kaniblu

join_by () {
    # Argument #1 is the separator. It can be multi-character.
    # Argument #2, 3, and so on, are the elements to be joined.
    # Usage: join_by ", " "${array[@]}"
    local SEPARATOR="$1"
    shift

    local F=0
    for x in "$@"
    do

kaniblu

torch.einsum("blm,mn->bln", torch.randn(3, 4, 5), torch.randn(5, 6)).size()  # torch.Size([3, 4, 6])

kaniblu

(torch.randn(3, 4, 5) @ torch.randn(5, 6)).size()  # torch.Size([3, 4, 6])

kaniblu

torch.randn(3, 4, 5).view(12, 5).mm(torch.randn(5, 6)).view(3, 4, 6).size()  # torch.Size([3, 4, 6])

kaniblu

torch.randn(3, 4, 5).mm(torch.randn(5, 6)).size()  
# RuntimeError: matrices expected, got 3D, 2D tensors at

kaniblu

import torch

(torch.randn(3, 4, 5).bmm(torch.randn(3, 5, 6))).size()  # torch.Size([3, 4, 6])

kaniblu

import torch

(torch.randn(3, 4) @ torch.randn(4, 5)).size()  # torch.Size([3, 5])

kaniblu

import torch

torch.mm(torch.randn(3, 4), torch.randn(4, 5)).size()  # torch.Size([3, 5])
torch.randn(3, 4).mm(torch.randn(4, 5)).size()  # torch.Size([3, 5])

kaniblu

__all__ = ["BLEUEvaluator"]

import bisect
import logging
import collections
from dataclasses import dataclass
from typing import Sequence, Optional, List

import torch
import numpy as np

kaniblu

from typing import Sequence
import numpy as np


def split_list(items: Sequence, ratios: Sequence[float]):
    ratios = np.cumsum(np.array(ratios) / sum(ratios)).tolist()
    indices = [0] + [int(round(len(items) * r)) for r in ratios]
    return [items[i:j] for i, j in zip(indices, indices[1:])]