AniketTec/github-issues huggingface dataset:
Title: Inconsistent Behavior Between `load_dataset` and `load_from_disk` When Loading Sharded Datasets
State: open

### Description

I encountered an inconsistency in behavior between `load_dataset` and `load_from_disk` when loading sharded datasets. Here is a minimal example to reproduce the issue:

#### Code 1: Using `load_dataset`
```python
from datasets import Dataset, load_dataset

# First save with max_shard_size=10
Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)

# Second save with max_shard_size=10
Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)

# Load the DatasetDict
loaded_datasetdict = load_dataset("my_sharded_datasetdict")
print(loaded_datasetdict)
```
**Output**:
- `train` has 1350 samples.
- `test` has 150 samples.

#### Code 2: Using `load_from_disk`
```python
from datasets import Dataset, load_from_disk

# First save with max_shard_size=10
Dataset.from_dict({"id": range(1000)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)

# Second save with max_shard_size=10
Dataset.from_dict({"id": range(500)}).train_test_split(test_size=0.1).save_to_disk("my_sharded_datasetdict", max_shard_size=10)

# Load the DatasetDict
loaded_datasetdict = load_from_disk("my_sharded_datasetdict")
print(loaded_datasetdict)
```
**Output**:
- `train` has 450 samples.
- `test` has 50 samples.

### Expected Behavior
I expected both `load_dataset` and `load_from_disk` to load the same dataset, as they are pointing to the same directory. However, the results differ significantly:
- `load_dataset` seems to merge all shards, resulting in a combined dataset.
- `load_from_disk` only loads the last saved dataset, ignoring previous shards.

### Questions
1. Is this behavior intentional? If so, could you clarify the difference between `load_dataset` and `load_from_disk` in the documentation?
2. If this is not intentional, could this be considered a bug?
3. What is the recommended way to handle cases where multiple datasets are saved to the same directory?


Thank you for your time and effort in maintaining this great library! I look forward to your feedback.

Comments:
Title: Support faster processing using pandas or polars functions in `IterableDataset.map()`
State: closed

Following the polars integration :)

Allow super fast processing using pandas or polars functions in `IterableDataset.map()` by adding support to pandas and polars formatting in `IterableDataset`

```python
import polars as pl
from datasets import Dataset

ds = Dataset.from_dict({"i": range(10)}).to_iterable_dataset()
ds = ds.with_format("polars")
ds = ds.map(lambda df: df.with_columns(pl.col("i").add(1).alias("i+1")), batched=True)
ds = ds.with_format(None)
print(next(iter(ds)))
# {'i': 0, 'i+1': 1}
```

It leverages arrow's zero-copy features from/to pandas and polars.

related to https://github.com/huggingface/datasets/issues/3444 #6762 

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7370). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

merging this and will make some docs and communications around using polars for optimizing data processing :)
Title: Importing dataset gives unhelpful error message when filenames in metadata.csv are not found in the directory
State: open

### Describe the bug

While importing an audiofolder dataset, where the names of the audiofiles don't correspond to the filenames in the metadata.csv, we get an unclear error message that is not helpful for the debugging, i.e. 
```
ValueError: Instruction "train" corresponds to no data!
```

### Steps to reproduce the bug

Assume an audiofolder with audiofiles, filename1.mp3, filename2.mp3 etc and a file metadata.csv which contains the columns file_name and sentence. The file_names are formatted like filename1.mp3, filename2.mp3 etc. 

Load the audio
```
from datasets import load_dataset
load_dataset("audiofolder", data_dir='/path/to/audiofolder')
```
When the file_names in the csv are not in sync with the filenames in the audiofolder, then we get an Error message:
```
File /opt/conda/lib/python3.12/site-packages/datasets/arrow_reader.py:251, in BaseReader.read(self, name, instructions, split_infos, in_memory)
    249 if not files:
    250     msg = f'Instruction "{instructions}" corresponds to no data!'
--> 251     raise ValueError(msg)
    252 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)

ValueError: Instruction "train" corresponds to no data!
```
load_dataset has a default value for the argument split = 'train'. 


### Expected behavior

It would be better to get an error report something like:
```
The metadata.csv file has different filenames than the files in the datadirectory. 
```
It would have saved me 4 hours of debugging. 

### Environment info

- `datasets` version: 3.2.0
- Platform: Linux-5.14.0-427.40.1.el9_4.x86_64-x86_64-with-glibc2.39
- Python version: 3.12.8
- `huggingface_hub` version: 0.27.0
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0

Comments:
I'd prefer even more verbose errors; like `"file123.mp3" is referenced in metadata.csv, but not found in the data directory '/path/to/audiofolder' ! (and 100+ more missing files)` Or something along those lines.
Title: Add with_split to DatasetDict.map
State: closed

#7356

Comments:
Can you check this out, @lhoestq?

cc @lhoestq @albertvillanova 

@lhoestq


@lhoestq


@lhoestq

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7368). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

@lhoestq 

@lhoestq please...

Thank you so much for reviewing this PR! Have a great weekend~
Title: Dataset.from_dict() can't handle large dict
State: open

### Describe the bug

I have 26,000,000 3-tuples. When I use Dataset.from_dict() to load, neither. py nor Jupiter notebook can run successfully. This is my code:
```
    # len(example_data) is 26,000,000, 'diff' is a text
    diff1_list = [example_data[i].texts[0] for i in range(len(example_data))]
    diff2_list = [example_data[i].texts[1] for i in range(len(example_data))]
    label_list = [example_data[i].label for i in range(len(example_data))]

    embedding_dataset = Dataset.from_dict({
        "diff1": diff1_list,
        "diff2": diff2_list,
        "label": label_list
    })
```

### Steps to reproduce the bug

1. Initialize a large 3-tuple, e.g. 26,000,000
2. Use Dataset.from_dict() to load

### Expected behavior

Dataset.from_dict() run successfully

### Environment info

sentence-transformers                   3.3.1

Comments:
Title: A parameter is specified but not used in datasets.arrow_dataset.Dataset.from_pandas()
State: open

### Describe the bug

I am interested in creating train, test and eval splits from a pandas Dataframe, therefore I was looking at the possibilities I can follow. I noticed the split parameter and was hopeful to use it in order to generate the 3 at once, however, while trying to understand the code, i noticed that it has no added value (correct me if I am wrong or misunderstood the code). 


from_pandas function code :

```python
    if info is not None and features is not None and info.features != features:
            raise ValueError(
                f"Features specified in `features` and `info.features` can't be different:\n{features}\n{info.features}"
            )
        features = features if features is not None else info.features if info is not None else None
        if info is None:
            info = DatasetInfo()
        info.features = features
        table = InMemoryTable.from_pandas(
            df=df,
            preserve_index=preserve_index,
        )
        if features is not None:
            # more expensive cast than InMemoryTable.from_pandas(..., schema=features.arrow_schema)
            # needed to support the str to Audio conversion for instance
            table = table.cast(features.arrow_schema)
        return cls(table, info=info, split=split)
```

### Steps to reproduce the bug

```python
from datasets import Dataset
# Filling the split parameter with whatever causes no harm at all
data = Dataset.from_pandas(self.raw_data, split='egiojegoierjgoiejgrefiergiuorenvuirgurthgi')
```

### Expected behavior

Would be great if there is no split parameter (if it isn't working), or to add a concrete example of how it can be used.

### Environment info

- `datasets` version: 3.2.0
- Platform: Linux-5.15.0-127-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.27.1
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0

Comments:
Title: ImportError: To support decoding images, please install 'Pillow'.
State: open

### Describe the bug

Following this tutorial locally using a macboko and VSCode: https://huggingface.co/docs/diffusers/en/tutorials/basic_training
This line of code: for i, image in enumerate(dataset[:4]["image"]):
throws: ImportError: To support decoding images, please install 'Pillow'.

Pillow is installed.

### Steps to reproduce the bug

Run the tutorial


### Expected behavior

Images should be rendered

### Environment info

MacBook, VSCode

Comments:
what's your `pip show Pillow` output

same issue.. my pip show Pillow output as below:

```
Name: pillow
Version: 11.1.0
Summary: Python Imaging Library (Fork)
Home-page: https://python-pillow.github.io/
Author: 
Author-email: "Jeffrey A. Clark" <aclark@aclark.net>
License: MIT-CMU
Location: [/opt/homebrew/lib/python3.10/site-packages](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/lib/python3.10/site-packages)
Requires: 
Required-by:
```

I encountered the same problem on Ubuntu system, my pip show Pillow output as below:

```
Name: pillow
Version: 10.4.0
Summary: Python Imaging Library (Fork)
Home-page: https://python-pillow.org/
Author: 
Author-email: "Jeffrey A. Clark" <[aclark@aclark.net](mailto:aclark@aclark.net)>
License: HPND
Location: /home/shunying/.local/lib/python3.8/site-packages
Requires: 
Required-by: 
```

Well, solved this by specifying the pip version to my conda virtual environment :)

I have also encountered this. It's a strange thing that's happening.

Checking the code `datasets` it uses `importlib.util.find_spec("PIL")` to verify if `PIL` is installed. While both `pip show` and `importlib` work correctly, I still got the error.

In my case, restarting and redoing the `datasets` import helped. Seems weird to me.
Title: Fix lock permission
State: open

All files except lock file have proper permission obeying `ACL` property if it is set.

If the cache directory has `ACL` property, it should be respected instead of just using `umask` for permission.
To fix it, just create a lock file and pass the created `mode`.

By creating a lock file with `touch()` before `FileLock` create it with `mode`,
- if `ACL` is not set, same as before
- if `ACL` is set, `ACL` is respected

If it is acceptable, it should be also applied to [`huggingface_hub`](https://github.com/huggingface/huggingface_hub/blob/2702ec2a2bd0124cc1fddfd72ccb1297b2478148/src/huggingface_hub/utils/_fixes.py#L95) I guess.

Comments:
Title: Fix remove_columns in the formatted case
State: open

`remove_columns` had no effect when running a function in `.map()` on dataset that is formatted

This aligns the logic of `map()` with the non formatted case and also with with https://github.com/huggingface/datasets/pull/7353

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7358). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: How about adding a feature to pass the key when performing map on DatasetDict?
State: closed
Labels: description: New feature or request
name: enhancement

### Feature request

Add a feature to pass the key of the DatasetDict when performing map

### Motivation

I often preprocess using map on DatasetDict. 
Sometimes, I need to preprocess train and valid data differently depending on the task. 
So, I thought it would be nice to pass the key (like train, valid) when performing map on DatasetDict. 

What do you think?

### Your contribution

I can submit a pull request to add the feature to pass the key of the DatasetDict when performing map.

Comments:
@lhoestq 
If it's okay with you, can I work on this?

Hi ! Can you give an example of what it would look like to use this new feature ?

Note that currently you can already do

```python
ds["train"] = ds["train"].map(process_train)
ds["test"] = ds["test"].map(process_test)
```

@lhoestq 
Thanks for the response! 
Let me clarify what I'm looking for with an example:

Currently, we need to write separate processing functions or call .map() separately:
```python
# Current approach
def process_train(example):
    # Training-specific processing
    return example

def process_valid(example):
    # Validation-specific processing
    return example

ds["train"] = ds["train"].map(process_train)
ds["valid"] = ds["valid"].map(process_valid)
```

What I'm proposing is to have a single processing function that knows which split it's processing:

```python
# Proposed feature
def process(example, split_key):
    if split_key == "train":
        # Training-specific processing
    elif split_key == "valid":
        # Validation-specific processing
    return example

# Using with_key=True to pass the split information
ds = ds.map(process, with_key=True)
```

This becomes particularly useful when:
1. The processing logic is heavily shared between splits but needs minor adjustments
2. You want to maintain the processing logic in one place for better maintainability
3. The processing function is complex and you want to avoid duplicating code

So I wanted to request this feature to achieve this kind of functionality. 
I've created a draft PR implementing this: https://github.com/huggingface/datasets/pull/7240/files


I see ! I think it makes sense, and it's more readable than doing something like this:
```python
from functools import partial
ds = DatasetDict({key: ds[key].map(partial(process, split_key=key)) for key in ds})
```

PS: you named the argument `with_key` in your example, but it might be even clearer with it's named `with_split` maybe no ?

@lhoestq I agree. 
It seems better to use `with_split`.
So can I open a PR with this change?

Sure !
Title: A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
State: closed

### Describe the bug

Following this tutorial: https://huggingface.co/docs/diffusers/en/tutorials/basic_training and running it locally using VSCode on my MacBook.  The first line in the tutorial fails:  from datasets import load_dataset
dataset = load_dataset('huggan/smithsonian_butterflies_subset', split="train"). with this error: 
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2. and ImportError: numpy.core.multiarray failed to import.   

Does from datasets import load_dataset really use NumPy 1.x?  

### Steps to reproduce the bug

Open VSCode.  create a new venv.  Create a new ipynb file.  Import pip install diffusers[training] try to run this line of code: from datasets import load_dataset

### Expected behavior

data is loaded

### Environment info

ran this: datasets-cli env
and got A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Comments:
recreated .venv and run this: pip install diffusers[training]==0.11.1
Title: changes to MappedExamplesIterable to resolve #7345
State: closed

modified `MappedExamplesIterable` and `test_iterable_dataset.py::test_mapped_examples_iterable_with_indices`

fix #7345

@lhoestq 

Comments:
I noticed that `Dataset.map` has a more complex output depending on `remove_columns`. In particular [this](https://github.com/huggingface/datasets/blob/6457be66e2ef88411281eddc4e7698866a3977f1/src/datasets/arrow_dataset.py#L3371) line removes columns from output if the input is being modified in place (i.e. `input_columns = None`). I tried to mimic this behaviour in `MappedExamplesIterable` by checking if the input and output point to the same dictionary object.

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7353). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Webdataset special columns in last position
State: closed

Place columns "__key__" and "__url__" in last position in the Dataset Viewer since they are not the main content

before:

<img width="1012" alt="image" src="https://github.com/user-attachments/assets/b556c1fe-2674-4ba0-9643-c074aa9716fd" />


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7349). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Catch OSError for arrow
State: closed

fixes https://github.com/huggingface/datasets/issues/7346

(also updated `ruff` and appleid style changes)

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7348). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
Title: Different behaviour of IterableDataset.map vs Dataset.map with remove_columns
State: closed

### Describe the bug

The following code

```python
import datasets as hf
ds1 = hf.Dataset.from_list([{'i': i} for i in [0,1]])
#ds1 = ds1.to_iterable_dataset()
ds2 = ds1.map(
    lambda i: {'i': i+1},
    input_columns = ['i'],
    remove_columns = ['i']
)   
list(ds2)
```

produces

```python
[{'i': 1}, {'i': 2}]
```

as expected. If the line that converts `ds1` to iterable is uncommented so that the `ds2` is a map of an `IterableDataset`, the result is 

```python
[{},{}]
```

I expected the output to be the same as before. It seems that in the second case the removed column is not added back into the output.

The issue seems to be [here](https://github.com/huggingface/datasets/blob/6c6a82a573f946c4a81069f56446caed15cee9c2/src/datasets/iterable_dataset.py#L1093): the columns are removed after the mapping which is not what we want (or what the [documentation says](https://github.com/huggingface/datasets/blob/6c6a82a573f946c4a81069f56446caed15cee9c2/src/datasets/iterable_dataset.py#L2370)) because we want the columns removed from the transformed example but then added if the map produced them.

This is `datasets==3.2.0` and `python==3.10`



### Steps to reproduce the bug

see above

### Expected behavior

see above

### Environment info

see above

Comments:
Good catch ! Do you think you can open a PR to fix this issue ?
Title: [Bug] Inconsistent behavior of data_files and data_dir in load_dataset method.
State: closed

### Describe the bug

Inconsistent operation of data_files and data_dir in load_dataset method.


### Steps to reproduce the bug

# First
I have three files, named 'train.json', 'val.json', 'test.json'.
Each one has a simple dict `{text:'aaa'}`.
Their path are `/data/train.json`,  `/data/val.json`,  `/data/test.json`
I load dataset with `data_files` argument:
```py
files = [os.path.join('./data',file) for file in os.listdir('./data')]
ds = load_dataset(
            path='json',
            data_files=files,)
```
And I get:
```py
DatasetDict({
    train: Dataset({
        features: ['text'],
        num_rows: 3
    })
})
```
However, If I load dataset with `data_dir` argument:
```py
ds = load_dataset(
            path='json',
            data_dir='./data',)
```
And I get:
```py
DatasetDict({
    train: Dataset({
        features: ['text'],
        num_rows: 1
    })
    validation: Dataset({
        features: ['text'],
        num_rows: 1
    })
    test: Dataset({
        features: ['text'],
        num_rows: 1
    })
})
```
Two results are not the same. Their behaviors are not equal, even if the statement [here](https://github.com/huggingface/datasets/blob/d0c152a979d91cc34b605c0298aebc650ab7dd27/src/datasets/load.py#L1790) said that their behaviors are equal. 


# Second
If some filename include 'test' while others do not, `load_dataset` only return `test` dataset and others files are **abandoned**.
Given two files named `test.json` and `1.json`
Each one has a simple dict `{text:'aaa'}`.
I load the dataset using:
```py
ds = load_dataset(
            path='json',
            data_dir='./data',)
```
Only `test` is returned, `1.json` is missing:
```py
DatasetDict({
    test: Dataset({
        features: ['text'],
        num_rows: 1
    })
})
```
Things do not change even I manually set `split='train'`

 



### Expected behavior

1. Fix the above bugs.
2. Although the document says that load_dataset method will `Find which file goes into which split (e.g. train/test) based on file and directory names or on the YAML configuration`, I hope I can manually decide whether to do so. Sometimes users may accidentally put a `test` string in the filename but they just want a single `train` dataset. If the number of files in `data_dir` is huge, it's not easy to find out what cause the second situation metioned above.

### Environment info

datasets==3.2.0
Ubuntu18.84

Comments:
Hi ! `data_files` with a list is equivalent to `data_files={"train": data_files}` with a train test only.

When no split are specified, they are inferred based on file names, and files with no apparent split are ignored

Thanks for your reply!
`files with no apparent split are ignored`. Is there a option that I can choose to ignored it or not as I mention aboved? Thanks!

To include all the files, the best way is to pass `data_files` yourself. There is no option to disable split detection at the moment

Thanks! I hope you guys can consider adding this option in the future. :)