/usr/local/lib/python3.6/site-packages/datasets/io
Edit: /usr/local/lib/python3.6/site-packages/datasets/io/csv.py (4739B)
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class CsvDatasetReader(AbstractDatasetReader):
def __init__(
self,
path_or_paths: NestedDataStructureLike[PathLike],
split: Optional[NamedSplit] = None,
features: Optional[Features] = None,
cache_dir: str = None,
keep_in_memory: bool = False,
**kwargs,
):
super().__init__(
path_or_paths, split=split, features=features, cache_dir=cache_dir, keep_in_memory=keep_in_memory, **kwargs
)
path_or_paths = path_or_paths if isinstance(path_or_paths, dict) else {self.split: path_or_paths}
self.builder = Csv(
cache_dir=cache_dir,
data_files=path_or_paths,
features=features,
**kwargs,
)
def read(self):
download_config = None
download_mode = None
ignore_verifications = False
use_auth_token = None
base_path = None
self.builder.download_and_prepare(
download_config=download_config,
download_mode=download_mode,
ignore_verifications=ignore_verifications,
# try_from_hf_gcs=try_from_hf_gcs,
base_path=base_path,
use_auth_token=use_auth_token,
)
# Build dataset for splits
dataset = self.builder.as_dataset(
split=self.split, ignore_verifications=ignore_verifications, in_memory=self.keep_in_memory
)
return dataset
class CsvDatasetWriter:
def __init__(
self,
dataset: Dataset,
path_or_buf: Union[PathLike, BinaryIO],
batch_size: Optional[int] = None,
num_proc: Optional[int] = None,
**to_csv_kwargs,
):
if num_proc is not None and num_proc <= 0:
raise ValueError(f"num_proc {num_proc} must be an integer > 0.")
self.dataset = dataset
self.path_or_buf = path_or_buf
self.batch_size = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
self.num_proc = num_proc
self.encoding = "utf-8"
self.to_csv_kwargs = to_csv_kwargs
def write(self) -> int:
_ = self.to_csv_kwargs.pop("path_or_buf", None)
if isinstance(self.path_or_buf, (str, bytes, os.PathLike)):
with open(self.path_or_buf, "wb+") as buffer:
written = self._write(file_obj=buffer, **self.to_csv_kwargs)
else:
written = self._write(file_obj=self.path_or_buf, **self.to_csv_kwargs)
return written
def _batch_csv(self, args):
offset, header, to_csv_kwargs = args
batch = query_table(
table=self.dataset.data,
key=slice(offset, offset + self.batch_size),
indices=self.dataset._indices,
)
csv_str = batch.to_pandas().to_csv(
path_or_buf=None, header=header if (offset == 0) else False, **to_csv_kwargs
)
return csv_str.encode(self.encoding)
def _write(self, file_obj: BinaryIO, header: bool = True, **to_csv_kwargs) -> int:
"""Writes the pyarrow table as CSV to a binary file handle.
Caller is responsible for opening and closing the handle.
"""
written = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0, len(self.dataset), self.batch_size),
unit="ba",
disable=not logging.is_progress_bar_enabled(),
desc="Creating CSV from Arrow format",
):
csv_str = self._batch_csv((offset, header, to_csv_kwargs))
written += file_obj.write(csv_str)
else:
num_rows, batch_size = len(self.dataset), self.batch_size
with multiprocessing.Pool(self.num_proc) as pool:
for csv_str in logging.tqdm(
pool.imap(
self._batch_csv,
[(offset, header, to_csv_kwargs) for offset in range(0, num_rows, batch_size)],
),
total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size,
unit="ba",
disable=not logging.is_progress_bar_enabled(),
desc="Creating CSV from Arrow format",
):
written += file_obj.write(csv_str)
return written