from __future__ import annotations

from datetime import datetime
import json
import numbers
from typing import Any
from typing import TYPE_CHECKING
from typing import Union

import numpy as np
from optuna.distributions import BaseDistribution
from optuna.distributions import CategoricalDistribution
from optuna.distributions import FloatDistribution
from optuna.distributions import IntDistribution
from optuna.study import StudySummary
from optuna.trial import FrozenTrial

from . import _note as note
from ._form_widget import get_form_widgets_json
from ._named_objectives import get_objective_names
from ._preference_setting import _SYSTEM_ATTR_FEEDBACK_COMPONENT
from ._preferential_history import _SYSTEM_ATTR_PREFIX_HISTORY
from .artifact._backend import list_study_artifacts
from .artifact._backend import list_trial_artifacts
from .preferential._study import _SYSTEM_ATTR_PREFERENTIAL_STUDY
from .preferential._system_attrs import get_preferences
from .preferential._system_attrs import is_preference_removed


if TYPE_CHECKING:
    from typing import Literal
    from typing import TypedDict

    from ._preferential_history import History
    from ._preferential_history import SerializedHistory

    Attribute = TypedDict(
        "Attribute",
        {
            "key": str,
            "value": str,
        },
    )
    AttributeSpec = TypedDict(
        "AttributeSpec",
        {
            "key": str,
            "sortable": bool,
        },
    )
    IntermediateValue = TypedDict(
        "IntermediateValue",
        {
            "step": int,
            "value": Union[float, Literal["inf", "-inf", "nan"]],
        },
    )

    FloatDistributionJSON = TypedDict(
        "FloatDistributionJSON",
        {
            "type": Literal["FloatDistribution"],
            "low": float,
            "high": float,
            "step": float,
            "log": bool,
        },
    )
    IntDistributionJSON = TypedDict(
        "IntDistributionJSON",
        {
            "type": Literal["IntDistribution"],
            "low": int,
            "high": int,
            "step": int,
            "log": bool,
        },
    )
    CategoricalDistributionChoiceJSON = TypedDict(
        "CategoricalDistributionChoiceJSON",
        {
            "pytype": str,
            "value": str,
        },
    )
    CategoricalDistributionJSON = TypedDict(
        "CategoricalDistributionJSON",
        {
            "type": Literal["CategoricalDistribution"],
            "choices": list[CategoricalDistributionChoiceJSON],
        },
    )
    DistributionJSON = Union[
        FloatDistributionJSON, IntDistributionJSON, CategoricalDistributionJSON
    ]


MAX_ATTR_LENGTH = 1024
CONSTRAINTS_KEY = "constraints"


def serialize_attrs(attrs: dict[str, Any]) -> list[Attribute]:
    serialized: list[Attribute] = []
    for k, v in attrs.items():
        value: str
        if isinstance(v, bytes):
            value = "<binary object>"
        elif isinstance(v, str):
            value = v
        elif isinstance(v, numbers.Real):
            value = str(v)
        else:
            value = json.dumps(v)
            value = value[:MAX_ATTR_LENGTH] if len(value) > MAX_ATTR_LENGTH else value
        serialized.append({"key": k, "value": value})
    return serialized


def serialize_study_summary(summary: StudySummary) -> dict[str, Any]:
    serialized = {
        "study_id": summary._study_id,
        "study_name": summary.study_name,
        "directions": [d.name.lower() for d in summary.directions],
        "user_attrs": serialize_attrs(summary.user_attrs),
        "is_preferential": getattr(summary, "_system_attrs", {}).get(
            _SYSTEM_ATTR_PREFERENTIAL_STUDY, False
        ),
    }

    if summary.datetime_start is not None:
        serialized["datetime_start"] = summary.datetime_start.isoformat()

    return serialized


def serialize_study_detail(
    summary: StudySummary,
    best_trials: list[FrozenTrial],
    trials: list[FrozenTrial],
    intersection: list[tuple[str, BaseDistribution]],
    union: list[tuple[str, BaseDistribution]],
    union_user_attrs: list[tuple[str, bool]],
    has_intermediate_values: bool,
    plotly_graph_objects: dict[str, str],
    skipped_trial_numbers: list[int],
) -> dict[str, Any]:
    serialized: dict[str, Any] = {
        "name": summary.study_name,
        "directions": [d.name.lower() for d in summary.directions],
        "user_attrs": serialize_attrs(summary.user_attrs),
    }
    system_attrs = getattr(summary, "system_attrs", {})
    serialized["artifacts"] = list_study_artifacts(system_attrs)
    if summary.datetime_start is not None:
        serialized["datetime_start"] = summary.datetime_start.isoformat()

    serialized["trials"] = [
        serialize_frozen_trial(summary._study_id, trial, system_attrs) for trial in trials
    ]
    serialized["best_trials"] = [
        serialize_frozen_trial(summary._study_id, trial, system_attrs) for trial in best_trials
    ]
    serialized["intersection_search_space"] = serialize_search_space(intersection)
    serialized["union_search_space"] = serialize_search_space(union)
    serialized["union_user_attrs"] = [{"key": a[0], "sortable": a[1]} for a in union_user_attrs]
    serialized["has_intermediate_values"] = has_intermediate_values
    serialized["note"] = note.get_note_from_system_attrs(system_attrs, None)
    serialized["is_preferential"] = system_attrs.get(_SYSTEM_ATTR_PREFERENTIAL_STUDY, False)
    objective_names = get_objective_names(system_attrs)
    if objective_names:
        serialized["objective_names"] = objective_names
    form_widgets = get_form_widgets_json(system_attrs)
    if form_widgets:
        serialized["form_widgets"] = form_widgets
    serialized["feedback_component_type"] = system_attrs.get(
        _SYSTEM_ATTR_FEEDBACK_COMPONENT,
        {
            "output_type": "note",
        },
    )
    if serialized["is_preferential"]:
        serialized["preference_history"] = serialize_preference_history(system_attrs)
        serialized["preferences"] = get_preferences(system_attrs)
        serialized["skipped_trial_numbers"] = skipped_trial_numbers
    serialized["plotly_graph_objects"] = [
        {"id": id_, "graph_object": graph_object}
        for id_, graph_object in plotly_graph_objects.items()
    ]
    return serialized


def serialize_preference_history(
    system_attrs: dict[str, Any],
) -> list[SerializedHistory]:
    histories: list[SerializedHistory] = []
    for k, v in system_attrs.items():
        if not k.startswith(_SYSTEM_ATTR_PREFIX_HISTORY):
            continue
        choice: dict[str, Any] = json.loads(v)
        if choice["mode"] == "ChooseWorst":
            history: History = {
                "mode": "ChooseWorst",
                "id": choice["id"],
                "timestamp": choice["timestamp"],
                "candidates": choice["candidates"],
                "clicked": choice["clicked"],
                "preferences": choice["preferences"],
            }
            histories.append(
                {
                    "history": history,
                    "is_removed": is_preference_removed(system_attrs, choice["id"]),
                }
            )

    histories.sort(key=lambda c: datetime.fromisoformat(c["history"]["timestamp"]))
    return histories


def serialize_frozen_trial(
    study_id: int, trial: FrozenTrial, study_system_attrs: dict[str, Any]
) -> dict[str, Any]:
    params = []
    for param_name, param_external_value in trial.params.items():
        distribution = trial.distributions.get(param_name)
        if distribution is None:
            continue
        params.append(
            {
                "name": param_name,
                "param_internal_value": distribution.to_internal_repr(param_external_value),
                "param_external_value": str(param_external_value),
                "param_external_pytyp": str(type(param_external_value)),
                "distribution": serialize_distribution(distribution),
            }
        )
    trial_system_attrs: dict[str, Any] = getattr(trial, "_system_attrs", {})
    fixed_params = trial_system_attrs.get("fixed_params", {})
    serialized = {
        "trial_id": trial._trial_id,
        "study_id": study_id,
        "number": trial.number,
        "state": trial.state.name.capitalize(),
        "params": params,
        "fixed_params": [
            {"name": param_name, "param_external_value": str(fixed_params.get(param_name, None))}
            for param_name in fixed_params
        ],
        "user_attrs": serialize_attrs(trial.user_attrs),
        "note": note.get_note_from_system_attrs(study_system_attrs, trial._trial_id),
        "artifacts": list_trial_artifacts(study_system_attrs, trial_system_attrs, trial),
        "constraints": trial_system_attrs.get(CONSTRAINTS_KEY, []),
    }

    serialized_intermediate_values: list[IntermediateValue] = []
    for step, value in trial.intermediate_values.items():
        serialized_value: Union[float, Literal["nan", "inf", "-inf"]]
        if np.isnan(value):
            serialized_value = "nan"
        elif np.isposinf(value):
            serialized_value = "inf"
        elif np.isneginf(value):
            serialized_value = "-inf"
        else:
            assert np.isfinite(value)
            serialized_value = value
        serialized_intermediate_values.append({"step": step, "value": serialized_value})
    serialized["intermediate_values"] = sorted(
        serialized_intermediate_values, key=lambda v: v["step"]
    )

    if trial.values is not None:
        serialized_values: list[Union[float, Literal["inf", "-inf"]]] = []
        for v in trial.values:
            assert not np.isnan(v), "Should not detect nan value"
            if np.isposinf(v):
                serialized_values.append("inf")
            elif np.isneginf(v):
                serialized_values.append("-inf")
            else:
                serialized_values.append(v)
        serialized["values"] = serialized_values

    if trial.datetime_start is not None:
        serialized["datetime_start"] = trial.datetime_start.isoformat()

    if trial.datetime_complete is not None:
        serialized["datetime_complete"] = trial.datetime_complete.isoformat()

    return serialized


def serialize_distribution(distribution: BaseDistribution) -> DistributionJSON:
    if isinstance(distribution, FloatDistribution):
        float_distribution: FloatDistributionJSON = {
            "type": "FloatDistribution",
            "low": getattr(distribution, "low"),
            "high": getattr(distribution, "high"),
            "step": getattr(distribution, "step"),
            "log": getattr(distribution, "log"),
        }
        return float_distribution
    if isinstance(distribution, IntDistribution):
        int_distribution: IntDistributionJSON = {
            "type": "IntDistribution",
            "low": getattr(distribution, "low"),
            "high": getattr(distribution, "high"),
            "step": getattr(distribution, "step"),
            "log": getattr(distribution, "log"),
        }
        return int_distribution
    if isinstance(distribution, CategoricalDistribution):
        categorical: CategoricalDistributionJSON = {
            "type": "CategoricalDistribution",
            "choices": [
                {"pytype": str(type(choice)), "value": str(choice)}
                for choice in distribution.choices
            ],
        }
        return categorical
    raise ValueError(f"Unexpected distribution {str(distribution)}")


def serialize_search_space(
    search_space: list[tuple[str, BaseDistribution]]
) -> list[dict[str, Any]]:
    serialized = []
    for param_name, distribution in search_space:
        serialized.append(
            {
                "name": param_name,
                "distribution": serialize_distribution(distribution),
            }
        )
    return serialized