text stringlengths 1 1.02k | class_index int64 0 271 | source stringclasses 76
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|---|---|---|
def _generate_tables(self, files):
pa_table_names = list(self.config.features) if self.config.features is not None else ["text"]
for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "... | 181 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/text/text.py |
batch = [line.rstrip("\n") for line in batch]
pa_table = pa.Table.from_arrays([pa.array(batch)], names=pa_table_names)
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_ta... | 181 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/text/text.py |
batch = batch.split("\n\n")
pa_table = pa.Table.from_arrays(
[pa.array([example for example in batch[:-1] if example])], names=pa_table_names
)
# Uncomment for debugging (will print the Arrow table size and elements)
... | 181 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/text/text.py |
text = f.read()
pa_table = pa.Table.from_arrays([pa.array([text])], names=pa_table_names)
yield file_idx, self._cast_table(pa_table) | 181 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/text/text.py |
class ParquetConfig(datasets.BuilderConfig):
"""BuilderConfig for Parquet."""
batch_size: Optional[int] = None
columns: Optional[List[str]] = None
features: Optional[datasets.Features] = None
filters: Optional[Union[ds.Expression, List[tuple], List[List[tuple]]]] = None
def __post_init__(self)... | 182 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/parquet/parquet.py |
class Parquet(datasets.ArrowBasedBuilder):
BUILDER_CONFIG_CLASS = ParquetConfig
def _info(self):
if (
self.config.columns is not None
and self.config.features is not None
and set(self.config.columns) != set(self.config.features)
):
raise ValueErro... | 183 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/parquet/parquet.py |
def _split_generators(self, dl_manager):
"""We handle string, list and dicts in datafiles"""
if not self.config.data_files:
raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}")
dl_manager.download_config.extract_on_the_fly = True
... | 183 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/parquet/parquet.py |
self.info.features = datasets.Features.from_arrow_schema(pq.read_schema(f))
break
splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={"files": files}))
if self.config.columns is not None and set(self.config.columns) != set(self.info.features):
self.info... | 183 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/parquet/parquet.py |
def _cast_table(self, pa_table: pa.Table) -> pa.Table:
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
pa_table = table_cast(pa_table, self.info.features.... | 183 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/parquet/parquet.py |
def _generate_tables(self, files):
if self.config.features is not None and self.config.columns is not None:
if sorted(field.name for field in self.info.features.arrow_schema) != sorted(self.config.columns):
raise ValueError(
f"Tried to load parquet data with colum... | 183 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/parquet/parquet.py |
for batch_idx, record_batch in enumerate(
parquet_fragment.to_batches(
batch_size=batch_size,
columns=self.config.columns,
filter=filter_expr,
batch_readahead=0,
... | 183 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/parquet/parquet.py |
logger.error(f"Failed to read file '{file}' with error {type(e)}: {e}")
raise | 183 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/parquet/parquet.py |
class VideoFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""BuilderConfig for ImageFolder."""
drop_labels: bool = None
drop_metadata: bool = None
def __post_init__(self):
super().__post_init__() | 184 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/videofolder/videofolder.py |
class VideoFolder(folder_based_builder.FolderBasedBuilder):
BASE_FEATURE = datasets.Video
BASE_COLUMN_NAME = "video"
BUILDER_CONFIG_CLASS = VideoFolderConfig
EXTENSIONS: List[str] # definition at the bottom of the script | 185 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/videofolder/videofolder.py |
class AudioFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""Builder Config for AudioFolder."""
drop_labels: bool = None
drop_metadata: bool = None
def __post_init__(self):
super().__post_init__() | 186 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/audiofolder/audiofolder.py |
class AudioFolder(folder_based_builder.FolderBasedBuilder):
BASE_FEATURE = datasets.Audio
BASE_COLUMN_NAME = "audio"
BUILDER_CONFIG_CLASS = AudioFolderConfig
EXTENSIONS: List[str] # definition at the bottom of the script | 187 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/audiofolder/audiofolder.py |
class SparkConfig(datasets.BuilderConfig):
"""BuilderConfig for Spark."""
features: Optional[datasets.Features] = None
def __post_init__(self):
super().__post_init__() | 188 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
class SparkExamplesIterable(_BaseExamplesIterable):
def __init__(
self,
df: "pyspark.sql.DataFrame",
partition_order=None,
):
super().__init__()
self.df = df
self.partition_order = partition_order or range(self.df.rdd.getNumPartitions())
def _init_state_dict(... | 189 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
def shard_data_sources(self, num_shards: int, index: int, contiguous=True) -> "SparkExamplesIterable":
partition_order = self.split_shard_indices_by_worker(num_shards=num_shards, index=index, contiguous=contiguous)
return SparkExamplesIterable(self.df, partition_order=partition_order)
@property
... | 189 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
class Spark(datasets.DatasetBuilder):
BUILDER_CONFIG_CLASS = SparkConfig
def __init__(
self,
df: "pyspark.sql.DataFrame",
cache_dir: str = None,
working_dir: str = None,
**config_kwargs,
):
import pyspark
self._spark = pyspark.sql.SparkSession.builde... | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
# Returns the path of the created file.
def create_cache_and_write_probe(context):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(cache_dir, exist_ok=True)
probe_file = os.path... | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
probe = (
self._spark.... | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
def get_arrow_batch_size(it):
for batch in it:
yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]}) | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
df_num_rows = self.df.count()
sample_num_rows = df_num_rows if df_num_rows <= 100 else 100
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
approx_bytes_per_row = (
self.df.limit(sample_num_rows)
.repartition(1)
.map... | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
def _prepare_split_single(
self,
fpath: str,
file_format: str,
max_shard_size: int,
) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
import pyspark
writer_class = ParquetWriter if file_format == "parquet" else ArrowWriter
working_fpath = os.path.join(self.... | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
def write_arrow(it):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
task_id = pyspark.TaskContext().taskAttemptId()
first_batch = next(it, None)
if first_batch is None:
# Some partitions might not receive any data.
... | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
num_examples, num_bytes = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]],
names=... | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
if writer._num_bytes > 0:
num_examples, num_bytes = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]],
names=["task_id", "num_examples", "num_bytes"],
)
... | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
stats = (
self.df.mapInArrow(write_arrow, "task_id: long, num_examples: long, num_bytes: long")
.groupBy("task_id")
.agg(
pyspark.sql.functions.sum("num_examples").alias("total_num_examples"),
pyspark.sql.functions.sum("num_bytes").alias("total_num_byt... | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
max_shard_size = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE)
self._repartition_df_if_needed(max_shard_size)
is_local = not is_remote_filesystem(self._fs)
path_join = os.path.join if is_local else posixpath.join
SUFFIX = "-TTTTT-SSSSS-of-NNNNN"
fname = f"{self.name... | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
for task_id, content in self._prepare_split_single(fpath, file_format, max_shard_size):
(
num_examples,
num_bytes,
num_shards,
shard_lengths,
) = content
if num_bytes > 0:
total_num_examples += num_exampl... | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
fs = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
task_id: int,
... | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
args = []
global_shard_id = 0
for i in range(len(task_id_and_num_shards)):
task_id, num_shards = task_id_and_num_shards[i]
for shard_id in range(num_shards):
args.append([task_id, shard_id, global_shard_id])
global_shard_id ... | 190 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/spark/spark.py |
class PandasConfig(datasets.BuilderConfig):
"""BuilderConfig for Pandas."""
features: Optional[datasets.Features] = None
def __post_init__(self):
super().__post_init__() | 191 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/pandas/pandas.py |
class Pandas(datasets.ArrowBasedBuilder):
BUILDER_CONFIG_CLASS = PandasConfig
def _info(self):
warnings.warn(
"The Pandas builder is deprecated and will be removed in the next major version of datasets.",
FutureWarning,
)
return datasets.DatasetInfo(features=self... | 192 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/pandas/pandas.py |
def _split_generators(self, dl_manager):
"""We handle string, list and dicts in datafiles"""
if not self.config.data_files:
raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}")
data_files = dl_manager.download_and_extract(self.con... | 192 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/pandas/pandas.py |
files = [dl_manager.iter_files(file) for file in files]
splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={"files": files}))
return splits | 192 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/pandas/pandas.py |
def _cast_table(self, pa_table: pa.Table) -> pa.Table:
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
pa_table = table_cast(pa_table, self.config.featu... | 192 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/pandas/pandas.py |
class SqlConfig(datasets.BuilderConfig):
"""BuilderConfig for SQL."""
sql: Union[str, "sqlalchemy.sql.Selectable"] = None
con: Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] = None
index_col: Optional[Union[str, List[str]]] = None
coerce_float: bool = T... | 193 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/sql/sql.py |
def create_config_id(
self,
config_kwargs: dict,
custom_features: Optional[datasets.Features] = None,
) -> str:
config_kwargs = config_kwargs.copy()
# We need to stringify the Selectable object to make its hash deterministic
# The process of stringifying is explained... | 193 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/sql/sql.py |
if isinstance(sql, sqlalchemy.sql.Selectable):
engine = sqlalchemy.create_engine(config_kwargs["con"].split("://")[0] + "://")
sql_str = str(sql.compile(dialect=engine.dialect))
config_kwargs["sql"] = sql_str
else:
raise Typ... | 193 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/sql/sql.py |
return super().create_config_id(config_kwargs, custom_features=custom_features)
@property
def pd_read_sql_kwargs(self):
pd_read_sql_kwargs = {
"index_col": self.index_col,
"columns": self.columns,
"params": self.params,
"coerce_float": self.coerce_float,
... | 193 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/sql/sql.py |
class Sql(datasets.ArrowBasedBuilder):
BUILDER_CONFIG_CLASS = SqlConfig
def _info(self):
return datasets.DatasetInfo(features=self.config.features)
def _split_generators(self, dl_manager):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={})]
def _cast_table(self,... | 194 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/sql/sql.py |
def _generate_tables(self):
chunksize = self.config.chunksize
sql_reader = pd.read_sql(
self.config.sql, self.config.con, chunksize=chunksize, **self.config.pd_read_sql_kwargs
)
sql_reader = [sql_reader] if chunksize is None else sql_reader
for chunk_idx, df in enumer... | 194 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/packaged_modules/sql/sql.py |
class DatasetViewerError(DatasetsError):
"""Dataset viewer error.
Raised when trying to use the dataset viewer HTTP API and when trying to access:
- a missing dataset, or
- a private/gated dataset and the user is not authenticated.
- unavailable /parquet or /info responses
""" | 195 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/_dataset_viewer.py |
class _NoDuplicateSafeLoader(yaml.SafeLoader):
def _check_no_duplicates_on_constructed_node(self, node):
keys = [self.constructed_objects[key_node] for key_node, _ in node.value]
keys = [tuple(key) if isinstance(key, list) else key for key in keys]
counter = Counter(keys)
duplicate_k... | 196 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/metadata.py |
class MetadataConfigs(Dict[str, Dict[str, Any]]):
"""Should be in format {config_name: {**config_params}}."""
FIELD_NAME: ClassVar[str] = METADATA_CONFIGS_FIELD
@staticmethod
def _raise_if_data_files_field_not_valid(metadata_config: dict):
yaml_data_files = metadata_config.get("data_files")
... | 197 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/metadata.py |
data_files:
- split: train
path:
- train/part1/*
- train/part2/*
- split: test
path: test/* | 197 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/metadata.py |
PS: some symbols like dashes '-' are not allowed in split names
"""
)
if not isinstance(yaml_data_files, (list, str)):
raise ValueError(yaml_error_message)
if isinstance(yaml_data_files, list):
for yaml_data_files_item in yaml_data_file... | 197 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/metadata.py |
@classmethod
def _from_exported_parquet_files_and_dataset_infos(
cls,
parquet_commit_hash: str,
exported_parquet_files: List[Dict[str, Any]],
dataset_infos: DatasetInfosDict,
) -> "MetadataConfigs":
metadata_configs = {
config_name: {
"data_fil... | 197 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/metadata.py |
}
if dataset_infos:
# Preserve order of configs and splits
metadata_configs = {
config_name: {
"data_files": [
data_file
for split_name in dataset_info.splits
for data_file in meta... | 197 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/metadata.py |
@classmethod
def from_dataset_card_data(cls, dataset_card_data: DatasetCardData) -> "MetadataConfigs":
if dataset_card_data.get(cls.FIELD_NAME):
metadata_configs = dataset_card_data[cls.FIELD_NAME]
if not isinstance(metadata_configs, list):
raise ValueError(f"Expected... | 197 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/metadata.py |
for param, value in config.items()
}
for metadata_config in metadata_configs
if (config := metadata_config.copy())
}
)
return cls() | 197 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/metadata.py |
def to_dataset_card_data(self, dataset_card_data: DatasetCardData) -> None:
if self:
for metadata_config in self.values():
self._raise_if_data_files_field_not_valid(metadata_config)
current_metadata_configs = self.from_dataset_card_data(dataset_card_data)
tota... | 197 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/metadata.py |
def get_default_config_name(self) -> Optional[str]:
default_config_name = None
for config_name, metadata_config in self.items():
if len(self) == 1 or config_name == "default" or metadata_config.get("default"):
if default_config_name is None:
default_config... | 197 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/metadata.py |
class Version:
"""Dataset version `MAJOR.MINOR.PATCH`.
Args:
version_str (`str`):
The dataset version.
description (`str`):
A description of what is new in this version.
major (`str`):
minor (`str`):
patch (`str`):
Example:
```py
>>>... | 198 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/version.py |
def _validate_operand(self, other):
if isinstance(other, str):
return Version(other)
elif isinstance(other, Version):
return other
raise TypeError(f"{other} (type {type(other)}) cannot be compared to version.")
def __eq__(self, other):
try:
other ... | 198 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/version.py |
class tracked_str(str):
origins = {}
def set_origin(self, origin: str):
if super().__repr__() not in self.origins:
self.origins[super().__repr__()] = origin
def get_origin(self):
return self.origins.get(super().__repr__(), str(self))
def __repr__(self) -> str:
if s... | 199 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/track.py |
class tracked_list(list):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.last_item = None
def __iter__(self) -> Iterator:
for x in super().__iter__():
self.last_item = x
yield x
self.last_item = None
def __repr__(... | 200 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/track.py |
class TrackedIterableFromGenerator(Iterable):
"""Utility class to create an iterable from a generator function, in order to reset the generator when needed."""
def __init__(self, generator, *args):
super().__init__()
self.generator = generator
self.args = args
self.last_item = N... | 201 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/track.py |
class NonMutableDict(dict):
"""Dict where keys can only be added but not modified.
Will raise an error if the user try to overwrite one key. The error message
can be customized during construction. It will be formatted using {key} for
the overwritten key.
"""
def __init__(self, *args, **kwargs... | 202 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/py_utils.py |
class classproperty(property): # pylint: disable=invalid-name
"""Descriptor to be used as decorator for @classmethods."""
def __get__(self, obj, objtype=None):
return self.fget.__get__(None, objtype)() | 203 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/py_utils.py |
class NestedDataStructure:
def __init__(self, data=None):
self.data = data if data is not None else []
def flatten(self, data=None):
data = data if data is not None else self.data
if isinstance(data, dict):
return self.flatten(list(data.values()))
elif isinstance(dat... | 204 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/py_utils.py |
class tqdm(old_tqdm):
"""
Class to override `disable` argument in case progress bars are globally disabled.
Taken from https://github.com/tqdm/tqdm/issues/619#issuecomment-619639324.
"""
def __init__(self, *args, **kwargs):
if are_progress_bars_disabled():
kwargs["disable"] = T... | 205 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/tqdm.py |
class _PatchedModuleObj:
"""Set all the modules components as attributes of the _PatchedModuleObj object."""
def __init__(self, module, attrs=None):
attrs = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("__"):
... | 206 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/patching.py |
class patch_submodule:
"""
Patch a submodule attribute of an object, by keeping all other submodules intact at all levels.
Example::
>>> import importlib
>>> from datasets.load import dataset_module_factory
>>> from datasets.streaming import patch_submodule, xjoin
>>>
... | 207 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/patching.py |
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path" | 207 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/patching.py |
for i in range(len(submodules)):
try:
submodule = import_module(".".join(submodules[: i + 1]))
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__... | 207 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/patching.py |
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(patched, key, _PatchedModuleObj(getattr(patched, key, None), attrs=self.attrs))
patched = getattr(patched, key)
# finally set the target attribute
... | 207 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/patching.py |
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join". | 207 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/patching.py |
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
attr_value = getattr(import_module(".".join(submodules)), target_attr)
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in ... | 207 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/patching.py |
raise RuntimeError(f"Tried to patch attribute {target_attr} instead of a submodule.") | 207 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/patching.py |
def __exit__(self, *exc_info):
for attr in list(self.original):
setattr(self.obj, attr, self.original.pop(attr))
def start(self):
"""Activate a patch."""
self.__enter__()
self._active_patches.append(self)
def stop(self):
"""Stop an active patch."""
t... | 207 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/patching.py |
class FileLock(FileLock_):
"""
A `filelock.FileLock` initializer that handles long paths.
It also uses the current umask for lock files.
"""
MAX_FILENAME_LENGTH = 255
def __init__(self, lock_file, *args, **kwargs):
# The "mode" argument is required if we want to use the current umask i... | 208 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/_filelock.py |
@classmethod
def hash_filename_if_too_long(cls, path: str) -> str:
path = os.path.abspath(os.path.expanduser(path))
filename = os.path.basename(path)
max_filename_length = cls.MAX_FILENAME_LENGTH
if issubclass(cls, UnixFileLock):
max_filename_length = min(max_filename_len... | 208 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/_filelock.py |
class TqdmCallback(fsspec.callbacks.TqdmCallback):
def __init__(self, tqdm_kwargs=None, *args, **kwargs):
if config.FSSPEC_VERSION < version.parse("2024.2.0"):
super().__init__(tqdm_kwargs, *args, **kwargs)
self._tqdm = _tqdm # replace tqdm module by datasets.utils.tqdm module
... | 209 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
class NonStreamableDatasetError(Exception):
pass | 210 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
class xPath(type(Path())):
"""Extension of `pathlib.Path` to support both local paths and remote URLs."""
def __str__(self):
path_str = super().__str__()
main_hop, *rest_hops = path_str.split("::")
if is_local_path(main_hop):
return main_hop
path_as_posix = path_str.... | 211 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
def glob(self, pattern, download_config: Optional[DownloadConfig] = None):
"""Glob function for argument of type :obj:`~pathlib.Path` that supports both local paths end remote URLs.
Args:
pattern (`str`): Pattern that resulting paths must match.
download_config : mainly use toke... | 211 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
Yields:
[`xPath`]
"""
posix_path = self.as_posix()
main_hop, *rest_hops = posix_path.split("::")
if is_local_path(main_hop):
yield from Path(main_hop).glob(pattern)
else:
# globbing inside a zip in a private repo requires authentication
... | 211 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
def rglob(self, pattern, **kwargs):
"""Rglob function for argument of type :obj:`~pathlib.Path` that supports both local paths end remote URLs.
Args:
pattern (`str`): Pattern that resulting paths must match.
Yields:
[`xPath`]
"""
return self.glob("**/" +... | 211 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
@property
def stem(self) -> str:
"""Stem function for argument of type :obj:`~pathlib.Path` that supports both local paths end remote URLs.
Returns:
`str`
"""
return PurePosixPath(self.as_posix().split("::")[0]).stem
@property
def suffix(self) -> str:
""... | 211 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
def joinpath(self, *p: Tuple[str, ...]) -> "xPath":
"""Extend :func:`xjoin` to support argument of type :obj:`~pathlib.Path`.
Args:
*p (`tuple` of `str`): Other path components.
Returns:
[`xPath`]
"""
return type(self)(xjoin(self.as_posix(), *p))
de... | 211 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
class ArchiveIterable(TrackedIterableFromGenerator):
"""An iterable of (path, fileobj) from a TAR archive, used by `iter_archive`"""
@staticmethod
def _iter_tar(f):
stream = tarfile.open(fileobj=f, mode="r|*")
for tarinfo in stream:
file_path = tarinfo.name
if not ta... | 212 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
@staticmethod
def _iter_zip(f):
zipf = zipfile.ZipFile(f)
for member in zipf.infolist():
file_path = member.filename
if member.is_dir():
continue
if file_path is None:
continue
if os.path.basename(file_path).startswith((... | 212 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
@classmethod
def _iter_from_urlpath(
cls, urlpath: str, download_config: Optional[DownloadConfig] = None
) -> Generator[Tuple, None, None]:
compression = _get_extraction_protocol(urlpath, download_config=download_config)
# Set block_size=0 to get faster streaming
# (e.g. for hf:/... | 212 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
class FilesIterable(TrackedIterableFromGenerator):
"""An iterable of paths from a list of directories or files""" | 213 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
@classmethod
def _iter_from_urlpaths(
cls, urlpaths: Union[str, List[str]], download_config: Optional[DownloadConfig] = None
) -> Generator[str, None, None]:
if not isinstance(urlpaths, list):
urlpaths = [urlpaths]
for urlpath in urlpaths:
if xisfile(urlpath, down... | 213 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
# skipping hidden files
continue
yield xjoin(dirpath, filename)
else:
raise FileNotFoundError(urlpath) | 213 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
@classmethod
def from_urlpaths(cls, urlpaths, download_config: Optional[DownloadConfig] = None) -> "FilesIterable":
return cls(cls._iter_from_urlpaths, urlpaths, download_config) | 213 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/file_utils.py |
class Pickler(dill.Pickler):
dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy())
_legacy_no_dict_keys_sorting = False
def save(self, obj, save_persistent_id=True):
obj_type = type(obj)
if obj_type not in self.dispatch:
if "regex" in sys.modules:
... | 214 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/_dill.py |
if issubclass(obj_type, torch.Tensor):
pklregister(obj_type)(_save_torchTensor)
if obj_type is torch.Generator:
pklregister(obj_type)(_save_torchGenerator)
# Unwrap `torch.compile`-ed modules
if issubclass(obj_type, torch.nn.Modul... | 214 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/_dill.py |
def _batch_setitems(self, items):
if self._legacy_no_dict_keys_sorting:
return super()._batch_setitems(items)
# Ignore the order of keys in a dict
try:
# Faster, but fails for unorderable elements
items = sorted(items)
except Exception: # TypeError, d... | 214 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/_dill.py |
class ExtractManager:
def __init__(self, cache_dir: Optional[str] = None):
self.extract_dir = (
os.path.join(cache_dir, config.EXTRACTED_DATASETS_DIR) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
self.extractor = Extractor
def _get_output_path(self, path: str) -> str:
... | 215 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/extract.py |
def extract(self, input_path: str, force_extract: bool = False) -> str:
extractor_format = self.extractor.infer_extractor_format(input_path)
if not extractor_format:
return input_path
output_path = self._get_output_path(input_path)
if self._do_extract(output_path, force_extra... | 215 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/extract.py |
class BaseExtractor(ABC):
@classmethod
@abstractmethod
def is_extractable(cls, path: Union[Path, str], **kwargs) -> bool: ...
@staticmethod
@abstractmethod
def extract(input_path: Union[Path, str], output_path: Union[Path, str]) -> None: ... | 216 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/extract.py |
class MagicNumberBaseExtractor(BaseExtractor, ABC):
magic_numbers: List[bytes] = []
@staticmethod
def read_magic_number(path: Union[Path, str], magic_number_length: int):
with open(path, "rb") as f:
return f.read(magic_number_length)
@classmethod
def is_extractable(cls, path: U... | 217 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/extract.py |
class TarExtractor(BaseExtractor):
@classmethod
def is_extractable(cls, path: Union[Path, str], **kwargs) -> bool:
return tarfile.is_tarfile(path)
@staticmethod
def safemembers(members, output_path):
"""
Fix for CVE-2007-4559
Desc:
Directory traversal vulnera... | 218 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/extract.py |
def badlink(info, base: str) -> bool:
# Links are interpreted relative to the directory containing the link
tip = resolved(os.path.join(base, os.path.dirname(info.name)))
return badpath(info.linkname, base=tip)
base = resolved(output_path)
for finfo in members:
... | 218 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/extract.py |
@staticmethod
def extract(input_path: Union[Path, str], output_path: Union[Path, str]) -> None:
os.makedirs(output_path, exist_ok=True)
tar_file = tarfile.open(input_path)
tar_file.extractall(output_path, members=TarExtractor.safemembers(tar_file, output_path))
tar_file.close() | 218 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/utils/extract.py |
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