modules/voice_conversion/fairseq/utils.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import argparse
import collections
import contextlib
import copy
import importlib
import logging
import os
import sys
import warnings
from itertools import accumulate
from typing import TYPE_CHECKING, Callable, Dict, List, Optional

import torch
import torch.nn.functional as F
from torch import Tensor

if TYPE_CHECKING:
    from fairseq.modules.multihead_attention import MultiheadAttention

try:
    from amp_C import multi_tensor_l2norm

    multi_tensor_l2norm_available = True
except ImportError:
    multi_tensor_l2norm_available = False

try:
    import torch_xla.core.xla_model as xm
except ImportError:
    xm = None


logger = logging.getLogger(__name__)


MANIFOLD_PATH_SEP = "|"


class FileContentsAction(argparse.Action):
    def __init__(self, option_strings, dest, nargs=None, **kwargs):
        if nargs is not None:
            raise ValueError("nargs not allowed")
        super(FileContentsAction, self).__init__(option_strings, dest, **kwargs)

    def __call__(self, parser, namespace, values, option_string=None):
        from fairseq.file_io import PathManager

        if PathManager.isfile(values):
            with PathManager.open(values) as f:
                argument = f.read().strip()
        else:
            argument = values
        setattr(namespace, self.dest, argument)


def split_paths(paths: str, separator=os.pathsep) -> List[str]:
    return (
        paths.split(separator) if "://" not in paths else paths.split(MANIFOLD_PATH_SEP)
    )


def load_ensemble_for_inference(filenames, task, model_arg_overrides=None):
    from fairseq import checkpoint_utils

    deprecation_warning(
        "utils.load_ensemble_for_inference is deprecated. "
        "Please use checkpoint_utils.load_model_ensemble instead."
    )
    return checkpoint_utils.load_model_ensemble(
        filenames, arg_overrides=model_arg_overrides, task=task
    )


def apply_to_sample(f, sample):
    if hasattr(sample, "__len__") and len(sample) == 0:
        return {}

    def _apply(x):
        if torch.is_tensor(x):
            return f(x)
        elif isinstance(x, collections.OrderedDict):
            # OrderedDict has attributes that needs to be preserved
            od = collections.OrderedDict(
                (key, _apply(value)) for key, value in x.items()
            )
            od.__dict__ = x.__dict__
            return od
        elif isinstance(x, dict):
            return {key: _apply(value) for key, value in x.items()}
        elif isinstance(x, list):
            return [_apply(x) for x in x]
        elif isinstance(x, tuple):
            return tuple(_apply(x) for x in x)
        elif isinstance(x, set):
            return {_apply(x) for x in x}
        else:
            return x

    return _apply(sample)


def move_to_cuda(sample, device=None):
    device = device or torch.cuda.current_device()

    def _move_to_cuda(tensor):
        # non_blocking is ignored if tensor is not pinned, so we can always set
        # to True (see github.com/PyTorchLightning/pytorch-lightning/issues/620)
        return tensor.to(device=device, non_blocking=True)

    return apply_to_sample(_move_to_cuda, sample)


def move_to_cpu(sample):
    def _move_to_cpu(tensor):
        # PyTorch has poor support for half tensors (float16) on CPU.
        # Move any such tensors to float32.
        if tensor.dtype in {torch.bfloat16, torch.float16}:
            tensor = tensor.to(dtype=torch.float32)
        return tensor.cpu()

    return apply_to_sample(_move_to_cpu, sample)


def move_to_tpu(sample):

    import torch_xla.core.xla_model as xm

    device = xm.xla_device()

    def _move_to_tpu(tensor):
        return tensor.to(device)

    return apply_to_sample(_move_to_tpu, sample)


def get_incremental_state(
    module: "MultiheadAttention",
    incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
    key: str,
) -> Optional[Dict[str, Optional[Tensor]]]:
    """Helper for getting incremental state for an nn.Module."""
    return module.get_incremental_state(incremental_state, key)


def set_incremental_state(
    module: "MultiheadAttention",
    incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
    key: str,
    value: Dict[str, Optional[Tensor]],
) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]:
    """Helper for setting incremental state for an nn.Module."""
    if incremental_state is not None:
        result = module.set_incremental_state(incremental_state, key, value)
        if result is not None:
            incremental_state = result
    return incremental_state


def load_align_dict(replace_unk):
    if replace_unk is None:
        align_dict = None
    elif isinstance(replace_unk, str) and len(replace_unk) > 0:
        # Load alignment dictionary for unknown word replacement if it was passed as an argument.
        align_dict = {}
        with open(replace_unk, "r") as f:
            for line in f:
                cols = line.split()
                align_dict[cols[0]] = cols[1]
    else:
        # No alignment dictionary provided but we still want to perform unknown word replacement by copying the
        # original source word.
        align_dict = {}
    return align_dict


def print_embed_overlap(embed_dict, vocab_dict):
    embed_keys = set(embed_dict.keys())
    vocab_keys = set(vocab_dict.symbols)
    overlap = len(embed_keys & vocab_keys)
    logger.info("found {}/{} types in embedding file".format(overlap, len(vocab_dict)))


def parse_embedding(embed_path):
    """Parse embedding text file into a dictionary of word and embedding tensors.

    The first line can have vocabulary size and dimension. The following lines
    should contain word and embedding separated by spaces.

    Example:
        2 5
        the -0.0230 -0.0264  0.0287  0.0171  0.1403
        at -0.0395 -0.1286  0.0275  0.0254 -0.0932
    """
    embed_dict = {}
    with open(embed_path) as f_embed:
        next(f_embed)  # skip header
        for line in f_embed:
            pieces = line.rstrip().split(" ")
            embed_dict[pieces[0]] = torch.Tensor(
                [float(weight) for weight in pieces[1:]]
            )
    return embed_dict


def load_embedding(embed_dict, vocab, embedding):
    for idx in range(len(vocab)):
        token = vocab[idx]
        if token in embed_dict:
            embedding.weight.data[idx] = embed_dict[token]
    return embedding


def replace_unk(hypo_str, src_str, alignment, align_dict, unk):
    from fairseq import tokenizer

    # Tokens are strings here
    hypo_tokens = tokenizer.tokenize_line(hypo_str)
    # TODO: Very rare cases where the replacement is '<eos>' should be handled gracefully
    src_tokens = tokenizer.tokenize_line(src_str) + ["<eos>"]
    for i, ht in enumerate(hypo_tokens):
        if ht == unk:
            src_token = src_tokens[alignment[i]]
            # Either take the corresponding value in the aligned dictionary or just copy the original value.
            hypo_tokens[i] = align_dict.get(src_token, src_token)
    return " ".join(hypo_tokens)


def post_process_prediction(
    hypo_tokens,
    src_str,
    alignment,
    align_dict,
    tgt_dict,
    remove_bpe=None,
    extra_symbols_to_ignore=None,
):
    hypo_str = tgt_dict.string(
        hypo_tokens, remove_bpe, extra_symbols_to_ignore=extra_symbols_to_ignore
    )
    if align_dict is not None:
        hypo_str = replace_unk(
            hypo_str, src_str, alignment, align_dict, tgt_dict.unk_string()
        )
    if align_dict is not None or remove_bpe is not None:
        # Convert back to tokens for evaluating with unk replacement or without BPE
        # Note that the dictionary can be modified inside the method.
        hypo_tokens = tgt_dict.encode_line(hypo_str, add_if_not_exist=True)
    return hypo_tokens, hypo_str, alignment


def make_positions(tensor, padding_idx: int, onnx_trace: bool = False):
    """Replace non-padding symbols with their position numbers.

    Position numbers begin at padding_idx+1. Padding symbols are ignored.
    """
    # The series of casts and type-conversions here are carefully
    # balanced to both work with ONNX export and XLA. In particular XLA
    # prefers ints, cumsum defaults to output longs, and ONNX doesn't know
    # how to handle the dtype kwarg in cumsum.
    mask = tensor.ne(padding_idx).int()
    return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx


def strip_pad(tensor, pad):
    return tensor[tensor.ne(pad)]


def buffered_arange(max):
    if not hasattr(buffered_arange, "buf"):
        buffered_arange.buf = torch.LongTensor()
    if max > buffered_arange.buf.numel():
        buffered_arange.buf.resize_(max)
        torch.arange(max, out=buffered_arange.buf)
    return buffered_arange.buf[:max]


def convert_padding_direction(
    src_tokens, padding_idx, right_to_left: bool = False, left_to_right: bool = False
):
    assert right_to_left ^ left_to_right
    pad_mask = src_tokens.eq(padding_idx)
    if not pad_mask.any():
        # no padding, return early
        return src_tokens
    if left_to_right and not pad_mask[:, 0].any():
        # already right padded
        return src_tokens
    if right_to_left and not pad_mask[:, -1].any():
        # already left padded
        return src_tokens
    max_len = src_tokens.size(1)
    buffered = torch.empty(0).long()
    if max_len > 0:
        torch.arange(max_len, out=buffered)
    range = buffered.type_as(src_tokens).expand_as(src_tokens)
    num_pads = pad_mask.long().sum(dim=1, keepdim=True)
    if right_to_left:
        index = torch.remainder(range - num_pads, max_len)
    else:
        index = torch.remainder(range + num_pads, max_len)
    return src_tokens.gather(1, index)


def item(tensor):
    # tpu-comment: making this a no-op for xla devices.
    if torch.is_tensor(tensor) and tensor.device.type == "xla":
        return tensor.detach()
    if hasattr(tensor, "item"):
        return tensor.item()
    if hasattr(tensor, "__getitem__"):
        return tensor[0]
    return tensor


def multi_tensor_total_norm(grads, chunk_size=2048 * 32) -> torch.Tensor:
    per_device_grads = {}
    norms = []
    for grad in grads:
        device = grad.device
        cur_device_grads = per_device_grads.get(device)
        if cur_device_grads is None:
            cur_device_grads = []
            per_device_grads[device] = cur_device_grads
        cur_device_grads.append(grad)
    for device in per_device_grads.keys():
        cur_device_grads = per_device_grads[device]
        if device.type == "cuda":
            # TODO(msb) return has_inf
            has_inf = torch.zeros((1, 1), dtype=torch.int, device=device)
            with torch.cuda.device(device):
                norm = multi_tensor_l2norm(
                    chunk_size, has_inf, [cur_device_grads], False
                )
            norms.append(norm[0].to(torch.cuda.current_device()))
        else:
            norms += [torch.norm(g, p=2, dtype=torch.float32) for g in cur_device_grads]
    total_norm = torch.norm(torch.stack(norms))
    return total_norm


@torch.no_grad()
def clip_grad_norm_(params, max_norm, aggregate_norm_fn=None) -> torch.Tensor:
    def grad_exists(p):
        return p is not None and getattr(p, "grad", None) is not None

    if isinstance(params, torch.Tensor):
        params = [params]
    params = list(params)
    grads = [
        p.grad.detach() for p in params if grad_exists(p) and not hasattr(p, "expert")
    ]
    expert_grads = [
        p.grad.detach() for p in params if grad_exists(p) and hasattr(p, "expert")
    ]

    if len(grads) == 0:
        if len(params) > 0:
            return params[0].new_tensor(0.0)
        else:
            return torch.tensor(0.0)

    if len(grads) == 1:
        total_norm = torch.norm(grads[0], p=2, dtype=torch.float32)
    else:
        if multi_tensor_l2norm_available:
            total_norm = multi_tensor_total_norm(grads)
        else:
            if torch.cuda.is_available():
                warnings.warn(
                    "amp_C fused kernels unavailable, disabling multi_tensor_l2norm; "
                    "you may get better performance by installing NVIDIA's apex library"
                )
                device = torch.cuda.current_device()
            elif grads[0].device.type == "xla":
                device = grads[0].device
            else:
                device = torch.device("cpu")
            total_norm = torch.norm(
                torch.stack(
                    [torch.norm(g, p=2, dtype=torch.float32).to(device) for g in grads]
                )
            )

    if aggregate_norm_fn is not None:
        total_norm = aggregate_norm_fn(total_norm)

    if max_norm > 0:
        max_norm = float(max_norm)
        clip_coef = (max_norm / (total_norm + 1e-6)).clamp_(max=1)
        for g in grads + expert_grads:
            g.mul_(clip_coef)
    return total_norm


def fill_with_neg_inf(t):
    """FP16-compatible function that fills a tensor with -inf."""
    return t.float().fill_(float("-inf")).type_as(t)


def _match_types(arg1, arg2):
    """Convert the numerical argument to the same type as the other argument"""

    def upgrade(arg_number, arg_structure):
        if isinstance(arg_structure, tuple):
            return tuple([arg_number] * len(arg_structure))
        elif isinstance(arg_structure, dict):
            arg = copy.deepcopy(arg_structure)
            for k in arg:
                arg[k] = upgrade(arg_number, arg_structure[k])
            return arg
        else:
            return arg_number

    if isinstance(arg1, float) or isinstance(arg1, int):
        return upgrade(arg1, arg2), arg2
    elif isinstance(arg2, float) or isinstance(arg2, int):
        return arg1, upgrade(arg2, arg1)

    return arg1, arg2


def resolve_max_positions(*args):
    """Resolve max position constraints from multiple sources."""

    def map_value_update(d1, d2):
        updated_value = copy.deepcopy(d1)
        for key in d2:
            if key not in updated_value:
                updated_value[key] = d2[key]
            else:
                updated_value[key] = min(d1[key], d2[key])
        return updated_value

    def nullsafe_min(l):
        minim = None
        for item in l:
            if minim is None:
                minim = item
            elif item is not None and item < minim:
                minim = item
        return minim

    max_positions = None
    for arg in args:
        if max_positions is None:
            max_positions = arg
        elif arg is not None:
            max_positions, arg = _match_types(max_positions, arg)
            if isinstance(arg, float) or isinstance(arg, int):
                max_positions = min(max_positions, arg)
            elif isinstance(arg, dict):
                max_positions = map_value_update(max_positions, arg)
            else:
                max_positions = tuple(map(nullsafe_min, zip(max_positions, arg)))

    return max_positions


def import_user_module(args):
    module_path = getattr(args, "user_dir", None)
    if module_path is not None:
        module_path = os.path.abspath(args.user_dir)
        if not os.path.exists(module_path) and not os.path.isfile(
            os.path.dirname(module_path)
        ):
            fairseq_rel_path = os.path.join(os.path.dirname(__file__), args.user_dir)
            if os.path.exists(fairseq_rel_path):
                module_path = fairseq_rel_path
            else:
                fairseq_rel_path = os.path.join(
                    os.path.dirname(__file__), "..", args.user_dir
                )
                if os.path.exists(fairseq_rel_path):
                    module_path = fairseq_rel_path
                else:
                    raise FileNotFoundError(module_path)

        # ensure that user modules are only imported once
        import_user_module.memo = getattr(import_user_module, "memo", set())
        if module_path not in import_user_module.memo:
            import_user_module.memo.add(module_path)

            module_parent, module_name = os.path.split(module_path)
            if module_name not in sys.modules:
                sys.path.insert(0, module_parent)
                importlib.import_module(module_name)

                tasks_path = os.path.join(module_path, "tasks")
                if os.path.exists(tasks_path):
                    from fairseq.tasks import import_tasks

                    import_tasks(tasks_path, f"{module_name}.tasks")

                models_path = os.path.join(module_path, "models")
                if os.path.exists(models_path):
                    from fairseq.models import import_models

                    import_models(models_path, f"{module_name}.models")
            elif module_path in sys.modules[module_name].__path__:
                logger.info(f"--user-dir={module_path} has already been imported.")
            else:
                raise ImportError(
                    "Failed to import --user-dir={} because the corresponding module name "
                    "({}) is not globally unique. Please rename the directory to "
                    "something unique and try again.".format(module_path, module_name)
                )


def softmax(x, dim: int, onnx_trace: bool = False):
    if onnx_trace:
        return F.softmax(x.float(), dim=dim)
    else:
        return F.softmax(x, dim=dim, dtype=torch.float32)


def log_softmax(x, dim: int, onnx_trace: bool = False):
    if onnx_trace:
        return F.log_softmax(x.float(), dim=dim)
    else:
        return F.log_softmax(x, dim=dim, dtype=torch.float32)


def get_perplexity(loss, round=2, base=2):
    from fairseq.logging.meters import safe_round

    if loss is None:
        return 0.0
    try:
        return safe_round(base**loss, round)
    except OverflowError:
        return float("inf")


def deprecation_warning(message, stacklevel=3):
    # don't use DeprecationWarning, since it's ignored by default
    warnings.warn(message, stacklevel=stacklevel)


def relu_squared(x: torch.Tensor):
    return F.relu(x).pow(2)


def get_activation_fn(activation: str) -> Callable:
    """Returns the activation function corresponding to `activation`"""
    from fairseq.modules import gelu, gelu_accurate

    if activation == "relu":
        return F.relu
    elif activation == "relu_squared":
        return relu_squared
    elif activation == "gelu":
        return gelu
    elif activation == "gelu_fast":
        deprecation_warning(
            "--activation-fn=gelu_fast has been renamed to gelu_accurate"
        )
        return gelu_accurate
    elif activation == "gelu_accurate":
        return gelu_accurate
    elif activation == "tanh":
        return torch.tanh
    elif activation == "linear":
        return lambda x: x
    elif activation == "swish":
        return torch.nn.SiLU
    else:
        raise RuntimeError("--activation-fn {} not supported".format(activation))


def get_available_activation_fns() -> List:
    return [
        "relu",
        "gelu",
        "gelu_fast",  # deprecated
        "gelu_accurate",
        "tanh",
        "linear",
    ]


@contextlib.contextmanager
def model_eval(model):
    is_training = model.training
    model.eval()
    yield
    model.train(is_training)


def has_parameters(module):
    try:
        next(module.parameters())
        return True
    except StopIteration:
        return False


def get_rng_state():
    state = {"torch_rng_state": torch.get_rng_state()}
    if xm is not None:
        state["xla_rng_state"] = xm.get_rng_state()
    if torch.cuda.is_available():
        state["cuda_rng_state"] = torch.cuda.get_rng_state()
    return state


def set_rng_state(state):
    torch.set_rng_state(state["torch_rng_state"])
    if xm is not None:
        xm.set_rng_state(state["xla_rng_state"])
    if torch.cuda.is_available():
        torch.cuda.set_rng_state(state["cuda_rng_state"])


class set_torch_seed(object):
    def __init__(self, seed):
        assert isinstance(seed, int)
        self.rng_state = get_rng_state()

        torch.manual_seed(seed)
        if xm is not None:
            xm.set_rng_state(seed)
        if torch.cuda.is_available():
            torch.cuda.manual_seed(seed)

    def __enter__(self):
        return self

    def __exit__(self, *exc):
        set_rng_state(self.rng_state)


def parse_alignment(line):
    """
    Parses a single line from the alingment file.

    Args:
        line (str): String containing the alignment of the format:
            <src_idx_1>-<tgt_idx_1> <src_idx_2>-<tgt_idx_2> ..
            <src_idx_m>-<tgt_idx_m>. All indices are 0 indexed.

    Returns:
        torch.IntTensor: packed alignments of shape (2 * m).
    """
    alignments = line.strip().split()
    parsed_alignment = torch.IntTensor(2 * len(alignments))
    for idx, alignment in enumerate(alignments):
        src_idx, tgt_idx = alignment.split("-")
        parsed_alignment[2 * idx] = int(src_idx)
        parsed_alignment[2 * idx + 1] = int(tgt_idx)
    return parsed_alignment


def get_token_to_word_mapping(tokens, exclude_list):
    n = len(tokens)
    word_start = [int(token not in exclude_list) for token in tokens]
    word_idx = list(accumulate(word_start))
    token_to_word = {i: word_idx[i] for i in range(n)}
    return token_to_word


def extract_hard_alignment(attn, src_sent, tgt_sent, pad, eos):
    tgt_valid = (
        ((tgt_sent != pad) & (tgt_sent != eos)).nonzero(as_tuple=False).squeeze(dim=-1)
    )
    src_invalid = (
        ((src_sent == pad) | (src_sent == eos)).nonzero(as_tuple=False).squeeze(dim=-1)
    )
    src_token_to_word = get_token_to_word_mapping(src_sent, [eos, pad])
    tgt_token_to_word = get_token_to_word_mapping(tgt_sent, [eos, pad])
    alignment = []
    if len(tgt_valid) != 0 and len(src_invalid) < len(src_sent):
        attn_valid = attn[tgt_valid]
        attn_valid[:, src_invalid] = float("-inf")
        _, src_indices = attn_valid.max(dim=1)
        for tgt_idx, src_idx in zip(tgt_valid, src_indices):
            alignment.append(
                (
                    src_token_to_word[src_idx.item()] - 1,
                    tgt_token_to_word[tgt_idx.item()] - 1,
                )
            )
    return alignment


def extract_soft_alignment(attn, src_sent, tgt_sent, pad, eos):
    tgt_valid = ((tgt_sent != pad)).nonzero(as_tuple=False)
    src_valid = ((src_sent != pad)).nonzero(as_tuple=False).squeeze(dim=-1)
    alignment = []
    if len(tgt_valid) != 0 and len(src_valid) != 0:
        attn_valid = attn[tgt_valid, src_valid]
        alignment = [
            ["{:.6f}".format(p) for p in src_probs.tolist()] for src_probs in attn_valid
        ]
    return alignment


def new_arange(x, *size):
    """
    Return a Tensor of `size` filled with a range function on the device of x.
    If size is empty, using the size of the variable x.
    """
    if len(size) == 0:
        size = x.size()
    return torch.arange(size[-1], device=x.device).expand(*size).contiguous()


def get_tpu_device():
    return xm.xla_device()


def tpu_data_loader(itr):
    import torch_xla.core.xla_model as xm
    import torch_xla.distributed.parallel_loader as pl

    from fairseq.data import iterators

    xm.rendezvous("tpu_data_loader")  # wait for all workers
    xm.mark_step()
    device = xm.xla_device()
    return iterators.CountingIterator(
        pl.ParallelLoader(itr, [device]).per_device_loader(device),
        start=getattr(itr, "n", 0),
        total=len(itr),
    )


def is_xla_tensor(tensor):
    return torch.is_tensor(tensor) and tensor.device.type == "xla"


def index_put(tensor, indices, value):
    if is_xla_tensor(tensor):
        for _ in range(indices.dim(), tensor.dim()):
            indices = indices.unsqueeze(-1)
        if indices.size(-1) < tensor.size(-1):
            indices = indices.expand_as(tensor)
        tensor = torch.mul(tensor, ~indices) + torch.mul(value, indices)
    else:
        tensor[indices] = value
    return tensor


def xla_device_to_cpu(dat):
    import torch_xla.core.xla_model as xm

    return xm._maybe_convert_to_cpu(dat)


class CudaEnvironment(object):
    def __init__(self):
        cur_device = torch.cuda.current_device()
        prop = torch.cuda.get_device_properties("cuda:{}".format(cur_device))
        self.name = prop.name
        self.major = prop.major
        self.minor = prop.minor
        self.total_memory_in_GB = prop.total_memory / 1024 / 1024 / 1024

    @staticmethod
    def pretty_print_cuda_env_list(cuda_env_list):
        """
        Given a list of CudaEnviorments, pretty print them
        """
        num_workers = len(cuda_env_list)
        center = "CUDA enviroments for all {} workers".format(num_workers)
        banner_len = 40 - len(center) // 2
        first_line = "*" * banner_len + center + "*" * banner_len
        logger.info(first_line)
        for r, env in enumerate(cuda_env_list):
            logger.info(
                "rank {:3d}: ".format(r)
                + "capabilities = {:2d}.{:<2d} ; ".format(env.major, env.minor)
                + "total memory = {:.3f} GB ; ".format(env.total_memory_in_GB)
                + "name = {:40s}".format(env.name)
            )
        logger.info(first_line)


def csv_str_list(x):
    return x.split(",")


def eval_str_list(x, type=float):
    if x is None:
        return None
    if isinstance(x, str):
        x = eval(x)
    try:
        return list(map(type, x))
    except TypeError:
        return [type(x)]


def eval_str_dict(x, type=dict):
    if x is None:
        return None
    if isinstance(x, str):
        x = eval(x)
    return x


def eval_bool(x, default=False):
    if x is None:
        return default
    try:
        return bool(eval(x))
    except TypeError:
        return default


def reset_logging():
    root = logging.getLogger()
    for handler in root.handlers:
        root.removeHandler(handler)
    root.setLevel(os.environ.get("LOGLEVEL", "INFO").upper())
    handler = logging.StreamHandler(sys.stdout)
    handler.setFormatter(
        logging.Formatter(
            fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
            datefmt="%Y-%m-%d %H:%M:%S",
        )
    )
    root.addHandler(handler)


def safe_getattr(obj, k, default=None):
    """Returns obj[k] if it exists and is not None, otherwise returns default."""
    from omegaconf import OmegaConf

    if OmegaConf.is_config(obj):
        return obj[k] if k in obj and obj[k] is not None else default

    return getattr(obj, k, default)


def safe_hasattr(obj, k):
    """Returns True if the given key exists and is not None."""
    return getattr(obj, k, None) is not None


def hotreload_function(name=None):
    """
    Decorator to function to enable hot-reload for debugging.
    It allows you to debug a function without having reloading all heavy models, dataset loading and
        preprocessing, allow faster debugging.
    If you want to change model or dataset loading, consider relaunching your code
    -----------------------------------
    This will run the decorated function func:
        if func run successful:
            It will pause, allow user to edit code, and prompt user to:
                Press enter to re-run the function with updated code
                Type "done" to finish the function, return output
                Type "disable" to stop pausing this function and let code continue without pause
                Ctril + C to terminal
        if func raise error:
            it will prompt user to
                1. Edit code, and press enter to retry
                2. Ctrl + C to terminate
                3. Type "raise" to raise that exception
    * Requirements:
        0. Fairseq was installed with `pip install --editable .`
        1. pip install jurigged[develoop]
        2. set environment HOTRELOAD_PAUSE=1 CUDA_LAUNCH_BLOCKING=1
        3. Run on only 1 GPU (no distributed)
    * How to use:
        1. in python, import and decorate the top-level function to be re-run after code edits:
            ```python
            from fairseq.utils import hotreload_function
            ....
            @hotreload_function("train_step")
            def train_step(self, sample ....):
                ....
            ....
            ```
        2. in bash run scripts:
            ```bash
            watch_dir=<home>/fairseq-py/fairseq/tasks # directory to watch for file changes
            export CUDA_VISIBLE_DEVICES=0 # single-gpu
            HOTRELOAD_PAUSE=1 CUDA_LAUNCH_BLOCKING=1 python -m jurigged -w ${watch_dir} --poll 2 -v train.py ......
            ```
    * NOTE:
        1. -w ${watch_dir} specify all the files to be watched for changes
            once functions, class, ... code are changed, all instances in the process will get updated (hot-reload)
    * Limitation:
        * Currently distributed debugging not working
        * Need to launch train.py locally (cannot submit jobs)
    """
    try:
        import jurigged
    except ImportError as e:
        logger.warning("Please install jurigged: pip install jurigged[develoop]")
        raise e
    from fairseq.distributed import utils as distributed_utils
    import traceback

    def hotreload_decorator(func):
        assert callable(func), f"not callable: {func}"
        jname = name or func.__name__
        logger.info(f"jurigged-hotreload:Apply jurigged on {jname}:{func.__name__}")
        HOTRELOAD_PAUSE = bool(os.environ.get("HOTRELOAD_PAUSE", 0))
        cublk = bool(os.environ.get("CUDA_LAUNCH_BLOCKING", 0))
        prefix = f"HOTRELOAD:{jname}:[cublk={cublk}]"
        hot_reload_state = {"disable": False}

        def func_wrapper(*args, **kwargs):
            if not HOTRELOAD_PAUSE or hot_reload_state["disable"]:
                return func(*args, **kwargs)
            world_size = distributed_utils.get_global_world_size()
            assert (
                world_size <= 1
            ), f"HOTRELOAD_PAUSE:{jname} currently cannot do distributed training"
            success = False
            while not success:
                try:
                    output = func(*args, **kwargs)
                    # success = True
                    end_action = input(
                        f"{prefix}: PAUSE, you may edit code now. Enter to re-run, ctrl+C to terminate, "
                        f'type "done" to continue (function still being watched), or type "disable" to stop pausing this function :'
                    )
                    if end_action.strip().lower() in ["disable", "done"]:
                        success = True
                    else:
                        logger.warning(
                            f"{prefix}: action={end_action} function will re-run now."
                        )
                except Exception as e:
                    action = input(
                        f"{prefix}:ERROR: \n{traceback.format_exc()}\n"
                        f'Edit code to try again: enter to continue, ctrl+C to terminate, or type "raise" to raise the exception: '
                    )
                    if action.strip().lower() == "raise":
                        raise e

            if end_action.strip().lower() == "disable":
                logger.warning(
                    f"{prefix}: Stop pausing {jname}. The function is still being watched and newly editted code will take effect "
                    f"if the {jname} is called again later."
                    f' "unset HOTRELOAD_PAUSE" before relaunch to disable hotreload and'
                    f" remove @hotreload_function decorator in the code."
                )
                hot_reload_state["disable"] = True
            return output

        return func_wrapper

    return hotreload_decorator