"""Stable prompt and artifact contract shared by the Studio and product SFT."""

from __future__ import annotations

import hashlib
import json
import re
from pathlib import Path
from typing import Any, Mapping, Sequence


CHAT_PROMPT_FORMAT = "smolgpt-fables-chat-v3"
SMOLLM3_CHAT_PROMPT_FORMAT = "smolgpt-fables-smollm3-chat-v5"
RAW_PROMPT_FORMAT = "smolgpt-fables-raw-v1"
PRODUCT_CONTEXT_LENGTH = 2048
SMOLLM3_PRODUCT_CONTEXT_LENGTH = 4096
CHAT_SYSTEM_PROMPT = (
    "You are SmolGPT-Fables. Output only the requested finished Markdown story "
    "continuation. Begin exactly with `### Scene 01:`. Emit exactly the requested "
    "number of consecutive, zero-padded `### Scene NN:` sections. Those scene "
    "headings are the only headings allowed in the output: never emit an H1, H2, "
    "any other H3, an H4 or deeper heading, or any other section before, between, "
    "or after them. Follow the canvas exactly and copy every required name and "
    "detail verbatim. Stop immediately after the requested final scene. Never "
    "repeat or quote the story title, metadata, canvas, `## Story`, or any instruction."
)
CHAT_PROMPT_TRANSFORM_VERSION = "scene-output-contract-v1"
CHAT_STORY_BOUNDARY = "## Story\n\n"
CHAT_OUTPUT_CONTRACT_TEMPLATE = (
    "Output contract (follow exactly): use only these H3 heading prefixes, in this "
    "order: {headings}. Write exactly {scene_count} scenes; emit no other heading "
    "or section; stop immediately after completing `### Scene {final_scene:02d}:`.\n\n"
)
SMOLLM3_PROMPT_TRANSFORM_VERSION = "natural-fable-scene-contract-v3"
SMOLLM3_STORY_BOUNDARY = "## Story\n\n"
SMOLLM3_SCENE_WORD_RANGE = (45, 115)
SMOLLM3_SYSTEM_PROMPT = (
    "You are SmolGPT-Fables. Write a vivid, complete fable from the user's canvas. "
    "Output only the finished story continuation. Begin with `### Scene 01:` and "
    "emit exactly the requested consecutive, zero-padded scene sections. A scene "
    "heading may include a short title after the colon. Use no other Markdown "
    "heading. Copy every required name, setting, and unusual detail verbatim. Make "
    "each character's described role, personality, and desire affect what they do. "
    "Write concrete action and dialogue instead of summarizing instructions. Keep "
    "each scene concise, make every scene change the situation, and resolve the "
    "ending target inside the final scene. Stop immediately after the final sentence; "
    "never add notes, analysis, a moral label, an ending section, or quoted canvas "
    "text. /no_think"
)
_SCENE_COUNT_PATTERN = re.compile(r"(?m)^- Scene Count: ([0-9]+)$")
_TARGET_SCENES_PATTERN = re.compile(r"(?m)^- Target scenes: ([0-9]+)$")
_MIN_SCENE_COUNT = 1
_MAX_SCENE_COUNT = 6
SUPPORTED_PROMPT_FORMATS = frozenset(
    {CHAT_PROMPT_FORMAT, SMOLLM3_CHAT_PROMPT_FORMAT, RAW_PROMPT_FORMAT}
)
CHAT_PROMPT_FORMATS = frozenset(
    {CHAT_PROMPT_FORMAT, SMOLLM3_CHAT_PROMPT_FORMAT}
)
COMMON_ARTIFACT_FILES = ("config.json", "tokenizer.json")
CUSTOM_CODE_FILES = ("configuration_smolgpt.py", "modeling_smolgpt.py")


def _render_output_contract(scene_count: int) -> str:
    headings = ", ".join(
        f"`### Scene {index:02d}:`" for index in range(1, scene_count + 1)
    )
    return CHAT_OUTPUT_CONTRACT_TEMPLATE.format(
        headings=headings,
        scene_count=scene_count,
        final_scene=scene_count,
    )


def prompt_contract_sha256() -> str:
    payload = {
        "format": CHAT_PROMPT_FORMAT,
        "system": CHAT_SYSTEM_PROMPT,
        "messages": ["system", "user", "assistant"],
        "assistant_only_loss": True,
        "prompt_transform": {
            "version": CHAT_PROMPT_TRANSFORM_VERSION,
            "scene_count_pattern": _SCENE_COUNT_PATTERN.pattern,
            "target_scenes_pattern": _TARGET_SCENES_PATTERN.pattern,
            "scene_count_range": [_MIN_SCENE_COUNT, _MAX_SCENE_COUNT],
            "story_boundary": CHAT_STORY_BOUNDARY,
            "rendered_output_contracts": {
                str(scene_count): _render_output_contract(scene_count)
                for scene_count in range(_MIN_SCENE_COUNT, _MAX_SCENE_COUNT + 1)
            },
        },
    }
    encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode()
    return hashlib.sha256(encoded).hexdigest()


def smollm3_render_output_contract(scene_count: int) -> str:
    """Render the exact SmolLM3 v5 output contract used for SFT."""

    if not _MIN_SCENE_COUNT <= scene_count <= _MAX_SCENE_COUNT:
        raise ValueError("v4 scene count must be between 1 and 6")
    headings = ", ".join(
        f"`### Scene {index:02d}:`"
        for index in range(1, scene_count + 1)
    )
    minimum, maximum = SMOLLM3_SCENE_WORD_RANGE
    return (
        "Output contract (follow exactly):\n"
        f"- Use these scene prefixes in order: {headings}.\n"
        f"- Write exactly {scene_count} scenes and {minimum}-{maximum} words per scene.\n"
        "- Use no heading except those scene headings.\n"
        "- Copy every item on `Must include` verbatim into the story.\n"
        "- Show the character-role details through decisions, action, or dialogue.\n"
        f"- Resolve the ending target in Scene {scene_count:02d} and stop.\n\n"
    )


def _canonical_smollm3_scene_count(prompt: str) -> int:
    scene_counts = _SCENE_COUNT_PATTERN.findall(prompt)
    target_counts = _TARGET_SCENES_PATTERN.findall(prompt)
    if len(scene_counts) != 1 or len(target_counts) != 1:
        raise ValueError("v4 prompt needs one Scene Count and one Target scenes line")
    scene_count = int(scene_counts[0])
    target_count = int(target_counts[0])
    if not _MIN_SCENE_COUNT <= scene_count <= _MAX_SCENE_COUNT:
        raise ValueError("v4 scene count must be between 1 and 6")
    if scene_count != target_count:
        raise ValueError("v4 Scene Count and Target scenes must match")
    return scene_count


def smollm3_transform_prompt(prompt: str) -> str:
    """Apply the exact SmolLM3 v5 prompt transform used for SFT."""

    scene_count = _canonical_smollm3_scene_count(prompt)
    if not prompt.endswith(SMOLLM3_STORY_BOUNDARY):
        raise ValueError("v4 prompt must end at the canonical Story boundary")
    return (
        prompt[: -len(SMOLLM3_STORY_BOUNDARY)]
        + smollm3_render_output_contract(scene_count)
        + SMOLLM3_STORY_BOUNDARY
    )


def smollm3_chat_messages(
    prompt: str,
    completion: str | None = None,
) -> list[dict[str, str]]:
    messages = [
        {"role": "system", "content": SMOLLM3_SYSTEM_PROMPT},
        {"role": "user", "content": smollm3_transform_prompt(prompt)},
    ]
    if completion is not None:
        messages.append({"role": "assistant", "content": completion})
    return messages


def smollm3_prompt_contract_sha256() -> str:
    """Hash the exact SmolLM3 v5 prompt contract used for SFT."""

    payload = {
        "format": SMOLLM3_CHAT_PROMPT_FORMAT,
        "system": SMOLLM3_SYSTEM_PROMPT,
        "messages": ["system", "user", "assistant"],
        "assistant_only_loss": True,
        "thinking": False,
        "context_length": SMOLLM3_PRODUCT_CONTEXT_LENGTH,
        "transform_version": SMOLLM3_PROMPT_TRANSFORM_VERSION,
        "story_boundary": SMOLLM3_STORY_BOUNDARY,
        "scene_count_pattern": _SCENE_COUNT_PATTERN.pattern,
        "target_scenes_pattern": _TARGET_SCENES_PATTERN.pattern,
        "scene_word_range": list(SMOLLM3_SCENE_WORD_RANGE),
        "rendered_contracts": {
            str(count): smollm3_render_output_contract(count)
            for count in range(_MIN_SCENE_COUNT, _MAX_SCENE_COUNT + 1)
        },
    }
    encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode()
    return hashlib.sha256(encoded).hexdigest()


def raw_prompt_contract_sha256() -> str:
    payload = {
        "format": RAW_PROMPT_FORMAT,
        "messages": ["raw-markdown-prompt"],
        "bos_prefix": True,
    }
    encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode()
    return hashlib.sha256(encoded).hexdigest()


def product_context_length_for_prompt_format(prompt_format: str) -> int | None:
    if prompt_format == CHAT_PROMPT_FORMAT:
        return PRODUCT_CONTEXT_LENGTH
    if prompt_format == SMOLLM3_CHAT_PROMPT_FORMAT:
        return SMOLLM3_PRODUCT_CONTEXT_LENGTH
    if prompt_format == RAW_PROMPT_FORMAT:
        return None
    raise ValueError(f"unsupported prompt format: {prompt_format}")


def _has_model_weights(root: Path) -> bool:
    return (root / "model.safetensors").is_file() or (
        root / "model.safetensors.index.json"
    ).is_file()


def validate_transformers_artifact(root: Path) -> tuple[Mapping[str, Any], str]:
    """Validate either the legacy custom export or a standard Transformers LM."""

    missing = [name for name in COMMON_ARTIFACT_FILES if not (root / name).is_file()]
    if missing:
        raise ValueError("model repository is missing: " + ", ".join(missing))
    if not _has_model_weights(root):
        raise ValueError("model repository is missing Safetensors weights")
    try:
        config = json.loads((root / "config.json").read_text(encoding="utf-8"))
    except (OSError, json.JSONDecodeError) as exc:
        raise ValueError(f"could not read config.json: {exc}") from exc
    if not isinstance(config, Mapping):
        raise ValueError("config.json must contain a JSON object")
    auto_map = config.get("auto_map")
    if isinstance(auto_map, Mapping) and auto_map.get("AutoModelForCausalLM"):
        missing_code = [name for name in CUSTOM_CODE_FILES if not (root / name).is_file()]
        if missing_code:
            raise ValueError("custom model repository is missing: " + ", ".join(missing_code))
        return config, "custom"
    architectures = config.get("architectures")
    if not isinstance(architectures, list) or not all(
        isinstance(value, str) and value for value in architectures
    ):
        raise ValueError("standard model config needs a non-empty architectures list")
    if not isinstance(config.get("model_type"), str) or not config["model_type"]:
        raise ValueError("standard model config needs model_type")
    return config, "standard"


def _expected_prompt_contract_sha256(prompt_format: str) -> str:
    if prompt_format == CHAT_PROMPT_FORMAT:
        return prompt_contract_sha256()
    if prompt_format == SMOLLM3_CHAT_PROMPT_FORMAT:
        return smollm3_prompt_contract_sha256()
    if prompt_format == RAW_PROMPT_FORMAT:
        return raw_prompt_contract_sha256()
    raise ValueError(f"unsupported prompt format: {prompt_format}")


def _manifest_prompt_binding(
    manifest: Mapping[str, Any],
    *,
    artifact_kind: str,
) -> tuple[str, str] | None:
    """Read legacy top-level or SmolLM3 nested bindings without ambiguity."""

    top_format_present = "prompt_format" in manifest
    top_hash_present = "prompt_contract_sha256" in manifest
    nested_present = "prompt_contract" in manifest
    if not top_format_present and not top_hash_present and not nested_present:
        if artifact_kind == "custom":
            return None
        raise ValueError("training manifest has no supported prompt_format")
    if top_format_present != top_hash_present:
        raise ValueError("training manifest prompt binding is incomplete")

    bindings: list[tuple[str, str]] = []
    if top_format_present:
        bindings.append(
            (manifest.get("prompt_format"), manifest.get("prompt_contract_sha256"))
        )
    if nested_present:
        nested = manifest.get("prompt_contract")
        if (
            not isinstance(nested, Mapping)
            or set(nested) != {"format", "sha256", "thinking"}
            or nested.get("thinking") is not False
        ):
            raise ValueError("training manifest nested prompt contract is invalid")
        bindings.append((nested.get("format"), nested.get("sha256")))
    if any(
        not isinstance(value, str)
        or not value
        or not isinstance(digest, str)
        or re.fullmatch(r"[0-9a-f]{64}", digest) is None
        for value, digest in bindings
    ):
        raise ValueError("training manifest prompt binding is invalid")
    if any(binding != bindings[0] for binding in bindings[1:]):
        raise ValueError("training manifest prompt bindings conflict")
    return bindings[0]


def prompt_format_for_artifact(root: Path, artifact_kind: str, tokenizer: Any) -> str:
    manifest_path = root / "training_manifest.json"
    if manifest_path.is_file():
        manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
        if not isinstance(manifest, Mapping):
            raise ValueError("training manifest must contain a JSON object")
        binding = _manifest_prompt_binding(manifest, artifact_kind=artifact_kind)
        if binding is None:
            # Checked-in pre-contract SmolGPT exports are unambiguously the raw
            # Markdown/BOS path. Any partially declared contract still fails.
            return RAW_PROMPT_FORMAT
        value, contract_hash = binding
        if value not in SUPPORTED_PROMPT_FORMATS:
            raise ValueError("training manifest has no supported prompt_format")
        if artifact_kind == "standard" and value not in CHAT_PROMPT_FORMATS:
            raise ValueError("standard product manifest must use the chat prompt format")
        if artifact_kind == "custom" and value == SMOLLM3_CHAT_PROMPT_FORMAT:
            raise ValueError("SmolLM3 chat-v5 requires a standard model artifact")
        expected_sha = _expected_prompt_contract_sha256(value)
        if contract_hash != expected_sha:
            raise ValueError("training manifest prompt contract hash does not match runtime")
        if value in CHAT_PROMPT_FORMATS and not getattr(
            tokenizer, "chat_template", None
        ):
            raise ValueError("chat prompt format requires a tokenizer chat_template")
        return str(value)
    if artifact_kind == "standard" and getattr(tokenizer, "chat_template", None):
        return CHAT_PROMPT_FORMAT
    return RAW_PROMPT_FORMAT


def _canonical_scene_count(prompt: str) -> int:
    scene_counts = _SCENE_COUNT_PATTERN.findall(prompt)
    target_counts = _TARGET_SCENES_PATTERN.findall(prompt)
    if len(scene_counts) != 1:
        raise ValueError(
            "chat-v3 prompt must contain exactly one canonical '- Scene Count: N' line"
        )
    if len(target_counts) != 1:
        raise ValueError(
            "chat-v3 prompt must contain exactly one canonical '- Target scenes: N' line"
        )
    scene_count = int(scene_counts[0])
    target_count = int(target_counts[0])
    if not _MIN_SCENE_COUNT <= scene_count <= _MAX_SCENE_COUNT:
        raise ValueError("chat-v3 Scene Count must be between 1 and 6")
    if not _MIN_SCENE_COUNT <= target_count <= _MAX_SCENE_COUNT:
        raise ValueError("chat-v3 Target scenes must be between 1 and 6")
    if scene_count != target_count:
        raise ValueError("chat-v3 Scene Count and Target scenes must match")
    return scene_count


def _transform_chat_prompt(prompt: str) -> str:
    scene_count = _canonical_scene_count(prompt)
    if not prompt.endswith(CHAT_STORY_BOUNDARY):
        raise ValueError("chat-v3 prompt must end at the canonical '## Story' boundary")
    return (
        prompt[: -len(CHAT_STORY_BOUNDARY)]
        + _render_output_contract(scene_count)
        + CHAT_STORY_BOUNDARY
    )


def chat_messages(prompt: str, completion: str | None = None) -> list[dict[str, str]]:
    messages = [
        {"role": "system", "content": CHAT_SYSTEM_PROMPT},
        {"role": "user", "content": _transform_chat_prompt(prompt)},
    ]
    if completion is not None:
        messages.append({"role": "assistant", "content": completion})
    return messages


def _chat_messages_for_format(
    prompt: str,
    completion: str | None,
    prompt_format: str,
) -> list[dict[str, str]]:
    if prompt_format == CHAT_PROMPT_FORMAT:
        return chat_messages(prompt, completion)
    if prompt_format == SMOLLM3_CHAT_PROMPT_FORMAT:
        return smollm3_chat_messages(prompt, completion)
    raise ValueError(f"unsupported chat prompt format: {prompt_format}")


def _apply_runtime_chat_template(
    tokenizer: Any,
    messages: list[dict[str, str]],
    *,
    prompt_format: str,
    add_generation_prompt: bool,
) -> Any:
    kwargs: dict[str, Any] = {
        "add_generation_prompt": add_generation_prompt,
        "tokenize": True,
    }
    if prompt_format == SMOLLM3_CHAT_PROMPT_FORMAT:
        kwargs["enable_thinking"] = False
    return tokenizer.apply_chat_template(messages, **kwargs)


def _flatten_token_ids(values: Any, *, context: str) -> list[int]:
    """Normalize chat-template outputs across supported Transformers versions."""

    if isinstance(values, Mapping):
        if "input_ids" not in values:
            raise ValueError(f"{context} returned no input_ids")
        values = values["input_ids"]
    if hasattr(values, "tolist"):
        values = values.tolist()
    if not isinstance(values, Sequence) or isinstance(values, (str, bytes, bytearray)):
        raise ValueError(f"{context} returned unsupported token IDs")
    normalized = list(values)
    if normalized and isinstance(normalized[0], Sequence) and not isinstance(
        normalized[0], (str, bytes, bytearray)
    ):
        if len(normalized) != 1:
            raise ValueError(f"{context} returned more than one token sequence")
        normalized = list(normalized[0])
    try:
        return [int(value) for value in normalized]
    except (TypeError, ValueError) as exc:
        raise ValueError(f"{context} returned non-integer token IDs") from exc


def generation_prompt_ids(tokenizer: Any, prompt: str, prompt_format: str) -> list[int]:
    if prompt_format in CHAT_PROMPT_FORMATS:
        values = _apply_runtime_chat_template(
            tokenizer,
            _chat_messages_for_format(prompt, None, prompt_format),
            prompt_format=prompt_format,
            add_generation_prompt=True,
        )
        context = (
            "SmolLM3 v5 generation template"
            if prompt_format == SMOLLM3_CHAT_PROMPT_FORMAT
            else "chat generation template"
        )
        return _flatten_token_ids(values, context=context)
    if prompt_format != RAW_PROMPT_FORMAT:
        raise ValueError(f"unsupported prompt format: {prompt_format}")
    return [
        int(tokenizer.bos_token_id),
        *(
            int(value)
            for value in tokenizer.encode(prompt, add_special_tokens=False)
        ),
    ]


def assistant_training_ids(
    tokenizer: Any,
    prompt: str,
    completion: str,
    *,
    max_length: int,
    prompt_format: str = CHAT_PROMPT_FORMAT,
) -> tuple[list[int], list[int]]:
    """Create one chat sequence with loss masked through the assistant header."""

    if prompt_format not in CHAT_PROMPT_FORMATS:
        raise ValueError("assistant training requires a supported chat prompt format")
    prefix = generation_prompt_ids(tokenizer, prompt, prompt_format)
    full = _flatten_token_ids(
        _apply_runtime_chat_template(
            tokenizer,
            _chat_messages_for_format(prompt, completion, prompt_format),
            prompt_format=prompt_format,
            add_generation_prompt=False,
        ),
        context=(
            "SmolLM3 v5 training template"
            if prompt_format == SMOLLM3_CHAT_PROMPT_FORMAT
            else "chat training template"
        ),
    )
    if full[: len(prefix)] != prefix:
        raise ValueError("chat template assistant prefix is not stable")
    if len(full) > max_length:
        raise ValueError(
            f"chat-formatted example has {len(full)} tokens; maximum is {max_length}"
        )
    if len(full) <= len(prefix):
        raise ValueError("chat-formatted example has no assistant completion tokens")
    labels = [-100] * len(prefix) + full[len(prefix) :]
    return full, labels


def aggregate_sha256(paths: Sequence[Path], root: Path) -> str:
    records = []
    for path in sorted(paths):
        records.append(
            {
                "path": path.relative_to(root).as_posix(),
                "sha256": hashlib.sha256(path.read_bytes()).hexdigest(),
            }
        )
    payload = json.dumps(records, sort_keys=True, separators=(",", ":")).encode()
    return hashlib.sha256(payload).hexdigest()