Title: how to convert a dict generator into a huggingface dataset. 
State: closed
Labels: description: Further information is requested
name: question

### Link

_No response_

### Description

Hey there, I have used seqio to get a well distributed mixture of samples from multiple dataset. However the resultant output from seqio is a python generator dict, which I cannot produce back into huggingface dataset.

The generator contains all the samples needed for training the model but I cannot convert it into a huggingface dataset.

The code looks like this:


```
for ex in seqio_data:
print(ex[“text”])
```

I need to convert the seqio_data (generator) into huggingface dataset.


the complete seqio code goes here:
```
import functools

import seqio
import tensorflow as tf
import t5.data
from datasets import load_dataset
from t5.data import postprocessors
from t5.data import preprocessors
from t5.evaluation import metrics
from seqio import FunctionDataSource, utils

TaskRegistry = seqio.TaskRegistry



def gen_dataset(split, shuffle=False, seed=None, column="text", dataset_params=None):
    dataset = load_dataset(**dataset_params)
    if shuffle:
        if seed:
            dataset = dataset.shuffle(seed=seed)
        else:
            dataset = dataset.shuffle()
    while True:
        for item in dataset[str(split)]:
            yield item[column]


def dataset_fn(split, shuffle_files, seed=None, dataset_params=None):
    return tf.data.Dataset.from_generator(
        functools.partial(gen_dataset, split, shuffle_files, seed, dataset_params=dataset_params),
        output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_name)
    )


@utils.map_over_dataset
def target_to_key(x, key_map, target_key):
    """Assign the value from the dataset to target_key in key_map"""
    return {**key_map, target_key: x}



dataset_name = 'oscar-corpus/OSCAR-2109'
subset= 'mr'
dataset_params = {"path": dataset_name, "language":subset, "use_auth_token":True}
dataset_shapes = None

TaskRegistry.add(
    "oscar_marathi_corpus",
    source=seqio.FunctionDataSource(
        dataset_fn=functools.partial(dataset_fn, dataset_params=dataset_params),
        splits=("train", "validation"),
        caching_permitted=False,
        num_input_examples=dataset_shapes,
    ),
preprocessors=[
functools.partial(
target_to_key, key_map={
"targets": None,
}, target_key="targets")],
    output_features={"targets": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)},
    metric_fns=[]
)

dataset = seqio.get_mixture_or_task("oscar_marathi_corpus").get_dataset(
    sequence_length=None,
    split="train",
    shuffle=True,
    num_epochs=1,
    shard_info=seqio.ShardInfo(index=0, num_shards=10),
    use_cached=False,
    seed=42
)
for _, ex in zip(range(5), dataset):
     print(ex['targets'].numpy().decode())
```


### Owner

_No response_

Comments:
@albertvillanova @lhoestq , could you please help me on this issue. 

Hi ! As mentioned on the [forum](https://discuss.huggingface.co/t/how-to-wrap-a-generator-with-hf-dataset/18464), the simplest for now would be to define a [dataset script](https://huggingface.co/docs/datasets/dataset_script) which can contain your generator. But we can also explore adding something like `ds = Dataset.from_iterable(seqio_dataset)`

@lhoestq , hey i did as you instructed, but sadly i cannot get pass through the download_manager, as i dont have anything to download. i was skipping the ` def _split_generators(self, dl_manager):` function. but i cannot get around it. I get a `NotImplementedError: `

the following is my code for the same: 



```
import datasets 
import functools
import glob 
from datasets import load_from_disk
import seqio
import tensorflow as tf
import t5.data
from datasets import load_dataset
from t5.data import postprocessors
from t5.data import preprocessors
from t5.evaluation import metrics
from seqio import FunctionDataSource, utils

TaskRegistry = seqio.TaskRegistry

data_path = glob.glob("/home/stephen/Desktop/MEGA_CORPUS/COMBINED_CORPUS/*", recursive=False)


def gen_dataset(split, shuffle=False, seed=None, column="text", dataset_path=None):
    dataset = load_from_disk(dataset_path)
    if shuffle:
        if seed:
            dataset = dataset.shuffle(seed=seed)
        else:
            dataset = dataset.shuffle()
    while True:
        for item in dataset[str(split)]:
            yield item[column]


def dataset_fn(split, shuffle_files, seed=None, dataset_path=None):
    return tf.data.Dataset.from_generator(
        functools.partial(gen_dataset, split, shuffle_files, seed, dataset_path=dataset_path),
        output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_path)
    )

@utils.map_over_dataset
def target_to_key(x, key_map, target_key):
    """Assign the value from the dataset to target_key in key_map"""
    return {**key_map, target_key: x}


_CITATION = "Not ready yet"
_DESCRIPTION = "a custom seqio based mixed samples on a given temperature value, that again returns a dataset in HF dataset format well samples on the Mixture temperature"
_HOMEPAGE = "ldcil.org"

class CustomSeqio(datasets.GeneratorBasedBuilder):

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                }
            ),
            homepage="https://ldcil.org",
            citation=_CITATION,)

def generate_examples(self):
    seqio_train_list = []
    for lang in data_path:
        dataset_name = lang.split("/")[-1]
        dataset_shapes = None 

        TaskRegistry.add(
        str(dataset_name),
        source=seqio.FunctionDataSource(
            dataset_fn=functools.partial(dataset_fn, dataset_path=lang),
            splits=("train", "test"),
            caching_permitted=False,
            num_input_examples=dataset_shapes,
        ),
        preprocessors=[
        functools.partial(
        target_to_key, key_map={
        "targets": None,
        }, target_key="targets")],
            output_features={"targets": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)},
            metric_fns=[]
        )

        seqio_train_dataset = seqio.get_mixture_or_task(dataset_name).get_dataset(
        sequence_length=None,
        split="train",
        shuffle=True,
        num_epochs=1,
        shard_info=seqio.ShardInfo(index=0, num_shards=10),
        use_cached=False,
        seed=42)
        seqio_train_list.append(seqio_train_dataset)
    
    lang_name_list = []
    for lang in data_path:
        lang_name = lang.split("/")[-1]
        lang_name_list.append(lang_name)

    seqio_mixture = seqio.MixtureRegistry.add(
        "seqio_mixture",
        lang_name_list,
        default_rate=0.7)
    
    seqio_mixture_dataset = seqio.get_mixture_or_task("seqio_mixture").get_dataset(
    sequence_length=None,
    split="train",
    shuffle=True,
    num_epochs=1,
    shard_info=seqio.ShardInfo(index=0, num_shards=10),
    use_cached=False,
    seed=42)

    for id, ex in  enumerate(seqio_mixture_dataset):
        yield id, {"text": ex["targets"].numpy().decode()}
```

and i load it by:

`seqio_mixture = load_dataset("seqio_loader")`

@lhoestq , just to make things clear ... 

the following is my original code, thats not in the HF dataset loading script: 

```
import functools
import seqio
import tensorflow as tf
import t5.data
from datasets import load_from_disk
from t5.data import postprocessors
from t5.data import preprocessors
from t5.evaluation import metrics
from seqio import FunctionDataSource, utils
import glob 

TaskRegistry = seqio.TaskRegistry



def gen_dataset(split, shuffle=False, seed=None, column="text", dataset_path=None):
    dataset = load_from_disk(dataset_path)
    if shuffle:
        if seed:
            dataset = dataset.shuffle(seed=seed)
        else:
            dataset = dataset.shuffle()
    while True:
        for item in dataset[str(split)]:
            yield item[column]


def dataset_fn(split, shuffle_files, seed=None, dataset_path=None):
    return tf.data.Dataset.from_generator(
        functools.partial(gen_dataset, split, shuffle_files, seed, dataset_path=dataset_path),
        output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_path)
    )


@utils.map_over_dataset
def target_to_key(x, key_map, target_key):
    """Assign the value from the dataset to target_key in key_map"""
    return {**key_map, target_key: x}

data_path = glob.glob("/home/stephen/Desktop/MEGA_CORPUS/COMBINED_CORPUS/*", recursive=False)

seqio_train_list = []

for lang in data_path:
    dataset_name = lang.split("/")[-1]
    dataset_shapes = None 

    TaskRegistry.add(
    str(dataset_name),
    source=seqio.FunctionDataSource(
        dataset_fn=functools.partial(dataset_fn, dataset_path=lang),
        splits=("train", "test"),
        caching_permitted=False,
        num_input_examples=dataset_shapes,
    ),
    preprocessors=[
    functools.partial(
    target_to_key, key_map={
    "targets": None,
    }, target_key="targets")],
        output_features={"targets": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)},
        metric_fns=[]
    )

    seqio_train_dataset = seqio.get_mixture_or_task(dataset_name).get_dataset(
    sequence_length=None,
    split="train",
    shuffle=True,
    num_epochs=1,
    shard_info=seqio.ShardInfo(index=0, num_shards=10),
    use_cached=False,
    seed=42)
    seqio_train_list.append(seqio_train_dataset)

lang_name_list = []
for lang in data_path:
    lang_name = lang.split("/")[-1]
    lang_name_list.append(lang_name)

seqio_mixture = seqio.MixtureRegistry.add(
  "seqio_mixture",
  lang_name_list,
  default_rate=0.7
)

seqio_mixture_dataset = seqio.get_mixture_or_task("seqio_mixture").get_dataset(
    sequence_length=None,
    split="train",
    shuffle=True,
    num_epochs=1,
    shard_info=seqio.ShardInfo(index=0, num_shards=10),
    use_cached=False,
    seed=42)

for _, ex in zip(range(15), seqio_mixture_dataset):
    print(ex["targets"].numpy().decode())
```

where the seqio_mixture_dataset is the generator that i wanted to be wrapped in HF dataset. 

also additionally, could you please tell me how do i set the `default_rate=0.7` args where `seqio_mixture` is defined to be made as a custom option in the HF load_dataset() method,

maybe like this: 
`seqio_mixture_dataset = datasets.load_dataset("seqio_loader",temperature=0.5)`

I like the idea of having `Dataset.from_iterable(iterable)` in the API. The only problem is that we also want to make this part cachable, which is tricky if `iterable` is a generator. 

Some resources on this issue:
* https://github.com/uqfoundation/dill/issues/311
* https://stackoverflow.com/questions/7180212/why-cant-generators-be-pickled
* https://github.com/tonyroberts/generator_tools - python package for pickling generators; pickles bytecode, so it creates version-specific dumps

For the caching maybe we can have `Dataset.from_generator` as TF and pickle+hash the generator function (not the generator object itself) ?

And then keep `Dataset.from_iterable` fo pickable objects like lists

@lhoestq, @mariosasko do you too have any examples where the dataset is a generator and needs to be wrapped into hf dataset ? 

@lhoestq, following to my previous question ... what possibly could be done in this [link1](https://github.com/huggingface/datasets/issues/4417#issuecomment-1146627404) [link2](https://github.com/huggingface/datasets/issues/4417#issuecomment-1146627593) case? do you have any ideas? 

@lhoestq +1 for the `Dataset.from_generator` idea.

Having thought about it, let's avoid adding `Dataset.from_iterable` to the API since dictionaries are technically iteralbles ("iterable" is a broad term in Python), and we already provide `Dataset.from_dict`. And for lists maybe we can add `Dataset.from_list` similar to `pa.Table.from_pylist`. WDYT?


Hi @StephennFernandes!

To fix the issues in the copied code, rename `generate_examples` to` _generate_examples` and add one level of indentation as this is a method of `GeneratorBasedBuilder` and define `_split_generators` as follows (again as a method of `GeneratorBasedBuilder):
```python
 def _split_generators(self, dl_manager):
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={},
            ),
        ]
```

And if you are feeling extra adventurous, you can try to use ArrowWriter to directly create a cache file:
```python
from datasets import Dataset
from datasets.arrow_writer import ArrowWriter

writer = ArrowWriter(path="path/to/cache_file.arrow", writer_batch_size=1000)

with writer:
    for ex in generator:
        writer.write(ex) 
    writer.finalize()

dset = Dataset.from_file("path/to/cache_file.arrow")
```



I have a problem which I think is very similar: I would like to "stream" data to a HF Array (memory-mapped) Dataset, where the final size of the dataset is unknown, but could be much larger than what fits into memory.
What I want to end up with is an Array Dataset which I can open using `Dataset.load_from_disk(dataset_path="somename")` and use e.g. as the training set. 

For this I would have thought there should be an API which allows me to open/create the dataset (and define the features etc), then write examples to the dataset, but I could not find a way to do this. 

I tried doing this and it looks like it works, but it feels very hacky and I am not sure if this might fail to update some of the fields in the json files which may turn out to be important:
```
from datasets import Dataset, Features, ClassLabel, Sequence, Value
from datasets.arrow_writer import ArrowWriter                                                                                                                                                   
# 1) define the features
features = Features(dict(
    id=Value(dtype="string"),
    tokens=Sequence(feature=Value(dtype="string")),
    ner_tags=Sequence(feature=ClassLabel(names=['O', 'B-corporation', 'I-corporation', 'B-creative-work', 'I-creative-work', 'B-group', 'I-group', 'B-location', 'I-location', 'B-person', 'I-person', 'B-product', 'I-product'])),
))
# 2) create empty dataset for examples with these features and store to disk
empty = dict(
    id = [],
    tokens = [],
    ner_tags = [],
)
ds = Dataset.from_dict(empty, features=features)
ds.save_to_disk(dataset_path="debug_ds1")

# 3) directly write all the examples to the arrow dataset 
with ArrowWriter(path="debug_ds1/dataset.arrow") as writer: 
    writer.write(dict(id=0, tokens=["a", "b"], ner_tags=[0, 0])) 
    writer.write(dict(id=1, tokens=["x", "y"], ner_tags=[1, 0])) 
    writer.finalize() 
 
ds2 = Dataset.load_from_disk(dataset_path="debug_ds1")
len(ds2)
```
Is there a cleaner/proper way to do this?

I like the sound of `Dataset.from_iterable` or `Dataset.from_generator` (should not from iterable be able to handle from generator too as all generators are iterables?) but how would I define the features for me examples there? 

Hi @johann-petrak! You can pass the features directly to ArrowWriter's initializer like so `ArrowWriter(..., features=features)`.

And the reason why I prefer `Dataset.from_generator` over `Dataset.from_iterable` is mentioned in one of my previous comments.

@mariosasko so at the moment we still have to create a fake `Dataset` first and then use `ArrowWriter` to write an actual dataset? I'm using the latest version of `datasets` on pypi but my final file is always empty. Is there anything wrong with the code below?

```python
    total = 0
    with ArrowWriter(path=str(final_data_path), features=features) as writer:
        for batch in loader:
            for traj in batch:
                for generator in question_generators:
                    for xi in generator(traj):
                        # print(f"Question: {xi.question}, answer: {xi.answer}")
                        total += 1
                        writer.write(
                            {
                                "id": f"qa_{total}",
                                "question": xi.question,
                                "answer": xi.answer,
                            }
                        )
        writer.finalize()
    print(f"Total #questions = {total}") # this prints 402
```

This works for me if I then (actually I also close the writer: `writer.close()`)  open the Arrow file as a dataset using `ds=Dataset.from_file(final_data_path)` then `ds.save_to_disk(somedir)`. The Dataset created that way contains the expected examples.

Oh thanks. That did the trick I believe. Shouldn't ArrowWriter have a context manager that does these operations?

You can just use `Dataset.from_file` to get your dataset, no need to do an extra `save_to_disk` somewhere else ;)

I was thinking that `save_to_disk` is necessary when one wants to re-use that dataset as a proper HF dataset later, no?
At least what I wanted to achieve is create a dataset that can be opened like any other local or remote dataset. 

`save_to_disk`/`load_from_disk` is indeed more general, e.g. it supports datasets that consist in several files, and saves some extra info in a dataset_info.json file (description, citation, split sizes, etc.)

If you have one single file it's fine to simply do `.from_file()`