Add files using upload-large-folder tool

env-llmeval/lib/python3.10/site-packages/datasets/__init__.py

@@ -0,0 +1,70 @@
+# ruff: noqa
+# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
+#
+# 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.
+
+__version__ = "2.18.0"
+
+from .arrow_dataset import Dataset
+from .arrow_reader import ReadInstruction
+from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
+from .combine import concatenate_datasets, interleave_datasets
+from .dataset_dict import DatasetDict, IterableDatasetDict
+from .download import *
+from .features import *
+from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
+from .info import DatasetInfo, MetricInfo
+from .inspect import (
+    get_dataset_config_info,
+    get_dataset_config_names,
+    get_dataset_default_config_name,
+    get_dataset_infos,
+    get_dataset_split_names,
+    inspect_dataset,
+    inspect_metric,
+    list_datasets,
+    list_metrics,
+)
+from .iterable_dataset import IterableDataset
+from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
+from .metric import Metric
+from .splits import (
+    NamedSplit,
+    NamedSplitAll,
+    Split,
+    SplitBase,
+    SplitDict,
+    SplitGenerator,
+    SplitInfo,
+    SubSplitInfo,
+    percent,
+)
+from .tasks import *
+from .utils import *
+from .utils import logging
+
+
+# deprecated modules
+from datasets import arrow_dataset as _arrow_dataset  # isort:skip
+from datasets import utils as _utils  # isort:skip
+from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip
+
+_arrow_dataset.concatenate_datasets = concatenate_datasets
+_utils.DownloadConfig = DownloadConfig
+_utils.DownloadManager = DownloadManager
+_utils.DownloadMode = DownloadMode
+_deprecated_download_manager.DownloadConfig = DownloadConfig
+_deprecated_download_manager.DownloadMode = DownloadMode
+_deprecated_download_manager.DownloadManager = DownloadManager
+
+del _arrow_dataset, _utils, _deprecated_download_manager

env-llmeval/lib/python3.10/site-packages/datasets/arrow_reader.py

@@ -0,0 +1,661 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""Arrow ArrowReader."""
+
+import copy
+import math
+import os
+import re
+import shutil
+from dataclasses import dataclass
+from functools import partial
+from pathlib import Path
+from typing import TYPE_CHECKING, List, Optional, Union
+
+import pyarrow as pa
+import pyarrow.parquet as pq
+from tqdm.contrib.concurrent import thread_map
+
+from .download.download_config import DownloadConfig
+from .naming import _split_re, filenames_for_dataset_split
+from .table import InMemoryTable, MemoryMappedTable, Table, concat_tables
+from .utils import logging
+from .utils import tqdm as hf_tqdm
+from .utils.file_utils import cached_path
+
+
+if TYPE_CHECKING:
+    from .info import DatasetInfo  # noqa: F401
+    from .splits import Split, SplitInfo  # noqa: F401
+
+
+logger = logging.get_logger(__name__)
+
+HF_GCP_BASE_URL = "https://storage.googleapis.com/huggingface-nlp/cache/datasets"
+
+_SUB_SPEC_RE = re.compile(
+    rf"""
+^
+ (?P<split>{_split_re[1:-1]})
+ (\[
+    ((?P<from>-?\d+)
+     (?P<from_pct>%)?)?
+    :
+    ((?P<to>-?\d+)
+     (?P<to_pct>%)?)?
+ \])?(\((?P<rounding>[^\)]*)\))?
+$
+""",  # remove ^ and $
+    re.X,
+)
+
+_ADDITION_SEP_RE = re.compile(r"\s*\+\s*")
+
+
+class DatasetNotOnHfGcsError(ConnectionError):
+    """When you can't get the dataset from the Hf google cloud storage"""
+
+    pass
+
+
+class MissingFilesOnHfGcsError(ConnectionError):
+    """When some files are missing on the Hf oogle cloud storage"""
+
+    pass
+
+
+@dataclass(frozen=True)
+class FileInstructions:
+    """The file instructions associated with a split ReadInstruction.
+
+    Attributes:
+        num_examples: `int`, The total number of examples
+        file_instructions: List[dict(filename, skip, take)], the files information.
+            The filenames contains the relative path, not absolute.
+            skip/take indicates which example read in the file: `ds.slice(skip, take)`
+    """
+
+    num_examples: int
+    file_instructions: List[dict]
+
+
+def make_file_instructions(
+    name: str,
+    split_infos: List["SplitInfo"],
+    instruction: Union[str, "ReadInstruction"],
+    filetype_suffix: Optional[str] = None,
+    prefix_path: Optional[str] = None,
+) -> FileInstructions:
+    """Returns instructions of the split dict.
+
+    Args:
+        name (`str`): Name of the dataset.
+        split_infos (`list` of `[SplitInfo]`): Dataset splits information.
+        instruction ([`ReadInstruction`] or `str`): Reading instruction for a dataset.
+        filetype_suffix (`str`, *optional*): Suffix of dataset files, e.g. 'arrow' or 'parquet'.
+        prefix_path (`str`, *optional*): Prefix of dataset files, e.g. directory name.
+
+    Returns:
+        [`FileInstructions`]
+    """
+    if not isinstance(name, str):
+        raise TypeError(f"Expected str 'name', but got: {type(name).__name__}")
+    elif not name:
+        raise ValueError("Expected non-empty str 'name'")
+    name2len = {info.name: info.num_examples for info in split_infos}
+    name2shard_lengths = {info.name: info.shard_lengths for info in split_infos}
+    name2filenames = {
+        info.name: filenames_for_dataset_split(
+            path=prefix_path,
+            dataset_name=name,
+            split=info.name,
+            filetype_suffix=filetype_suffix,
+            shard_lengths=name2shard_lengths[info.name],
+        )
+        for info in split_infos
+    }
+    if not isinstance(instruction, ReadInstruction):
+        instruction = ReadInstruction.from_spec(instruction)
+    # Create the absolute instruction (per split)
+    absolute_instructions = instruction.to_absolute(name2len)
+
+    # For each split, return the files instruction (skip/take)
+    file_instructions = []
+    num_examples = 0
+    for abs_instr in absolute_instructions:
+        split_length = name2len[abs_instr.splitname]
+        filenames = name2filenames[abs_instr.splitname]
+        shard_lengths = name2shard_lengths[abs_instr.splitname]
+        from_ = 0 if abs_instr.from_ is None else abs_instr.from_
+        to = split_length if abs_instr.to is None else abs_instr.to
+        if shard_lengths is None:  # not sharded
+            for filename in filenames:
+                take = to - from_
+                if take == 0:
+                    continue
+                num_examples += take
+                file_instructions.append({"filename": filename, "skip": from_, "take": take})
+        else:  # sharded
+            index_start = 0  # Beginning (included) of moving window.
+            index_end = 0  # End (excluded) of moving window.
+            for filename, shard_length in zip(filenames, shard_lengths):
+                index_end += shard_length
+                if from_ < index_end and to > index_start:  # There is something to take.
+                    skip = from_ - index_start if from_ > index_start else 0
+                    take = to - index_start - skip if to < index_end else -1
+                    if take == 0:
+                        continue
+                    file_instructions.append({"filename": filename, "skip": skip, "take": take})
+                    num_examples += shard_length - skip if take == -1 else take
+                index_start += shard_length
+    return FileInstructions(
+        num_examples=num_examples,
+        file_instructions=file_instructions,
+    )
+
+
+class BaseReader:
+    """
+    Build a Dataset object out of Instruction instance(s).
+    """
+
+    def __init__(self, path: str, info: Optional["DatasetInfo"]):
+        """Initializes ArrowReader.
+
+        Args:
+            path (str): path where tfrecords are stored.
+            info (DatasetInfo): info about the dataset.
+        """
+        self._path: str = path
+        self._info: Optional["DatasetInfo"] = info
+        self._filetype_suffix: Optional[str] = None
+
+    def _get_table_from_filename(self, filename_skip_take, in_memory=False) -> Table:
+        """Returns a Dataset instance from given (filename, skip, take)."""
+        raise NotImplementedError
+
+    def _read_files(self, files, in_memory=False) -> Table:
+        """Returns Dataset for given file instructions.
+
+        Args:
+            files: List[dict(filename, skip, take)], the files information.
+                The filenames contain the absolute path, not relative.
+                skip/take indicates which example read in the file: `ds.slice(skip, take)`
+            in_memory (bool, default False): Whether to copy the data in-memory.
+        """
+        if len(files) == 0 or not all(isinstance(f, dict) for f in files):
+            raise ValueError("please provide valid file informations")
+        files = copy.deepcopy(files)
+        for f in files:
+            f["filename"] = os.path.join(self._path, f["filename"])
+
+        pa_tables = thread_map(
+            partial(self._get_table_from_filename, in_memory=in_memory),
+            files,
+            tqdm_class=hf_tqdm,
+            desc="Loading dataset shards",
+            # set `disable=None` rather than `disable=False` by default to disable progress bar when no TTY attached
+            disable=len(files) <= 16 or None,
+        )
+        pa_tables = [t for t in pa_tables if len(t) > 0]
+        if not pa_tables and (self._info is None or self._info.features is None):
+            raise ValueError(
+                "Tried to read an empty table. Please specify at least info.features to create an empty table with the right type."
+            )
+        pa_tables = pa_tables or [InMemoryTable.from_batches([], schema=pa.schema(self._info.features.type))]
+        pa_table = concat_tables(pa_tables) if len(pa_tables) != 1 else pa_tables[0]
+        return pa_table
+
+    def get_file_instructions(self, name, instruction, split_infos):
+        """Return list of dict {'filename': str, 'skip': int, 'take': int}"""
+        file_instructions = make_file_instructions(
+            name, split_infos, instruction, filetype_suffix=self._filetype_suffix, prefix_path=self._path
+        )
+        files = file_instructions.file_instructions
+        return files
+
+    def read(
+        self,
+        name,
+        instructions,
+        split_infos,
+        in_memory=False,
+    ):
+        """Returns Dataset instance(s).
+
+        Args:
+            name (str): name of the dataset.
+            instructions (ReadInstruction): instructions to read.
+                Instruction can be string and will then be passed to the Instruction
+                constructor as it.
+            split_infos (list of SplitInfo proto): the available splits for dataset.
+            in_memory (bool, default False): Whether to copy the data in-memory.
+
+        Returns:
+             kwargs to build a single Dataset instance.
+        """
+
+        files = self.get_file_instructions(name, instructions, split_infos)
+        if not files:
+            msg = f'Instruction "{instructions}" corresponds to no data!'
+            raise ValueError(msg)
+        return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
+
+    def read_files(
+        self,
+        files: List[dict],
+        original_instructions: Union[None, "ReadInstruction", "Split"] = None,
+        in_memory=False,
+    ):
+        """Returns single Dataset instance for the set of file instructions.
+
+        Args:
+            files: List[dict(filename, skip, take)], the files information.
+                The filenames contains the relative path, not absolute.
+                skip/take indicates which example read in the file: `ds.skip().take()`
+            original_instructions: store the original instructions used to build the dataset split in the dataset.
+            in_memory (bool, default False): Whether to copy the data in-memory.
+
+        Returns:
+            kwargs to build a Dataset instance.
+        """
+        # Prepend path to filename
+        pa_table = self._read_files(files, in_memory=in_memory)
+        # If original_instructions is not None, convert it to a human-readable NamedSplit
+        if original_instructions is not None:
+            from .splits import Split  # noqa
+
+            split = Split(str(original_instructions))
+        else:
+            split = None
+        dataset_kwargs = {"arrow_table": pa_table, "info": self._info, "split": split}
+        return dataset_kwargs
+
+    def download_from_hf_gcs(self, download_config: DownloadConfig, relative_data_dir):
+        """
+        Download the dataset files from the Hf GCS
+
+        Args:
+            dl_cache_dir: `str`, the local cache directory used to download files
+            relative_data_dir: `str`, the relative directory of the remote files from
+                the `datasets` directory on GCS.
+
+        """
+        remote_cache_dir = HF_GCP_BASE_URL + "/" + relative_data_dir.replace(os.sep, "/")
+        try:
+            remote_dataset_info = os.path.join(remote_cache_dir, "dataset_info.json")
+            downloaded_dataset_info = cached_path(
+                remote_dataset_info.replace(os.sep, "/"), download_config=download_config
+            )
+            shutil.move(downloaded_dataset_info, os.path.join(self._path, "dataset_info.json"))
+            if self._info is not None:
+                self._info.update(self._info.from_directory(self._path))
+        except FileNotFoundError as err:
+            raise DatasetNotOnHfGcsError(err) from None
+        try:
+            for split in self._info.splits:
+                file_instructions = self.get_file_instructions(
+                    name=self._info.builder_name,
+                    instruction=split,
+                    split_infos=self._info.splits.values(),
+                )
+                for file_instruction in file_instructions:
+                    file_to_download = str(Path(file_instruction["filename"]).relative_to(self._path))
+                    remote_prepared_filename = os.path.join(remote_cache_dir, file_to_download)
+                    downloaded_prepared_filename = cached_path(
+                        remote_prepared_filename.replace(os.sep, "/"), download_config=download_config
+                    )
+                    shutil.move(downloaded_prepared_filename, file_instruction["filename"])
+        except FileNotFoundError as err:
+            raise MissingFilesOnHfGcsError(err) from None
+
+
+class ArrowReader(BaseReader):
+    """
+    Build a Dataset object out of Instruction instance(s).
+    This Reader uses either memory mapping or file descriptors (in-memory) on arrow files.
+    """
+
+    def __init__(self, path: str, info: Optional["DatasetInfo"]):
+        """Initializes ArrowReader.
+
+        Args:
+            path (str): path where Arrow files are stored.
+            info (DatasetInfo): info about the dataset.
+        """
+        super().__init__(path, info)
+        self._filetype_suffix = "arrow"
+
+    def _get_table_from_filename(self, filename_skip_take, in_memory=False) -> Table:
+        """Returns a Dataset instance from given (filename, skip, take)."""
+        filename, skip, take = (
+            filename_skip_take["filename"],
+            filename_skip_take["skip"] if "skip" in filename_skip_take else None,
+            filename_skip_take["take"] if "take" in filename_skip_take else None,
+        )
+        table = ArrowReader.read_table(filename, in_memory=in_memory)
+        if take == -1:
+            take = len(table) - skip
+        # here we don't want to slice an empty table, or it may segfault
+        if skip is not None and take is not None and not (skip == 0 and take == len(table)):
+            table = table.slice(skip, take)
+        return table
+
+    @staticmethod
+    def read_table(filename, in_memory=False) -> Table:
+        """
+        Read table from file.
+
+        Args:
+            filename (str): File name of the table.
+            in_memory (bool, default=False): Whether to copy the data in-memory.
+
+        Returns:
+            pyarrow.Table
+        """
+        table_cls = InMemoryTable if in_memory else MemoryMappedTable
+        return table_cls.from_file(filename)
+
+
+class ParquetReader(BaseReader):
+    """
+    Build a Dataset object out of Instruction instance(s).
+    This Reader uses memory mapping on parquet files.
+    """
+
+    def __init__(self, path: str, info: Optional["DatasetInfo"]):
+        """Initializes ParquetReader.
+
+        Args:
+            path (str): path where tfrecords are stored.
+            info (DatasetInfo): info about the dataset.
+        """
+        super().__init__(path, info)
+        self._filetype_suffix = "parquet"
+
+    def _get_table_from_filename(self, filename_skip_take, **kwargs):
+        """Returns a Dataset instance from given (filename, skip, take)."""
+        filename, skip, take = (
+            filename_skip_take["filename"],
+            filename_skip_take["skip"] if "skip" in filename_skip_take else None,
+            filename_skip_take["take"] if "take" in filename_skip_take else None,
+        )
+        # Parquet read_table always loads data in memory, independently of memory_map
+        pa_table = pq.read_table(filename, memory_map=True)
+        # here we don't want to slice an empty table, or it may segfault
+        if skip is not None and take is not None and not (skip == 0 and take == len(pa_table)):
+            pa_table = pa_table.slice(skip, take)
+        return pa_table
+
+
+@dataclass(frozen=True)
+class _AbsoluteInstruction:
+    """A machine friendly slice: defined absolute positive boundaries."""
+
+    splitname: str
+    from_: int  # uint (starting index).
+    to: int  # uint (ending index).
+
+
+@dataclass(frozen=True)
+class _RelativeInstruction:
+    """Represents a single parsed slicing instruction, can use % and negatives."""
+
+    splitname: str
+    from_: Optional[int] = None  # int (starting index) or None if no lower boundary.
+    to: Optional[int] = None  # int (ending index) or None if no upper boundary.
+    unit: Optional[str] = None
+    rounding: Optional[str] = None
+
+    def __post_init__(self):
+        if self.unit is not None and self.unit not in ["%", "abs"]:
+            raise ValueError("unit must be either % or abs")
+        if self.rounding is not None and self.rounding not in ["closest", "pct1_dropremainder"]:
+            raise ValueError("rounding must be either closest or pct1_dropremainder")
+        if self.unit != "%" and self.rounding is not None:
+            raise ValueError("It is forbidden to specify rounding if not using percent slicing.")
+        if self.unit == "%" and self.from_ is not None and abs(self.from_) > 100:
+            raise ValueError("Percent slice boundaries must be > -100 and < 100.")
+        if self.unit == "%" and self.to is not None and abs(self.to) > 100:
+            raise ValueError("Percent slice boundaries must be > -100 and < 100.")
+        # Update via __dict__ due to instance being "frozen"
+        self.__dict__["rounding"] = "closest" if self.rounding is None and self.unit == "%" else self.rounding
+
+
+def _str_to_read_instruction(spec):
+    """Returns ReadInstruction for given string."""
+    res = _SUB_SPEC_RE.match(spec)
+    if not res:
+        raise ValueError(f"Unrecognized instruction format: {spec}")
+    unit = "%" if res.group("from_pct") or res.group("to_pct") else "abs"
+    return ReadInstruction(
+        split_name=res.group("split"),
+        rounding=res.group("rounding"),
+        from_=int(res.group("from")) if res.group("from") else None,
+        to=int(res.group("to")) if res.group("to") else None,
+        unit=unit,
+    )
+
+
+def _pct_to_abs_pct1(boundary, num_examples):
+    # Using math.trunc here, since -99.5% should give -99%, not -100%.
+    if num_examples < 100:
+        msg = (
+            'Using "pct1_dropremainder" rounding on a split with less than 100 '
+            "elements is forbidden: it always results in an empty dataset."
+        )
+        raise ValueError(msg)
+    return boundary * math.trunc(num_examples / 100.0)
+
+
+def _pct_to_abs_closest(boundary, num_examples):
+    return int(round(boundary * num_examples / 100.0))
+
+
+def _rel_to_abs_instr(rel_instr, name2len):
+    """Returns _AbsoluteInstruction instance for given RelativeInstruction.
+
+    Args:
+        rel_instr: RelativeInstruction instance.
+        name2len: dict {split_name: num_examples}.
+    """
+    pct_to_abs = _pct_to_abs_closest if rel_instr.rounding == "closest" else _pct_to_abs_pct1
+    split = rel_instr.splitname
+    if split not in name2len:
+        raise ValueError(f'Unknown split "{split}". Should be one of {list(name2len)}.')
+    num_examples = name2len[split]
+    from_ = rel_instr.from_
+    to = rel_instr.to
+    if rel_instr.unit == "%":
+        from_ = 0 if from_ is None else pct_to_abs(from_, num_examples)
+        to = num_examples if to is None else pct_to_abs(to, num_examples)
+    else:
+        from_ = 0 if from_ is None else from_
+        to = num_examples if to is None else to
+    if from_ < 0:
+        from_ = max(num_examples + from_, 0)
+    if to < 0:
+        to = max(num_examples + to, 0)
+    from_ = min(from_, num_examples)
+    to = min(to, num_examples)
+    return _AbsoluteInstruction(split, from_, to)
+
+
+class ReadInstruction:
+    """Reading instruction for a dataset.
+
+    Examples::
+
+      # The following lines are equivalent:
+      ds = datasets.load_dataset('mnist', split='test[:33%]')
+      ds = datasets.load_dataset('mnist', split=datasets.ReadInstruction.from_spec('test[:33%]'))
+      ds = datasets.load_dataset('mnist', split=datasets.ReadInstruction('test', to=33, unit='%'))
+      ds = datasets.load_dataset('mnist', split=datasets.ReadInstruction(
+          'test', from_=0, to=33, unit='%'))
+
+      # The following lines are equivalent:
+      ds = datasets.load_dataset('mnist', split='test[:33%]+train[1:-1]')
+      ds = datasets.load_dataset('mnist', split=datasets.ReadInstruction.from_spec(
+          'test[:33%]+train[1:-1]'))
+      ds = datasets.load_dataset('mnist', split=(
+          datasets.ReadInstruction('test', to=33, unit='%') +
+          datasets.ReadInstruction('train', from_=1, to=-1, unit='abs')))
+
+      # The following lines are equivalent:
+      ds = datasets.load_dataset('mnist', split='test[:33%](pct1_dropremainder)')
+      ds = datasets.load_dataset('mnist', split=datasets.ReadInstruction.from_spec(
+          'test[:33%](pct1_dropremainder)'))
+      ds = datasets.load_dataset('mnist', split=datasets.ReadInstruction(
+          'test', from_=0, to=33, unit='%', rounding="pct1_dropremainder"))
+
+      # 10-fold validation:
+      tests = datasets.load_dataset(
+          'mnist',
+          [datasets.ReadInstruction('train', from_=k, to=k+10, unit='%')
+          for k in range(0, 100, 10)])
+      trains = datasets.load_dataset(
+          'mnist',
+          [datasets.ReadInstruction('train', to=k, unit='%') + datasets.ReadInstruction('train', from_=k+10, unit='%')
+          for k in range(0, 100, 10)])
+
+    """
+
+    def _init(self, relative_instructions):
+        # Private initializer.
+        self._relative_instructions = relative_instructions
+
+    @classmethod
+    def _read_instruction_from_relative_instructions(cls, relative_instructions):
+        """Returns ReadInstruction obj initialized with relative_instructions."""
+        # Use __new__ to bypass __init__ used by public API and not conveniant here.
+        result = cls.__new__(cls)
+        result._init(relative_instructions)  # pylint: disable=protected-access
+        return result
+
+    def __init__(self, split_name, rounding=None, from_=None, to=None, unit=None):
+        """Initialize ReadInstruction.
+
+        Args:
+            split_name (str): name of the split to read. Eg: 'train'.
+            rounding (str, optional): The rounding behaviour to use when percent slicing is
+                used. Ignored when slicing with absolute indices.
+                Possible values:
+                 - 'closest' (default): The specified percentages are rounded to the
+                     closest value. Use this if you want specified percents to be as
+                     much exact as possible.
+                 - 'pct1_dropremainder': the specified percentages are treated as
+                     multiple of 1%. Use this option if you want consistency. Eg:
+                         len(5%) == 5 * len(1%).
+                     Using this option, one might not be able to use the full set of
+                     examples, if the number of those is not a multiple of 100.
+            from_ (int):
+            to (int): alternative way of specifying slicing boundaries. If any of
+                {from_, to, unit} argument is used, slicing cannot be specified as
+                string.
+            unit (str): optional, one of:
+                '%': to set the slicing unit as percents of the split size.
+                'abs': to set the slicing unit as absolute numbers.
+        """
+        # This constructor is not always called. See factory method
+        # `_read_instruction_from_relative_instructions`. Common init instructions
+        # MUST be placed in the _init method.
+        self._init([_RelativeInstruction(split_name, from_, to, unit, rounding)])
+
+    @classmethod
+    def from_spec(cls, spec):
+        """Creates a `ReadInstruction` instance out of a string spec.
+
+        Args:
+            spec (`str`):
+                Split(s) + optional slice(s) to read + optional rounding
+                if percents are used as the slicing unit. A slice can be specified,
+                using absolute numbers (`int`) or percentages (`int`).
+
+        Examples:
+
+            ```
+            test: test split.
+            test + validation: test split + validation split.
+            test[10:]: test split, minus its first 10 records.
+            test[:10%]: first 10% records of test split.
+            test[:20%](pct1_dropremainder): first 10% records, rounded with the pct1_dropremainder rounding.
+            test[:-5%]+train[40%:60%]: first 95% of test + middle 20% of train.
+            ```
+
+        Returns:
+            ReadInstruction instance.
+        """
+        spec = str(spec)  # Need to convert to str in case of NamedSplit instance.
+        subs = _ADDITION_SEP_RE.split(spec)
+        if not subs:
+            raise ValueError(f"No instructions could be built out of {spec}")
+        instruction = _str_to_read_instruction(subs[0])
+        return sum((_str_to_read_instruction(sub) for sub in subs[1:]), instruction)
+
+    def to_spec(self):
+        rel_instr_specs = []
+        for rel_instr in self._relative_instructions:
+            rel_instr_spec = rel_instr.splitname
+            if rel_instr.from_ is not None or rel_instr.to is not None:
+                from_ = rel_instr.from_
+                to = rel_instr.to
+                unit = rel_instr.unit
+                rounding = rel_instr.rounding
+                unit = unit if unit == "%" else ""
+                from_ = str(from_) + unit if from_ is not None else ""
+                to = str(to) + unit if to is not None else ""
+                slice_str = f"[{from_}:{to}]"
+                rounding_str = (
+                    f"({rounding})" if unit == "%" and rounding is not None and rounding != "closest" else ""
+                )
+                rel_instr_spec += slice_str + rounding_str
+            rel_instr_specs.append(rel_instr_spec)
+        return "+".join(rel_instr_specs)
+
+    def __add__(self, other):
+        """Returns a new ReadInstruction obj, result of appending other to self."""
+        if not isinstance(other, ReadInstruction):
+            msg = "ReadInstruction can only be added to another ReadInstruction obj."
+            raise TypeError(msg)
+        self_ris = self._relative_instructions
+        other_ris = other._relative_instructions  # pylint: disable=protected-access
+        if (
+            self_ris[0].unit != "abs"
+            and other_ris[0].unit != "abs"
+            and self._relative_instructions[0].rounding != other_ris[0].rounding
+        ):
+            raise ValueError("It is forbidden to sum ReadInstruction instances with different rounding values.")
+        return self._read_instruction_from_relative_instructions(self_ris + other_ris)
+
+    def __str__(self):
+        return self.to_spec()
+
+    def __repr__(self):
+        return f"ReadInstruction({self._relative_instructions})"
+
+    def to_absolute(self, name2len):
+        """Translate instruction into a list of absolute instructions.
+
+        Those absolute instructions are then to be added together.
+
+        Args:
+            name2len (`dict`):
+                Associating split names to number of examples.
+
+        Returns:
+            list of _AbsoluteInstruction instances (corresponds to the + in spec).
+        """
+        return [_rel_to_abs_instr(rel_instr, name2len) for rel_instr in self._relative_instructions]

env-llmeval/lib/python3.10/site-packages/datasets/arrow_writer.py

@@ -0,0 +1,745 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
+#
+# 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
+#
+# 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.
+
+# Lint as: python3
+"""To write records into Parquet files."""
+
+import errno
+import json
+import os
+import sys
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
+
+import fsspec
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+from . import config
+from .features import Features, Image, Value
+from .features.features import (
+    FeatureType,
+    _ArrayXDExtensionType,
+    cast_to_python_objects,
+    generate_from_arrow_type,
+    get_nested_type,
+    list_of_np_array_to_pyarrow_listarray,
+    numpy_to_pyarrow_listarray,
+    to_pyarrow_listarray,
+)
+from .filesystems import is_remote_filesystem
+from .info import DatasetInfo
+from .keyhash import DuplicatedKeysError, KeyHasher
+from .table import array_cast, cast_array_to_feature, embed_table_storage, table_cast
+from .utils import logging
+from .utils import tqdm as hf_tqdm
+from .utils.file_utils import hash_url_to_filename
+from .utils.py_utils import asdict, first_non_null_value
+
+
+logger = logging.get_logger(__name__)
+
+type_ = type  # keep python's type function
+
+
+class SchemaInferenceError(ValueError):
+    pass
+
+
+class TypedSequence:
+    """
+    This data container generalizes the typing when instantiating pyarrow arrays, tables or batches.
+
+    More specifically it adds several features:
+    - Support extension types like ``datasets.features.Array2DExtensionType``:
+        By default pyarrow arrays don't return extension arrays. One has to call
+        ``pa.ExtensionArray.from_storage(type, pa.array(data, type.storage_type))``
+        in order to get an extension array.
+    - Support for ``try_type`` parameter that can be used instead of ``type``:
+        When an array is transformed, we like to keep the same type as before if possible.
+        For example when calling :func:`datasets.Dataset.map`, we don't want to change the type
+        of each column by default.
+    - Better error message when a pyarrow array overflows.
+
+    Example::
+
+        from datasets.features import Array2D, Array2DExtensionType, Value
+        from datasets.arrow_writer import TypedSequence
+        import pyarrow as pa
+
+        arr = pa.array(TypedSequence([1, 2, 3], type=Value("int32")))
+        assert arr.type == pa.int32()
+
+        arr = pa.array(TypedSequence([1, 2, 3], try_type=Value("int32")))
+        assert arr.type == pa.int32()
+
+        arr = pa.array(TypedSequence(["foo", "bar"], try_type=Value("int32")))
+        assert arr.type == pa.string()
+
+        arr = pa.array(TypedSequence([[[1, 2, 3]]], type=Array2D((1, 3), "int64")))
+        assert arr.type == Array2DExtensionType((1, 3), "int64")
+
+        table = pa.Table.from_pydict({
+            "image": TypedSequence([[[1, 2, 3]]], type=Array2D((1, 3), "int64"))
+        })
+        assert table["image"].type == Array2DExtensionType((1, 3), "int64")
+
+    """
+
+    def __init__(
+        self,
+        data: Iterable,
+        type: Optional[FeatureType] = None,
+        try_type: Optional[FeatureType] = None,
+        optimized_int_type: Optional[FeatureType] = None,
+    ):
+        # assert type is None or try_type is None,
+        if type is not None and try_type is not None:
+            raise ValueError("You cannot specify both type and try_type")
+        # set attributes
+        self.data = data
+        self.type = type
+        self.try_type = try_type  # is ignored if it doesn't match the data
+        self.optimized_int_type = optimized_int_type
+        # when trying a type (is ignored if data is not compatible)
+        self.trying_type = self.try_type is not None
+        self.trying_int_optimization = optimized_int_type is not None and type is None and try_type is None
+        # used to get back the inferred type after __arrow_array__() is called once
+        self._inferred_type = None
+
+    def get_inferred_type(self) -> FeatureType:
+        """Return the inferred feature type.
+        This is done by converting the sequence to an Arrow array, and getting the corresponding
+        feature type.
+
+        Since building the Arrow array can be expensive, the value of the inferred type is cached
+        as soon as pa.array is called on the typed sequence.
+
+        Returns:
+            FeatureType: inferred feature type of the sequence.
+        """
+        if self._inferred_type is None:
+            self._inferred_type = generate_from_arrow_type(pa.array(self).type)
+        return self._inferred_type
+
+    @staticmethod
+    def _infer_custom_type_and_encode(data: Iterable) -> Tuple[Iterable, Optional[FeatureType]]:
+        """Implement type inference for custom objects like PIL.Image.Image -> Image type.
+
+        This function is only used for custom python objects that can't be direclty passed to build
+        an Arrow array. In such cases is infers the feature type to use, and it encodes the data so
+        that they can be passed to an Arrow array.
+
+        Args:
+            data (Iterable): array of data to infer the type, e.g. a list of PIL images.
+
+        Returns:
+            Tuple[Iterable, Optional[FeatureType]]: a tuple with:
+                - the (possibly encoded) array, if the inferred feature type requires encoding
+                - the inferred feature type if the array is made of supported custom objects like
+                    PIL images, else None.
+        """
+        if config.PIL_AVAILABLE and "PIL" in sys.modules:
+            import PIL.Image
+
+            non_null_idx, non_null_value = first_non_null_value(data)
+            if isinstance(non_null_value, PIL.Image.Image):
+                return [Image().encode_example(value) if value is not None else None for value in data], Image()
+        return data, None
+
+    def __arrow_array__(self, type: Optional[pa.DataType] = None):
+        """This function is called when calling pa.array(typed_sequence)"""
+
+        if type is not None:
+            raise ValueError("TypedSequence is supposed to be used with pa.array(typed_sequence, type=None)")
+        del type  # make sure we don't use it
+        data = self.data
+        # automatic type inference for custom objects
+        if self.type is None and self.try_type is None:
+            data, self._inferred_type = self._infer_custom_type_and_encode(data)
+        if self._inferred_type is None:
+            type = self.try_type if self.trying_type else self.type
+        else:
+            type = self._inferred_type
+        pa_type = get_nested_type(type) if type is not None else None
+        optimized_int_pa_type = (
+            get_nested_type(self.optimized_int_type) if self.optimized_int_type is not None else None
+        )
+        trying_cast_to_python_objects = False
+        try:
+            # custom pyarrow types
+            if isinstance(pa_type, _ArrayXDExtensionType):
+                storage = to_pyarrow_listarray(data, pa_type)
+                return pa.ExtensionArray.from_storage(pa_type, storage)
+
+            # efficient np array to pyarrow array
+            if isinstance(data, np.ndarray):
+                out = numpy_to_pyarrow_listarray(data)
+            elif isinstance(data, list) and data and isinstance(first_non_null_value(data)[1], np.ndarray):
+                out = list_of_np_array_to_pyarrow_listarray(data)
+            else:
+                trying_cast_to_python_objects = True
+                out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))
+            # use smaller integer precisions if possible
+            if self.trying_int_optimization:
+                if pa.types.is_int64(out.type):
+                    out = out.cast(optimized_int_pa_type)
+                elif pa.types.is_list(out.type):
+                    if pa.types.is_int64(out.type.value_type):
+                        out = array_cast(out, pa.list_(optimized_int_pa_type))
+                    elif pa.types.is_list(out.type.value_type) and pa.types.is_int64(out.type.value_type.value_type):
+                        out = array_cast(out, pa.list_(pa.list_(optimized_int_pa_type)))
+            # otherwise we can finally use the user's type
+            elif type is not None:
+                # We use cast_array_to_feature to support casting to custom types like Audio and Image
+                # Also, when trying type "string", we don't want to convert integers or floats to "string".
+                # We only do it if trying_type is False - since this is what the user asks for.
+                out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
+            return out
+        except (
+            TypeError,
+            pa.lib.ArrowInvalid,
+            pa.lib.ArrowNotImplementedError,
+        ) as e:  # handle type errors and overflows
+            # Ignore ArrowNotImplementedError caused by trying type, otherwise re-raise
+            if not self.trying_type and isinstance(e, pa.lib.ArrowNotImplementedError):
+                raise
+
+            if self.trying_type:
+                try:  # second chance
+                    if isinstance(data, np.ndarray):
+                        return numpy_to_pyarrow_listarray(data)
+                    elif isinstance(data, list) and data and any(isinstance(value, np.ndarray) for value in data):
+                        return list_of_np_array_to_pyarrow_listarray(data)
+                    else:
+                        trying_cast_to_python_objects = True
+                        return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))
+                except pa.lib.ArrowInvalid as e:
+                    if "overflow" in str(e):
+                        raise OverflowError(
+                            f"There was an overflow with type {type_(data)}. Try to reduce writer_batch_size to have batches smaller than 2GB.\n({e})"
+                        ) from None
+                    elif self.trying_int_optimization and "not in range" in str(e):
+                        optimized_int_pa_type_str = np.dtype(optimized_int_pa_type.to_pandas_dtype()).name
+                        logger.info(
+                            f"Failed to cast a sequence to {optimized_int_pa_type_str}. Falling back to int64."
+                        )
+                        return out
+                    elif trying_cast_to_python_objects and "Could not convert" in str(e):
+                        out = pa.array(
+                            cast_to_python_objects(data, only_1d_for_numpy=True, optimize_list_casting=False)
+                        )
+                        if type is not None:
+                            out = cast_array_to_feature(out, type, allow_number_to_str=True)
+                        return out
+                    else:
+                        raise
+            elif "overflow" in str(e):
+                raise OverflowError(
+                    f"There was an overflow with type {type_(data)}. Try to reduce writer_batch_size to have batches smaller than 2GB.\n({e})"
+                ) from None
+            elif self.trying_int_optimization and "not in range" in str(e):
+                optimized_int_pa_type_str = np.dtype(optimized_int_pa_type.to_pandas_dtype()).name
+                logger.info(f"Failed to cast a sequence to {optimized_int_pa_type_str}. Falling back to int64.")
+                return out
+            elif trying_cast_to_python_objects and "Could not convert" in str(e):
+                out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True, optimize_list_casting=False))
+                if type is not None:
+                    out = cast_array_to_feature(out, type, allow_number_to_str=True)
+                return out
+            else:
+                raise
+
+
+class OptimizedTypedSequence(TypedSequence):
+    def __init__(
+        self,
+        data,
+        type: Optional[FeatureType] = None,
+        try_type: Optional[FeatureType] = None,
+        col: Optional[str] = None,
+        optimized_int_type: Optional[FeatureType] = None,
+    ):
+        optimized_int_type_by_col = {
+            "attention_mask": Value("int8"),  # binary tensor
+            "special_tokens_mask": Value("int8"),
+            "input_ids": Value("int32"),  # typical vocab size: 0-50k (max ~500k, never > 1M)
+            "token_type_ids": Value(
+                "int8"
+            ),  # binary mask; some (XLNetModel) use an additional token represented by a 2
+        }
+        if type is None and try_type is None:
+            optimized_int_type = optimized_int_type_by_col.get(col, None)
+        super().__init__(data, type=type, try_type=try_type, optimized_int_type=optimized_int_type)
+
+
+class ArrowWriter:
+    """Shuffles and writes Examples to Arrow files."""
+
+    _WRITER_CLASS = pa.RecordBatchStreamWriter
+
+    def __init__(
+        self,
+        schema: Optional[pa.Schema] = None,
+        features: Optional[Features] = None,
+        path: Optional[str] = None,
+        stream: Optional[pa.NativeFile] = None,
+        fingerprint: Optional[str] = None,
+        writer_batch_size: Optional[int] = None,
+        hash_salt: Optional[str] = None,
+        check_duplicates: Optional[bool] = False,
+        disable_nullable: bool = False,
+        update_features: bool = False,
+        with_metadata: bool = True,
+        unit: str = "examples",
+        embed_local_files: bool = False,
+        storage_options: Optional[dict] = None,
+    ):
+        if path is None and stream is None:
+            raise ValueError("At least one of path and stream must be provided.")
+        if features is not None:
+            self._features = features
+            self._schema = None
+        elif schema is not None:
+            self._schema: pa.Schema = schema
+            self._features = Features.from_arrow_schema(self._schema)
+        else:
+            self._features = None
+            self._schema = None
+
+        if hash_salt is not None:
+            # Create KeyHasher instance using split name as hash salt
+            self._hasher = KeyHasher(hash_salt)
+        else:
+            self._hasher = KeyHasher("")
+
+        self._check_duplicates = check_duplicates
+        self._disable_nullable = disable_nullable
+
+        if stream is None:
+            fs_token_paths = fsspec.get_fs_token_paths(path, storage_options=storage_options)
+            self._fs: fsspec.AbstractFileSystem = fs_token_paths[0]
+            self._path = (
+                fs_token_paths[2][0]
+                if not is_remote_filesystem(self._fs)
+                else self._fs.unstrip_protocol(fs_token_paths[2][0])
+            )
+            self.stream = self._fs.open(fs_token_paths[2][0], "wb")
+            self._closable_stream = True
+        else:
+            self._fs = None
+            self._path = None
+            self.stream = stream
+            self._closable_stream = False
+
+        self.fingerprint = fingerprint
+        self.disable_nullable = disable_nullable
+        self.writer_batch_size = writer_batch_size or config.DEFAULT_MAX_BATCH_SIZE
+        self.update_features = update_features
+        self.with_metadata = with_metadata
+        self.unit = unit
+        self.embed_local_files = embed_local_files
+
+        self._num_examples = 0
+        self._num_bytes = 0
+        self.current_examples: List[Tuple[Dict[str, Any], str]] = []
+        self.current_rows: List[pa.Table] = []
+        self.pa_writer: Optional[pa.RecordBatchStreamWriter] = None
+        self.hkey_record = []
+
+    def __len__(self):
+        """Return the number of writed and staged examples"""
+        return self._num_examples + len(self.current_examples) + len(self.current_rows)
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.close()
+
+    def close(self):
+        # Try closing if opened; if closed: pyarrow.lib.ArrowInvalid: Invalid operation on closed file
+        if self.pa_writer:  # it might be None
+            try:
+                self.pa_writer.close()
+            except Exception:  # pyarrow.lib.ArrowInvalid, OSError
+                pass
+        if self._closable_stream and not self.stream.closed:
+            self.stream.close()  # This also closes self.pa_writer if it is opened
+
+    def _build_writer(self, inferred_schema: pa.Schema):
+        schema = self.schema
+        inferred_features = Features.from_arrow_schema(inferred_schema)
+        if self._features is not None:
+            if self.update_features:  # keep original features it they match, or update them
+                fields = {field.name: field for field in self._features.type}
+                for inferred_field in inferred_features.type:
+                    name = inferred_field.name
+                    if name in fields:
+                        if inferred_field == fields[name]:
+                            inferred_features[name] = self._features[name]
+                self._features = inferred_features
+                schema: pa.Schema = inferred_schema
+        else:
+            self._features = inferred_features
+            schema: pa.Schema = inferred_features.arrow_schema
+        if self.disable_nullable:
+            schema = pa.schema(pa.field(field.name, field.type, nullable=False) for field in schema)
+        if self.with_metadata:
+            schema = schema.with_metadata(self._build_metadata(DatasetInfo(features=self._features), self.fingerprint))
+        else:
+            schema = schema.with_metadata({})
+        self._schema = schema
+        self.pa_writer = self._WRITER_CLASS(self.stream, schema)
+
+    @property
+    def schema(self):
+        _schema = (
+            self._schema
+            if self._schema is not None
+            else (pa.schema(self._features.type) if self._features is not None else None)
+        )
+        if self._disable_nullable and _schema is not None:
+            _schema = pa.schema(pa.field(field.name, field.type, nullable=False) for field in _schema)
+        return _schema if _schema is not None else []
+
+    @staticmethod
+    def _build_metadata(info: DatasetInfo, fingerprint: Optional[str] = None) -> Dict[str, str]:
+        info_keys = ["features"]  # we can add support for more DatasetInfo keys in the future
+        info_as_dict = asdict(info)
+        metadata = {}
+        metadata["info"] = {key: info_as_dict[key] for key in info_keys}
+        if fingerprint is not None:
+            metadata["fingerprint"] = fingerprint
+        return {"huggingface": json.dumps(metadata)}
+
+    def write_examples_on_file(self):
+        """Write stored examples from the write-pool of examples. It makes a table out of the examples and write it."""
+        if not self.current_examples:
+            return
+        # preserve the order the columns
+        if self.schema:
+            schema_cols = set(self.schema.names)
+            examples_cols = self.current_examples[0][0].keys()  # .keys() preserves the order (unlike set)
+            common_cols = [col for col in self.schema.names if col in examples_cols]
+            extra_cols = [col for col in examples_cols if col not in schema_cols]
+            cols = common_cols + extra_cols
+        else:
+            cols = list(self.current_examples[0][0])
+        batch_examples = {}
+        for col in cols:
+            # We use row[0][col] since current_examples contains (example, key) tuples.
+            # Morever, examples could be Arrow arrays of 1 element.
+            # This can happen in `.map()` when we want to re-write the same Arrow data
+            if all(isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) for row in self.current_examples):
+                arrays = [row[0][col] for row in self.current_examples]
+                arrays = [
+                    chunk
+                    for array in arrays
+                    for chunk in (array.chunks if isinstance(array, pa.ChunkedArray) else [array])
+                ]
+                batch_examples[col] = pa.concat_arrays(arrays)
+            else:
+                batch_examples[col] = [
+                    row[0][col].to_pylist()[0] if isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) else row[0][col]
+                    for row in self.current_examples
+                ]
+        self.write_batch(batch_examples=batch_examples)
+        self.current_examples = []
+
+    def write_rows_on_file(self):
+        """Write stored rows from the write-pool of rows. It concatenates the single-row tables and it writes the resulting table."""
+        if not self.current_rows:
+            return
+        table = pa.concat_tables(self.current_rows)
+        self.write_table(table)
+        self.current_rows = []
+
+    def write(
+        self,
+        example: Dict[str, Any],
+        key: Optional[Union[str, int, bytes]] = None,
+        writer_batch_size: Optional[int] = None,
+    ):
+        """Add a given (Example,Key) pair to the write-pool of examples which is written to file.
+
+        Args:
+            example: the Example to add.
+            key: Optional, a unique identifier(str, int or bytes) associated with each example
+        """
+        # Utilize the keys and duplicate checking when `self._check_duplicates` is passed True
+        if self._check_duplicates:
+            # Create unique hash from key and store as (key, example) pairs
+            hash = self._hasher.hash(key)
+            self.current_examples.append((example, hash))
+            # Maintain record of keys and their respective hashes for checking duplicates
+            self.hkey_record.append((hash, key))
+        else:
+            # Store example as a tuple so as to keep the structure of `self.current_examples` uniform
+            self.current_examples.append((example, ""))
+
+        if writer_batch_size is None:
+            writer_batch_size = self.writer_batch_size
+        if writer_batch_size is not None and len(self.current_examples) >= writer_batch_size:
+            if self._check_duplicates:
+                self.check_duplicate_keys()
+                # Re-intializing to empty list for next batch
+                self.hkey_record = []
+
+            self.write_examples_on_file()
+
+    def check_duplicate_keys(self):
+        """Raises error if duplicates found in a batch"""
+        tmp_record = set()
+        for hash, key in self.hkey_record:
+            if hash in tmp_record:
+                duplicate_key_indices = [
+                    str(self._num_examples + index)
+                    for index, (duplicate_hash, _) in enumerate(self.hkey_record)
+                    if duplicate_hash == hash
+                ]
+
+                raise DuplicatedKeysError(key, duplicate_key_indices)
+            else:
+                tmp_record.add(hash)
+
+    def write_row(self, row: pa.Table, writer_batch_size: Optional[int] = None):
+        """Add a given single-row Table to the write-pool of rows which is written to file.
+
+        Args:
+            row: the row to add.
+        """
+        if len(row) != 1:
+            raise ValueError(f"Only single-row pyarrow tables are allowed but got table with {len(row)} rows.")
+        self.current_rows.append(row)
+        if writer_batch_size is None:
+            writer_batch_size = self.writer_batch_size
+        if writer_batch_size is not None and len(self.current_rows) >= writer_batch_size:
+            self.write_rows_on_file()
+
+    def write_batch(
+        self,
+        batch_examples: Dict[str, List],
+        writer_batch_size: Optional[int] = None,
+    ):
+        """Write a batch of Example to file.
+        Ignores the batch if it appears to be empty,
+        preventing a potential schema update of unknown types.
+
+        Args:
+            batch_examples: the batch of examples to add.
+        """
+        if batch_examples and len(next(iter(batch_examples.values()))) == 0:
+            return
+        features = None if self.pa_writer is None and self.update_features else self._features
+        try_features = self._features if self.pa_writer is None and self.update_features else None
+        arrays = []
+        inferred_features = Features()
+        # preserve the order the columns
+        if self.schema:
+            schema_cols = set(self.schema.names)
+            batch_cols = batch_examples.keys()  # .keys() preserves the order (unlike set)
+            common_cols = [col for col in self.schema.names if col in batch_cols]
+            extra_cols = [col for col in batch_cols if col not in schema_cols]
+            cols = common_cols + extra_cols
+        else:
+            cols = list(batch_examples)
+        for col in cols:
+            col_values = batch_examples[col]
+            col_type = features[col] if features else None
+            if isinstance(col_values, (pa.Array, pa.ChunkedArray)):
+                array = cast_array_to_feature(col_values, col_type) if col_type is not None else col_values
+                arrays.append(array)
+                inferred_features[col] = generate_from_arrow_type(col_values.type)
+            else:
+                col_try_type = try_features[col] if try_features is not None and col in try_features else None
+                typed_sequence = OptimizedTypedSequence(col_values, type=col_type, try_type=col_try_type, col=col)
+                arrays.append(pa.array(typed_sequence))
+                inferred_features[col] = typed_sequence.get_inferred_type()
+        schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema
+        pa_table = pa.Table.from_arrays(arrays, schema=schema)
+        self.write_table(pa_table, writer_batch_size)
+
+    def write_table(self, pa_table: pa.Table, writer_batch_size: Optional[int] = None):
+        """Write a Table to file.
+
+        Args:
+            example: the Table to add.
+        """
+        if writer_batch_size is None:
+            writer_batch_size = self.writer_batch_size
+        if self.pa_writer is None:
+            self._build_writer(inferred_schema=pa_table.schema)
+        pa_table = pa_table.combine_chunks()
+        pa_table = table_cast(pa_table, self._schema)
+        if self.embed_local_files:
+            pa_table = embed_table_storage(pa_table)
+        self._num_bytes += pa_table.nbytes
+        self._num_examples += pa_table.num_rows
+        self.pa_writer.write_table(pa_table, writer_batch_size)
+
+    def finalize(self, close_stream=True):
+        self.write_rows_on_file()
+        # In case current_examples < writer_batch_size, but user uses finalize()
+        if self._check_duplicates:
+            self.check_duplicate_keys()
+            # Re-intializing to empty list for next batch
+            self.hkey_record = []
+        self.write_examples_on_file()
+        # If schema is known, infer features even if no examples were written
+        if self.pa_writer is None and self.schema:
+            self._build_writer(self.schema)
+        if self.pa_writer is not None:
+            self.pa_writer.close()
+            self.pa_writer = None
+            if close_stream:
+                self.stream.close()
+        else:
+            if close_stream:
+                self.stream.close()
+            raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
+        logger.debug(
+            f"Done writing {self._num_examples} {self.unit} in {self._num_bytes} bytes {self._path if self._path else ''}."
+        )
+        return self._num_examples, self._num_bytes
+
+
+class ParquetWriter(ArrowWriter):
+    _WRITER_CLASS = pq.ParquetWriter
+
+
+class BeamWriter:
+    """
+    Shuffles and writes Examples to Arrow files.
+    The Arrow files are converted from Parquet files that are the output of Apache Beam pipelines.
+    """
+
+    def __init__(
+        self,
+        features: Optional[Features] = None,
+        schema: Optional[pa.Schema] = None,
+        path: Optional[str] = None,
+        namespace: Optional[str] = None,
+        cache_dir: Optional[str] = None,
+    ):
+        if features is None and schema is None:
+            raise ValueError("At least one of features and schema must be provided.")
+        if path is None:
+            raise ValueError("Path must be provided.")
+
+        if features is not None:
+            self._features: Features = features
+            self._schema: pa.Schema = features.arrow_schema
+        else:
+            self._schema: pa.Schema = schema
+            self._features: Features = Features.from_arrow_schema(schema)
+
+        self._path = path
+        self._parquet_path = os.path.splitext(path)[0]  # remove extension
+        self._namespace = namespace or "default"
+        self._num_examples = None
+        self._cache_dir = cache_dir or config.HF_DATASETS_CACHE
+
+    def write_from_pcollection(self, pcoll_examples):
+        """Add the final steps of the beam pipeline: write to parquet files."""
+        import apache_beam as beam
+
+        def inc_num_examples(example):
+            beam.metrics.Metrics.counter(self._namespace, "num_examples").inc()
+
+        # count examples
+        _ = pcoll_examples | "Count N. Examples" >> beam.Map(inc_num_examples)
+
+        # save dataset
+        return (
+            pcoll_examples
+            | "Get values" >> beam.Values()
+            | "Save to parquet"
+            >> beam.io.parquetio.WriteToParquet(
+                self._parquet_path, self._schema, shard_name_template="-SSSSS-of-NNNNN.parquet"
+            )
+        )
+
+    def finalize(self, metrics_query_result: dict):
+        """
+        Run after the pipeline has finished.
+        It converts the resulting parquet files to arrow and it completes the info from the pipeline metrics.
+
+        Args:
+            metrics_query_result: `dict` obtained from pipeline_results.metrics().query(m_filter). Make sure
+                that the filter keeps only the metrics for the considered split, under the namespace `split_name`.
+        """
+
+        # Beam FileSystems require the system's path separator in the older versions
+        fs, _, [parquet_path] = fsspec.get_fs_token_paths(self._parquet_path)
+        parquet_path = str(Path(parquet_path)) if not is_remote_filesystem(fs) else fs.unstrip_protocol(parquet_path)
+
+        shards = fs.glob(parquet_path + "*.parquet")
+        num_bytes = sum(fs.sizes(shards))
+        shard_lengths = get_parquet_lengths(shards)
+
+        # Convert to arrow
+        if self._path.endswith(".arrow"):
+            logger.info(f"Converting parquet files {self._parquet_path} to arrow {self._path}")
+            try:  # stream conversion
+                num_bytes = 0
+                for shard in hf_tqdm(shards, unit="shards"):
+                    with fs.open(shard, "rb") as source:
+                        with fs.open(shard.replace(".parquet", ".arrow"), "wb") as destination:
+                            shard_num_bytes, _ = parquet_to_arrow(source, destination)
+                            num_bytes += shard_num_bytes
+            except OSError as e:  # broken pipe can happen if the connection is unstable, do local conversion instead
+                if e.errno != errno.EPIPE:  # not a broken pipe
+                    raise
+                logger.warning(
+                    "Broken Pipe during stream conversion from parquet to arrow. Using local convert instead"
+                )
+                local_convert_dir = os.path.join(self._cache_dir, "beam_convert")
+                os.makedirs(local_convert_dir, exist_ok=True)
+                num_bytes = 0
+                for shard in hf_tqdm(shards, unit="shards"):
+                    local_parquet_path = os.path.join(local_convert_dir, hash_url_to_filename(shard) + ".parquet")
+                    fs.download(shard, local_parquet_path)
+                    local_arrow_path = local_parquet_path.replace(".parquet", ".arrow")
+                    shard_num_bytes, _ = parquet_to_arrow(local_parquet_path, local_arrow_path)
+                    num_bytes += shard_num_bytes
+                    remote_arrow_path = shard.replace(".parquet", ".arrow")
+                    fs.upload(local_arrow_path, remote_arrow_path)
+
+        # Save metrics
+        counters_dict = {metric.key.metric.name: metric.result for metric in metrics_query_result["counters"]}
+        self._num_examples = counters_dict["num_examples"]
+        self._num_bytes = num_bytes
+        self._shard_lengths = shard_lengths
+        return self._num_examples, self._num_bytes
+
+
+def get_parquet_lengths(sources) -> List[int]:
+    shard_lengths = []
+    for source in hf_tqdm(sources, unit="parquet files"):
+        parquet_file = pa.parquet.ParquetFile(source)
+        shard_lengths.append(parquet_file.metadata.num_rows)
+    return shard_lengths
+
+
+def parquet_to_arrow(source, destination) -> List[int]:
+    """Convert parquet file to arrow file. Inputs can be str paths or file-like objects"""
+    stream = None if isinstance(destination, str) else destination
+    parquet_file = pa.parquet.ParquetFile(source)
+    # Beam can create empty Parquet files, so we need to pass the source Parquet file's schema
+    with ArrowWriter(schema=parquet_file.schema_arrow, path=destination, stream=stream) as writer:
+        for record_batch in parquet_file.iter_batches():
+            pa_table = pa.Table.from_batches([record_batch])
+            writer.write_table(pa_table)
+        num_bytes, num_examples = writer.finalize()
+    return num_bytes, num_examples

env-llmeval/lib/python3.10/site-packages/datasets/combine.py

@@ -0,0 +1,215 @@
+from typing import List, Optional, TypeVar
+
+from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
+from .dataset_dict import DatasetDict, IterableDatasetDict
+from .info import DatasetInfo
+from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
+from .splits import NamedSplit
+from .utils import logging
+from .utils.py_utils import Literal
+
+
+logger = logging.get_logger(__name__)
+
+
+DatasetType = TypeVar("DatasetType", Dataset, IterableDataset)
+
+
+def interleave_datasets(
+    datasets: List[DatasetType],
+    probabilities: Optional[List[float]] = None,
+    seed: Optional[int] = None,
+    info: Optional[DatasetInfo] = None,
+    split: Optional[NamedSplit] = None,
+    stopping_strategy: Literal["first_exhausted", "all_exhausted"] = "first_exhausted",
+) -> DatasetType:
+    """
+    Interleave several datasets (sources) into a single dataset.
+    The new dataset is constructed by alternating between the sources to get the examples.
+
+    You can use this function on a list of [`Dataset`] objects, or on a list of [`IterableDataset`] objects.
+
+        - If `probabilities` is `None` (default) the new dataset is constructed by cycling between each source to get the examples.
+        - If `probabilities` is not `None`, the new dataset is constructed by getting examples from a random source at a time according to the provided probabilities.
+
+    The resulting dataset ends when one of the source datasets runs out of examples except when `oversampling` is `True`,
+    in which case, the resulting dataset ends when all datasets have ran out of examples at least one time.
+
+    Note for iterable datasets:
+
+    In a distributed setup or in PyTorch DataLoader workers, the stopping strategy is applied per process.
+    Therefore the "first_exhausted" strategy on an sharded iterable dataset can generate less samples in total (up to 1 missing sample per subdataset per worker).
+
+    Args:
+        datasets (`List[Dataset]` or `List[IterableDataset]`):
+            List of datasets to interleave.
+        probabilities (`List[float]`, *optional*, defaults to `None`):
+            If specified, the new dataset is constructed by sampling
+            examples from one source at a time according to these probabilities.
+        seed (`int`, *optional*, defaults to `None`):
+            The random seed used to choose a source for each example.
+        info ([`DatasetInfo`], *optional*):
+            Dataset information, like description, citation, etc.
+            <Added version="2.4.0"/>
+        split ([`NamedSplit`], *optional*):
+            Name of the dataset split.
+            <Added version="2.4.0"/>
+        stopping_strategy (`str`, defaults to `first_exhausted`):
+            Two strategies are proposed right now, `first_exhausted` and `all_exhausted`.
+            By default, `first_exhausted` is an undersampling strategy, i.e the dataset construction is stopped as soon as one dataset has ran out of samples.
+            If the strategy is `all_exhausted`,  we use an oversampling strategy, i.e the dataset construction is stopped as soon as every samples of every dataset has been added at least once.
+            Note that if the strategy is `all_exhausted`, the interleaved dataset size can get enormous:
+            - with no probabilities, the resulting dataset will have `max_length_datasets*nb_dataset` samples.
+            - with given probabilities, the resulting dataset will have more samples if some datasets have really low probability of visiting.
+    Returns:
+        [`Dataset`] or [`IterableDataset`]: Return type depends on the input `datasets`
+        parameter. `Dataset` if the input is a list of `Dataset`, `IterableDataset` if the input is a list of
+        `IterableDataset`.
+
+    Example:
+
+        For regular datasets (map-style):
+
+        ```python
+        >>> from datasets import Dataset, interleave_datasets
+        >>> d1 = Dataset.from_dict({"a": [0, 1, 2]})
+        >>> d2 = Dataset.from_dict({"a": [10, 11, 12]})
+        >>> d3 = Dataset.from_dict({"a": [20, 21, 22]})
+        >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42, stopping_strategy="all_exhausted")
+        >>> dataset["a"]
+        [10, 0, 11, 1, 2, 20, 12, 10, 0, 1, 2, 21, 0, 11, 1, 2, 0, 1, 12, 2, 10, 0, 22]
+        >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)
+        >>> dataset["a"]
+        [10, 0, 11, 1, 2]
+        >>> dataset = interleave_datasets([d1, d2, d3])
+        >>> dataset["a"]
+        [0, 10, 20, 1, 11, 21, 2, 12, 22]
+        >>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted")
+        >>> dataset["a"]
+        [0, 10, 20, 1, 11, 21, 2, 12, 22]
+        >>> d1 = Dataset.from_dict({"a": [0, 1, 2]})
+        >>> d2 = Dataset.from_dict({"a": [10, 11, 12, 13]})
+        >>> d3 = Dataset.from_dict({"a": [20, 21, 22, 23, 24]})
+        >>> dataset = interleave_datasets([d1, d2, d3])
+        >>> dataset["a"]
+        [0, 10, 20, 1, 11, 21, 2, 12, 22]
+        >>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted")
+        >>> dataset["a"]
+        [0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 10, 24]
+        >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)
+        >>> dataset["a"]
+        [10, 0, 11, 1, 2]
+        >>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42, stopping_strategy="all_exhausted")
+        >>> dataset["a"]
+        [10, 0, 11, 1, 2, 20, 12, 13, ..., 0, 1, 2, 0, 24]
+        For datasets in streaming mode (iterable):
+
+        >>> from datasets import load_dataset, interleave_datasets
+        >>> d1 = load_dataset("oscar", "unshuffled_deduplicated_en", split="train", streaming=True)
+        >>> d2 = load_dataset("oscar", "unshuffled_deduplicated_fr", split="train", streaming=True)
+        >>> dataset = interleave_datasets([d1, d2])
+        >>> iterator = iter(dataset)
+        >>> next(iterator)
+        {'text': 'Mtendere Village was inspired by the vision...}
+        >>> next(iterator)
+        {'text': "Média de débat d'idées, de culture...}
+        ```
+    """
+    from .arrow_dataset import Dataset
+    from .iterable_dataset import IterableDataset
+
+    if not datasets:
+        raise ValueError("Unable to interleave an empty list of datasets.")
+    for i, dataset in enumerate(datasets):
+        if not isinstance(dataset, (Dataset, IterableDataset)):
+            if isinstance(dataset, (DatasetDict, IterableDatasetDict)):
+                if not dataset:
+                    raise ValueError(
+                        f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
+                        "is an empty dataset dictionary."
+                    )
+                raise ValueError(
+                    f"Dataset at position {i} has at least one split: {list(dataset)}\n"
+                    f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(dataset))}']"
+                )
+            raise ValueError(
+                f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(dataset).__name__}."
+            )
+        if i == 0:
+            dataset_type, other_type = (
+                (Dataset, IterableDataset) if isinstance(dataset, Dataset) else (IterableDataset, Dataset)
+            )
+        elif not isinstance(dataset, dataset_type):
+            raise ValueError(
+                f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."
+            )
+    if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
+        raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy.")
+    if dataset_type is Dataset:
+        return _interleave_map_style_datasets(
+            datasets, probabilities, seed, info=info, split=split, stopping_strategy=stopping_strategy
+        )
+    else:
+        return _interleave_iterable_datasets(
+            datasets, probabilities, seed, info=info, split=split, stopping_strategy=stopping_strategy
+        )
+
+
+def concatenate_datasets(
+    dsets: List[DatasetType],
+    info: Optional[DatasetInfo] = None,
+    split: Optional[NamedSplit] = None,
+    axis: int = 0,
+) -> DatasetType:
+    """
+    Converts a list of [`Dataset`] with the same schema into a single [`Dataset`].
+
+    Args:
+        dsets (`List[datasets.Dataset]`):
+            List of Datasets to concatenate.
+        info (`DatasetInfo`, *optional*):
+            Dataset information, like description, citation, etc.
+        split (`NamedSplit`, *optional*):
+            Name of the dataset split.
+        axis (`{0, 1}`, defaults to `0`):
+            Axis to concatenate over, where `0` means over rows (vertically) and `1` means over columns
+            (horizontally).
+
+            <Added version="1.6.0"/>
+
+    Example:
+
+    ```py
+    >>> ds3 = concatenate_datasets([ds1, ds2])
+    ```
+    """
+
+    if not dsets:
+        raise ValueError("Unable to concatenate an empty list of datasets.")
+    for i, dataset in enumerate(dsets):
+        if not isinstance(dataset, (Dataset, IterableDataset)):
+            if isinstance(dataset, (DatasetDict, IterableDatasetDict)):
+                if not dataset:
+                    raise ValueError(
+                        f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
+                        "is an empty dataset dictionary."
+                    )
+                raise ValueError(
+                    f"Dataset at position {i} has at least one split: {list(dataset)}\n"
+                    f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(dataset))}']"
+                )
+            raise ValueError(
+                f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(dataset).__name__}."
+            )
+        if i == 0:
+            dataset_type, other_type = (
+                (Dataset, IterableDataset) if isinstance(dataset, Dataset) else (IterableDataset, Dataset)
+            )
+        elif not isinstance(dataset, dataset_type):
+            raise ValueError(
+                f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."
+            )
+    if dataset_type is Dataset:
+        return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)
+    else:
+        return _concatenate_iterable_datasets(dsets, info=info, split=split, axis=axis)

env-llmeval/lib/python3.10/site-packages/datasets/config.py

@@ -0,0 +1,259 @@
+import importlib
+import importlib.metadata
+import logging
+import os
+import platform
+from pathlib import Path
+from typing import Optional
+
+from packaging import version
+
+
+logger = logging.getLogger(__name__.split(".", 1)[0])  # to avoid circular import from .utils.logging
+
+# Datasets
+S3_DATASETS_BUCKET_PREFIX = "https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"
+CLOUDFRONT_DATASETS_DISTRIB_PREFIX = "https://cdn-datasets.huggingface.co/datasets/datasets"
+REPO_DATASETS_URL = "https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"
+
+# Metrics
+S3_METRICS_BUCKET_PREFIX = "https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
+CLOUDFRONT_METRICS_DISTRIB_PREFIX = "https://cdn-datasets.huggingface.co/datasets/metric"
+REPO_METRICS_URL = "https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"
+
+# Hub
+HF_ENDPOINT = os.environ.get("HF_ENDPOINT", "https://huggingface.co")
+HUB_DATASETS_URL = HF_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
+HUB_DATASETS_HFFS_URL = "hf://datasets/{repo_id}@{revision}/{path}"
+HUB_DEFAULT_VERSION = "main"
+
+PY_VERSION = version.parse(platform.python_version())
+
+# General environment variables accepted values for booleans
+ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"}
+ENV_VARS_FALSE_VALUES = {"0", "OFF", "NO", "FALSE"}
+ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"})
+ENV_VARS_FALSE_AND_AUTO_VALUES = ENV_VARS_FALSE_VALUES.union({"AUTO"})
+
+
+# Imports
+DILL_VERSION = version.parse(importlib.metadata.version("dill"))
+FSSPEC_VERSION = version.parse(importlib.metadata.version("fsspec"))
+PANDAS_VERSION = version.parse(importlib.metadata.version("pandas"))
+PYARROW_VERSION = version.parse(importlib.metadata.version("pyarrow"))
+HF_HUB_VERSION = version.parse(importlib.metadata.version("huggingface_hub"))
+
+USE_TF = os.environ.get("USE_TF", "AUTO").upper()
+USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
+USE_JAX = os.environ.get("USE_JAX", "AUTO").upper()
+
+TORCH_VERSION = "N/A"
+TORCH_AVAILABLE = False
+
+if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
+    TORCH_AVAILABLE = importlib.util.find_spec("torch") is not None
+    if TORCH_AVAILABLE:
+        try:
+            TORCH_VERSION = version.parse(importlib.metadata.version("torch"))
+            logger.info(f"PyTorch version {TORCH_VERSION} available.")
+        except importlib.metadata.PackageNotFoundError:
+            pass
+else:
+    logger.info("Disabling PyTorch because USE_TF is set")
+
+TF_VERSION = "N/A"
+TF_AVAILABLE = False
+
+if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
+    TF_AVAILABLE = importlib.util.find_spec("tensorflow") is not None
+    if TF_AVAILABLE:
+        # For the metadata, we have to look for both tensorflow and tensorflow-cpu
+        for package in [
+            "tensorflow",
+            "tensorflow-cpu",
+            "tensorflow-gpu",
+            "tf-nightly",
+            "tf-nightly-cpu",
+            "tf-nightly-gpu",
+            "intel-tensorflow",
+            "tensorflow-rocm",
+            "tensorflow-macos",
+        ]:
+            try:
+                TF_VERSION = version.parse(importlib.metadata.version(package))
+            except importlib.metadata.PackageNotFoundError:
+                continue
+            else:
+                break
+        else:
+            TF_AVAILABLE = False
+    if TF_AVAILABLE:
+        if TF_VERSION.major < 2:
+            logger.info(f"TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.")
+            TF_AVAILABLE = False
+        else:
+            logger.info(f"TensorFlow version {TF_VERSION} available.")
+else:
+    logger.info("Disabling Tensorflow because USE_TORCH is set")
+
+
+JAX_VERSION = "N/A"
+JAX_AVAILABLE = False
+
+if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
+    JAX_AVAILABLE = importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
+    if JAX_AVAILABLE:
+        try:
+            JAX_VERSION = version.parse(importlib.metadata.version("jax"))
+            logger.info(f"JAX version {JAX_VERSION} available.")
+        except importlib.metadata.PackageNotFoundError:
+            pass
+else:
+    logger.info("Disabling JAX because USE_JAX is set to False")
+
+
+USE_BEAM = os.environ.get("USE_BEAM", "AUTO").upper()
+BEAM_VERSION = "N/A"
+BEAM_AVAILABLE = False
+if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
+    try:
+        BEAM_VERSION = version.parse(importlib.metadata.version("apache_beam"))
+        BEAM_AVAILABLE = True
+        logger.info(f"Apache Beam version {BEAM_VERSION} available.")
+    except importlib.metadata.PackageNotFoundError:
+        pass
+else:
+    logger.info("Disabling Apache Beam because USE_BEAM is set to False")
+
+
+# Optional tools for data loading
+SQLALCHEMY_AVAILABLE = importlib.util.find_spec("sqlalchemy") is not None
+
+# Optional tools for feature decoding
+PIL_AVAILABLE = importlib.util.find_spec("PIL") is not None
+IS_OPUS_SUPPORTED = importlib.util.find_spec("soundfile") is not None and version.parse(
+    importlib.import_module("soundfile").__libsndfile_version__
+) >= version.parse("1.0.31")
+IS_MP3_SUPPORTED = importlib.util.find_spec("soundfile") is not None and version.parse(
+    importlib.import_module("soundfile").__libsndfile_version__
+) >= version.parse("1.1.0")
+
+# Optional compression tools
+RARFILE_AVAILABLE = importlib.util.find_spec("rarfile") is not None
+ZSTANDARD_AVAILABLE = importlib.util.find_spec("zstandard") is not None
+LZ4_AVAILABLE = importlib.util.find_spec("lz4") is not None
+PY7ZR_AVAILABLE = importlib.util.find_spec("py7zr") is not None
+
+# Cache location
+DEFAULT_XDG_CACHE_HOME = "~/.cache"
+XDG_CACHE_HOME = os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
+DEFAULT_HF_CACHE_HOME = os.path.join(XDG_CACHE_HOME, "huggingface")
+HF_CACHE_HOME = os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))
+
+DEFAULT_HF_DATASETS_CACHE = os.path.join(HF_CACHE_HOME, "datasets")
+HF_DATASETS_CACHE = Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))
+
+DEFAULT_HF_METRICS_CACHE = os.path.join(HF_CACHE_HOME, "metrics")
+HF_METRICS_CACHE = Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))
+
+DEFAULT_HF_MODULES_CACHE = os.path.join(HF_CACHE_HOME, "modules")
+HF_MODULES_CACHE = Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))
+
+DOWNLOADED_DATASETS_DIR = "downloads"
+DEFAULT_DOWNLOADED_DATASETS_PATH = os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
+DOWNLOADED_DATASETS_PATH = Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))
+
+EXTRACTED_DATASETS_DIR = "extracted"
+DEFAULT_EXTRACTED_DATASETS_PATH = os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
+EXTRACTED_DATASETS_PATH = Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))
+
+# Download count for the website
+HF_UPDATE_DOWNLOAD_COUNTS = (
+    os.environ.get("HF_UPDATE_DOWNLOAD_COUNTS", "AUTO").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
+)
+
+# Remote dataset scripts support
+__HF_DATASETS_TRUST_REMOTE_CODE = os.environ.get("HF_DATASETS_TRUST_REMOTE_CODE", "1")
+HF_DATASETS_TRUST_REMOTE_CODE: Optional[bool] = (
+    True
+    if __HF_DATASETS_TRUST_REMOTE_CODE.upper() in ENV_VARS_TRUE_VALUES
+    else False
+    if __HF_DATASETS_TRUST_REMOTE_CODE.upper() in ENV_VARS_FALSE_VALUES
+    else None
+)
+TIME_OUT_REMOTE_CODE = 15
+
+# Datasets-server
+USE_PARQUET_EXPORT = True
+
+# Batch size constants. For more info, see:
+# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
+DEFAULT_MAX_BATCH_SIZE = 1000
+
+# Size of the preloaded record batch in `Dataset.__iter__`
+ARROW_READER_BATCH_SIZE_IN_DATASET_ITER = 10
+
+# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
+MAX_SHARD_SIZE = "500MB"
+
+# Parquet configuration
+PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS = 100
+PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS = 100
+PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS = 100
+
+# Offline mode
+HF_DATASETS_OFFLINE = os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES
+
+# Here, `True` will disable progress bars globally without possibility of enabling it
+# programmatically. `False` will enable them without possibility of disabling them.
+# If environment variable is not set (None), then the user is free to enable/disable
+# them programmatically.
+# TL;DR: env variable has priority over code
+__HF_DATASETS_DISABLE_PROGRESS_BARS = os.environ.get("HF_DATASETS_DISABLE_PROGRESS_BARS")
+HF_DATASETS_DISABLE_PROGRESS_BARS: Optional[bool] = (
+    __HF_DATASETS_DISABLE_PROGRESS_BARS.upper() in ENV_VARS_TRUE_VALUES
+    if __HF_DATASETS_DISABLE_PROGRESS_BARS is not None
+    else None
+)
+
+# In-memory
+DEFAULT_IN_MEMORY_MAX_SIZE = 0  # Disabled
+IN_MEMORY_MAX_SIZE = float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))
+
+# File names
+DATASET_ARROW_FILENAME = "dataset.arrow"
+DATASET_INDICES_FILENAME = "indices.arrow"
+DATASET_STATE_JSON_FILENAME = "state.json"
+DATASET_INFO_FILENAME = "dataset_info.json"
+DATASETDICT_INFOS_FILENAME = "dataset_infos.json"
+LICENSE_FILENAME = "LICENSE"
+METRIC_INFO_FILENAME = "metric_info.json"
+DATASETDICT_JSON_FILENAME = "dataset_dict.json"
+METADATA_CONFIGS_FIELD = "configs"
+REPOCARD_FILENAME = "README.md"
+REPOYAML_FILENAME = ".huggingface.yaml"
+
+MODULE_NAME_FOR_DYNAMIC_MODULES = "datasets_modules"
+
+MAX_DATASET_CONFIG_ID_READABLE_LENGTH = 255
+
+# Temporary cache directory prefix
+TEMP_CACHE_DIR_PREFIX = "hf_datasets-"
+
+# Streaming
+STREAMING_READ_MAX_RETRIES = 20
+STREAMING_READ_RETRY_INTERVAL = 5
+
+# Datasets without script
+DATA_FILES_MAX_NUMBER_FOR_MODULE_INFERENCE = 200
+GLOBBED_DATA_FILES_MAX_NUMBER_FOR_MODULE_INFERENCE = 10
+ARCHIVED_DATA_FILES_MAX_NUMBER_FOR_MODULE_INFERENCE = 200
+
+# Progress bars
+PBAR_REFRESH_TIME_INTERVAL = 0.05  # 20 progress updates per sec
+
+# Maximum number of uploaded files per commit
+UPLOADS_MAX_NUMBER_PER_COMMIT = 50
+
+# Backward compatibiliy
+MAX_TABLE_NBYTES_FOR_PICKLING = 4 << 30

env-llmeval/lib/python3.10/site-packages/datasets/data_files.py

@@ -0,0 +1,806 @@
+import os
+import re
+from functools import partial
+from glob import has_magic
+from pathlib import Path, PurePath
+from typing import Callable, Dict, List, Optional, Set, Tuple, Union
+
+import huggingface_hub
+from fsspec import get_fs_token_paths
+from fsspec.implementations.http import HTTPFileSystem
+from huggingface_hub import HfFileSystem
+from packaging import version
+from tqdm.contrib.concurrent import thread_map
+
+from . import config
+from .download import DownloadConfig
+from .download.streaming_download_manager import _prepare_path_and_storage_options, xbasename, xjoin
+from .naming import _split_re
+from .splits import Split
+from .utils import logging
+from .utils import tqdm as hf_tqdm
+from .utils.file_utils import is_local_path, is_relative_path
+from .utils.py_utils import glob_pattern_to_regex, string_to_dict
+
+
+SANITIZED_DEFAULT_SPLIT = str(Split.TRAIN)
+
+
+logger = logging.get_logger(__name__)
+
+
+class Url(str):
+    pass
+
+
+class EmptyDatasetError(FileNotFoundError):
+    pass
+
+
+SPLIT_PATTERN_SHARDED = "data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*"
+
+SPLIT_KEYWORDS = {
+    Split.TRAIN: ["train", "training"],
+    Split.VALIDATION: ["validation", "valid", "dev", "val"],
+    Split.TEST: ["test", "testing", "eval", "evaluation"],
+}
+NON_WORDS_CHARS = "-._ 0-9"
+if config.FSSPEC_VERSION < version.parse("2023.9.0"):
+    KEYWORDS_IN_PATH_NAME_BASE_PATTERNS = ["{keyword}[{sep}/]**", "**[{sep}/]{keyword}[{sep}/]**"]
+elif config.FSSPEC_VERSION < version.parse("2023.12.0"):
+    KEYWORDS_IN_PATH_NAME_BASE_PATTERNS = ["{keyword}[{sep}/]**", "**/*[{sep}/]{keyword}[{sep}/]**"]
+else:
+    KEYWORDS_IN_PATH_NAME_BASE_PATTERNS = [
+        "**/{keyword}[{sep}]*",
+        "**/{keyword}/**",
+        "**/*[{sep}]{keyword}[{sep}]*",
+        "**/*[{sep}]{keyword}[{sep}]*/**",
+        "**/{keyword}[{sep}]*/**",
+        "**/*[{sep}]{keyword}/**",
+    ]
+
+DEFAULT_SPLITS = [Split.TRAIN, Split.VALIDATION, Split.TEST]
+DEFAULT_PATTERNS_SPLIT_IN_PATH_NAME = {
+    split: [
+        pattern.format(keyword=keyword, sep=NON_WORDS_CHARS)
+        for keyword in SPLIT_KEYWORDS[split]
+        for pattern in KEYWORDS_IN_PATH_NAME_BASE_PATTERNS
+    ]
+    for split in DEFAULT_SPLITS
+}
+
+DEFAULT_PATTERNS_ALL = {
+    Split.TRAIN: ["**"],
+}
+
+ALL_SPLIT_PATTERNS = [SPLIT_PATTERN_SHARDED]
+ALL_DEFAULT_PATTERNS = [
+    DEFAULT_PATTERNS_SPLIT_IN_PATH_NAME,
+    DEFAULT_PATTERNS_ALL,
+]
+if config.FSSPEC_VERSION < version.parse("2023.9.0"):
+    METADATA_PATTERNS = [
+        "metadata.csv",
+        "**/metadata.csv",
+        "metadata.jsonl",
+        "**/metadata.jsonl",
+    ]  # metadata file for ImageFolder and AudioFolder
+else:
+    METADATA_PATTERNS = [
+        "**/metadata.csv",
+        "**/metadata.jsonl",
+    ]  # metadata file for ImageFolder and AudioFolder
+WILDCARD_CHARACTERS = "*[]"
+FILES_TO_IGNORE = [
+    "README.md",
+    "config.json",
+    "dataset_info.json",
+    "dataset_infos.json",
+    "dummy_data.zip",
+    "dataset_dict.json",
+]
+
+
+def contains_wildcards(pattern: str) -> bool:
+    return any(wilcard_character in pattern for wilcard_character in WILDCARD_CHARACTERS)
+
+
+def sanitize_patterns(patterns: Union[Dict, List, str]) -> Dict[str, Union[List[str], "DataFilesList"]]:
+    """
+    Take the data_files patterns from the user, and format them into a dictionary.
+    Each key is the name of the split, and each value is a list of data files patterns (paths or urls).
+    The default split is "train".
+
+    Returns:
+        patterns: dictionary of split_name -> list of patterns
+    """
+    if isinstance(patterns, dict):
+        return {str(key): value if isinstance(value, list) else [value] for key, value in patterns.items()}
+    elif isinstance(patterns, str):
+        return {SANITIZED_DEFAULT_SPLIT: [patterns]}
+    elif isinstance(patterns, list):
+        if any(isinstance(pattern, dict) for pattern in patterns):
+            for pattern in patterns:
+                if not (
+                    isinstance(pattern, dict)
+                    and len(pattern) == 2
+                    and "split" in pattern
+                    and isinstance(pattern.get("path"), (str, list))
+                ):
+                    raise ValueError(
+                        f"Expected each split to have a 'path' key which can be a string or a list of strings, but got {pattern}"
+                    )
+            splits = [pattern["split"] for pattern in patterns]
+            if len(set(splits)) != len(splits):
+                raise ValueError(f"Some splits are duplicated in data_files: {splits}")
+            return {
+                str(pattern["split"]): pattern["path"] if isinstance(pattern["path"], list) else [pattern["path"]]
+                for pattern in patterns
+            }
+        else:
+            return {SANITIZED_DEFAULT_SPLIT: patterns}
+    else:
+        return sanitize_patterns(list(patterns))
+
+
+def _is_inside_unrequested_special_dir(matched_rel_path: str, pattern: str) -> bool:
+    """
+    When a path matches a pattern, we additionnally check if it's inside a special directory
+    we ignore by default (if it starts with a double underscore).
+
+    Users can still explicitly request a filepath inside such a directory if "__pycache__" is
+    mentioned explicitly in the requested pattern.
+
+    Some examples:
+
+    base directory:
+
+        ./
+        └── __pycache__
+            └── b.txt
+
+    >>> _is_inside_unrequested_special_dir("__pycache__/b.txt", "**")
+    True
+    >>> _is_inside_unrequested_special_dir("__pycache__/b.txt", "*/b.txt")
+    True
+    >>> _is_inside_unrequested_special_dir("__pycache__/b.txt", "__pycache__/*")
+    False
+    >>> _is_inside_unrequested_special_dir("__pycache__/b.txt", "__*/*")
+    False
+    """
+    # We just need to check if every special directories from the path is present explicly in the pattern.
+    # Since we assume that the path matches the pattern, it's equivalent to counting that both
+    # the parent path and the parent pattern have the same number of special directories.
+    data_dirs_to_ignore_in_path = [part for part in PurePath(matched_rel_path).parent.parts if part.startswith("__")]
+    data_dirs_to_ignore_in_pattern = [part for part in PurePath(pattern).parent.parts if part.startswith("__")]
+    return len(data_dirs_to_ignore_in_path) != len(data_dirs_to_ignore_in_pattern)
+
+
+def _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(matched_rel_path: str, pattern: str) -> bool:
+    """
+    When a path matches a pattern, we additionnally check if it's a hidden file or if it's inside
+    a hidden directory we ignore by default, i.e. if the file name or a parent directory name starts with a dot.
+
+    Users can still explicitly request a filepath that is hidden or is inside a hidden directory
+    if the hidden part is mentioned explicitly in the requested pattern.
+
+    Some examples:
+
+    base directory:
+
+        ./
+        └── .hidden_file.txt
+
+    >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_file.txt", "**")
+    True
+    >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_file.txt", ".*")
+    False
+
+    base directory:
+
+        ./
+        └── .hidden_dir
+            └── a.txt
+
+    >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/a.txt", "**")
+    True
+    >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/a.txt", ".*/*")
+    False
+    >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/a.txt", ".hidden_dir/*")
+    False
+
+    base directory:
+
+        ./
+        └── .hidden_dir
+            └── .hidden_file.txt
+
+    >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/.hidden_file.txt", "**")
+    True
+    >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/.hidden_file.txt", ".*/*")
+    True
+    >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/.hidden_file.txt", ".*/.*")
+    False
+    >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/.hidden_file.txt", ".hidden_dir/*")
+    True
+    >>> _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(".hidden_dir/.hidden_file.txt", ".hidden_dir/.*")
+    False
+    """
+    # We just need to check if every hidden part from the path is present explicly in the pattern.
+    # Since we assume that the path matches the pattern, it's equivalent to counting that both
+    # the path and the pattern have the same number of hidden parts.
+    hidden_directories_in_path = [
+        part for part in PurePath(matched_rel_path).parts if part.startswith(".") and not set(part) == {"."}
+    ]
+    hidden_directories_in_pattern = [
+        part for part in PurePath(pattern).parts if part.startswith(".") and not set(part) == {"."}
+    ]
+    return len(hidden_directories_in_path) != len(hidden_directories_in_pattern)
+
+
+def _get_data_files_patterns(pattern_resolver: Callable[[str], List[str]]) -> Dict[str, List[str]]:
+    """
+    Get the default pattern from a directory or repository by testing all the supported patterns.
+    The first patterns to return a non-empty list of data files is returned.
+
+    In order, it first tests if SPLIT_PATTERN_SHARDED works, otherwise it tests the patterns in ALL_DEFAULT_PATTERNS.
+    """
+    # first check the split patterns like data/{split}-00000-of-00001.parquet
+    for split_pattern in ALL_SPLIT_PATTERNS:
+        pattern = split_pattern.replace("{split}", "*")
+        try:
+            data_files = pattern_resolver(pattern)
+        except FileNotFoundError:
+            continue
+        if len(data_files) > 0:
+            splits: Set[str] = {
+                string_to_dict(xbasename(p), glob_pattern_to_regex(xbasename(split_pattern)))["split"]
+                for p in data_files
+            }
+            if any(not re.match(_split_re, split) for split in splits):
+                raise ValueError(f"Split name should match '{_split_re}'' but got '{splits}'.")
+            sorted_splits = [str(split) for split in DEFAULT_SPLITS if split in splits] + sorted(
+                splits - set(DEFAULT_SPLITS)
+            )
+            return {split: [split_pattern.format(split=split)] for split in sorted_splits}
+    # then check the default patterns based on train/valid/test splits
+    for patterns_dict in ALL_DEFAULT_PATTERNS:
+        non_empty_splits = []
+        for split, patterns in patterns_dict.items():
+            for pattern in patterns:
+                try:
+                    data_files = pattern_resolver(pattern)
+                except FileNotFoundError:
+                    continue
+                if len(data_files) > 0:
+                    non_empty_splits.append(split)
+                    break
+        if non_empty_splits:
+            return {split: patterns_dict[split] for split in non_empty_splits}
+    raise FileNotFoundError(f"Couldn't resolve pattern {pattern} with resolver {pattern_resolver}")
+
+
+def _get_metadata_files_patterns(pattern_resolver: Callable[[str], List[str]]) -> List[str]:
+    """
+    Get the supported metadata patterns from a directory or repository.
+    """
+    non_empty_patterns = []
+    for pattern in METADATA_PATTERNS:
+        try:
+            metadata_files = pattern_resolver(pattern)
+            if len(metadata_files) > 0:
+                non_empty_patterns.append(pattern)
+        except FileNotFoundError:
+            pass
+    if non_empty_patterns:
+        return non_empty_patterns
+    raise FileNotFoundError(f"Couldn't resolve pattern {pattern} with resolver {pattern_resolver}")
+
+
+def resolve_pattern(
+    pattern: str,
+    base_path: str,
+    allowed_extensions: Optional[List[str]] = None,
+    download_config: Optional[DownloadConfig] = None,
+) -> List[str]:
+    """
+    Resolve the paths and URLs of the data files from the pattern passed by the user.
+
+    You can use patterns to resolve multiple local files. Here are a few examples:
+    - *.csv to match all the CSV files at the first level
+    - **.csv to match all the CSV files at any level
+    - data/* to match all the files inside "data"
+    - data/** to match all the files inside "data" and its subdirectories
+
+    The patterns are resolved using the fsspec glob. In fsspec>=2023.12.0 this is equivalent to
+    Python's glob.glob, Path.glob, Path.match and fnmatch where ** is unsupported with a prefix/suffix
+    other than a forward slash /.
+
+    More generally:
+    - '*' matches any character except a forward-slash (to match just the file or directory name)
+    - '**' matches any character including a forward-slash /
+
+    Hidden files and directories (i.e. whose names start with a dot) are ignored, unless they are explicitly requested.
+    The same applies to special directories that start with a double underscore like "__pycache__".
+    You can still include one if the pattern explicilty mentions it:
+    - to include a hidden file: "*/.hidden.txt" or "*/.*"
+    - to include a hidden directory: ".hidden/*" or ".*/*"
+    - to include a special directory: "__special__/*" or "__*/*"
+
+    Example::
+
+        >>> from datasets.data_files import resolve_pattern
+        >>> base_path = "."
+        >>> resolve_pattern("docs/**/*.py", base_path)
+        [/Users/mariosasko/Desktop/projects/datasets/docs/source/_config.py']
+
+    Args:
+        pattern (str): Unix pattern or paths or URLs of the data files to resolve.
+            The paths can be absolute or relative to base_path.
+            Remote filesystems using fsspec are supported, e.g. with the hf:// protocol.
+        base_path (str): Base path to use when resolving relative paths.
+        allowed_extensions (Optional[list], optional): White-list of file extensions to use. Defaults to None (all extensions).
+            For example: allowed_extensions=[".csv", ".json", ".txt", ".parquet"]
+    Returns:
+        List[str]: List of paths or URLs to the local or remote files that match the patterns.
+    """
+    if is_relative_path(pattern):
+        pattern = xjoin(base_path, pattern)
+    elif is_local_path(pattern):
+        base_path = os.path.splitdrive(pattern)[0] + os.sep
+    else:
+        base_path = ""
+    pattern, storage_options = _prepare_path_and_storage_options(pattern, download_config=download_config)
+    fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options)
+    fs_base_path = base_path.split("::")[0].split("://")[-1] or fs.root_marker
+    fs_pattern = pattern.split("::")[0].split("://")[-1]
+    files_to_ignore = set(FILES_TO_IGNORE) - {xbasename(pattern)}
+    protocol = fs.protocol if isinstance(fs.protocol, str) else fs.protocol[0]
+    protocol_prefix = protocol + "://" if protocol != "file" else ""
+    glob_kwargs = {}
+    if protocol == "hf" and config.HF_HUB_VERSION >= version.parse("0.20.0"):
+        # 10 times faster glob with detail=True (ignores costly info like lastCommit)
+        glob_kwargs["expand_info"] = False
+    matched_paths = [
+        filepath if filepath.startswith(protocol_prefix) else protocol_prefix + filepath
+        for filepath, info in fs.glob(pattern, detail=True, **glob_kwargs).items()
+        if info["type"] == "file"
+        and (xbasename(filepath) not in files_to_ignore)
+        and not _is_inside_unrequested_special_dir(
+            os.path.relpath(filepath, fs_base_path), os.path.relpath(fs_pattern, fs_base_path)
+        )
+        and not _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir(
+            os.path.relpath(filepath, fs_base_path), os.path.relpath(fs_pattern, fs_base_path)
+        )
+    ]  # ignore .ipynb and __pycache__, but keep /../
+    if allowed_extensions is not None:
+        out = [
+            filepath
+            for filepath in matched_paths
+            if any("." + suffix in allowed_extensions for suffix in xbasename(filepath).split(".")[1:])
+        ]
+        if len(out) < len(matched_paths):
+            invalid_matched_files = list(set(matched_paths) - set(out))
+            logger.info(
+                f"Some files matched the pattern '{pattern}' but don't have valid data file extensions: {invalid_matched_files}"
+            )
+    else:
+        out = matched_paths
+    if not out:
+        error_msg = f"Unable to find '{pattern}'"
+        if allowed_extensions is not None:
+            error_msg += f" with any supported extension {list(allowed_extensions)}"
+        raise FileNotFoundError(error_msg)
+    return out
+
+
+def get_data_patterns(base_path: str, download_config: Optional[DownloadConfig] = None) -> Dict[str, List[str]]:
+    """
+    Get the default pattern from a directory testing all the supported patterns.
+    The first patterns to return a non-empty list of data files is returned.
+
+    Some examples of supported patterns:
+
+    Input:
+
+        my_dataset_repository/
+        ├── README.md
+        └── dataset.csv
+
+    Output:
+
+        {"train": ["**"]}
+
+    Input:
+
+        my_dataset_repository/
+        ├── README.md
+        ├── train.csv
+        └── test.csv
+
+        my_dataset_repository/
+        ├── README.md
+        └── data/
+            ├── train.csv
+            └── test.csv
+
+        my_dataset_repository/
+        ├── README.md
+        ├── train_0.csv
+        ├── train_1.csv
+        ├── train_2.csv
+        ├── train_3.csv
+        ├── test_0.csv
+        └── test_1.csv
+
+    Output:
+
+        {'train': ['train[-._ 0-9/]**', '**/*[-._ 0-9/]train[-._ 0-9/]**', 'training[-._ 0-9/]**', '**/*[-._ 0-9/]training[-._ 0-9/]**'],
+         'test': ['test[-._ 0-9/]**', '**/*[-._ 0-9/]test[-._ 0-9/]**', 'testing[-._ 0-9/]**', '**/*[-._ 0-9/]testing[-._ 0-9/]**', ...]}
+
+    Input:
+
+        my_dataset_repository/
+        ├── README.md
+        └── data/
+            ├── train/
+            │   ├── shard_0.csv
+            │   ├── shard_1.csv
+            │   ├── shard_2.csv
+            │   └── shard_3.csv
+            └── test/
+                ├── shard_0.csv
+                └── shard_1.csv
+
+    Output:
+
+        {'train': ['train[-._ 0-9/]**', '**/*[-._ 0-9/]train[-._ 0-9/]**', 'training[-._ 0-9/]**', '**/*[-._ 0-9/]training[-._ 0-9/]**'],
+         'test': ['test[-._ 0-9/]**', '**/*[-._ 0-9/]test[-._ 0-9/]**', 'testing[-._ 0-9/]**', '**/*[-._ 0-9/]testing[-._ 0-9/]**', ...]}
+
+    Input:
+
+        my_dataset_repository/
+        ├── README.md
+        └── data/
+            ├── train-00000-of-00003.csv
+            ├── train-00001-of-00003.csv
+            ├── train-00002-of-00003.csv
+            ├── test-00000-of-00001.csv
+            ├── random-00000-of-00003.csv
+            ├── random-00001-of-00003.csv
+            └── random-00002-of-00003.csv
+
+    Output:
+
+        {'train': ['data/train-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*'],
+         'test': ['data/test-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*'],
+         'random': ['data/random-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*']}
+
+    In order, it first tests if SPLIT_PATTERN_SHARDED works, otherwise it tests the patterns in ALL_DEFAULT_PATTERNS.
+    """
+    resolver = partial(resolve_pattern, base_path=base_path, download_config=download_config)
+    try:
+        return _get_data_files_patterns(resolver)
+    except FileNotFoundError:
+        raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None
+
+
+def get_metadata_patterns(
+    base_path: str,
+    download_config: Optional[DownloadConfig] = None,
+) -> List[str]:
+    """
+    Get the supported metadata patterns from a local directory.
+    """
+    resolver = partial(resolve_pattern, base_path=base_path, download_config=download_config)
+    try:
+        return _get_metadata_files_patterns(resolver)
+    except FileNotFoundError:
+        raise FileNotFoundError(f"The directory at {base_path} doesn't contain any metadata file") from None
+
+
+def _get_single_origin_metadata(
+    data_file: str,
+    download_config: Optional[DownloadConfig] = None,
+) -> Tuple[str]:
+    data_file, storage_options = _prepare_path_and_storage_options(data_file, download_config=download_config)
+    fs, _, _ = get_fs_token_paths(data_file, storage_options=storage_options)
+    if isinstance(fs, HfFileSystem):
+        resolved_path = fs.resolve_path(data_file)
+        return (resolved_path.repo_id, resolved_path.revision)
+    elif isinstance(fs, HTTPFileSystem) and data_file.startswith(config.HF_ENDPOINT):
+        hffs = HfFileSystem(endpoint=config.HF_ENDPOINT, token=download_config.token)
+        data_file = "hf://" + data_file[len(config.HF_ENDPOINT) + 1 :].replace("/resolve/", "@", 1)
+        resolved_path = hffs.resolve_path(data_file)
+        return (resolved_path.repo_id, resolved_path.revision)
+    info = fs.info(data_file)
+    # s3fs uses "ETag", gcsfs uses "etag", and for local we simply check mtime
+    for key in ["ETag", "etag", "mtime"]:
+        if key in info:
+            return (str(info[key]),)
+    return ()
+
+
+def _get_origin_metadata(
+    data_files: List[str],
+    max_workers=64,
+    download_config: Optional[DownloadConfig] = None,
+) -> Tuple[str]:
+    return thread_map(
+        partial(_get_single_origin_metadata, download_config=download_config),
+        data_files,
+        max_workers=max_workers,
+        tqdm_class=hf_tqdm,
+        desc="Resolving data files",
+        # set `disable=None` rather than `disable=False` by default to disable progress bar when no TTY attached
+        disable=len(data_files) <= 16 or None,
+    )
+
+
+class DataFilesList(List[str]):
+    """
+    List of data files (absolute local paths or URLs).
+    It has two construction methods given the user's data files patterns :
+    - ``from_hf_repo``: resolve patterns inside a dataset repository
+    - ``from_local_or_remote``: resolve patterns from a local path
+
+    Moreover DataFilesList has an additional attribute ``origin_metadata``.
+    It can store:
+    - the last modified time of local files
+    - ETag of remote files
+    - commit sha of a dataset repository
+
+    Thanks to this additional attribute, it is possible to hash the list
+    and get a different hash if and only if at least one file changed.
+    This is useful for caching Dataset objects that are obtained from a list of data files.
+    """
+
+    def __init__(self, data_files: List[str], origin_metadata: List[Tuple[str]]):
+        super().__init__(data_files)
+        self.origin_metadata = origin_metadata
+
+    def __add__(self, other):
+        return DataFilesList([*self, *other], self.origin_metadata + other.origin_metadata)
+
+    @classmethod
+    def from_hf_repo(
+        cls,
+        patterns: List[str],
+        dataset_info: huggingface_hub.hf_api.DatasetInfo,
+        base_path: Optional[str] = None,
+        allowed_extensions: Optional[List[str]] = None,
+        download_config: Optional[DownloadConfig] = None,
+    ) -> "DataFilesList":
+        base_path = f"hf://datasets/{dataset_info.id}@{dataset_info.sha}/{base_path or ''}".rstrip("/")
+        return cls.from_patterns(
+            patterns, base_path=base_path, allowed_extensions=allowed_extensions, download_config=download_config
+        )
+
+    @classmethod
+    def from_local_or_remote(
+        cls,
+        patterns: List[str],
+        base_path: Optional[str] = None,
+        allowed_extensions: Optional[List[str]] = None,
+        download_config: Optional[DownloadConfig] = None,
+    ) -> "DataFilesList":
+        base_path = base_path if base_path is not None else Path().resolve().as_posix()
+        return cls.from_patterns(
+            patterns, base_path=base_path, allowed_extensions=allowed_extensions, download_config=download_config
+        )
+
+    @classmethod
+    def from_patterns(
+        cls,
+        patterns: List[str],
+        base_path: Optional[str] = None,
+        allowed_extensions: Optional[List[str]] = None,
+        download_config: Optional[DownloadConfig] = None,
+    ) -> "DataFilesList":
+        base_path = base_path if base_path is not None else Path().resolve().as_posix()
+        data_files = []
+        for pattern in patterns:
+            try:
+                data_files.extend(
+                    resolve_pattern(
+                        pattern,
+                        base_path=base_path,
+                        allowed_extensions=allowed_extensions,
+                        download_config=download_config,
+                    )
+                )
+            except FileNotFoundError:
+                if not has_magic(pattern):
+                    raise
+        origin_metadata = _get_origin_metadata(data_files, download_config=download_config)
+        return cls(data_files, origin_metadata)
+
+    def filter_extensions(self, extensions: List[str]) -> "DataFilesList":
+        pattern = "|".join("\\" + ext for ext in extensions)
+        pattern = re.compile(f".*({pattern})(\\..+)?$")
+        return DataFilesList(
+            [data_file for data_file in self if pattern.match(data_file)],
+            origin_metadata=self.origin_metadata,
+        )
+
+
+class DataFilesDict(Dict[str, DataFilesList]):
+    """
+    Dict of split_name -> list of data files (absolute local paths or URLs).
+    It has two construction methods given the user's data files patterns :
+    - ``from_hf_repo``: resolve patterns inside a dataset repository
+    - ``from_local_or_remote``: resolve patterns from a local path
+
+    Moreover each list is a DataFilesList. It is possible to hash the dictionary
+    and get a different hash if and only if at least one file changed.
+    For more info, see ``DataFilesList``.
+
+    This is useful for caching Dataset objects that are obtained from a list of data files.
+
+    Changing the order of the keys of this dictionary also doesn't change its hash.
+    """
+
+    @classmethod
+    def from_local_or_remote(
+        cls,
+        patterns: Dict[str, Union[List[str], DataFilesList]],
+        base_path: Optional[str] = None,
+        allowed_extensions: Optional[List[str]] = None,
+        download_config: Optional[DownloadConfig] = None,
+    ) -> "DataFilesDict":
+        out = cls()
+        for key, patterns_for_key in patterns.items():
+            out[key] = (
+                DataFilesList.from_local_or_remote(
+                    patterns_for_key,
+                    base_path=base_path,
+                    allowed_extensions=allowed_extensions,
+                    download_config=download_config,
+                )
+                if not isinstance(patterns_for_key, DataFilesList)
+                else patterns_for_key
+            )
+        return out
+
+    @classmethod
+    def from_hf_repo(
+        cls,
+        patterns: Dict[str, Union[List[str], DataFilesList]],
+        dataset_info: huggingface_hub.hf_api.DatasetInfo,
+        base_path: Optional[str] = None,
+        allowed_extensions: Optional[List[str]] = None,
+        download_config: Optional[DownloadConfig] = None,
+    ) -> "DataFilesDict":
+        out = cls()
+        for key, patterns_for_key in patterns.items():
+            out[key] = (
+                DataFilesList.from_hf_repo(
+                    patterns_for_key,
+                    dataset_info=dataset_info,
+                    base_path=base_path,
+                    allowed_extensions=allowed_extensions,
+                    download_config=download_config,
+                )
+                if not isinstance(patterns_for_key, DataFilesList)
+                else patterns_for_key
+            )
+        return out
+
+    @classmethod
+    def from_patterns(
+        cls,
+        patterns: Dict[str, Union[List[str], DataFilesList]],
+        base_path: Optional[str] = None,
+        allowed_extensions: Optional[List[str]] = None,
+        download_config: Optional[DownloadConfig] = None,
+    ) -> "DataFilesDict":
+        out = cls()
+        for key, patterns_for_key in patterns.items():
+            out[key] = (
+                DataFilesList.from_patterns(
+                    patterns_for_key,
+                    base_path=base_path,
+                    allowed_extensions=allowed_extensions,
+                    download_config=download_config,
+                )
+                if not isinstance(patterns_for_key, DataFilesList)
+                else patterns_for_key
+            )
+        return out
+
+    def filter_extensions(self, extensions: List[str]) -> "DataFilesDict":
+        out = type(self)()
+        for key, data_files_list in self.items():
+            out[key] = data_files_list.filter_extensions(extensions)
+        return out
+
+
+class DataFilesPatternsList(List[str]):
+    """
+    List of data files patterns (absolute local paths or URLs).
+    For each pattern there should also be a list of allowed extensions
+    to keep, or a None ot keep all the files for the pattern.
+    """
+
+    def __init__(
+        self,
+        patterns: List[str],
+        allowed_extensions: List[Optional[List[str]]],
+    ):
+        super().__init__(patterns)
+        self.allowed_extensions = allowed_extensions
+
+    def __add__(self, other):
+        return DataFilesList([*self, *other], self.allowed_extensions + other.allowed_extensions)
+
+    @classmethod
+    def from_patterns(
+        cls, patterns: List[str], allowed_extensions: Optional[List[str]] = None
+    ) -> "DataFilesPatternsDict":
+        return cls(patterns, [allowed_extensions] * len(patterns))
+
+    def resolve(
+        self,
+        base_path: str,
+        download_config: Optional[DownloadConfig] = None,
+    ) -> "DataFilesList":
+        base_path = base_path if base_path is not None else Path().resolve().as_posix()
+        data_files = []
+        for pattern, allowed_extensions in zip(self, self.allowed_extensions):
+            try:
+                data_files.extend(
+                    resolve_pattern(
+                        pattern,
+                        base_path=base_path,
+                        allowed_extensions=allowed_extensions,
+                        download_config=download_config,
+                    )
+                )
+            except FileNotFoundError:
+                if not has_magic(pattern):
+                    raise
+        origin_metadata = _get_origin_metadata(data_files, download_config=download_config)
+        return DataFilesList(data_files, origin_metadata)
+
+    def filter_extensions(self, extensions: List[str]) -> "DataFilesList":
+        return DataFilesPatternsList(
+            self, [allowed_extensions + extensions for allowed_extensions in self.allowed_extensions]
+        )
+
+
+class DataFilesPatternsDict(Dict[str, DataFilesPatternsList]):
+    """
+    Dict of split_name -> list of data files patterns (absolute local paths or URLs).
+    """
+
+    @classmethod
+    def from_patterns(
+        cls, patterns: Dict[str, List[str]], allowed_extensions: Optional[List[str]] = None
+    ) -> "DataFilesPatternsDict":
+        out = cls()
+        for key, patterns_for_key in patterns.items():
+            out[key] = (
+                DataFilesPatternsList.from_patterns(
+                    patterns_for_key,
+                    allowed_extensions=allowed_extensions,
+                )
+                if not isinstance(patterns_for_key, DataFilesPatternsList)
+                else patterns_for_key
+            )
+        return out
+
+    def resolve(
+        self,
+        base_path: str,
+        download_config: Optional[DownloadConfig] = None,
+    ) -> "DataFilesDict":
+        out = DataFilesDict()
+        for key, data_files_patterns_list in self.items():
+            out[key] = data_files_patterns_list.resolve(base_path, download_config)
+        return out
+
+    def filter_extensions(self, extensions: List[str]) -> "DataFilesPatternsDict":
+        out = type(self)()
+        for key, data_files_patterns_list in self.items():
+            out[key] = data_files_patterns_list.filter_extensions(extensions)
+        return out

env-llmeval/lib/python3.10/site-packages/datasets/distributed.py

@@ -0,0 +1,39 @@
+from typing import TypeVar
+
+from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
+from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset
+
+
+DatasetType = TypeVar("DatasetType", Dataset, IterableDataset)
+
+
+def split_dataset_by_node(dataset: DatasetType, rank: int, world_size: int) -> DatasetType:
+    """
+    Split a dataset for the node at rank `rank` in a pool of nodes of size `world_size`.
+
+    For map-style datasets:
+
+    Each node is assigned a chunk of data, e.g. rank 0 is given the first chunk of the dataset.
+    To maximize data loading throughput, chunks are made of contiguous data on disk if possible.
+
+    For iterable datasets:
+
+    If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`),
+    then the shards are evenly assigned across the nodes, which is the most optimized.
+    Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples.
+
+    Args:
+        dataset ([`Dataset`] or [`IterableDataset`]):
+            The dataset to split by node.
+        rank (`int`):
+            Rank of the current node.
+        world_size (`int`):
+            Total number of nodes.
+
+    Returns:
+        [`Dataset`] or [`IterableDataset`]: The dataset to be used on the node at rank `rank`.
+    """
+    if isinstance(dataset, Dataset):
+        return _split_by_node_map_style_dataset(dataset, rank=rank, world_size=world_size)
+    else:
+        return _split_by_node_iterable_dataset(dataset, rank=rank, world_size=world_size)

env-llmeval/lib/python3.10/site-packages/datasets/exceptions.py

@@ -0,0 +1,85 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright 2023 The HuggingFace Authors.
+from typing import Any, Dict, List, Optional, Union
+
+from huggingface_hub import HfFileSystem
+
+from . import config
+from .table import CastError
+from .utils.track import TrackedIterable, tracked_list, tracked_str
+
+
+class DatasetsError(Exception):
+    """Base class for exceptions in this library."""
+
+
+class DefunctDatasetError(DatasetsError):
+    """The dataset has been defunct."""
+
+
+class FileNotFoundDatasetsError(DatasetsError, FileNotFoundError):
+    """FileNotFoundError raised by this library."""
+
+
+class DataFilesNotFoundError(FileNotFoundDatasetsError):
+    """No (supported) data files found."""
+
+
+class DatasetNotFoundError(FileNotFoundDatasetsError):
+    """Dataset not found.
+
+    Raised when trying to access:
+    - a missing dataset, or
+    - a private/gated dataset and the user is not authenticated.
+    """
+
+
+class DatasetBuildError(DatasetsError):
+    pass
+
+
+class ManualDownloadError(DatasetBuildError):
+    pass
+
+
+class FileFormatError(DatasetBuildError):
+    pass
+
+
+class DatasetGenerationError(DatasetBuildError):
+    pass
+
+
+class DatasetGenerationCastError(DatasetGenerationError):
+    @classmethod
+    def from_cast_error(
+        cls,
+        cast_error: CastError,
+        builder_name: str,
+        gen_kwargs: Dict[str, Any],
+        token: Optional[Union[bool, str]],
+    ) -> "DatasetGenerationCastError":
+        explanation_message = (
+            f"\n\nAll the data files must have the same columns, but at some point {cast_error.details()}"
+        )
+        formatted_tracked_gen_kwargs: List[str] = []
+        for gen_kwarg in gen_kwargs.values():
+            if not isinstance(gen_kwarg, (tracked_str, tracked_list, TrackedIterable)):
+                continue
+            while isinstance(gen_kwarg, (tracked_list, TrackedIterable)) and gen_kwarg.last_item is not None:
+                gen_kwarg = gen_kwarg.last_item
+            if isinstance(gen_kwarg, tracked_str):
+                gen_kwarg = gen_kwarg.get_origin()
+            if isinstance(gen_kwarg, str) and gen_kwarg.startswith("hf://"):
+                resolved_path = HfFileSystem(endpoint=config.HF_ENDPOINT, token=token).resolve_path(gen_kwarg)
+                gen_kwarg = "hf://" + resolved_path.unresolve()
+                if "@" + resolved_path.revision in gen_kwarg:
+                    gen_kwarg = (
+                        gen_kwarg.replace("@" + resolved_path.revision, "", 1)
+                        + f" (at revision {resolved_path.revision})"
+                    )
+            formatted_tracked_gen_kwargs.append(str(gen_kwarg))
+        if formatted_tracked_gen_kwargs:
+            explanation_message += f"\n\nThis happened while the {builder_name} dataset builder was generating data using\n\n{', '.join(formatted_tracked_gen_kwargs)}"
+        help_message = "\n\nPlease either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)"
+        return cls("An error occurred while generating the dataset" + explanation_message + help_message)

env-llmeval/lib/python3.10/site-packages/datasets/filesystems/__init__.py

@@ -0,0 +1,86 @@
+import importlib
+import shutil
+import threading
+import warnings
+from typing import List
+
+import fsspec
+import fsspec.asyn
+from fsspec.implementations.local import LocalFileSystem
+
+from ..utils.deprecation_utils import deprecated
+from . import compression
+
+
+_has_s3fs = importlib.util.find_spec("s3fs") is not None
+
+if _has_s3fs:
+    from .s3filesystem import S3FileSystem  # noqa: F401
+
+COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSystem] = [
+    compression.Bz2FileSystem,
+    compression.GzipFileSystem,
+    compression.Lz4FileSystem,
+    compression.XzFileSystem,
+    compression.ZstdFileSystem,
+]
+
+# Register custom filesystems
+for fs_class in COMPRESSION_FILESYSTEMS:
+    if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
+        warnings.warn(f"A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.")
+    fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
+
+
+@deprecated(
+    "This function is deprecated and will be removed in a future version. Please use `fsspec.core.strip_protocol` instead."
+)
+def extract_path_from_uri(dataset_path: str) -> str:
+    """
+    Preprocesses `dataset_path` and removes remote filesystem (e.g. removing `s3://`).
+
+    Args:
+        dataset_path (`str`):
+            Path (e.g. `dataset/train`) or remote uri (e.g. `s3://my-bucket/dataset/train`) of the dataset directory.
+    """
+    if "://" in dataset_path:
+        dataset_path = dataset_path.split("://")[1]
+    return dataset_path
+
+
+def is_remote_filesystem(fs: fsspec.AbstractFileSystem) -> bool:
+    """
+    Checks if `fs` is a remote filesystem.
+
+    Args:
+        fs (`fsspec.spec.AbstractFileSystem`):
+            An abstract super-class for pythonic file-systems, e.g. `fsspec.filesystem(\'file\')` or [`datasets.filesystems.S3FileSystem`].
+    """
+    return not isinstance(fs, LocalFileSystem)
+
+
+def rename(fs: fsspec.AbstractFileSystem, src: str, dst: str):
+    """
+    Renames the file `src` in `fs` to `dst`.
+    """
+    if not is_remote_filesystem(fs):
+        # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
+        shutil.move(fs._strip_protocol(src), fs._strip_protocol(dst))
+    else:
+        fs.mv(src, dst, recursive=True)
+
+
+def _reset_fsspec_lock() -> None:
+    """
+    Clear reference to the loop and thread.
+    This is necessary otherwise HTTPFileSystem hangs in the ML training loop.
+    Only required for fsspec >= 0.9.0
+    See https://github.com/fsspec/gcsfs/issues/379
+    """
+    if hasattr(fsspec.asyn, "reset_lock"):
+        # for future fsspec>2022.05.0
+        fsspec.asyn.reset_lock()
+    else:
+        fsspec.asyn.iothread[0] = None
+        fsspec.asyn.loop[0] = None
+        fsspec.asyn.lock = threading.Lock()

env-llmeval/lib/python3.10/site-packages/datasets/filesystems/compression.py

@@ -0,0 +1,178 @@
+import os
+from typing import Optional
+
+import fsspec
+from fsspec.archive import AbstractArchiveFileSystem
+from fsspec.utils import DEFAULT_BLOCK_SIZE
+
+
+class BaseCompressedFileFileSystem(AbstractArchiveFileSystem):
+    """Read contents of compressed file as a filesystem with one file inside."""
+
+    root_marker = ""
+    protocol: str = (
+        None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
+    )
+    compression: str = None  # compression type in fsspec. ex: "gzip"
+    extension: str = None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
+
+    def __init__(
+        self, fo: str = "", target_protocol: Optional[str] = None, target_options: Optional[dict] = None, **kwargs
+    ):
+        """
+        The compressed file system can be instantiated from any compressed file.
+        It reads the contents of compressed file as a filesystem with one file inside, as if it was an archive.
+
+        The single file inside the filesystem is named after the compresssed file,
+        without the compression extension at the end of the filename.
+
+        Args:
+            fo (:obj:``str``): Path to compressed file. Will fetch file using ``fsspec.open()``
+            mode (:obj:``str``): Currently, only 'rb' accepted
+            target_protocol(:obj:``str``, optional): To override the FS protocol inferred from a URL.
+            target_options (:obj:``dict``, optional): Kwargs passed when instantiating the target FS.
+        """
+        super().__init__(self, **kwargs)
+        # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
+        self.file = fsspec.open(
+            fo,
+            mode="rb",
+            protocol=target_protocol,
+            compression=self.compression,
+            client_kwargs={
+                "requote_redirect_url": False,  # see https://github.com/huggingface/datasets/pull/5459
+                "trust_env": True,  # Enable reading proxy env variables.
+                **(target_options or {}).pop("client_kwargs", {}),  # To avoid issues if it was already passed.
+            },
+            **(target_options or {}),
+        )
+        self.compressed_name = os.path.basename(self.file.path.split("::")[0])
+        self.uncompressed_name = (
+            self.compressed_name[: self.compressed_name.rindex(".")]
+            if "." in self.compressed_name
+            else self.compressed_name
+        )
+        self.dir_cache = None
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        # compressed file paths are always relative to the archive root
+        return super()._strip_protocol(path).lstrip("/")
+
+    def _get_dirs(self):
+        if self.dir_cache is None:
+            f = {**self.file.fs.info(self.file.path), "name": self.uncompressed_name}
+            self.dir_cache = {f["name"]: f}
+
+    def cat(self, path: str):
+        return self.file.open().read()
+
+    def _open(
+        self,
+        path: str,
+        mode: str = "rb",
+        block_size=None,
+        autocommit=True,
+        cache_options=None,
+        **kwargs,
+    ):
+        path = self._strip_protocol(path)
+        if mode != "rb":
+            raise ValueError(f"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'")
+        return self.file.open()
+
+
+class Bz2FileSystem(BaseCompressedFileFileSystem):
+    """Read contents of BZ2 file as a filesystem with one file inside."""
+
+    protocol = "bz2"
+    compression = "bz2"
+    extension = ".bz2"
+
+
+class GzipFileSystem(BaseCompressedFileFileSystem):
+    """Read contents of GZIP file as a filesystem with one file inside."""
+
+    protocol = "gzip"
+    compression = "gzip"
+    extension = ".gz"
+
+
+class Lz4FileSystem(BaseCompressedFileFileSystem):
+    """Read contents of LZ4 file as a filesystem with one file inside."""
+
+    protocol = "lz4"
+    compression = "lz4"
+    extension = ".lz4"
+
+
+class XzFileSystem(BaseCompressedFileFileSystem):
+    """Read contents of .xz (LZMA) file as a filesystem with one file inside."""
+
+    protocol = "xz"
+    compression = "xz"
+    extension = ".xz"
+
+
+class ZstdFileSystem(BaseCompressedFileFileSystem):
+    """
+    Read contents of zstd file as a filesystem with one file inside.
+
+    Note that reading in binary mode with fsspec isn't supported yet:
+    https://github.com/indygreg/python-zstandard/issues/136
+    """
+
+    protocol = "zstd"
+    compression = "zstd"
+    extension = ".zst"
+
+    def __init__(
+        self,
+        fo: str,
+        mode: str = "rb",
+        target_protocol: Optional[str] = None,
+        target_options: Optional[dict] = None,
+        block_size: int = DEFAULT_BLOCK_SIZE,
+        **kwargs,
+    ):
+        super().__init__(
+            fo=fo,
+            mode=mode,
+            target_protocol=target_protocol,
+            target_options=target_options,
+            block_size=block_size,
+            **kwargs,
+        )
+        # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
+        #
+        # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
+        #     out.close = close
+        # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
+        #
+        # see https://github.com/intake/filesystem_spec/issues/725
+        _enter = self.file.__enter__
+
+        class WrappedFile:
+            def __init__(self, file_):
+                self._file = file_
+
+            def __enter__(self):
+                self._file.__enter__()
+                return self
+
+            def __exit__(self, *args, **kwargs):
+                self._file.__exit__(*args, **kwargs)
+
+            def __iter__(self):
+                return iter(self._file)
+
+            def __next__(self):
+                return next(self._file)
+
+            def __getattr__(self, attr):
+                return getattr(self._file, attr)
+
+        def fixed_enter(*args, **kwargs):
+            return WrappedFile(_enter(*args, **kwargs))
+
+        self.file.__enter__ = fixed_enter

env-llmeval/lib/python3.10/site-packages/datasets/filesystems/s3filesystem.py

@@ -0,0 +1,116 @@
+import s3fs
+
+from ..utils.deprecation_utils import deprecated
+
+
+@deprecated("Use s3fs.S3FileSystem instead.")
+class S3FileSystem(s3fs.S3FileSystem):
+    """
+    `datasets.filesystems.S3FileSystem` is a subclass of [`s3fs.S3FileSystem`](https://s3fs.readthedocs.io/en/latest/api.html).
+
+    Users can use this class to access S3 as if it were a file system. It exposes a filesystem-like API (ls, cp, open, etc.) on top of S3 storage. Provide credentials either explicitly (`key=`, `secret=`) or with boto's credential methods. See botocore documentation for more information. If no credentials are available, use `anon=True`.
+
+    Args:
+        anon (`bool`, default to `False`):
+            Whether to use anonymous connection (public buckets only). If `False`, uses the key/secret given,
+            or boto's credential resolver (client_kwargs, environment, variables, config files, EC2 IAM server, in that order).
+        key (`str`):
+            If not anonymous, use this access key ID, if specified.
+        secret (`str`):
+            If not anonymous, use this secret access key, if specified.
+        token (`str`):
+            If not anonymous, use this security token, if specified.
+        use_ssl (`bool`, defaults to `True`):
+            Whether to use SSL in connections to S3; may be faster without, but insecure. If `use_ssl` is
+            also set in `client_kwargs`, the value set in `client_kwargs` will take priority.
+        s3_additional_kwargs (`dict`):
+            Parameters that are used when calling S3 API methods. Typically used for things
+            like ServerSideEncryption.
+        client_kwargs (`dict`):
+            Parameters for the botocore client.
+        requester_pays (`bool`, defaults to `False`):
+            Whether `RequesterPays` buckets are supported.
+        default_block_size (`int`):
+            If given, the default block size value used for `open()`, if no specific value is given at all time.
+            The built-in default is 5MB.
+        default_fill_cache (`bool`, defaults to `True`):
+            Whether to use cache filling with open by default. Refer to `S3File.open`.
+        default_cache_type (`str`, defaults to `bytes`):
+            If given, the default `cache_type` value used for `open()`. Set to `none` if no
+            caching is desired. See fsspec's documentation for other available `cache_type` values.
+        version_aware (`bool`, defaults to `False`):
+            Whether to support bucket versioning. If enable this will require the user to have
+            the necessary IAM permissions for dealing with versioned objects.
+        cache_regions (`bool`, defaults to `False`):
+            Whether to cache bucket regions. Whenever a new bucket is used, it will
+            first find out which region it belongs to and then use the client for that region.
+        asynchronous (`bool`, defaults to `False`):
+            Whether this instance is to be used from inside coroutines.
+        config_kwargs (`dict`):
+            Parameters passed to `botocore.client.Config`.
+        **kwargs:
+            Other parameters for core session.
+        session (`aiobotocore.session.AioSession`):
+            Session to be used for all connections. This session will be used inplace of creating
+            a new session inside S3FileSystem. For example: `aiobotocore.session.AioSession(profile='test_user')`.
+        skip_instance_cache (`bool`):
+            Control reuse of instances. Passed on to `fsspec`.
+        use_listings_cache (`bool`):
+            Control reuse of directory listings. Passed on to `fsspec`.
+        listings_expiry_time (`int` or `float`):
+            Control reuse of directory listings. Passed on to `fsspec`.
+        max_paths (`int`): Control reuse of directory listings. Passed on to `fsspec`.
+
+    Examples:
+
+    Listing files from public S3 bucket.
+
+    ```py
+    >>> import datasets
+    >>> s3 = datasets.filesystems.S3FileSystem(anon=True)  # doctest: +SKIP
+    >>> s3.ls('public-datasets/imdb/train')  # doctest: +SKIP
+    ['dataset_info.json.json','dataset.arrow','state.json']
+    ```
+
+    Listing files from private S3 bucket using `aws_access_key_id` and `aws_secret_access_key`.
+
+    ```py
+    >>> import datasets
+    >>> s3 = datasets.filesystems.S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key)  # doctest: +SKIP
+    >>> s3.ls('my-private-datasets/imdb/train')  # doctest: +SKIP
+    ['dataset_info.json.json','dataset.arrow','state.json']
+    ```
+
+    Using `S3Filesystem` with `botocore.session.Session` and custom `aws_profile`.
+
+    ```py
+    >>> import botocore
+    >>> from datasets.filesystems import S3Filesystem
+
+    >>> s3_session = botocore.session.Session(profile_name='my_profile_name')
+    >>> s3 = S3FileSystem(session=s3_session)  # doctest: +SKIP
+    ```
+
+    Loading dataset from S3 using `S3Filesystem` and [`load_from_disk`].
+
+    ```py
+    >>> from datasets import load_from_disk
+    >>> from datasets.filesystems import S3Filesystem
+
+    >>> s3 = S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key)  # doctest: +SKIP
+    >>> dataset = load_from_disk('s3://my-private-datasets/imdb/train', storage_options=s3.storage_options)  # doctest: +SKIP
+    >>> print(len(dataset))
+    25000
+    ```
+
+    Saving dataset to S3 using `S3Filesystem` and [`Dataset.save_to_disk`].
+
+    ```py
+    >>> from datasets import load_dataset
+    >>> from datasets.filesystems import S3Filesystem
+
+    >>> dataset = load_dataset("imdb")
+    >>> s3 = S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key)  # doctest: +SKIP
+    >>> dataset.save_to_disk('s3://my-private-datasets/imdb/train', storage_options=s3.storage_options)  # doctest: +SKIP
+    ```
+    """

env-llmeval/lib/python3.10/site-packages/datasets/fingerprint.py

@@ -0,0 +1,494 @@
+import inspect
+import os
+import random
+import shutil
+import tempfile
+import weakref
+from functools import wraps
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import xxhash
+
+from . import config
+from .naming import INVALID_WINDOWS_CHARACTERS_IN_PATH
+from .utils._dill import dumps
+from .utils.deprecation_utils import deprecated
+from .utils.logging import get_logger
+
+
+if TYPE_CHECKING:
+    from .arrow_dataset import Dataset
+
+
+logger = get_logger(__name__)
+
+
+# Fingerprinting allows to have one deterministic fingerprint per dataset state.
+# A dataset fingerprint is updated after each transform.
+# Re-running the same transforms on a dataset in a different session results in the same fingerprint.
+# This is possible thanks to a custom hashing function that works with most python objects.
+
+# Fingerprinting is the main mechanism that enables caching.
+# The caching mechanism allows to reload an existing cache file if it's already been computed.
+
+
+#################
+# Caching
+#################
+
+_CACHING_ENABLED = True
+_TEMP_DIR_FOR_TEMP_CACHE_FILES: Optional["_TempCacheDir"] = None
+_DATASETS_WITH_TABLE_IN_TEMP_DIR: Optional[weakref.WeakSet] = None
+
+
+class _TempCacheDir:
+    """
+    A temporary directory for storing cached Arrow files with a cleanup that frees references to the Arrow files
+    before deleting the directory itself to avoid permission errors on Windows.
+    """
+
+    def __init__(self):
+        self.name = tempfile.mkdtemp(prefix=config.TEMP_CACHE_DIR_PREFIX)
+        self._finalizer = weakref.finalize(self, self._cleanup)
+
+    def _cleanup(self):
+        for dset in get_datasets_with_cache_file_in_temp_dir():
+            dset.__del__()
+        if os.path.exists(self.name):
+            try:
+                shutil.rmtree(self.name)
+            except Exception as e:
+                raise OSError(
+                    f"An error occured while trying to delete temporary cache directory {self.name}. Please delete it manually."
+                ) from e
+
+    def cleanup(self):
+        if self._finalizer.detach():
+            self._cleanup()
+
+
+def maybe_register_dataset_for_temp_dir_deletion(dataset):
+    """
+    This function registers the datasets that have cache files in _TEMP_DIR_FOR_TEMP_CACHE_FILES in order
+    to properly delete them before deleting the temporary directory.
+    The temporary directory _TEMP_DIR_FOR_TEMP_CACHE_FILES is used when caching is disabled.
+    """
+    if _TEMP_DIR_FOR_TEMP_CACHE_FILES is None:
+        return
+
+    global _DATASETS_WITH_TABLE_IN_TEMP_DIR
+    if _DATASETS_WITH_TABLE_IN_TEMP_DIR is None:
+        _DATASETS_WITH_TABLE_IN_TEMP_DIR = weakref.WeakSet()
+    if any(
+        Path(_TEMP_DIR_FOR_TEMP_CACHE_FILES.name) in Path(cache_file["filename"]).parents
+        for cache_file in dataset.cache_files
+    ):
+        _DATASETS_WITH_TABLE_IN_TEMP_DIR.add(dataset)
+
+
+def get_datasets_with_cache_file_in_temp_dir():
+    return list(_DATASETS_WITH_TABLE_IN_TEMP_DIR) if _DATASETS_WITH_TABLE_IN_TEMP_DIR is not None else []
+
+
+def enable_caching():
+    """
+    When applying transforms on a dataset, the data are stored in cache files.
+    The caching mechanism allows to reload an existing cache file if it's already been computed.
+
+    Reloading a dataset is possible since the cache files are named using the dataset fingerprint, which is updated
+    after each transform.
+
+    If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
+    More precisely, if the caching is disabled:
+    - cache files are always recreated
+    - cache files are written to a temporary directory that is deleted when session closes
+    - cache files are named using a random hash instead of the dataset fingerprint
+    - use [`~datasets.Dataset.save_to_disk`] to save a transformed dataset or it will be deleted when session closes
+    - caching doesn't affect [`~datasets.load_dataset`]. If you want to regenerate a dataset from scratch you should use
+    the `download_mode` parameter in [`~datasets.load_dataset`].
+    """
+    global _CACHING_ENABLED
+    _CACHING_ENABLED = True
+
+
+def disable_caching():
+    """
+    When applying transforms on a dataset, the data are stored in cache files.
+    The caching mechanism allows to reload an existing cache file if it's already been computed.
+
+    Reloading a dataset is possible since the cache files are named using the dataset fingerprint, which is updated
+    after each transform.
+
+    If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
+    More precisely, if the caching is disabled:
+    - cache files are always recreated
+    - cache files are written to a temporary directory that is deleted when session closes
+    - cache files are named using a random hash instead of the dataset fingerprint
+    - use [`~datasets.Dataset.save_to_disk`] to save a transformed dataset or it will be deleted when session closes
+    - caching doesn't affect [`~datasets.load_dataset`]. If you want to regenerate a dataset from scratch you should use
+    the `download_mode` parameter in [`~datasets.load_dataset`].
+    """
+    global _CACHING_ENABLED
+    _CACHING_ENABLED = False
+
+
+@deprecated(
+    "Use datasets.enable_caching() or datasets.disable_caching() instead. This function will be removed in a future version of datasets."
+)
+def set_caching_enabled(boolean: bool):
+    """
+    When applying transforms on a dataset, the data are stored in cache files.
+    The caching mechanism allows to reload an existing cache file if it's already been computed.
+
+    Reloading a dataset is possible since the cache files are named using the dataset fingerprint, which is updated
+    after each transform.
+
+    If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
+    More precisely, if the caching is disabled:
+    - cache files are always recreated
+    - cache files are written to a temporary directory that is deleted when session closes
+    - cache files are named using a random hash instead of the dataset fingerprint
+    - use :func:`datasets.Dataset.save_to_disk` to save a transformed dataset or it will be deleted when session closes
+    - caching doesn't affect :func:`datasets.load_dataset`. If you want to regenerate a dataset from scratch you should use
+    the ``download_mode`` parameter in :func:`datasets.load_dataset`.
+    """
+    global _CACHING_ENABLED
+    _CACHING_ENABLED = bool(boolean)
+
+
+def is_caching_enabled() -> bool:
+    """
+    When applying transforms on a dataset, the data are stored in cache files.
+    The caching mechanism allows to reload an existing cache file if it's already been computed.
+
+    Reloading a dataset is possible since the cache files are named using the dataset fingerprint, which is updated
+    after each transform.
+
+    If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets.
+    More precisely, if the caching is disabled:
+    - cache files are always recreated
+    - cache files are written to a temporary directory that is deleted when session closes
+    - cache files are named using a random hash instead of the dataset fingerprint
+    - use [`~datasets.Dataset.save_to_disk`]] to save a transformed dataset or it will be deleted when session closes
+    - caching doesn't affect [`~datasets.load_dataset`]. If you want to regenerate a dataset from scratch you should use
+    the `download_mode` parameter in [`~datasets.load_dataset`].
+    """
+    global _CACHING_ENABLED
+    return bool(_CACHING_ENABLED)
+
+
+def get_temporary_cache_files_directory() -> str:
+    """Return a directory that is deleted when session closes."""
+    global _TEMP_DIR_FOR_TEMP_CACHE_FILES
+    if _TEMP_DIR_FOR_TEMP_CACHE_FILES is None:
+        _TEMP_DIR_FOR_TEMP_CACHE_FILES = _TempCacheDir()
+    return _TEMP_DIR_FOR_TEMP_CACHE_FILES.name
+
+
+#################
+# Hashing
+#################
+
+
+@deprecated("Use `copyreg.pickle` to register a custom reducer.")
+def hashregister(*types):
+    def proxy(func):
+        for t in types:
+            Hasher.dispatch[t] = func
+        return func
+
+    return proxy
+
+
+class Hasher:
+    """Hasher that accepts python objects as inputs."""
+
+    dispatch: Dict = {}
+
+    def __init__(self):
+        self.m = xxhash.xxh64()
+
+    @classmethod
+    def hash_bytes(cls, value: Union[bytes, List[bytes]]) -> str:
+        value = [value] if isinstance(value, bytes) else value
+        m = xxhash.xxh64()
+        for x in value:
+            m.update(x)
+        return m.hexdigest()
+
+    @classmethod
+    @deprecated("Use `Hasher.hash` instead.")
+    def hash_default(cls, value: Any) -> str:
+        return cls.hash(value)
+
+    @classmethod
+    def hash(cls, value: Any) -> str:
+        return cls.hash_bytes(dumps(value))
+
+    def update(self, value: Any) -> None:
+        header_for_update = f"=={type(value)}=="
+        value_for_update = self.hash(value)
+        self.m.update(header_for_update.encode("utf8"))
+        self.m.update(value_for_update.encode("utf-8"))
+
+    def hexdigest(self) -> str:
+        return self.m.hexdigest()
+
+
+#################
+# Fingerprinting
+#################
+
+fingerprint_rng = random.Random()
+# we show a warning only once when fingerprinting fails to avoid spam
+fingerprint_warnings: Dict[str, bool] = {}
+
+
+def generate_fingerprint(dataset: "Dataset") -> str:
+    state = dataset.__dict__
+    hasher = Hasher()
+    for key in sorted(state):
+        if key == "_fingerprint":
+            continue
+        hasher.update(key)
+        hasher.update(state[key])
+    # hash data files last modification timestamps as well
+    for cache_file in dataset.cache_files:
+        hasher.update(os.path.getmtime(cache_file["filename"]))
+    return hasher.hexdigest()
+
+
+def generate_random_fingerprint(nbits: int = 64) -> str:
+    return f"{fingerprint_rng.getrandbits(nbits):0{nbits//4}x}"
+
+
+def update_fingerprint(fingerprint, transform, transform_args):
+    global fingerprint_warnings
+    hasher = Hasher()
+    hasher.update(fingerprint)
+    try:
+        hasher.update(transform)
+    except:  # noqa various errors might raise here from pickle or dill
+        if _CACHING_ENABLED:
+            if not fingerprint_warnings.get("update_fingerprint_transform_hash_failed", False):
+                logger.warning(
+                    f"Transform {transform} couldn't be hashed properly, a random hash was used instead. "
+                    "Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
+                    "If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. "
+                    "This warning is only showed once. Subsequent hashing failures won't be showed."
+                )
+                fingerprint_warnings["update_fingerprint_transform_hash_failed"] = True
+            else:
+                logger.info(f"Transform {transform} couldn't be hashed properly, a random hash was used instead.")
+        else:
+            logger.info(
+                f"Transform {transform} couldn't be hashed properly, a random hash was used instead. This doesn't affect caching since it's disabled."
+            )
+
+        return generate_random_fingerprint()
+    for key in sorted(transform_args):
+        hasher.update(key)
+        try:
+            hasher.update(transform_args[key])
+        except:  # noqa various errors might raise here from pickle or dill
+            if _CACHING_ENABLED:
+                if not fingerprint_warnings.get("update_fingerprint_transform_hash_failed", False):
+                    logger.warning(
+                        f"Parameter '{key}'={transform_args[key]} of the transform {transform} couldn't be hashed properly, a random hash was used instead. "
+                        "Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. "
+                        "If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. "
+                        "This warning is only showed once. Subsequent hashing failures won't be showed."
+                    )
+                    fingerprint_warnings["update_fingerprint_transform_hash_failed"] = True
+                else:
+                    logger.info(
+                        f"Parameter '{key}'={transform_args[key]} of the transform {transform} couldn't be hashed properly, a random hash was used instead."
+                    )
+            else:
+                logger.info(
+                    f"Parameter '{key}'={transform_args[key]} of the transform {transform} couldn't be hashed properly, a random hash was used instead. This doesn't affect caching since it's disabled."
+                )
+            return generate_random_fingerprint()
+    return hasher.hexdigest()
+
+
+def validate_fingerprint(fingerprint: str, max_length=64):
+    """
+    Make sure the fingerprint is a non-empty string that is not longer that max_length=64 by default,
+    so that the fingerprint can be used to name cache files without issues.
+    """
+    if not isinstance(fingerprint, str) or not fingerprint:
+        raise ValueError(f"Invalid fingerprint '{fingerprint}': it should be a non-empty string.")
+    for invalid_char in INVALID_WINDOWS_CHARACTERS_IN_PATH:
+        if invalid_char in fingerprint:
+            raise ValueError(
+                f"Invalid fingerprint. Bad characters from black list '{INVALID_WINDOWS_CHARACTERS_IN_PATH}' found in '{fingerprint}'. "
+                f"They could create issues when creating cache files."
+            )
+    if len(fingerprint) > max_length:
+        raise ValueError(
+            f"Invalid fingerprint. Maximum lenth is {max_length} but '{fingerprint}' has length {len(fingerprint)}."
+            "It could create issues when creating cache files."
+        )
+
+
+def format_transform_for_fingerprint(func: Callable, version: Optional[str] = None) -> str:
+    """
+    Format a transform to the format that will be used to update the fingerprint.
+    """
+    transform = f"{func.__module__}.{func.__qualname__}"
+    if version is not None:
+        transform += f"@{version}"
+    return transform
+
+
+def format_kwargs_for_fingerprint(
+    func: Callable,
+    args: Tuple,
+    kwargs: Dict[str, Any],
+    use_kwargs: Optional[List[str]] = None,
+    ignore_kwargs: Optional[List[str]] = None,
+    randomized_function: bool = False,
+) -> Dict[str, Any]:
+    """
+    Format the kwargs of a transform to the format that will be used to update the fingerprint.
+    """
+    kwargs_for_fingerprint = kwargs.copy()
+    if args:
+        params = [p.name for p in inspect.signature(func).parameters.values() if p != p.VAR_KEYWORD]
+        args = args[1:]  # assume the first argument is the dataset
+        params = params[1:]
+        kwargs_for_fingerprint.update(zip(params, args))
+    else:
+        del kwargs_for_fingerprint[
+            next(iter(inspect.signature(func).parameters))
+        ]  # assume the first key is the dataset
+
+    # keep the right kwargs to be hashed to generate the fingerprint
+
+    if use_kwargs:
+        kwargs_for_fingerprint = {k: v for k, v in kwargs_for_fingerprint.items() if k in use_kwargs}
+    if ignore_kwargs:
+        kwargs_for_fingerprint = {k: v for k, v in kwargs_for_fingerprint.items() if k not in ignore_kwargs}
+    if randomized_function:  # randomized functions have `seed` and `generator` parameters
+        if kwargs_for_fingerprint.get("seed") is None and kwargs_for_fingerprint.get("generator") is None:
+            _, seed, pos, *_ = np.random.get_state()
+            seed = seed[pos] if pos < 624 else seed[0]
+            kwargs_for_fingerprint["generator"] = np.random.default_rng(seed)
+
+    # remove kwargs that are the default values
+
+    default_values = {
+        p.name: p.default for p in inspect.signature(func).parameters.values() if p.default != inspect._empty
+    }
+    for default_varname, default_value in default_values.items():
+        if default_varname in kwargs_for_fingerprint and kwargs_for_fingerprint[default_varname] == default_value:
+            kwargs_for_fingerprint.pop(default_varname)
+    return kwargs_for_fingerprint
+
+
+def fingerprint_transform(
+    inplace: bool,
+    use_kwargs: Optional[List[str]] = None,
+    ignore_kwargs: Optional[List[str]] = None,
+    fingerprint_names: Optional[List[str]] = None,
+    randomized_function: bool = False,
+    version: Optional[str] = None,
+):
+    """
+    Wrapper for dataset transforms to update the dataset fingerprint using ``update_fingerprint``
+    Args:
+        inplace (:obj:`bool`):  If inplace is True, the fingerprint of the dataset is updated inplace.
+            Otherwise, a parameter "new_fingerprint" is passed to the wrapped method that should take care of
+            setting the fingerprint of the returned Dataset.
+        use_kwargs (:obj:`List[str]`, optional): optional white list of argument names to take into account
+            to update the fingerprint to the wrapped method that should take care of
+            setting the fingerprint of the returned Dataset. By default all the arguments are used.
+        ignore_kwargs (:obj:`List[str]`, optional): optional black list of argument names to take into account
+            to update the fingerprint. Note that ignore_kwargs prevails on use_kwargs.
+        fingerprint_names (:obj:`List[str]`, optional, defaults to ["new_fingerprint"]):
+            If the dataset transforms is not inplace and returns a DatasetDict, then it can require
+            several fingerprints (one per dataset in the DatasetDict). By specifying fingerprint_names,
+            one fingerprint named after each element of fingerprint_names is going to be passed.
+        randomized_function (:obj:`bool`, defaults to False): If the dataset transform is random and has
+            optional parameters "seed" and "generator", then you can set randomized_function to True.
+            This way, even if users set "seed" and "generator" to None, then the fingerprint is
+            going to be randomly generated depending on numpy's current state. In this case, the
+            generator is set to np.random.default_rng(np.random.get_state()[1][0]).
+        version (:obj:`str`, optional): version of the transform. The version is taken into account when
+            computing the fingerprint. If a datase transform changes (or at least if the output data
+            that are cached changes), then one should increase the version. If the version stays the
+            same, then old cached data could be reused that are not compatible with the new transform.
+            It should be in the format "MAJOR.MINOR.PATCH".
+    """
+
+    if use_kwargs is not None and not isinstance(use_kwargs, list):
+        raise ValueError(f"use_kwargs is supposed to be a list, not {type(use_kwargs)}")
+
+    if ignore_kwargs is not None and not isinstance(ignore_kwargs, list):
+        raise ValueError(f"ignore_kwargs is supposed to be a list, not {type(use_kwargs)}")
+
+    if inplace and fingerprint_names:
+        raise ValueError("fingerprint_names are only used when inplace is False")
+
+    fingerprint_names = fingerprint_names if fingerprint_names is not None else ["new_fingerprint"]
+
+    def _fingerprint(func):
+        if not inplace and not all(name in func.__code__.co_varnames for name in fingerprint_names):
+            raise ValueError(f"function {func} is missing parameters {fingerprint_names} in signature")
+
+        if randomized_function:  # randomized function have seed and generator parameters
+            if "seed" not in func.__code__.co_varnames:
+                raise ValueError(f"'seed' must be in {func}'s signature")
+            if "generator" not in func.__code__.co_varnames:
+                raise ValueError(f"'generator' must be in {func}'s signature")
+        # this call has to be outside the wrapper or since __qualname__ changes in multiprocessing
+        transform = format_transform_for_fingerprint(func, version=version)
+
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            kwargs_for_fingerprint = format_kwargs_for_fingerprint(
+                func,
+                args,
+                kwargs,
+                use_kwargs=use_kwargs,
+                ignore_kwargs=ignore_kwargs,
+                randomized_function=randomized_function,
+            )
+
+            if args:
+                dataset: Dataset = args[0]
+                args = args[1:]
+            else:
+                dataset: Dataset = kwargs.pop(next(iter(inspect.signature(func).parameters)))
+
+            # compute new_fingerprint and add it to the args of not in-place transforms
+            if inplace:
+                new_fingerprint = update_fingerprint(dataset._fingerprint, transform, kwargs_for_fingerprint)
+            else:
+                for fingerprint_name in fingerprint_names:  # transforms like `train_test_split` have several hashes
+                    if kwargs.get(fingerprint_name) is None:
+                        kwargs_for_fingerprint["fingerprint_name"] = fingerprint_name
+                        kwargs[fingerprint_name] = update_fingerprint(
+                            dataset._fingerprint, transform, kwargs_for_fingerprint
+                        )
+                    else:
+                        validate_fingerprint(kwargs[fingerprint_name])
+
+            # Call actual function
+
+            out = func(dataset, *args, **kwargs)
+
+            # Update fingerprint of in-place transforms + update in-place history of transforms
+
+            if inplace:  # update after calling func so that the fingerprint doesn't change if the function fails
+                dataset._fingerprint = new_fingerprint
+
+            return out
+
+        wrapper._decorator_name_ = "fingerprint"
+        return wrapper
+
+    return _fingerprint

env-llmeval/lib/python3.10/site-packages/datasets/info.py

@@ -0,0 +1,592 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""DatasetInfo and MetricInfo record information we know about a dataset and a metric.
+
+This includes things that we know about the dataset statically, i.e.:
+ - description
+ - canonical location
+ - does it have validation and tests splits
+ - size
+ - etc.
+
+This also includes the things that can and should be computed once we've
+processed the dataset as well:
+ - number of examples (in each split)
+ - etc.
+"""
+
+import copy
+import dataclasses
+import json
+import os
+import posixpath
+import warnings
+from dataclasses import dataclass
+from pathlib import Path
+from typing import ClassVar, Dict, List, Optional, Union
+
+import fsspec
+from huggingface_hub import DatasetCard, DatasetCardData
+
+from . import config
+from .features import Features, Value
+from .splits import SplitDict
+from .tasks import TaskTemplate, task_template_from_dict
+from .utils import Version
+from .utils.logging import get_logger
+from .utils.py_utils import asdict, unique_values
+
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class SupervisedKeysData:
+    input: str = ""
+    output: str = ""
+
+
+@dataclass
+class DownloadChecksumsEntryData:
+    key: str = ""
+    value: str = ""
+
+
+class MissingCachedSizesConfigError(Exception):
+    """The expected cached sizes of the download file are missing."""
+
+
+class NonMatchingCachedSizesError(Exception):
+    """The prepared split doesn't have expected sizes."""
+
+
+@dataclass
+class PostProcessedInfo:
+    features: Optional[Features] = None
+    resources_checksums: Optional[dict] = None
+
+    def __post_init__(self):
+        # Convert back to the correct classes when we reload from dict
+        if self.features is not None and not isinstance(self.features, Features):
+            self.features = Features.from_dict(self.features)
+
+    @classmethod
+    def from_dict(cls, post_processed_info_dict: dict) -> "PostProcessedInfo":
+        field_names = {f.name for f in dataclasses.fields(cls)}
+        return cls(**{k: v for k, v in post_processed_info_dict.items() if k in field_names})
+
+
+@dataclass
+class DatasetInfo:
+    """Information about a dataset.
+
+    `DatasetInfo` documents datasets, including its name, version, and features.
+    See the constructor arguments and properties for a full list.
+
+    Not all fields are known on construction and may be updated later.
+
+    Attributes:
+        description (`str`):
+            A description of the dataset.
+        citation (`str`):
+            A BibTeX citation of the dataset.
+        homepage (`str`):
+            A URL to the official homepage for the dataset.
+        license (`str`):
+            The dataset's license. It can be the name of the license or a paragraph containing the terms of the license.
+        features ([`Features`], *optional*):
+            The features used to specify the dataset's column types.
+        post_processed (`PostProcessedInfo`, *optional*):
+            Information regarding the resources of a possible post-processing of a dataset. For example, it can contain the information of an index.
+        supervised_keys (`SupervisedKeysData`, *optional*):
+            Specifies the input feature and the label for supervised learning if applicable for the dataset (legacy from TFDS).
+        builder_name (`str`, *optional*):
+            The name of the `GeneratorBasedBuilder` subclass used to create the dataset. Usually matched to the corresponding script name. It is also the snake_case version of the dataset builder class name.
+        config_name (`str`, *optional*):
+            The name of the configuration derived from [`BuilderConfig`].
+        version (`str` or [`Version`], *optional*):
+            The version of the dataset.
+        splits (`dict`, *optional*):
+            The mapping between split name and metadata.
+        download_checksums (`dict`, *optional*):
+            The mapping between the URL to download the dataset's checksums and corresponding metadata.
+        download_size (`int`, *optional*):
+            The size of the files to download to generate the dataset, in bytes.
+        post_processing_size (`int`, *optional*):
+            Size of the dataset in bytes after post-processing, if any.
+        dataset_size (`int`, *optional*):
+            The combined size in bytes of the Arrow tables for all splits.
+        size_in_bytes (`int`, *optional*):
+            The combined size in bytes of all files associated with the dataset (downloaded files + Arrow files).
+        task_templates (`List[TaskTemplate]`, *optional*):
+            The task templates to prepare the dataset for during training and evaluation. Each template casts the dataset's [`Features`] to standardized column names and types as detailed in `datasets.tasks`.
+        **config_kwargs (additional keyword arguments):
+            Keyword arguments to be passed to the [`BuilderConfig`] and used in the [`DatasetBuilder`].
+    """
+
+    # Set in the dataset scripts
+    description: str = dataclasses.field(default_factory=str)
+    citation: str = dataclasses.field(default_factory=str)
+    homepage: str = dataclasses.field(default_factory=str)
+    license: str = dataclasses.field(default_factory=str)
+    features: Optional[Features] = None
+    post_processed: Optional[PostProcessedInfo] = None
+    supervised_keys: Optional[SupervisedKeysData] = None
+    task_templates: Optional[List[TaskTemplate]] = None
+
+    # Set later by the builder
+    builder_name: Optional[str] = None
+    dataset_name: Optional[str] = None  # for packaged builders, to be different from builder_name
+    config_name: Optional[str] = None
+    version: Optional[Union[str, Version]] = None
+    # Set later by `download_and_prepare`
+    splits: Optional[dict] = None
+    download_checksums: Optional[dict] = None
+    download_size: Optional[int] = None
+    post_processing_size: Optional[int] = None
+    dataset_size: Optional[int] = None
+    size_in_bytes: Optional[int] = None
+
+    _INCLUDED_INFO_IN_YAML: ClassVar[List[str]] = [
+        "config_name",
+        "download_size",
+        "dataset_size",
+        "features",
+        "splits",
+    ]
+
+    def __post_init__(self):
+        # Convert back to the correct classes when we reload from dict
+        if self.features is not None and not isinstance(self.features, Features):
+            self.features = Features.from_dict(self.features)
+        if self.post_processed is not None and not isinstance(self.post_processed, PostProcessedInfo):
+            self.post_processed = PostProcessedInfo.from_dict(self.post_processed)
+        if self.version is not None and not isinstance(self.version, Version):
+            if isinstance(self.version, str):
+                self.version = Version(self.version)
+            else:
+                self.version = Version.from_dict(self.version)
+        if self.splits is not None and not isinstance(self.splits, SplitDict):
+            self.splits = SplitDict.from_split_dict(self.splits)
+        if self.supervised_keys is not None and not isinstance(self.supervised_keys, SupervisedKeysData):
+            if isinstance(self.supervised_keys, (tuple, list)):
+                self.supervised_keys = SupervisedKeysData(*self.supervised_keys)
+            else:
+                self.supervised_keys = SupervisedKeysData(**self.supervised_keys)
+
+        # Parse and make a list of templates
+        if self.task_templates is not None:
+            if isinstance(self.task_templates, (list, tuple)):
+                templates = [
+                    template if isinstance(template, TaskTemplate) else task_template_from_dict(template)
+                    for template in self.task_templates
+                ]
+                self.task_templates = [template for template in templates if template is not None]
+            elif isinstance(self.task_templates, TaskTemplate):
+                self.task_templates = [self.task_templates]
+            else:
+                template = task_template_from_dict(self.task_templates)
+                self.task_templates = [template] if template is not None else []
+
+        # Align task templates with features
+        if self.task_templates is not None:
+            self.task_templates = list(self.task_templates)
+            if self.features is not None:
+                self.task_templates = [
+                    template.align_with_features(self.features) for template in (self.task_templates)
+                ]
+
+    def write_to_directory(
+        self, dataset_info_dir, pretty_print=False, fs="deprecated", storage_options: Optional[dict] = None
+    ):
+        """Write `DatasetInfo` and license (if present) as JSON files to `dataset_info_dir`.
+
+        Args:
+            dataset_info_dir (`str`):
+                Destination directory.
+            pretty_print (`bool`, defaults to `False`):
+                If `True`, the JSON will be pretty-printed with the indent level of 4.
+            fs (`fsspec.spec.AbstractFileSystem`, *optional*):
+                Instance of the remote filesystem used to download the files from.
+
+                <Deprecated version="2.9.0">
+
+                `fs` was deprecated in version 2.9.0 and will be removed in 3.0.0.
+                Please use `storage_options` instead, e.g. `storage_options=fs.storage_options`.
+
+                </Deprecated>
+
+            storage_options (`dict`, *optional*):
+                Key/value pairs to be passed on to the file-system backend, if any.
+
+                <Added version="2.9.0"/>
+
+        Example:
+
+        ```py
+        >>> from datasets import load_dataset
+        >>> ds = load_dataset("rotten_tomatoes", split="validation")
+        >>> ds.info.write_to_directory("/path/to/directory/")
+        ```
+        """
+        if fs != "deprecated":
+            warnings.warn(
+                "'fs' was deprecated in favor of 'storage_options' in version 2.9.0 and will be removed in 3.0.0.\n"
+                "You can remove this warning by passing 'storage_options=fs.storage_options' instead.",
+                FutureWarning,
+            )
+            storage_options = fs.storage_options
+
+        fs: fsspec.AbstractFileSystem
+        fs, _, _ = fsspec.get_fs_token_paths(dataset_info_dir, storage_options=storage_options)
+        with fs.open(posixpath.join(dataset_info_dir, config.DATASET_INFO_FILENAME), "wb") as f:
+            self._dump_info(f, pretty_print=pretty_print)
+        if self.license:
+            with fs.open(posixpath.join(dataset_info_dir, config.LICENSE_FILENAME), "wb") as f:
+                self._dump_license(f)
+
+    def _dump_info(self, file, pretty_print=False):
+        """Dump info in `file` file-like object open in bytes mode (to support remote files)"""
+        file.write(json.dumps(asdict(self), indent=4 if pretty_print else None).encode("utf-8"))
+
+    def _dump_license(self, file):
+        """Dump license in `file` file-like object open in bytes mode (to support remote files)"""
+        file.write(self.license.encode("utf-8"))
+
+    @classmethod
+    def from_merge(cls, dataset_infos: List["DatasetInfo"]):
+        dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None]
+
+        if len(dataset_infos) > 0 and all(dataset_infos[0] == dset_info for dset_info in dataset_infos):
+            # if all dataset_infos are equal we don't need to merge. Just return the first.
+            return dataset_infos[0]
+
+        description = "\n\n".join(unique_values(info.description for info in dataset_infos)).strip()
+        citation = "\n\n".join(unique_values(info.citation for info in dataset_infos)).strip()
+        homepage = "\n\n".join(unique_values(info.homepage for info in dataset_infos)).strip()
+        license = "\n\n".join(unique_values(info.license for info in dataset_infos)).strip()
+        features = None
+        supervised_keys = None
+        task_templates = None
+
+        # Find common task templates across all dataset infos
+        all_task_templates = [info.task_templates for info in dataset_infos if info.task_templates is not None]
+        if len(all_task_templates) > 1:
+            task_templates = list(set(all_task_templates[0]).intersection(*all_task_templates[1:]))
+        elif len(all_task_templates):
+            task_templates = list(set(all_task_templates[0]))
+        # If no common task templates found, replace empty list with None
+        task_templates = task_templates if task_templates else None
+
+        return cls(
+            description=description,
+            citation=citation,
+            homepage=homepage,
+            license=license,
+            features=features,
+            supervised_keys=supervised_keys,
+            task_templates=task_templates,
+        )
+
+    @classmethod
+    def from_directory(
+        cls, dataset_info_dir: str, fs="deprecated", storage_options: Optional[dict] = None
+    ) -> "DatasetInfo":
+        """Create [`DatasetInfo`] from the JSON file in `dataset_info_dir`.
+
+        This function updates all the dynamically generated fields (num_examples,
+        hash, time of creation,...) of the [`DatasetInfo`].
+
+        This will overwrite all previous metadata.
+
+        Args:
+            dataset_info_dir (`str`):
+                The directory containing the metadata file. This
+                should be the root directory of a specific dataset version.
+            fs (`fsspec.spec.AbstractFileSystem`, *optional*):
+                Instance of the remote filesystem used to download the files from.
+
+                <Deprecated version="2.9.0">
+
+                `fs` was deprecated in version 2.9.0 and will be removed in 3.0.0.
+                Please use `storage_options` instead, e.g. `storage_options=fs.storage_options`.
+
+                </Deprecated>
+
+            storage_options (`dict`, *optional*):
+                Key/value pairs to be passed on to the file-system backend, if any.
+
+                <Added version="2.9.0"/>
+
+        Example:
+
+        ```py
+        >>> from datasets import DatasetInfo
+        >>> ds_info = DatasetInfo.from_directory("/path/to/directory/")
+        ```
+        """
+        if fs != "deprecated":
+            warnings.warn(
+                "'fs' was deprecated in favor of 'storage_options' in version 2.9.0 and will be removed in 3.0.0.\n"
+                "You can remove this warning by passing 'storage_options=fs.storage_options' instead.",
+                FutureWarning,
+            )
+            storage_options = fs.storage_options
+
+        fs: fsspec.AbstractFileSystem
+        fs, _, _ = fsspec.get_fs_token_paths(dataset_info_dir, storage_options=storage_options)
+        logger.info(f"Loading Dataset info from {dataset_info_dir}")
+        if not dataset_info_dir:
+            raise ValueError("Calling DatasetInfo.from_directory() with undefined dataset_info_dir.")
+        with fs.open(posixpath.join(dataset_info_dir, config.DATASET_INFO_FILENAME), "r", encoding="utf-8") as f:
+            dataset_info_dict = json.load(f)
+        return cls.from_dict(dataset_info_dict)
+
+    @classmethod
+    def from_dict(cls, dataset_info_dict: dict) -> "DatasetInfo":
+        field_names = {f.name for f in dataclasses.fields(cls)}
+        return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
+
+    def update(self, other_dataset_info: "DatasetInfo", ignore_none=True):
+        self_dict = self.__dict__
+        self_dict.update(
+            **{
+                k: copy.deepcopy(v)
+                for k, v in other_dataset_info.__dict__.items()
+                if (v is not None or not ignore_none)
+            }
+        )
+
+    def copy(self) -> "DatasetInfo":
+        return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
+
+    def _to_yaml_dict(self) -> dict:
+        yaml_dict = {}
+        dataset_info_dict = asdict(self)
+        for key in dataset_info_dict:
+            if key in self._INCLUDED_INFO_IN_YAML:
+                value = getattr(self, key)
+                if hasattr(value, "_to_yaml_list"):  # Features, SplitDict
+                    yaml_dict[key] = value._to_yaml_list()
+                elif hasattr(value, "_to_yaml_string"):  # Version
+                    yaml_dict[key] = value._to_yaml_string()
+                else:
+                    yaml_dict[key] = value
+        return yaml_dict
+
+    @classmethod
+    def _from_yaml_dict(cls, yaml_data: dict) -> "DatasetInfo":
+        yaml_data = copy.deepcopy(yaml_data)
+        if yaml_data.get("features") is not None:
+            yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
+        if yaml_data.get("splits") is not None:
+            yaml_data["splits"] = SplitDict._from_yaml_list(yaml_data["splits"])
+        field_names = {f.name for f in dataclasses.fields(cls)}
+        return cls(**{k: v for k, v in yaml_data.items() if k in field_names})
+
+
+class DatasetInfosDict(Dict[str, DatasetInfo]):
+    def write_to_directory(self, dataset_infos_dir, overwrite=False, pretty_print=False) -> None:
+        total_dataset_infos = {}
+        dataset_infos_path = os.path.join(dataset_infos_dir, config.DATASETDICT_INFOS_FILENAME)
+        dataset_readme_path = os.path.join(dataset_infos_dir, config.REPOCARD_FILENAME)
+        if not overwrite:
+            total_dataset_infos = self.from_directory(dataset_infos_dir)
+        total_dataset_infos.update(self)
+        if os.path.exists(dataset_infos_path):
+            # for backward compatibility, let's update the JSON file if it exists
+            with open(dataset_infos_path, "w", encoding="utf-8") as f:
+                dataset_infos_dict = {
+                    config_name: asdict(dset_info) for config_name, dset_info in total_dataset_infos.items()
+                }
+                json.dump(dataset_infos_dict, f, indent=4 if pretty_print else None)
+        # Dump the infos in the YAML part of the README.md file
+        if os.path.exists(dataset_readme_path):
+            dataset_card = DatasetCard.load(dataset_readme_path)
+            dataset_card_data = dataset_card.data
+        else:
+            dataset_card = None
+            dataset_card_data = DatasetCardData()
+        if total_dataset_infos:
+            total_dataset_infos.to_dataset_card_data(dataset_card_data)
+            dataset_card = (
+                DatasetCard("---\n" + str(dataset_card_data) + "\n---\n") if dataset_card is None else dataset_card
+            )
+            dataset_card.save(Path(dataset_readme_path))
+
+    @classmethod
+    def from_directory(cls, dataset_infos_dir) -> "DatasetInfosDict":
+        logger.info(f"Loading Dataset Infos from {dataset_infos_dir}")
+        # Load the info from the YAML part of README.md
+        if os.path.exists(os.path.join(dataset_infos_dir, config.REPOCARD_FILENAME)):
+            dataset_card_data = DatasetCard.load(Path(dataset_infos_dir) / config.REPOCARD_FILENAME).data
+            if "dataset_info" in dataset_card_data:
+                return cls.from_dataset_card_data(dataset_card_data)
+        if os.path.exists(os.path.join(dataset_infos_dir, config.DATASETDICT_INFOS_FILENAME)):
+            # this is just to have backward compatibility with dataset_infos.json files
+            with open(os.path.join(dataset_infos_dir, config.DATASETDICT_INFOS_FILENAME), encoding="utf-8") as f:
+                return cls(
+                    {
+                        config_name: DatasetInfo.from_dict(dataset_info_dict)
+                        for config_name, dataset_info_dict in json.load(f).items()
+                    }
+                )
+        else:
+            return cls()
+
+    @classmethod
+    def from_dataset_card_data(cls, dataset_card_data: DatasetCardData) -> "DatasetInfosDict":
+        if isinstance(dataset_card_data.get("dataset_info"), (list, dict)):
+            if isinstance(dataset_card_data["dataset_info"], list):
+                return cls(
+                    {
+                        dataset_info_yaml_dict.get("config_name", "default"): DatasetInfo._from_yaml_dict(
+                            dataset_info_yaml_dict
+                        )
+                        for dataset_info_yaml_dict in dataset_card_data["dataset_info"]
+                    }
+                )
+            else:
+                dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
+                dataset_info.config_name = dataset_card_data["dataset_info"].get("config_name", "default")
+                return cls({dataset_info.config_name: dataset_info})
+        else:
+            return cls()
+
+    def to_dataset_card_data(self, dataset_card_data: DatasetCardData) -> None:
+        if self:
+            # first get existing metadata info
+            if "dataset_info" in dataset_card_data and isinstance(dataset_card_data["dataset_info"], dict):
+                dataset_metadata_infos = {
+                    dataset_card_data["dataset_info"].get("config_name", "default"): dataset_card_data["dataset_info"]
+                }
+            elif "dataset_info" in dataset_card_data and isinstance(dataset_card_data["dataset_info"], list):
+                dataset_metadata_infos = {
+                    config_metadata["config_name"]: config_metadata
+                    for config_metadata in dataset_card_data["dataset_info"]
+                }
+            else:
+                dataset_metadata_infos = {}
+            # update/rewrite existing metadata info with the one to dump
+            total_dataset_infos = {
+                **dataset_metadata_infos,
+                **{config_name: dset_info._to_yaml_dict() for config_name, dset_info in self.items()},
+            }
+            # the config_name from the dataset_infos_dict takes over the config_name of the DatasetInfo
+            for config_name, dset_info_yaml_dict in total_dataset_infos.items():
+                dset_info_yaml_dict["config_name"] = config_name
+            if len(total_dataset_infos) == 1:
+                # use a struct instead of a list of configurations, since there's only one
+                dataset_card_data["dataset_info"] = next(iter(total_dataset_infos.values()))
+                config_name = dataset_card_data["dataset_info"].pop("config_name", None)
+                if config_name != "default":
+                    # if config_name is not "default" preserve it and put at the first position
+                    dataset_card_data["dataset_info"] = {
+                        "config_name": config_name,
+                        **dataset_card_data["dataset_info"],
+                    }
+            else:
+                dataset_card_data["dataset_info"] = []
+                for config_name, dataset_info_yaml_dict in sorted(total_dataset_infos.items()):
+                    # add the config_name field in first position
+                    dataset_info_yaml_dict.pop("config_name", None)
+                    dataset_info_yaml_dict = {"config_name": config_name, **dataset_info_yaml_dict}
+                    dataset_card_data["dataset_info"].append(dataset_info_yaml_dict)
+
+
+@dataclass
+class MetricInfo:
+    """Information about a metric.
+
+    `MetricInfo` documents a metric, including its name, version, and features.
+    See the constructor arguments and properties for a full list.
+
+    Note: Not all fields are known on construction and may be updated later.
+    """
+
+    # Set in the dataset scripts
+    description: str
+    citation: str
+    features: Features
+    inputs_description: str = dataclasses.field(default_factory=str)
+    homepage: str = dataclasses.field(default_factory=str)
+    license: str = dataclasses.field(default_factory=str)
+    codebase_urls: List[str] = dataclasses.field(default_factory=list)
+    reference_urls: List[str] = dataclasses.field(default_factory=list)
+    streamable: bool = False
+    format: Optional[str] = None
+
+    # Set later by the builder
+    metric_name: Optional[str] = None
+    config_name: Optional[str] = None
+    experiment_id: Optional[str] = None
+
+    def __post_init__(self):
+        if self.format is not None:
+            for key, value in self.features.items():
+                if not isinstance(value, Value):
+                    raise ValueError(
+                        f"When using 'numpy' format, all features should be a `datasets.Value` feature. "
+                        f"Here {key} is an instance of {value.__class__.__name__}"
+                    )
+
+    def write_to_directory(self, metric_info_dir, pretty_print=False):
+        """Write `MetricInfo` as JSON to `metric_info_dir`.
+        Also save the license separately in LICENCE.
+        If `pretty_print` is True, the JSON will be pretty-printed with the indent level of 4.
+
+        Example:
+
+        ```py
+        >>> from datasets import load_metric
+        >>> metric = load_metric("accuracy")
+        >>> metric.info.write_to_directory("/path/to/directory/")
+        ```
+        """
+        with open(os.path.join(metric_info_dir, config.METRIC_INFO_FILENAME), "w", encoding="utf-8") as f:
+            json.dump(asdict(self), f, indent=4 if pretty_print else None)
+
+        if self.license:
+            with open(os.path.join(metric_info_dir, config.LICENSE_FILENAME), "w", encoding="utf-8") as f:
+                f.write(self.license)
+
+    @classmethod
+    def from_directory(cls, metric_info_dir) -> "MetricInfo":
+        """Create MetricInfo from the JSON file in `metric_info_dir`.
+
+        Args:
+            metric_info_dir: `str` The directory containing the metadata file. This
+                should be the root directory of a specific dataset version.
+
+        Example:
+
+        ```py
+        >>> from datasets import MetricInfo
+        >>> metric_info = MetricInfo.from_directory("/path/to/directory/")
+        ```
+        """
+        logger.info(f"Loading Metric info from {metric_info_dir}")
+        if not metric_info_dir:
+            raise ValueError("Calling MetricInfo.from_directory() with undefined metric_info_dir.")
+
+        with open(os.path.join(metric_info_dir, config.METRIC_INFO_FILENAME), encoding="utf-8") as f:
+            metric_info_dict = json.load(f)
+        return cls.from_dict(metric_info_dict)
+
+    @classmethod
+    def from_dict(cls, metric_info_dict: dict) -> "MetricInfo":
+        field_names = {f.name for f in dataclasses.fields(cls)}
+        return cls(**{k: v for k, v in metric_info_dict.items() if k in field_names})

env-llmeval/lib/python3.10/site-packages/datasets/inspect.py

@@ -0,0 +1,581 @@
+# Copyright 2020 The HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""List and inspect datasets."""
+
+import inspect
+import os
+import shutil
+import warnings
+from pathlib import Path, PurePath
+from typing import Dict, List, Mapping, Optional, Sequence, Union
+
+import huggingface_hub
+
+from . import config
+from .download.download_config import DownloadConfig
+from .download.download_manager import DownloadMode
+from .download.streaming_download_manager import StreamingDownloadManager
+from .info import DatasetInfo
+from .load import (
+    dataset_module_factory,
+    get_dataset_builder_class,
+    import_main_class,
+    load_dataset_builder,
+    metric_module_factory,
+)
+from .utils.deprecation_utils import deprecated
+from .utils.file_utils import relative_to_absolute_path
+from .utils.logging import get_logger
+from .utils.version import Version
+
+
+logger = get_logger(__name__)
+
+
+class SplitsNotFoundError(ValueError):
+    pass
+
+
+@deprecated("Use 'huggingface_hub.list_datasets' instead.")
+def list_datasets(with_community_datasets=True, with_details=False):
+    """List all the datasets scripts available on the Hugging Face Hub.
+
+    Args:
+        with_community_datasets (`bool`, *optional*, defaults to `True`):
+            Include the community provided datasets.
+        with_details (`bool`, *optional*, defaults to `False`):
+            Return the full details on the datasets instead of only the short name.
+
+    Example:
+
+    ```py
+    >>> from datasets import list_datasets
+    >>> list_datasets()
+    ['acronym_identification',
+     'ade_corpus_v2',
+     'adversarial_qa',
+     'aeslc',
+     'afrikaans_ner_corpus',
+     'ag_news',
+     ...
+    ]
+    ```
+    """
+    datasets = huggingface_hub.list_datasets(full=with_details)
+    if not with_community_datasets:
+        datasets = [dataset for dataset in datasets if "/" not in dataset.id]
+    if not with_details:
+        datasets = [dataset.id for dataset in datasets]
+    return list(datasets)
+
+
+@deprecated(
+    "Use 'evaluate.list_evaluation_modules' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate"
+)
+def list_metrics(with_community_metrics=True, with_details=False):
+    """List all the metrics script available on the Hugging Face Hub.
+
+    <Deprecated version="2.5.0">
+
+    Use `evaluate.list_evaluation_modules` instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate
+
+    </Deprecated>
+
+    Args:
+        with_community_metrics (:obj:`bool`, optional, default ``True``): Include the community provided metrics.
+        with_details (:obj:`bool`, optional, default ``False``): Return the full details on the metrics instead of only the short name.
+
+    Example:
+
+    ```py
+    >>> from datasets import list_metrics
+    >>> list_metrics()
+    ['accuracy',
+     'bertscore',
+     'bleu',
+     'bleurt',
+     'cer',
+     'chrf',
+     ...
+    ]
+    ```
+    """
+    metrics = huggingface_hub.list_metrics()
+    if not with_community_metrics:
+        metrics = [metric for metric in metrics if "/" not in metric.id]
+    if not with_details:
+        metrics = [metric.id for metric in metrics]
+    return metrics
+
+
+@deprecated("Clone the dataset repository from the Hugging Face Hub instead.")
+def inspect_dataset(path: str, local_path: str, download_config: Optional[DownloadConfig] = None, **download_kwargs):
+    """
+    Allow inspection/modification of a dataset script by copying on local drive at local_path.
+
+    Args:
+        path (`str`): Path to the dataset processing script with the dataset builder. Can be either:
+
+            - a local path to processing script or the directory containing the script (if the script has the same name
+                as the directory),
+                e.g. `'./dataset/squad'` or `'./dataset/squad/squad.py'`.
+            - a dataset identifier on the Hugging Face Hub (list all available datasets and ids with [`list_datasets`])
+                e.g. `'squad'`, `'glue'` or `'openai/webtext'`.
+        local_path (`str`):
+            Path to the local folder to copy the dataset script to.
+        download_config ([`DownloadConfig`], *optional*):
+            Specific download configuration parameters.
+        **download_kwargs (additional keyword arguments):
+            Optional arguments for [`DownloadConfig`] which will override
+            the attributes of `download_config` if supplied.
+    """
+    if download_config is None:
+        download_config = DownloadConfig(**download_kwargs)
+    if os.path.isfile(path):
+        path = str(Path(path).parent)
+    if os.path.isdir(path):
+        shutil.copytree(path, local_path, dirs_exist_ok=True)
+    else:
+        huggingface_hub.HfApi(endpoint=config.HF_ENDPOINT, token=download_config.token).snapshot_download(
+            repo_id=path, repo_type="dataset", local_dir=local_path, force_download=download_config.force_download
+        )
+    print(
+        f"The dataset {path} can be inspected at {local_path}. "
+        f'You can modify this loading script  if it has one and use it with `datasets.load_dataset("{PurePath(local_path).as_posix()}")`.'
+    )
+
+
+@deprecated(
+    "Use 'evaluate.inspect_evaluation_module' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate"
+)
+def inspect_metric(path: str, local_path: str, download_config: Optional[DownloadConfig] = None, **download_kwargs):
+    r"""
+    Allow inspection/modification of a metric script by copying it on local drive at local_path.
+
+    <Deprecated version="2.5.0">
+
+    Use `evaluate.inspect_evaluation_module` instead, from the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate
+
+    </Deprecated>
+
+    Args:
+        path (``str``): path to the dataset processing script with the dataset builder. Can be either:
+
+            - a local path to processing script or the directory containing the script (if the script has the same name as the directory),
+                e.g. ``'./dataset/squad'`` or ``'./dataset/squad/squad.py'``
+            - a dataset identifier on the Hugging Face Hub (list all available datasets and ids with ``datasets.list_datasets()``)
+                e.g. ``'squad'``, ``'glue'`` or ``'openai/webtext'``
+        local_path (``str``): path to the local folder to copy the datset script to.
+        download_config (Optional ``datasets.DownloadConfig``): specific download configuration parameters.
+        **download_kwargs (additional keyword arguments): optional attributes for DownloadConfig() which will override the attributes in download_config if supplied.
+    """
+    metric_module = metric_module_factory(path, download_config=download_config, **download_kwargs)
+    metric_cls = import_main_class(metric_module.module_path, dataset=False)
+    module_source_path = inspect.getsourcefile(metric_cls)
+    module_source_dirpath = os.path.dirname(module_source_path)
+    for dirpath, dirnames, filenames in os.walk(module_source_dirpath):
+        dst_dirpath = os.path.join(local_path, os.path.relpath(dirpath, module_source_dirpath))
+        os.makedirs(dst_dirpath, exist_ok=True)
+        # skipping hidden directories; prune the search
+        dirnames[:] = [dirname for dirname in dirnames if not dirname.startswith((".", "__"))]
+        for filename in filenames:
+            shutil.copy2(os.path.join(dirpath, filename), os.path.join(dst_dirpath, filename))
+        shutil.copystat(dirpath, dst_dirpath)
+    local_path = relative_to_absolute_path(local_path)
+    print(
+        f"The processing scripts for metric {path} can be inspected at {local_path}. "
+        f"The main class is in {module_source_dirpath}. "
+        f'You can modify this processing scripts and use it with `datasets.load_metric("{PurePath(local_path).as_posix()}")`.'
+    )
+
+
+def get_dataset_infos(
+    path: str,
+    data_files: Optional[Union[Dict, List, str]] = None,
+    download_config: Optional[DownloadConfig] = None,
+    download_mode: Optional[Union[DownloadMode, str]] = None,
+    revision: Optional[Union[str, Version]] = None,
+    token: Optional[Union[bool, str]] = None,
+    use_auth_token="deprecated",
+    **config_kwargs,
+):
+    """Get the meta information about a dataset, returned as a dict mapping config name to DatasetInfoDict.
+
+    Args:
+        path (`str`): path to the dataset processing script with the dataset builder. Can be either:
+
+            - a local path to processing script or the directory containing the script (if the script has the same name as the directory),
+                e.g. `'./dataset/squad'` or `'./dataset/squad/squad.py'`
+            - a dataset identifier on the Hugging Face Hub (list all available datasets and ids with [`datasets.list_datasets`])
+                e.g. `'squad'`, `'glue'` or``'openai/webtext'`
+        revision (`Union[str, datasets.Version]`, *optional*):
+            If specified, the dataset module will be loaded from the datasets repository at this version.
+            By default:
+            - it is set to the local version of the lib.
+            - it will also try to load it from the main branch if it's not available at the local version of the lib.
+            Specifying a version that is different from your local version of the lib might cause compatibility issues.
+        download_config ([`DownloadConfig`], *optional*):
+            Specific download configuration parameters.
+        download_mode ([`DownloadMode`] or `str`, defaults to `REUSE_DATASET_IF_EXISTS`):
+            Download/generate mode.
+        data_files (`Union[Dict, List, str]`, *optional*):
+            Defining the data_files of the dataset configuration.
+        token (`str` or `bool`, *optional*):
+            Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
+            If `True`, or not specified, will get token from `"~/.huggingface"`.
+        use_auth_token (`str` or `bool`, *optional*):
+            Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
+            If `True`, or not specified, will get token from `"~/.huggingface"`.
+
+            <Deprecated version="2.14.0">
+
+            `use_auth_token` was deprecated in favor of `token` in version 2.14.0 and will be removed in 3.0.0.
+
+            </Deprecated>
+
+        **config_kwargs (additional keyword arguments):
+            Optional attributes for builder class which will override the attributes if supplied.
+
+    Example:
+
+    ```py
+    >>> from datasets import get_dataset_infos
+    >>> get_dataset_infos('rotten_tomatoes')
+    {'default': DatasetInfo(description="Movie Review Dataset.\nThis is a dataset of containing 5,331 positive and 5,331 negative processed\nsentences from Rotten Tomatoes movie reviews...), ...}
+    ```
+    """
+    if use_auth_token != "deprecated":
+        warnings.warn(
+            "'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\n"
+            "You can remove this warning by passing 'token=<use_auth_token>' instead.",
+            FutureWarning,
+        )
+        token = use_auth_token
+
+    config_names = get_dataset_config_names(
+        path=path,
+        revision=revision,
+        download_config=download_config,
+        download_mode=download_mode,
+        data_files=data_files,
+        token=token,
+    )
+    return {
+        config_name: get_dataset_config_info(
+            path=path,
+            config_name=config_name,
+            data_files=data_files,
+            download_config=download_config,
+            download_mode=download_mode,
+            revision=revision,
+            token=token,
+            **config_kwargs,
+        )
+        for config_name in config_names
+    }
+
+
+def get_dataset_config_names(
+    path: str,
+    revision: Optional[Union[str, Version]] = None,
+    download_config: Optional[DownloadConfig] = None,
+    download_mode: Optional[Union[DownloadMode, str]] = None,
+    dynamic_modules_path: Optional[str] = None,
+    data_files: Optional[Union[Dict, List, str]] = None,
+    **download_kwargs,
+):
+    """Get the list of available config names for a particular dataset.
+
+    Args:
+        path (`str`): path to the dataset processing script with the dataset builder. Can be either:
+
+            - a local path to processing script or the directory containing the script (if the script has the same name as the directory),
+                e.g. `'./dataset/squad'` or `'./dataset/squad/squad.py'`
+            - a dataset identifier on the Hugging Face Hub (list all available datasets and ids with [`datasets.list_datasets`])
+                e.g. `'squad'`, `'glue'` or `'openai/webtext'`
+        revision (`Union[str, datasets.Version]`, *optional*):
+            If specified, the dataset module will be loaded from the datasets repository at this version.
+            By default:
+            - it is set to the local version of the lib.
+            - it will also try to load it from the main branch if it's not available at the local version of the lib.
+            Specifying a version that is different from your local version of the lib might cause compatibility issues.
+        download_config ([`DownloadConfig`], *optional*):
+            Specific download configuration parameters.
+        download_mode ([`DownloadMode`] or `str`, defaults to `REUSE_DATASET_IF_EXISTS`):
+            Download/generate mode.
+        dynamic_modules_path (`str`, defaults to `~/.cache/huggingface/modules/datasets_modules`):
+            Optional path to the directory in which the dynamic modules are saved. It must have been initialized with `init_dynamic_modules`.
+            By default the datasets and metrics are stored inside the `datasets_modules` module.
+        data_files (`Union[Dict, List, str]`, *optional*):
+            Defining the data_files of the dataset configuration.
+        **download_kwargs (additional keyword arguments):
+            Optional attributes for [`DownloadConfig`] which will override the attributes in `download_config` if supplied,
+            for example `token`.
+
+    Example:
+
+    ```py
+    >>> from datasets import get_dataset_config_names
+    >>> get_dataset_config_names("glue")
+    ['cola',
+     'sst2',
+     'mrpc',
+     'qqp',
+     'stsb',
+     'mnli',
+     'mnli_mismatched',
+     'mnli_matched',
+     'qnli',
+     'rte',
+     'wnli',
+     'ax']
+    ```
+    """
+    dataset_module = dataset_module_factory(
+        path,
+        revision=revision,
+        download_config=download_config,
+        download_mode=download_mode,
+        dynamic_modules_path=dynamic_modules_path,
+        data_files=data_files,
+        **download_kwargs,
+    )
+    builder_cls = get_dataset_builder_class(dataset_module, dataset_name=os.path.basename(path))
+    return list(builder_cls.builder_configs.keys()) or [
+        dataset_module.builder_kwargs.get("config_name", builder_cls.DEFAULT_CONFIG_NAME or "default")
+    ]
+
+
+def get_dataset_default_config_name(
+    path: str,
+    revision: Optional[Union[str, Version]] = None,
+    download_config: Optional[DownloadConfig] = None,
+    download_mode: Optional[Union[DownloadMode, str]] = None,
+    dynamic_modules_path: Optional[str] = None,
+    data_files: Optional[Union[Dict, List, str]] = None,
+    **download_kwargs,
+) -> Optional[str]:
+    """Get the default config name for a particular dataset.
+
+    Args:
+        path (`str`): path to the dataset processing script with the dataset builder. Can be either:
+
+            - a local path to processing script or the directory containing the script (if the script has the same name as the directory),
+                e.g. `'./dataset/squad'` or `'./dataset/squad/squad.py'`
+            - a dataset identifier on the Hugging Face Hub (list all available datasets and ids with [`datasets.list_datasets`])
+                e.g. `'squad'`, `'glue'` or `'openai/webtext'`
+        revision (`Union[str, datasets.Version]`, *optional*):
+            If specified, the dataset module will be loaded from the datasets repository at this version.
+            By default:
+            - it is set to the local version of the lib.
+            - it will also try to load it from the main branch if it's not available at the local version of the lib.
+            Specifying a version that is different from your local version of the lib might cause compatibility issues.
+        download_config ([`DownloadConfig`], *optional*):
+            Specific download configuration parameters.
+        download_mode ([`DownloadMode`] or `str`, defaults to `REUSE_DATASET_IF_EXISTS`):
+            Download/generate mode.
+        dynamic_modules_path (`str`, defaults to `~/.cache/huggingface/modules/datasets_modules`):
+            Optional path to the directory in which the dynamic modules are saved. It must have been initialized with `init_dynamic_modules`.
+            By default the datasets and metrics are stored inside the `datasets_modules` module.
+        data_files (`Union[Dict, List, str]`, *optional*):
+            Defining the data_files of the dataset configuration.
+        **download_kwargs (additional keyword arguments):
+            Optional attributes for [`DownloadConfig`] which will override the attributes in `download_config` if supplied,
+            for example `token`.
+
+    Returns:
+        Optional[str]
+
+    Example:
+
+    ```py
+    >>> from datasets import get_dataset_default_config_name
+    >>> get_dataset_default_config_name("openbookqa")
+    'main'
+    ```
+    """
+    dataset_module = dataset_module_factory(
+        path,
+        revision=revision,
+        download_config=download_config,
+        download_mode=download_mode,
+        dynamic_modules_path=dynamic_modules_path,
+        data_files=data_files,
+        **download_kwargs,
+    )
+    builder_cls = get_dataset_builder_class(dataset_module, dataset_name=os.path.basename(path))
+    builder_configs = list(builder_cls.builder_configs.keys())
+    if builder_configs:
+        default_config_name = builder_configs[0] if len(builder_configs) == 1 else None
+    else:
+        default_config_name = "default"
+    return builder_cls.DEFAULT_CONFIG_NAME or default_config_name
+
+
+def get_dataset_config_info(
+    path: str,
+    config_name: Optional[str] = None,
+    data_files: Optional[Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]] = None,
+    download_config: Optional[DownloadConfig] = None,
+    download_mode: Optional[Union[DownloadMode, str]] = None,
+    revision: Optional[Union[str, Version]] = None,
+    token: Optional[Union[bool, str]] = None,
+    use_auth_token="deprecated",
+    **config_kwargs,
+) -> DatasetInfo:
+    """Get the meta information (DatasetInfo) about a dataset for a particular config
+
+    Args:
+        path (``str``): path to the dataset processing script with the dataset builder. Can be either:
+
+            - a local path to processing script or the directory containing the script (if the script has the same name as the directory),
+                e.g. ``'./dataset/squad'`` or ``'./dataset/squad/squad.py'``
+            - a dataset identifier on the Hugging Face Hub (list all available datasets and ids with ``datasets.list_datasets()``)
+                e.g. ``'squad'``, ``'glue'`` or ``'openai/webtext'``
+        config_name (:obj:`str`, optional): Defining the name of the dataset configuration.
+        data_files (:obj:`str` or :obj:`Sequence` or :obj:`Mapping`, optional): Path(s) to source data file(s).
+        download_config (:class:`~download.DownloadConfig`, optional): Specific download configuration parameters.
+        download_mode (:class:`DownloadMode` or :obj:`str`, default ``REUSE_DATASET_IF_EXISTS``): Download/generate mode.
+        revision (:class:`~utils.Version` or :obj:`str`, optional): Version of the dataset script to load.
+            As datasets have their own git repository on the Datasets Hub, the default version "main" corresponds to their "main" branch.
+            You can specify a different version than the default "main" by using a commit SHA or a git tag of the dataset repository.
+        token (``str`` or :obj:`bool`, optional): Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
+            If True, or not specified, will get token from `"~/.huggingface"`.
+        use_auth_token (``str`` or :obj:`bool`, optional): Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
+            If True, or not specified, will get token from `"~/.huggingface"`.
+
+            <Deprecated version="2.14.0">
+
+            `use_auth_token` was deprecated in favor of `token` in version 2.14.0 and will be removed in 3.0.0.
+
+            </Deprecated>
+
+        **config_kwargs (additional keyword arguments): optional attributes for builder class which will override the attributes if supplied.
+
+    """
+    if use_auth_token != "deprecated":
+        warnings.warn(
+            "'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\n"
+            "You can remove this warning by passing 'token=<use_auth_token>' instead.",
+            FutureWarning,
+        )
+        token = use_auth_token
+
+    builder = load_dataset_builder(
+        path,
+        name=config_name,
+        data_files=data_files,
+        download_config=download_config,
+        download_mode=download_mode,
+        revision=revision,
+        token=token,
+        **config_kwargs,
+    )
+    info = builder.info
+    if info.splits is None:
+        download_config = download_config.copy() if download_config else DownloadConfig()
+        if token is not None:
+            download_config.token = token
+        builder._check_manual_download(
+            StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
+        )
+        try:
+            info.splits = {
+                split_generator.name: {"name": split_generator.name, "dataset_name": path}
+                for split_generator in builder._split_generators(
+                    StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
+                )
+            }
+        except Exception as err:
+            raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
+    return info
+
+
+def get_dataset_split_names(
+    path: str,
+    config_name: Optional[str] = None,
+    data_files: Optional[Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]] = None,
+    download_config: Optional[DownloadConfig] = None,
+    download_mode: Optional[Union[DownloadMode, str]] = None,
+    revision: Optional[Union[str, Version]] = None,
+    token: Optional[Union[bool, str]] = None,
+    use_auth_token="deprecated",
+    **config_kwargs,
+):
+    """Get the list of available splits for a particular config and dataset.
+
+    Args:
+        path (`str`): path to the dataset processing script with the dataset builder. Can be either:
+
+            - a local path to processing script or the directory containing the script (if the script has the same name as the directory),
+                e.g. `'./dataset/squad'` or `'./dataset/squad/squad.py'`
+            - a dataset identifier on the Hugging Face Hub (list all available datasets and ids with [`datasets.list_datasets`])
+                e.g. `'squad'`, `'glue'` or `'openai/webtext'`
+        config_name (`str`, *optional*):
+            Defining the name of the dataset configuration.
+        data_files (`str` or `Sequence` or `Mapping`, *optional*):
+            Path(s) to source data file(s).
+        download_config ([`DownloadConfig`], *optional*):
+            Specific download configuration parameters.
+        download_mode ([`DownloadMode`] or `str`, defaults to `REUSE_DATASET_IF_EXISTS`):
+            Download/generate mode.
+        revision ([`Version`] or `str`, *optional*):
+            Version of the dataset script to load.
+            As datasets have their own git repository on the Datasets Hub, the default version "main" corresponds to their "main" branch.
+            You can specify a different version than the default "main" by using a commit SHA or a git tag of the dataset repository.
+        token (`str` or `bool`, *optional*):
+            Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
+            If `True`, or not specified, will get token from `"~/.huggingface"`.
+        use_auth_token (`str` or `bool`, *optional*):
+            Optional string or boolean to use as Bearer token for remote files on the Datasets Hub.
+            If `True`, or not specified, will get token from `"~/.huggingface"`.
+
+            <Deprecated version="2.14.0">
+
+            `use_auth_token` was deprecated in favor of `token` in version 2.14.0 and will be removed in 3.0.0.
+
+            </Deprecated>
+
+        **config_kwargs (additional keyword arguments):
+            Optional attributes for builder class which will override the attributes if supplied.
+
+    Example:
+
+    ```py
+    >>> from datasets import get_dataset_split_names
+    >>> get_dataset_split_names('rotten_tomatoes')
+    ['train', 'validation', 'test']
+    ```
+    """
+    if use_auth_token != "deprecated":
+        warnings.warn(
+            "'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\n"
+            "You can remove this warning by passing 'token=<use_auth_token>' instead.",
+            FutureWarning,
+        )
+        token = use_auth_token
+
+    info = get_dataset_config_info(
+        path,
+        config_name=config_name,
+        data_files=data_files,
+        download_config=download_config,
+        download_mode=download_mode,
+        revision=revision,
+        token=token,
+        **config_kwargs,
+    )
+    return list(info.splits.keys())

env-llmeval/lib/python3.10/site-packages/datasets/keyhash.py

@@ -0,0 +1,104 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+
+"""
+Hashing function for dataset keys using `hashlib.md5`
+
+Requirements for the hash function:
+
+- Provides a uniformly distributed hash from random space
+- Adequately fast speed
+- Working with multiple input types (in this case, `str`, `int` or `bytes`)
+- Should be platform independent (generates same hash on different OS and systems)
+
+The hashing function provides a unique 128-bit integer hash of the key provided.
+
+The split name is being used here as the hash salt to avoid having same hashes
+in different splits due to same keys
+"""
+
+from typing import Union
+
+from huggingface_hub.utils import insecure_hashlib
+
+
+def _as_bytes(hash_data: Union[str, int, bytes]) -> bytes:
+    """
+    Returns the input hash_data in its bytes form
+
+    Args:
+    hash_data: the hash salt/key to be converted to bytes
+    """
+    if isinstance(hash_data, bytes):
+        # Data already in bytes, returns as it as
+        return hash_data
+    elif isinstance(hash_data, str):
+        # We keep the data as it as for it ot be later encoded to UTF-8
+        # However replace `\\` with `/` for Windows compatibility
+        hash_data = hash_data.replace("\\", "/")
+    elif isinstance(hash_data, int):
+        hash_data = str(hash_data)
+    else:
+        # If data is not of the required type, raise error
+        raise InvalidKeyError(hash_data)
+
+    return hash_data.encode("utf-8")
+
+
+class InvalidKeyError(Exception):
+    """Raises an error when given key is of invalid datatype."""
+
+    def __init__(self, hash_data):
+        self.prefix = "\nFAILURE TO GENERATE DATASET: Invalid key type detected"
+        self.err_msg = f"\nFound Key {hash_data} of type {type(hash_data)}"
+        self.suffix = "\nKeys should be either str, int or bytes type"
+        super().__init__(f"{self.prefix}{self.err_msg}{self.suffix}")
+
+
+class DuplicatedKeysError(Exception):
+    """Raise an error when duplicate key found."""
+
+    def __init__(self, key, duplicate_key_indices, fix_msg=""):
+        self.key = key
+        self.duplicate_key_indices = duplicate_key_indices
+        self.fix_msg = fix_msg
+        self.prefix = "Found multiple examples generated with the same key"
+        if len(duplicate_key_indices) <= 20:
+            self.err_msg = f"\nThe examples at index {', '.join(duplicate_key_indices)} have the key {key}"
+        else:
+            self.err_msg = f"\nThe examples at index {', '.join(duplicate_key_indices[:20])}... ({len(duplicate_key_indices) - 20} more) have the key {key}"
+        self.suffix = "\n" + fix_msg if fix_msg else ""
+        super().__init__(f"{self.prefix}{self.err_msg}{self.suffix}")
+
+
+class KeyHasher:
+    """KeyHasher class for providing hash using md5"""
+
+    def __init__(self, hash_salt: str):
+        self._split_md5 = insecure_hashlib.md5(_as_bytes(hash_salt))
+
+    def hash(self, key: Union[str, int, bytes]) -> int:
+        """Returns 128-bits unique hash of input key
+
+        Args:
+        key: the input key to be hashed (should be str, int or bytes)
+
+        Returns: 128-bit int hash key"""
+        md5 = self._split_md5.copy()
+        byte_key = _as_bytes(key)
+        md5.update(byte_key)
+        # Convert to integer with hexadecimal conversion
+        return int(md5.hexdigest(), 16)

env-llmeval/lib/python3.10/site-packages/datasets/metric.py

@@ -0,0 +1,652 @@
+# Copyright 2020 The HuggingFace Datasets Authors
+#
+# 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.
+
+# Lint as: python3
+"""Metrics base class."""
+
+import os
+import types
+import uuid
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import pyarrow as pa
+from filelock import BaseFileLock, Timeout
+
+from . import config
+from .arrow_dataset import Dataset
+from .arrow_reader import ArrowReader
+from .arrow_writer import ArrowWriter
+from .download.download_config import DownloadConfig
+from .download.download_manager import DownloadManager
+from .features import Features
+from .info import DatasetInfo, MetricInfo
+from .naming import camelcase_to_snakecase
+from .utils._filelock import FileLock
+from .utils.deprecation_utils import deprecated
+from .utils.logging import get_logger
+from .utils.py_utils import copyfunc, temp_seed
+
+
+logger = get_logger(__name__)
+
+
+class FileFreeLock(BaseFileLock):
+    """Thread lock until a file **cannot** be locked"""
+
+    def __init__(self, lock_file, *args, **kwargs):
+        self.filelock = FileLock(lock_file)
+        super().__init__(self.filelock.lock_file, *args, **kwargs)
+
+    def _acquire(self):
+        try:
+            self.filelock.acquire(timeout=0.01, poll_intervall=0.02)  # Try to lock once
+        except Timeout:
+            # We couldn't acquire the lock, the file is locked!
+            self._context.lock_file_fd = self.filelock.lock_file
+        else:
+            # We were able to acquire the lock, the file is not yet locked!
+            self.filelock.release()
+            self._context.lock_file_fd = None
+
+    def _release(self):
+        self._context.lock_file_fd = None
+
+
+# lists - summarize long lists similarly to NumPy
+# arrays/tensors - let the frameworks control formatting
+def summarize_if_long_list(obj):
+    if not type(obj) == list or len(obj) <= 6:  # noqa: E721
+        return f"{obj}"
+
+    def format_chunk(chunk):
+        return ", ".join(repr(x) for x in chunk)
+
+    return f"[{format_chunk(obj[:3])}, ..., {format_chunk(obj[-3:])}]"
+
+
+class MetricInfoMixin:
+    """This base class exposes some attributes of MetricInfo
+    at the base level of the Metric for easy access.
+
+    <Deprecated version="2.5.0">
+
+    Use the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate
+
+    </Deprecated>
+
+    """
+
+    def __init__(self, info: MetricInfo):
+        self._metric_info = info
+
+    @property
+    def info(self):
+        """:class:`datasets.MetricInfo` object containing all the metadata in the metric."""
+        return self._metric_info
+
+    @property
+    def name(self) -> str:
+        return self._metric_info.metric_name
+
+    @property
+    def experiment_id(self) -> Optional[str]:
+        return self._metric_info.experiment_id
+
+    @property
+    def description(self) -> str:
+        return self._metric_info.description
+
+    @property
+    def citation(self) -> str:
+        return self._metric_info.citation
+
+    @property
+    def features(self) -> Features:
+        return self._metric_info.features
+
+    @property
+    def inputs_description(self) -> str:
+        return self._metric_info.inputs_description
+
+    @property
+    def homepage(self) -> Optional[str]:
+        return self._metric_info.homepage
+
+    @property
+    def license(self) -> str:
+        return self._metric_info.license
+
+    @property
+    def codebase_urls(self) -> Optional[List[str]]:
+        return self._metric_info.codebase_urls
+
+    @property
+    def reference_urls(self) -> Optional[List[str]]:
+        return self._metric_info.reference_urls
+
+    @property
+    def streamable(self) -> bool:
+        return self._metric_info.streamable
+
+    @property
+    def format(self) -> Optional[str]:
+        return self._metric_info.format
+
+
+class Metric(MetricInfoMixin):
+    """A Metric is the base class and common API for all metrics.
+
+    <Deprecated version="2.5.0">
+
+    Use the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate
+
+    </Deprecated>
+
+    Args:
+        config_name (``str``): This is used to define a hash specific to a metrics computation script and prevents the metric's data
+            to be overridden when the metric loading script is modified.
+        keep_in_memory (:obj:`bool`): keep all predictions and references in memory. Not possible in distributed settings.
+        cache_dir (``str``): Path to a directory in which temporary prediction/references data will be stored.
+            The data directory should be located on a shared file-system in distributed setups.
+        num_process (``int``): specify the total number of nodes in a distributed settings.
+            This is useful to compute metrics in distributed setups (in particular non-additive metrics like F1).
+        process_id (``int``): specify the id of the current process in a distributed setup (between 0 and num_process-1)
+            This is useful to compute metrics in distributed setups (in particular non-additive metrics like F1).
+        seed (:obj:`int`, optional): If specified, this will temporarily set numpy's random seed when :func:`datasets.Metric.compute` is run.
+        experiment_id (``str``): A specific experiment id. This is used if several distributed evaluations share the same file system.
+            This is useful to compute metrics in distributed setups (in particular non-additive metrics like F1).
+        max_concurrent_cache_files (``int``): Max number of concurrent metrics cache files (default 10000).
+        timeout (``Union[int, float]``): Timeout in second for distributed setting synchronization.
+    """
+
+    @deprecated("Use the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate")
+    def __init__(
+        self,
+        config_name: Optional[str] = None,
+        keep_in_memory: bool = False,
+        cache_dir: Optional[str] = None,
+        num_process: int = 1,
+        process_id: int = 0,
+        seed: Optional[int] = None,
+        experiment_id: Optional[str] = None,
+        max_concurrent_cache_files: int = 10000,
+        timeout: Union[int, float] = 100,
+        **kwargs,
+    ):
+        # prepare info
+        self.config_name = config_name or "default"
+        info = self._info()
+        info.metric_name = camelcase_to_snakecase(self.__class__.__name__)
+        info.config_name = self.config_name
+        info.experiment_id = experiment_id or "default_experiment"
+        MetricInfoMixin.__init__(self, info)  # For easy access on low level
+
+        # Safety checks on num_process and process_id
+        if not isinstance(process_id, int) or process_id < 0:
+            raise ValueError("'process_id' should be a number greater than 0")
+        if not isinstance(num_process, int) or num_process <= process_id:
+            raise ValueError("'num_process' should be a number greater than process_id")
+        if keep_in_memory and num_process != 1:
+            raise ValueError("Using 'keep_in_memory' is not possible in distributed setting (num_process > 1).")
+
+        self.num_process = num_process
+        self.process_id = process_id
+        self.max_concurrent_cache_files = max_concurrent_cache_files
+
+        self.keep_in_memory = keep_in_memory
+        self._data_dir_root = os.path.expanduser(cache_dir or config.HF_METRICS_CACHE)
+        self.data_dir = self._build_data_dir()
+        if seed is None:
+            _, seed, pos, *_ = np.random.get_state()
+            self.seed: int = seed[pos] if pos < 624 else seed[0]
+        else:
+            self.seed: int = seed
+        self.timeout: Union[int, float] = timeout
+
+        # Update 'compute' and 'add' docstring
+        # methods need to be copied otherwise it changes the docstrings of every instance
+        self.compute = types.MethodType(copyfunc(self.compute), self)
+        self.add_batch = types.MethodType(copyfunc(self.add_batch), self)
+        self.add = types.MethodType(copyfunc(self.add), self)
+        self.compute.__func__.__doc__ += self.info.inputs_description
+        self.add_batch.__func__.__doc__ += self.info.inputs_description
+        self.add.__func__.__doc__ += self.info.inputs_description
+
+        # self.arrow_schema = pa.schema(field for field in self.info.features.type)
+        self.buf_writer = None
+        self.writer = None
+        self.writer_batch_size = None
+        self.data = None
+
+        # This is the cache file we store our predictions/references in
+        # Keep it None for now so we can (cloud)pickle the object
+        self.cache_file_name = None
+        self.filelock = None
+        self.rendez_vous_lock = None
+
+        # This is all the cache files on which we have a lock when we are in a distributed setting
+        self.file_paths = None
+        self.filelocks = None
+
+    def __len__(self):
+        """Return the number of examples (predictions or predictions/references pair)
+        currently stored in the metric's cache.
+        """
+        return 0 if self.writer is None else len(self.writer)
+
+    def __repr__(self):
+        return (
+            f'Metric(name: "{self.name}", features: {self.features}, '
+            f'usage: """{self.inputs_description}""", '
+            f"stored examples: {len(self)})"
+        )
+
+    def _build_data_dir(self):
+        """Path of this metric in cache_dir:
+        Will be:
+            self._data_dir_root/self.name/self.config_name/self.hash (if not none)/
+        If any of these element is missing or if ``with_version=False`` the corresponding subfolders are dropped.
+        """
+        builder_data_dir = self._data_dir_root
+        builder_data_dir = os.path.join(builder_data_dir, self.name, self.config_name)
+        os.makedirs(builder_data_dir, exist_ok=True)
+        return builder_data_dir
+
+    def _create_cache_file(self, timeout=1) -> Tuple[str, FileLock]:
+        """Create a new cache file. If the default cache file is used, we generated a new hash."""
+        file_path = os.path.join(self.data_dir, f"{self.experiment_id}-{self.num_process}-{self.process_id}.arrow")
+        filelock = None
+        for i in range(self.max_concurrent_cache_files):
+            filelock = FileLock(file_path + ".lock")
+            try:
+                filelock.acquire(timeout=timeout)
+            except Timeout:
+                # If we have reached the max number of attempts or we are not allow to find a free name (distributed setup)
+                # We raise an error
+                if self.num_process != 1:
+                    raise ValueError(
+                        f"Error in _create_cache_file: another metric instance is already using the local cache file at {file_path}. "
+                        f"Please specify an experiment_id (currently: {self.experiment_id}) to avoid collision "
+                        f"between distributed metric instances."
+                    ) from None
+                if i == self.max_concurrent_cache_files - 1:
+                    raise ValueError(
+                        f"Cannot acquire lock, too many metric instance are operating concurrently on this file system."
+                        f"You should set a larger value of max_concurrent_cache_files when creating the metric "
+                        f"(current value is {self.max_concurrent_cache_files})."
+                    ) from None
+                # In other cases (allow to find new file name + not yet at max num of attempts) we can try to sample a new hashing name.
+                file_uuid = str(uuid.uuid4())
+                file_path = os.path.join(
+                    self.data_dir, f"{self.experiment_id}-{file_uuid}-{self.num_process}-{self.process_id}.arrow"
+                )
+            else:
+                break
+
+        return file_path, filelock
+
+    def _get_all_cache_files(self) -> Tuple[List[str], List[FileLock]]:
+        """Get a lock on all the cache files in a distributed setup.
+        We wait for timeout second to let all the distributed node finish their tasks (default is 100 seconds).
+        """
+        if self.num_process == 1:
+            if self.cache_file_name is None:
+                raise ValueError(
+                    "Metric cache file doesn't exist. Please make sure that you call `add` or `add_batch` "
+                    "at least once before calling `compute`."
+                )
+            file_paths = [self.cache_file_name]
+        else:
+            file_paths = [
+                os.path.join(self.data_dir, f"{self.experiment_id}-{self.num_process}-{process_id}.arrow")
+                for process_id in range(self.num_process)
+            ]
+
+        # Let's acquire a lock on each process files to be sure they are finished writing
+        filelocks = []
+        for process_id, file_path in enumerate(file_paths):
+            if process_id == 0:  # process 0 already has its lock file
+                filelocks.append(self.filelock)
+            else:
+                filelock = FileLock(file_path + ".lock")
+                try:
+                    filelock.acquire(timeout=self.timeout)
+                except Timeout:
+                    raise ValueError(
+                        f"Cannot acquire lock on cached file {file_path} for process {process_id}."
+                    ) from None
+                else:
+                    filelocks.append(filelock)
+
+        return file_paths, filelocks
+
+    def _check_all_processes_locks(self):
+        expected_lock_file_names = [
+            os.path.join(self.data_dir, f"{self.experiment_id}-{self.num_process}-{process_id}.arrow.lock")
+            for process_id in range(self.num_process)
+        ]
+        for expected_lock_file_name in expected_lock_file_names:
+            nofilelock = FileFreeLock(expected_lock_file_name)
+            try:
+                nofilelock.acquire(timeout=self.timeout)
+            except Timeout:
+                raise ValueError(
+                    f"Expected to find locked file {expected_lock_file_name} from process {self.process_id} but it doesn't exist."
+                ) from None
+            else:
+                nofilelock.release()
+
+    def _check_rendez_vous(self):
+        expected_lock_file_name = os.path.join(self.data_dir, f"{self.experiment_id}-{self.num_process}-0.arrow.lock")
+        nofilelock = FileFreeLock(expected_lock_file_name)
+        try:
+            nofilelock.acquire(timeout=self.timeout)
+        except Timeout:
+            raise ValueError(
+                f"Expected to find locked file {expected_lock_file_name} from process {self.process_id} but it doesn't exist."
+            ) from None
+        else:
+            nofilelock.release()
+        lock_file_name = os.path.join(self.data_dir, f"{self.experiment_id}-{self.num_process}-rdv.lock")
+        rendez_vous_lock = FileLock(lock_file_name)
+        try:
+            rendez_vous_lock.acquire(timeout=self.timeout)
+        except Timeout:
+            raise ValueError(f"Couldn't acquire lock on {lock_file_name} from process {self.process_id}.") from None
+        else:
+            rendez_vous_lock.release()
+
+    def _finalize(self):
+        """Close all the writing process and load/gather the data
+        from all the nodes if main node or all_process is True.
+        """
+        if self.writer is not None:
+            self.writer.finalize()
+        self.writer = None
+        # release the locks of the processes > 0 so that process 0 can lock them to read + delete the data
+        if self.filelock is not None and self.process_id > 0:
+            self.filelock.release()
+
+        if self.keep_in_memory:
+            # Read the predictions and references
+            reader = ArrowReader(path=self.data_dir, info=DatasetInfo(features=self.features))
+            self.data = Dataset.from_buffer(self.buf_writer.getvalue())
+
+        elif self.process_id == 0:
+            # Let's acquire a lock on each node files to be sure they are finished writing
+            file_paths, filelocks = self._get_all_cache_files()
+
+            # Read the predictions and references
+            try:
+                reader = ArrowReader(path="", info=DatasetInfo(features=self.features))
+                self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths]))
+            except FileNotFoundError:
+                raise ValueError(
+                    "Error in finalize: another metric instance is already using the local cache file. "
+                    "Please specify an experiment_id to avoid collision between distributed metric instances."
+                ) from None
+
+            # Store file paths and locks and we will release/delete them after the computation.
+            self.file_paths = file_paths
+            self.filelocks = filelocks
+
+    def compute(self, *, predictions=None, references=None, **kwargs) -> Optional[dict]:
+        """Compute the metrics.
+
+        Usage of positional arguments is not allowed to prevent mistakes.
+
+        Args:
+            predictions (list/array/tensor, optional): Predictions.
+            references (list/array/tensor, optional): References.
+            **kwargs (optional): Keyword arguments that will be forwarded to the metrics :meth:`_compute`
+                method (see details in the docstring).
+
+        Return:
+            dict or None
+
+            - Dictionary with the metrics if this metric is run on the main process (``process_id == 0``).
+            - None if the metric is not run on the main process (``process_id != 0``).
+
+        Example:
+
+        ```py
+        >>> from datasets import load_metric
+        >>> metric = load_metric("accuracy")
+        >>> accuracy = metric.compute(predictions=model_prediction, references=labels)
+        ```
+        """
+        all_kwargs = {"predictions": predictions, "references": references, **kwargs}
+        if predictions is None and references is None:
+            missing_kwargs = {k: None for k in self.features if k not in all_kwargs}
+            all_kwargs.update(missing_kwargs)
+        else:
+            missing_inputs = [k for k in self.features if k not in all_kwargs]
+            if missing_inputs:
+                raise ValueError(
+                    f"Metric inputs are missing: {missing_inputs}. All required inputs are {list(self.features)}"
+                )
+        inputs = {input_name: all_kwargs[input_name] for input_name in self.features}
+        compute_kwargs = {k: kwargs[k] for k in kwargs if k not in self.features}
+
+        if any(v is not None for v in inputs.values()):
+            self.add_batch(**inputs)
+        self._finalize()
+
+        self.cache_file_name = None
+        self.filelock = None
+
+        if self.process_id == 0:
+            self.data.set_format(type=self.info.format)
+
+            inputs = {input_name: self.data[input_name] for input_name in self.features}
+            with temp_seed(self.seed):
+                output = self._compute(**inputs, **compute_kwargs)
+
+            if self.buf_writer is not None:
+                self.buf_writer = None
+                del self.data
+                self.data = None
+            else:
+                # Release locks and delete all the cache files. Process 0 is released last.
+                for filelock, file_path in reversed(list(zip(self.filelocks, self.file_paths))):
+                    logger.info(f"Removing {file_path}")
+                    del self.data
+                    self.data = None
+                    del self.writer
+                    self.writer = None
+                    os.remove(file_path)
+                    filelock.release()
+
+            return output
+        else:
+            return None
+
+    def add_batch(self, *, predictions=None, references=None, **kwargs):
+        """Add a batch of predictions and references for the metric's stack.
+
+        Args:
+            predictions (list/array/tensor, optional): Predictions.
+            references (list/array/tensor, optional): References.
+
+        Example:
+
+        ```py
+        >>> from datasets import load_metric
+        >>> metric = load_metric("accuracy")
+        >>> metric.add_batch(predictions=model_prediction, references=labels)
+        ```
+        """
+        bad_inputs = [input_name for input_name in kwargs if input_name not in self.features]
+        if bad_inputs:
+            raise ValueError(f"Bad inputs for metric: {bad_inputs}. All required inputs are {list(self.features)}")
+        batch = {"predictions": predictions, "references": references, **kwargs}
+        batch = {intput_name: batch[intput_name] for intput_name in self.features}
+        batch = self.info.features.encode_batch(batch)
+        if self.writer is None:
+            self._init_writer()
+        try:
+            self.writer.write_batch(batch)
+        except pa.ArrowInvalid:
+            if any(len(batch[c]) != len(next(iter(batch.values()))) for c in batch):
+                col0 = next(iter(batch))
+                bad_col = [c for c in batch if len(batch[c]) != len(batch[col0])][0]
+                error_msg = (
+                    f"Mismatch in the number of {col0} ({len(batch[col0])}) and {bad_col} ({len(batch[bad_col])})"
+                )
+            elif sorted(self.features) != ["references", "predictions"]:
+                error_msg = f"Metric inputs don't match the expected format.\n" f"Expected format: {self.features},\n"
+                error_msg_inputs = ",\n".join(
+                    f"Input {input_name}: {summarize_if_long_list(batch[input_name])}" for input_name in self.features
+                )
+                error_msg += error_msg_inputs
+            else:
+                error_msg = (
+                    f"Predictions and/or references don't match the expected format.\n"
+                    f"Expected format: {self.features},\n"
+                    f"Input predictions: {summarize_if_long_list(predictions)},\n"
+                    f"Input references: {summarize_if_long_list(references)}"
+                )
+            raise ValueError(error_msg) from None
+
+    def add(self, *, prediction=None, reference=None, **kwargs):
+        """Add one prediction and reference for the metric's stack.
+
+        Args:
+            prediction (list/array/tensor, optional): Predictions.
+            reference (list/array/tensor, optional): References.
+
+        Example:
+
+        ```py
+        >>> from datasets import load_metric
+        >>> metric = load_metric("accuracy")
+        >>> metric.add(predictions=model_predictions, references=labels)
+        ```
+        """
+        bad_inputs = [input_name for input_name in kwargs if input_name not in self.features]
+        if bad_inputs:
+            raise ValueError(f"Bad inputs for metric: {bad_inputs}. All required inputs are {list(self.features)}")
+        example = {"predictions": prediction, "references": reference, **kwargs}
+        example = {intput_name: example[intput_name] for intput_name in self.features}
+        example = self.info.features.encode_example(example)
+        if self.writer is None:
+            self._init_writer()
+        try:
+            self.writer.write(example)
+        except pa.ArrowInvalid:
+            error_msg = f"Metric inputs don't match the expected format.\n" f"Expected format: {self.features},\n"
+            error_msg_inputs = ",\n".join(
+                f"Input {input_name}: {summarize_if_long_list(example[input_name])}" for input_name in self.features
+            )
+            error_msg += error_msg_inputs
+            raise ValueError(error_msg) from None
+
+    def _init_writer(self, timeout=1):
+        if self.num_process > 1:
+            if self.process_id == 0:
+                file_path = os.path.join(self.data_dir, f"{self.experiment_id}-{self.num_process}-rdv.lock")
+                self.rendez_vous_lock = FileLock(file_path)
+                try:
+                    self.rendez_vous_lock.acquire(timeout=timeout)
+                except TimeoutError:
+                    raise ValueError(
+                        f"Error in _init_writer: another metric instance is already using the local cache file at {file_path}. "
+                        f"Please specify an experiment_id (currently: {self.experiment_id}) to avoid collision "
+                        f"between distributed metric instances."
+                    ) from None
+
+        if self.keep_in_memory:
+            self.buf_writer = pa.BufferOutputStream()
+            self.writer = ArrowWriter(
+                features=self.info.features, stream=self.buf_writer, writer_batch_size=self.writer_batch_size
+            )
+        else:
+            self.buf_writer = None
+
+            # Get cache file name and lock it
+            if self.cache_file_name is None or self.filelock is None:
+                cache_file_name, filelock = self._create_cache_file()  # get ready
+                self.cache_file_name = cache_file_name
+                self.filelock = filelock
+
+            self.writer = ArrowWriter(
+                features=self.info.features, path=self.cache_file_name, writer_batch_size=self.writer_batch_size
+            )
+        # Setup rendez-vous here if
+        if self.num_process > 1:
+            if self.process_id == 0:
+                self._check_all_processes_locks()  # wait for everyone to be ready
+                self.rendez_vous_lock.release()  # let everyone go
+            else:
+                self._check_rendez_vous()  # wait for master to be ready and to let everyone go
+
+    def _info(self) -> MetricInfo:
+        """Construct the MetricInfo object. See `MetricInfo` for details.
+
+        Warning: This function is only called once and the result is cached for all
+        following .info() calls.
+
+        Returns:
+            info: (MetricInfo) The metrics information
+        """
+        raise NotImplementedError
+
+    def download_and_prepare(
+        self,
+        download_config: Optional[DownloadConfig] = None,
+        dl_manager: Optional[DownloadManager] = None,
+    ):
+        """Downloads and prepares dataset for reading.
+
+        Args:
+            download_config (:class:`DownloadConfig`, optional): Specific download configuration parameters.
+            dl_manager (:class:`DownloadManager`, optional): Specific download manager to use.
+        """
+        if dl_manager is None:
+            if download_config is None:
+                download_config = DownloadConfig()
+                download_config.cache_dir = os.path.join(self.data_dir, "downloads")
+                download_config.force_download = False
+
+            dl_manager = DownloadManager(
+                dataset_name=self.name, download_config=download_config, data_dir=self.data_dir
+            )
+
+        self._download_and_prepare(dl_manager)
+
+    def _download_and_prepare(self, dl_manager):
+        """Downloads and prepares resources for the metric.
+
+        This is the internal implementation to overwrite called when user calls
+        `download_and_prepare`. It should download all required resources for the metric.
+
+        Args:
+            dl_manager (:class:`DownloadManager`): `DownloadManager` used to download and cache data.
+        """
+        return None
+
+    def _compute(self, *, predictions=None, references=None, **kwargs) -> Dict[str, Any]:
+        """This method defines the common API for all the metrics in the library"""
+        raise NotImplementedError
+
+    def __del__(self):
+        if hasattr(self, "filelock") and self.filelock is not None:
+            self.filelock.release()
+        if hasattr(self, "rendez_vous_lock") and self.rendez_vous_lock is not None:
+            self.rendez_vous_lock.release()
+        if hasattr(self, "writer"):  # in case it was already deleted
+            del self.writer
+        if hasattr(self, "data"):  # in case it was already deleted
+            del self.data

env-llmeval/lib/python3.10/site-packages/datasets/naming.py

@@ -0,0 +1,84 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""Utilities for file names."""
+
+import itertools
+import os
+import re
+
+
+_uppercase_uppercase_re = re.compile(r"([A-Z]+)([A-Z][a-z])")
+_lowercase_uppercase_re = re.compile(r"([a-z\d])([A-Z])")
+
+_single_underscore_re = re.compile(r"(?<!_)_(?!_)")
+_multiple_underscores_re = re.compile(r"(_{2,})")
+
+_split_re = r"^\w+(\.\w+)*$"
+
+INVALID_WINDOWS_CHARACTERS_IN_PATH = r"<>:/\|?*"
+
+
+def camelcase_to_snakecase(name):
+    """Convert camel-case string to snake-case."""
+    name = _uppercase_uppercase_re.sub(r"\1_\2", name)
+    name = _lowercase_uppercase_re.sub(r"\1_\2", name)
+    return name.lower()
+
+
+def snakecase_to_camelcase(name):
+    """Convert snake-case string to camel-case string."""
+    name = _single_underscore_re.split(name)
+    name = [_multiple_underscores_re.split(n) for n in name]
+    return "".join(n.capitalize() for n in itertools.chain.from_iterable(name) if n != "")
+
+
+def filename_prefix_for_name(name):
+    if os.path.basename(name) != name:
+        raise ValueError(f"Should be a dataset name, not a path: {name}")
+    return camelcase_to_snakecase(name)
+
+
+def filename_prefix_for_split(name, split):
+    if os.path.basename(name) != name:
+        raise ValueError(f"Should be a dataset name, not a path: {name}")
+    if not re.match(_split_re, split):
+        raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'.")
+    return f"{filename_prefix_for_name(name)}-{split}"
+
+
+def filepattern_for_dataset_split(dataset_name, split, data_dir, filetype_suffix=None):
+    prefix = filename_prefix_for_split(dataset_name, split)
+    if filetype_suffix:
+        prefix += f".{filetype_suffix}"
+    filepath = os.path.join(data_dir, prefix)
+    return f"{filepath}*"
+
+
+def filenames_for_dataset_split(path, dataset_name, split, filetype_suffix=None, shard_lengths=None):
+    prefix = filename_prefix_for_split(dataset_name, split)
+    prefix = os.path.join(path, prefix)
+
+    if shard_lengths:
+        num_shards = len(shard_lengths)
+        filenames = [f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(num_shards)]
+        if filetype_suffix:
+            filenames = [filename + f".{filetype_suffix}" for filename in filenames]
+        return filenames
+    else:
+        filename = prefix
+        if filetype_suffix:
+            filename += f".{filetype_suffix}"
+        return [filename]

env-llmeval/lib/python3.10/site-packages/datasets/search.py

@@ -0,0 +1,779 @@
+import importlib.util
+import os
+import tempfile
+from pathlib import PurePath
+from typing import TYPE_CHECKING, Dict, List, NamedTuple, Optional, Union
+
+import fsspec
+import numpy as np
+
+from .utils import logging
+from .utils import tqdm as hf_tqdm
+
+
+if TYPE_CHECKING:
+    from .arrow_dataset import Dataset  # noqa: F401
+
+    try:
+        from elasticsearch import Elasticsearch  # noqa: F401
+
+    except ImportError:
+        pass
+    try:
+        import faiss  # noqa: F401
+
+    except ImportError:
+        pass
+
+_has_elasticsearch = importlib.util.find_spec("elasticsearch") is not None
+_has_faiss = importlib.util.find_spec("faiss") is not None
+
+
+logger = logging.get_logger(__name__)
+
+
+class MissingIndex(Exception):
+    pass
+
+
+class SearchResults(NamedTuple):
+    scores: List[float]
+    indices: List[int]
+
+
+class BatchedSearchResults(NamedTuple):
+    total_scores: List[List[float]]
+    total_indices: List[List[int]]
+
+
+class NearestExamplesResults(NamedTuple):
+    scores: List[float]
+    examples: dict
+
+
+class BatchedNearestExamplesResults(NamedTuple):
+    total_scores: List[List[float]]
+    total_examples: List[dict]
+
+
+class BaseIndex:
+    """Base class for indexing"""
+
+    def search(self, query, k: int = 10, **kwargs) -> SearchResults:
+        """
+        To implement.
+        This method has to return the scores and the indices of the retrieved examples given a certain query.
+        """
+        raise NotImplementedError
+
+    def search_batch(self, queries, k: int = 10, **kwargs) -> BatchedSearchResults:
+        """Find the nearest examples indices to the query.
+
+        Args:
+            queries (`Union[List[str], np.ndarray]`): The queries as a list of strings if `column` is a text index or as a numpy array if `column` is a vector index.
+            k (`int`): The number of examples to retrieve per query.
+
+        Ouput:
+            total_scores (`List[List[float]`): The retrieval scores of the retrieved examples per query.
+            total_indices (`List[List[int]]`): The indices of the retrieved examples per query.
+        """
+        total_scores, total_indices = [], []
+        for query in queries:
+            scores, indices = self.search(query, k)
+            total_scores.append(scores)
+            total_indices.append(indices)
+        return BatchedSearchResults(total_scores, total_indices)
+
+    def save(self, file: Union[str, PurePath]):
+        """Serialize the index on disk"""
+        raise NotImplementedError
+
+    @classmethod
+    def load(cls, file: Union[str, PurePath]) -> "BaseIndex":
+        """Deserialize the index from disk"""
+        raise NotImplementedError
+
+
+class ElasticSearchIndex(BaseIndex):
+    """
+    Sparse index using Elasticsearch. It is used to index text and run queries based on BM25 similarity.
+    An Elasticsearch server needs to be accessible, and a python client is declared with
+    ```
+    es_client = Elasticsearch([{'host': 'localhost', 'port': '9200'}])
+    ```
+    for example.
+    """
+
+    def __init__(
+        self,
+        host: Optional[str] = None,
+        port: Optional[int] = None,
+        es_client: Optional["Elasticsearch"] = None,
+        es_index_name: Optional[str] = None,
+        es_index_config: Optional[dict] = None,
+    ):
+        if not _has_elasticsearch:
+            raise ImportError(
+                "You must install ElasticSearch to use ElasticSearchIndex. To do so you can run `pip install elasticsearch==7.7.1 for example`"
+            )
+        if es_client is not None and (host is not None or port is not None):
+            raise ValueError("Please specify either `es_client` or `(host, port)`, but not both.")
+        host = host or "localhost"
+        port = port or 9200
+
+        import elasticsearch.helpers  # noqa: F401 - need this to properly load all the es features
+        from elasticsearch import Elasticsearch  # noqa: F811
+
+        self.es_client = es_client if es_client is not None else Elasticsearch([{"host": host, "port": str(port)}])
+        self.es_index_name = (
+            es_index_name
+            if es_index_name is not None
+            else "huggingface_datasets_" + os.path.basename(tempfile.NamedTemporaryFile().name)
+        )
+        self.es_index_config = (
+            es_index_config
+            if es_index_config is not None
+            else {
+                "settings": {
+                    "number_of_shards": 1,
+                    "analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}},
+                },
+                "mappings": {"properties": {"text": {"type": "text", "analyzer": "standard", "similarity": "BM25"}}},
+            }
+        )
+
+    def add_documents(self, documents: Union[List[str], "Dataset"], column: Optional[str] = None):
+        """
+        Add documents to the index.
+        If the documents are inside a certain column, you can specify it using the `column` argument.
+        """
+        index_name = self.es_index_name
+        index_config = self.es_index_config
+        self.es_client.indices.create(index=index_name, body=index_config)
+        number_of_docs = len(documents)
+        progress = hf_tqdm(unit="docs", total=number_of_docs)
+        successes = 0
+
+        def passage_generator():
+            if column is not None:
+                for i, example in enumerate(documents):
+                    yield {"text": example[column], "_id": i}
+            else:
+                for i, example in enumerate(documents):
+                    yield {"text": example, "_id": i}
+
+        # create the ES index
+        import elasticsearch as es
+
+        for ok, action in es.helpers.streaming_bulk(
+            client=self.es_client,
+            index=index_name,
+            actions=passage_generator(),
+        ):
+            progress.update(1)
+            successes += ok
+        if successes != len(documents):
+            logger.warning(
+                f"Some documents failed to be added to ElasticSearch. Failures: {len(documents)-successes}/{len(documents)}"
+            )
+        logger.info(f"Indexed {successes:d} documents")
+
+    def search(self, query: str, k=10, **kwargs) -> SearchResults:
+        """Find the nearest examples indices to the query.
+
+        Args:
+            query (`str`): The query as a string.
+            k (`int`): The number of examples to retrieve.
+
+        Ouput:
+            scores (`List[List[float]`): The retrieval scores of the retrieved examples.
+            indices (`List[List[int]]`): The indices of the retrieved examples.
+        """
+        response = self.es_client.search(
+            index=self.es_index_name,
+            body={"query": {"multi_match": {"query": query, "fields": ["text"], "type": "cross_fields"}}, "size": k},
+            **kwargs,
+        )
+        hits = response["hits"]["hits"]
+        return SearchResults([hit["_score"] for hit in hits], [int(hit["_id"]) for hit in hits])
+
+    def search_batch(self, queries, k: int = 10, max_workers=10, **kwargs) -> BatchedSearchResults:
+        import concurrent.futures
+
+        total_scores, total_indices = [None] * len(queries), [None] * len(queries)
+        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+            future_to_index = {executor.submit(self.search, query, k, **kwargs): i for i, query in enumerate(queries)}
+            for future in concurrent.futures.as_completed(future_to_index):
+                index = future_to_index[future]
+                results: SearchResults = future.result()
+                total_scores[index] = results.scores
+                total_indices[index] = results.indices
+        return BatchedSearchResults(total_indices=total_indices, total_scores=total_scores)
+
+
+class FaissIndex(BaseIndex):
+    """
+    Dense index using Faiss. It is used to index vectors.
+    Faiss is a library for efficient similarity search and clustering of dense vectors.
+    It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
+    You can find more information about Faiss here:
+    - For index types and the string factory: https://github.com/facebookresearch/faiss/wiki/The-index-factory
+    - For GPU settings: https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU
+    """
+
+    def __init__(
+        self,
+        device: Optional[Union[int, List[int]]] = None,
+        string_factory: Optional[str] = None,
+        metric_type: Optional[int] = None,
+        custom_index: Optional["faiss.Index"] = None,
+    ):
+        """
+        Create a Dense index using Faiss. You can specify `device` if you want to run it on GPU (`device` must be the GPU index).
+        You can find more information about Faiss here:
+        - For `string factory`: https://github.com/facebookresearch/faiss/wiki/The-index-factory
+        """
+        if string_factory is not None and custom_index is not None:
+            raise ValueError("Please specify either `string_factory` or `custom_index` but not both.")
+        if device is not None and custom_index is not None:
+            raise ValueError(
+                "Cannot pass both 'custom_index' and 'device'. "
+                "Pass 'custom_index' already transferred to the target device instead."
+            )
+        self.device = device
+        self.string_factory = string_factory
+        self.metric_type = metric_type
+        self.faiss_index = custom_index
+        if not _has_faiss:
+            raise ImportError(
+                "You must install Faiss to use FaissIndex. To do so you can run `conda install -c pytorch faiss-cpu` or `conda install -c pytorch faiss-gpu`. "
+                "A community supported package is also available on pypi: `pip install faiss-cpu` or `pip install faiss-gpu`. "
+                "Note that pip may not have the latest version of FAISS, and thus, some of the latest features and bug fixes may not be available."
+            )
+
+    def add_vectors(
+        self,
+        vectors: Union[np.array, "Dataset"],
+        column: Optional[str] = None,
+        batch_size: int = 1000,
+        train_size: Optional[int] = None,
+        faiss_verbose: Optional[bool] = None,
+    ):
+        """
+        Add vectors to the index.
+        If the arrays are inside a certain column, you can specify it using the `column` argument.
+        """
+        import faiss  # noqa: F811
+
+        # Create index
+        if self.faiss_index is None:
+            size = len(vectors[0]) if column is None else len(vectors[0][column])
+            if self.string_factory is not None:
+                if self.metric_type is None:
+                    index = faiss.index_factory(size, self.string_factory)
+                else:
+                    index = faiss.index_factory(size, self.string_factory, self.metric_type)
+            else:
+                if self.metric_type is None:
+                    index = faiss.IndexFlat(size)
+                else:
+                    index = faiss.IndexFlat(size, self.metric_type)
+
+            self.faiss_index = self._faiss_index_to_device(index, self.device)
+            logger.info(f"Created faiss index of type {type(self.faiss_index)}")
+
+        # Set verbosity level
+        if faiss_verbose is not None:
+            self.faiss_index.verbose = faiss_verbose
+            if hasattr(self.faiss_index, "index") and self.faiss_index.index is not None:
+                self.faiss_index.index.verbose = faiss_verbose
+            if hasattr(self.faiss_index, "quantizer") and self.faiss_index.quantizer is not None:
+                self.faiss_index.quantizer.verbose = faiss_verbose
+            if hasattr(self.faiss_index, "clustering_index") and self.faiss_index.clustering_index is not None:
+                self.faiss_index.clustering_index.verbose = faiss_verbose
+
+        # Train
+        if train_size is not None:
+            train_vecs = vectors[:train_size] if column is None else vectors[:train_size][column]
+            logger.info(f"Training the index with the first {len(train_vecs)} vectors")
+            self.faiss_index.train(train_vecs)
+        else:
+            logger.info("Ignored the training step of the faiss index as `train_size` is None.")
+
+        # Add vectors
+        logger.info(f"Adding {len(vectors)} vectors to the faiss index")
+        for i in hf_tqdm(range(0, len(vectors), batch_size)):
+            vecs = vectors[i : i + batch_size] if column is None else vectors[i : i + batch_size][column]
+            self.faiss_index.add(vecs)
+
+    @staticmethod
+    def _faiss_index_to_device(index: "faiss.Index", device: Optional[Union[int, List[int]]] = None) -> "faiss.Index":
+        """
+        Sends a faiss index to a device.
+        A device can either be a positive integer (GPU id), a negative integer (all GPUs),
+            or a list of positive integers (select GPUs to use), or `None` for CPU.
+        """
+
+        # If device is not specified, then it runs on CPU.
+        if device is None:
+            return index
+
+        import faiss  # noqa: F811
+
+        # If the device id is given as an integer
+        if isinstance(device, int):
+            # Positive integers are directly mapped to GPU ids
+            if device > -1:
+                faiss_res = faiss.StandardGpuResources()
+                index = faiss.index_cpu_to_gpu(faiss_res, device, index)
+            # And negative integers mean using all GPUs
+            else:
+                index = faiss.index_cpu_to_all_gpus(index)
+        # Device ids given as a list mean mapping to those devices specified.
+        elif isinstance(device, (list, tuple)):
+            index = faiss.index_cpu_to_gpus_list(index, gpus=list(device))
+        else:
+            raise TypeError(
+                f"The argument type: {type(device)} is not expected. "
+                + "Please pass in either nothing, a positive int, a negative int, or a list of positive ints."
+            )
+
+        return index
+
+    def search(self, query: np.array, k=10, **kwargs) -> SearchResults:
+        """Find the nearest examples indices to the query.
+
+        Args:
+            query (`np.array`): The query as a numpy array.
+            k (`int`): The number of examples to retrieve.
+
+        Ouput:
+            scores (`List[List[float]`): The retrieval scores of the retrieved examples.
+            indices (`List[List[int]]`): The indices of the retrieved examples.
+        """
+        if len(query.shape) != 1 and (len(query.shape) != 2 or query.shape[0] != 1):
+            raise ValueError("Shape of query is incorrect, it has to be either a 1D array or 2D (1, N)")
+
+        queries = query.reshape(1, -1)
+        if not queries.flags.c_contiguous:
+            queries = np.asarray(queries, order="C")
+        scores, indices = self.faiss_index.search(queries, k, **kwargs)
+        return SearchResults(scores[0], indices[0].astype(int))
+
+    def search_batch(self, queries: np.array, k=10, **kwargs) -> BatchedSearchResults:
+        """Find the nearest examples indices to the queries.
+
+        Args:
+            queries (`np.array`): The queries as a numpy array.
+            k (`int`): The number of examples to retrieve.
+
+        Ouput:
+            total_scores (`List[List[float]`): The retrieval scores of the retrieved examples per query.
+            total_indices (`List[List[int]]`): The indices of the retrieved examples per query.
+        """
+        if len(queries.shape) != 2:
+            raise ValueError("Shape of query must be 2D")
+        if not queries.flags.c_contiguous:
+            queries = np.asarray(queries, order="C")
+        scores, indices = self.faiss_index.search(queries, k, **kwargs)
+        return BatchedSearchResults(scores, indices.astype(int))
+
+    def save(self, file: Union[str, PurePath], storage_options: Optional[Dict] = None):
+        """Serialize the FaissIndex on disk"""
+        import faiss  # noqa: F811
+
+        if self.device is not None and isinstance(self.device, (int, list, tuple)):
+            index = faiss.index_gpu_to_cpu(self.faiss_index)
+        else:
+            index = self.faiss_index
+
+        with fsspec.open(str(file), "wb", **(storage_options or {})) as f:
+            faiss.write_index(index, faiss.BufferedIOWriter(faiss.PyCallbackIOWriter(f.write)))
+
+    @classmethod
+    def load(
+        cls,
+        file: Union[str, PurePath],
+        device: Optional[Union[int, List[int]]] = None,
+        storage_options: Optional[Dict] = None,
+    ) -> "FaissIndex":
+        """Deserialize the FaissIndex from disk"""
+        import faiss  # noqa: F811
+
+        # Instances of FaissIndex is essentially just a wrapper for faiss indices.
+        faiss_index = cls(device=device)
+        with fsspec.open(str(file), "rb", **(storage_options or {})) as f:
+            index = faiss.read_index(faiss.BufferedIOReader(faiss.PyCallbackIOReader(f.read)))
+        faiss_index.faiss_index = faiss_index._faiss_index_to_device(index, faiss_index.device)
+        return faiss_index
+
+
+class IndexableMixin:
+    """Add indexing features to `datasets.Dataset`"""
+
+    def __init__(self):
+        self._indexes: Dict[str, BaseIndex] = {}
+
+    def __len__(self):
+        raise NotImplementedError
+
+    def __getitem__(self, key):
+        raise NotImplementedError
+
+    def is_index_initialized(self, index_name: str) -> bool:
+        return index_name in self._indexes
+
+    def _check_index_is_initialized(self, index_name: str):
+        if not self.is_index_initialized(index_name):
+            raise MissingIndex(
+                f"Index with index_name '{index_name}' not initialized yet. Please make sure that you call `add_faiss_index` or `add_elasticsearch_index` first."
+            )
+
+    def list_indexes(self) -> List[str]:
+        """List the `colindex_nameumns`/identifiers of all the attached indexes."""
+        return list(self._indexes)
+
+    def get_index(self, index_name: str) -> BaseIndex:
+        """List the `index_name`/identifiers of all the attached indexes.
+
+        Args:
+            index_name (`str`): Index name.
+
+        Returns:
+            [`BaseIndex`]
+        """
+        self._check_index_is_initialized(index_name)
+        return self._indexes[index_name]
+
+    def add_faiss_index(
+        self,
+        column: str,
+        index_name: Optional[str] = None,
+        device: Optional[Union[int, List[int]]] = None,
+        string_factory: Optional[str] = None,
+        metric_type: Optional[int] = None,
+        custom_index: Optional["faiss.Index"] = None,
+        batch_size: int = 1000,
+        train_size: Optional[int] = None,
+        faiss_verbose: bool = False,
+    ):
+        """Add a dense index using Faiss for fast retrieval.
+        The index is created using the vectors of the specified column.
+        You can specify `device` if you want to run it on GPU (`device` must be the GPU index, see more below).
+        You can find more information about Faiss here:
+        - For `string factory`: https://github.com/facebookresearch/faiss/wiki/The-index-factory
+
+        Args:
+            column (`str`): The column of the vectors to add to the index.
+            index_name (Optional `str`): The index_name/identifier of the index. This is the index_name that is used to call `.get_nearest` or `.search`.
+                By default it corresponds to `column`.
+            device (Optional `Union[int, List[int]]`): If positive integer, this is the index of the GPU to use. If negative integer, use all GPUs.
+                If a list of positive integers is passed in, run only on those GPUs. By default it uses the CPU.
+            string_factory (Optional `str`): This is passed to the index factory of Faiss to create the index. Default index class is IndexFlatIP.
+            metric_type (Optional `int`): Type of metric. Ex: `faiss.METRIC_INNER_PRODUCT` or `faiss.METRIC_L2`.
+            custom_index (Optional `faiss.Index`): Custom Faiss index that you already have instantiated and configured for your needs.
+            batch_size (Optional `int`): Size of the batch to use while adding vectors to the FaissIndex. Default value is 1000.
+                <Added version="2.4.0"/>
+            train_size (Optional `int`): If the index needs a training step, specifies how many vectors will be used to train the index.
+            faiss_verbose (`bool`, defaults to False): Enable the verbosity of the Faiss index.
+        """
+        index_name = index_name if index_name is not None else column
+        faiss_index = FaissIndex(
+            device=device, string_factory=string_factory, metric_type=metric_type, custom_index=custom_index
+        )
+        faiss_index.add_vectors(
+            self, column=column, batch_size=batch_size, train_size=train_size, faiss_verbose=faiss_verbose
+        )
+        self._indexes[index_name] = faiss_index
+
+    def add_faiss_index_from_external_arrays(
+        self,
+        external_arrays: np.array,
+        index_name: str,
+        device: Optional[Union[int, List[int]]] = None,
+        string_factory: Optional[str] = None,
+        metric_type: Optional[int] = None,
+        custom_index: Optional["faiss.Index"] = None,
+        batch_size: int = 1000,
+        train_size: Optional[int] = None,
+        faiss_verbose: bool = False,
+    ):
+        """Add a dense index using Faiss for fast retrieval.
+        The index is created using the vectors of `external_arrays`.
+        You can specify `device` if you want to run it on GPU (`device` must be the GPU index).
+        You can find more information about Faiss here:
+        - For `string factory`: https://github.com/facebookresearch/faiss/wiki/The-index-factory
+
+        Args:
+            external_arrays (`np.array`): If you want to use arrays from outside the lib for the index, you can set `external_arrays`.
+                It will use `external_arrays` to create the Faiss index instead of the arrays in the given `column`.
+            index_name (`str`): The index_name/identifier of the index. This is the index_name that is used to call `.get_nearest` or `.search`.
+            device (Optional `Union[int, List[int]]`): If positive integer, this is the index of the GPU to use. If negative integer, use all GPUs.
+                If a list of positive integers is passed in, run only on those GPUs. By default it uses the CPU.
+            string_factory (Optional `str`): This is passed to the index factory of Faiss to create the index. Default index class is IndexFlatIP.
+            metric_type (Optional `int`): Type of metric. Ex: `faiss.METRIC_INNER_PRODUCT` or `faiss.METRIC_L2`.
+            custom_index (Optional `faiss.Index`): Custom Faiss index that you already have instantiated and configured for your needs.
+            batch_size (Optional `int`): Size of the batch to use while adding vectors to the FaissIndex. Default value is 1000.
+                <Added version="2.4.0"/>
+            train_size (Optional `int`): If the index needs a training step, specifies how many vectors will be used to train the index.
+            faiss_verbose (`bool`, defaults to False): Enable the verbosity of the Faiss index.
+        """
+        faiss_index = FaissIndex(
+            device=device, string_factory=string_factory, metric_type=metric_type, custom_index=custom_index
+        )
+        faiss_index.add_vectors(
+            external_arrays, column=None, batch_size=batch_size, train_size=train_size, faiss_verbose=faiss_verbose
+        )
+        self._indexes[index_name] = faiss_index
+
+    def save_faiss_index(self, index_name: str, file: Union[str, PurePath], storage_options: Optional[Dict] = None):
+        """Save a FaissIndex on disk.
+
+        Args:
+            index_name (`str`): The index_name/identifier of the index. This is the index_name that is used to call `.get_nearest` or `.search`.
+            file (`str`): The path to the serialized faiss index on disk or remote URI (e.g. `"s3://my-bucket/index.faiss"`).
+            storage_options (`dict`, *optional*):
+                Key/value pairs to be passed on to the file-system backend, if any.
+
+                <Added version="2.11.0"/>
+
+        """
+        index = self.get_index(index_name)
+        if not isinstance(index, FaissIndex):
+            raise ValueError(f"Index '{index_name}' is not a FaissIndex but a '{type(index)}'")
+        index.save(file, storage_options=storage_options)
+        logger.info(f"Saved FaissIndex {index_name} at {file}")
+
+    def load_faiss_index(
+        self,
+        index_name: str,
+        file: Union[str, PurePath],
+        device: Optional[Union[int, List[int]]] = None,
+        storage_options: Optional[Dict] = None,
+    ):
+        """Load a FaissIndex from disk.
+
+        If you want to do additional configurations, you can have access to the faiss index object by doing
+        `.get_index(index_name).faiss_index` to make it fit your needs.
+
+        Args:
+            index_name (`str`): The index_name/identifier of the index. This is the index_name that is used to
+                call `.get_nearest` or `.search`.
+            file (`str`): The path to the serialized faiss index on disk or remote URI (e.g. `"s3://my-bucket/index.faiss"`).
+            device (Optional `Union[int, List[int]]`): If positive integer, this is the index of the GPU to use. If negative integer, use all GPUs.
+                If a list of positive integers is passed in, run only on those GPUs. By default it uses the CPU.
+            storage_options (`dict`, *optional*):
+                Key/value pairs to be passed on to the file-system backend, if any.
+
+                <Added version="2.11.0"/>
+
+        """
+        index = FaissIndex.load(file, device=device, storage_options=storage_options)
+        if index.faiss_index.ntotal != len(self):
+            raise ValueError(
+                f"Index size should match Dataset size, but Index '{index_name}' at {file} has {index.faiss_index.ntotal} elements while the dataset has {len(self)} examples."
+            )
+        self._indexes[index_name] = index
+        logger.info(f"Loaded FaissIndex {index_name} from {file}")
+
+    def add_elasticsearch_index(
+        self,
+        column: str,
+        index_name: Optional[str] = None,
+        host: Optional[str] = None,
+        port: Optional[int] = None,
+        es_client: Optional["Elasticsearch"] = None,
+        es_index_name: Optional[str] = None,
+        es_index_config: Optional[dict] = None,
+    ):
+        """Add a text index using ElasticSearch for fast retrieval.
+
+        Args:
+            column (`str`): The column of the documents to add to the index.
+            index_name (Optional `str`): The index_name/identifier of the index. This is the index name that is used to call `.get_nearest` or `.search`.
+                By default it corresponds to `column`.
+            host (Optional `str`, defaults to localhost):
+                host of where ElasticSearch is running
+            port (Optional `str`, defaults to 9200):
+                port of where ElasticSearch is running
+            es_client (Optional `elasticsearch.Elasticsearch`):
+                The elasticsearch client used to create the index if host and port are None.
+            es_index_name (Optional `str`): The elasticsearch index name used to create the index.
+            es_index_config (Optional `dict`):
+                The configuration of the elasticsearch index.
+                Default config is:
+
+        Config::
+
+            {
+                "settings": {
+                    "number_of_shards": 1,
+                    "analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}},
+                },
+                "mappings": {
+                    "properties": {
+                        "text": {
+                            "type": "text",
+                            "analyzer": "standard",
+                            "similarity": "BM25"
+                        },
+                    }
+                },
+            }
+        """
+        index_name = index_name if index_name is not None else column
+        es_index = ElasticSearchIndex(
+            host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config
+        )
+        es_index.add_documents(self, column=column)
+        self._indexes[index_name] = es_index
+
+    def load_elasticsearch_index(
+        self,
+        index_name: str,
+        es_index_name: str,
+        host: Optional[str] = None,
+        port: Optional[int] = None,
+        es_client: Optional["Elasticsearch"] = None,
+        es_index_config: Optional[dict] = None,
+    ):
+        """Load an existing text index using ElasticSearch for fast retrieval.
+
+        Args:
+            index_name (`str`):
+                The `index_name`/identifier of the index. This is the index name that is used to call `get_nearest` or `search`.
+            es_index_name (`str`):
+                The name of elasticsearch index to load.
+            host (`str`, *optional*, defaults to `localhost`):
+                Host of where ElasticSearch is running.
+            port (`str`, *optional*, defaults to `9200`):
+                Port of where ElasticSearch is running.
+            es_client (`elasticsearch.Elasticsearch`, *optional*):
+                The elasticsearch client used to create the index if host and port are `None`.
+            es_index_config (`dict`, *optional*):
+                The configuration of the elasticsearch index.
+                Default config is:
+                    ```
+                    {
+                        "settings": {
+                            "number_of_shards": 1,
+                            "analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}},
+                        },
+                        "mappings": {
+                            "properties": {
+                                "text": {
+                                    "type": "text",
+                                    "analyzer": "standard",
+                                    "similarity": "BM25"
+                                },
+                            }
+                        },
+                    }
+                    ```
+        """
+        self._indexes[index_name] = ElasticSearchIndex(
+            host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config
+        )
+
+    def drop_index(self, index_name: str):
+        """Drop the index with the specified column.
+
+        Args:
+            index_name (`str`):
+                The `index_name`/identifier of the index.
+        """
+        del self._indexes[index_name]
+
+    def search(self, index_name: str, query: Union[str, np.array], k: int = 10, **kwargs) -> SearchResults:
+        """Find the nearest examples indices in the dataset to the query.
+
+        Args:
+            index_name (`str`):
+                The name/identifier of the index.
+            query (`Union[str, np.ndarray]`):
+                The query as a string if `index_name` is a text index or as a numpy array if `index_name` is a vector index.
+            k (`int`):
+                The number of examples to retrieve.
+
+        Returns:
+            `(scores, indices)`:
+                A tuple of `(scores, indices)` where:
+                - **scores** (`List[List[float]`): the retrieval scores from either FAISS (`IndexFlatL2` by default) or ElasticSearch of the retrieved examples
+                - **indices** (`List[List[int]]`): the indices of the retrieved examples
+        """
+        self._check_index_is_initialized(index_name)
+        return self._indexes[index_name].search(query, k, **kwargs)
+
+    def search_batch(
+        self, index_name: str, queries: Union[List[str], np.array], k: int = 10, **kwargs
+    ) -> BatchedSearchResults:
+        """Find the nearest examples indices in the dataset to the query.
+
+        Args:
+            index_name (`str`):
+                The `index_name`/identifier of the index.
+            queries (`Union[List[str], np.ndarray]`):
+                The queries as a list of strings if `index_name` is a text index or as a numpy array if `index_name` is a vector index.
+            k (`int`):
+                The number of examples to retrieve per query.
+
+        Returns:
+            `(total_scores, total_indices)`:
+                A tuple of `(total_scores, total_indices)` where:
+                - **total_scores** (`List[List[float]`): the retrieval scores from either FAISS (`IndexFlatL2` by default) or ElasticSearch of the retrieved examples per query
+                - **total_indices** (`List[List[int]]`): the indices of the retrieved examples per query
+        """
+        self._check_index_is_initialized(index_name)
+        return self._indexes[index_name].search_batch(queries, k, **kwargs)
+
+    def get_nearest_examples(
+        self, index_name: str, query: Union[str, np.array], k: int = 10, **kwargs
+    ) -> NearestExamplesResults:
+        """Find the nearest examples in the dataset to the query.
+
+        Args:
+            index_name (`str`):
+                The index_name/identifier of the index.
+            query (`Union[str, np.ndarray]`):
+                The query as a string if `index_name` is a text index or as a numpy array if `index_name` is a vector index.
+            k (`int`):
+                The number of examples to retrieve.
+
+        Returns:
+            `(scores, examples)`:
+                A tuple of `(scores, examples)` where:
+                - **scores** (`List[float]`): the retrieval scores from either FAISS (`IndexFlatL2` by default) or ElasticSearch of the retrieved examples
+                - **examples** (`dict`): the retrieved examples
+        """
+        self._check_index_is_initialized(index_name)
+        scores, indices = self.search(index_name, query, k, **kwargs)
+        top_indices = [i for i in indices if i >= 0]
+        return NearestExamplesResults(scores[: len(top_indices)], self[top_indices])
+
+    def get_nearest_examples_batch(
+        self, index_name: str, queries: Union[List[str], np.array], k: int = 10, **kwargs
+    ) -> BatchedNearestExamplesResults:
+        """Find the nearest examples in the dataset to the query.
+
+        Args:
+            index_name (`str`):
+                The `index_name`/identifier of the index.
+            queries (`Union[List[str], np.ndarray]`):
+                The queries as a list of strings if `index_name` is a text index or as a numpy array if `index_name` is a vector index.
+            k (`int`):
+                The number of examples to retrieve per query.
+
+        Returns:
+            `(total_scores, total_examples)`:
+                A tuple of `(total_scores, total_examples)` where:
+                - **total_scores** (`List[List[float]`): the retrieval scores from either FAISS (`IndexFlatL2` by default) or ElasticSearch of the retrieved examples per query
+                - **total_examples** (`List[dict]`): the retrieved examples per query
+        """
+        self._check_index_is_initialized(index_name)
+        total_scores, total_indices = self.search_batch(index_name, queries, k, **kwargs)
+        total_scores = [
+            scores_i[: len([i for i in indices_i if i >= 0])]
+            for scores_i, indices_i in zip(total_scores, total_indices)
+        ]
+        total_samples = [self[[i for i in indices if i >= 0]] for indices in total_indices]
+        return BatchedNearestExamplesResults(total_scores, total_samples)

env-llmeval/lib/python3.10/site-packages/datasets/splits.py

@@ -0,0 +1,635 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""Splits related API."""
+
+import abc
+import collections
+import copy
+import dataclasses
+import re
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Union
+
+from .arrow_reader import FileInstructions, make_file_instructions
+from .naming import _split_re
+from .utils.py_utils import NonMutableDict, asdict
+
+
+@dataclass
+class SplitInfo:
+    name: str = dataclasses.field(default="", metadata={"include_in_asdict_even_if_is_default": True})
+    num_bytes: int = dataclasses.field(default=0, metadata={"include_in_asdict_even_if_is_default": True})
+    num_examples: int = dataclasses.field(default=0, metadata={"include_in_asdict_even_if_is_default": True})
+    shard_lengths: Optional[List[int]] = None
+
+    # Deprecated
+    # For backward compatibility, this field needs to always be included in files like
+    # dataset_infos.json and dataset_info.json files
+    # To do so, we always include it in the output of datasets.utils.py_utils.asdict(split_info)
+    dataset_name: Optional[str] = dataclasses.field(
+        default=None, metadata={"include_in_asdict_even_if_is_default": True}
+    )
+
+    @property
+    def file_instructions(self):
+        """Returns the list of dict(filename, take, skip)."""
+        # `self.dataset_name` is assigned in `SplitDict.add()`.
+        instructions = make_file_instructions(
+            name=self.dataset_name,
+            split_infos=[self],
+            instruction=str(self.name),
+        )
+        return instructions.file_instructions
+
+
+@dataclass
+class SubSplitInfo:
+    """Wrapper around a sub split info.
+    This class expose info on the subsplit:
+    ```
+    ds, info = datasets.load_dataset(..., split='train[75%:]', with_info=True)
+    info.splits['train[75%:]'].num_examples
+    ```
+    """
+
+    instructions: FileInstructions
+
+    @property
+    def num_examples(self):
+        """Returns the number of example in the subsplit."""
+        return self.instructions.num_examples
+
+    @property
+    def file_instructions(self):
+        """Returns the list of dict(filename, take, skip)."""
+        return self.instructions.file_instructions
+
+
+class SplitBase(metaclass=abc.ABCMeta):
+    # pylint: disable=line-too-long
+    """Abstract base class for Split compositionality.
+
+    See the
+    [guide on splits](../loading#slice-splits)
+    for more information.
+
+    There are three parts to the composition:
+        1) The splits are composed (defined, merged, split,...) together before
+             calling the `.as_dataset()` function. This is done with the `__add__`,
+             `__getitem__`, which return a tree of `SplitBase` (whose leaf
+             are the `NamedSplit` objects)
+
+        ```
+        split = datasets.Split.TRAIN + datasets.Split.TEST.subsplit(datasets.percent[:50])
+        ```
+
+        2) The `SplitBase` is forwarded to the `.as_dataset()` function
+             to be resolved into actual read instruction. This is done by the
+             `.get_read_instruction()` method which takes the real dataset splits
+             (name, number of shards,...) and parse the tree to return a
+             `SplitReadInstruction()` object
+
+        ```
+        read_instruction = split.get_read_instruction(self.info.splits)
+        ```
+
+        3) The `SplitReadInstruction` is then used in the `tf.data.Dataset` pipeline
+             to define which files to read and how to skip examples within file.
+
+    """
+
+    # pylint: enable=line-too-long
+
+    @abc.abstractmethod
+    def get_read_instruction(self, split_dict):
+        """Parse the descriptor tree and compile all read instructions together.
+
+        Args:
+            split_dict: `dict`, The `dict[split_name, SplitInfo]` of the dataset
+
+        Returns:
+            split_read_instruction: `SplitReadInstruction`
+        """
+        raise NotImplementedError("Abstract method")
+
+    def __eq__(self, other):
+        """Equality: datasets.Split.TRAIN == 'train'."""
+        if isinstance(other, (NamedSplit, str)):
+            return False
+        raise NotImplementedError("Equality is not implemented between merged/sub splits.")
+
+    def __ne__(self, other):
+        """InEquality: datasets.Split.TRAIN != 'test'."""
+        return not self.__eq__(other)
+
+    def __add__(self, other):
+        """Merging: datasets.Split.TRAIN + datasets.Split.TEST."""
+        return _SplitMerged(self, other)
+
+    def subsplit(self, arg=None, k=None, percent=None, weighted=None):  # pylint: disable=redefined-outer-name
+        """Divides this split into subsplits.
+
+        There are 3 ways to define subsplits, which correspond to the 3
+        arguments `k` (get `k` even subsplits), `percent` (get a slice of the
+        dataset with `datasets.percent`), and `weighted` (get subsplits with proportions
+        specified by `weighted`).
+
+        Example::
+
+        ```
+        # 50% train, 50% test
+        train, test = split.subsplit(k=2)
+        # 50% train, 25% test, 25% validation
+        train, test, validation = split.subsplit(weighted=[2, 1, 1])
+        # Extract last 20%
+        subsplit = split.subsplit(datasets.percent[-20:])
+        ```
+
+        Warning: k and weighted will be converted into percent which mean that
+        values below the percent will be rounded up or down. The final split may be
+        bigger to deal with remainders. For instance:
+
+        ```
+        train, test, valid = split.subsplit(k=3)  # 33%, 33%, 34%
+        s1, s2, s3, s4 = split.subsplit(weighted=[2, 2, 1, 1])  # 33%, 33%, 16%, 18%
+        ```
+
+        Args:
+            arg: If no kwargs are given, `arg` will be interpreted as one of
+                `k`, `percent`, or `weighted` depending on the type.
+                For example:
+                ```
+                split.subsplit(10)  # Equivalent to split.subsplit(k=10)
+                split.subsplit(datasets.percent[:-20])  # percent=datasets.percent[:-20]
+                split.subsplit([1, 1, 2])  # weighted=[1, 1, 2]
+                ```
+            k: `int` If set, subdivide the split into `k` equal parts.
+            percent: `datasets.percent slice`, return a single subsplit corresponding to
+                a slice of the original split. For example:
+                `split.subsplit(datasets.percent[-20:])  # Last 20% of the dataset`.
+            weighted: `list[int]`, return a list of subsplits whose proportions match
+                the normalized sum of the list. For example:
+                `split.subsplit(weighted=[1, 1, 2])  # 25%, 25%, 50%`.
+
+        Returns:
+            A subsplit or list of subsplits extracted from this split object.
+        """
+        # Note that the percent kwargs redefine the outer name datasets.percent. This
+        # is done for consistency (.subsplit(percent=datasets.percent[:40]))
+        if sum(bool(x) for x in (arg, k, percent, weighted)) != 1:
+            raise ValueError("Only one argument of subsplit should be set.")
+
+        # Auto deduce k
+        if isinstance(arg, int):
+            k = arg
+        elif isinstance(arg, slice):
+            percent = arg
+        elif isinstance(arg, list):
+            weighted = arg
+
+        if not (k or percent or weighted):
+            raise ValueError(
+                f"Invalid split argument {arg}. Only list, slice and int supported. "
+                "One of k, weighted or percent should be set to a non empty value."
+            )
+
+        def assert_slices_coverage(slices):
+            # Ensure that the expended slices cover all percents.
+            assert sum((list(range(*s.indices(100))) for s in slices), []) == list(range(100))
+
+        if k:
+            if not 0 < k <= 100:
+                raise ValueError(f"Subsplit k should be between 0 and 100, got {k}")
+            shift = 100 // k
+            slices = [slice(i * shift, (i + 1) * shift) for i in range(k)]
+            # Round up last element to ensure all elements are taken
+            slices[-1] = slice(slices[-1].start, 100)
+            # Internal check to ensure full coverage
+            assert_slices_coverage(slices)
+            return tuple(_SubSplit(self, s) for s in slices)
+        elif percent:
+            return _SubSplit(self, percent)
+        elif weighted:
+            # Normalize the weighted sum
+            total = sum(weighted)
+            weighted = [100 * x // total for x in weighted]
+            # Create the slice for each of the elements
+            start = 0
+            stop = 0
+            slices = []
+            for v in weighted:
+                stop += v
+                slices.append(slice(start, stop))
+                start = stop
+            # Round up last element to ensure all elements are taken
+            slices[-1] = slice(slices[-1].start, 100)
+            # Internal check to ensure full coverage
+            assert_slices_coverage(slices)
+            return tuple(_SubSplit(self, s) for s in slices)
+        else:
+            # Should not be possible
+            raise ValueError("Could not determine the split")
+
+
+# 2 requirements:
+# 1. datasets.percent be sliceable
+# 2. datasets.percent be documented
+#
+# Instances are not documented, so we want datasets.percent to be a class, but to
+# have it be sliceable, we need this metaclass.
+class PercentSliceMeta(type):
+    def __getitem__(cls, slice_value):
+        if not isinstance(slice_value, slice):
+            raise ValueError(f"datasets.percent should only be called with slice, not {slice_value}")
+        return slice_value
+
+
+class PercentSlice(metaclass=PercentSliceMeta):
+    # pylint: disable=line-too-long
+    """Syntactic sugar for defining slice subsplits: `datasets.percent[75:-5]`.
+
+    See the
+    [guide on splits](../loading#slice-splits)
+    for more information.
+    """
+
+    # pylint: enable=line-too-long
+    pass
+
+
+percent = PercentSlice  # pylint: disable=invalid-name
+
+
+class _SplitMerged(SplitBase):
+    """Represent two split descriptors merged together."""
+
+    def __init__(self, split1, split2):
+        self._split1 = split1
+        self._split2 = split2
+
+    def get_read_instruction(self, split_dict):
+        read_instruction1 = self._split1.get_read_instruction(split_dict)
+        read_instruction2 = self._split2.get_read_instruction(split_dict)
+        return read_instruction1 + read_instruction2
+
+    def __repr__(self):
+        return f"({repr(self._split1)} + {repr(self._split2)})"
+
+
+class _SubSplit(SplitBase):
+    """Represent a sub split of a split descriptor."""
+
+    def __init__(self, split, slice_value):
+        self._split = split
+        self._slice_value = slice_value
+
+    def get_read_instruction(self, split_dict):
+        return self._split.get_read_instruction(split_dict)[self._slice_value]
+
+    def __repr__(self):
+        slice_str = "{start}:{stop}"
+        if self._slice_value.step is not None:
+            slice_str += ":{step}"
+        slice_str = slice_str.format(
+            start="" if self._slice_value.start is None else self._slice_value.start,
+            stop="" if self._slice_value.stop is None else self._slice_value.stop,
+            step=self._slice_value.step,
+        )
+        return f"{repr(self._split)}(datasets.percent[{slice_str}])"
+
+
+class NamedSplit(SplitBase):
+    """Descriptor corresponding to a named split (train, test, ...).
+
+    Example:
+        Each descriptor can be composed with other using addition or slice:
+
+            ```py
+            split = datasets.Split.TRAIN.subsplit(datasets.percent[0:25]) + datasets.Split.TEST
+            ```
+
+        The resulting split will correspond to 25% of the train split merged with
+        100% of the test split.
+
+        A split cannot be added twice, so the following will fail:
+
+            ```py
+            split = (
+                    datasets.Split.TRAIN.subsplit(datasets.percent[:25]) +
+                    datasets.Split.TRAIN.subsplit(datasets.percent[75:])
+            )  # Error
+            split = datasets.Split.TEST + datasets.Split.ALL  # Error
+            ```
+
+        The slices can be applied only one time. So the following are valid:
+
+            ```py
+            split = (
+                    datasets.Split.TRAIN.subsplit(datasets.percent[:25]) +
+                    datasets.Split.TEST.subsplit(datasets.percent[:50])
+            )
+            split = (datasets.Split.TRAIN + datasets.Split.TEST).subsplit(datasets.percent[:50])
+            ```
+
+        But this is not valid:
+
+            ```py
+            train = datasets.Split.TRAIN
+            test = datasets.Split.TEST
+            split = train.subsplit(datasets.percent[:25]).subsplit(datasets.percent[:25])
+            split = (train.subsplit(datasets.percent[:25]) + test).subsplit(datasets.percent[:50])
+            ```
+    """
+
+    def __init__(self, name):
+        self._name = name
+        split_names_from_instruction = [split_instruction.split("[")[0] for split_instruction in name.split("+")]
+        for split_name in split_names_from_instruction:
+            if not re.match(_split_re, split_name):
+                raise ValueError(f"Split name should match '{_split_re}' but got '{split_name}'.")
+
+    def __str__(self):
+        return self._name
+
+    def __repr__(self):
+        return f"NamedSplit({self._name!r})"
+
+    def __eq__(self, other):
+        """Equality: datasets.Split.TRAIN == 'train'."""
+        if isinstance(other, NamedSplit):
+            return self._name == other._name  # pylint: disable=protected-access
+        elif isinstance(other, SplitBase):
+            return False
+        elif isinstance(other, str):  # Other should be string
+            return self._name == other
+        else:
+            raise ValueError(f"Equality not supported between split {self} and {other}")
+
+    def __lt__(self, other):
+        return self._name < other._name  # pylint: disable=protected-access
+
+    def __hash__(self):
+        return hash(self._name)
+
+    def get_read_instruction(self, split_dict):
+        return SplitReadInstruction(split_dict[self._name])
+
+
+class NamedSplitAll(NamedSplit):
+    """Split corresponding to the union of all defined dataset splits."""
+
+    def __init__(self):
+        super().__init__("all")
+
+    def __repr__(self):
+        return "NamedSplitAll()"
+
+    def get_read_instruction(self, split_dict):
+        # Merge all dataset split together
+        read_instructions = [SplitReadInstruction(s) for s in split_dict.values()]
+        return sum(read_instructions, SplitReadInstruction())
+
+
+class Split:
+    # pylint: disable=line-too-long
+    """`Enum` for dataset splits.
+
+    Datasets are typically split into different subsets to be used at various
+    stages of training and evaluation.
+
+    - `TRAIN`: the training data.
+    - `VALIDATION`: the validation data. If present, this is typically used as
+      evaluation data while iterating on a model (e.g. changing hyperparameters,
+      model architecture, etc.).
+    - `TEST`: the testing data. This is the data to report metrics on. Typically
+      you do not want to use this during model iteration as you may overfit to it.
+    - `ALL`: the union of all defined dataset splits.
+
+    All splits, including compositions inherit from `datasets.SplitBase`.
+
+    See the [guide](../load_hub#splits) on splits for more information.
+
+    Example:
+
+    ```py
+    >>> datasets.SplitGenerator(
+    ...     name=datasets.Split.TRAIN,
+    ...     gen_kwargs={"split_key": "train", "files": dl_manager.download_and extract(url)},
+    ... ),
+    ... datasets.SplitGenerator(
+    ...     name=datasets.Split.VALIDATION,
+    ...     gen_kwargs={"split_key": "validation", "files": dl_manager.download_and extract(url)},
+    ... ),
+    ... datasets.SplitGenerator(
+    ...     name=datasets.Split.TEST,
+    ...     gen_kwargs={"split_key": "test", "files": dl_manager.download_and extract(url)},
+    ... )
+    ```
+    """
+
+    # pylint: enable=line-too-long
+    TRAIN = NamedSplit("train")
+    TEST = NamedSplit("test")
+    VALIDATION = NamedSplit("validation")
+    ALL = NamedSplitAll()
+
+    def __new__(cls, name):
+        """Create a custom split with datasets.Split('custom_name')."""
+        return NamedSplitAll() if name == "all" else NamedSplit(name)
+
+
+# Similar to SplitInfo, but contain an additional slice info
+SlicedSplitInfo = collections.namedtuple(
+    "SlicedSplitInfo",
+    [
+        "split_info",
+        "slice_value",
+    ],
+)  # noqa: E231
+
+
+class SplitReadInstruction:
+    """Object containing the reading instruction for the dataset.
+
+    Similarly to `SplitDescriptor` nodes, this object can be composed with itself,
+    but the resolution happens instantaneously, instead of keeping track of the
+    tree, such as all instructions are compiled and flattened in a single
+    SplitReadInstruction object containing the list of files and slice to use.
+
+    Once resolved, the instructions can be accessed with:
+
+    ```
+    read_instructions.get_list_sliced_split_info()  # List of splits to use
+    ```
+
+    """
+
+    def __init__(self, split_info=None):
+        self._splits = NonMutableDict(error_msg="Overlap between splits. Split {key} has been added with " "itself.")
+
+        if split_info:
+            self.add(SlicedSplitInfo(split_info=split_info, slice_value=None))
+
+    def add(self, sliced_split):
+        """Add a SlicedSplitInfo the read instructions."""
+        # TODO(epot): Check that the number of examples per shard % 100 == 0
+        # Otherwise the slices value may be unbalanced and not exactly reflect the
+        # requested slice.
+        self._splits[sliced_split.split_info.name] = sliced_split
+
+    def __add__(self, other):
+        """Merging split together."""
+        # Will raise error if a split has already be added (NonMutableDict)
+        # TODO(epot): If a split is already added but there is no overlap between
+        # the slices, should merge the slices (ex: [:10] + [80:])
+        split_instruction = SplitReadInstruction()
+        split_instruction._splits.update(self._splits)  # pylint: disable=protected-access
+        split_instruction._splits.update(other._splits)  # pylint: disable=protected-access
+        return split_instruction
+
+    def __getitem__(self, slice_value):
+        """Sub-splits."""
+        # Will raise an error if a split has already been sliced
+        split_instruction = SplitReadInstruction()
+        for v in self._splits.values():
+            if v.slice_value is not None:
+                raise ValueError(f"Trying to slice Split {v.split_info.name} which has already been sliced")
+            v = v._asdict()
+            v["slice_value"] = slice_value
+            split_instruction.add(SlicedSplitInfo(**v))
+        return split_instruction
+
+    def get_list_sliced_split_info(self):
+        return list(self._splits.values())
+
+
+class SplitDict(dict):
+    """Split info object."""
+
+    def __init__(self, *args, dataset_name=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.dataset_name = dataset_name
+
+    def __getitem__(self, key: Union[SplitBase, str]):
+        # 1st case: The key exists: `info.splits['train']`
+        if str(key) in self:
+            return super().__getitem__(str(key))
+        # 2nd case: Uses instructions: `info.splits['train[50%]']`
+        else:
+            instructions = make_file_instructions(
+                name=self.dataset_name,
+                split_infos=self.values(),
+                instruction=key,
+            )
+            return SubSplitInfo(instructions)
+
+    def __setitem__(self, key: Union[SplitBase, str], value: SplitInfo):
+        if key != value.name:
+            raise ValueError(f"Cannot add elem. (key mismatch: '{key}' != '{value.name}')")
+        super().__setitem__(key, value)
+
+    def add(self, split_info: SplitInfo):
+        """Add the split info."""
+        if split_info.name in self:
+            raise ValueError(f"Split {split_info.name} already present")
+        split_info.dataset_name = self.dataset_name
+        super().__setitem__(split_info.name, split_info)
+
+    @property
+    def total_num_examples(self):
+        """Return the total number of examples."""
+        return sum(s.num_examples for s in self.values())
+
+    @classmethod
+    def from_split_dict(cls, split_infos: Union[List, Dict], dataset_name: Optional[str] = None):
+        """Returns a new SplitDict initialized from a Dict or List of `split_infos`."""
+        if isinstance(split_infos, dict):
+            split_infos = list(split_infos.values())
+
+        if dataset_name is None:
+            dataset_name = split_infos[0].get("dataset_name") if split_infos else None
+
+        split_dict = cls(dataset_name=dataset_name)
+
+        for split_info in split_infos:
+            if isinstance(split_info, dict):
+                split_info = SplitInfo(**split_info)
+            split_dict.add(split_info)
+
+        return split_dict
+
+    def to_split_dict(self):
+        """Returns a list of SplitInfo protos that we have."""
+        out = []
+        for split_name, split_info in self.items():
+            split_info = copy.deepcopy(split_info)
+            split_info.name = split_name
+            out.append(split_info)
+        return out
+
+    def copy(self):
+        return SplitDict.from_split_dict(self.to_split_dict(), self.dataset_name)
+
+    def _to_yaml_list(self) -> list:
+        out = [asdict(s) for s in self.to_split_dict()]
+        # we don't need the shard lengths in YAML, since it depends on max_shard_size and num_proc
+        for split_info_dict in out:
+            split_info_dict.pop("shard_lengths", None)
+        # we don't need the dataset_name attribute that is deprecated
+        for split_info_dict in out:
+            split_info_dict.pop("dataset_name", None)
+        return out
+
+    @classmethod
+    def _from_yaml_list(cls, yaml_data: list) -> "SplitDict":
+        return cls.from_split_dict(yaml_data)
+
+
+@dataclass
+class SplitGenerator:
+    """Defines the split information for the generator.
+
+    This should be used as returned value of
+    `GeneratorBasedBuilder._split_generators`.
+    See `GeneratorBasedBuilder._split_generators` for more info and example
+    of usage.
+
+    Args:
+        name (`str`):
+            Name of the `Split` for which the generator will
+            create the examples.
+        **gen_kwargs (additional keyword arguments):
+            Keyword arguments to forward to the `DatasetBuilder._generate_examples` method
+            of the builder.
+
+    Example:
+
+    ```py
+    >>> datasets.SplitGenerator(
+    ...     name=datasets.Split.TRAIN,
+    ...     gen_kwargs={"split_key": "train", "files": dl_manager.download_and_extract(url)},
+    ... )
+    ```
+    """
+
+    name: str
+    gen_kwargs: Dict = dataclasses.field(default_factory=dict)
+    split_info: SplitInfo = dataclasses.field(init=False)
+
+    def __post_init__(self):
+        self.name = str(self.name)  # Make sure we convert NamedSplits in strings
+        NamedSplit(self.name)  # check that it's a valid split name
+        self.split_info = SplitInfo(name=self.name)

env-llmeval/lib/python3.10/site-packages/datasets/streaming.py

@@ -0,0 +1,140 @@
+import importlib
+import inspect
+from functools import wraps
+from typing import TYPE_CHECKING, Optional
+
+from .download.download_config import DownloadConfig
+from .download.streaming_download_manager import (
+    xbasename,
+    xdirname,
+    xet_parse,
+    xexists,
+    xgetsize,
+    xglob,
+    xgzip_open,
+    xisdir,
+    xisfile,
+    xjoin,
+    xlistdir,
+    xnumpy_load,
+    xopen,
+    xpandas_read_csv,
+    xpandas_read_excel,
+    xPath,
+    xpyarrow_parquet_read_table,
+    xrelpath,
+    xsio_loadmat,
+    xsplit,
+    xsplitext,
+    xwalk,
+    xxml_dom_minidom_parse,
+)
+from .utils.logging import get_logger
+from .utils.patching import patch_submodule
+from .utils.py_utils import get_imports
+
+
+logger = get_logger(__name__)
+
+
+if TYPE_CHECKING:
+    from .builder import DatasetBuilder
+
+
+def extend_module_for_streaming(module_path, download_config: Optional[DownloadConfig] = None):
+    """Extend the module to support streaming.
+
+    We patch some functions in the module to use `fsspec` to support data streaming:
+    - We use `fsspec.open` to open and read remote files. We patch the module function:
+      - `open`
+    - We use the "::" hop separator to join paths and navigate remote compressed/archive files. We patch the module
+      functions:
+      - `os.path.join`
+      - `pathlib.Path.joinpath` and `pathlib.Path.__truediv__` (called when using the "/" operator)
+
+    The patched functions are replaced with custom functions defined to work with the
+    :class:`~download.streaming_download_manager.StreamingDownloadManager`.
+
+    Args:
+        module_path: Path to the module to be extended.
+        download_config : mainly use use_auth_token or storage_options to support different platforms and auth types.
+    """
+
+    module = importlib.import_module(module_path)
+
+    # TODO(QL): always update the module to add subsequent new authentication without removing old ones
+    if hasattr(module, "_patched_for_streaming") and module._patched_for_streaming:
+        if isinstance(module._patched_for_streaming, DownloadConfig):
+            module._patched_for_streaming.token = download_config.token
+            module._patched_for_streaming.storage_options = download_config.storage_options
+        return
+
+    def wrap_auth(function):
+        @wraps(function)
+        def wrapper(*args, **kwargs):
+            return function(*args, download_config=download_config, **kwargs)
+
+        wrapper._decorator_name_ = "wrap_auth"
+        return wrapper
+
+    # open files in a streaming fashion
+    patch_submodule(module, "open", wrap_auth(xopen)).start()
+    patch_submodule(module, "os.listdir", wrap_auth(xlistdir)).start()
+    patch_submodule(module, "os.walk", wrap_auth(xwalk)).start()
+    patch_submodule(module, "glob.glob", wrap_auth(xglob)).start()
+    # allow to navigate in remote zip files
+    patch_submodule(module, "os.path.join", xjoin).start()
+    patch_submodule(module, "os.path.dirname", xdirname).start()
+    patch_submodule(module, "os.path.basename", xbasename).start()
+    patch_submodule(module, "os.path.relpath", xrelpath).start()
+    patch_submodule(module, "os.path.split", xsplit).start()
+    patch_submodule(module, "os.path.splitext", xsplitext).start()
+    # allow checks on paths
+    patch_submodule(module, "os.path.exists", wrap_auth(xexists)).start()
+    patch_submodule(module, "os.path.isdir", wrap_auth(xisdir)).start()
+    patch_submodule(module, "os.path.isfile", wrap_auth(xisfile)).start()
+    patch_submodule(module, "os.path.getsize", wrap_auth(xgetsize)).start()
+    patch_submodule(module, "pathlib.Path", xPath).start()
+    # file readers
+    patch_submodule(module, "gzip.open", wrap_auth(xgzip_open)).start()
+    patch_submodule(module, "numpy.load", wrap_auth(xnumpy_load)).start()
+    patch_submodule(module, "pandas.read_csv", wrap_auth(xpandas_read_csv), attrs=["__version__"]).start()
+    patch_submodule(module, "pandas.read_excel", wrap_auth(xpandas_read_excel), attrs=["__version__"]).start()
+    patch_submodule(module, "scipy.io.loadmat", wrap_auth(xsio_loadmat), attrs=["__version__"]).start()
+    patch_submodule(module, "xml.etree.ElementTree.parse", wrap_auth(xet_parse)).start()
+    patch_submodule(module, "xml.dom.minidom.parse", wrap_auth(xxml_dom_minidom_parse)).start()
+    # pyarrow: do not patch pyarrow attribute in packaged modules
+    if not module.__name__.startswith("datasets.packaged_modules."):
+        patch_submodule(module, "pyarrow.parquet.read_table", wrap_auth(xpyarrow_parquet_read_table)).start()
+    module._patched_for_streaming = download_config
+
+
+def extend_dataset_builder_for_streaming(builder: "DatasetBuilder"):
+    """Extend the dataset builder module and the modules imported by it to support streaming.
+
+    Args:
+        builder (:class:`DatasetBuilder`): Dataset builder instance.
+    """
+    # this extends the open and os.path.join functions for data streaming
+    download_config = DownloadConfig(storage_options=builder.storage_options, token=builder.token)
+    extend_module_for_streaming(builder.__module__, download_config=download_config)
+    # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
+    if not builder.__module__.startswith("datasets."):  # check that it's not a packaged builder like csv
+        for imports in get_imports(inspect.getfile(builder.__class__)):
+            if imports[0] == "internal":
+                internal_import_name = imports[1]
+                internal_module_name = ".".join(builder.__module__.split(".")[:-1] + [internal_import_name])
+                extend_module_for_streaming(internal_module_name, download_config=download_config)
+
+    # builders can inherit from other builders that might use streaming functionality
+    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
+    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
+    from .builder import DatasetBuilder
+
+    parent_builder_modules = [
+        cls.__module__
+        for cls in type(builder).__mro__[1:]  # make sure it's not the same module we've already patched
+        if issubclass(cls, DatasetBuilder) and cls.__module__ != DatasetBuilder.__module__
+    ]  # check it's not a standard builder from datasets.builder
+    for module in parent_builder_modules:
+        extend_module_for_streaming(module, download_config=download_config)

env-llmeval/lib/python3.10/site-packages/datasets/table.py

@@ -0,0 +1,2360 @@
+import copy
+import os
+from functools import partial
+from itertools import groupby
+from typing import TYPE_CHECKING, Callable, Iterator, List, Optional, Tuple, TypeVar, Union
+
+import numpy as np
+import pyarrow as pa
+import pyarrow.compute as pc
+import pyarrow.types
+
+from . import config
+from .utils.logging import get_logger
+
+
+if TYPE_CHECKING:
+    from .features.features import Features, FeatureType
+
+
+logger = get_logger(__name__)
+
+
+def inject_arrow_table_documentation(arrow_table_method):
+    def wrapper(fn):
+        fn.__doc__ = arrow_table_method.__doc__ + (fn.__doc__ if fn.__doc__ is not None else "")
+        fn.__doc__ = fn.__doc__.replace("pyarrow.Table", "Table")
+        if hasattr(arrow_table_method, "__annotations__"):
+            fn.__annotations__ = arrow_table_method.__annotations__
+        return fn
+
+    return wrapper
+
+
+def _in_memory_arrow_table_from_file(filename: str) -> pa.Table:
+    in_memory_stream = pa.input_stream(filename)
+    opened_stream = pa.ipc.open_stream(in_memory_stream)
+    pa_table = opened_stream.read_all()
+    return pa_table
+
+
+def _in_memory_arrow_table_from_buffer(buffer: pa.Buffer) -> pa.Table:
+    stream = pa.BufferReader(buffer)
+    opened_stream = pa.ipc.open_stream(stream)
+    table = opened_stream.read_all()
+    return table
+
+
+def _memory_mapped_record_batch_reader_from_file(filename: str) -> pa.RecordBatchStreamReader:
+    memory_mapped_stream = pa.memory_map(filename)
+    return pa.ipc.open_stream(memory_mapped_stream)
+
+
+def read_schema_from_file(filename: str) -> pa.Schema:
+    """
+    Infer arrow table schema from file without loading whole file into memory.
+    Usefull especially while having very big files.
+    """
+    with pa.memory_map(filename) as memory_mapped_stream:
+        schema = pa.ipc.open_stream(memory_mapped_stream).schema
+    return schema
+
+
+def _memory_mapped_arrow_table_from_file(filename: str) -> pa.Table:
+    opened_stream = _memory_mapped_record_batch_reader_from_file(filename)
+    pa_table = opened_stream.read_all()
+    return pa_table
+
+
+def _deepcopy(x, memo: dict):
+    """deepcopy a regular class instance"""
+    cls = x.__class__
+    result = cls.__new__(cls)
+    memo[id(x)] = result
+    for k, v in x.__dict__.items():
+        setattr(result, k, copy.deepcopy(v, memo))
+    return result
+
+
+def _interpolation_search(arr: List[int], x: int) -> int:
+    """
+    Return the position i of a sorted array so that arr[i] <= x < arr[i+1]
+
+    Args:
+        arr (`List[int]`): non-empty sorted list of integers
+        x (`int`): query
+
+    Returns:
+        `int`: the position i so that arr[i] <= x < arr[i+1]
+
+    Raises:
+        `IndexError`: if the array is empty or if the query is outside the array values
+    """
+    i, j = 0, len(arr) - 1
+    while i < j and arr[i] <= x < arr[j]:
+        k = i + ((j - i) * (x - arr[i]) // (arr[j] - arr[i]))
+        if arr[k] <= x < arr[k + 1]:
+            return k
+        elif arr[k] < x:
+            i, j = k + 1, j
+        else:
+            i, j = i, k
+    raise IndexError(f"Invalid query '{x}' for size {arr[-1] if len(arr) else 'none'}.")
+
+
+class IndexedTableMixin:
+    def __init__(self, table: pa.Table):
+        self._schema: pa.Schema = table.schema
+        self._batches: List[pa.RecordBatch] = [
+            recordbatch for recordbatch in table.to_batches() if len(recordbatch) > 0
+        ]
+        self._offsets: np.ndarray = np.cumsum([0] + [len(b) for b in self._batches], dtype=np.int64)
+
+    def fast_gather(self, indices: Union[List[int], np.ndarray]) -> pa.Table:
+        """
+        Create a pa.Table by gathering the records at the records at the specified indices. Should be faster
+        than pa.concat_tables(table.fast_slice(int(i) % table.num_rows, 1) for i in indices) since NumPy can compute
+        the binary searches in parallel, highly optimized C
+        """
+        if not len(indices):
+            raise ValueError("Indices must be non-empty")
+        batch_indices = np.searchsorted(self._offsets, indices, side="right") - 1
+        return pa.Table.from_batches(
+            [
+                self._batches[batch_idx].slice(i - self._offsets[batch_idx], 1)
+                for batch_idx, i in zip(batch_indices, indices)
+            ],
+            schema=self._schema,
+        )
+
+    def fast_slice(self, offset=0, length=None) -> pa.Table:
+        """
+        Slice the Table using interpolation search.
+        The behavior is the same as `pyarrow.Table.slice` but it's significantly faster.
+
+        Interpolation search is used to find the start and end indexes of the batches we want to keep.
+        The batches to keep are then concatenated to form the sliced Table.
+        """
+        if offset < 0:
+            raise IndexError("Offset must be non-negative")
+        elif offset >= self._offsets[-1] or (length is not None and length <= 0):
+            return pa.Table.from_batches([], schema=self._schema)
+        i = _interpolation_search(self._offsets, offset)
+        if length is None or length + offset >= self._offsets[-1]:
+            batches = self._batches[i:]
+            batches[0] = batches[0].slice(offset - self._offsets[i])
+        else:
+            j = _interpolation_search(self._offsets, offset + length - 1)
+            batches = self._batches[i : j + 1]
+            batches[-1] = batches[-1].slice(0, offset + length - self._offsets[j])
+            batches[0] = batches[0].slice(offset - self._offsets[i])
+        return pa.Table.from_batches(batches, schema=self._schema)
+
+
+class Table(IndexedTableMixin):
+    """
+    Wraps a pyarrow Table by using composition.
+    This is the base class for `InMemoryTable`, `MemoryMappedTable` and `ConcatenationTable`.
+
+    It implements all the basic attributes/methods of the pyarrow Table class except
+    the Table transforms: `slice, filter, flatten, combine_chunks, cast, add_column,
+    append_column, remove_column, set_column, rename_columns` and `drop`.
+
+    The implementation of these methods differs for the subclasses.
+    """
+
+    def __init__(self, table: pa.Table):
+        super().__init__(table)
+        self.table = table
+
+    def __deepcopy__(self, memo: dict):
+        # arrow tables are immutable, so there's no need to copy self.table
+        # moreover calling deepcopy on a pyarrow table seems to make pa.total_allocated_bytes() decrease for some reason
+        # by adding it to the memo, self.table won't be copied
+        memo[id(self.table)] = self.table
+        # same for the recordbatches used by the index
+        memo[id(self._batches)] = list(self._batches)
+        return _deepcopy(self, memo)
+
+    def validate(self, *args, **kwargs):
+        """
+        Perform validation checks.  An exception is raised if validation fails.
+
+        By default only cheap validation checks are run.  Pass `full=True`
+        for thorough validation checks (potentially `O(n)`).
+
+        Args:
+            full (`bool`, defaults to `False`):
+                If `True`, run expensive checks, otherwise cheap checks only.
+
+        Raises:
+            `pa.lib.ArrowInvalid`: if validation fails
+        """
+        return self.table.validate(*args, **kwargs)
+
+    def equals(self, *args, **kwargs):
+        """
+        Check if contents of two tables are equal.
+
+        Args:
+            other ([`~datasets.table.Table`]):
+                Table to compare against.
+            check_metadata `bool`, defaults to `False`):
+                Whether schema metadata equality should be checked as well.
+
+        Returns:
+            `bool`
+        """
+        args = tuple(arg.table if isinstance(arg, Table) else arg for arg in args)
+        kwargs = {k: v.table if isinstance(v, Table) else v for k, v in kwargs}
+        return self.table.equals(*args, **kwargs)
+
+    def to_batches(self, *args, **kwargs):
+        """
+        Convert Table to list of (contiguous) `RecordBatch` objects.
+
+        Args:
+            max_chunksize (`int`, defaults to `None`):
+                Maximum size for `RecordBatch` chunks. Individual chunks may be
+                smaller depending on the chunk layout of individual columns.
+
+        Returns:
+            `List[pyarrow.RecordBatch]`
+        """
+        return self.table.to_batches(*args, **kwargs)
+
+    def to_pydict(self, *args, **kwargs):
+        """
+        Convert the Table to a `dict` or `OrderedDict`.
+
+        Returns:
+            `dict`
+        """
+        return self.table.to_pydict(*args, **kwargs)
+
+    def to_pylist(self, *args, **kwargs):
+        """
+        Convert the Table to a list
+
+        Returns:
+            `list`
+        """
+        return self.table.to_pylist(*args, **kwargs)
+
+    def to_pandas(self, *args, **kwargs):
+        """
+        Convert to a pandas-compatible NumPy array or DataFrame, as appropriate.
+
+        Args:
+            memory_pool (`MemoryPool`, defaults to `None`):
+                Arrow MemoryPool to use for allocations. Uses the default memory
+                pool is not passed.
+            strings_to_categorical (`bool`, defaults to `False`):
+                Encode string (UTF8) and binary types to `pandas.Categorical`.
+            categories (`list`, defaults to `empty`):
+                List of fields that should be returned as `pandas.Categorical`. Only
+                applies to table-like data structures.
+            zero_copy_only (`bool`, defaults to `False`):
+                Raise an `ArrowException` if this function call would require copying
+                the underlying data.
+            integer_object_nulls (`bool`, defaults to `False`):
+                Cast integers with nulls to objects.
+            date_as_object (`bool`, defaults to `True`):
+                Cast dates to objects. If `False`, convert to `datetime64[ns]` dtype.
+            timestamp_as_object (`bool`, defaults to `False`):
+                Cast non-nanosecond timestamps (`np.datetime64`) to objects. This is
+                useful if you have timestamps that don't fit in the normal date
+                range of nanosecond timestamps (1678 CE-2262 CE).
+                If `False`, all timestamps are converted to `datetime64[ns]` dtype.
+            use_threads (`bool`, defaults to `True`):
+                Whether to parallelize the conversion using multiple threads.
+            deduplicate_objects (`bool`, defaults to `False`):
+                Do not create multiple copies Python objects when created, to save
+                on memory use. Conversion will be slower.
+            ignore_metadata (`bool`, defaults to `False`):
+                If `True`, do not use the 'pandas' metadata to reconstruct the
+                DataFrame index, if present.
+            safe (`bool`, defaults to `True`):
+                For certain data types, a cast is needed in order to store the
+                data in a pandas DataFrame or Series (e.g. timestamps are always
+                stored as nanoseconds in pandas). This option controls whether it
+                is a safe cast or not.
+            split_blocks (`bool`, defaults to `False`):
+                If `True`, generate one internal "block" for each column when
+                creating a pandas.DataFrame from a `RecordBatch` or `Table`. While this
+                can temporarily reduce memory note that various pandas operations
+                can trigger "consolidation" which may balloon memory use.
+            self_destruct (`bool`, defaults to `False`):
+                EXPERIMENTAL: If `True`, attempt to deallocate the originating Arrow
+                memory while converting the Arrow object to pandas. If you use the
+                object after calling `to_pandas` with this option it will crash your
+                program.
+            types_mapper (`function`, defaults to `None`):
+                A function mapping a pyarrow DataType to a pandas `ExtensionDtype`.
+                This can be used to override the default pandas type for conversion
+                of built-in pyarrow types or in absence of `pandas_metadata` in the
+                Table schema. The function receives a pyarrow DataType and is
+                expected to return a pandas `ExtensionDtype` or `None` if the
+                default conversion should be used for that type. If you have
+                a dictionary mapping, you can pass `dict.get` as function.
+
+        Returns:
+            `pandas.Series` or `pandas.DataFrame`: `pandas.Series` or `pandas.DataFrame` depending on type of object
+        """
+        return self.table.to_pandas(*args, **kwargs)
+
+    def to_string(self, *args, **kwargs):
+        return self.table.to_string(*args, **kwargs)
+
+    def to_reader(self, max_chunksize: Optional[int] = None):
+        """
+        Convert the Table to a RecordBatchReader.
+
+        Note that this method is zero-copy, it merely exposes the same data under a different API.
+
+        Args:
+            max_chunksize (`int`, defaults to `None`)
+                Maximum size for RecordBatch chunks. Individual chunks may be smaller depending
+                on the chunk layout of individual columns.
+
+        Returns:
+            `pyarrow.RecordBatchReader`
+        """
+        return self.table.to_reader(max_chunksize=max_chunksize)
+
+    def field(self, *args, **kwargs):
+        """
+        Select a schema field by its column name or numeric index.
+
+        Args:
+            i (`Union[int, str]`):
+                The index or name of the field to retrieve.
+
+        Returns:
+            `pyarrow.Field`
+        """
+        return self.table.field(*args, **kwargs)
+
+    def column(self, *args, **kwargs):
+        """
+        Select a column by its column name, or numeric index.
+
+        Args:
+            i (`Union[int, str]`):
+                The index or name of the column to retrieve.
+
+        Returns:
+            `pyarrow.ChunkedArray`
+        """
+        return self.table.column(*args, **kwargs)
+
+    def itercolumns(self, *args, **kwargs):
+        """
+        Iterator over all columns in their numerical order.
+
+        Yields:
+            `pyarrow.ChunkedArray`
+        """
+        return self.table.itercolumns(*args, **kwargs)
+
+    @property
+    def schema(self):
+        """
+        Schema of the table and its columns.
+
+        Returns:
+            `pyarrow.Schema`
+        """
+        return self.table.schema
+
+    @property
+    def columns(self):
+        """
+        List of all columns in numerical order.
+
+        Returns:
+            `List[pa.ChunkedArray]`
+        """
+        return self.table.columns
+
+    @property
+    def num_columns(self):
+        """
+        Number of columns in this table.
+
+        Returns:
+            int
+        """
+        return self.table.num_columns
+
+    @property
+    def num_rows(self):
+        """
+        Number of rows in this table.
+
+        Due to the definition of a table, all columns have the same number of
+        rows.
+
+        Returns:
+            int
+        """
+        return self.table.num_rows
+
+    @property
+    def shape(self):
+        """
+        Dimensions of the table: (#rows, #columns).
+
+        Returns:
+            `(int, int)`: Number of rows and number of columns.
+        """
+        return self.table.shape
+
+    @property
+    def nbytes(self):
+        """
+        Total number of bytes consumed by the elements of the table.
+        """
+        return self.table.nbytes
+
+    @property
+    def column_names(self):
+        """
+        Names of the table's columns.
+        """
+        return self.table.column_names
+
+    def __eq__(self, other):
+        return self.equals(other)
+
+    def __getitem__(self, i):
+        return self.table[i]
+
+    def __len__(self):
+        return len(self.table)
+
+    def __repr__(self):
+        return self.table.__repr__().replace("pyarrow.Table", self.__class__.__name__)
+
+    def __str__(self):
+        return self.table.__str__().replace("pyarrow.Table", self.__class__.__name__)
+
+    def slice(self, *args, **kwargs):
+        """
+        Compute zero-copy slice of this Table.
+
+        Args:
+            offset (`int`, defaults to `0`):
+                Offset from start of table to slice.
+            length (`int`, defaults to `None`):
+                Length of slice (default is until end of table starting from
+                offset).
+
+        Returns:
+            `datasets.table.Table`
+        """
+        raise NotImplementedError()
+
+    def filter(self, *args, **kwargs):
+        """
+        Select records from a Table. See `pyarrow.compute.filter` for full usage.
+        """
+        raise NotImplementedError()
+
+    def flatten(self, *args, **kwargs):
+        """
+        Flatten this Table.  Each column with a struct type is flattened
+        into one column per struct field.  Other columns are left unchanged.
+
+        Args:
+            memory_pool (`MemoryPool`, defaults to `None`):
+                For memory allocations, if required, otherwise use default pool.
+
+        Returns:
+            `datasets.table.Table`
+        """
+        raise NotImplementedError()
+
+    def combine_chunks(self, *args, **kwargs):
+        """
+        Make a new table by combining the chunks this table has.
+
+        All the underlying chunks in the `ChunkedArray` of each column are
+        concatenated into zero or one chunk.
+
+        Args:
+            memory_pool (`MemoryPool`, defaults to `None`):
+                For memory allocations, if required, otherwise use default pool.
+
+        Returns:
+            `datasets.table.Table`
+        """
+        raise NotImplementedError()
+
+    def cast(self, *args, **kwargs):
+        """
+        Cast table values to another schema.
+
+        Args:
+            target_schema (`Schema`):
+                Schema to cast to, the names and order of fields must match.
+            safe (`bool`, defaults to `True`):
+                Check for overflows or other unsafe conversions.
+
+        Returns:
+            `datasets.table.Table`
+        """
+        raise NotImplementedError()
+
+    def replace_schema_metadata(self, *args, **kwargs):
+        """
+        EXPERIMENTAL: Create shallow copy of table by replacing schema
+        key-value metadata with the indicated new metadata (which may be None,
+        which deletes any existing metadata
+
+        Args:
+            metadata (`dict`, defaults to `None`):
+
+        Returns:
+            `datasets.table.Table`: shallow_copy
+        """
+        raise NotImplementedError()
+
+    def add_column(self, *args, **kwargs):
+        """
+        Add column to Table at position.
+
+        A new table is returned with the column added, the original table
+        object is left unchanged.
+
+        Args:
+            i (`int`):
+                Index to place the column at.
+            field_ (`Union[str, pyarrow.Field]`):
+                If a string is passed then the type is deduced from the column
+                data.
+            column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
+                Column data.
+
+        Returns:
+            `datasets.table.Table`: New table with the passed column added.
+        """
+        raise NotImplementedError()
+
+    def append_column(self, *args, **kwargs):
+        """
+        Append column at end of columns.
+
+        Args:
+            field_ (`Union[str, pyarrow.Field]`):
+                If a string is passed then the type is deduced from the column
+                data.
+            column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
+                Column data.
+
+        Returns:
+            `datasets.table.Table`:  New table with the passed column added.
+        """
+        raise NotImplementedError()
+
+    def remove_column(self, *args, **kwargs):
+        """
+        Create new Table with the indicated column removed.
+
+        Args:
+            i (`int`):
+                Index of column to remove.
+
+        Returns:
+            `datasets.table.Table`: New table without the column.
+        """
+        raise NotImplementedError()
+
+    def set_column(self, *args, **kwargs):
+        """
+        Replace column in Table at position.
+
+        Args:
+            i (`int`):
+                Index to place the column at.
+            field_ (`Union[str, pyarrow.Field]`):
+                If a string is passed then the type is deduced from the column
+                data.
+            column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
+                Column data.
+
+        Returns:
+            `datasets.table.Table`: New table with the passed column set.
+        """
+        raise NotImplementedError()
+
+    def rename_columns(self, *args, **kwargs):
+        """
+        Create new table with columns renamed to provided names.
+        """
+        raise NotImplementedError()
+
+    def drop(self, *args, **kwargs):
+        """
+        Drop one or more columns and return a new table.
+
+        Args:
+            columns (`List[str]`):
+                List of field names referencing existing columns.
+
+        Raises:
+            `KeyError` : if any of the passed columns name are not existing.
+
+        Returns:
+            `datasets.table.Table`: New table without the columns.
+        """
+        raise NotImplementedError()
+
+    def select(self, *args, **kwargs):
+        """
+        Select columns of the table.
+
+        Returns a new table with the specified columns, and metadata preserved.
+
+        Args:
+            columns (:obj:`Union[List[str], List[int]]`):
+                The column names or integer indices to select.
+
+        Returns:
+            `datasets.table.Table`: table with only a subset of the columns
+        """
+        raise NotImplementedError()
+
+
+class TableBlock(Table):
+    """
+    `TableBlock` is the allowed class inside a `ConcanetationTable`.
+    Only `MemoryMappedTable` and `InMemoryTable` are `TableBlock`.
+    This is because we don't want a `ConcanetationTable` made out of other `ConcanetationTables`.
+    """
+
+    pass
+
+
+class InMemoryTable(TableBlock):
+    """
+    The table is said in-memory when it is loaded into the user's RAM.
+
+    Pickling it does copy all the data using memory.
+    Its implementation is simple and uses the underlying pyarrow Table methods directly.
+
+    This is different from the `MemoryMapped` table, for which pickling doesn't copy all the
+    data in memory. For a `MemoryMapped`, unpickling instead reloads the table from the disk.
+
+    `InMemoryTable` must be used when data fit in memory, while `MemoryMapped` are reserved for
+    data bigger than memory or when you want the memory footprint of your application to
+    stay low.
+    """
+
+    @classmethod
+    def from_file(cls, filename: str):
+        table = _in_memory_arrow_table_from_file(filename)
+        return cls(table)
+
+    @classmethod
+    def from_buffer(cls, buffer: pa.Buffer):
+        table = _in_memory_arrow_table_from_buffer(buffer)
+        return cls(table)
+
+    @classmethod
+    def from_pandas(cls, *args, **kwargs):
+        """
+        Convert pandas.DataFrame to an Arrow Table.
+
+        The column types in the resulting Arrow Table are inferred from the
+        dtypes of the pandas.Series in the DataFrame. In the case of non-object
+        Series, the NumPy dtype is translated to its Arrow equivalent. In the
+        case of `object`, we need to guess the datatype by looking at the
+        Python objects in this Series.
+
+        Be aware that Series of the `object` dtype don't carry enough
+        information to always lead to a meaningful Arrow type. In the case that
+        we cannot infer a type, e.g. because the DataFrame is of length 0 or
+        the Series only contains `None/nan` objects, the type is set to
+        null. This behavior can be avoided by constructing an explicit schema
+        and passing it to this function.
+
+        Args:
+            df (`pandas.DataFrame`):
+            schema (`pyarrow.Schema`, *optional*):
+                The expected schema of the Arrow Table. This can be used to
+                indicate the type of columns if we cannot infer it automatically.
+                If passed, the output will have exactly this schema. Columns
+                specified in the schema that are not found in the DataFrame columns
+                or its index will raise an error. Additional columns or index
+                levels in the DataFrame which are not specified in the schema will
+                be ignored.
+            preserve_index (`bool`, *optional*):
+                Whether to store the index as an additional column in the resulting
+                `Table`. The default of None will store the index as a column,
+                except for RangeIndex which is stored as metadata only. Use
+                `preserve_index=True` to force it to be stored as a column.
+            nthreads (`int`, defaults to `None` (may use up to system CPU count threads))
+                If greater than 1, convert columns to Arrow in parallel using
+                indicated number of threads.
+            columns (`List[str]`, *optional*):
+               List of column to be converted. If `None`, use all columns.
+            safe (`bool`, defaults to `True`):
+               Check for overflows or other unsafe conversions,
+
+        Returns:
+            `datasets.table.Table`:
+
+        Examples:
+        ```python
+        >>> import pandas as pd
+        >>> import pyarrow as pa
+        >>> df = pd.DataFrame({
+            ...     'int': [1, 2],
+            ...     'str': ['a', 'b']
+            ... })
+        >>> pa.Table.from_pandas(df)
+        <pyarrow.lib.Table object at 0x7f05d1fb1b40>
+        ```
+        """
+        return cls(pa.Table.from_pandas(*args, **kwargs))
+
+    @classmethod
+    def from_arrays(cls, *args, **kwargs):
+        """
+        Construct a Table from Arrow arrays.
+
+        Args:
+            arrays (`List[Union[pyarrow.Array, pyarrow.ChunkedArray]]`):
+                Equal-length arrays that should form the table.
+            names (`List[str]`, *optional*):
+                Names for the table columns. If not passed, schema must be passed.
+            schema (`Schema`, defaults to `None`):
+                Schema for the created table. If not passed, names must be passed.
+            metadata (`Union[dict, Mapping]`, defaults to `None`):
+                Optional metadata for the schema (if inferred).
+
+        Returns:
+            `datasets.table.Table`
+        """
+        return cls(pa.Table.from_arrays(*args, **kwargs))
+
+    @classmethod
+    def from_pydict(cls, *args, **kwargs):
+        """
+        Construct a Table from Arrow arrays or columns.
+
+        Args:
+            mapping (`Union[dict, Mapping]`):
+                A mapping of strings to Arrays or Python lists.
+            schema (`Schema`, defaults to `None`):
+                If not passed, will be inferred from the Mapping values
+            metadata (`Union[dict, Mapping]`, defaults to `None`):
+                Optional metadata for the schema (if inferred).
+
+        Returns:
+            `datasets.table.Table`
+        """
+        return cls(pa.Table.from_pydict(*args, **kwargs))
+
+    @classmethod
+    def from_pylist(cls, mapping, *args, **kwargs):
+        """
+        Construct a Table from list of rows / dictionaries.
+
+        Args:
+            mapping (`List[dict]`):
+                A mapping of strings to row values.
+            schema (`Schema`, defaults to `None`):
+                If not passed, will be inferred from the Mapping values
+            metadata (`Union[dict, Mapping]`, defaults to `None`):
+                Optional metadata for the schema (if inferred).
+
+        Returns:
+            `datasets.table.Table`
+        """
+        return cls(pa.Table.from_pylist(mapping, *args, **kwargs))
+
+    @classmethod
+    def from_batches(cls, *args, **kwargs):
+        """
+        Construct a Table from a sequence or iterator of Arrow `RecordBatches`.
+
+        Args:
+            batches (`Union[Sequence[pyarrow.RecordBatch], Iterator[pyarrow.RecordBatch]]`):
+                Sequence of `RecordBatch` to be converted, all schemas must be equal.
+            schema (`Schema`, defaults to `None`):
+                If not passed, will be inferred from the first `RecordBatch`.
+
+        Returns:
+            `datasets.table.Table`:
+        """
+        return cls(pa.Table.from_batches(*args, **kwargs))
+
+    def slice(self, offset=0, length=None):
+        """
+        Compute zero-copy slice of this Table.
+
+        Args:
+            offset (`int`, defaults to `0`):
+                Offset from start of table to slice.
+            length (`int`, defaults to `None`):
+                Length of slice (default is until end of table starting from
+                offset).
+
+        Returns:
+            `datasets.table.Table`
+        """
+        # Use fast slicing here
+        return InMemoryTable(self.fast_slice(offset=offset, length=length))
+
+    def filter(self, *args, **kwargs):
+        """
+        Select records from a Table. See `pyarrow.compute.filter` for full usage.
+        """
+        return InMemoryTable(self.table.filter(*args, **kwargs))
+
+    def flatten(self, *args, **kwargs):
+        """
+        Flatten this Table.  Each column with a struct type is flattened
+        into one column per struct field.  Other columns are left unchanged.
+
+        Args:
+            memory_pool (`MemoryPool`, defaults to `None`):
+                For memory allocations, if required, otherwise use default pool.
+
+        Returns:
+            `datasets.table.Table`
+        """
+        return InMemoryTable(table_flatten(self.table, *args, **kwargs))
+
+    def combine_chunks(self, *args, **kwargs):
+        """
+        Make a new table by combining the chunks this table has.
+
+        All the underlying chunks in the `ChunkedArray` of each column are
+        concatenated into zero or one chunk.
+
+        Args:
+            memory_pool (`MemoryPool`, defaults to `None`):
+                For memory allocations, if required, otherwise use default pool.
+
+        Returns:
+            `datasets.table.Table`
+        """
+        return InMemoryTable(self.table.combine_chunks(*args, **kwargs))
+
+    def cast(self, *args, **kwargs):
+        """
+        Cast table values to another schema.
+
+        Args:
+            target_schema (`Schema`):
+                Schema to cast to, the names and order of fields must match.
+            safe (`bool`, defaults to `True`):
+                Check for overflows or other unsafe conversions.
+
+        Returns:
+            `datasets.table.Table`
+        """
+        return InMemoryTable(table_cast(self.table, *args, **kwargs))
+
+    def replace_schema_metadata(self, *args, **kwargs):
+        """
+        EXPERIMENTAL: Create shallow copy of table by replacing schema
+        key-value metadata with the indicated new metadata (which may be `None`,
+        which deletes any existing metadata).
+
+        Args:
+            metadata (`dict`, defaults to `None`):
+
+        Returns:
+            `datasets.table.Table`: shallow_copy
+        """
+        return InMemoryTable(self.table.replace_schema_metadata(*args, **kwargs))
+
+    def add_column(self, *args, **kwargs):
+        """
+        Add column to Table at position.
+
+        A new table is returned with the column added, the original table
+        object is left unchanged.
+
+        Args:
+            i (`int`):
+                Index to place the column at.
+            field_ (`Union[str, pyarrow.Field]`):
+                If a string is passed then the type is deduced from the column
+                data.
+            column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
+                Column data.
+
+        Returns:
+            `datasets.table.Table`: New table with the passed column added.
+        """
+        return InMemoryTable(self.table.add_column(*args, **kwargs))
+
+    def append_column(self, *args, **kwargs):
+        """
+        Append column at end of columns.
+
+        Args:
+            field_ (`Union[str, pyarrow.Field]`):
+                If a string is passed then the type is deduced from the column
+                data.
+            column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
+                Column data.
+
+        Returns:
+            `datasets.table.Table`:
+                New table with the passed column added.
+        """
+        return InMemoryTable(self.table.append_column(*args, **kwargs))
+
+    def remove_column(self, *args, **kwargs):
+        """
+        Create new Table with the indicated column removed.
+
+        Args:
+            i (`int`):
+                Index of column to remove.
+
+        Returns:
+            `datasets.table.Table`:
+                New table without the column.
+        """
+        return InMemoryTable(self.table.remove_column(*args, **kwargs))
+
+    def set_column(self, *args, **kwargs):
+        """
+        Replace column in Table at position.
+
+        Args:
+            i (`int`):
+                Index to place the column at.
+            field_ (`Union[str, pyarrow.Field]`):
+                If a string is passed then the type is deduced from the column
+                data.
+            column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
+                Column data.
+
+        Returns:
+            `datasets.table.Table`:
+                New table with the passed column set.
+        """
+        return InMemoryTable(self.table.set_column(*args, **kwargs))
+
+    def rename_columns(self, *args, **kwargs):
+        """
+        Create new table with columns renamed to provided names.
+        """
+        return InMemoryTable(self.table.rename_columns(*args, **kwargs))
+
+    def drop(self, *args, **kwargs):
+        """
+        Drop one or more columns and return a new table.
+
+        Args:
+            columns (`List[str]`):
+                List of field names referencing existing columns.
+
+        Raises:
+            `KeyError` : if any of the passed columns name are not existing.
+
+        Returns:
+            `datasets.table.Table`:
+                New table without the columns.
+        """
+        return InMemoryTable(self.table.drop(*args, **kwargs))
+
+    def select(self, *args, **kwargs):
+        """
+        Select columns of the table.
+
+        Returns a new table with the specified columns, and metadata preserved.
+
+        Args:
+            columns (:obj:`Union[List[str], List[int]]`):
+                The column names or integer indices to select.
+
+        Returns:
+            :class:`datasets.table.Table`: New table with the specified columns, and metadata preserved.
+        """
+        return InMemoryTable(self.table.select(*args, **kwargs))
+
+
+# The MemoryMappedTable needs replays to properly reload tables from the disk
+Replay = Tuple[str, tuple, dict]
+
+
+class MemoryMappedTable(TableBlock):
+    """
+    The table is said memory mapped when it doesn't use the user's RAM but loads the data
+    from the disk instead.
+
+    Pickling it doesn't copy the data into memory.
+    Instead, only the path to the memory mapped arrow file is pickled, as well as the list
+    of transforms to "replay" when reloading the table from the disk.
+
+    Its implementation requires to store an history of all the transforms that were applied
+    to the underlying pyarrow Table, so that they can be "replayed" when reloading the Table
+    from the disk.
+
+    This is different from the `InMemoryTable` table, for which pickling does copy all the
+    data in memory.
+
+    `InMemoryTable` must be used when data fit in memory, while `MemoryMapped` are reserved for
+    data bigger than memory or when you want the memory footprint of your application to
+    stay low.
+    """
+
+    def __init__(self, table: pa.Table, path: str, replays: Optional[List[Replay]] = None):
+        super().__init__(table)
+        self.path = os.path.abspath(path)
+        self.replays: List[Replay] = replays if replays is not None else []
+
+    @classmethod
+    def from_file(cls, filename: str, replays=None):
+        table = _memory_mapped_arrow_table_from_file(filename)
+        table = cls._apply_replays(table, replays)
+        return cls(table, filename, replays)
+
+    def __getstate__(self):
+        return {"path": self.path, "replays": self.replays}
+
+    def __setstate__(self, state):
+        path = state["path"]
+        replays = state["replays"]
+        table = _memory_mapped_arrow_table_from_file(path)
+        table = self._apply_replays(table, replays)
+        MemoryMappedTable.__init__(self, table, path=path, replays=replays)
+
+    @staticmethod
+    def _apply_replays(table: pa.Table, replays: Optional[List[Replay]] = None) -> pa.Table:
+        if replays is not None:
+            for name, args, kwargs in replays:
+                if name == "cast":
+                    table = table_cast(table, *args, **kwargs)
+                elif name == "flatten":
+                    table = table_flatten(table, *args, **kwargs)
+                else:
+                    table = getattr(table, name)(*args, **kwargs)
+        return table
+
+    def _append_replay(self, replay: Replay) -> List[Replay]:
+        replays = copy.deepcopy(self.replays)
+        replays.append(replay)
+        return replays
+
+    def slice(self, offset=0, length=None):
+        """
+        Compute zero-copy slice of this Table.
+
+        Args:
+            offset (`int`, defaults to `0`):
+                Offset from start of table to slice.
+            length (`int`, defaults to `None`):
+                Length of slice (default is until end of table starting from
+                offset).
+
+        Returns:
+            `datasets.table.Table`
+        """
+        replay = ("slice", (offset, length), {})
+        replays = self._append_replay(replay)
+        # Use fast slicing here
+        return MemoryMappedTable(self.fast_slice(offset=offset, length=length), self.path, replays)
+
+    def filter(self, *args, **kwargs):
+        """
+        Select records from a Table. See `pyarrow.compute.filter` for full usage.
+        """
+        replay = ("filter", copy.deepcopy(args), copy.deepcopy(kwargs))
+        replays = self._append_replay(replay)
+        return MemoryMappedTable(self.table.filter(*args, **kwargs), self.path, replays)
+
+    def flatten(self, *args, **kwargs):
+        """
+        Flatten this Table.  Each column with a struct type is flattened
+        into one column per struct field.  Other columns are left unchanged.
+
+        Args:
+            memory_pool (`MemoryPool`, defaults to `None`):
+                For memory allocations, if required, otherwise use default pool.
+
+        Returns:
+            `datasets.table.Table`
+        """
+        replay = ("flatten", copy.deepcopy(args), copy.deepcopy(kwargs))
+        replays = self._append_replay(replay)
+        return MemoryMappedTable(table_flatten(self.table, *args, **kwargs), self.path, replays)
+
+    def combine_chunks(self, *args, **kwargs):
+        """
+        Make a new table by combining the chunks this table has.
+
+        All the underlying chunks in the ChunkedArray of each column are
+        concatenated into zero or one chunk.
+
+        Args:
+            memory_pool (`MemoryPool`, defaults to `None`):
+                For memory allocations, if required, otherwise use default pool.
+
+        Returns:
+            `datasets.table.Table`
+        """
+        replay = ("combine_chunks", copy.deepcopy(args), copy.deepcopy(kwargs))
+        replays = self._append_replay(replay)
+        return MemoryMappedTable(self.table.combine_chunks(*args, **kwargs), self.path, replays)
+
+    def cast(self, *args, **kwargs):
+        """
+        Cast table values to another schema
+
+        Args:
+            target_schema (`Schema`):
+                Schema to cast to, the names and order of fields must match.
+            safe (`bool`, defaults to `True`):
+                Check for overflows or other unsafe conversions.
+
+        Returns:
+            `datasets.table.Table`
+        """
+        replay = ("cast", copy.deepcopy(args), copy.deepcopy(kwargs))
+        replays = self._append_replay(replay)
+        return MemoryMappedTable(table_cast(self.table, *args, **kwargs), self.path, replays)
+
+    def replace_schema_metadata(self, *args, **kwargs):
+        """
+        EXPERIMENTAL: Create shallow copy of table by replacing schema
+        key-value metadata with the indicated new metadata (which may be None,
+        which deletes any existing metadata.
+
+        Args:
+            metadata (`dict`, defaults to `None`):
+
+        Returns:
+            `datasets.table.Table`: shallow_copy
+        """
+        replay = ("replace_schema_metadata", copy.deepcopy(args), copy.deepcopy(kwargs))
+        replays = self._append_replay(replay)
+        return MemoryMappedTable(self.table.replace_schema_metadata(*args, **kwargs), self.path, replays)
+
+    def add_column(self, *args, **kwargs):
+        """
+        Add column to Table at position.
+
+        A new table is returned with the column added, the original table
+        object is left unchanged.
+
+        Args:
+            i (`int`):
+                Index to place the column at.
+            field_ (`Union[str, pyarrow.Field]`):
+                If a string is passed then the type is deduced from the column
+                data.
+            column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
+                Column data.
+
+        Returns:
+            `datasets.table.Table`: New table with the passed column added.
+        """
+        replay = ("add_column", copy.deepcopy(args), copy.deepcopy(kwargs))
+        replays = self._append_replay(replay)
+        return MemoryMappedTable(self.table.add_column(*args, **kwargs), self.path, replays)
+
+    def append_column(self, *args, **kwargs):
+        """
+        Append column at end of columns.
+
+        Args:
+            field_ (`Union[str, pyarrow.Field]`):
+                If a string is passed then the type is deduced from the column
+                data.
+            column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
+                Column data.
+
+        Returns:
+            `datasets.table.Table`:
+                New table with the passed column added.
+        """
+        replay = ("append_column", copy.deepcopy(args), copy.deepcopy(kwargs))
+        replays = self._append_replay(replay)
+        return MemoryMappedTable(self.table.append_column(*args, **kwargs), self.path, replays)
+
+    def remove_column(self, *args, **kwargs):
+        """
+        Create new Table with the indicated column removed.
+
+        Args:
+            i (`int`):
+                Index of column to remove.
+
+        Returns:
+            `datasets.table.Table`:
+                New table without the column.
+        """
+        replay = ("remove_column", copy.deepcopy(args), copy.deepcopy(kwargs))
+        replays = self._append_replay(replay)
+        return MemoryMappedTable(self.table.remove_column(*args, **kwargs), self.path, replays)
+
+    def set_column(self, *args, **kwargs):
+        """
+        Replace column in Table at position.
+
+        Args:
+            i (`int`):
+                Index to place the column at.
+            field_ (`Union[str, pyarrow.Field]`):
+                If a string is passed then the type is deduced from the column
+                data.
+            column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
+                Column data.
+
+        Returns:
+            `datasets.table.Table`:
+                New table with the passed column set.
+        """
+        replay = ("set_column", copy.deepcopy(args), copy.deepcopy(kwargs))
+        replays = self._append_replay(replay)
+        return MemoryMappedTable(self.table.set_column(*args, **kwargs), self.path, replays)
+
+    def rename_columns(self, *args, **kwargs):
+        """
+        Create new table with columns renamed to provided names.
+        """
+        replay = ("rename_columns", copy.deepcopy(args), copy.deepcopy(kwargs))
+        replays = self._append_replay(replay)
+        return MemoryMappedTable(self.table.rename_columns(*args, **kwargs), self.path, replays)
+
+    def drop(self, *args, **kwargs):
+        """
+        Drop one or more columns and return a new table.
+
+        Args:
+            columns (`List[str]`):
+                List of field names referencing existing columns.
+
+        Raises:
+            `KeyError` : if any of the passed columns name are not existing.
+
+        Returns:
+            `datasets.table.Table`:
+                New table without the columns.
+        """
+        replay = ("drop", copy.deepcopy(args), copy.deepcopy(kwargs))
+        replays = self._append_replay(replay)
+        return MemoryMappedTable(self.table.drop(*args, **kwargs), self.path, replays)
+
+    def select(self, *args, **kwargs):
+        """
+        Select columns of the table.
+
+        Returns a new table with the specified columns, and metadata preserved.
+
+        Args:
+            columns (:obj:`Union[List[str], List[int]]`):
+                The column names or integer indices to select.
+
+        Returns:
+            :class:`datasets.table.Table`: New table with the specified columns, and metadata preserved.
+        """
+        replay = ("select", copy.deepcopy(args), copy.deepcopy(kwargs))
+        replays = self._append_replay(replay)
+        return MemoryMappedTable(self.table.select(*args, **kwargs), self.path, replays)
+
+
+# A ConcatenationTable is the concatenation of several tables.
+# The ``blocks`` attributes stores a list of list of blocks.
+# The first axis concatenates the tables along the axis 0 (it appends rows),
+# while the second axis concatenates tables along the axis 1 (it appends columns).
+TableBlockContainer = TypeVar("TableBlockContainer", TableBlock, List[TableBlock], List[List[TableBlock]])
+
+
+class ConcatenationTable(Table):
+    """
+    The table comes from the concatenation of several tables called blocks.
+    It enables concatenation on both axis 0 (append rows) and axis 1 (append columns).
+
+    The underlying tables are called "blocks" and can be either `InMemoryTable`
+    or `MemoryMappedTable` objects.
+    This allows to combine tables that come from memory or that are memory mapped.
+    When a `ConcatenationTable` is pickled, then each block is pickled:
+    - the `InMemoryTable` objects are pickled by copying all the data in memory.
+    - the MemoryMappedTable objects are pickled without copying the data into memory.
+    Instead, only the path to the memory mapped arrow file is pickled, as well as the list
+    of transforms to "replays" when reloading the table from the disk.
+
+    Its implementation requires to store each block separately.
+    The `blocks` attributes stores a list of list of blocks.
+    The first axis concatenates the tables along the axis 0 (it appends rows),
+    while the second axis concatenates tables along the axis 1 (it appends columns).
+
+    If some columns are missing when concatenating on axis 0, they are filled with null values.
+    This is done using `pyarrow.concat_tables(tables, promote=True)`.
+
+    You can access the fully combined table by accessing the `ConcatenationTable.table` attribute,
+    and the blocks by accessing the `ConcatenationTable.blocks` attribute.
+    """
+
+    def __init__(self, table: pa.Table, blocks: List[List[TableBlock]]):
+        super().__init__(table)
+        self.blocks = blocks
+        # Check that all the blocks have the right type.
+        # Only InMemoryTable and MemoryMappedTable are allowed.
+        for subtables in blocks:
+            for subtable in subtables:
+                if not isinstance(subtable, TableBlock):
+                    raise TypeError(
+                        "The blocks of a ConcatenationTable must be InMemoryTable or MemoryMappedTable objects"
+                        f", but got {subtable}."
+                    )
+
+    def __getstate__(self):
+        return {"blocks": self.blocks}
+
+    def __setstate__(self, state):
+        blocks = state["blocks"]
+        table = self._concat_blocks_horizontally_and_vertically(blocks)
+        ConcatenationTable.__init__(self, table, blocks=blocks)
+
+    @staticmethod
+    def _concat_blocks(blocks: List[Union[TableBlock, pa.Table]], axis: int = 0) -> pa.Table:
+        pa_tables = [table.table if hasattr(table, "table") else table for table in blocks]
+        if axis == 0:
+            # we set promote=True to fill missing columns with null values
+            if config.PYARROW_VERSION.major < 14:
+                return pa.concat_tables(pa_tables, promote=True)
+            else:
+                return pa.concat_tables(pa_tables, promote_options="default")
+        elif axis == 1:
+            for i, table in enumerate(pa_tables):
+                if i == 0:
+                    pa_table = table
+                else:
+                    for name, col in zip(table.column_names, table.columns):
+                        pa_table = pa_table.append_column(name, col)
+            return pa_table
+        else:
+            raise ValueError("'axis' must be either 0 or 1")
+
+    @classmethod
+    def _concat_blocks_horizontally_and_vertically(cls, blocks: List[List[TableBlock]]) -> pa.Table:
+        pa_tables_to_concat_vertically = []
+        for i, tables in enumerate(blocks):
+            if not tables:
+                continue
+            pa_table_horizontally_concatenated = cls._concat_blocks(tables, axis=1)
+            pa_tables_to_concat_vertically.append(pa_table_horizontally_concatenated)
+        return cls._concat_blocks(pa_tables_to_concat_vertically, axis=0)
+
+    @classmethod
+    def _merge_blocks(cls, blocks: TableBlockContainer, axis: Optional[int] = None) -> TableBlockContainer:
+        if axis is not None:
+            merged_blocks = []
+            for is_in_memory, block_group in groupby(blocks, key=lambda x: isinstance(x, InMemoryTable)):
+                if is_in_memory:
+                    block_group = [InMemoryTable(cls._concat_blocks(list(block_group), axis=axis))]
+                merged_blocks += list(block_group)
+        else:  # both
+            merged_blocks = [cls._merge_blocks(row_block, axis=1) for row_block in blocks]
+            if all(len(row_block) == 1 for row_block in merged_blocks):
+                merged_blocks = cls._merge_blocks(
+                    [block for row_block in merged_blocks for block in row_block], axis=0
+                )
+        return merged_blocks
+
+    @classmethod
+    def _consolidate_blocks(cls, blocks: TableBlockContainer) -> TableBlockContainer:
+        if isinstance(blocks, TableBlock):
+            return blocks
+        elif isinstance(blocks[0], TableBlock):
+            return cls._merge_blocks(blocks, axis=0)
+        else:
+            return cls._merge_blocks(blocks)
+
+    @classmethod
+    def from_blocks(cls, blocks: TableBlockContainer) -> "ConcatenationTable":
+        blocks = cls._consolidate_blocks(blocks)
+        if isinstance(blocks, TableBlock):
+            table = blocks
+            return cls(table.table, [[table]])
+        elif isinstance(blocks[0], TableBlock):
+            table = cls._concat_blocks(blocks, axis=0)
+            blocks = [[t] for t in blocks]
+            return cls(table, blocks)
+        else:
+            table = cls._concat_blocks_horizontally_and_vertically(blocks)
+            return cls(table, blocks)
+
+    @classmethod
+    def from_tables(cls, tables: List[Union[pa.Table, Table]], axis: int = 0) -> "ConcatenationTable":
+        """Create `ConcatenationTable` from list of tables.
+
+        Args:
+            tables (list of `Table` or list of `pyarrow.Table`):
+                List of tables.
+            axis (`{0, 1}`, defaults to `0`, meaning over rows):
+                Axis to concatenate over, where `0` means over rows (vertically) and `1` means over columns
+                (horizontally).
+
+                <Added version="1.6.0"/>
+        """
+
+        def to_blocks(table: Union[pa.Table, Table]) -> List[List[TableBlock]]:
+            if isinstance(table, pa.Table):
+                return [[InMemoryTable(table)]]
+            elif isinstance(table, ConcatenationTable):
+                return copy.deepcopy(table.blocks)
+            else:
+                return [[table]]
+
+        def _slice_row_block(row_block: List[TableBlock], length: int) -> Tuple[List[TableBlock], List[TableBlock]]:
+            sliced = [table.slice(0, length) for table in row_block]
+            remainder = [table.slice(length, len(row_block[0]) - length) for table in row_block]
+            return sliced, remainder
+
+        def _split_both_like(
+            result: List[List[TableBlock]], blocks: List[List[TableBlock]]
+        ) -> Tuple[List[List[TableBlock]], List[List[TableBlock]]]:
+            """
+            Make sure each row_block contain the same num_rows to be able to concatenate them on axis=1.
+
+            To do so, we modify both blocks sets to have the same row_blocks boundaries.
+            For example, if `result` has 2 row_blocks of 3 rows and `blocks` has 3 row_blocks of 2 rows,
+            we modify both to have 4 row_blocks of size 2, 1, 1 and 2:
+
+                    [ x   x   x | x   x   x ]
+                +   [ y   y | y   y | y   y ]
+                -----------------------------
+                =   [ x   x | x | x | x   x ]
+                    [ y   y | y | y | y   y ]
+
+            """
+            result, blocks = list(result), list(blocks)
+            new_result, new_blocks = [], []
+            while result and blocks:
+                # we slice the longest row block to save two row blocks of same length
+                # and we replace the long row block by its remainder if necessary
+                if len(result[0][0]) > len(blocks[0][0]):
+                    new_blocks.append(blocks[0])
+                    sliced, result[0] = _slice_row_block(result[0], len(blocks.pop(0)[0]))
+                    new_result.append(sliced)
+                elif len(result[0][0]) < len(blocks[0][0]):
+                    new_result.append(result[0])
+                    sliced, blocks[0] = _slice_row_block(blocks[0], len(result.pop(0)[0]))
+                    new_blocks.append(sliced)
+                else:
+                    new_result.append(result.pop(0))
+                    new_blocks.append(blocks.pop(0))
+            if result or blocks:
+                raise ValueError("Failed to concatenate on axis=1 because tables don't have the same number of rows")
+            return new_result, new_blocks
+
+        def _extend_blocks(
+            result: List[List[TableBlock]], blocks: List[List[TableBlock]], axis: int = 0
+        ) -> List[List[TableBlock]]:
+            if axis == 0:
+                result.extend(blocks)
+            elif axis == 1:
+                # We make sure each row_block have the same num_rows
+                result, blocks = _split_both_like(result, blocks)
+                for i, row_block in enumerate(blocks):
+                    result[i].extend(row_block)
+            return result
+
+        blocks = to_blocks(tables[0])
+        for table in tables[1:]:
+            table_blocks = to_blocks(table)
+            blocks = _extend_blocks(blocks, table_blocks, axis=axis)
+        return cls.from_blocks(blocks)
+
+    @property
+    def _slices(self):
+        offset = 0
+        for tables in self.blocks:
+            length = len(tables[0])
+            yield (offset, length)
+            offset += length
+
+    def slice(self, offset=0, length=None):
+        """
+        Compute zero-copy slice of this Table.
+
+        Args:
+            offset (`int`, defaults to `0`):
+                Offset from start of table to slice.
+            length (`int`, defaults to `None`):
+                Length of slice (default is until end of table starting from
+                offset).
+
+        Returns:
+            `datasets.table.Table`
+        """
+        table = self.table.slice(offset, length=length)
+        length = length if length is not None else self.num_rows - offset
+        blocks = []
+        for tables in self.blocks:
+            n_rows = len(tables[0])
+            if length == 0:
+                break
+            elif n_rows <= offset:
+                offset = offset - n_rows
+            elif n_rows <= offset + length:
+                blocks.append([t.slice(offset) for t in tables])
+                length, offset = length + offset - n_rows, 0
+            else:
+                blocks.append([t.slice(offset, length) for t in tables])
+                length, offset = 0, 0
+        return ConcatenationTable(table, blocks)
+
+    def filter(self, mask, *args, **kwargs):
+        """
+        Select records from a Table. See `pyarrow.compute.filter` for full usage.
+        """
+        table = self.table.filter(mask, *args, **kwargs)
+        blocks = []
+        for (offset, length), tables in zip(self._slices, self.blocks):
+            submask = mask.slice(offset, length)
+            blocks.append([t.filter(submask, *args, **kwargs) for t in tables])
+        return ConcatenationTable(table, blocks)
+
+    def flatten(self, *args, **kwargs):
+        """
+        Flatten this Table.  Each column with a struct type is flattened
+        into one column per struct field.  Other columns are left unchanged.
+
+        Args:
+            memory_pool (`MemoryPool`, defaults to `None`):
+                For memory allocations, if required, otherwise use default pool.
+
+        Returns:
+            `datasets.table.Table`
+        """
+        table = table_flatten(self.table, *args, **kwargs)
+        blocks = []
+        for tables in self.blocks:
+            blocks.append([t.flatten(*args, **kwargs) for t in tables])
+        return ConcatenationTable(table, blocks)
+
+    def combine_chunks(self, *args, **kwargs):
+        """
+        Make a new table by combining the chunks this table has.
+
+        All the underlying chunks in the `ChunkedArray` of each column are
+        concatenated into zero or one chunk.
+
+        Args:
+            memory_pool (`MemoryPool`, defaults to `None`):
+                For memory allocations, if required, otherwise use default pool.
+
+        Returns:
+            `datasets.table.Table`
+        """
+        table = self.table.combine_chunks(*args, **kwargs)
+        blocks = []
+        for tables in self.blocks:
+            blocks.append([t.combine_chunks(*args, **kwargs) for t in tables])
+        return ConcatenationTable(table, blocks)
+
+    def cast(self, target_schema, *args, **kwargs):
+        """
+        Cast table values to another schema.
+
+        Args:
+            target_schema (`Schema`):
+                Schema to cast to, the names and order of fields must match.
+            safe (`bool`, defaults to `True`):
+                Check for overflows or other unsafe conversions.
+
+        Returns:
+            `datasets.table.Table`
+        """
+        from .features import Features
+
+        table = table_cast(self.table, target_schema, *args, **kwargs)
+        target_features = Features.from_arrow_schema(target_schema)
+        blocks = []
+        for subtables in self.blocks:
+            new_tables = []
+            fields = list(target_schema)
+            for subtable in subtables:
+                subfields = []
+                for name in subtable.column_names:
+                    subfields.append(fields.pop(next(i for i, field in enumerate(fields) if field.name == name)))
+                subfeatures = Features({subfield.name: target_features[subfield.name] for subfield in subfields})
+                subschema = subfeatures.arrow_schema
+                new_tables.append(subtable.cast(subschema, *args, **kwargs))
+            blocks.append(new_tables)
+        return ConcatenationTable(table, blocks)
+
+    def replace_schema_metadata(self, *args, **kwargs):
+        """
+        EXPERIMENTAL: Create shallow copy of table by replacing schema
+        key-value metadata with the indicated new metadata (which may be `None`,
+        which deletes any existing metadata).
+
+        Args:
+            metadata (`dict`, defaults to `None`):
+
+        Returns:
+            `datasets.table.Table`: shallow_copy
+        """
+        table = self.table.replace_schema_metadata(*args, **kwargs)
+        blocks = []
+        for tables in self.blocks:
+            blocks.append([t.replace_schema_metadata(*args, **kwargs) for t in tables])
+        return ConcatenationTable(table, self.blocks)
+
+    def add_column(self, *args, **kwargs):
+        """
+        Add column to Table at position.
+
+        A new table is returned with the column added, the original table
+        object is left unchanged.
+
+        Args:
+            i (`int`):
+                Index to place the column at.
+            field_ (`Union[str, pyarrow.Field]`):
+                If a string is passed then the type is deduced from the column
+                data.
+            column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
+                Column data.
+
+        Returns:
+            `datasets.table.Table`: New table with the passed column added.
+        """
+        raise NotImplementedError()
+
+    def append_column(self, *args, **kwargs):
+        """
+        Append column at end of columns.
+
+        Args:
+            field_ (`Union[str, pyarrow.Field]`):
+                If a string is passed then the type is deduced from the column
+                data.
+            column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
+                Column data.
+
+        Returns:
+            `datasets.table.Table`:
+                New table with the passed column added.
+        """
+        raise NotImplementedError()
+
+    def remove_column(self, i, *args, **kwargs):
+        """
+        Create new Table with the indicated column removed.
+
+        Args:
+            i (`int`):
+                Index of column to remove.
+
+        Returns:
+            `datasets.table.Table`:
+                New table without the column.
+        """
+        table = self.table.remove_column(i, *args, **kwargs)
+        name = self.table.column_names[i]
+        blocks = []
+        for tables in self.blocks:
+            blocks.append(
+                [
+                    t.remove_column(t.column_names.index(name), *args, **kwargs) if name in t.column_names else t
+                    for t in tables
+                ]
+            )
+        return ConcatenationTable(table, blocks)
+
+    def set_column(self, *args, **kwargs):
+        """
+        Replace column in Table at position.
+
+        Args:
+            i (`int`):
+                Index to place the column at.
+            field_ (`Union[str, pyarrow.Field]`):
+                If a string is passed then the type is deduced from the column
+                data.
+            column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
+                Column data.
+
+        Returns:
+            `datasets.table.Table`:
+                New table with the passed column set.
+        """
+        raise NotImplementedError()
+
+    def rename_columns(self, names, *args, **kwargs):
+        """
+        Create new table with columns renamed to provided names.
+        """
+        table = self.table.rename_columns(names, *args, **kwargs)
+        names = dict(zip(self.table.column_names, names))
+        blocks = []
+        for tables in self.blocks:
+            blocks.append(
+                [t.rename_columns([names[name] for name in t.column_names], *args, **kwargs) for t in tables]
+            )
+        return ConcatenationTable(table, blocks)
+
+    def drop(self, columns, *args, **kwargs):
+        """
+        Drop one or more columns and return a new table.
+
+        Args:
+            columns (`List[str]`):
+                List of field names referencing existing columns.
+
+        Raises:
+            `KeyError` : if any of the passed columns name are not existing.
+
+        Returns:
+            `datasets.table.Table`:
+                New table without the columns.
+        """
+        table = self.table.drop(columns, *args, **kwargs)
+        blocks = []
+        for tables in self.blocks:
+            blocks.append([t.drop([c for c in columns if c in t.column_names], *args, **kwargs) for t in tables])
+        return ConcatenationTable(table, blocks)
+
+    def select(self, columns, *args, **kwargs):
+        """
+        Select columns of the table.
+
+        Returns a new table with the specified columns, and metadata preserved.
+
+        Args:
+            columns (:obj:`Union[List[str], List[int]]`):
+                The column names or integer indices to select.
+
+        Returns:
+            :class:`datasets.table.Table`: New table with the specified columns, and metadata preserved.
+        """
+        table = self.table.select(columns, *args, **kwargs)
+        blocks = []
+        for tables in self.blocks:
+            blocks.append([t.select([c for c in columns if c in t.column_names], *args, **kwargs) for t in tables])
+        return ConcatenationTable(table, blocks)
+
+
+def concat_tables(tables: List[Table], axis: int = 0) -> Table:
+    """
+    Concatenate tables.
+
+    Args:
+        tables (list of `Table`):
+            List of tables to be concatenated.
+        axis (`{0, 1}`, defaults to `0`, meaning over rows):
+            Axis to concatenate over, where `0` means over rows (vertically) and `1` means over columns
+            (horizontally).
+
+            <Added version="1.6.0"/>
+    Returns:
+        `datasets.table.Table`:
+            If the number of input tables is > 1, then the returned table is a `datasets.table.ConcatenationTable`.
+            Otherwise if there's only one table, it is returned as is.
+    """
+    tables = list(tables)
+    if len(tables) == 1:
+        return tables[0]
+    return ConcatenationTable.from_tables(tables, axis=axis)
+
+
+def list_table_cache_files(table: Table) -> List[str]:
+    """
+    Get the cache files that are loaded by the table.
+    Cache file are used when parts of the table come from the disk via memory mapping.
+
+    Returns:
+        `List[str]`:
+            A list of paths to the cache files loaded by the table.
+    """
+    if isinstance(table, ConcatenationTable):
+        cache_files = []
+        for subtables in table.blocks:
+            for subtable in subtables:
+                cache_files += list_table_cache_files(subtable)
+        return cache_files
+    elif isinstance(table, MemoryMappedTable):
+        return [table.path]
+    else:
+        return []
+
+
+def _wrap_for_chunked_arrays(func):
+    """Apply the function on each chunk of a `pyarrow.ChunkedArray`, or on the array directly"""
+
+    def wrapper(array, *args, **kwargs):
+        if isinstance(array, pa.ChunkedArray):
+            return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
+        else:
+            return func(array, *args, **kwargs)
+
+    return wrapper
+
+
+def _are_list_values_of_length(array: pa.ListArray, length: int) -> bool:
+    """Check if all the sub-lists of a `pa.ListArray` have the specified length."""
+    return pc.all(pc.equal(array.value_lengths(), length)).as_py() or array.null_count == len(array)
+
+
+def _combine_list_array_offsets_with_mask(array: pa.ListArray) -> pa.Array:
+    """Add the null bitmap to the offsets of a `pa.ListArray`."""
+    offsets = array.offsets
+    if array.null_count > 0:
+        offsets = pa.concat_arrays(
+            [
+                pc.replace_with_mask(offsets[:-1], array.is_null(), pa.nulls(len(array), pa.int32())),
+                offsets[-1:],
+            ]
+        )
+    return offsets
+
+
+def _storage_type(type: pa.DataType) -> pa.DataType:
+    """Convert a (possibly nested) `pa.ExtensionType` to its storage type."""
+    if isinstance(type, pa.ExtensionType):
+        return _storage_type(type.storage_type)
+    elif isinstance(type, pa.StructType):
+        return pa.struct([pa.field(field.name, _storage_type(field.type)) for field in type])
+    elif isinstance(type, pa.ListType):
+        return pa.list_(_storage_type(type.value_type))
+    elif isinstance(type, pa.FixedSizeListType):
+        return pa.list_(_storage_type(type.value_type), type.list_size)
+    return type
+
+
+@_wrap_for_chunked_arrays
+def array_cast(array: pa.Array, pa_type: pa.DataType, allow_number_to_str=True):
+    """Improved version of `pa.Array.cast`
+
+    It supports casting `pa.StructArray` objects to re-order the fields.
+    It also let you control certain aspects of the casting, e.g. whether
+    to disable numbers (`floats` or `ints`) to strings.
+
+    Args:
+        array (`pa.Array`):
+            PyArrow array to cast
+        pa_type (`pa.DataType`):
+            Target PyArrow type
+        allow_number_to_str (`bool`, defaults to `True`):
+            Whether to allow casting numbers to strings.
+            Defaults to `True`.
+
+    Raises:
+        `pa.ArrowInvalidError`: if the arrow data casting fails
+        `TypeError`: if the target type is not supported according, e.g.
+
+            - if a field is missing
+            - if casting from numbers to strings and `allow_number_to_str` is `False`
+
+    Returns:
+        `List[pyarrow.Array]`: the casted array
+    """
+    _c = partial(array_cast, allow_number_to_str=allow_number_to_str)
+    if isinstance(array, pa.ExtensionArray):
+        array = array.storage
+    if isinstance(pa_type, pa.ExtensionType):
+        return pa_type.wrap_array(_c(array, pa_type.storage_type))
+    elif array.type == pa_type:
+        return array
+    elif pa.types.is_struct(array.type):
+        if pa.types.is_struct(pa_type) and ({field.name for field in pa_type} == {field.name for field in array.type}):
+            if array.type.num_fields == 0:
+                return array
+            arrays = [_c(array.field(field.name), field.type) for field in pa_type]
+            return pa.StructArray.from_arrays(arrays, fields=list(pa_type), mask=array.is_null())
+    elif pa.types.is_list(array.type):
+        if pa.types.is_fixed_size_list(pa_type):
+            if _are_list_values_of_length(array, pa_type.list_size):
+                if array.null_count > 0:
+                    # Ensure each null value in the array translates to [null] * pa_type.list_size in the array's values array
+                    array_type = array.type
+                    storage_type = _storage_type(array_type)
+                    if array_type != storage_type:
+                        # Temporarily convert to the storage type to support extension types in the slice operation
+                        array = _c(array, storage_type)
+                        array = pc.list_slice(array, 0, pa_type.list_size, return_fixed_size_list=True)
+                        array = _c(array, array_type)
+                    else:
+                        array = pc.list_slice(array, 0, pa_type.list_size, return_fixed_size_list=True)
+                    array_values = array.values
+                    if config.PYARROW_VERSION.major < 15:
+                        return pa.Array.from_buffers(
+                            pa_type,
+                            len(array),
+                            [array.is_valid().buffers()[1]],
+                            children=[_c(array_values, pa_type.value_type)],
+                        )
+                    else:
+                        return pa.FixedSizeListArray.from_arrays(
+                            _c(array_values, pa_type.value_type), pa_type.list_size, mask=array.is_null()
+                        )
+                else:
+                    array_values = array.values[
+                        array.offset * pa_type.length : (array.offset + len(array)) * pa_type.length
+                    ]
+                    return pa.FixedSizeListArray.from_arrays(_c(array_values, pa_type.value_type), pa_type.list_size)
+        elif pa.types.is_list(pa_type):
+            # Merge offsets with the null bitmap to avoid the "Null bitmap with offsets slice not supported" ArrowNotImplementedError
+            array_offsets = _combine_list_array_offsets_with_mask(array)
+            return pa.ListArray.from_arrays(array_offsets, _c(array.values, pa_type.value_type))
+    elif pa.types.is_fixed_size_list(array.type):
+        if pa.types.is_fixed_size_list(pa_type):
+            if pa_type.list_size == array.type.list_size:
+                array_values = array.values[
+                    array.offset * array.type.list_size : (array.offset + len(array)) * array.type.list_size
+                ]
+                if config.PYARROW_VERSION.major < 15:
+                    return pa.Array.from_buffers(
+                        pa_type,
+                        len(array),
+                        [array.is_valid().buffers()[1]],
+                        children=[_c(array_values, pa_type.value_type)],
+                    )
+                else:
+                    return pa.FixedSizeListArray.from_arrays(
+                        _c(array_values, pa_type.value_type), pa_type.list_size, mask=array.is_null()
+                    )
+        elif pa.types.is_list(pa_type):
+            array_offsets = (np.arange(len(array) + 1) + array.offset) * array.type.list_size
+            return pa.ListArray.from_arrays(array_offsets, _c(array.values, pa_type.value_type), mask=array.is_null())
+    else:
+        if (
+            not allow_number_to_str
+            and pa.types.is_string(pa_type)
+            and (pa.types.is_floating(array.type) or pa.types.is_integer(array.type))
+        ):
+            raise TypeError(
+                f"Couldn't cast array of type {array.type} to {pa_type} since allow_number_to_str is set to {allow_number_to_str}"
+            )
+        if pa.types.is_null(pa_type) and not pa.types.is_null(array.type):
+            raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
+        return array.cast(pa_type)
+    raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
+
+
+@_wrap_for_chunked_arrays
+def cast_array_to_feature(array: pa.Array, feature: "FeatureType", allow_number_to_str=True):
+    """Cast an array to the arrow type that corresponds to the requested feature type.
+    For custom features like [`Audio`] or [`Image`], it takes into account the "cast_storage" methods
+    they defined to enable casting from other arrow types.
+
+    Args:
+        array (`pa.Array`):
+            The PyArrow array to cast.
+        feature (`datasets.features.FeatureType`):
+            The target feature type.
+        allow_number_to_str (`bool`, defaults to `True`):
+            Whether to allow casting numbers to strings.
+            Defaults to `True`.
+
+    Raises:
+        `pa.ArrowInvalidError`: if the arrow data casting fails
+        `TypeError`: if the target type is not supported according, e.g.
+
+            - if a field is missing
+            - if casting from numbers to strings and `allow_number_to_str` is `False`
+
+    Returns:
+        array (`pyarrow.Array`): the casted array
+    """
+    from .features.features import Sequence, get_nested_type
+
+    _c = partial(cast_array_to_feature, allow_number_to_str=allow_number_to_str)
+
+    if isinstance(array, pa.ExtensionArray):
+        array = array.storage
+    if hasattr(feature, "cast_storage"):
+        return feature.cast_storage(array)
+
+    elif pa.types.is_struct(array.type):
+        # feature must be a dict or Sequence(subfeatures_dict)
+        if isinstance(feature, Sequence) and isinstance(feature.feature, dict):
+            feature = {
+                name: Sequence(subfeature, length=feature.length) for name, subfeature in feature.feature.items()
+            }
+        if isinstance(feature, dict) and {field.name for field in array.type} == set(feature):
+            if array.type.num_fields == 0:
+                return array
+            arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
+            return pa.StructArray.from_arrays(arrays, names=list(feature), mask=array.is_null())
+    elif pa.types.is_list(array.type):
+        # feature must be either [subfeature] or Sequence(subfeature)
+        if isinstance(feature, list):
+            casted_array_values = _c(array.values, feature[0])
+            if casted_array_values.type == array.values.type:
+                return array
+            else:
+                # Merge offsets with the null bitmap to avoid the "Null bitmap with offsets slice not supported" ArrowNotImplementedError
+                array_offsets = _combine_list_array_offsets_with_mask(array)
+                return pa.ListArray.from_arrays(array_offsets, casted_array_values)
+        elif isinstance(feature, Sequence):
+            if feature.length > -1:
+                if _are_list_values_of_length(array, feature.length):
+                    if array.null_count > 0:
+                        # Ensure each null value in the array translates to [null] * pa_type.list_size in the array's values array
+                        array_type = array.type
+                        storage_type = _storage_type(array_type)
+                        if array_type != storage_type:
+                            # Temporarily convert to the storage type to support extension types in the slice operation
+                            array = array_cast(array, storage_type, allow_number_to_str=allow_number_to_str)
+                            array = pc.list_slice(array, 0, feature.length, return_fixed_size_list=True)
+                            array = array_cast(array, array_type, allow_number_to_str=allow_number_to_str)
+                        else:
+                            array = pc.list_slice(array, 0, feature.length, return_fixed_size_list=True)
+                        array_values = array.values
+                        casted_array_values = _c(array_values, feature.feature)
+                        if config.PYARROW_VERSION.major < 15:
+                            return pa.Array.from_buffers(
+                                pa.list_(casted_array_values.type, feature.length),
+                                len(array),
+                                [array.is_valid().buffers()[1]],
+                                children=[casted_array_values],
+                            )
+                        else:
+                            return pa.FixedSizeListArray.from_arrays(
+                                casted_array_values, feature.length, mask=array.is_null()
+                            )
+                    else:
+                        array_values = array.values[
+                            array.offset * feature.length : (array.offset + len(array)) * feature.length
+                        ]
+                        return pa.FixedSizeListArray.from_arrays(_c(array_values, feature.feature), feature.length)
+            else:
+                casted_array_values = _c(array.values, feature.feature)
+                if casted_array_values.type == array.values.type:
+                    return array
+                else:
+                    # Merge offsets with the null bitmap to avoid the "Null bitmap with offsets slice not supported" ArrowNotImplementedError
+                    array_offsets = _combine_list_array_offsets_with_mask(array)
+                    return pa.ListArray.from_arrays(array_offsets, casted_array_values)
+    elif pa.types.is_fixed_size_list(array.type):
+        # feature must be either [subfeature] or Sequence(subfeature)
+        if isinstance(feature, list):
+            array_offsets = (np.arange(len(array) + 1) + array.offset) * array.type.list_size
+            return pa.ListArray.from_arrays(array_offsets, _c(array.values, feature[0]), mask=array.is_null())
+        elif isinstance(feature, Sequence):
+            if feature.length > -1:
+                if feature.length == array.type.list_size:
+                    array_values = array.values[
+                        array.offset * array.type.list_size : (array.offset + len(array)) * array.type.list_size
+                    ]
+                    casted_array_values = _c(array_values, feature.feature)
+                    if config.PYARROW_VERSION.major < 15:
+                        return pa.Array.from_buffers(
+                            pa.list_(casted_array_values.type, feature.length),
+                            len(array),
+                            [array.is_valid().buffers()[1]],
+                            children=[casted_array_values],
+                        )
+                    else:
+                        return pa.FixedSizeListArray.from_arrays(
+                            casted_array_values, feature.length, mask=array.is_null()
+                        )
+            else:
+                array_offsets = (np.arange(len(array) + 1) + array.offset) * array.type.list_size
+                return pa.ListArray.from_arrays(array_offsets, _c(array.values, feature.feature), mask=array.is_null())
+    if pa.types.is_null(array.type):
+        return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str)
+    elif not isinstance(feature, (Sequence, dict, list, tuple)):
+        return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
+    raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
+
+
+@_wrap_for_chunked_arrays
+def embed_array_storage(array: pa.Array, feature: "FeatureType"):
+    """Embed data into an arrays's storage.
+    For custom features like Audio or Image, it takes into account the "embed_storage" methods
+    they define to embed external data (e.g. an image file) into an array.
+
+    <Added version="2.4.0"/>
+
+    Args:
+        array (`pa.Array`):
+            The PyArrow array in which to embed data.
+        feature (`datasets.features.FeatureType`):
+            Array features.
+
+    Raises:
+        `TypeError`: if the target type is not supported according, e.g.
+
+            - if a field is missing
+
+    Returns:
+         array (`pyarrow.Array`): the casted array
+    """
+    from .features import Sequence
+
+    _e = embed_array_storage
+
+    if isinstance(array, pa.ExtensionArray):
+        array = array.storage
+    if hasattr(feature, "embed_storage"):
+        return feature.embed_storage(array)
+    elif pa.types.is_struct(array.type):
+        # feature must be a dict or Sequence(subfeatures_dict)
+        if isinstance(feature, Sequence) and isinstance(feature.feature, dict):
+            feature = {
+                name: Sequence(subfeature, length=feature.length) for name, subfeature in feature.feature.items()
+            }
+        if isinstance(feature, dict):
+            arrays = [_e(array.field(name), subfeature) for name, subfeature in feature.items()]
+            return pa.StructArray.from_arrays(arrays, names=list(feature), mask=array.is_null())
+    elif pa.types.is_list(array.type):
+        # feature must be either [subfeature] or Sequence(subfeature)
+        # Merge offsets with the null bitmap to avoid the "Null bitmap with offsets slice not supported" ArrowNotImplementedError
+        array_offsets = _combine_list_array_offsets_with_mask(array)
+        if isinstance(feature, list):
+            return pa.ListArray.from_arrays(array_offsets, _e(array.values, feature[0]))
+        if isinstance(feature, Sequence) and feature.length == -1:
+            return pa.ListArray.from_arrays(array_offsets, _e(array.values, feature.feature))
+    elif pa.types.is_fixed_size_list(array.type):
+        # feature must be Sequence(subfeature)
+        if isinstance(feature, Sequence) and feature.length > -1:
+            array_values = array.values[
+                array.offset * array.type.list_size : (array.offset + len(array)) * array.type.list_size
+            ]
+            embedded_array_values = _e(array_values, feature.feature)
+            if config.PYARROW_VERSION.major < 15:
+                return pa.Array.from_buffers(
+                    pa.list_(array_values.type, feature.length),
+                    len(array),
+                    [array.is_valid().buffers()[1]],
+                    children=[embedded_array_values],
+                )
+            else:
+                return pa.FixedSizeListArray.from_arrays(embedded_array_values, feature.length, mask=array.is_null())
+    if not isinstance(feature, (Sequence, dict, list, tuple)):
+        return array
+    raise TypeError(f"Couldn't embed array of type\n{array.type}\nwith\n{feature}")
+
+
+class CastError(ValueError):
+    """When it's not possible to cast an Arrow table to a specific schema or set of features"""
+
+    def __init__(self, *args, table_column_names: List[str], requested_column_names: List[str]) -> None:
+        super().__init__(*args)
+        self.table_column_names = table_column_names
+        self.requested_column_names = requested_column_names
+
+    def details(self):
+        new_columns = set(self.table_column_names) - set(self.requested_column_names)
+        missing_columns = set(self.requested_column_names) - set(self.table_column_names)
+        if new_columns and missing_columns:
+            return f"there are {len(new_columns)} new columns ({', '.join(new_columns)}) and {len(missing_columns)} missing columns ({', '.join(missing_columns)})."
+        elif new_columns:
+            return f"there are {len(new_columns)} new columns ({new_columns})"
+        else:
+            return f"there are {len(missing_columns)} missing columns ({missing_columns})"
+
+
+def cast_table_to_features(table: pa.Table, features: "Features"):
+    """Cast a table to the arrow schema that corresponds to the requested features.
+
+    Args:
+        table (`pyarrow.Table`):
+            PyArrow table to cast.
+        features ([`Features`]):
+            Target features.
+
+    Returns:
+        table (`pyarrow.Table`): the casted table
+    """
+    if sorted(table.column_names) != sorted(features):
+        raise CastError(
+            f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match",
+            table_column_names=table.column_names,
+            requested_column_names=list(features),
+        )
+    arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
+    return pa.Table.from_arrays(arrays, schema=features.arrow_schema)
+
+
+def cast_table_to_schema(table: pa.Table, schema: pa.Schema):
+    """Cast a table to the arrow schema. Different from `cast_table_to_features`, this method can preserve nullability.
+
+    Args:
+        table (`pa.Table`):
+            PyArrow table to cast.
+        features ([`Features`]):
+            Target features.
+
+    Returns:
+        `pa.Table`: the casted table
+    """
+    from .features import Features
+
+    features = Features.from_arrow_schema(schema)
+    if sorted(table.column_names) != sorted(features):
+        raise CastError(
+            f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match",
+            table_column_names=table.column_names,
+            requested_column_names=list(features),
+        )
+    arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
+    return pa.Table.from_arrays(arrays, schema=schema)
+
+
+def embed_table_storage(table: pa.Table):
+    """Embed external data into a table's storage.
+
+    <Added version="2.4.0"/>
+
+    Args:
+        table (`pyarrow.Table`):
+            PyArrow table in which to embed data.
+
+    Returns:
+        table (`pyarrow.Table`): the table with embedded data
+    """
+    from .features.features import Features, require_storage_embed
+
+    features = Features.from_arrow_schema(table.schema)
+    arrays = [
+        embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
+        for name, feature in features.items()
+    ]
+    return pa.Table.from_arrays(arrays, schema=features.arrow_schema)
+
+
+def table_cast(table: pa.Table, schema: pa.Schema):
+    """Improved version of `pa.Table.cast`.
+
+    It supports casting to feature types stored in the schema metadata.
+
+    Args:
+        table (`pyarrow.Table`):
+            PyArrow table to cast.
+        schema (`pyarrow.Schema`):
+            Target PyArrow schema.
+
+    Returns:
+        table (`pyarrow.Table`): the casted table
+    """
+    if table.schema != schema:
+        return cast_table_to_schema(table, schema)
+    elif table.schema.metadata != schema.metadata:
+        return table.replace_schema_metadata(schema.metadata)
+    else:
+        return table
+
+
+def table_flatten(table: pa.Table):
+    """Improved version of `pa.Table.flatten`.
+
+    It behaves as `pa.Table.flatten` in a sense it does 1-step flatten of the columns with a struct type into one column per struct field,
+    but updates the metadata and skips decodable features unless the `decode` attribute of these features is set to False.
+
+    Args:
+        table (`pa.Table`):
+            PyArrow table to flatten.
+
+    Returns:
+        `Table`: the flattened table
+    """
+    from .features import Features
+
+    features = Features.from_arrow_schema(table.schema)
+    if any(hasattr(subfeature, "flatten") and subfeature.flatten() == subfeature for subfeature in features.values()):
+        flat_arrays = []
+        flat_column_names = []
+        for field in table.schema:
+            array = table.column(field.name)
+            subfeature = features[field.name]
+            if pa.types.is_struct(field.type) and (
+                not hasattr(subfeature, "flatten") or subfeature.flatten() != subfeature
+            ):
+                flat_arrays.extend(array.flatten())
+                flat_column_names.extend([f"{field.name}.{subfield.name}" for subfield in field.type])
+            else:
+                flat_arrays.append(array)
+                flat_column_names.append(field.name)
+        flat_table = pa.Table.from_arrays(
+            flat_arrays,
+            names=flat_column_names,
+        )
+    else:
+        flat_table = table.flatten()
+    # Preserve complex types in the metadata
+    flat_features = features.flatten(max_depth=2)
+    flat_features = Features({column_name: flat_features[column_name] for column_name in flat_table.column_names})
+    return flat_table.replace_schema_metadata(flat_features.arrow_schema.metadata)
+
+
+def table_visitor(table: pa.Table, function: Callable[[pa.Array], None]):
+    """Visit all arrays in a table and apply a function to them.
+
+    Args:
+        table (`pyarrow.Table`):
+            PyArrow table to visit.
+        function (`Callable[[pa.Array], None]`):
+            Function to apply to each array.
+    """
+    from .features import Features, Sequence
+
+    features = Features.from_arrow_schema(table.schema)
+
+    def _visit(array, feature):
+        if isinstance(array, pa.ChunkedArray):
+            for chunk in array.chunks:
+                _visit(chunk, feature)
+        else:
+            if isinstance(array, pa.ExtensionArray):
+                array = array.storage
+            function(array, feature)
+            if pa.types.is_struct(array.type) and not hasattr(feature, "cast_storage"):
+                if isinstance(feature, Sequence) and isinstance(feature.feature, dict):
+                    feature = {
+                        name: Sequence(subfeature, length=feature.length)
+                        for name, subfeature in feature.feature.items()
+                    }
+                for name, subfeature in feature.items():
+                    _visit(array.field(name), subfeature)
+            elif pa.types.is_list(array.type):
+                if isinstance(feature, list):
+                    _visit(array.values, feature[0])
+                elif isinstance(feature, Sequence):
+                    _visit(array.values, feature.feature)
+
+    for name, feature in features.items():
+        _visit(table[name], feature)
+
+
+def table_iter(table: Table, batch_size: int, drop_last_batch=False) -> Iterator[pa.Table]:
+    """Iterate over sub-tables of size `batch_size`.
+
+    Args:
+        table (`pyarrow.Table`):
+            PyArrow table to iterate over.
+        batch_size (`int`):
+            Size of each sub-table to yield.
+        drop_last_batch (`bool`, defaults to `False`):
+            Drop the last batch if it is smaller than `batch_size`.
+    """
+    chunks_buffer = []
+    chunks_buffer_size = 0
+    for chunk in table.to_reader(max_chunksize=batch_size):
+        if len(chunk) == 0:
+            continue
+        elif chunks_buffer_size + len(chunk) < batch_size:
+            chunks_buffer.append(chunk)
+            chunks_buffer_size += len(chunk)
+            continue
+        elif chunks_buffer_size + len(chunk) == batch_size:
+            chunks_buffer.append(chunk)
+            yield pa.Table.from_batches(chunks_buffer)
+            chunks_buffer = []
+            chunks_buffer_size = 0
+        else:
+            cropped_chunk_length = batch_size - chunks_buffer_size
+            chunks_buffer.append(chunk.slice(0, cropped_chunk_length))
+            yield pa.Table.from_batches(chunks_buffer)
+            chunks_buffer = [chunk.slice(cropped_chunk_length, len(chunk) - cropped_chunk_length)]
+            chunks_buffer_size = len(chunk) - cropped_chunk_length
+    if not drop_last_batch and chunks_buffer:
+        yield pa.Table.from_batches(chunks_buffer)

env-llmeval/lib/python3.10/site-packages/datasets/tasks/__init__.py

@@ -0,0 +1,46 @@
+from typing import Optional
+
+from ..utils.logging import get_logger
+from .audio_classification import AudioClassification
+from .automatic_speech_recognition import AutomaticSpeechRecognition
+from .base import TaskTemplate
+from .image_classification import ImageClassification
+from .language_modeling import LanguageModeling
+from .question_answering import QuestionAnsweringExtractive
+from .summarization import Summarization
+from .text_classification import TextClassification
+
+
+__all__ = [
+    "AutomaticSpeechRecognition",
+    "AudioClassification",
+    "ImageClassification",
+    "LanguageModeling",
+    "QuestionAnsweringExtractive",
+    "Summarization",
+    "TaskTemplate",
+    "TextClassification",
+]
+
+logger = get_logger(__name__)
+
+
+NAME2TEMPLATE = {
+    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
+    AudioClassification.task: AudioClassification,
+    ImageClassification.task: ImageClassification,
+    LanguageModeling.task: LanguageModeling,
+    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
+    Summarization.task: Summarization,
+    TextClassification.task: TextClassification,
+}
+
+
+def task_template_from_dict(task_template_dict: dict) -> Optional[TaskTemplate]:
+    """Create one of the supported task templates in :py:mod:`datasets.tasks` from a dictionary."""
+    task_name = task_template_dict.get("task")
+    if task_name is None:
+        logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(NAME2TEMPLATE)}")
+        return None
+    template = NAME2TEMPLATE.get(task_name)
+    return template.from_dict(task_template_dict)

env-llmeval/lib/python3.10/site-packages/datasets/tasks/audio_classification.py

@@ -0,0 +1,33 @@
+import copy
+from dataclasses import dataclass, field
+from typing import ClassVar, Dict
+
+from ..features import Audio, ClassLabel, Features
+from .base import TaskTemplate
+
+
+@dataclass(frozen=True)
+class AudioClassification(TaskTemplate):
+    task: str = field(default="audio-classification", metadata={"include_in_asdict_even_if_is_default": True})
+    input_schema: ClassVar[Features] = Features({"audio": Audio()})
+    label_schema: ClassVar[Features] = Features({"labels": ClassLabel})
+    audio_column: str = "audio"
+    label_column: str = "labels"
+
+    def align_with_features(self, features):
+        if self.label_column not in features:
+            raise ValueError(f"Column {self.label_column} is not present in features.")
+        if not isinstance(features[self.label_column], ClassLabel):
+            raise ValueError(f"Column {self.label_column} is not a ClassLabel.")
+        task_template = copy.deepcopy(self)
+        label_schema = self.label_schema.copy()
+        label_schema["labels"] = features[self.label_column]
+        task_template.__dict__["label_schema"] = label_schema
+        return task_template
+
+    @property
+    def column_mapping(self) -> Dict[str, str]:
+        return {
+            self.audio_column: "audio",
+            self.label_column: "labels",
+        }

env-llmeval/lib/python3.10/site-packages/datasets/tasks/automatic_speech_recognition.py

@@ -0,0 +1,30 @@
+import copy
+from dataclasses import dataclass, field
+from typing import ClassVar, Dict
+
+from ..features import Audio, Features, Value
+from .base import TaskTemplate
+
+
+@dataclass(frozen=True)
+class AutomaticSpeechRecognition(TaskTemplate):
+    task: str = field(default="automatic-speech-recognition", metadata={"include_in_asdict_even_if_is_default": True})
+    input_schema: ClassVar[Features] = Features({"audio": Audio()})
+    label_schema: ClassVar[Features] = Features({"transcription": Value("string")})
+    audio_column: str = "audio"
+    transcription_column: str = "transcription"
+
+    def align_with_features(self, features):
+        if self.audio_column not in features:
+            raise ValueError(f"Column {self.audio_column} is not present in features.")
+        if not isinstance(features[self.audio_column], Audio):
+            raise ValueError(f"Column {self.audio_column} is not an Audio type.")
+        task_template = copy.deepcopy(self)
+        input_schema = self.input_schema.copy()
+        input_schema["audio"] = features[self.audio_column]
+        task_template.__dict__["input_schema"] = input_schema
+        return task_template
+
+    @property
+    def column_mapping(self) -> Dict[str, str]:
+        return {self.audio_column: "audio", self.transcription_column: "transcription"}

env-llmeval/lib/python3.10/site-packages/datasets/tasks/base.py

@@ -0,0 +1,39 @@
+import abc
+import copy
+import dataclasses
+from dataclasses import dataclass
+from typing import ClassVar, Dict, Type, TypeVar
+
+from ..features import Features
+
+
+T = TypeVar("T", bound="TaskTemplate")
+
+
+@dataclass(frozen=True)
+class TaskTemplate(abc.ABC):
+    # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
+    task: str
+    input_schema: ClassVar[Features]
+    label_schema: ClassVar[Features]
+
+    def align_with_features(self: T, features: Features) -> T:
+        """
+        Align features with the task template.
+        """
+        # No-op
+        return copy.deepcopy(self)
+
+    @property
+    def features(self) -> Features:
+        return Features(**self.input_schema, **self.label_schema)
+
+    @property
+    @abc.abstractmethod
+    def column_mapping(self) -> Dict[str, str]:
+        raise NotImplementedError
+
+    @classmethod
+    def from_dict(cls: Type[T], template_dict: dict) -> T:
+        field_names = {f.name for f in dataclasses.fields(cls)}
+        return cls(**{k: v for k, v in template_dict.items() if k in field_names})

env-llmeval/lib/python3.10/site-packages/datasets/tasks/image_classification.py

@@ -0,0 +1,33 @@
+import copy
+from dataclasses import dataclass, field
+from typing import ClassVar, Dict
+
+from ..features import ClassLabel, Features, Image
+from .base import TaskTemplate
+
+
+@dataclass(frozen=True)
+class ImageClassification(TaskTemplate):
+    task: str = field(default="image-classification", metadata={"include_in_asdict_even_if_is_default": True})
+    input_schema: ClassVar[Features] = Features({"image": Image()})
+    label_schema: ClassVar[Features] = Features({"labels": ClassLabel})
+    image_column: str = "image"
+    label_column: str = "labels"
+
+    def align_with_features(self, features):
+        if self.label_column not in features:
+            raise ValueError(f"Column {self.label_column} is not present in features.")
+        if not isinstance(features[self.label_column], ClassLabel):
+            raise ValueError(f"Column {self.label_column} is not a ClassLabel.")
+        task_template = copy.deepcopy(self)
+        label_schema = self.label_schema.copy()
+        label_schema["labels"] = features[self.label_column]
+        task_template.__dict__["label_schema"] = label_schema
+        return task_template
+
+    @property
+    def column_mapping(self) -> Dict[str, str]:
+        return {
+            self.image_column: "image",
+            self.label_column: "labels",
+        }

env-llmeval/lib/python3.10/site-packages/datasets/tasks/language_modeling.py

@@ -0,0 +1,18 @@
+from dataclasses import dataclass, field
+from typing import ClassVar, Dict
+
+from ..features import Features, Value
+from .base import TaskTemplate
+
+
+@dataclass(frozen=True)
+class LanguageModeling(TaskTemplate):
+    task: str = field(default="language-modeling", metadata={"include_in_asdict_even_if_is_default": True})
+
+    input_schema: ClassVar[Features] = Features({"text": Value("string")})
+    label_schema: ClassVar[Features] = Features({})
+    text_column: str = "text"
+
+    @property
+    def column_mapping(self) -> Dict[str, str]:
+        return {self.text_column: "text"}

env-llmeval/lib/python3.10/site-packages/datasets/tasks/question_answering.py

@@ -0,0 +1,29 @@
+from dataclasses import dataclass, field
+from typing import ClassVar, Dict
+
+from ..features import Features, Sequence, Value
+from .base import TaskTemplate
+
+
+@dataclass(frozen=True)
+class QuestionAnsweringExtractive(TaskTemplate):
+    # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
+    task: str = field(default="question-answering-extractive", metadata={"include_in_asdict_even_if_is_default": True})
+    input_schema: ClassVar[Features] = Features({"question": Value("string"), "context": Value("string")})
+    label_schema: ClassVar[Features] = Features(
+        {
+            "answers": Sequence(
+                {
+                    "text": Value("string"),
+                    "answer_start": Value("int32"),
+                }
+            )
+        }
+    )
+    question_column: str = "question"
+    context_column: str = "context"
+    answers_column: str = "answers"
+
+    @property
+    def column_mapping(self) -> Dict[str, str]:
+        return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}

env-llmeval/lib/python3.10/site-packages/datasets/tasks/summarization.py

@@ -0,0 +1,19 @@
+from dataclasses import dataclass, field
+from typing import ClassVar, Dict
+
+from ..features import Features, Value
+from .base import TaskTemplate
+
+
+@dataclass(frozen=True)
+class Summarization(TaskTemplate):
+    # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
+    task: str = field(default="summarization", metadata={"include_in_asdict_even_if_is_default": True})
+    input_schema: ClassVar[Features] = Features({"text": Value("string")})
+    label_schema: ClassVar[Features] = Features({"summary": Value("string")})
+    text_column: str = "text"
+    summary_column: str = "summary"
+
+    @property
+    def column_mapping(self) -> Dict[str, str]:
+        return {self.text_column: "text", self.summary_column: "summary"}

env-llmeval/lib/python3.10/site-packages/datasets/tasks/text_classification.py

@@ -0,0 +1,34 @@
+import copy
+from dataclasses import dataclass, field
+from typing import ClassVar, Dict
+
+from ..features import ClassLabel, Features, Value
+from .base import TaskTemplate
+
+
+@dataclass(frozen=True)
+class TextClassification(TaskTemplate):
+    # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
+    task: str = field(default="text-classification", metadata={"include_in_asdict_even_if_is_default": True})
+    input_schema: ClassVar[Features] = Features({"text": Value("string")})
+    label_schema: ClassVar[Features] = Features({"labels": ClassLabel})
+    text_column: str = "text"
+    label_column: str = "labels"
+
+    def align_with_features(self, features):
+        if self.label_column not in features:
+            raise ValueError(f"Column {self.label_column} is not present in features.")
+        if not isinstance(features[self.label_column], ClassLabel):
+            raise ValueError(f"Column {self.label_column} is not a ClassLabel.")
+        task_template = copy.deepcopy(self)
+        label_schema = self.label_schema.copy()
+        label_schema["labels"] = features[self.label_column]
+        task_template.__dict__["label_schema"] = label_schema
+        return task_template
+
+    @property
+    def column_mapping(self) -> Dict[str, str]:
+        return {
+            self.text_column: "text",
+            self.label_column: "labels",
+        }

env-llmeval/lib/python3.10/site-packages/dill-0.3.8.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

env-llmeval/lib/python3.10/site-packages/dill-0.3.8.dist-info/LICENSE

@@ -0,0 +1,35 @@
+Copyright (c) 2004-2016 California Institute of Technology.
+Copyright (c) 2016-2024 The Uncertainty Quantification Foundation.
+All rights reserved.
+
+This software is available subject to the conditions and terms laid
+out below. By downloading and using this software you are agreeing
+to the following conditions.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+    - Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+
+    - Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+
+    - Neither the names of the copyright holders nor the names of any of
+      the contributors may be used to endorse or promote products derived
+      from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
+TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
+OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
+OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
+ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+

env-llmeval/lib/python3.10/site-packages/dill-0.3.8.dist-info/METADATA

@@ -0,0 +1,280 @@
+Metadata-Version: 2.1
+Name: dill
+Version: 0.3.8
+Summary: serialize all of Python
+Home-page: https://github.com/uqfoundation/dill
+Author: Mike McKerns
+Author-email: [email protected]
+Maintainer: Mike McKerns
+Maintainer-email: [email protected]
+License: BSD-3-Clause
+Download-URL: https://pypi.org/project/dill/#files
+Project-URL: Documentation, http://dill.rtfd.io
+Project-URL: Source Code, https://github.com/uqfoundation/dill
+Project-URL: Bug Tracker, https://github.com/uqfoundation/dill/issues
+Platform: Linux
+Platform: Windows
+Platform: Mac
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Software Development
+Requires-Python: >=3.8
+Provides-Extra: graph
+Requires-Dist: objgraph (>=1.7.2) ; extra == 'graph'
+Provides-Extra: profile
+Requires-Dist: gprof2dot (>=2022.7.29) ; extra == 'profile'
+Provides-Extra: readline
+
+-----------------------------
+dill: serialize all of Python
+-----------------------------
+
+About Dill
+==========
+
+``dill`` extends Python's ``pickle`` module for serializing and de-serializing
+Python objects to the majority of the built-in Python types. Serialization
+is the process of converting an object to a byte stream, and the inverse
+of which is converting a byte stream back to a Python object hierarchy.
+
+``dill`` provides the user the same interface as the ``pickle`` module, and
+also includes some additional features. In addition to pickling Python
+objects, ``dill`` provides the ability to save the state of an interpreter
+session in a single command.  Hence, it would be feasible to save an
+interpreter session, close the interpreter, ship the pickled file to
+another computer, open a new interpreter, unpickle the session and
+thus continue from the 'saved' state of the original interpreter
+session.
+
+``dill`` can be used to store Python objects to a file, but the primary
+usage is to send Python objects across the network as a byte stream.
+``dill`` is quite flexible, and allows arbitrary user defined classes
+and functions to be serialized.  Thus ``dill`` is not intended to be
+secure against erroneously or maliciously constructed data. It is
+left to the user to decide whether the data they unpickle is from
+a trustworthy source.
+
+``dill`` is part of ``pathos``, a Python framework for heterogeneous computing.
+``dill`` is in active development, so any user feedback, bug reports, comments,
+or suggestions are highly appreciated.  A list of issues is located at
+https://github.com/uqfoundation/dill/issues, with a legacy list maintained at
+https://uqfoundation.github.io/project/pathos/query.
+
+
+Major Features
+==============
+
+``dill`` can pickle the following standard types:
+
+    - none, type, bool, int, float, complex, bytes, str,
+    - tuple, list, dict, file, buffer, builtin,
+    - Python classes, namedtuples, dataclasses, metaclasses,
+    - instances of classes,
+    - set, frozenset, array, functions, exceptions
+
+``dill`` can also pickle more 'exotic' standard types:
+
+    - functions with yields, nested functions, lambdas,
+    - cell, method, unboundmethod, module, code, methodwrapper,
+    - methoddescriptor, getsetdescriptor, memberdescriptor, wrapperdescriptor,
+    - dictproxy, slice, notimplemented, ellipsis, quit
+
+``dill`` cannot yet pickle these standard types:
+
+    - frame, generator, traceback
+
+``dill`` also provides the capability to:
+
+    - save and load Python interpreter sessions
+    - save and extract the source code from functions and classes
+    - interactively diagnose pickling errors
+
+
+Current Release
+===============
+
+The latest released version of ``dill`` is available from:
+
+    https://pypi.org/project/dill
+
+``dill`` is distributed under a 3-clause BSD license.
+
+
+Development Version
+===================
+
+You can get the latest development version with all the shiny new features at:
+
+    https://github.com/uqfoundation
+
+If you have a new contribution, please submit a pull request.
+
+
+Installation
+============
+
+``dill`` can be installed with ``pip``::
+
+    $ pip install dill
+
+To optionally include the ``objgraph`` diagnostic tool in the install::
+
+    $ pip install dill[graph]
+
+To optionally include the ``gprof2dot`` diagnostic tool in the install::
+
+    $ pip install dill[profile]
+
+For windows users, to optionally install session history tools::
+
+    $ pip install dill[readline]
+
+
+Requirements
+============
+
+``dill`` requires:
+
+    - ``python`` (or ``pypy``), **>=3.8**
+    - ``setuptools``, **>=42**
+
+Optional requirements:
+
+    - ``objgraph``, **>=1.7.2**
+    - ``gprof2dot``, **>=2022.7.29**
+    - ``pyreadline``, **>=1.7.1** (on windows)
+
+
+Basic Usage
+===========
+
+``dill`` is a drop-in replacement for ``pickle``. Existing code can be
+updated to allow complete pickling using::
+
+    >>> import dill as pickle
+
+or::
+
+    >>> from dill import dumps, loads
+
+``dumps`` converts the object to a unique byte string, and ``loads`` performs
+the inverse operation::
+
+    >>> squared = lambda x: x**2
+    >>> loads(dumps(squared))(3)
+    9
+
+There are a number of options to control serialization which are provided
+as keyword arguments to several ``dill`` functions:
+
+* with *protocol*, the pickle protocol level can be set. This uses the
+  same value as the ``pickle`` module, *DEFAULT_PROTOCOL*.
+* with *byref=True*, ``dill`` to behave a lot more like pickle with
+  certain objects (like modules) pickled by reference as opposed to
+  attempting to pickle the object itself.
+* with *recurse=True*, objects referred to in the global dictionary are
+  recursively traced and pickled, instead of the default behavior of
+  attempting to store the entire global dictionary.
+* with *fmode*, the contents of the file can be pickled along with the file
+  handle, which is useful if the object is being sent over the wire to a
+  remote system which does not have the original file on disk. Options are
+  *HANDLE_FMODE* for just the handle, *CONTENTS_FMODE* for the file content
+  and *FILE_FMODE* for content and handle.
+* with *ignore=False*, objects reconstructed with types defined in the
+  top-level script environment use the existing type in the environment
+  rather than a possibly different reconstructed type.
+
+The default serialization can also be set globally in *dill.settings*.
+Thus, we can modify how ``dill`` handles references to the global dictionary
+locally or globally::
+
+    >>> import dill.settings
+    >>> dumps(absolute) == dumps(absolute, recurse=True)
+    False
+    >>> dill.settings['recurse'] = True
+    >>> dumps(absolute) == dumps(absolute, recurse=True)
+    True
+
+``dill`` also includes source code inspection, as an alternate to pickling::
+
+    >>> import dill.source
+    >>> print(dill.source.getsource(squared))
+    squared = lambda x:x**2
+
+To aid in debugging pickling issues, use *dill.detect* which provides
+tools like pickle tracing::
+
+    >>> import dill.detect
+    >>> with dill.detect.trace():
+    >>>     dumps(squared)
+    ┬ F1: <function <lambda> at 0x7fe074f8c280>
+    ├┬ F2: <function _create_function at 0x7fe074c49c10>
+    │└ # F2 [34 B]
+    ├┬ Co: <code object <lambda> at 0x7fe07501eb30, file "<stdin>", line 1>
+    │├┬ F2: <function _create_code at 0x7fe074c49ca0>
+    ││└ # F2 [19 B]
+    │└ # Co [87 B]
+    ├┬ D1: <dict object at 0x7fe0750d4680>
+    │└ # D1 [22 B]
+    ├┬ D2: <dict object at 0x7fe074c5a1c0>
+    │└ # D2 [2 B]
+    ├┬ D2: <dict object at 0x7fe074f903c0>
+    │├┬ D2: <dict object at 0x7fe074f8ebc0>
+    ││└ # D2 [2 B]
+    │└ # D2 [23 B]
+    └ # F1 [180 B]
+
+With trace, we see how ``dill`` stored the lambda (``F1``) by first storing
+``_create_function``, the underlying code object (``Co``) and ``_create_code``
+(which is used to handle code objects), then we handle the reference to
+the global dict (``D2``) plus other dictionaries (``D1`` and ``D2``) that
+save the lambda object's state. A ``#`` marks when the object is actually stored.
+
+
+More Information
+================
+
+Probably the best way to get started is to look at the documentation at
+http://dill.rtfd.io. Also see ``dill.tests`` for a set of scripts that
+demonstrate how ``dill`` can serialize different Python objects. You can
+run the test suite with ``python -m dill.tests``. The contents of any
+pickle file can be examined with ``undill``.  As ``dill`` conforms to
+the ``pickle`` interface, the examples and documentation found at
+http://docs.python.org/library/pickle.html also apply to ``dill``
+if one will ``import dill as pickle``. The source code is also generally
+well documented, so further questions may be resolved by inspecting the
+code itself. Please feel free to submit a ticket on github, or ask a
+question on stackoverflow (**@Mike McKerns**).
+If you would like to share how you use ``dill`` in your work, please send
+an email (to **mmckerns at uqfoundation dot org**).
+
+
+Citation
+========
+
+If you use ``dill`` to do research that leads to publication, we ask that you
+acknowledge use of ``dill`` by citing the following in your publication::
+
+    M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis,
+    "Building a framework for predictive science", Proceedings of
+    the 10th Python in Science Conference, 2011;
+    http://arxiv.org/pdf/1202.1056
+
+    Michael McKerns and Michael Aivazis,
+    "pathos: a framework for heterogeneous computing", 2010- ;
+    https://uqfoundation.github.io/project/pathos
+
+Please see https://uqfoundation.github.io/project/pathos or
+http://arxiv.org/pdf/1202.1056 for further information.
+