"""In-memory access to the prompt-sharded Causal-Forcing predictor dataset."""

from __future__ import annotations

import time
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Iterable

import torch
from safetensors import safe_open

from wan.modules.causal_model import causal_rope_apply


TOKENS_PER_FRAME = 30 * 52
FRAMES_PER_CHUNK = 3
TOKENS_PER_CHUNK = TOKENS_PER_FRAME * FRAMES_PER_CHUNK


@dataclass
class PromptCommon:
    hidden: list[list[torch.Tensor]]
    noisy: dict[tuple[int, int], torch.Tensor]
    flow: dict[tuple[int, int], torch.Tensor]
    timestep: dict[tuple[int, int], torch.Tensor]


@dataclass
class LayerPromptCache:
    history_k: torch.Tensor
    history_v: torch.Tensor
    cross_k: torch.Tensor
    cross_v: torch.Tensor


class OfflinePredictorStore:
    """Keep common trajectories in RAM; load one block's KV cache at a time."""

    def __init__(
        self,
        root: str | Path,
        prompt_ids: Iterable[int],
        num_chunks: int = 7,
        max_history_chunks: int = 7,
        layer_cache_device: torch.device | str | None = None,
    ) -> None:
        self.root = Path(root).resolve()
        self.prompt_ids = sorted(set(int(value) for value in prompt_ids))
        self.num_chunks = int(num_chunks)
        self.max_history_chunks = int(max_history_chunks)
        self.layer_cache_device = (
            torch.device(layer_cache_device)
            if layer_cache_device is not None
            else torch.device("cpu")
        )
        if self.num_chunks < 2:
            raise ValueError("num_chunks must be at least 2")
        if self.max_history_chunks < 1:
            raise ValueError("max_history_chunks must be positive")
        self.common: dict[int, PromptCommon] = {}
        self.layer_cache: dict[int, LayerPromptCache] = {}
        self.layer_caches: dict[int, dict[int, LayerPromptCache]] = {}
        self.layer_id: int | None = None
        self._load_common()

    def _load_common(self) -> None:
        started = time.perf_counter()
        for offset, prompt_id in enumerate(self.prompt_ids, start=1):
            path = (
                self.root
                / f"prompt_{prompt_id:04d}"
                / "trajectory.safetensors"
            )
            context_path = (
                self.root
                / f"prompt_{prompt_id:04d}"
                / "chunk0_context"
                / "trajectory.safetensors"
            )
            hidden: list[list[torch.Tensor]] = []
            noisy: dict[tuple[int, int], torch.Tensor] = {}
            flow: dict[tuple[int, int], torch.Tensor] = {}
            timestep: dict[tuple[int, int], torch.Tensor] = {}
            with safe_open(
                context_path, framework="pt", device="cpu"
            ) as context_handle, safe_open(
                path, framework="pt", device="cpu"
            ) as handle:
                for chunk in range(self.num_chunks):
                    chunk_hidden = []
                    for step in range(4):
                        prefix = f"chunk_{chunk:02d}_step_{step:02d}"
                        source = context_handle if chunk == 0 else handle
                        chunk_hidden.append(
                            source.get_tensor(
                                f"{prefix}_final_hidden"
                            ).squeeze(0)
                        )
                        if chunk >= 1 and step >= 1:
                            noisy[(chunk, step)] = handle.get_tensor(
                                f"{prefix}_noisy_latent"
                            ).squeeze(0)
                            flow[(chunk, step)] = handle.get_tensor(
                                f"{prefix}_flow"
                            ).squeeze(0)
                            timestep[(chunk, step)] = handle.get_tensor(
                                f"{prefix}_timestep"
                            ).squeeze(0)
                    hidden.append(chunk_hidden)
            self.common[prompt_id] = PromptCommon(
                hidden=hidden,
                noisy=noisy,
                flow=flow,
                timestep=timestep,
            )
            if offset % 10 == 0 or offset == len(self.prompt_ids):
                elapsed = time.perf_counter() - started
                print(
                    f"[data] common {offset}/{len(self.prompt_ids)} "
                    f"({elapsed:.1f}s)",
                    flush=True,
                )

    @torch.inference_mode()
    def load_layer_cache(
        self,
        layer_id: int,
        teacher_model: torch.nn.Module,
        device: torch.device,
    ) -> None:
        """Project clean prefeatures once with frozen Teacher K/V weights."""
        self.layer_caches = {}
        self.layer_cache = {}
        self.layer_id = int(layer_id)
        teacher_block = teacher_model.blocks[layer_id]
        heads = teacher_block.num_heads
        head_dim = teacher_block.dim // heads
        if teacher_model.freqs.device != device:
            teacher_model.freqs = teacher_model.freqs.to(device)

        started = time.perf_counter()
        history_chunks = self.num_chunks - 1
        grid_sizes = torch.tensor(
            [[history_chunks * FRAMES_PER_CHUNK, 30, 52]], dtype=torch.long
        )
        for offset, prompt_id in enumerate(self.prompt_ids, start=1):
            prompt_dir = self.root / f"prompt_{prompt_id:04d}"
            prefeature_path = (
                prompt_dir
                / "clean_prefeatures"
                / f"block_{layer_id:02d}.safetensors"
            )
            context_prefeature_path = (
                prompt_dir
                / "chunk0_context"
                / "clean_prefeatures"
                / f"block_{layer_id:02d}.safetensors"
            )
            with safe_open(
                prefeature_path, framework="pt", device="cpu"
            ) as handle, safe_open(
                context_prefeature_path, framework="pt", device="cpu"
            ) as context_handle:
                prefeature = torch.cat(
                    [
                        (
                            context_handle.get_tensor("chunk_00")
                            if chunk == 0
                            else handle.get_tensor(f"chunk_{chunk:02d}")
                        )
                        for chunk in range(history_chunks)
                    ],
                    dim=1,
                )
            prefeature = prefeature.to(
                device=device,
                dtype=torch.bfloat16,
                non_blocking=False,
            )
            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
                key = teacher_block.self_attn.norm_k(
                    teacher_block.self_attn.k(prefeature)
                ).view(1, -1, heads, head_dim)
                value = teacher_block.self_attn.v(prefeature).view(
                    1, -1, heads, head_dim
                )
                key = causal_rope_apply(
                    key,
                    grid_sizes,
                    teacher_model.freqs,
                    start_frame=0,
                )
            key = (
                key.reshape(
                    1, history_chunks, TOKENS_PER_CHUNK, heads, head_dim
                )
                .squeeze(0)
                .to(device=self.layer_cache_device, dtype=torch.bfloat16)
                .contiguous()
            )
            value = (
                value.reshape(
                    1, history_chunks, TOKENS_PER_CHUNK, heads, head_dim
                )
                .squeeze(0)
                .to(device=self.layer_cache_device, dtype=torch.bfloat16)
                .contiguous()
            )

            cross_path = prompt_dir / "cross_attention.safetensors"
            with safe_open(
                cross_path, framework="pt", device="cpu"
            ) as handle:
                cross_k = handle.get_tensor(
                    f"block_{layer_id:02d}_k"
                ).squeeze(0).to(self.layer_cache_device)
                cross_v = handle.get_tensor(
                    f"block_{layer_id:02d}_v"
                ).squeeze(0).to(self.layer_cache_device)
            self.layer_cache[prompt_id] = LayerPromptCache(
                history_k=key,
                history_v=value,
                cross_k=cross_k,
                cross_v=cross_v,
            )
            del prefeature, key, value
            if offset % 10 == 0 or offset == len(self.prompt_ids):
                elapsed = time.perf_counter() - started
                print(
                    f"[data] block {layer_id:02d} cache "
                    f"{offset}/{len(self.prompt_ids)} ({elapsed:.1f}s)",
                    flush=True,
                )
        torch.cuda.empty_cache()

    @torch.inference_mode()
    def load_layer_caches(
        self,
        layer_ids: Iterable[int],
        teacher_model: torch.nn.Module,
        device: torch.device,
    ) -> None:
        """Load Teacher-layer caches, retaining overlap with the previous group."""
        requested = list(dict.fromkeys(int(value) for value in layer_ids))
        if not requested:
            raise ValueError("At least one layer cache is required")
        loaded: dict[int, dict[int, LayerPromptCache]] = {
            layer_id: self.layer_caches[layer_id]
            for layer_id in requested
            if layer_id in self.layer_caches
        }
        reused = sorted(loaded)
        # Drop layers that are no longer requested before projecting a new one.
        # The dictionaries in ``loaded`` keep only the overlapping layers alive.
        self.layer_caches = {}
        self.layer_cache = {}
        self.layer_id = None
        if reused:
            print(f"[data] reusing layer caches {reused}", flush=True)
        for layer_id in requested:
            if layer_id in loaded:
                continue
            self.load_layer_cache(layer_id, teacher_model, device)
            loaded[layer_id] = self.layer_cache
        self.layer_caches = loaded

    def batch_layers(
        self,
        prompt_ids: list[int],
        chunk: int,
        target_step: int,
        layer_ids: Iterable[int],
    ) -> dict[str, Any]:
        """Build one sample batch with independent K/V inputs for each block."""
        requested = [int(value) for value in layer_ids]
        if not requested:
            raise ValueError("At least one layer ID is required")
        missing = sorted(set(requested) - set(self.layer_caches))
        if missing:
            raise RuntimeError(f"Layer caches not loaded: {missing}")

        output = self._base_batch(prompt_ids, chunk, target_step)
        output["layer_ids"] = requested

        for position, layer_id in enumerate(requested):
            cache = [self.layer_caches[layer_id][prompt_id] for prompt_id in prompt_ids]
            output[f"history_k_{position}"] = torch.stack(
                [
                    item.history_k[:chunk].reshape(
                        chunk * TOKENS_PER_CHUNK,
                        item.history_k.shape[-2],
                        item.history_k.shape[-1],
                    )
                    for item in cache
                ]
            )
            output[f"history_v_{position}"] = torch.stack(
                [
                    item.history_v[:chunk].reshape(
                        chunk * TOKENS_PER_CHUNK,
                        item.history_v.shape[-2],
                        item.history_v.shape[-1],
                    )
                    for item in cache
                ]
            )
            output[f"cross_k_{position}"] = torch.stack(
                [item.cross_k for item in cache]
            )
            output[f"cross_v_{position}"] = torch.stack(
                [item.cross_v for item in cache]
            )
        return output

    def batch(
        self,
        prompt_ids: list[int],
        chunk: int,
        target_step: int,
    ) -> dict[str, Any]:
        if self.layer_id is None or not self.layer_cache:
            raise RuntimeError("load_layer_cache must be called first")
        output = self._base_batch(prompt_ids, chunk, target_step)
        cache = [self.layer_cache[prompt_id] for prompt_id in prompt_ids]
        history_start = max(0, chunk - self.max_history_chunks)
        history_chunks = chunk - history_start
        output.update(
            {
                "history_k": torch.stack(
                    [
                        item.history_k[history_start:chunk].reshape(
                            history_chunks * TOKENS_PER_CHUNK,
                            item.history_k.shape[-2],
                            item.history_k.shape[-1],
                        )
                        for item in cache
                    ]
                ),
                "history_v": torch.stack(
                    [
                        item.history_v[history_start:chunk].reshape(
                            history_chunks * TOKENS_PER_CHUNK,
                            item.history_v.shape[-2],
                            item.history_v.shape[-1],
                        )
                        for item in cache
                    ]
                ),
                "cross_k": torch.stack([item.cross_k for item in cache]),
                "cross_v": torch.stack([item.cross_v for item in cache]),
            }
        )
        return output

    def _base_batch(
        self,
        prompt_ids: list[int],
        chunk: int,
        target_step: int,
    ) -> dict[str, Any]:
        if chunk < 1 or chunk >= self.num_chunks:
            raise ValueError(
                f"Trainable chunk must be 1..{self.num_chunks - 1}, got {chunk}"
            )
        if target_step < 1 or target_step > 3:
            raise ValueError(
                f"Target denoising step must be 1..3, got {target_step}"
            )
        anchor_step = target_step - 1

        common = [self.common[prompt_id] for prompt_id in prompt_ids]
        return {
            "prompt_ids": prompt_ids,
            "chunk": chunk,
            "anchor_step": anchor_step,
            "target_step": target_step,
            "noisy_latent": torch.stack(
                [item.noisy[(chunk, target_step)] for item in common]
            ),
            "anchor_hidden": torch.stack(
                [item.hidden[chunk][anchor_step] for item in common]
            ),
            "previous_hidden": torch.stack(
                [item.hidden[chunk - 1][target_step] for item in common]
            ),
            "target_hidden": torch.stack(
                [item.hidden[chunk][target_step] for item in common]
            ),
            "target_flow": torch.stack(
                [item.flow[(chunk, target_step)] for item in common]
            ),
            "timestep": torch.stack(
                [item.timestep[(chunk, target_step)] for item in common]
            ),
        }