"""Typed, validated configuration loading for CellTriage.

WHAT: Pydantic schemas for all five YAML configs, plus loaders that parse and
validate them, and cross-config consistency checks.

WHY a schema rather than ``yaml.safe_load``: this project encodes its
methodological commitments as configuration. Its hard constraints are
expressible as invariants over these files -- escape cost must
dominate overkill cost, feature selection must be fitted inside CV folds,
neural networks are forbidden, and every excluded cell must carry a reason. A
plain dict load would let any of those be violated by a typo and the run would
proceed, producing numbers that look fine and are not. Validation turns each
commitment into something that must be *deliberately* defeated rather than
accidentally lost.

The validators here are therefore not defensive boilerplate; each one
corresponds to a stated project rule and is annotated with which.
"""

from __future__ import annotations

from pathlib import Path
from typing import Any, Literal

import yaml
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator

from src.utils.paths import CONFIG_DIR

# ---------------------------------------------------------------------------
# Base
# ---------------------------------------------------------------------------


class _StrictModel(BaseModel):
    """Base model that forbids unknown keys.

    WHY ``extra='forbid'``: a mistyped key in a YAML file is otherwise silently
    ignored, so the pipeline runs with the default rather than the intended
    value. For a file that holds cost assumptions and leakage controls, a typo
    that changes behaviour without complaining is the worst possible failure
    mode.
    """

    model_config = ConfigDict(extra="forbid", frozen=False)


# ---------------------------------------------------------------------------
# grading.yaml
# ---------------------------------------------------------------------------


class GradeSpec(_StrictModel):
    """One product tier."""

    min_cycles: int = Field(ge=0, description="Inclusive lower bound on observed cycle life.")
    tier: str = Field(min_length=1, description="Human-readable application tier.")


class LabellingSpec(_StrictModel):
    boundary_inclusive: bool


class GradingConfig(_StrictModel):
    """Grade boundaries and product tiers (``configs/grading.yaml``)."""

    grades: dict[str, GradeSpec]
    warranty_target_cycles: int = Field(ge=0)
    grade_order: list[str] = Field(min_length=2)
    shipped_grades: list[str] = Field(min_length=1)
    labelling: LabellingSpec

    @model_validator(mode="after")
    def _check_grade_structure(self) -> "GradingConfig":
        if set(self.grade_order) != set(self.grades):
            raise ValueError(
                f"grade_order {self.grade_order} does not match the grades defined "
                f"({sorted(self.grades)}). Every grade must appear exactly once."
            )
        if len(set(self.grade_order)) != len(self.grade_order):
            raise ValueError(f"grade_order contains duplicates: {self.grade_order}")

        # Ordering is load-bearing: it determines which off-diagonal cost-matrix
        # entries count as escapes. If the order were not strictly decreasing in
        # min_cycles, "more demanding tier" would be undefined.
        thresholds = [self.grades[g].min_cycles for g in self.grade_order]
        if thresholds != sorted(thresholds, reverse=True) or len(set(thresholds)) != len(thresholds):
            raise ValueError(
                f"grade_order must be strictly decreasing in min_cycles; got "
                f"{dict(zip(self.grade_order, thresholds))}."
            )
        if thresholds[-1] != 0:
            raise ValueError(
                f"The least demanding grade ('{self.grade_order[-1]}') must have "
                f"min_cycles == 0 so that every cell receives a grade; got {thresholds[-1]}."
            )

        unknown_shipped = set(self.shipped_grades) - set(self.grade_order)
        if unknown_shipped:
            raise ValueError(f"shipped_grades references undefined grades: {sorted(unknown_shipped)}")
        if self.grade_order[-1] in self.shipped_grades:
            raise ValueError(
                f"The scrap grade '{self.grade_order[-1]}' cannot be a shipped grade; "
                "escape rate is defined over shipped cells only."
            )

        highest, lowest_shipped = thresholds[0], self.grades[self.grade_order[-2]].min_cycles
        if not lowest_shipped <= self.warranty_target_cycles <= highest:
            raise ValueError(
                f"warranty_target_cycles ({self.warranty_target_cycles}) must lie between the "
                f"lowest shipped-grade boundary ({lowest_shipped}) and the highest "
                f"({highest})."
            )
        return self

    def grade_for(self, cycle_life: float) -> str:
        """Assign a grade to an observed cycle life.

        Boundary handling is explicit (see ``labelling.boundary_inclusive``):
        a value exactly on a boundary goes to the higher tier. An off-by-one
        here silently moves cells between classes and makes the reported class
        balance irreproducible.
        """
        for grade in self.grade_order:
            threshold = self.grades[grade].min_cycles
            if (cycle_life >= threshold) if self.labelling.boundary_inclusive else (cycle_life > threshold):
                return grade
        return self.grade_order[-1]


# ---------------------------------------------------------------------------
# costs.yaml
# ---------------------------------------------------------------------------


class SensitivitySpec(_StrictModel):
    escape_overkill_ratios: list[float] = Field(min_length=2)

    @field_validator("escape_overkill_ratios")
    @classmethod
    def _ascending_and_positive(cls, v: list[float]) -> list[float]:
        if any(r <= 0 for r in v):
            raise ValueError(f"escape_overkill_ratios must all be positive; got {v}")
        if v != sorted(v):
            raise ValueError(f"escape_overkill_ratios must be ascending; got {v}")
        return v


class UnitsSpec(_StrictModel):
    basis: str
    currency: str | None = None


class CostsConfig(_StrictModel):
    """Cost matrix and chamber economics (``configs/costs.yaml``)."""

    cost_matrix: dict[str, list[float]]
    cycle_test_cost: float = Field(gt=0)
    chamber_slots: int = Field(gt=0)
    batch_size: int = Field(gt=0)
    sensitivity: SensitivitySpec
    units: UnitsSpec

    @model_validator(mode="after")
    def _check_matrix(self) -> "CostsConfig":
        n = len(self.cost_matrix)
        for row_key, row in self.cost_matrix.items():
            if not row_key.startswith("true_"):
                raise ValueError(f"Cost-matrix row keys must be 'true_<GRADE>'; got '{row_key}'.")
            if len(row) != n:
                raise ValueError(
                    f"Cost matrix must be square: row '{row_key}' has {len(row)} entries, expected {n}."
                )
            if any(c < 0 for c in row):
                raise ValueError(f"Cost-matrix entries must be non-negative; row '{row_key}' = {row}.")

        rows = self.row_grades()
        for i, grade in enumerate(rows):
            diagonal = self.cost_matrix[f"true_{grade}"][i]
            if diagonal != 0.0:
                raise ValueError(
                    f"Diagonal entry for grade '{grade}' must be 0 (a correct assignment costs "
                    f"nothing); got {diagonal}."
                )

        escapes = self.escape_costs()
        overkills = self.overkill_costs()
        if not escapes or not overkills:
            raise ValueError("Cost matrix must contain at least one escape and one overkill entry.")

        # PROJECT RULE: escape cost must dominate overkill cost. A pack's life
        # is governed by its weakest cell, so an escaped cell carries warranty
        # and recall exposure while an overkilled cell costs only its
        # manufacturing value. If this inversion were ever introduced by an
        # edit, every decision-theoretic conclusion in the project would flip
        # without any other symptom.
        if min(escapes.values()) <= max(overkills.values()):
            raise ValueError(
                f"Every escape cost must exceed every overkill cost. "
                f"Cheapest escape {min(escapes.values())} ({min(escapes, key=escapes.get)}) does not "
                f"exceed costliest overkill {max(overkills.values())} "
                f"({max(overkills, key=overkills.get)})."
            )

        # Capacity must actually bind, otherwise Phase 8D's allocation problem
        # is vacuous: if every cell can be held, there is nothing to allocate.
        if self.chamber_slots >= self.batch_size:
            raise ValueError(
                f"chamber_slots ({self.chamber_slots}) must be smaller than batch_size "
                f"({self.batch_size}); otherwise the capacity constraint does not bind and the "
                "allocation problem in Phase 8D is trivial."
            )
        return self

    def row_grades(self) -> list[str]:
        """Grade labels in matrix row order, most demanding first."""
        return [key.removeprefix("true_") for key in self.cost_matrix]

    def _off_diagonal(self, kind: Literal["escape", "overkill"]) -> dict[tuple[str, str], float]:
        """Return off-diagonal entries of the requested kind, keyed (true, assigned).

        Rows are true grades and columns assigned grades, both ordered most
        demanding first. A cell assigned to a MORE demanding tier than it can
        sustain is an escape, which means ``col_index < row_index`` -- the
        lower-left triangle. See the orientation note in ``configs/costs.yaml``:
        the build plan's prose says "above the diagonal" but its own numbers
        place escapes below it, and the numbers govern.
        """
        grades = self.row_grades()
        out: dict[tuple[str, str], float] = {}
        for i, true_grade in enumerate(grades):
            row = self.cost_matrix[f"true_{true_grade}"]
            for j, assigned_grade in enumerate(grades):
                if i == j:
                    continue
                is_escape = j < i
                if (kind == "escape") == is_escape:
                    out[(true_grade, assigned_grade)] = row[j]
        return out

    def escape_costs(self) -> dict[tuple[str, str], float]:
        """Costs of assigning a cell to a tier it cannot sustain."""
        return self._off_diagonal("escape")

    def overkill_costs(self) -> dict[tuple[str, str], float]:
        """Costs of downgrading or scrapping a capable cell."""
        return self._off_diagonal("overkill")

    def escape_overkill_ratio(self) -> float:
        """Mean escape cost divided by mean overkill cost, for the sensitivity sweep."""
        escapes = list(self.escape_costs().values())
        overkills = list(self.overkill_costs().values())
        return (sum(escapes) / len(escapes)) / (sum(overkills) / len(overkills))


# ---------------------------------------------------------------------------
# data.yaml
# ---------------------------------------------------------------------------


class DatasetSpec(_StrictModel):
    name: str
    chemistry: str
    cell_model: str
    nominal_capacity_ah: float = Field(gt=0)
    nominal_voltage_v: float = Field(gt=0)
    cycling_temperature_c: float
    discharge_protocol: str
    eol_capacity_fraction: float = Field(gt=0, lt=1)


class SourceFileSpec(_StrictModel):
    """One downloadable source file, with the provenance needed to re-fetch it."""

    name: str = Field(min_length=1)
    url: str = Field(pattern=r"^https://")
    size_bytes: int = Field(gt=0)
    batch: str = Field(min_length=1)
    #: Populated by src/data/downloader.py from the downloaded bytes. Null until
    #: the file has actually been fetched and hashed.
    sha256: str | None = Field(default=None, pattern=r"^[0-9a-f]{64}$")


class SourceSpec(_StrictModel):
    verified: bool
    primary_citation: str
    extension_citation: str
    files: list[SourceFileSpec] = Field(default_factory=list)
    format: str
    verified_on: str | None = None
    landing_page: str | None = None
    total_size_bytes: int | None = None
    extension_located: bool = False

    @model_validator(mode="after")
    def _verified_requires_evidence(self) -> "SourceSpec":
        # Task 2.0 rule: do not hardcode a URL you have not verified. Flipping
        # `verified` to true without a digest for every file would defeat the
        # check this exists to enforce -- a URL that merely resolves is not a
        # URL whose contents have been verified.
        if self.verified:
            if not self.files:
                raise ValueError("source.verified is true but source.files is empty.")
            undigested = [f.name for f in self.files if not f.sha256]
            if undigested:
                raise ValueError(
                    f"source.verified is true but these files have no sha256 digest: {undigested}. "
                    "A digest can only come from bytes that were actually downloaded."
                )

        if self.total_size_bytes is not None and self.files:
            declared = sum(f.size_bytes for f in self.files)
            if declared != self.total_size_bytes:
                raise ValueError(
                    f"source.total_size_bytes ({self.total_size_bytes}) does not equal the sum of the "
                    f"per-file sizes ({declared})."
                )
        return self


class PathsSpec(_StrictModel):
    raw: str
    interim: str
    processed: str
    cells: str


class BatchSpec(_StrictModel):
    date: str
    role: Literal["in_distribution", "out_of_distribution"]
    expected_cells_raw: int = Field(gt=0)
    expected_cells_used: int = Field(ge=0)
    verified: bool
    note: str | None = None

    @model_validator(mode="after")
    def _used_not_more_than_raw(self) -> "BatchSpec":
        if self.expected_cells_used > self.expected_cells_raw:
            raise ValueError(
                f"expected_cells_used ({self.expected_cells_used}) exceeds expected_cells_raw "
                f"({self.expected_cells_raw})."
            )
        return self


class ContinuationSpec(_StrictModel):
    primary: str
    continued_as: str
    known_extra_cycles: int = Field(gt=0)
    verified: bool

    @model_validator(mode="after")
    def _distinct_records(self) -> "ContinuationSpec":
        if self.primary == self.continued_as:
            raise ValueError(f"A cell cannot continue as itself: '{self.primary}'.")
        return self


class ExclusionSpec(_StrictModel):
    """One excluded cell.

    ``reason`` is required and must be substantive. PROJECT RULE: never
    silently drop a cell -- always log the cell ID and the reason. Making the
    reason a required, minimum-length field means an undocumented exclusion
    cannot be added to this file at all.
    """

    cell_id: str = Field(min_length=1)
    batch: str = Field(min_length=1)
    reason: str = Field(min_length=15)
    verified: bool

    @field_validator("reason")
    @classmethod
    def _reason_is_substantive(cls, v: str) -> str:
        placeholders = {"tbd", "todo", "n/a", "na", "unknown", "excluded", "see above", "-"}
        if v.strip().lower().rstrip(".") in placeholders:
            raise ValueError(f"Exclusion reason '{v}' is a placeholder, not a reason.")
        return v


class ReconciliationSpec(_StrictModel):
    expected_cells_used: int = Field(gt=0)
    expected_cells_used_with_extension: int = Field(gt=0)
    verified: bool

    @model_validator(mode="after")
    def _extension_is_larger(self) -> "ReconciliationSpec":
        if self.expected_cells_used_with_extension < self.expected_cells_used:
            raise ValueError("The extended corpus cannot contain fewer cells than the primary one.")
        return self


class ParsingSpec(_StrictModel):
    """Parsing and storage policy for Phase 2."""

    within_cycle_max_cycle: int = Field(gt=0)
    parquet_compression: Literal["zstd", "snappy", "gzip", "brotli", "none"]


class CycleLifeRecomputeSpec(_StrictModel):
    method: Literal["first_below_threshold"]
    smoothing_window: int = Field(ge=1)
    min_cycle: int = Field(ge=1)
    #: Fraction above the end-of-life threshold still counted as having reached
    #: it. Derived empirically in Phase 2, not assumed -- see the extensive
    #: rationale in configs/data.yaml. Capped well below 10% because a large
    #: tolerance would start absorbing genuinely censored cells.
    eol_tolerance_fraction: float = Field(ge=0.0, le=0.05)


class PlaceholderRowSpec(_StrictModel):
    expect_at_most_one: bool
    expect_at_first_cycle: bool
    #: Batches empirically confirmed to carry the all-zero placeholder row.
    batches_with_placeholder: list[str] = Field(default_factory=list)


class ValidationSpec(_StrictModel):
    capacity_max_fraction_of_nominal: float = Field(gt=1.0)
    voltage_range_v: list[float] = Field(min_length=2, max_length=2)
    protocol_voltage_range_v: list[float] = Field(min_length=2, max_length=2)
    temperature_range_c: list[float] = Field(min_length=2, max_length=2)
    require_monotonic_cycle_index: bool
    forbid_duplicate_cycle_index: bool
    cycle_life_disagreement_tolerance: int = Field(ge=0)
    cycle_life_recompute: CycleLifeRecomputeSpec
    placeholder_row: PlaceholderRowSpec
    systematic_violation_fraction: float = Field(gt=0.0, le=0.5)
    systematic_cell_fraction: float = Field(gt=0.0, le=1.0)
    max_cycle_duration_minutes: float = Field(gt=0.0)

    @model_validator(mode="after")
    def _validation_bound_contains_protocol(self) -> "ValidationSpec":
        # The validation bound exists to catch mis-parsing, so it must be at
        # least as wide as the protocol setpoints. A bound TIGHTER than the
        # protocol would flag correctly parsed data as impossible.
        if (self.voltage_range_v[0] > self.protocol_voltage_range_v[0]
                or self.voltage_range_v[1] < self.protocol_voltage_range_v[1]):
            raise ValueError(
                f"voltage_range_v {self.voltage_range_v} must contain the protocol range "
                f"{self.protocol_voltage_range_v}; a bound tighter than the protocol would reject "
                "correctly parsed data."
            )
        return self

    @field_validator("voltage_range_v", "temperature_range_c")
    @classmethod
    def _ordered_range(cls, v: list[float]) -> list[float]:
        if v[0] >= v[1]:
            raise ValueError(f"Range must be [low, high] with low < high; got {v}.")
        return v


class DataConfig(_StrictModel):
    """Dataset structure, joins and exclusions (``configs/data.yaml``)."""

    dataset: DatasetSpec
    source: SourceSpec
    paths: PathsSpec
    batches: dict[str, BatchSpec]
    continuations: list[ContinuationSpec]
    exclusions: list[ExclusionSpec]
    reconciliation: ReconciliationSpec
    parsing: ParsingSpec
    validation: ValidationSpec

    @model_validator(mode="after")
    def _check_cross_references(self) -> "DataConfig":
        known_batches = set(self.batches)

        unknown = {e.cell_id: e.batch for e in self.exclusions if e.batch not in known_batches}
        if unknown:
            raise ValueError(f"Exclusions reference undefined batches: {unknown}")

        ids = [e.cell_id for e in self.exclusions]
        duplicates = {c for c in ids if ids.count(c) > 1}
        if duplicates:
            raise ValueError(f"Duplicate cell IDs in the exclusion list: {sorted(duplicates)}")

        # A cell cannot be both joined into another record and excluded: the
        # two operations would disagree about whether it exists.
        continuation_ids = {c.primary for c in self.continuations} | {
            c.continued_as for c in self.continuations
        }
        conflict = continuation_ids & set(ids)
        if conflict:
            raise ValueError(
                f"Cells appear in both the continuation joins and the exclusion list: {sorted(conflict)}. "
                "A cell cannot be simultaneously joined and dropped."
            )

        if not any(b.role == "out_of_distribution" for b in self.batches.values()):
            raise ValueError(
                "At least one batch must be marked out_of_distribution; RQ4 has no test set otherwise."
            )
        return self

    def excluded_cell_ids(self) -> set[str]:
        return {e.cell_id for e in self.exclusions}

    def unverified_assumptions(self) -> list[str]:
        """List every config entry still carrying ``verified: false``.

        WHY: Phase 2 must close each of these out. Surfacing them as a list
        means the outstanding assumptions are printable rather than buried in
        the YAML.
        """
        pending: list[str] = []
        if not self.source.verified:
            pending.append("source (URLs, checksums, file sizes)")
        pending.extend(f"batches.{name} (cell counts)" for name, b in self.batches.items() if not b.verified)
        pending.extend(
            f"continuations.{c.primary}->{c.continued_as} (extra-cycle count)"
            for c in self.continuations
            if not c.verified
        )
        pending.extend(f"exclusions.{e.cell_id} (stated reason)" for e in self.exclusions if not e.verified)
        if not self.reconciliation.verified:
            pending.append("reconciliation (final cell count)")
        return pending


# ---------------------------------------------------------------------------
# features.yaml
# ---------------------------------------------------------------------------


class CurveSpec(_StrictModel):
    voltage_min_v: float
    voltage_max_v: float
    n_grid_points: int = Field(gt=1)
    baseline_cycle: int = Field(gt=0)
    min_baseline_cycle: int = Field(gt=0)
    interpolation: str

    @model_validator(mode="after")
    def _check_grid(self) -> "CurveSpec":
        if self.voltage_min_v >= self.voltage_max_v:
            raise ValueError(
                f"voltage_min_v ({self.voltage_min_v}) must be below voltage_max_v ({self.voltage_max_v})."
            )
        if self.min_baseline_cycle > self.baseline_cycle:
            raise ValueError(
                f"min_baseline_cycle ({self.min_baseline_cycle}) cannot exceed baseline_cycle "
                f"({self.baseline_cycle})."
            )
        return self


class FeatureGroupSpec(_StrictModel):
    enabled: bool
    module: str
    in_line_measurable: bool
    is_process_recipe: bool
    rationale: str = Field(min_length=20)


class MutualInformationSpec(_StrictModel):
    enabled: bool
    n_neighbors: int = Field(gt=0)


class StabilitySelectionSpec(_StrictModel):
    enabled: bool
    n_bootstrap: int = Field(gt=0)
    sample_fraction: float = Field(gt=0, le=1)
    selection_frequency_threshold: float = Field(gt=0, le=1)


class SelectionSpec(_StrictModel):
    fit_inside_cv_only: bool
    variance_threshold: float = Field(ge=0)
    correlation_threshold: float = Field(gt=0, le=1)
    mutual_information: MutualInformationSpec
    stability_selection: StabilitySelectionSpec

    @field_validator("fit_inside_cv_only")
    @classmethod
    def _must_fit_inside_cv(cls, v: bool) -> bool:
        # HARD CONSTRAINT: with n ~ 124-169 cells, fitting selection on the full
        # dataset is the fastest way to produce a fake result. This validator
        # exists so that disabling the rule requires deliberately editing a
        # schema, not just flipping a YAML flag.
        if not v:
            raise ValueError(
                "selection.fit_inside_cv_only must be true. Feature selection fitted outside "
                "cross-validation folds leaks the evaluation set into model construction."
            )
        return v


class PreprocessingSpec(_StrictModel):
    scaler: str
    impute_strategy: str


class FeatureOutputSpec(_StrictModel):
    directory: str
    filename_template: str
    float_precision: str


class FeaturesConfig(_StrictModel):
    """Budgets, feature groups and selection rules (``configs/features.yaml``)."""

    budgets: list[int] = Field(min_length=1)
    reference_budget: int = Field(gt=0)
    curve: CurveSpec
    groups: dict[str, FeatureGroupSpec]
    feature_sets: dict[str, list[str]]
    selection: SelectionSpec
    preprocessing: PreprocessingSpec
    output: FeatureOutputSpec

    @field_validator("budgets")
    @classmethod
    def _ascending_unique_positive(cls, v: list[int]) -> list[int]:
        if any(b <= 0 for b in v):
            raise ValueError(f"Budgets must be positive cycle counts; got {v}.")
        if v != sorted(v) or len(set(v)) != len(v):
            raise ValueError(f"Budgets must be strictly ascending and unique; got {v}.")
        return v

    @model_validator(mode="after")
    def _check_consistency(self) -> "FeaturesConfig":
        if self.reference_budget not in self.budgets:
            raise ValueError(
                f"reference_budget ({self.reference_budget}) must be one of the configured budgets "
                f"{self.budgets}."
            )
        for set_name, members in self.feature_sets.items():
            unknown = set(members) - set(self.groups)
            if unknown:
                raise ValueError(f"feature_sets.{set_name} references undefined groups: {sorted(unknown)}")
            if len(set(members)) != len(members):
                raise ValueError(f"feature_sets.{set_name} contains duplicate groups.")

        # RQ4 control. Reporting results with and without process-recipe
        # descriptors is required, so a feature set free of them must exist.
        recipe_groups = {name for name, g in self.groups.items() if g.is_process_recipe}
        if recipe_groups:
            if not any(recipe_groups.isdisjoint(m) for m in self.feature_sets.values()):
                raise ValueError(
                    f"No feature set excludes the process-recipe groups {sorted(recipe_groups)}. "
                    "RQ4 requires results reported both with and without them."
                )

        # The baseline cycle for DeltaQ(V) must be reachable at the smallest
        # budget, otherwise the curve features are undefined there and the
        # smallest budget silently yields an all-NaN column.
        if self.curve.min_baseline_cycle > min(self.budgets):
            raise ValueError(
                f"curve.min_baseline_cycle ({self.curve.min_baseline_cycle}) exceeds the smallest "
                f"budget ({min(self.budgets)}); DeltaQ(V) features would be undefined there."
            )
        return self

    def enabled_groups(self) -> list[str]:
        return [name for name, group in self.groups.items() if group.enabled]


# ---------------------------------------------------------------------------
# models.yaml
# ---------------------------------------------------------------------------


class ModelConstraintsSpec(_StrictModel):
    allow_neural_networks: bool

    @field_validator("allow_neural_networks")
    @classmethod
    def _no_neural_networks(cls, v: bool) -> bool:
        # A methodological commitment of the project, made structural so it
        # cannot be relaxed by editing a config file.
        if v:
            raise ValueError(
                "allow_neural_networks must be false. This project is committed to classical "
                "supervised ML: feature engineering rather than estimator capacity is the "
                "bottleneck, and exact TreeSHAP attributions are what make a scrap decision "
                "auditable by a process engineer."
            )
        return v


class TargetSpec(_StrictModel):
    name: str
    transform: Literal["log10", "log", "none"]


class CVFoldSpec(_StrictModel):
    n_splits: int = Field(ge=2)
    n_repeats: int = Field(ge=1)
    purpose: str | None = None


class CVSpec(_StrictModel):
    group_key: str
    outer: CVFoldSpec
    inner: CVFoldSpec


class SplitSchemeSpec(_StrictModel):
    enabled: bool
    role: str
    train_batches: list[str] | None = None
    test_batches: list[str] | None = None
    n_folds: int | None = Field(default=None, ge=2)

    @model_validator(mode="after")
    def _no_batch_overlap(self) -> "SplitSchemeSpec":
        # HARD CONSTRAINT: no cell in two splits. At batch granularity this is
        # the strongest check available before the data is parsed.
        if self.train_batches and self.test_batches:
            overlap = set(self.train_batches) & set(self.test_batches)
            if overlap:
                raise ValueError(f"Batches appear in both train and test: {sorted(overlap)}")
        return self


class SeedsSpec(_StrictModel):
    global_: int = Field(alias="global")
    numpy: int
    outer_repeats: list[int]
    bootstrap: int

    model_config = ConfigDict(extra="forbid", populate_by_name=True)

    @field_validator("outer_repeats")
    @classmethod
    def _unique_seeds(cls, v: list[int]) -> list[int]:
        if len(set(v)) != len(v):
            raise ValueError(f"Outer-repeat seeds must be unique, else repeats are not independent; got {v}.")
        return v


class BootstrapSpec(_StrictModel):
    n_resamples: int = Field(ge=1000)
    confidence_level: float = Field(gt=0, lt=1)
    method: str


class MetricsSpec(_StrictModel):
    statistical: list[str] = Field(min_length=1)
    manufacturing: list[str] = Field(min_length=1)
    primary: str

    @model_validator(mode="after")
    def _primary_is_declared(self) -> "MetricsSpec":
        if self.primary not in (*self.statistical, *self.manufacturing):
            raise ValueError(f"metrics.primary '{self.primary}' is not among the declared metrics.")
        return self


class SearchDimSpec(_StrictModel):
    type: Literal["float", "int", "categorical"]
    low: float | None = None
    high: float | None = None
    log: bool = False
    choices: list[Any] | None = None

    @model_validator(mode="after")
    def _check_bounds(self) -> "SearchDimSpec":
        if self.type == "categorical":
            if not self.choices:
                raise ValueError("A categorical search dimension requires 'choices'.")
            return self
        if self.low is None or self.high is None:
            raise ValueError(f"A {self.type} search dimension requires 'low' and 'high'.")
        if self.low >= self.high:
            raise ValueError(f"Search bounds must satisfy low < high; got low={self.low}, high={self.high}.")
        if self.log and self.low <= 0:
            raise ValueError(f"Log-scaled search requires a positive lower bound; got {self.low}.")
        return self


class ModelSpec(_StrictModel):
    enabled: bool
    estimator: str
    role: str | None = None
    params: dict[str, Any] = Field(default_factory=dict)
    search_space: dict[str, SearchDimSpec] = Field(default_factory=dict)

    @field_validator("estimator")
    @classmethod
    def _reject_neural_estimators(cls, v: str) -> str:
        # Defence in depth behind the allow_neural_networks flag: catches an
        # estimator that would violate the constraint even if the flag were
        # somehow bypassed.
        forbidden = ("MLP", "torch", "tensorflow", "keras", "neural_network")
        if any(token.lower() in v.lower() for token in forbidden):
            raise ValueError(f"Estimator '{v}' appears to be a neural network, which this project forbids.")
        return v


class HyperparameterSearchSpec(_StrictModel):
    engine: str
    n_trials: int = Field(gt=0)
    timeout_seconds: int = Field(gt=0)
    scope: str
    sampler: str
    seed: int

    @field_validator("scope")
    @classmethod
    def _inner_fold_only(cls, v: str) -> str:
        # PROJECT RULE: never tune hyperparameters on the test set.
        if v != "inner_fold_only":
            raise ValueError(
                f"hyperparameter_search.scope must be 'inner_fold_only'; got '{v}'. Tuning against "
                "outer-fold performance is tuning on the test set."
            )
        return v


class QuantileSpec(_StrictModel):
    enabled: bool
    estimator: str
    objective: str
    quantiles: list[float] = Field(min_length=2)

    @field_validator("quantiles")
    @classmethod
    def _valid_quantiles(cls, v: list[float]) -> list[float]:
        if any(not 0 < q < 1 for q in v):
            raise ValueError(f"Quantiles must lie strictly in (0, 1); got {v}.")
        if v != sorted(v) or len(set(v)) != len(v):
            raise ValueError(f"Quantiles must be strictly ascending and unique; got {v}.")
        return v

    @model_validator(mode="after")
    def _symmetric_for_cqr(self) -> "QuantileSpec":
        # Conformalized quantile regression needs symmetric lower/upper pairs
        # to form intervals at a nominal level.
        for q in self.quantiles:
            if not any(abs((1 - q) - other) < 1e-9 for other in self.quantiles):
                raise ValueError(
                    f"Quantile {q} has no complementary quantile {1 - q:.2f}; CQR intervals require "
                    "symmetric pairs."
                )
        return self


class SecondaryClassifierSpec(_StrictModel):
    enabled: bool
    role: str
    estimators: list[str] = Field(min_length=1)
    class_weight: str | None = None
    min_class_count_warning: int = Field(ge=1)


class GradingRouteSpec(_StrictModel):
    """How A/B/C grades are produced (reviewed decision after Phase 3)."""

    primary: Literal["ordinal_from_regression", "direct_classification"]
    secondary: SecondaryClassifierSpec

    @model_validator(mode="after")
    def _secondary_is_not_the_headline(self) -> "GradingRouteSpec":
        # The secondary route exists for interpretability only. Requiring it to
        # declare that in its own role string keeps the distinction visible at
        # the point of use rather than only in a design document.
        if self.secondary.enabled and "secondary" not in self.secondary.role.lower():
            raise ValueError(
                "grading_route.secondary.role must state that it is a secondary view; it must "
                "never be reported as the headline grading result."
            )
        return self


class EnsembleSpec(_StrictModel):
    enabled: bool
    meta_learner: str
    use_out_of_fold_only: bool

    @field_validator("use_out_of_fold_only")
    @classmethod
    def _oof_only(cls, v: bool) -> bool:
        if not v:
            raise ValueError(
                "ensemble.use_out_of_fold_only must be true; stacking on in-fold predictions leaks "
                "base-model training performance into the meta-learner."
            )
        return v


class RegistrySpec(_StrictModel):
    directory: str
    record_provenance: bool


class ModelsConfig(_StrictModel):
    """Model spaces, CV protocol and seeds (``configs/models.yaml``)."""

    constraints: ModelConstraintsSpec
    target: TargetSpec
    cv: CVSpec
    splits: dict[str, SplitSchemeSpec]
    seeds: SeedsSpec
    bootstrap: BootstrapSpec
    metrics: MetricsSpec
    hyperparameter_search: HyperparameterSearchSpec
    models: dict[str, ModelSpec]
    quantile: QuantileSpec
    grading_route: GradingRouteSpec
    ensemble: EnsembleSpec
    registry: RegistrySpec

    @model_validator(mode="after")
    def _check_protocol(self) -> "ModelsConfig":
        if len(self.seeds.outer_repeats) != self.cv.outer.n_repeats:
            raise ValueError(
                f"seeds.outer_repeats has {len(self.seeds.outer_repeats)} entries but "
                f"cv.outer.n_repeats is {self.cv.outer.n_repeats}; every repeat must have a fixed seed "
                "for the run to be reproducible."
            )
        if not any(m.role == "floor" for m in self.models.values()):
            raise ValueError(
                "At least one dummy model must be declared with role 'floor'. Without a floor, a "
                "reported error is not interpretable."
            )
        if not any(m.role == "severson_reproduction_and_baseline" for m in self.models.values()):
            raise ValueError(
                "No model is designated for the Severson reproduction (Gate 2), which is the "
                "credibility anchor of the project."
            )
        return self

    def enabled_models(self) -> list[str]:
        return [name for name, spec in self.models.items() if spec.enabled]


# ---------------------------------------------------------------------------
# Bundle and loaders
# ---------------------------------------------------------------------------

CONFIG_SCHEMAS: dict[str, type[BaseModel]] = {
    "data": DataConfig,
    "features": FeaturesConfig,
    "models": ModelsConfig,
    "grading": GradingConfig,
    "costs": CostsConfig,
}


class ConfigBundle(_StrictModel):
    """All five validated configs, plus cross-file consistency checks.

    WHY cross-file validation: the individually valid files can still disagree
    with each other -- a cost matrix whose grades do not match the grading
    scheme is the obvious case, and it would produce a decision layer that
    indexes the wrong tier without ever raising.
    """

    data: DataConfig
    features: FeaturesConfig
    models: ModelsConfig
    grading: GradingConfig
    costs: CostsConfig

    @model_validator(mode="after")
    def _check_cross_config(self) -> "ConfigBundle":
        if self.costs.row_grades() != self.grading.grade_order:
            raise ValueError(
                f"Cost-matrix grades {self.costs.row_grades()} must match grading.grade_order "
                f"{self.grading.grade_order} in both membership and order; otherwise the decision "
                "layer indexes the wrong tier."
            )

        configured_batches = set(self.data.batches)
        for name, scheme in self.models.splits.items():
            for field in ("train_batches", "test_batches"):
                declared = getattr(scheme, field) or []
                unknown = set(declared) - configured_batches
                if unknown:
                    raise ValueError(
                        f"splits.{name}.{field} references batches not defined in data.yaml: {sorted(unknown)}"
                    )

        ood_batches = {n for n, b in self.data.batches.items() if b.role == "out_of_distribution"}
        holdout = self.models.splits.get("batch_holdout")
        if holdout and holdout.enabled and set(holdout.test_batches or []) != ood_batches:
            raise ValueError(
                f"batch_holdout tests on {holdout.test_batches} but data.yaml marks {sorted(ood_batches)} "
                "as out_of_distribution. The OOD split must match the declared OOD batches."
            )
        return self

    def unverified_assumptions(self) -> list[str]:
        """Outstanding ``verified: false`` entries that Phase 2 must close out."""
        return self.data.unverified_assumptions()


def load_config(name: str, config_dir: Path | None = None) -> BaseModel:
    """Load and validate a single config by stem (e.g. ``"costs"``).

    Raises:
        KeyError: If ``name`` is not one of the five known configs.
        FileNotFoundError: If the YAML file is absent.
        pydantic.ValidationError: If the contents violate the schema.
    """
    if name not in CONFIG_SCHEMAS:
        raise KeyError(f"Unknown config '{name}'. Known configs: {sorted(CONFIG_SCHEMAS)}.")
    path = (config_dir or CONFIG_DIR) / f"{name}.yaml"
    if not path.exists():
        raise FileNotFoundError(f"Config file not found: {path}")
    with path.open("r", encoding="utf-8") as handle:
        raw = yaml.safe_load(handle)
    if raw is None:
        raise ValueError(f"Config file is empty: {path}")
    return CONFIG_SCHEMAS[name].model_validate(raw)


def load_configs(config_dir: Path | None = None) -> ConfigBundle:
    """Load and validate all five configs together, including cross-file checks."""
    return ConfigBundle.model_validate({name: load_config(name, config_dir) for name in CONFIG_SCHEMAS})


def run_config_validation(config_dir: Path | None = None) -> ConfigBundle:
    """Validate every config and log a summary. Entry point for Phase 1 acceptance."""
    from src.utils.logger import get_logger, log_section

    logger = get_logger("config")
    log_section(logger, "Configuration validation")

    bundle = load_configs(config_dir)

    logger.info("All %d configs loaded and validated against their schemas.", len(CONFIG_SCHEMAS))
    logger.info(
        "Grades: %s | warranty target: %d cycles",
        ", ".join(f"{g}>={bundle.grading.grades[g].min_cycles}" for g in bundle.grading.grade_order),
        bundle.grading.warranty_target_cycles,
    )
    logger.info(
        "Cost matrix: mean escape/overkill ratio = %.1f (sweep: %s)",
        bundle.costs.escape_overkill_ratio(),
        bundle.costs.sensitivity.escape_overkill_ratios,
    )
    logger.info("Budgets: %s (reference %d)", bundle.features.budgets, bundle.features.reference_budget)
    logger.info("Enabled feature groups: %s", ", ".join(bundle.features.enabled_groups()))
    logger.info("Enabled models: %s", ", ".join(bundle.models.enabled_models()))
    logger.info("Exclusions declared: %d cells, each with a reason.", len(bundle.data.exclusions))

    pending = bundle.unverified_assumptions()
    logger.warning("Unverified assumptions outstanding for Phase 2: %d", len(pending))
    for item in pending:
        logger.warning("  [ ] %s", item)

    return bundle


if __name__ == "__main__":
    run_config_validation()