return output_queue.get()