"""Frozen training config and immutable planned-run manifest builders."""

from __future__ import annotations

import json
import math
from dataclasses import asdict, dataclass
from datetime import datetime
from pathlib import Path
from typing import Any, Literal

from ..atomic_io import atomic_write_json
from ..hashing import canonical_json, canonical_json_hash, is_git_revision, is_sha256
from .evi_po_contract import EVI_PO_ADAPTER_VERSION
from .papo_contract import (
    PAPO_ADAPTER_VERSION,
    PAPO_UPSTREAM_SOURCE_SHA256,
    PAPO_UPSTREAM_SOURCE_URL,
)
from .rewards import arm_trainer_kind, canonical_arm

TrainerKind = Literal["sft", "grpo", "papo", "evi_po"]
RunMode = Literal["sft", "smoke", "main"]
QWEN35_2B_REVISION = "15852e8c16360a2fea060d615a32b45270f8a8fc"
TRAINING_CONTRACT_VERSION = "evi_v2_constrained_answer_256_v1"
# Constrained decoding removes irrelevant formatting/runaway-generation noise
# while preserving every certified answer, including the public abstention
# token.  vLLM completes the regex and emits EOS; the 256-token cap remains a
# hard safety boundary rather than a routinely reached training event.
ANSWER_SCHEMA_REGEX = r"<answer>(<UNANSWERABLE>|[^<>\r\n]{1,128})</answer>"


class RunArtifactError(RuntimeError):
    """Raised when a frozen run identity or write-once artifact drifts."""


@dataclass(frozen=True)
class RuntimeTrainingConfig:
    """Fully resolved values required before a GPU launcher may run."""

    run_id: str
    trainer_kind: TrainerKind
    arm: str
    seed: int
    model_path: str
    model_revision: str
    model_snapshot_sha256: str
    dataset_path: str
    dataset_asset_root: str
    output_dir: str
    initial_checkpoint_path: str
    environment_lock_path: str
    environment_lock_sha256: str
    evaluation_manifest_path: str
    evaluation_manifest_sha256: str
    system_prompt_sha256: str
    precision: str
    attention_implementation: str
    max_prompt_tokens: int
    max_completion_tokens: int
    total_context_tokens: int
    per_device_train_batch_size: int
    gradient_accumulation_steps: int
    world_size: int
    generations_per_prompt: int
    checkpoint_interval: int
    max_optimizer_steps: int
    max_completion_tokens_per_run: int
    learning_rate: float
    lora_rank: int
    lora_alpha: int
    lora_dropout: float
    lora_target_modules: tuple[str, ...]
    comparison_slot_manifest_sha256: str | None = None
    backend_entrypoint: str | None = None
    use_vllm: bool = True
    run_mode: RunMode = "main"
    compatibility_gate_path: str | None = None
    evi_direction_loss_weight: float = 0.0
    evi_evidence_loss_weight: float = 0.0
    evi_direction_margin: float = 0.0
    papo_mask_ratio: float = 0.6
    papo_perception_loss_weight: float = 0.02

    def __post_init__(self) -> None:
        if not isinstance(self.run_id, str) or not self.run_id:
            raise RunArtifactError("run_id must be non-empty")
        if self.trainer_kind not in {"sft", "grpo", "papo", "evi_po"}:
            raise RunArtifactError(f"unsupported trainer kind: {self.trainer_kind}")
        if self.run_mode not in {"sft", "smoke", "main"}:
            raise RunArtifactError(f"unsupported run mode: {self.run_mode}")
        if self.trainer_kind == "sft" and self.run_mode != "sft":
            raise RunArtifactError("SFT trainer requires run_mode=sft")
        if self.trainer_kind != "sft" and self.run_mode not in {"smoke", "main"}:
            raise RunArtifactError("RL trainer requires run_mode=smoke or main")
        if self.trainer_kind != "sft":
            try:
                canonical = canonical_arm(self.arm)
            except ValueError as exc:
                raise RunArtifactError(f"unsupported RL arm: {self.arm}") from exc
            if canonical != self.arm:
                object.__setattr__(self, "arm", canonical)
        if self.trainer_kind != "sft" and self.trainer_kind != arm_trainer_kind(self.arm):
            raise RunArtifactError(
                f"trainer kind {self.trainer_kind!r} does not match arm {self.arm!r}"
            )
        evi_values = (
            self.evi_direction_loss_weight,
            self.evi_evidence_loss_weight,
            self.evi_direction_margin,
        )
        if any(
            isinstance(value, bool)
            or not isinstance(value, int | float)
            or not math.isfinite(float(value))
            or value < 0
            for value in evi_values
        ):
            raise RunArtifactError("EVI-PO weights and margin must be finite and non-negative")
        if self.trainer_kind != "evi_po" and any(value != 0.0 for value in evi_values):
            raise RunArtifactError("non-EVI trainers may not carry EVI-PO objective values")
        if self.trainer_kind != "sft":
            if not self.comparison_slot_manifest_sha256:
                raise RunArtifactError(
                    "RL runs require comparison_slot_manifest_sha256 for schedule fairness"
                )
            if not is_sha256(self.comparison_slot_manifest_sha256):
                raise RunArtifactError("comparison_slot_manifest_sha256 must be a SHA-256")
        elif self.comparison_slot_manifest_sha256 is not None and not is_sha256(
            self.comparison_slot_manifest_sha256
        ):
            raise RunArtifactError("comparison_slot_manifest_sha256 must be a SHA-256")
        if isinstance(self.seed, bool) or not isinstance(self.seed, int) or self.seed < 0:
            raise RunArtifactError("seed must be a non-negative integer")
        if not isinstance(self.model_revision, str) or not is_git_revision(self.model_revision):
            raise RunArtifactError("model_revision must be an exact 40-hex revision")
        if self.model_revision != QWEN35_2B_REVISION:
            raise RunArtifactError("model_revision does not match pinned Qwen3.5-2B")
        for name in (
            "model_snapshot_sha256",
            "environment_lock_sha256",
            "evaluation_manifest_sha256",
            "system_prompt_sha256",
        ):
            if not is_sha256(str(getattr(self, name))):
                raise RunArtifactError(f"{name} must be a SHA-256")
        numeric_positive = (
            "max_prompt_tokens",
            "max_completion_tokens",
            "total_context_tokens",
            "per_device_train_batch_size",
            "gradient_accumulation_steps",
            "world_size",
            "generations_per_prompt",
            "checkpoint_interval",
            "max_completion_tokens_per_run",
            "lora_rank",
            "lora_alpha",
        )
        for name in numeric_positive:
            value = getattr(self, name)
            if isinstance(value, bool) or not isinstance(value, int) or value <= 0:
                raise RunArtifactError(f"{name} must be a positive integer")
        if self.max_prompt_tokens + self.max_completion_tokens != self.total_context_tokens:
            raise RunArtifactError("prompt + completion tokens must equal total context")
        if (
            isinstance(self.learning_rate, bool)
            or not isinstance(self.learning_rate, int | float)
            or not math.isfinite(self.learning_rate)
            or self.learning_rate <= 0
        ):
            raise RunArtifactError("learning_rate must be finite and positive")
        if (
            isinstance(self.lora_dropout, bool)
            or not isinstance(self.lora_dropout, int | float)
            or not math.isfinite(self.lora_dropout)
            or not 0 <= self.lora_dropout < 1
        ):
            raise RunArtifactError("lora_dropout must be finite and in [0,1)")
        if self.lora_target_modules != ("all-linear",):
            raise RunArtifactError("controlled runs require lora_target_modules=all-linear")
        for name in (
            "model_path",
            "dataset_path",
            "dataset_asset_root",
            "output_dir",
            "initial_checkpoint_path",
            "environment_lock_path",
            "evaluation_manifest_path",
        ):
            value = getattr(self, name)
            if (
                not isinstance(value, str)
                or not value.strip()
                or value.strip()
                in {
                    "required",
                    "set_by_common_oom_smoke",
                    "set_to_match_global_batch",
                    "TODO",
                    "TBD",
                }
            ):
                raise RunArtifactError(f"{name} is unresolved")
        if self.trainer_kind == "sft":
            if self.max_optimizer_steps != -1:
                raise RunArtifactError("SFT requires max_optimizer_steps=-1 for one epoch")
            if Path(self.initial_checkpoint_path).resolve() != Path(self.model_path).resolve():
                raise RunArtifactError(
                    "SFT initial_checkpoint_path must equal model_path (base initialization)"
                )
        elif self.run_mode == "smoke" and self.max_optimizer_steps != 20:
            raise RunArtifactError("controlled RL smoke requires exactly 20 optimizer steps")
        elif self.run_mode == "main" and self.max_optimizer_steps != 5750:
            raise RunArtifactError("controlled main RL runs require exactly 5,750 optimizer steps")
        if self.run_mode == "main":
            if (
                not isinstance(self.compatibility_gate_path, str)
                or not self.compatibility_gate_path.strip()
            ):
                raise RunArtifactError("main RL runs require compatibility_gate_path")
        elif self.compatibility_gate_path is not None:
            raise RunArtifactError("SFT and smoke plans may not carry a compatibility gate")
        if self.precision != "bf16":
            raise RunArtifactError("Qwen3.5 controlled runs require bf16 precision")
        if self.attention_implementation != "flash_attention_2":
            raise RunArtifactError("Qwen3.5 controlled runs require flash_attention_2")
        if not isinstance(self.use_vllm, bool):
            raise RunArtifactError("use_vllm must be boolean")
        if self.backend_entrypoint != "explicit_learning.training.backend:run":
            raise RunArtifactError("controlled runs require explicit_learning.training.backend:run")
        if (self.lora_rank, self.lora_alpha, self.lora_dropout) != (64, 128, 0.0):
            raise RunArtifactError("controlled runs require LoRA r=64, alpha=128, dropout=0")
        if self.learning_rate != 1e-6:
            raise RunArtifactError("controlled runs require learning_rate=1e-6")
        if (self.max_prompt_tokens, self.max_completion_tokens, self.total_context_tokens) != (
            4096,
            256,
            4352,
        ):
            raise RunArtifactError("controlled runs require a 4096/256/4352 token envelope")
        expected_generations = 1 if self.trainer_kind == "sft" else 4
        if self.generations_per_prompt != expected_generations:
            raise RunArtifactError(
                f"{self.trainer_kind} requires generations_per_prompt={expected_generations}"
            )
        if (
            self.trainer_kind != "sft"
            and self.global_prompt_batch_size % self.generations_per_prompt != 0
        ):
            raise RunArtifactError(
                "global prompt batch size must be divisible by generations_per_prompt"
            )
        token_cap = 6_000_000 if self.trainer_kind == "sft" else 24_000_000
        if self.max_completion_tokens_per_run > token_cap:
            raise RunArtifactError(
                f"{self.trainer_kind} token budget exceeds protocol cap {token_cap}"
            )

    @property
    def global_prompt_batch_size(self) -> int:
        return self.per_device_train_batch_size * self.gradient_accumulation_steps * self.world_size

    def to_dict(self) -> dict[str, Any]:
        value = asdict(self)
        value["lora_target_modules"] = list(self.lora_target_modules)
        value["global_prompt_batch_size"] = self.global_prompt_batch_size
        value["effective_completion_batch_size"] = self.global_prompt_batch_size
        value["unique_prompt_groups_per_optimizer_step"] = (
            self.global_prompt_batch_size
            if self.trainer_kind == "sft"
            else self.global_prompt_batch_size // self.generations_per_prompt
        )
        value["train_full_weights"] = False
        value["base_model_resource"] = "qwen35_2b"
        value["base_model_repo_id"] = "Qwen/Qwen3.5-2B"
        value["training_contract_version"] = TRAINING_CONTRACT_VERSION
        value["completion_only_sft_loss"] = self.trainer_kind == "sft"
        value["assistant_only_sft_loss"] = False
        value["reject_overlength_samples"] = True
        # No-thinking protocol: the system prompt, SFT gold, structured rollout
        # regex, and reward all share the same concise answer contract.
        value["enable_thinking"] = False
        value["structured_output_regex"] = (
            None if self.trainer_kind == "sft" else ANSWER_SCHEMA_REGEX
        )
        value["optimizer"] = {
            "name": "adamw_torch",
            "learning_rate": self.learning_rate,
            "weight_decay": 0.0,
            "betas": [0.9, 0.999],
            "eps": 1.0e-8,
        }
        value["scheduler"] = {"name": "cosine", "warmup_ratio": 0.03}
        value["gradient_checkpointing"] = True
        value["max_grad_norm"] = 1.0
        value["num_train_epochs"] = 1
        value["max_optimizer_steps"] = self.max_optimizer_steps
        value["temperature"] = None if self.trainer_kind == "sft" else 1.0
        value["top_p"] = None if self.trainer_kind == "sft" else 1.0
        value["loss_type"] = (
            "assistant_only_cross_entropy"
            if self.trainer_kind == "sft"
            else ("grpo" if self.trainer_kind == "papo" else "dr_grpo")
        )
        value["beta"] = (
            None if self.trainer_kind == "sft" else (0.01 if self.trainer_kind == "papo" else 0.0)
        )
        value["freeze_vision_tower"] = False
        value["freeze_multimodal_projector"] = False
        value["lora_exclude_modules"] = ["embeddings", "lm_head"]
        value["papo_config"] = (
            {
                "variant": "PAPO-G",
                "mask_ratio": self.papo_mask_ratio,
                "mask_type": "random",
                "perception_loss_weight": self.papo_perception_loss_weight,
                "der_loss_weight1": 0.0,
                "der_loss_weight2": 0.0,
                "require_gpu_contract_probe": self.run_mode == "smoke",
                "adapter_version": PAPO_ADAPTER_VERSION,
                "upstream_source_url": PAPO_UPSTREAM_SOURCE_URL,
                "upstream_source_sha256": PAPO_UPSTREAM_SOURCE_SHA256,
            }
            if self.trainer_kind == "papo"
            else None
        )
        value["evi_po_config"] = (
            {
                "adapter_version": EVI_PO_ADAPTER_VERSION,
                "lambda_direction": self.evi_direction_loss_weight,
                "lambda_evidence": self.evi_evidence_loss_weight,
                "margin": self.evi_direction_margin,
                "required_relationships": ["FULL", "CONTROL", "MISSING"],
                "optional_relationships": ["SUBSTITUTE"],
                "candidate_support": "group_answers_plus_unanswerable",
                "evidence_supervision_relation": "FULL",
                "evidence_sources": [
                    "executor_dependency_nodes_projected_through_node_map",
                    "source_annotation_explicit_mask",
                    "source_annotation_bboxes",
                ],
                "attention_capture": "last_full_attention_layer_eager_forward_hook",
                "require_gpu_contract_probe": (
                    self.run_mode == "smoke"
                    and (
                        self.evi_direction_loss_weight > 0.0 or self.evi_evidence_loss_weight > 0.0
                    )
                ),
                "zero_weight_reduction": "exact_parent_grpo_path",
            }
            if self.trainer_kind == "evi_po"
            else None
        )
        value["vllm_config"] = (
            {
                "mode": "colocate",
                # Throughput-only knob: this is the fraction of GPU memory vLLM
                # reserves for its KV cache during the rollout (generation) phase.
                # Raising it lets more completions generate concurrently, which
                # speeds up RL rollout — the dominant runtime cost — WITHOUT
                # changing which tokens are sampled (seed + prompt + sampling
                # params are unchanged), so completions, gradients, and the
                # trained model are identical. The experimental batch sizes
                # (per_device_train_batch_size, gradient_accumulation_steps,
                # generations_per_prompt) are untouched. With enable_sleep_mode
                # vLLM frees its KV cache for the batch-1 LoRA training step, so
                # the rollout cap and the training step do not contend: a high
                # cap enlarges only the rollout KV cache. 0.85 on 80GB H100s
                # keeps ~67GB for vLLM weights+KV during rollout while the
                # sleep-mode training step reclaims the KV portion. Lower if a
                # colocate OOM appears (the smoke gate requires 0).
                "gpu_memory_utilization": 0.85,
                "max_model_length": self.total_context_tokens,
                "tensor_parallel_size": 1,
                "enable_sleep_mode": True,
                "structured_outputs_regex": ANSWER_SCHEMA_REGEX,
            }
            if self.use_vllm
            else None
        )
        value["token_budget_kind"] = (
            "assistant_tokens" if self.trainer_kind == "sft" else "sampled_completion_tokens"
        )
        value["sampled_completion_token_budget_role"] = (
            None if self.trainer_kind == "sft" else "hard_safety_ceiling_not_equality_target"
        )
        value["requires_forced_five_step_process_resume"] = self.run_mode == "smoke"
        return value


def build_run_manifest(
    config: RuntimeTrainingConfig,
    *,
    source_config_sha256: str,
    dataset_manifest_sha256: str,
    initial_checkpoint_sha256: str,
    code_commit: str,
    created_at: str,
    launcher_command: tuple[str, ...],
) -> tuple[dict[str, Any], dict[str, Any]]:
    """Return frozen config and a manifest that says ``planned``, never trained."""

    for name, value in (
        ("source_config_sha256", source_config_sha256),
        ("dataset_manifest_sha256", dataset_manifest_sha256),
        ("initial_checkpoint_sha256", initial_checkpoint_sha256),
    ):
        if not isinstance(value, str) or not is_sha256(value):
            raise RunArtifactError(f"{name} must be a SHA-256")
    if not isinstance(code_commit, str) or not is_git_revision(code_commit):
        raise RunArtifactError("code_commit must be an exact 40-hex revision")
    if not isinstance(created_at, str) or not created_at:
        raise RunArtifactError("created_at must be supplied")
    try:
        parsed_created_at = datetime.fromisoformat(created_at.replace("Z", "+00:00"))
    except ValueError as exc:
        raise RunArtifactError("created_at must be ISO-8601") from exc
    if parsed_created_at.tzinfo is None:
        raise RunArtifactError("created_at must include a timezone")
    if not launcher_command or any(not str(part) for part in launcher_command):
        raise RunArtifactError("launcher_command must be explicit and non-empty")
    frozen = {
        "schema_version": 1,
        "source_config_sha256": source_config_sha256,
        "runtime": config.to_dict(),
    }
    frozen_sha = canonical_json_hash(frozen)
    manifest = {
        "schema_version": 1,
        "kind": "training_run_plan",
        "status": "planned",
        "trained": False,
        "run_id": config.run_id,
        "arm": config.arm,
        "seed": config.seed,
        "trainer_kind": config.trainer_kind,
        "run_mode": config.run_mode,
        "code_commit": code_commit,
        "created_at": created_at,
        "frozen_config_sha256": frozen_sha,
        "dataset_manifest_sha256": dataset_manifest_sha256,
        "initial_checkpoint_sha256": initial_checkpoint_sha256,
        "model_snapshot_sha256": config.model_snapshot_sha256,
        "environment_lock_sha256": config.environment_lock_sha256,
        "evaluation_manifest_sha256": config.evaluation_manifest_sha256,
        "system_prompt_sha256": config.system_prompt_sha256,
        "comparison_slot_manifest_sha256": config.comparison_slot_manifest_sha256,
        "launcher_command": list(launcher_command),
    }
    return frozen, manifest


def _write_once(path: Path, value: dict[str, Any]) -> None:
    if path.exists():
        try:
            existing = json.loads(path.read_text(encoding="utf-8"))
        except json.JSONDecodeError as exc:
            raise RunArtifactError(f"existing frozen artifact is invalid JSON: {path}") from exc
        if canonical_json(existing) != canonical_json(value):
            raise RunArtifactError(f"refusing to overwrite drifted frozen artifact: {path}")
        return
    atomic_write_json(path, value)


def write_frozen_run(
    run_dir: str | Path,
    *,
    frozen_config: dict[str, Any],
    run_manifest: dict[str, Any],
) -> tuple[Path, Path]:
    directory = Path(run_dir)
    directory.mkdir(parents=True, exist_ok=True)
    config_path = directory / "frozen-config.json"
    manifest_path = directory / "run-manifest.json"
    _write_once(config_path, frozen_config)
    _write_once(manifest_path, run_manifest)
    return config_path, manifest_path