"""Typed, reviewable Evidence units stored in the workspace repository."""

from __future__ import annotations

import base64
import binascii
import json
import re
from pathlib import Path, PurePosixPath
from typing import Literal

import sqlglot
import yaml
from pydantic import AnyHttpUrl, BaseModel, ConfigDict, Field, field_validator, model_validator
from sqlglot import exp

from tht.evidence.contracts import validate_canonical_uri


class StrictModel(BaseModel):
    """Reject undeclared fields in the repository's canonical format."""

    model_config = ConfigDict(extra="forbid")


EVIDENCE_KINDS = (
    "glossary",
    "domain",
    "enum",
    "example",
    "mapping",
    "normalization",
    "formula",
    "reference",
)
EVIDENCE_PURPOSES = (
    "disambiguation",
    "rewriting",
    "schema_linking",
    "sql_generation",
)
EvidenceKind = Literal[*EVIDENCE_KINDS]
EvidencePurpose = Literal[*EVIDENCE_PURPOSES]
_IDENTIFIER = r"[A-Za-z_][A-Za-z0-9_$]*"
_TABLE_IDENTIFIER = re.compile(rf"^{_IDENTIFIER}\.{_IDENTIFIER}$")
_COLUMN_IDENTIFIER = re.compile(rf"^{_IDENTIFIER}\.{_IDENTIFIER}\.{_IDENTIFIER}$")
MAX_CURATED_FILE_BYTES = 10 * 1024 * 1024


class EvidenceScope(StrictModel):
    concepts: tuple[str, ...] = ()
    tables: tuple[str, ...] = ()
    columns: tuple[str, ...] = ()

    @field_validator("tables")
    @classmethod
    def _validate_tables(cls, value: tuple[str, ...]) -> tuple[str, ...]:
        return _validate_identifiers(value, _TABLE_IDENTIFIER, "tables")

    @field_validator("columns")
    @classmethod
    def _validate_columns(cls, value: tuple[str, ...]) -> tuple[str, ...]:
        return _validate_identifiers(value, _COLUMN_IDENTIFIER, "columns")


class EvidenceProvenance(StrictModel):
    model_config = ConfigDict(extra="forbid", frozen=True)

    source_file: str
    source_sha256: str
    supporting_excerpts: tuple[str, ...]

    @field_validator("source_file")
    @classmethod
    def _validate_source_file(cls, value: str) -> str:
        return validate_source_file(value)

    @field_validator("source_sha256")
    @classmethod
    def _validate_sha256(cls, value: str) -> str:
        if not re.fullmatch(r"sha256:[0-9a-f]{64}", value):
            raise ValueError("source_sha256 must be a sha256 digest")
        return value

    @field_validator("supporting_excerpts")
    @classmethod
    def _validate_excerpts(cls, value: tuple[str, ...]) -> tuple[str, ...]:
        if not 1 <= len(value) <= 5:
            raise ValueError("supporting_excerpts must contain one to five items")
        if any(not excerpt.strip() or len(excerpt) > 1000 for excerpt in value):
            raise ValueError("supporting excerpts must be nonempty and at most 1000 characters")
        return value


class ReviewItem(StrictModel):
    code: str
    message: str
    field: str | None = None


class FormulaPayload(StrictModel):
    concept: str
    columns: tuple[str, ...]
    sql: str

    @field_validator("columns")
    @classmethod
    def _validate_columns(cls, value: tuple[str, ...]) -> tuple[str, ...]:
        return _validate_identifiers(value, _COLUMN_IDENTIFIER, "columns")

    @field_validator("sql")
    @classmethod
    def _validate_expression(cls, value: str) -> str:
        try:
            statements = [statement for statement in sqlglot.parse(value, read="postgres") if statement]
        except sqlglot.errors.ParseError as error:
            raise ValueError("formula.sql must be valid PostgreSQL") from error
        if len(statements) != 1:
            raise ValueError("formula.sql must contain exactly one expression")
        expression = statements[0]
        if expression.find(exp.Select) is not None or expression.find(exp.With) is not None:
            raise ValueError("formula.sql must not contain a query")
        if any(
            expression.find(statement_type) is not None
            for statement_type in (
                exp.Insert,
                exp.Update,
                exp.Delete,
                exp.Create,
                exp.Drop,
                exp.Alter,
                exp.Merge,
                exp.TruncateTable,
                exp.Grant,
                exp.Revoke,
                exp.Command,
                exp.Values,
                exp.Set,
                exp.Table,
            )
        ):
            raise ValueError("formula.sql must not contain DDL or DML")
        return value


class ReferencePayload(StrictModel):
    url: AnyHttpUrl
    label: str
    description: str

    @field_validator("url")
    @classmethod
    def _reject_credentials(cls, value: AnyHttpUrl) -> AnyHttpUrl:
        validate_canonical_uri(str(value))
        return value


class GlossaryPayload(StrictModel):
    definition: str
    synonyms: tuple[str, ...] = ()
    variants: tuple[str, ...] = ()


class DomainPayload(StrictModel):
    rule: str


class EnumPayload(StrictModel):
    column: str
    values: dict[str, str]

    @field_validator("column")
    @classmethod
    def _validate_column(cls, value: str) -> str:
        _validate_identifiers((value,), _COLUMN_IDENTIFIER, "column")
        return value


class ExamplePayload(StrictModel):
    question: str
    interpretation: str


class MappingPayload(StrictModel):
    concept: str
    tables: tuple[str, ...]
    columns: tuple[str, ...]

    @field_validator("tables")
    @classmethod
    def _validate_tables(cls, value: tuple[str, ...]) -> tuple[str, ...]:
        return _validate_identifiers(value, _TABLE_IDENTIFIER, "tables")

    @field_validator("columns")
    @classmethod
    def _validate_columns(cls, value: tuple[str, ...]) -> tuple[str, ...]:
        return _validate_identifiers(value, _COLUMN_IDENTIFIER, "columns")


class NormalizationPayload(StrictModel):
    input: str
    output: str
    rule: str


EvidencePayload = (
    GlossaryPayload
    | DomainPayload
    | EnumPayload
    | ExamplePayload
    | MappingPayload
    | NormalizationPayload
    | FormulaPayload
    | ReferencePayload
)


_PAYLOAD_TYPE_BY_KIND = {
    "glossary": GlossaryPayload,
    "domain": DomainPayload,
    "enum": EnumPayload,
    "example": ExamplePayload,
    "mapping": MappingPayload,
    "normalization": NormalizationPayload,
    "formula": FormulaPayload,
    "reference": ReferencePayload,
}
_EVIDENCE_ID = re.compile(r"^evidence:[a-z0-9]+(?:-[a-z0-9]+)*$")


class CuratedEvidence(StrictModel):
    schema_version: Literal[1, 2, 3]
    id: str
    title: str
    kind: EvidenceKind
    purposes: tuple[EvidencePurpose, ...]
    applies_to: EvidenceScope = Field(default_factory=EvidenceScope)
    language: str
    provenance: EvidenceProvenance
    review_items: tuple[ReviewItem, ...] = ()
    payload: EvidencePayload

    @model_validator(mode="after")
    def _validate_kind_payload(self) -> CuratedEvidence:
        if not is_evidence_id(self.id):
            raise ValueError("id must use the evidence:<slug> form")
        expected = _PAYLOAD_TYPE_BY_KIND.get(self.kind)
        if expected is not None and not isinstance(self.payload, expected):
            raise ValueError(f"{self.kind} requires its typed payload")
        return self


_V2_LABELS = {
    "en": {
        "column": "Column",
        "columns": "Columns",
        "concept": "Concept",
        "definition": "Definition",
        "description": "Description",
        "empty": "No items",
        "input": "Input",
        "interpretation": "Interpretation",
        "label": "Label",
        "output": "Output",
        "question": "Question",
        "review_items": "Review items",
        "field": "Field",
        "rule": "Rule",
        "sql": "SQL",
        "supporting_excerpts": "Supporting excerpts",
        "synonyms": "Synonyms",
        "tables": "Tables",
        "url": "URL",
        "value": "Value",
        "values": "Values",
        "meaning": "Meaning",
        "variants": "Variants",
        "applies_to": "Applies to",
        "concepts": "Concepts",
        "technical_details": "Technical details and provenance",
        "purposes": "Purposes",
    },
    "it": {
        "column": "Colonna",
        "columns": "Colonne",
        "concept": "Concetto",
        "definition": "Definizione",
        "description": "Descrizione",
        "empty": "Nessun elemento",
        "input": "Input",
        "interpretation": "Interpretazione",
        "label": "Etichetta",
        "output": "Output",
        "question": "Domanda",
        "review_items": "Elementi da rivedere",
        "field": "Campo",
        "rule": "Regola",
        "sql": "SQL",
        "supporting_excerpts": "Estratti di supporto",
        "synonyms": "Sinonimi",
        "tables": "Tabelle",
        "url": "URL",
        "value": "Valore",
        "values": "Valori",
        "meaning": "Significato",
        "variants": "Varianti",
        "applies_to": "Ambito di applicazione",
        "concepts": "Concetti",
        "technical_details": "Dettagli tecnici e provenienza",
        "purposes": "Scopi",
    },
}
_V2_FIELD = re.compile(
    r"<!-- tht:field:([a-z_]+) -->\n(.*?)\n<!-- /tht:field:\1 -->",
    re.DOTALL,
)
_V2_EXCERPT_SEPARATOR = "<!-- tht:excerpt-separator -->"
_V2_EMPTY_LIST = "<!-- tht:empty-list -->"
_V2_REVIEW_SEPARATOR = "<!-- tht:review-separator -->"
_V2_REVIEW_FIELD = "<!-- tht:review-field -->"
_V3_METADATA = re.compile(r"\A<!-- tht:metadata:([A-Za-z0-9+/=]+) -->\n")
_V3_KIND_LABELS = {
    "en": {
        "glossary": "Glossary",
        "domain": "Domain",
        "enum": "Enumeration",
        "example": "Example",
        "mapping": "Mapping",
        "normalization": "Normalization",
        "formula": "Formula",
        "reference": "Reference",
    },
    "it": {
        "glossary": "Glossario",
        "domain": "Dominio",
        "enum": "Enumerazione",
        "example": "Esempio",
        "mapping": "Mappatura",
        "normalization": "Normalizzazione",
        "formula": "Formula",
        "reference": "Riferimento",
    },
}
_V3_PURPOSE_LABELS = {
    "en": {
        "disambiguation": "Disambiguation",
        "rewriting": "Rewriting",
        "schema_linking": "Schema linking",
        "sql_generation": "SQL generation",
    },
    "it": {
        "disambiguation": "Disambiguazione",
        "rewriting": "Riscrittura",
        "schema_linking": "Collegamento allo schema",
        "sql_generation": "Generazione SQL",
    },
}


def _v2_labels(language: str) -> dict[str, str]:
    return _V2_LABELS["it" if language.lower().startswith("it") else "en"]


def _render_v2_field(name: str, label: str, content: str) -> str:
    closing_marker = f"<!-- /tht:field:{name} -->"
    if "<!-- tht:field:" in content or "<!-- /tht:field:" in content:
        raise ValueError(f"curated evidence {name} contains a reserved marker")
    return (
        f"<!-- tht:field:{name} -->\n"
        f"## {label}\n\n"
        f"{content}\n"
        f"{closing_marker}"
    )


def _render_v2_excerpt(value: str) -> str:
    if _V2_EXCERPT_SEPARATOR in value:
        raise ValueError("supporting excerpt contains a reserved marker")
    quoted = "\n".join(">" if not line else f"> {line}" for line in value.split("\n"))
    return quoted


def _render_v2_list(
    values: tuple[str, ...], *, code: bool = False, empty_label: str = "No items",
) -> str:
    if not values:
        return f"{_V2_EMPTY_LIST}\n_{empty_label}._"
    if any("\n" in value for value in values):
        raise ValueError("curated evidence list values must be single-line")
    if code and any("`" in value for value in values):
        raise ValueError("curated evidence code values must not contain backticks")
    return "\n".join(f"- `{value}`" if code else f"- {value}" for value in values)


def _parse_v2_list(
    value: str, *, code: bool = False, empty_label: str = "No items",
) -> tuple[str, ...]:
    if value == f"{_V2_EMPTY_LIST}\n_{empty_label}._":
        return ()
    parsed: list[str] = []
    for line in value.split("\n"):
        if not line.startswith("- "):
            raise ValueError("curated evidence list is malformed")
        item = line[2:]
        if code:
            if len(item) < 2 or not item.startswith("`") or not item.endswith("`"):
                raise ValueError("curated evidence code list is malformed")
            item = item[1:-1]
        parsed.append(item)
    return tuple(parsed)


def _escape_v2_table_value(value: str) -> str:
    return value.replace("\\", "\\\\").replace("|", "\\|").replace("\n", "\\n")


def _unescape_v2_table_value(value: str) -> str:
    output: list[str] = []
    index = 0
    while index < len(value):
        if value[index] != "\\":
            output.append(value[index])
            index += 1
            continue
        if index + 1 >= len(value):
            raise ValueError("curated evidence table escape is malformed")
        escaped = value[index + 1]
        if escaped not in {"\\", "|", "n"}:
            raise ValueError("curated evidence table escape is malformed")
        output.append("\n" if escaped == "n" else escaped)
        index += 2
    return "".join(output)


def _render_v2_values(values: dict[str, str], labels: dict[str, str]) -> str:
    if any("`" in value for value in values):
        raise ValueError("curated evidence enum values must not contain backticks")
    rows = [
        f"| {labels['value']} | {labels['meaning']} |",
        "| --- | --- |",
    ]
    if not values:
        rows.append(f"| _{labels['empty']}._ | |")
        return "\n".join(rows)
    rows.extend(
        f"| `{_escape_v2_table_value(value)}` | {_escape_v2_table_value(meaning)} |"
        for value, meaning in sorted(values.items())
    )
    return "\n".join(rows)


def _parse_v2_values(value: str, labels: dict[str, str]) -> dict[str, str]:
    lines = value.split("\n")
    if (
        len(lines) < 3
        or lines[0] != f"| {labels['value']} | {labels['meaning']} |"
        or lines[1] != "| --- | --- |"
    ):
        raise ValueError("curated evidence values table is malformed")
    if lines[2:] == [f"| _{labels['empty']}._ | |"]:
        return {}
    parsed: dict[str, str] = {}
    for line in lines[2:]:
        if not line.startswith("| ") or not line.endswith(" |"):
            raise ValueError("curated evidence values table is malformed")
        cells = re.split(r"(?<!\\)\s\|\s", line[2:-2], maxsplit=1)
        if len(cells) != 2 or not cells[0].startswith("`") or not cells[0].endswith("`"):
            raise ValueError("curated evidence values table is malformed")
        key = _unescape_v2_table_value(cells[0][1:-1])
        if key in parsed:
            raise ValueError("curated evidence enum value appears more than once")
        parsed[key] = _unescape_v2_table_value(cells[1])
    return parsed


def _render_v3_values(values: dict[str, str], labels: dict[str, str]) -> str:
    if not values:
        return f"{_V2_EMPTY_LIST}\n_{labels['empty']}._"
    if any("`" in value for value in values):
        raise ValueError("curated evidence enum values must not contain backticks")
    rendered: list[str] = []
    for value, meaning in sorted(values.items()):
        lines = meaning.split("\n")
        rendered.append(f"- `{value}`: {lines[0]}")
        rendered.extend(f"  {line}" for line in lines[1:])
    return "\n".join(rendered)


def _parse_v3_values(value: str, labels: dict[str, str]) -> dict[str, str]:
    if value == f"{_V2_EMPTY_LIST}\n_{labels['empty']}._":
        return {}
    parsed: dict[str, list[str]] = {}
    current: str | None = None
    for line in value.split("\n"):
        match = re.fullmatch(r"- `([^`]+)`: ?(.*)", line)
        if match is not None:
            current = match.group(1)
            if current in parsed:
                raise ValueError("curated evidence enum value appears more than once")
            parsed[current] = [match.group(2)]
            continue
        if current is None or not line.startswith("  "):
            raise ValueError("curated evidence values list is malformed")
        parsed[current].append(line[2:])
    if not parsed:
        raise ValueError("curated evidence values list is malformed")
    return {key: "\n".join(lines) for key, lines in parsed.items()}


def _render_v2_payload(value: CuratedEvidence, labels: dict[str, str]) -> list[str]:
    payload = value.payload
    if value.kind == "glossary":
        fields = [_render_v2_field("definition", labels["definition"], payload.definition)]
        if payload.synonyms:
            fields.append(_render_v2_field(
                "synonyms", labels["synonyms"], _render_v2_list(payload.synonyms),
            ))
        if payload.variants:
            fields.append(_render_v2_field(
                "variants", labels["variants"], _render_v2_list(payload.variants),
            ))
        return fields
    if value.kind == "domain":
        return [_render_v2_field("rule", labels["rule"], payload.rule)]
    if value.kind == "enum":
        return [
            _render_v2_field("column", labels["column"], f"`{payload.column}`"),
            _render_v2_field("values", labels["values"], _render_v2_values(payload.values, labels)),
        ]
    if value.kind == "example":
        return [
            _render_v2_field("question", labels["question"], payload.question),
            _render_v2_field("interpretation", labels["interpretation"], payload.interpretation),
        ]
    if value.kind == "mapping":
        return [
            _render_v2_field("concept", labels["concept"], payload.concept),
            _render_v2_field("tables", labels["tables"], _render_v2_list(
                payload.tables, code=True, empty_label=labels["empty"],
            )),
            _render_v2_field("columns", labels["columns"], _render_v2_list(
                payload.columns, code=True, empty_label=labels["empty"],
            )),
        ]
    if value.kind == "normalization":
        return [
            _render_v2_field("input", labels["input"], payload.input),
            _render_v2_field("output", labels["output"], payload.output),
            _render_v2_field("rule", labels["rule"], payload.rule),
        ]
    if value.kind == "formula":
        if "```" in payload.sql:
            raise ValueError("curated evidence SQL contains a reserved Markdown fence")
        return [
            _render_v2_field("concept", labels["concept"], payload.concept),
            _render_v2_field("columns", labels["columns"], _render_v2_list(
                payload.columns, code=True, empty_label=labels["empty"],
            )),
            _render_v2_field("sql", labels["sql"], f"```sql\n{payload.sql}\n```"),
        ]
    if value.kind == "reference":
        return [
            _render_v2_field("label", labels["label"], payload.label),
            _render_v2_field("url", labels["url"], f"<{payload.url}>"),
            _render_v2_field("description", labels["description"], payload.description),
        ]
    raise ValueError(f"unsupported curated evidence kind {value.kind}")


def _render_v3_payload(value: CuratedEvidence, labels: dict[str, str]) -> list[str]:
    if value.kind != "enum":
        return _render_v2_payload(value, labels)
    payload = value.payload
    return [
        _render_v2_field("column", labels["column"], f"`{payload.column}`"),
        _render_v2_field("values", labels["values"], _render_v3_values(
            payload.values, labels,
        )),
    ]


def _render_v2_review_items(value: CuratedEvidence, labels: dict[str, str]) -> str:
    rendered: list[str] = []
    for item in value.review_items:
        if "`" in item.code or (item.field is not None and "`" in item.field):
            raise ValueError("curated evidence review identifiers must not contain backticks")
        if _V2_REVIEW_SEPARATOR in item.message or _V2_REVIEW_FIELD in item.message:
            raise ValueError("curated evidence review message contains a reserved marker")
        block = f"### `{item.code}`\n\n{item.message}"
        if item.field is not None:
            block += f"\n\n{_V2_REVIEW_FIELD}\n**{labels['field']}:** `{item.field}`"
        rendered.append(block)
    return f"\n{_V2_REVIEW_SEPARATOR}\n".join(rendered)


def _render_v2_body(value: CuratedEvidence) -> str:
    if "\n" in value.title:
        raise ValueError("curated evidence title must be single-line in v2")
    labels = _v2_labels(value.language)
    fields = [
        *_render_v2_payload(value, labels),
        _render_v2_field(
            "supporting_excerpts",
            labels["supporting_excerpts"],
            f"\n{_V2_EXCERPT_SEPARATOR}\n".join(
                _render_v2_excerpt(excerpt)
                for excerpt in value.provenance.supporting_excerpts
            ),
        ),
    ]
    if value.review_items:
        fields.append(_render_v2_field(
            "review_items",
            labels["review_items"],
            _render_v2_review_items(value, labels),
        ))
    return f"# {value.title}\n\n" + "\n\n".join(fields) + "\n"


def _render_v3_block(name: str, content: str) -> str:
    if "<!-- tht:field:" in content or "<!-- /tht:field:" in content:
        raise ValueError(f"curated evidence {name} contains a reserved marker")
    return (
        f"<!-- tht:field:{name} -->\n"
        f"{content}\n"
        f"<!-- /tht:field:{name} -->"
    )


def _v3_locale(value: CuratedEvidence) -> str:
    return "it" if value.language.lower().startswith("it") else "en"


def _render_v3_overview(value: CuratedEvidence, labels: dict[str, str]) -> str:
    locale = _v3_locale(value)
    language = "Italiano" if locale == "it" else "English"
    kind = _V3_KIND_LABELS[locale][value.kind]
    purposes = " · ".join(_V3_PURPOSE_LABELS[locale][purpose] for purpose in value.purposes)
    if not purposes:
        purposes = labels["empty"]
    return _render_v3_block(
        "overview",
        f"> **{kind}** · {language}\n>\n> **{labels['purposes']}:** {purposes}",
    )


def _render_v3_scope(value: CuratedEvidence, labels: dict[str, str]) -> str:
    sections: list[str] = []
    for label, values, code in (
        (labels["concepts"], value.applies_to.concepts, False),
        (labels["tables"], value.applies_to.tables, True),
        (labels["columns"], value.applies_to.columns, True),
    ):
        if values:
            sections.append(
                f"### {label}\n\n"
                f"{_render_v2_list(values, code=code, empty_label=labels['empty'])}"
            )
    content = "\n\n".join(sections) if sections else f"_{labels['empty']}._"
    return _render_v3_block(
        "applies_to",
        f"## {labels['applies_to']}\n\n{content}",
    )


def _render_v3_provenance(value: CuratedEvidence, labels: dict[str, str]) -> str:
    locale = _v3_locale(value)
    technical_labels = {
        "en": {
            "schema": "Schema version",
            "kind": "Kind",
            "language": "Language",
            "source": "Source file",
        },
        "it": {
            "schema": "Versione schema",
            "kind": "Tipo",
            "language": "Lingua",
            "source": "File sorgente",
        },
    }[locale]
    content = (
        "<details>\n"
        f"<summary>{labels['technical_details']}</summary>\n\n"
        f"- **ID:** `{value.id}`\n"
        f"- **{technical_labels['schema']}:** `{value.schema_version}`\n"
        f"- **{technical_labels['kind']}:** `{value.kind}`\n"
        f"- **{technical_labels['language']}:** `{value.language}`\n"
        f"- **{technical_labels['source']}:** `{value.provenance.source_file}`\n"
        f"- **SHA-256:** `{value.provenance.source_sha256}`\n\n"
        "</details>"
    )
    return _render_v3_block("provenance", content)


def _render_v3_metadata(value: CuratedEvidence) -> str:
    data = value.model_dump(mode="json", exclude={"payload", "review_items"})
    data["provenance"].pop("supporting_excerpts")
    encoded = base64.b64encode(json.dumps(
        data,
        ensure_ascii=False,
        separators=(",", ":"),
        sort_keys=True,
    ).encode("utf-8")).decode("ascii")
    return f"<!-- tht:metadata:{encoded} -->"


def _render_v3_body(value: CuratedEvidence) -> str:
    if "\n" in value.title:
        raise ValueError("curated evidence title must be single-line in v3")
    labels = _v2_labels(value.language)
    fields = [
        _render_v3_overview(value, labels),
        _render_v3_scope(value, labels),
        *_render_v3_payload(value, labels),
        _render_v2_field(
            "supporting_excerpts",
            labels["supporting_excerpts"],
            f"\n{_V2_EXCERPT_SEPARATOR}\n".join(
                _render_v2_excerpt(excerpt)
                for excerpt in value.provenance.supporting_excerpts
            ),
        ),
    ]
    if value.review_items:
        fields.append(_render_v2_field(
            "review_items",
            labels["review_items"],
            _render_v2_review_items(value, labels),
        ))
    fields.append(_render_v3_provenance(value, labels))
    return f"# {value.title}\n\n" + "\n\n".join(fields) + "\n"


def _parse_v2_field_content(name: str, block: str) -> str:
    try:
        heading, content = block.split("\n\n", 1)
    except ValueError as error:
        raise ValueError(f"curated evidence field {name} is malformed") from error
    if not heading.startswith("## ") or not content:
        raise ValueError(f"curated evidence field {name} is malformed")
    return content


def _parse_v2_excerpts(block: str) -> tuple[str, ...]:
    excerpts: list[str] = []
    for raw_excerpt in block.split(f"\n{_V2_EXCERPT_SEPARATOR}\n"):
        lines = raw_excerpt.split("\n")
        if any(line != ">" and not line.startswith("> ") for line in lines):
            raise ValueError("curated evidence supporting excerpt is malformed")
        excerpts.append("\n".join(line[2:] if line.startswith("> ") else "" for line in lines))
    if not excerpts:
        raise ValueError("curated evidence supporting excerpts are malformed")
    return tuple(excerpts)


def _parse_inline_code(value: str, name: str) -> str:
    if len(value) < 2 or not value.startswith("`") or not value.endswith("`"):
        raise ValueError(f"curated evidence field {name} must be inline code")
    return value[1:-1]


def _parse_v2_payload(
    kind: str, fields: dict[str, str], labels: dict[str, str],
) -> tuple[dict, set[str]]:
    if kind == "glossary":
        expected = {"definition"}
        payload: dict = {"definition": fields.get("definition")}
        for name in ("synonyms", "variants"):
            if name in fields:
                expected.add(name)
                payload[name] = _parse_v2_list(fields[name], empty_label=labels["empty"])
            else:
                payload[name] = ()
        return payload, expected
    if kind == "domain":
        return {"rule": fields.get("rule")}, {"rule"}
    if kind == "enum":
        return {
            "column": _parse_inline_code(fields.get("column", ""), "column"),
            "values": _parse_v2_values(fields.get("values", ""), labels),
        }, {"column", "values"}
    if kind == "example":
        return {
            "question": fields.get("question"),
            "interpretation": fields.get("interpretation"),
        }, {"question", "interpretation"}
    if kind == "mapping":
        return {
            "concept": fields.get("concept"),
            "tables": _parse_v2_list(
                fields.get("tables", ""), code=True, empty_label=labels["empty"],
            ),
            "columns": _parse_v2_list(
                fields.get("columns", ""), code=True, empty_label=labels["empty"],
            ),
        }, {"concept", "tables", "columns"}
    if kind == "normalization":
        return {
            "input": fields.get("input"),
            "output": fields.get("output"),
            "rule": fields.get("rule"),
        }, {"input", "output", "rule"}
    if kind == "formula":
        sql = fields.get("sql", "")
        if not sql.startswith("```sql\n") or not sql.endswith("\n```"):
            raise ValueError("curated evidence SQL block is malformed")
        return {
            "concept": fields.get("concept"),
            "columns": _parse_v2_list(
                fields.get("columns", ""), code=True, empty_label=labels["empty"],
            ),
            "sql": sql.removeprefix("```sql\n").removesuffix("\n```"),
        }, {"concept", "columns", "sql"}
    if kind == "reference":
        url = fields.get("url", "")
        if not url.startswith("<") or not url.endswith(">"):
            raise ValueError("curated evidence reference URL is malformed")
        return {
            "label": fields.get("label"),
            "url": url[1:-1],
            "description": fields.get("description"),
        }, {"label", "url", "description"}
    raise ValueError("curated evidence body kind is unsupported")


def _parse_v3_payload(
    kind: str, fields: dict[str, str], labels: dict[str, str],
) -> tuple[dict, set[str]]:
    if kind != "enum":
        return _parse_v2_payload(kind, fields, labels)
    return {
        "column": _parse_inline_code(fields.get("column", ""), "column"),
        "values": _parse_v3_values(fields.get("values", ""), labels),
    }, {"column", "values"}


def _parse_v2_review_items(value: str) -> tuple[ReviewItem, ...]:
    items: list[ReviewItem] = []
    for raw_item in value.split(f"\n{_V2_REVIEW_SEPARATOR}\n"):
        try:
            heading, detail = raw_item.split("\n\n", 1)
        except ValueError as error:
            raise ValueError("curated evidence review item is malformed") from error
        if not heading.startswith("### `") or not heading.endswith("`"):
            raise ValueError("curated evidence review item code is malformed")
        code = heading.removeprefix("### `").removesuffix("`")
        field = None
        marker = f"\n\n{_V2_REVIEW_FIELD}\n"
        if marker in detail:
            message, rendered_field = detail.split(marker, 1)
            match = re.fullmatch(r"\*\*[^*]+:\*\* `([^`]+)`", rendered_field)
            if match is None:
                raise ValueError("curated evidence review item field is malformed")
            field = match.group(1)
        else:
            message = detail
        if not code or not message:
            raise ValueError("curated evidence review item is malformed")
        items.append(ReviewItem(code=code, message=message, field=field))
    return tuple(items)


def _parse_v2_body(data: dict, body: str) -> dict:
    kind = data.get("kind")
    body_owned = {"payload", "review_items"}
    if isinstance(kind, str):
        body_owned.add(kind)
    if body_owned.intersection(data):
        raise ValueError("curated evidence v2 frontmatter contains body-owned fields")
    title = data.get("title")
    if not isinstance(title, str) or not body.startswith(f"# {title}\n"):
        raise ValueError("curated evidence body title must match its metadata")
    fields: dict[str, str] = {}
    for match in _V2_FIELD.finditer(body):
        name = match.group(1)
        if name in fields:
            raise ValueError(f"curated evidence field {name} appears more than once")
        fields[name] = _parse_v2_field_content(name, match.group(2))
    skeleton = _V2_FIELD.sub("", body).strip()
    if skeleton != f"# {title}":
        raise ValueError("curated evidence body contains unstructured content")
    labels = _v2_labels(str(data.get("language", "")))
    payload, payload_fields = _parse_v2_payload(kind, fields, labels)
    common_fields = {"supporting_excerpts"}
    if "review_items" in fields:
        common_fields.add("review_items")
    if set(fields) != payload_fields | common_fields:
        raise ValueError("curated evidence body fields do not match its kind")
    provenance = data.get("provenance")
    if not isinstance(provenance, dict) or "supporting_excerpts" in provenance:
        raise ValueError("curated evidence v2 provenance is malformed")
    provenance["supporting_excerpts"] = _parse_v2_excerpts(fields["supporting_excerpts"])
    data["review_items"] = (
        _parse_v2_review_items(fields["review_items"])
        if "review_items" in fields
        else []
    )
    data["payload"] = payload
    return data


def _parse_v3_body(data: dict, body: str) -> dict:
    kind = data.get("kind")
    body_owned = {"payload", "review_items"}
    if isinstance(kind, str):
        body_owned.add(kind)
    if body_owned.intersection(data):
        raise ValueError("curated evidence v3 metadata contains body-owned fields")
    title = data.get("title")
    if not isinstance(title, str) or not body.startswith(f"# {title}\n"):
        raise ValueError("curated evidence body title must match its metadata")
    fields: dict[str, str] = {}
    for match in _V2_FIELD.finditer(body):
        name = match.group(1)
        if name in fields:
            raise ValueError(f"curated evidence field {name} appears more than once")
        raw_content = match.group(2)
        fields[name] = (
            raw_content
            if name in {"overview", "applies_to", "provenance"}
            else _parse_v2_field_content(name, raw_content)
        )
    skeleton = _V2_FIELD.sub("", body).strip()
    if skeleton != f"# {title}":
        raise ValueError("curated evidence body contains unstructured content")
    labels = _v2_labels(str(data.get("language", "")))
    payload, payload_fields = _parse_v3_payload(kind, fields, labels)
    common_fields = {"overview", "applies_to", "supporting_excerpts", "provenance"}
    if "review_items" in fields:
        common_fields.add("review_items")
    if set(fields) != payload_fields | common_fields:
        raise ValueError("curated evidence body fields do not match its kind")
    provenance = data.get("provenance")
    if not isinstance(provenance, dict) or "supporting_excerpts" in provenance:
        raise ValueError("curated evidence v3 provenance is malformed")
    provenance["supporting_excerpts"] = _parse_v2_excerpts(fields["supporting_excerpts"])
    data["review_items"] = (
        _parse_v2_review_items(fields["review_items"])
        if "review_items" in fields
        else []
    )
    data["payload"] = payload
    return data


def _parse_v3_document(text: str) -> dict:
    match = _V3_METADATA.match(text)
    if match is None:
        raise ValueError("curated evidence v3 metadata is malformed")
    try:
        decoded = base64.b64decode(match.group(1), validate=True).decode("utf-8")
        raw = json.loads(decoded)
        data = dict(raw)
    except (binascii.Error, UnicodeDecodeError, json.JSONDecodeError, TypeError, ValueError) as error:
        raise ValueError("curated evidence v3 metadata is malformed") from error
    if data.get("schema_version") != 3:
        raise ValueError("curated evidence v3 metadata has the wrong schema version")
    return _parse_v3_body(data, text[match.end():])


def parse_curated_markdown(text: str, *, path: Path | None = None) -> CuratedEvidence:
    """Parse one canonical Curated Evidence Markdown document."""
    if text.startswith("<!-- tht:metadata:"):
        data = _parse_v3_document(text)
    else:
        if not text.startswith("---\n"):
            raise ValueError("curated evidence requires canonical metadata")
        try:
            _, frontmatter, body = text.split("---\n", 2)
        except ValueError as error:
            raise ValueError("curated evidence frontmatter is malformed") from error
        raw = yaml.safe_load(frontmatter)
        try:
            data = dict(raw)
        except (TypeError, ValueError) as error:
            raise ValueError("curated evidence frontmatter must be a mapping") from error
        if data.get("schema_version") == 2:
            data = _parse_v2_body(data, body)
        else:
            if body.strip():
                raise ValueError("curated evidence must not contain an ignored body")
            kind = data.get("kind")
            if "payload" not in data and kind in _PAYLOAD_TYPE_BY_KIND:
                data["payload"] = data.pop(kind, None)
    evidence = CuratedEvidence.model_validate(data)
    if evidence.schema_version == 3 and dump_curated_markdown(evidence) != text:
        raise ValueError("curated evidence v3 presentation is not canonical")
    if path is not None:
        _validate_kind_directory(path, evidence.kind)
    return evidence


def dump_curated_markdown(value: CuratedEvidence) -> str:
    """Render one canonical Curated Evidence Markdown document."""
    if value.schema_version == 3:
        return f"{_render_v3_metadata(value)}\n{_render_v3_body(value)}"
    if value.schema_version == 2:
        data = value.model_dump(mode="json", exclude={"payload", "review_items"})
        data["provenance"].pop("supporting_excerpts")
        frontmatter = yaml.safe_dump(data, allow_unicode=True, sort_keys=False)
        return f"---\n{frontmatter}---\n{_render_v2_body(value)}"
    data = value.model_dump(mode="json", exclude={"payload"})
    data[value.kind] = value.payload.model_dump(mode="json")
    frontmatter = yaml.safe_dump(data, allow_unicode=True, sort_keys=False)
    return f"---\n{frontmatter}---\n"


def load_curated_tree(root: Path) -> list[CuratedEvidence]:
    """Load canonical Evidence units in stable path order from a curated root."""
    if not root.is_dir():
        return []
    documents: list[CuratedEvidence] = []
    for path in sorted(root.rglob("*.md")):
        if path.name.upper().startswith("README"):
            continue
        if path.stat().st_size > MAX_CURATED_FILE_BYTES:
            raise ValueError("curated evidence exceeds the size limit")
        try:
            text = path.read_text(encoding="utf-8")
        except UnicodeDecodeError as error:
            raise ValueError("curated evidence must be UTF-8") from error
        documents.append(parse_curated_markdown(text, path=path))
    return documents


def _validate_kind_directory(path: Path, kind: EvidenceKind) -> None:
    parts = path.parts
    try:
        curated_index = parts.index("curated")
    except ValueError:
        return
    if len(parts) <= curated_index + 1 or parts[curated_index + 1] != kind:
        raise ValueError("curated evidence kind must match its directory")


def _validate_identifiers(
    values: tuple[str, ...], pattern: re.Pattern[str], field: str,
) -> tuple[str, ...]:
    if any(pattern.fullmatch(value) is None for value in values):
        raise ValueError(f"{field} must use canonical schema identifiers")
    return values


def validate_source_file(value: str) -> str:
    """Validate a repository-relative, credential-free Source Evidence path."""
    path = PurePosixPath(value)
    if (
        path.is_absolute()
        or ".." in path.parts
        or not path.parts
        or path.parts[0] != "source"
        or not value.endswith((".md", ".txt", ".sql.md"))
    ):
        raise ValueError("source_file must be a supported path below source/")
    return value


def is_evidence_id(value: str) -> bool:
    """Whether a value uses the stable public Evidence identifier format."""
    return _EVIDENCE_ID.fullmatch(value) is not None