other_transfer/src/physics_model/sampler.py

"""Batch sampler that never mixes image and text-only rows in one micro-batch."""

from __future__ import annotations

import random
from collections.abc import Iterator, Sequence

from torch.utils.data import BatchSampler, Sampler


class HasImageBatchSampler(BatchSampler):
    def __init__(
        self,
        has_image: Sequence[bool],
        batch_size: int,
        *,
        shuffle: bool = True,
        seed: int = 42,
        drop_last: bool = False,
        epoch: int = 0,
        num_replicas: int = 1,
        rank: int = 0,
    ) -> None:
        if batch_size < 1:
            raise ValueError("batch_size must be >= 1")
        self.has_image = list(has_image)
        self.batch_size = batch_size
        self.shuffle = shuffle
        self.seed = seed
        self.drop_last = drop_last
        self.epoch = epoch
        self.num_replicas = max(1, int(num_replicas))
        self.rank = int(rank)
        if not (0 <= self.rank < self.num_replicas):
            raise ValueError(f"rank {rank} out of range for {self.num_replicas} replicas")
        self.image_indices = [i for i, flag in enumerate(self.has_image) if flag]
        self.text_indices = [i for i, flag in enumerate(self.has_image) if not flag]
        dummy = _IndexSampler(len(self.has_image))
        super().__init__(dummy, batch_size, drop_last)

    def _shard(self, indices: list[int]) -> list[int]:
        if self.num_replicas == 1:
            return list(indices)
        return list(indices[self.rank :: self.num_replicas])

    def set_epoch(self, epoch: int) -> None:
        self.epoch = epoch

    def _batches_for(self, indices: list[int], rng: random.Random) -> list[list[int]]:
        order = list(indices)
        if self.shuffle:
            rng.shuffle(order)
        batches = [order[i : i + self.batch_size] for i in range(0, len(order), self.batch_size)]
        if self.drop_last and batches and len(batches[-1]) < self.batch_size:
            batches = batches[:-1]
        return [b for b in batches if b]

    def __iter__(self) -> Iterator[list[int]]:
        rng = random.Random(self.seed + self.epoch)
        image = self._shard(self.image_indices)
        text = self._shard(self.text_indices)
        batches = self._batches_for(image, rng) + self._batches_for(text, rng)
        if self.shuffle:
            rng.shuffle(batches)
        yield from batches

    def __len__(self) -> int:
        def count(n: int) -> int:
            if n == 0:
                return 0
            if self.drop_last:
                return n // self.batch_size
            return (n + self.batch_size - 1) // self.batch_size

        return count(len(self._shard(self.image_indices))) + count(len(self._shard(self.text_indices)))


class _IndexSampler(Sampler[int]):
    def __init__(self, length: int) -> None:
        self._length = length

    def __iter__(self) -> Iterator[int]:
        return iter(range(self._length))

    def __len__(self) -> int:
        return self._length


class HasImageRepeatSampler(Sampler[int]):
    """RepeatSampler that never mixes image and text unique prompts in one generation chunk."""

    def __init__(
        self,
        has_image: Sequence[bool],
        *,
        mini_repeat_count: int,
        batch_size: int,
        repeat_count: int = 1,
        shuffle: bool = True,
        seed: int | None = 42,
    ) -> None:
        if mini_repeat_count < 1 or batch_size < 1 or repeat_count < 1:
            raise ValueError("mini_repeat_count, batch_size, and repeat_count must be >= 1")
        self.has_image = [bool(v) for v in has_image]
        self.mini_repeat_count = int(mini_repeat_count)
        self.batch_size = int(batch_size)
        self.repeat_count = int(repeat_count)
        self.shuffle = bool(shuffle)
        self.seed = seed
        self.image_indices = [i for i, flag in enumerate(self.has_image) if flag]
        self.text_indices = [i for i, flag in enumerate(self.has_image) if not flag]

    def __iter__(self) -> Iterator[int]:
        rng = random.Random(self.seed)
        chunks: list[list[int]] = []
        for pool in (self.image_indices, self.text_indices):
            order = list(pool)
            if self.shuffle:
                rng.shuffle(order)
            for i in range(0, len(order), self.batch_size):
                chunk = order[i : i + self.batch_size]
                if len(chunk) == self.batch_size:
                    chunks.append(chunk)
        if self.shuffle:
            rng.shuffle(chunks)
        for chunk in chunks:
            for _ in range(self.repeat_count):
                for index in chunk:
                    for _ in range(self.mini_repeat_count):
                        yield index

    def __len__(self) -> int:
        def full(n: int) -> int:
            return (n // self.batch_size) * self.batch_size

        unique = full(len(self.image_indices)) + full(len(self.text_indices))
        return unique * self.mini_repeat_count * self.repeat_count