topological sorting of definitions by caller hierarchy
This commit is contained in:
Matthias Bisping
parent
3b18fc6158
commit
6e7645e319
@ -5,11 +5,6 @@ from funcy import compose, second
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from image_prediction.stitching.utils import make_coord_getter
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def group_by_coordinate(pairs, coord_getter):
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pairs = sorted(pairs, key=coord_getter)
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return map(compose(list, second), groupby(pairs, coord_getter))
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class CoordGrouper:
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def __init__(self, axis):
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self.c1_getter = make_coord_getter(f"{other_axis(axis)}1")
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@ -24,3 +19,8 @@ class CoordGrouper:
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def other_axis(axis):
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return "y" if axis == "x" else "x"
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def group_by_coordinate(pairs, coord_getter):
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pairs = sorted(pairs, key=coord_getter)
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return map(compose(list, second), groupby(pairs, coord_getter))
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@ -13,71 +13,51 @@ from image_prediction.utils.generic import until
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from test.utils.stitching import HorizontalKeyMapper, VerticalKeyMapper
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def make_pair_merger(axis):
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return {"y": merge_pair_vertically, "x": merge_pair_horizontally}[axis]
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def no_new_merges(pairs1, pairs2):
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return len(pairs1) == len(pairs2)
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def merge_along_both_axes(pairs: Iterable[ImageMetadataPair]):
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pairs = merge_along_axis(pairs, "x")
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pairs = list(merge_along_axis(pairs, "y"))
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return pairs
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def merge_along_axis(pairs: Iterable[ImageMetadataPair], axis):
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def group_pairs_within_groups_by_greater_coordinate(groups):
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return map(CoordGrouper(axis).group_pairs_by_greater_coordinate, groups)
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def merge_groups_along_orthogonal_axis(groups):
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return map(make_group_merger(axis), groups)
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def group_pairs_by_lesser_coordinate(pairs):
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return CoordGrouper(axis).group_pairs_by_lesser_coordinate(pairs)
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return rcompose(
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group_pairs_by_lesser_coordinate, # pairs -> groups of pairs aligned on one edge
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group_pairs_within_groups_by_greater_coordinate, # -> groups of pairs fully aligned on orthogonal axis
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flatten_groups_once, # groups of groups of pairs -> groups of pairs
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merge_groups_along_orthogonal_axis,
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flatten_groups_once, # groups of pairs -> pairs
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)(pairs)
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def make_group_merger(axis):
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return {"y": merge_group_vertically, "x": merge_group_horizontally}[axis]
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def merge_metadata_horizontally(m1: dict, m2: dict):
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m1, m2 = map(HorizontalKeyMapper, [m1, m2])
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return merge_metadata(m1, m2)
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def merge_group_vertically(group: Iterable[ImageMetadataPair]):
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return merge_group(group, "y")
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def merge_metadata_vertically(m1: dict, m2: dict):
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m1, m2 = map(VerticalKeyMapper, [m1, m2])
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return merge_metadata(m1, m2)
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def merge_group_horizontally(group: Iterable[ImageMetadataPair]):
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return merge_group(group, "x")
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def merge_metadata(m1: dict, m2: dict):
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c1 = min(m1.c1, m2.c1)
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c2 = max(m1.c2, m2.c2)
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dim = m1.dim + m2.dim
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merged = deepcopy(m1)
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merged.dim = dim
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merged.c1 = c1
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merged.c2 = c2
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return merged.wrapped
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def merge_pair_horizontally(p1: ImageMetadataPair, p2: ImageMetadataPair):
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metadata_merged = merge_metadata_horizontally(p1.metadata, p2.metadata)
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image_concatenated = concat_images_horizontally(p1.image, p2.image, metadata_merged)
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return ImageMetadataPair(image_concatenated, metadata_merged)
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def merge_pair_vertically(p1: ImageMetadataPair, p2: ImageMetadataPair):
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metadata_merged = merge_metadata_vertically(p1.metadata, p2.metadata)
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image_concatenated = concat_images_vertically(p1.image, p2.image, metadata_merged)
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return ImageMetadataPair(image_concatenated, metadata_merged)
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def concat_images_horizontally(im1: Image, im2: Image, metadata: dict):
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return concat_images(im1, im2, metadata, 0)
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def concat_images_vertically(im1: Image, im2: Image, metadata: dict):
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return concat_images(im1, im2, metadata, 1)
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def concat_images(im1: Image, im2: Image, metadata: dict, axis):
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im_aggr = Image.new(im1.mode, (metadata[Info.WIDTH], metadata[Info.HEIGHT]))
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images = [im1, im2]
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offsets = [0, *[im.size[axis] for im in images]]
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for im, offset in zip(images, offsets):
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box = (offset, 0) if not axis else (0, offset)
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im_aggr.paste(im, box=box)
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return im_aggr
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def merge_group(group: Iterable[ImageMetadataPair], direction):
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reduce_group = make_merger_aggregator(direction)
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return until(no_new_merges, reduce_group, group)
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def make_merger_aggregator(direction) -> Callable[[Iterable[ImageMetadataPair]], Iterable[ImageMetadataPair]]:
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@ -108,44 +88,64 @@ def make_merger_aggregator(direction) -> Callable[[Iterable[ImageMetadataPair]],
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return merger_aggregator
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def merge_group(group: Iterable[ImageMetadataPair], direction):
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reduce_group = make_merger_aggregator(direction)
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return until(no_new_merges, reduce_group, group)
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def make_pair_merger(axis):
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return {"y": merge_pair_vertically, "x": merge_pair_horizontally}[axis]
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def merge_group_horizontally(group: Iterable[ImageMetadataPair]):
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return merge_group(group, "x")
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def merge_pair_vertically(p1: ImageMetadataPair, p2: ImageMetadataPair):
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metadata_merged = merge_metadata_vertically(p1.metadata, p2.metadata)
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image_concatenated = concat_images_vertically(p1.image, p2.image, metadata_merged)
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return ImageMetadataPair(image_concatenated, metadata_merged)
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def merge_group_vertically(group: Iterable[ImageMetadataPair]):
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return merge_group(group, "y")
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def merge_pair_horizontally(p1: ImageMetadataPair, p2: ImageMetadataPair):
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metadata_merged = merge_metadata_horizontally(p1.metadata, p2.metadata)
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image_concatenated = concat_images_horizontally(p1.image, p2.image, metadata_merged)
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return ImageMetadataPair(image_concatenated, metadata_merged)
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def merge_along_axis(pairs: Iterable[ImageMetadataPair], axis):
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def group_pairs_within_groups_by_greater_coordinate(groups):
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return map(CoordGrouper(axis).group_pairs_by_greater_coordinate, groups)
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def merge_groups_along_orthogonal_axis(groups):
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return map(make_group_merger(axis), groups)
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def group_pairs_by_lesser_coordinate(pairs):
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return CoordGrouper(axis).group_pairs_by_lesser_coordinate(pairs)
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return rcompose(
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group_pairs_by_lesser_coordinate, # pairs -> groups of pairs aligned on one edge
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group_pairs_within_groups_by_greater_coordinate, # -> groups of pairs fully aligned on orthogonal axis
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flatten_groups_once, # groups of groups of pairs -> groups of pairs
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merge_groups_along_orthogonal_axis,
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flatten_groups_once, # groups of pairs -> pairs
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)(pairs)
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def merge_metadata_vertically(m1: dict, m2: dict):
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m1, m2 = map(VerticalKeyMapper, [m1, m2])
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return merge_metadata(m1, m2)
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def merge_along_both_axes(pairs: Iterable[ImageMetadataPair]):
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pairs = merge_along_axis(pairs, "x")
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pairs = list(merge_along_axis(pairs, "y"))
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return pairs
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def merge_metadata_horizontally(m1: dict, m2: dict):
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m1, m2 = map(HorizontalKeyMapper, [m1, m2])
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return merge_metadata(m1, m2)
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def no_new_merges(pairs1, pairs2):
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return len(pairs1) == len(pairs2)
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def merge_metadata(m1: dict, m2: dict):
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c1 = min(m1.c1, m2.c1)
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c2 = max(m1.c2, m2.c2)
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dim = m1.dim + m2.dim
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merged = deepcopy(m1)
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merged.dim = dim
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merged.c1 = c1
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merged.c2 = c2
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return merged.wrapped
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def concat_images_vertically(im1: Image, im2: Image, metadata: dict):
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return concat_images(im1, im2, metadata, 1)
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def concat_images_horizontally(im1: Image, im2: Image, metadata: dict):
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return concat_images(im1, im2, metadata, 0)
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def concat_images(im1: Image, im2: Image, metadata: dict, axis):
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im_aggr = Image.new(im1.mode, (metadata[Info.WIDTH], metadata[Info.HEIGHT]))
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images = [im1, im2]
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offsets = [0, *[im.size[axis] for im in images]]
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for im, offset in zip(images, offsets):
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box = (offset, 0) if not axis else (0, offset)
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im_aggr.paste(im, box=box)
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return im_aggr
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@ -3,11 +3,8 @@ from itertools import chain
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from image_prediction.info import Info
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def make_getter(key):
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def getter(pair):
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return pair.metadata[key]
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return getter
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def flatten_groups_once(groups):
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return chain.from_iterable(groups)
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def make_coord_getter(c):
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@ -19,12 +16,15 @@ def make_coord_getter(c):
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}[c]
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def make_getter(key):
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def getter(pair):
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return pair.metadata[key]
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return getter
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def make_length_getter(dim):
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return {
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"width": make_getter(Info.WIDTH),
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"height": make_getter(Info.HEIGHT),
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}[dim]
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def flatten_groups_once(groups):
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return chain.from_iterable(groups)
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