"""
emotion_engine.py — HuggingFace Datasets loading script

Registered as the dataset builder for the emotion-engine HF repo.
Supports loading by condition name or "all" for the full dataset.

Usage:
    from datasets import load_dataset

    # Single condition
    ds = load_dataset("PremC1F/emotion-engine", "condition_d")

    # All conditions concatenated
    ds = load_dataset("PremC1F/emotion-engine", "all")
"""

import datasets

_CITATION = """
@article{kanaparthi2026emotion,
  author  = {Kanaparthi, Prem Babu},
  title   = {Emergent Emotional Appraisal in Generative Agents
             via Predictive World Modeling},
  year    = {2026},
}
"""

_DESCRIPTION = """
204,520 agent-step records from a five-condition controlled experiment comparing
emotion-engine architectures in a 12-agent ghost-survival simulation (Ghost Town).

Five conditions:
  baseline_0  — no emotion system
  condition_a — hand-coded OCC appraisal rules
  condition_b — behavioral cloning from condition_a
  condition_c — emotion dynamics model
  condition_d — predictive world modeling (proposed)

Six scenarios per condition:
  standard_night, high_ghost_pressure, storm_scarcity,
  ally_death, betrayal_refusal, crowded_shelter

8 random seeds per scenario.

Each record contains: full 14-dim observation, 6-dim emotion vector,
8-dim latent representation, 12 future-event predictions (Condition D),
action taken, and outcome metadata.
"""

_HOMEPAGE = "https://github.com/PremC1F/emotion-engine"
_LICENSE  = "MIT"

_CONDITIONS = [
    "baseline_0",
    "condition_a",
    "condition_b",
    "condition_c",
    "condition_d",
]

_FEATURES = datasets.Features({
    # Identity
    "run_id":    datasets.Value("string"),
    "step":      datasets.Value("int32"),
    "agent_id":  datasets.Value("string"),
    "condition": datasets.Value("string"),
    "seed":      datasets.Value("int32"),
    "scenario":  datasets.Value("string"),

    # Observation (14 dims)
    "obs_visible_ghosts":          datasets.Value("int32"),
    "obs_visible_deaths":          datasets.Value("int32"),
    "obs_nearby_allies":           datasets.Value("int32"),
    "obs_trusted_allies":          datasets.Value("int32"),
    "obs_supplies_seen":           datasets.Value("int32"),
    "obs_in_shelter":              datasets.Value("bool"),
    "obs_nearest_refuge_distance": datasets.Value("float32"),
    "obs_steps_since_ghost_seen":  datasets.Value("int32"),
    "obs_steps_since_ally_died":   datasets.Value("int32"),
    "obs_steps_since_betrayal":    datasets.Value("int32"),
    "obs_ally_deaths_witnessed":   datasets.Value("int32"),
    "obs_betrayals_received":      datasets.Value("int32"),
    "obs_average_trust":           datasets.Value("float32"),
    "obs_graph_tension":           datasets.Value("float32"),

    # Affect / emotion
    "affect_fear":      datasets.Value("float32"),
    "affect_grief":     datasets.Value("float32"),
    "affect_trust":     datasets.Value("float32"),
    "affect_stress":    datasets.Value("float32"),
    "affect_relief":    datasets.Value("float32"),
    "affect_suspicion": datasets.Value("float32"),

    # Latent representation (8 dims)
    "latent_0": datasets.Value("float32"),
    "latent_1": datasets.Value("float32"),
    "latent_2": datasets.Value("float32"),
    "latent_3": datasets.Value("float32"),
    "latent_4": datasets.Value("float32"),
    "latent_5": datasets.Value("float32"),
    "latent_6": datasets.Value("float32"),
    "latent_7": datasets.Value("float32"),

    # Future-event predictions (Condition D; zero for others)
    "pred_ghost_nearby_t3":     datasets.Value("float32"),
    "pred_my_death_t5":         datasets.Value("float32"),
    "pred_health_drop_t3":      datasets.Value("float32"),
    "pred_nearby_death_t5":     datasets.Value("float32"),
    "pred_help_success_t5":     datasets.Value("float32"),
    "pred_refusal_received_t5": datasets.Value("float32"),
    "pred_tie_increase_t5":     datasets.Value("float32"),
    "pred_shelter_achieved_t2": datasets.Value("float32"),
    "pred_storm_onset_t3":      datasets.Value("float32"),
    "pred_scarcity_t5":         datasets.Value("float32"),
    "pred_graph_tension_t3":    datasets.Value("float32"),
    "pred_valence_t5":          datasets.Value("float32"),

    # Action & outcome
    "action":          datasets.Value("string"),
    "goal":            datasets.Value("string"),
    "reward_survival": datasets.Value("float32"),
    "reward_shelter":  datasets.Value("float32"),
    "reward_health":   datasets.Value("float32"),
    "reward_social":   datasets.Value("float32"),
    "total_reward":    datasets.Value("float32"),
    "alive":           datasets.Value("bool"),
    "health":          datasets.Value("float32"),
    "sheltered":       datasets.Value("bool"),
    "time_of_day":     datasets.Value("string"),
})


class EmotionEngineConfig(datasets.BuilderConfig):
    def __init__(self, condition="all", **kwargs):
        super().__init__(**kwargs)
        self.condition = condition


class EmotionEngine(datasets.GeneratorBasedBuilder):
    """Ghost Town emotion engine dataset — 204,520 agent-step records."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIG_CLASS = EmotionEngineConfig

    BUILDER_CONFIGS = [
        EmotionEngineConfig(
            name="all",
            version=VERSION,
            description="All five conditions concatenated (204,520 records)",
            condition="all",
        ),
    ] + [
        EmotionEngineConfig(
            name=c,
            version=VERSION,
            description=f"Condition {c} only (~40k records, 6 scenarios, 8 seeds)",
            condition=c,
        )
        for c in _CONDITIONS
    ]

    DEFAULT_CONFIG_NAME = "condition_d"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=_FEATURES,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        if self.config.condition == "all":
            files = {
                c: dl_manager.download(f"data/{c}.parquet")
                for c in _CONDITIONS
            }
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={"filepaths": list(files.values())},
                )
            ]
        else:
            filepath = dl_manager.download(
                f"data/{self.config.condition}.parquet"
            )
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={"filepaths": [filepath]},
                )
            ]

    def _generate_examples(self, filepaths):
        import pandas as pd
        idx = 0
        for path in filepaths:
            df = pd.read_parquet(path)
            for _, row in df.iterrows():
                yield idx, row.to_dict()
                idx += 1