loaders.py

"""This section describes unitxt loaders.

Loaders: Generators of Unitxt Multistreams from existing date sources
==============================================================

Unitxt is all about readily preparing of any given data source for feeding into any given language model, and then,
postprocessing the model's output, preparing it for any given evaluator.

Through that journey, the data advances in the form of Unitxt Multistream, undergoing a sequential application
of various off the shelf operators (i.e, picked from Unitxt catalog), or operators easily implemented by inheriting.
The journey starts by a Unitxt Loeader bearing a Multistream from the given datasource.
A loader, therefore, is the first item on any Unitxt Recipe.

Unitxt catalog contains several loaders for the most popular datasource formats.
All these loaders inherit from Loader, and hence, implementing a loader to expand over a new type of datasource, is
straight forward.

Operators in Unitxt catalog:
LoadHF : loads from Huggingface dataset.
LoadCSV: loads from csv (comma separated value) files
LoadFromKaggle: loads datasets from the kaggle.com community site
LoadFromIBMCloud: loads a dataset from the IBM cloud.
------------------------
"""
import importlib
import itertools
import os
import tempfile
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Dict, Mapping, Optional, Sequence, Union

import pandas as pd
from datasets import load_dataset as hf_load_dataset
from tqdm import tqdm

from .logging_utils import get_logger
from .operator import SourceOperator
from .stream import MultiStream, Stream

logger = get_logger()
try:
    import ibm_boto3

    # from ibm_botocore.client import ClientError

    ibm_boto3_available = True
except ImportError:
    ibm_boto3_available = False


class Loader(SourceOperator):
    # The loader_limit an optional parameter used to control the maximum number of instances to load from the the source.
    # It is usually provided to the loader via the recipe (see standard.py)
    # The loader can use this value to limit the amount of data downloaded from the source
    # to reduce loading time.  However, this may not always be possible, so the
    # loader may ingore this.  In any case, the recipe, will limit the number of instances in the returned
    # stream, after load is complete.
    loader_limit: int = None
    pass


class LoadHF(Loader):
    path: str
    name: Optional[str] = None
    data_dir: Optional[str] = None
    split: Optional[str] = None
    data_files: Optional[
        Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]
    ] = None
    streaming: bool = True

    def process(self):
        try:
            with tempfile.TemporaryDirectory() as dir_to_be_deleted:
                dataset = hf_load_dataset(
                    self.path,
                    name=self.name,
                    data_dir=self.data_dir,
                    data_files=self.data_files,
                    streaming=self.streaming,
                    cache_dir=None if self.streaming else dir_to_be_deleted,
                    split=self.split,
                )
            if self.split is not None:
                dataset = {self.split: dataset}
        except (
            NotImplementedError
        ):  # streaming is not supported for zipped files so we load without streaming
            with tempfile.TemporaryDirectory() as dir_to_be_deleted:
                dataset = hf_load_dataset(
                    self.path,
                    name=self.name,
                    data_dir=self.data_dir,
                    data_files=self.data_files,
                    streaming=False,
                    keep_in_memory=True,
                    cache_dir=dir_to_be_deleted,
                    split=self.split,
                )
            if self.split is None:
                for split in dataset.keys():
                    dataset[split] = dataset[split].to_iterable_dataset()
            else:
                dataset = {self.split: dataset}

        return MultiStream.from_iterables(dataset)


class LoadCSV(Loader):
    files: Dict[str, str]
    chunksize: int = 1000

    def load_csv(self, file):
        for chunk in pd.read_csv(file, chunksize=self.chunksize):
            for _index, row in chunk.iterrows():
                yield row.to_dict()

    def process(self):
        return MultiStream(
            {
                name: Stream(generator=self.load_csv, gen_kwargs={"file": file})
                for name, file in self.files.items()
            }
        )


class MissingKaggleCredentialsError(ValueError):
    pass


# TODO write how to obtain kaggle credentials
class LoadFromKaggle(Loader):
    url: str

    def verify(self):
        super().verify()
        if importlib.util.find_spec("opendatasets") is None:
            raise ImportError(
                "Please install opendatasets in order to use the LoadFromKaggle loader (using `pip install opendatasets`) "
            )
        if not os.path.isfile("kaggle.json"):
            raise MissingKaggleCredentialsError(
                "Please obtain kaggle credentials https://christianjmills.com/posts/kaggle-obtain-api-key-tutorial/ and save them to local ./kaggle.json file"
            )

    def prepare(self):
        super().prepare()
        from opendatasets import download

        self.downloader = download

    def process(self):
        with TemporaryDirectory() as temp_directory:
            self.downloader(self.url, temp_directory)
            dataset = hf_load_dataset(temp_directory, streaming=False)

        return MultiStream.from_iterables(dataset)


class LoadFromIBMCloud(Loader):
    endpoint_url_env: str
    aws_access_key_id_env: str
    aws_secret_access_key_env: str
    bucket_name: str
    data_dir: str = None

    # Can be either:
    # 1. a list of file names, the split of each file is determined by the file name pattern
    # 2. Mapping: split -> file_name, e.g. {"test" : "test.json", "train": "train.json"}
    # 3. Mapping: split -> file_names, e.g. {"test" : ["test1.json", "test2.json"], "train": ["train.json"]}
    data_files: Union[Sequence[str], Mapping[str, Union[str, Sequence[str]]]]
    caching: bool = True

    def _download_from_cos(self, cos, bucket_name, item_name, local_file):
        logger.info(f"Downloading {item_name} from {bucket_name} COS")
        try:
            response = cos.Object(bucket_name, item_name).get()
            size = response["ContentLength"]
            body = response["Body"]
        except Exception as e:
            raise Exception(
                f"Unabled to access {item_name} in {bucket_name} in COS", e
            ) from e

        if self.loader_limit is not None:
            if item_name.endswith(".jsonl"):
                first_lines = list(
                    itertools.islice(body.iter_lines(), self.loader_limit)
                )
                with open(local_file, "wb") as downloaded_file:
                    for line in first_lines:
                        downloaded_file.write(line)
                        downloaded_file.write(b"\n")
                logger.info(
                    f"\nDownload successful limited to {self.loader_limit} lines"
                )
                return

        progress_bar = tqdm(total=size, unit="iB", unit_scale=True)

        def upload_progress(chunk):
            progress_bar.update(chunk)

        try:
            cos.Bucket(bucket_name).download_file(
                item_name, local_file, Callback=upload_progress
            )
            logger.info("\nDownload Successful")
        except Exception as e:
            raise Exception(
                f"Unabled to download {item_name} in {bucket_name}", e
            ) from e

    def prepare(self):
        super().prepare()
        self.endpoint_url = os.getenv(self.endpoint_url_env)
        self.aws_access_key_id = os.getenv(self.aws_access_key_id_env)
        self.aws_secret_access_key = os.getenv(self.aws_secret_access_key_env)
        root_dir = os.getenv("UNITXT_IBM_COS_CACHE", None) or os.getcwd()
        self.cache_dir = os.path.join(root_dir, "ibmcos_datasets")

        if not os.path.exists(self.cache_dir):
            Path(self.cache_dir).mkdir(parents=True, exist_ok=True)

    def verify(self):
        super().verify()
        assert ibm_boto3_available, "Please install ibm_boto3 in order to use the LoadFromIBMCloud loader (using `pip install ibm-cos-sdk`) "
        assert (
            self.endpoint_url is not None
        ), f"Please set the {self.endpoint_url_env} environmental variable"
        assert (
            self.aws_access_key_id is not None
        ), f"Please set {self.aws_access_key_id_env} environmental variable"
        assert (
            self.aws_secret_access_key is not None
        ), f"Please set {self.aws_secret_access_key_env} environmental variable"

    def process(self):
        cos = ibm_boto3.resource(
            "s3",
            aws_access_key_id=self.aws_access_key_id,
            aws_secret_access_key=self.aws_secret_access_key,
            endpoint_url=self.endpoint_url,
        )
        local_dir = os.path.join(self.cache_dir, self.bucket_name, self.data_dir)
        if not os.path.exists(local_dir):
            Path(local_dir).mkdir(parents=True, exist_ok=True)

        if isinstance(self.data_files, Mapping):
            data_files_names = list(self.data_files.values())
            if not isinstance(data_files_names[0], str):
                data_files_names = list(itertools.chain(*data_files_names))
        else:
            data_files_names = self.data_files

        for data_file in data_files_names:
            local_file = os.path.join(local_dir, data_file)
            if not self.caching or not os.path.exists(local_file):
                # Build object key based on parameters. Slash character is not
                # allowed to be part of object key in IBM COS.
                object_key = (
                    self.data_dir + "/" + data_file
                    if self.data_dir is not None
                    else data_file
                )
                self._download_from_cos(
                    cos, self.bucket_name, object_key, local_dir + "/" + data_file
                )

        if isinstance(self.data_files, list):
            dataset = hf_load_dataset(local_dir, streaming=False)
        else:
            dataset = hf_load_dataset(
                local_dir, streaming=False, data_files=self.data_files
            )

        return MultiStream.from_iterables(dataset)