"""Reynolds 2004 siRNA efficacy rules as feature engineering.

Reference: Reynolds et al., Nature Biotechnology 22, 326-330 (2004).
"Rational siRNA design for RNA interference".

The 8 Reynolds criteria (sense strand, 1-indexed positions from the 5' end):
  1. GC content 30-52%
  2. No internal repeats (no >4 consecutive identical bases)
  3. A or T at position 19 (3' end of sense strand)
  4. A at position 3
  5. T at position 10
  6. A or G at position 13
  7. T at position 16
  8. Thermodynamic asymmetry: lower binding energy at the 5' antisense end
     than at the 3' antisense end. We approximate binding energy by GC
     content of a 7-nt window at each antisense end (since G:C pairs have
     3 H-bonds vs A:T/U's 2). The continuous feature is::

         thermo_asymmetry = 0.5 * (GC_5prime_antisense - GC_3prime_antisense)

     Criterion 8 is "met" when thermo_asymmetry < 0 (i.e. the 5' antisense
     end is less GC-rich = less stable than the 3' antisense end).

Pure Python + pandas. No torch dependency. Works on Windows and Linux.
"""

from __future__ import annotations

from typing import Dict, List

import pandas as pd


class ReynoldsFeaturizer:
    """Compute Reynolds 2004 siRNA efficacy features for 21-nt sequences.

    The featurizer is stateless and safe to reuse across calls.
    """

    SIRNA_LEN = 21

    # 7-nt windows used to approximate thermodynamic asymmetry.
    # The 5' end of the antisense strand pairs with the 3' end of the
    # sense strand (sense indices 14..20), and vice versa.
    _WINDOW_LEN = 7
    _SENSE_3PRIME_START = 14  # inclusive, sense[14:21] = sense 3' end
    _SENSE_5PRIME_END = 7     # exclusive, sense[0:7]  = sense 5' end

    # ---- public API ----------------------------------------------------- #
    def featurize(self, sirna_seq: str) -> Dict[str, float]:
        """Return a dict of Reynolds features for a 21-nt siRNA (sense strand).

        Accepts RNA (U) or DNA (T) input; U is normalized to T.
        """
        seq = self._normalize(sirna_seq)
        if len(seq) != self.SIRNA_LEN:
            raise ValueError(
                f"siRNA must be {self.SIRNA_LEN} nt long, got {len(seq)} "
                f"(input: {sirna_seq!r})"
            )

        # Criterion 1: GC content 30-52%
        gc_count = seq.count("G") + seq.count("C")
        gc_content = gc_count / self.SIRNA_LEN
        c1 = int(0.30 <= gc_content <= 0.52)

        # Criterion 2: no internal repeats (no >4 consecutive identical bases)
        max_run = self._longest_run(seq)
        no_repeats = max_run <= 4
        c2 = int(no_repeats)

        # Criterion 3: A or T at position 19 (1-indexed) -> index 18
        at_pos19 = seq[18] in ("A", "T")
        c3 = int(at_pos19)

        # Criterion 4: A at position 3 -> index 2
        a_pos3 = seq[2] == "A"
        c4 = int(a_pos3)

        # Criterion 5: T at position 10 -> index 9
        t_pos10 = seq[9] == "T"
        c5 = int(t_pos10)

        # Criterion 6: A or G at position 13 -> index 12
        ag_pos13 = seq[12] in ("A", "G")
        c6 = int(ag_pos13)

        # Criterion 7: T at position 16 -> index 15
        t_pos16 = seq[15] == "T"
        c7 = int(t_pos16)

        # Criterion 8: thermodynamic asymmetry (continuous feature + binary met/not-met)
        thermo_asymmetry = self._thermo_asymmetry(seq)
        c8 = int(thermo_asymmetry < 0)

        reynolds_score = c1 + c2 + c3 + c4 + c5 + c6 + c7 + c8

        return {
            "gc_content": float(gc_content),
            "no_repeats": c2,
            "at_pos19": c3,
            "a_pos3": c4,
            "t_pos10": c5,
            "ag_pos13": c6,
            "t_pos16": c7,
            "thermo_asymmetry": float(thermo_asymmetry),
            "reynolds_score": int(reynolds_score),
        }

    def featurize_batch(self, seqs: List[str]) -> pd.DataFrame:
        """Featurize a list of siRNA sequences; returns a DataFrame in input order."""
        if not seqs:
            return pd.DataFrame(
                columns=[
                    "gc_content",
                    "no_repeats",
                    "at_pos19",
                    "a_pos3",
                    "t_pos10",
                    "ag_pos13",
                    "t_pos16",
                    "thermo_asymmetry",
                    "reynolds_score",
                ]
            )
        rows = [self.featurize(s) for s in seqs]
        return pd.DataFrame(rows)

    # ---- helpers -------------------------------------------------------- #
    @staticmethod
    def _normalize(seq: str) -> str:
        if not isinstance(seq, str):
            raise TypeError(f"siRNA sequence must be str, got {type(seq).__name__}")
        return seq.upper().replace("U", "T")

    @staticmethod
    def _longest_run(seq: str) -> int:
        if not seq:
            return 0
        max_run = 1
        cur_run = 1
        for i in range(1, len(seq)):
            if seq[i] == seq[i - 1]:
                cur_run += 1
                if cur_run > max_run:
                    max_run = cur_run
            else:
                cur_run = 1
        return max_run

    def _thermo_asymmetry(self, seq: str) -> float:
        """Approximate thermodynamic asymmetry as 0.5 * (GC_5p_antisense - GC_3p_antisense).

        The 5' antisense end pairs with the 3' sense end (sense[14:21]),
        and the 3' antisense end pairs with the 5' sense end (sense[0:7]).
        GC content is preserved under reverse-complement, so we can read
        it directly off the sense strand.
        """
        five_prime_window = seq[self._SENSE_3PRIME_START : self.SIRNA_LEN]
        three_prime_window = seq[0 : self._SENSE_5PRIME_END]
        gc_5p = (
            five_prime_window.count("G") + five_prime_window.count("C")
        ) / self._WINDOW_LEN
        gc_3p = (
            three_prime_window.count("G") + three_prime_window.count("C")
        ) / self._WINDOW_LEN
        return 0.5 * (gc_5p - gc_3p)


__all__ = ["ReynoldsFeaturizer"]