from __future__ import annotations

from dataclasses import dataclass
from pathlib import Path

NOTE_VOCABULARY_SIZE = 8
SEQUENCE_LENGTH = 5
ART_INPUT_LENGTH = 41
ART_CATEGORY_LIMIT = 25
HOPFIELD_WEIGHT_DIMENSION = 64
SALIERI_NODE_COUNT = 61

NoteSequence = tuple[int, ...]


@dataclass(frozen=True)
class LegacyPaths:
    root: Path

    @property
    def sequence_data(self) -> Path:
        return self.root / "SEQUENCE.DAT"

    @property
    def salieri_config(self) -> Path:
        return self.root / "S61.DAT"

    @property
    def salieri_weights(self) -> Path:
        return self.root / "S61.WT"

    @property
    def hopfield_weights(self) -> Path:
        return self.root / "HTN.DAT"


@dataclass(frozen=True)
class SalieriConfig:
    learning_rate: float
    alpha: float
    n_input: int
    n_hidden: int
    n_output: int
    training_iterations: int
    error_tolerance: float
    data_file: str
    report_file: str
    weight_file: str


@dataclass(frozen=True)
class LegacyBPWeights:
    vector_length: int
    weights: tuple[tuple[float, ...], ...]
    thetas: tuple[float, ...]


@dataclass(frozen=True)
class CompositionContext:
    notes: NoteSequence = (0, 0, 0, 0, 0)
    delta_vigilance: bool = False
    new_category: bool = False
    is_classical: bool = False
    candidate_note: int = 0
    since_novelty: int = 0
    frustration: int = 0
    note_count: int = 0


@dataclass(frozen=True)
class CompositionRecord:
    notes: tuple[int, ...]
    per_note_seconds: tuple[float, ...] = ()
    total_seconds: float = 0.0


@dataclass(frozen=True)
class CompositionRunReport:
    notes: tuple[int, ...]
    per_note_seconds: tuple[float, ...]
    total_seconds: float
    parameters: dict[str, object]
    note_count: int
    alphabet_size: int
    unigram_entropy_bits: float
    conditional_entropy_bits: float
    normalized_entropy: float
    predictability: float
    redundancy: float