# Dataclasses and enums for storing neuron explanations, their scores, and related data. Also,
# related helper functions.

from __future__ import annotations

import json
import math
import os.path as osp
from dataclasses import dataclass
from enum import Enum
from typing import Any

from neuron_explainer.activations.activations import NeuronId
from neuron_explainer.fast_dataclasses import FastDataclass, loads, register_dataclass
from neuron_explainer.file_utils import CustomFileHandler, file_exists, read_single_async


class ActivationScale(str, Enum):
    """Which "units" are stored in the expected_activations/distribution_values fields of a
    SequenceSimulation.

    This enum identifies whether the values represent real activations of the neuron or something
    else. Different scales are not necessarily related by a linear transformation.
    """

    NEURON_ACTIVATIONS = "neuron_activations"
    """Values represent real activations of the neuron."""
    SIMULATED_NORMALIZED_ACTIVATIONS = "simulated_normalized_activations"
    """
    Values represent simulated activations of the neuron, normalized to the range [0, 10]. This
    scale is arbitrary and should not be interpreted as a neuron activation.
    """
    HUMAN_PREDICTED_ACTIVATIONS = "human_predicted_activations"
    """
    Values represent human predictions of the neuron's activation, normalized to the range [0, 2]:
        0=not active
        1=weakly/possibly active
        2=strongly/definitely active
    Not used at present.
    """


@register_dataclass
@dataclass
class SequenceSimulation(FastDataclass):
    """The result of a simulation of neuron activations on one text sequence."""

    tokens: list[str]
    """The sequence of tokens that was simulated."""
    expected_activations: list[float]
    """Expected value of the possibly-normalized activation for each token in the sequence."""
    activation_scale: ActivationScale
    """What scale is used for values in the expected_activations field."""
    distribution_values: list[list[float]]
    """
    For each token in the sequence, a list of values from the discrete distribution of activations
    produced from simulation. Tokens will be included here if and only if they are in the top K=15
    tokens predicted by the simulator, and excluded otherwise.

    May be transformed to another unit by calibration. When we simulate a neuron, we produce a
    discrete distribution with values in the arbitrary discretized space of the neuron, e.g. 10%
    chance of 0, 70% chance of 1, 20% chance of 2. Which we store as distribution_values =
    [0, 1, 2], distribution_probabilities = [0.1, 0.7, 0.2]. When we tranform the distribution to
    the real activation units, we can correspondingly tranform the values of this distribution
    to get a distribution in the units of the neuron. e.g. if the mapping from the discretized space
    to the real activation unit of the neuron is f(x) = x/2, then the distribution becomes 10%
    chance of 0, 70% chance of 0.5, 20% chance of 1. Which we store as distribution_values =
    [0, 0.5, 1], distribution_probabilities = [0.1, 0.7, 0.2].
    """
    distribution_probabilities: list[list[float]]
    """
    For each token in the sequence, the probability of the corresponding value in
    distribution_values.
    """

    uncalibrated_simulation: "SequenceSimulation" | None = None
    """The result of the simulation before calibration."""


SequenceSimulation.field_renamed("unit", "activation_scale")
SequenceSimulation.field_deleted("response")


@register_dataclass
@dataclass
class ScoredSequenceSimulation(FastDataclass):
    """
    SequenceSimulation result with a score (for that sequence only) and ground truth activations.
    """

    sequence_simulation: SequenceSimulation
    """The result of a simulation of neuron activations."""
    true_activations: list[float]
    """Ground truth activations on the sequence (not normalized)"""
    ev_correlation_score: float
    """
    Correlation coefficient between the expected values of the normalized activations from the
    simulation and the unnormalized true activations of the neuron on the text sequence.
    """
    rsquared_score: float | None = None
    """R^2 of the simulated activations."""
    absolute_dev_explained_score: float | None = None
    """
    Score based on absolute difference between real and simulated activations.
    absolute_dev_explained_score = 1 - mean(abs(real-predicted))/ mean(abs(real))
    """

    def __eq__(self, other: Any) -> bool:
        if not isinstance(other, ScoredSequenceSimulation):
            return False
        if len(self.__dict__.keys()) != len(other.__dict__.keys()):
            return False
        # Since NaN != NaN in Python, we need to make an exception for this case when checking for equality
        # of two ScoredSequenceSimulation objects.
        for field_name in self.__dict__.keys():
            if field_name not in other.__dict__:
                return False
            self_val, other_val = self.__dict__[field_name], other.__dict__[field_name]
            if self_val != other_val:
                if not (
                    isinstance(self_val, float)
                    and math.isnan(self_val)
                    and isinstance(other_val, float)
                    and math.isnan(other_val)
                ):
                    return False
        return True


ScoredSequenceSimulation.field_renamed("simulation", "sequence_simulation")


@register_dataclass
@dataclass
class ScoredSimulation(FastDataclass):
    """Result of scoring a neuron simulation on multiple sequences."""

    scored_sequence_simulations: list[ScoredSequenceSimulation]
    """ScoredSequenceSimulation for each sequence"""
    ev_correlation_score: float | None = None
    """
    Correlation coefficient between the expected values of the normalized activations from the
    simulation and the unnormalized true activations on a dataset created from all score_results.
    (Note that this is not equivalent to averaging across sequences.)
    """
    rsquared_score: float | None = None
    """R^2 of the simulated activations."""
    absolute_dev_explained_score: float | None = None
    """
    Score based on absolute difference between real and simulated activations.
    absolute_dev_explained_score = 1 - mean(abs(real-predicted))/ mean(abs(real)).
    """

    def get_preferred_score(self) -> float | None:
        """
        This method may return None in cases where the score is undefined, for example if the
        normalized activations were all zero, yielding a correlation coefficient of NaN.
        """
        return self.ev_correlation_score


@register_dataclass
@dataclass
class ScoredExplanation(FastDataclass):
    """Simulator parameters and the results of scoring it on multiple sequences"""

    explanation: str

    scored_simulation: ScoredSimulation
    """Result of scoring the neuron simulator on multiple sequences."""

    def get_preferred_score(self) -> float | None:
        """
        This method may return None in cases where the score is undefined, for example if the
        normalized activations were all zero, yielding a correlation coefficient of NaN.
        """
        return self.scored_simulation.get_preferred_score()


ScoredExplanation.was_previously_named("ScoredExplanationOrBaseline")
ScoredExplanation.field_renamed("explanation_or_baseline", "explanation")


@register_dataclass
@dataclass
class NeuronSimulationResults(FastDataclass):
    """Simulation results and scores for a neuron."""

    neuron_id: NeuronId
    scored_explanations: list[ScoredExplanation]


NeuronSimulationResults.field_renamed("scored_explanation_or_baseline_list", "scored_explanations")


@register_dataclass
@dataclass
class AttentionSimulation(FastDataclass):
    tokens: list[str]
    token_pair_coords: tuple[int, int]
    """The coordinates of the token pair that we're simulating attention for."""
    token_pair_label: int
    """Either 0 or 1 for negative or positive label, respectively."""
    simulation_prediction: float
    """The predicted label for the token pair from the attention simulator."""


@register_dataclass
@dataclass
class ScoredAttentionSimulation(FastDataclass):
    """Result of scoring an attention head simulation on multiple sequences."""

    attention_simulations: list[AttentionSimulation]
    """ScoredSequenceSimulation for each sequence"""
    roc_auc_score: float | None = None
    """
    Area under the ROC curve for the attention predictions. Each AttentionSimulation is
    essentially a single binary classification.
    """

    def get_preferred_score(self) -> float | None:
        return self.roc_auc_score


@register_dataclass
@dataclass
class ScoredAttentionExplanation(FastDataclass):
    """Simulator parameters and the results of scoring it on multiple sequences"""

    explanation: str

    scored_attention_simulation: ScoredAttentionSimulation
    """Result of scoring the neuron simulator on multiple sequences."""

    def get_preferred_score(self) -> float | None:
        """
        This method may return None in cases where the score is undefined, for example if the
        normalized activations were all zero, yielding a correlation coefficient of NaN.
        """
        return self.scored_attention_simulation.get_preferred_score()


@register_dataclass
@dataclass
class AttentionSimulationResults(FastDataclass):
    """Simulation results and scores for an attention head."""

    # Typing this as NeuronId is not ideal but I'm not sure if we want to rename the type to something more general.
    attention_head_id: NeuronId
    scored_explanations: list[ScoredAttentionExplanation]


AttentionSimulationResults.field_renamed("attention_id", "attention_head_id")


def load_neuron_explanations(
    explanations_path: str, layer_index: str | int, neuron_index: str | int
) -> NeuronSimulationResults | None:
    """Load scored explanations for the specified neuron."""
    file = osp.join(explanations_path, str(layer_index), f"{neuron_index}.jsonl")
    if not file_exists(file):
        return None
    with CustomFileHandler(file) as f:
        for line in f:
            return loads(line)
    return None


async def load_neuron_explanations_async(
    explanations_path: str, layer_index: str | int, neuron_index: str | int
) -> NeuronSimulationResults | None:
    """Load scored explanations for the specified neuron, asynchronously."""
    return await read_explanation_file(
        osp.join(explanations_path, str(layer_index), f"{neuron_index}.jsonl")
    )


async def read_file(filename: str) -> str | None:
    """Read the contents of the given file as a string, asynchronously. File can be a
    local file or a remote file."""
    try:
        raw_contents = await read_single_async(filename)
    except FileNotFoundError:
        return None
    lines = []
    for line in raw_contents.decode("utf-8").split("\n"):
        if len(line) > 0:
            lines.append(line)
    assert len(lines) == 1, filename
    return lines[0]


async def read_explanation_file(explanation_filename: str) -> NeuronSimulationResults | None:
    """Load scored explanations from the given filename, asynchronously."""
    line = await read_file(explanation_filename)
    return loads(line) if line is not None else None


async def read_json_file(filename: str) -> dict | None:
    """Read the contents of the given file as a JSON object, asynchronously."""
    line = await read_file(filename)
    return json.loads(line) if line is not None else None