text stringlengths 1 93.6k |
|---|
v = ckp.pop(k)
|
if isinstance(v, np.ndarray):
|
v = torch.tensor(v)
|
else:
|
assert isinstance(v, torch.tensor), type(v)
|
r[k] = v
|
return r
|
class Config:
|
_pointer = {}
|
def __init__(self, dictionary: dict, name: str = "root", level=0):
|
self._name = name
|
self._level = level
|
d = {}
|
for k, v in dictionary.items():
|
if v is None:
|
raise ValueError()
|
k = copy.deepcopy(k)
|
v = copy.deepcopy(v)
|
if isinstance(v, dict):
|
v = Config(v, name=k, level=level + 1)
|
d[k] = v
|
setattr(self, k, v)
|
self._pointer = d
|
def __repr__(self):
|
return str(list((self._pointer.keys())))
|
def __setattr__(self, key, val):
|
self.__dict__[key] = val
|
self.__dict__[key.upper()] = val
|
levels = key.split(".")
|
last_level = len(levels) - 1
|
pointer = self._pointer
|
if len(levels) > 1:
|
for i, l in enumerate(levels):
|
if hasattr(self, l) and isinstance(getattr(self, l), Config):
|
setattr(getattr(self, l), ".".join(levels[i:]), val)
|
if l == last_level:
|
pointer[l] = val
|
else:
|
pointer = pointer[l]
|
def to_dict(self):
|
return self._pointer
|
def dump_yaml(self, data, file_name):
|
with open(f"{file_name}", "w") as stream:
|
dump(data, stream)
|
def dump_json(self, data, file_name):
|
with open(f"{file_name}", "w") as stream:
|
json.dump(data, stream)
|
@staticmethod
|
def load_yaml(config):
|
with open(config) as stream:
|
data = load(stream, Loader=Loader)
|
return data
|
def __str__(self):
|
t = " "
|
if self._name != "root":
|
r = f"{t * (self._level-1)}{self._name}:\n"
|
else:
|
r = ""
|
level = self._level
|
for i, (k, v) in enumerate(self._pointer.items()):
|
if isinstance(v, Config):
|
r += f"{t * (self._level)}{v}\n"
|
self._level += 1
|
else:
|
r += f"{t * (self._level)}{k}: {v} ({type(v).__name__})\n"
|
self._level = level
|
return r[:-1]
|
@classmethod
|
def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
|
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
|
return cls(config_dict)
|
@classmethod
|
def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs):
|
cache_dir = kwargs.pop("cache_dir", None)
|
force_download = kwargs.pop("force_download", False)
|
resume_download = kwargs.pop("resume_download", False)
|
proxies = kwargs.pop("proxies", None)
|
local_files_only = kwargs.pop("local_files_only", False)
|
if os.path.isdir(pretrained_model_name_or_path):
|
config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
|
elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
|
config_file = pretrained_model_name_or_path
|
else:
|
config_file = hf_bucket_url(pretrained_model_name_or_path, filename=CONFIG_NAME, use_cdn=False)
|
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