def __init__(self, processor_num: int = None, ):
self.processor_num = cpu_count() if processor_num is None \
else min(processor_num, cpu_count())
LOGGER.debug('Building Pathos multi-processing pool with {} cores.'.format(self.processor_num))
self._pool = Pool(self.processor_num)
# -*- coding: utf-8 -*-
import os
import socket
import getpass
from ncc import LOGGER
HOSTNAME = socket.gethostname()
USERNAME = getpass.getuser()
# register your hostname or username
DEFAULT_DIR = '~/.ncc'
DEFAULT_DIR = os.path.expanduser(DEFAULT_DIR)
LIBS_DIR = os.path.join(os.path.dirname(__file__), 'tree-sitter-libs')
LOGGER.debug('Host Name: {}; User Name: {}; Default data directory: {}'.format(
HOSTNAME, USERNAME, DEFAULT_DIR))
__all__ = (
HOSTNAME,
USERNAME,
DEFAULT_DIR,
LIBS_DIR,
LOGGER,
)
from botocore.exceptions import ClientError
from filelock import FileLock
from tqdm.auto import tqdm
from ncc import LOGGER
from ncc import __VERSION__
try:
USE_TF = os.environ.get("USE_TF", "AUTO").upper()
USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
if USE_TORCH in ("1", "ON", "YES", "AUTO") and USE_TF not in ("1", "ON",
"YES"):
import torch
_torch_available = True # pylint: disable=invalid-name
LOGGER.debug("PyTorch version {} available.".format(torch.__version__))
else:
LOGGER.info("Disabling PyTorch because USE_TF is set")
_torch_available = False
except ImportError:
_torch_available = False # pylint: disable=invalid-name
try:
USE_TF = os.environ.get("USE_TF", "AUTO").upper()
USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
if USE_TF in ("1", "ON", "YES", "AUTO") and USE_TORCH not in ("1", "ON",
"YES"):
import tensorflow as tf
assert hasattr(tf, "__version__") and int(tf.__version__[0]) >= 2
