data.py

import os
from typing import Dict, Optional, Union

import datasets

from .api import __file__ as _
from .artifact import __file__ as _
from .augmentors import __file__ as _
from .benchmark import __file__ as _
from .blocks import __file__ as _
from .card import __file__ as _
from .catalog import __file__ as _
from .collections import __file__ as _
from .collections_operators import __file__ as _
from .dataclass import __file__ as _
from .dataset_utils import __file__ as _
from .dataset_utils import get_dataset_artifact
from .deprecation_utils import __file__ as _
from .dialog_operators import __file__ as _
from .dict_utils import __file__ as _
from .error_utils import __file__ as _
from .eval_utils import __file__ as _
from .evaluate_cli import __file__ as _
from .file_utils import __file__ as _
from .formats import __file__ as _
from .fusion import __file__ as _
from .generator_utils import __file__ as _
from .hf_utils import __file__ as _
from .hf_utils import verify_versions_compatibility
from .image_operators import __file__ as _
from .inference import __file__ as _
from .instructions import __file__ as _
from .llm_as_judge import __file__ as _
from .llm_as_judge_chat_templates import __file__ as _
from .llm_as_judge_constants import __file__ as _
from .llm_as_judge_from_template import __file__ as _
from .llm_as_judge_operators import __file__ as _
from .llm_as_judge_utils import __file__ as _
from .loaders import __file__ as _
from .logging_utils import __file__ as _
from .logging_utils import get_logger
from .metric import __file__ as _
from .metric_utils import __file__ as _
from .metrics import __file__ as _
from .normalizers import __file__ as _
from .operator import __file__ as _
from .operators import __file__ as _
from .parsing_utils import __file__ as _
from .processors import __file__ as _
from .random_utils import __file__ as _
from .recipe import __file__ as _
from .register import __file__ as _
from .schema import __file__ as _
from .schema import loads_batch, loads_instance
from .serializers import __file__ as _
from .settings_utils import __file__ as _
from .settings_utils import get_constants
from .span_lableing_operators import __file__ as _
from .split_utils import __file__ as _
from .splitters import __file__ as _
from .sql_utils import __file__ as _
from .standard import __file__ as _
from .stream import __file__ as _
from .stream_operators import __file__ as _
from .string_operators import __file__ as _
from .struct_data_operators import __file__ as _
from .system_prompts import __file__ as _
from .task import __file__ as _
from .templates import __file__ as _
from .text_utils import __file__ as _
from .tool_calling import __file__ as _
from .type_utils import __file__ as _
from .types import __file__ as _
from .utils import __file__ as _
from .utils import is_package_installed
from .validate import __file__ as _
from .version import __file__ as _

logger = get_logger()
constants = get_constants()


class Dataset(datasets.GeneratorBasedBuilder):
    VERSION = constants.version

    @property
    def generators(self):
        if not hasattr(self, "_generators") or self._generators is None:
            if is_package_installed("unitxt"):
                verify_versions_compatibility("dataset", self.VERSION)

                from unitxt.dataset_utils import \
                    get_dataset_artifact as get_dataset_artifact_installed

                logger.info("Loading with installed unitxt library...")
                dataset = get_dataset_artifact_installed(self.config.name)
            else:
                logger.info("Loading with huggingface unitxt copy...")
                dataset = get_dataset_artifact(self.config.name)

            self._generators = dataset()

        return self._generators

    def _info(self):
        return datasets.DatasetInfo()

    def _split_generators(self, _):
        return [
            datasets.SplitGenerator(name=name, gen_kwargs={"split_name": name})
            for name in self.generators.keys()
        ]

    def _generate_examples(self, split_name):
        generator = self.generators[split_name]
        yield from enumerate(generator)

    def _download_and_prepare(
        self, dl_manager, verification_mode, **prepare_splits_kwargs
    ):
        return super()._download_and_prepare(
            dl_manager, "no_checks", **prepare_splits_kwargs
        )

    def as_streaming_dataset(self, split: Optional[str] = None, base_path: Optional[str] = None) -> Union[Dict[str, datasets.IterableDataset], datasets.IterableDataset]:
        return (
            super()
            .as_streaming_dataset(split, base_path=base_path)
            .map(loads_instance)
        )

    def as_dataset(
        self,
        split: Optional[datasets.Split] = None,
        run_post_process=True,
        verification_mode: Optional[Union[datasets.VerificationMode, str]] = None,
        in_memory=False,
    ) -> Union[datasets.Dataset, datasets.DatasetDict]:
        """Return a Dataset for the specified split.

        Args:
            split (`datasets.Split`):
                Which subset of the data to return.
            run_post_process (`bool`, defaults to `True`):
                Whether to run post-processing dataset transforms and/or add
                indexes.
            verification_mode ([`VerificationMode`] or `str`, defaults to `BASIC_CHECKS`):
                Verification mode determining the checks to run on the
                downloaded/processed dataset information (checksums/size/splits/...).
            in_memory (`bool`, defaults to `False`):
                Whether to copy the data in-memory.

        Returns:
            datasets.Dataset

        :Example:

        .. code-block:: python

            from datasets import load_dataset_builder
            builder = load_dataset_builder('rotten_tomatoes')
            builder.download_and_prepare()
            ds = builder.as_dataset(split='train')
            print(ds)
            # prints:
            # Dataset({
            #     features: ['text', 'label'],
            #     num_rows: 8530
            # })
        """
        return (
            super()
            .as_dataset(split, run_post_process, verification_mode, in_memory)
            .with_transform(loads_batch)
        )