utils.py

import os
import pathlib
import re
import collections
import functools
import inspect
import sys
import fnmatch
from typing import List, Union

import torch

from omegaconf import OmegaConf


class ExitCodeError(Exception):
    pass


def sh(x):
    if os.system(x):
        raise ExitCodeError()


def escaped_split(text, sep_char, maxsplit=-1):
    """Split text into a list on occurrences of the given separation
    character `sep_char`. The separation character may be escaped by a
    backslash to avoid splitting at that location.

    The separation character must be a string of size 1.

    If `maxsplit` is given, at most `maxsplit` splits are done (thus,
    the list will have at most `maxsplit + 1` elements). If `maxsplit`
    is not specified or less than 0, then there is no limit on the
    number of splits (all possible splits are made).
    """
    assert (
        len(sep_char) == 1
    ), "separation string must be a single character for escaped splitting"

    if maxsplit == 0:
        return text
    maxsplit = max(0, maxsplit)

    return re.split(r"(?<!\\)" + sep_char, text, maxsplit)


def simple_parse_args_string(args_string):
    """
    Parses something like
        args1=val1,arg2=val2
    Into a dictionary
    """
    args_string = args_string.strip()
    if not args_string:
        return {}
    arg_list = args_string.split(",")
    args_dict = OmegaConf.to_object(OmegaConf.from_dotlist(arg_list))
    return args_dict


def join_iters(iters):
    for iter in iters:
        yield from iter


def chunks(iter, n):
    arr = []
    for x in iter:
        arr.append(x)
        if len(arr) == n:
            yield arr
            arr = []

    if arr:
        yield arr


def group(arr, fn):
    res = collections.defaultdict(list)

    for ob in arr:
        res[fn(ob)].append(ob)

    return list(res.values())


def _is_json_task(task_name):
    return task_name == "json" or task_name.startswith("json=")


class MultiChoice:
    def __init__(self, choices):
        self.choices = choices

    # Simple wildcard support (linux filename patterns)
    def __contains__(self, values):
        for value in values.split(","):
            if len(fnmatch.filter(self.choices, value)) == 0 and not _is_json_task(
                value
            ):
                return False

        return True

    def __iter__(self):
        for choice in self.choices:
            yield choice


# Returns a list containing all values of the source_list that
# match at least one of the patterns
def pattern_match(patterns, source_list):
    task_names = set()
    for pattern in patterns:
        if _is_json_task(pattern):
            task_names.add(pattern)

        for matching in fnmatch.filter(source_list, pattern):
            task_names.add(matching)
    return sorted(list(task_names))


def general_detokenize(string):
    string = string.replace(" n't", "n't")
    string = string.replace(" )", ")")
    string = string.replace("( ", "(")
    string = string.replace('" ', '"')
    string = string.replace(' "', '"')
    string = re.sub(r" (['.,])", r"\1", string)
    return string


def get_rolling_token_windows(token_list, prefix_token, max_seq_len, context_len):
    """
    - context_len allows for a rolling window context, allowing each prediction window to potentially
      condition on some context

    :param token_list: list
        List of tokens to be PREDICTED
    :param max_seq_len: int
        max_seq_len of model (or max_seq_len we want to use)
    :param context_len: int
        Amount of desired token context for prediction. Needs to be at least 1.
    :param prefix_token: token
        Dummy token like <eos> so the first token has something to condition on
    :return: generator
        Generator of tuples
            (input_tokens, pred_tokens)
        Note: Score only the last len(pred_tokens) logits of the LM
    """
    assert 1 <= context_len <= max_seq_len
    if not token_list:
        return
    # +1 offset, going from input->preds
    pred_len = max_seq_len - context_len + 1
    predicted = 0

    # Special handling for first window: predict all tokens
    first_seq_len = min(max_seq_len, len(token_list))
    yield ([prefix_token] + token_list[: first_seq_len - 1], token_list[:first_seq_len])
    predicted += first_seq_len

    while predicted < len(token_list):
        window_pred_len = min(len(token_list) - predicted, pred_len)
        window_end = predicted + window_pred_len

        yield (
            token_list[window_end - max_seq_len - 1 : window_end - 1],
            token_list[window_end - window_pred_len : window_end],
        )
        predicted += window_pred_len


def make_disjoint_window(pair):
    """Takes output from get_rolling_token_windows and makes the context not overlap with the continuation"""
    a, b = pair
    return a[: len(a) - (len(b) - 1)], b


def select_continuation_from_batch_left_padding(
    generations: Union[List[List[int]], torch.Tensor], max_context_size: int
):
    """Select the continuation from the batch, removing prompts of different lengths.
    Args:
        generations (Union[List[List[int]], torch.Tensor]):
            A tensor or list-of-lists of shape [batch_size, sequence length].
        max_context_size (int):
            The size of the biggest context; generations will proceed from that
            index.
    Example:
        PAD     PAD Continue : The dog chased the cat  [every       day of the week]
        Riddle  me    this   : The  dog chased the  cat [yesterday] PAD PAD PAD PAD
    Output:
        [every day of the week]
        [yesterday]  PAD PAD PAD PAD
    """
    return generations[:, max_context_size:]


class Reorderer:
    def __init__(self, arr, fn):
        self.size = len(arr)
        arr = list(enumerate(arr))
        arr = group(arr, lambda x: fn(x[1]))
        arr = [([y[0] for y in x], x[0][1]) for x in arr]
        arr.sort(key=lambda x: fn(x[1]))

        self.arr = arr

    def get_reordered(self):
        return [x[1] for x in self.arr]

    def get_original(self, newarr):
        res = [None] * self.size
        cov = [False] * self.size

        for (inds, _), v in zip(self.arr, newarr):
            for ind in inds:
                res[ind] = v
                cov[ind] = True

        assert all(cov)

        return res


def positional_deprecated(fn):
    """
    A decorator to nudge users into passing only keyword args (`kwargs`) to the
    wrapped function, `fn`.
    """

    @functools.wraps(fn)
    def _wrapper(*args, **kwargs):
        if len(args) != 1 if inspect.ismethod(fn) else 0:
            print(
                f"WARNING: using {fn.__name__} with positional arguments is "
                "deprecated and will be disallowed in a future version of "
                "lm-evaluation-harness!"
            )
        return fn(*args, **kwargs)

    return _wrapper


@positional_deprecated
def find_test_root(start_path: pathlib.Path) -> pathlib.Path:
    """
    Search upward in the directory tree to a maximum of three layers
    to find and return the package root (containing the 'tests' folder)
    """
    cur_path = start_path.resolve()
    max_layers = 3
    for _ in range(max_layers):
        if (cur_path / "tests" / "test_version_stable.py").exists():
            return cur_path
        else:
            cur_path = cur_path.parent.resolve()
    raise FileNotFoundError(
        f"Unable to find package root within {max_layers} upwards" + f"of {start_path}"
    )


@positional_deprecated
def run_task_tests(task_list: List[str]):
    """
    Find the package root and run the tests for the given tasks
    """
    import pytest

    package_root = find_test_root(start_path=pathlib.Path(__file__))
    task_string = " or ".join(task_list)
    args = [
        f"{package_root}/tests/test_version_stable.py",
        f"--rootdir={package_root}",
        "-k",
        f"{task_string}",
    ]
    sys.path.append(str(package_root))
    pytest_return_val = pytest.main(args)
    if pytest_return_val:
        raise ValueError(
            f"Not all tests for the specified tasks ({task_list}) ran successfully! Error code: {pytest_return_val}"
        )