ai-content-maker/.venv/Lib/site-packages/huggingface_hub/lfs.py

542 lines
19 KiB
Python

# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Git LFS related type definitions and utilities"""
import inspect
import io
import os
import re
import warnings
from contextlib import AbstractContextManager
from dataclasses import dataclass
from math import ceil
from os.path import getsize
from pathlib import Path
from typing import TYPE_CHECKING, BinaryIO, Dict, Iterable, List, Optional, Tuple, TypedDict
from urllib.parse import unquote
from huggingface_hub.constants import ENDPOINT, HF_HUB_ENABLE_HF_TRANSFER, REPO_TYPES_URL_PREFIXES
from .utils import (
build_hf_headers,
fix_hf_endpoint_in_url,
get_session,
hf_raise_for_status,
http_backoff,
logging,
tqdm,
validate_hf_hub_args,
)
from .utils.sha import sha256, sha_fileobj
if TYPE_CHECKING:
from ._commit_api import CommitOperationAdd
logger = logging.get_logger(__name__)
OID_REGEX = re.compile(r"^[0-9a-f]{40}$")
LFS_MULTIPART_UPLOAD_COMMAND = "lfs-multipart-upload"
LFS_HEADERS = {
"Accept": "application/vnd.git-lfs+json",
"Content-Type": "application/vnd.git-lfs+json",
}
@dataclass
class UploadInfo:
"""
Dataclass holding required information to determine whether a blob
should be uploaded to the hub using the LFS protocol or the regular protocol
Args:
sha256 (`bytes`):
SHA256 hash of the blob
size (`int`):
Size in bytes of the blob
sample (`bytes`):
First 512 bytes of the blob
"""
sha256: bytes
size: int
sample: bytes
@classmethod
def from_path(cls, path: str):
size = getsize(path)
with io.open(path, "rb") as file:
sample = file.peek(512)[:512]
sha = sha_fileobj(file)
return cls(size=size, sha256=sha, sample=sample)
@classmethod
def from_bytes(cls, data: bytes):
sha = sha256(data).digest()
return cls(size=len(data), sample=data[:512], sha256=sha)
@classmethod
def from_fileobj(cls, fileobj: BinaryIO):
sample = fileobj.read(512)
fileobj.seek(0, io.SEEK_SET)
sha = sha_fileobj(fileobj)
size = fileobj.tell()
fileobj.seek(0, io.SEEK_SET)
return cls(size=size, sha256=sha, sample=sample)
@validate_hf_hub_args
def post_lfs_batch_info(
upload_infos: Iterable[UploadInfo],
token: Optional[str],
repo_type: str,
repo_id: str,
revision: Optional[str] = None,
endpoint: Optional[str] = None,
headers: Optional[Dict[str, str]] = None,
) -> Tuple[List[dict], List[dict]]:
"""
Requests the LFS batch endpoint to retrieve upload instructions
Learn more: https://github.com/git-lfs/git-lfs/blob/main/docs/api/batch.md
Args:
upload_infos (`Iterable` of `UploadInfo`):
`UploadInfo` for the files that are being uploaded, typically obtained
from `CommitOperationAdd.upload_info`
repo_type (`str`):
Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`.
repo_id (`str`):
A namespace (user or an organization) and a repo name separated
by a `/`.
revision (`str`, *optional*):
The git revision to upload to.
headers (`dict`, *optional*):
Additional headers to include in the request
Returns:
`LfsBatchInfo`: 2-tuple:
- First element is the list of upload instructions from the server
- Second element is an list of errors, if any
Raises:
`ValueError`: If an argument is invalid or the server response is malformed
`HTTPError`: If the server returned an error
"""
endpoint = endpoint if endpoint is not None else ENDPOINT
url_prefix = ""
if repo_type in REPO_TYPES_URL_PREFIXES:
url_prefix = REPO_TYPES_URL_PREFIXES[repo_type]
batch_url = f"{endpoint}/{url_prefix}{repo_id}.git/info/lfs/objects/batch"
payload: Dict = {
"operation": "upload",
"transfers": ["basic", "multipart"],
"objects": [
{
"oid": upload.sha256.hex(),
"size": upload.size,
}
for upload in upload_infos
],
"hash_algo": "sha256",
}
if revision is not None:
payload["ref"] = {"name": unquote(revision)} # revision has been previously 'quoted'
headers = {
**LFS_HEADERS,
**build_hf_headers(token=token),
**(headers or {}),
}
resp = get_session().post(batch_url, headers=headers, json=payload)
hf_raise_for_status(resp)
batch_info = resp.json()
objects = batch_info.get("objects", None)
if not isinstance(objects, list):
raise ValueError("Malformed response from server")
return (
[_validate_batch_actions(obj) for obj in objects if "error" not in obj],
[_validate_batch_error(obj) for obj in objects if "error" in obj],
)
class PayloadPartT(TypedDict):
partNumber: int
etag: str
class CompletionPayloadT(TypedDict):
"""Payload that will be sent to the Hub when uploading multi-part."""
oid: str
parts: List[PayloadPartT]
def lfs_upload(
operation: "CommitOperationAdd",
lfs_batch_action: Dict,
token: Optional[str] = None,
headers: Optional[Dict[str, str]] = None,
endpoint: Optional[str] = None,
) -> None:
"""
Handles uploading a given object to the Hub with the LFS protocol.
Can be a No-op if the content of the file is already present on the hub large file storage.
Args:
operation (`CommitOperationAdd`):
The add operation triggering this upload.
lfs_batch_action (`dict`):
Upload instructions from the LFS batch endpoint for this object. See [`~utils.lfs.post_lfs_batch_info`] for
more details.
headers (`dict`, *optional*):
Headers to include in the request, including authentication and user agent headers.
Raises:
- `ValueError` if `lfs_batch_action` is improperly formatted
- `HTTPError` if the upload resulted in an error
"""
# 0. If LFS file is already present, skip upload
_validate_batch_actions(lfs_batch_action)
actions = lfs_batch_action.get("actions")
if actions is None:
# The file was already uploaded
logger.debug(f"Content of file {operation.path_in_repo} is already present upstream - skipping upload")
return
# 1. Validate server response (check required keys in dict)
upload_action = lfs_batch_action["actions"]["upload"]
_validate_lfs_action(upload_action)
verify_action = lfs_batch_action["actions"].get("verify")
if verify_action is not None:
_validate_lfs_action(verify_action)
# 2. Upload file (either single part or multi-part)
header = upload_action.get("header", {})
chunk_size = header.get("chunk_size")
upload_url = fix_hf_endpoint_in_url(upload_action["href"], endpoint=endpoint)
if chunk_size is not None:
try:
chunk_size = int(chunk_size)
except (ValueError, TypeError):
raise ValueError(
f"Malformed response from LFS batch endpoint: `chunk_size` should be an integer. Got '{chunk_size}'."
)
_upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_url)
else:
_upload_single_part(operation=operation, upload_url=upload_url)
# 3. Verify upload went well
if verify_action is not None:
_validate_lfs_action(verify_action)
verify_url = fix_hf_endpoint_in_url(verify_action["href"], endpoint)
verify_resp = get_session().post(
verify_url,
headers=build_hf_headers(token=token, headers=headers),
json={"oid": operation.upload_info.sha256.hex(), "size": operation.upload_info.size},
)
hf_raise_for_status(verify_resp)
logger.debug(f"{operation.path_in_repo}: Upload successful")
def _validate_lfs_action(lfs_action: dict):
"""validates response from the LFS batch endpoint"""
if not (
isinstance(lfs_action.get("href"), str)
and (lfs_action.get("header") is None or isinstance(lfs_action.get("header"), dict))
):
raise ValueError("lfs_action is improperly formatted")
return lfs_action
def _validate_batch_actions(lfs_batch_actions: dict):
"""validates response from the LFS batch endpoint"""
if not (isinstance(lfs_batch_actions.get("oid"), str) and isinstance(lfs_batch_actions.get("size"), int)):
raise ValueError("lfs_batch_actions is improperly formatted")
upload_action = lfs_batch_actions.get("actions", {}).get("upload")
verify_action = lfs_batch_actions.get("actions", {}).get("verify")
if upload_action is not None:
_validate_lfs_action(upload_action)
if verify_action is not None:
_validate_lfs_action(verify_action)
return lfs_batch_actions
def _validate_batch_error(lfs_batch_error: dict):
"""validates response from the LFS batch endpoint"""
if not (isinstance(lfs_batch_error.get("oid"), str) and isinstance(lfs_batch_error.get("size"), int)):
raise ValueError("lfs_batch_error is improperly formatted")
error_info = lfs_batch_error.get("error")
if not (
isinstance(error_info, dict)
and isinstance(error_info.get("message"), str)
and isinstance(error_info.get("code"), int)
):
raise ValueError("lfs_batch_error is improperly formatted")
return lfs_batch_error
def _upload_single_part(operation: "CommitOperationAdd", upload_url: str) -> None:
"""
Uploads `fileobj` as a single PUT HTTP request (basic LFS transfer protocol)
Args:
upload_url (`str`):
The URL to PUT the file to.
fileobj:
The file-like object holding the data to upload.
Returns: `requests.Response`
Raises: `requests.HTTPError` if the upload resulted in an error
"""
with operation.as_file(with_tqdm=True) as fileobj:
# S3 might raise a transient 500 error -> let's retry if that happens
response = http_backoff("PUT", upload_url, data=fileobj, retry_on_status_codes=(500, 502, 503, 504))
hf_raise_for_status(response)
def _upload_multi_part(operation: "CommitOperationAdd", header: Dict, chunk_size: int, upload_url: str) -> None:
"""
Uploads file using HF multipart LFS transfer protocol.
"""
# 1. Get upload URLs for each part
sorted_parts_urls = _get_sorted_parts_urls(header=header, upload_info=operation.upload_info, chunk_size=chunk_size)
# 2. Upload parts (either with hf_transfer or in pure Python)
use_hf_transfer = HF_HUB_ENABLE_HF_TRANSFER
if (
HF_HUB_ENABLE_HF_TRANSFER
and not isinstance(operation.path_or_fileobj, str)
and not isinstance(operation.path_or_fileobj, Path)
):
warnings.warn(
"hf_transfer is enabled but does not support uploading from bytes or BinaryIO, falling back to regular"
" upload"
)
use_hf_transfer = False
response_headers = (
_upload_parts_hf_transfer(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)
if use_hf_transfer
else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)
)
# 3. Send completion request
completion_res = get_session().post(
upload_url,
json=_get_completion_payload(response_headers, operation.upload_info.sha256.hex()),
headers=LFS_HEADERS,
)
hf_raise_for_status(completion_res)
def _get_sorted_parts_urls(header: Dict, upload_info: UploadInfo, chunk_size: int) -> List[str]:
sorted_part_upload_urls = [
upload_url
for _, upload_url in sorted(
[
(int(part_num, 10), upload_url)
for part_num, upload_url in header.items()
if part_num.isdigit() and len(part_num) > 0
],
key=lambda t: t[0],
)
]
num_parts = len(sorted_part_upload_urls)
if num_parts != ceil(upload_info.size / chunk_size):
raise ValueError("Invalid server response to upload large LFS file")
return sorted_part_upload_urls
def _get_completion_payload(response_headers: List[Dict], oid: str) -> CompletionPayloadT:
parts: List[PayloadPartT] = []
for part_number, header in enumerate(response_headers):
etag = header.get("etag")
if etag is None or etag == "":
raise ValueError(f"Invalid etag (`{etag}`) returned for part {part_number + 1}")
parts.append(
{
"partNumber": part_number + 1,
"etag": etag,
}
)
return {"oid": oid, "parts": parts}
def _upload_parts_iteratively(
operation: "CommitOperationAdd", sorted_parts_urls: List[str], chunk_size: int
) -> List[Dict]:
headers = []
with operation.as_file(with_tqdm=True) as fileobj:
for part_idx, part_upload_url in enumerate(sorted_parts_urls):
with SliceFileObj(
fileobj,
seek_from=chunk_size * part_idx,
read_limit=chunk_size,
) as fileobj_slice:
# S3 might raise a transient 500 error -> let's retry if that happens
part_upload_res = http_backoff(
"PUT", part_upload_url, data=fileobj_slice, retry_on_status_codes=(500, 502, 503, 504)
)
hf_raise_for_status(part_upload_res)
headers.append(part_upload_res.headers)
return headers # type: ignore
def _upload_parts_hf_transfer(
operation: "CommitOperationAdd", sorted_parts_urls: List[str], chunk_size: int
) -> List[Dict]:
# Upload file using an external Rust-based package. Upload is faster but support less features (no progress bars).
try:
from hf_transfer import multipart_upload
except ImportError:
raise ValueError(
"Fast uploading using 'hf_transfer' is enabled (HF_HUB_ENABLE_HF_TRANSFER=1) but 'hf_transfer' package is"
" not available in your environment. Try `pip install hf_transfer`."
)
supports_callback = "callback" in inspect.signature(multipart_upload).parameters
if not supports_callback:
warnings.warn(
"You are using an outdated version of `hf_transfer`. Consider upgrading to latest version to enable progress bars using `pip install -U hf_transfer`."
)
total = operation.upload_info.size
desc = operation.path_in_repo
if len(desc) > 40:
desc = f"(…){desc[-40:]}"
# set `disable=None` rather than `disable=False` by default to disable progress bar when no TTY attached
# see https://github.com/huggingface/huggingface_hub/pull/2000
disable = True if (logger.getEffectiveLevel() == logging.NOTSET) else None
with tqdm(unit="B", unit_scale=True, total=total, initial=0, desc=desc, disable=disable) as progress:
try:
output = multipart_upload(
file_path=operation.path_or_fileobj,
parts_urls=sorted_parts_urls,
chunk_size=chunk_size,
max_files=128,
parallel_failures=127, # could be removed
max_retries=5,
**({"callback": progress.update} if supports_callback else {}),
)
except Exception as e:
raise RuntimeError(
"An error occurred while uploading using `hf_transfer`. Consider disabling HF_HUB_ENABLE_HF_TRANSFER for"
" better error handling."
) from e
if not supports_callback:
progress.update(total)
return output
class SliceFileObj(AbstractContextManager):
"""
Utility context manager to read a *slice* of a seekable file-like object as a seekable, file-like object.
This is NOT thread safe
Inspired by stackoverflow.com/a/29838711/593036
Credits to @julien-c
Args:
fileobj (`BinaryIO`):
A file-like object to slice. MUST implement `tell()` and `seek()` (and `read()` of course).
`fileobj` will be reset to its original position when exiting the context manager.
seek_from (`int`):
The start of the slice (offset from position 0 in bytes).
read_limit (`int`):
The maximum number of bytes to read from the slice.
Attributes:
previous_position (`int`):
The previous position
Examples:
Reading 200 bytes with an offset of 128 bytes from a file (ie bytes 128 to 327):
```python
>>> with open("path/to/file", "rb") as file:
... with SliceFileObj(file, seek_from=128, read_limit=200) as fslice:
... fslice.read(...)
```
Reading a file in chunks of 512 bytes
```python
>>> import os
>>> chunk_size = 512
>>> file_size = os.getsize("path/to/file")
>>> with open("path/to/file", "rb") as file:
... for chunk_idx in range(ceil(file_size / chunk_size)):
... with SliceFileObj(file, seek_from=chunk_idx * chunk_size, read_limit=chunk_size) as fslice:
... chunk = fslice.read(...)
```
"""
def __init__(self, fileobj: BinaryIO, seek_from: int, read_limit: int):
self.fileobj = fileobj
self.seek_from = seek_from
self.read_limit = read_limit
def __enter__(self):
self._previous_position = self.fileobj.tell()
end_of_stream = self.fileobj.seek(0, os.SEEK_END)
self._len = min(self.read_limit, end_of_stream - self.seek_from)
# ^^ The actual number of bytes that can be read from the slice
self.fileobj.seek(self.seek_from, io.SEEK_SET)
return self
def __exit__(self, exc_type, exc_value, traceback):
self.fileobj.seek(self._previous_position, io.SEEK_SET)
def read(self, n: int = -1):
pos = self.tell()
if pos >= self._len:
return b""
remaining_amount = self._len - pos
data = self.fileobj.read(remaining_amount if n < 0 else min(n, remaining_amount))
return data
def tell(self) -> int:
return self.fileobj.tell() - self.seek_from
def seek(self, offset: int, whence: int = os.SEEK_SET) -> int:
start = self.seek_from
end = start + self._len
if whence in (os.SEEK_SET, os.SEEK_END):
offset = start + offset if whence == os.SEEK_SET else end + offset
offset = max(start, min(offset, end))
whence = os.SEEK_SET
elif whence == os.SEEK_CUR:
cur_pos = self.fileobj.tell()
offset = max(start - cur_pos, min(offset, end - cur_pos))
else:
raise ValueError(f"whence value {whence} is not supported")
return self.fileobj.seek(offset, whence) - self.seek_from
def __iter__(self):
yield self.read(n=4 * 1024 * 1024)