Enum used in map functions will raise a RecursionError with dill.
 ## Describe the bug

Enums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https://github.com/uqfoundation/dill/issues/250#issuecomment-852566284

In my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above.

## Steps to reproduce the bug
```python
from datasets import load_dataset
from enum import Enum

class A(Enum):
    a = 'a'

def main():
    a = A.a

    def f(x):
        return {} if a == a.a else x

    ds = load_dataset('cnn_dailymail', '3.0.0')['test']
    ds = ds.map(f, num_proc=15)

if __name__ == "__main__":
    main()
```

## Expected results
The known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood.

## Actual results

```python
  File "/home/xxxx/miniconda3/lib/python3.8/site-packages/dill/_dill.py", line 1373, in save_type
    pickler.save_reduce(_create_type, (type(obj), obj.__name__,
  File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 690, in save_reduce
    save(args)
  File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 558, in save
    f(self, obj)  # Call unbound method with explicit self
  File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 899, in save_tuple
    save(element)
  File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 534, in save
    self.framer.commit_frame()
  File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 220, in commit_frame
    if f.tell() >= self._FRAME_SIZE_TARGET or force:
RecursionError: maximum recursion depth exceeded while calling a Python object
```

## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 3.0.0

 I have the same bug.
the code is as follows:
![image](https://user-images.githubusercontent.com/84262181/139785849-620dd4ac-86ce-4212-8163-942bbca305aa.png)
the error is:
![image](https://user-images.githubusercontent.com/84262181/139785899-88a9bd75-c60b-45a5-b819-830c7c096f3d.png)

Look for the solution for this bug.


Enum used in map functions will raise a RecursionError with dill.
 ## Describe the bug

Enums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https://github.com/uqfoundation/dill/issues/250#issuecomment-852566284

In my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above.

## Steps to reproduce the bug
```python
from datasets import load_dataset
from enum import Enum

class A(Enum):
    a = 'a'

def main():
    a = A.a

    def f(x):
        return {} if a == a.a else x

    ds = load_dataset('cnn_dailymail', '3.0.0')['test']
    ds = ds.map(f, num_proc=15)

if __name__ == "__main__":
    main()
```

## Expected results
The known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood.

## Actual results

```python
  File "/home/xxxx/miniconda3/lib/python3.8/site-packages/dill/_dill.py", line 1373, in save_type
    pickler.save_reduce(_create_type, (type(obj), obj.__name__,
  File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 690, in save_reduce
    save(args)
  File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 558, in save
    f(self, obj)  # Call unbound method with explicit self
  File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 899, in save_tuple
    save(element)
  File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 534, in save
    self.framer.commit_frame()
  File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 220, in commit_frame
    if f.tell() >= self._FRAME_SIZE_TARGET or force:
RecursionError: maximum recursion depth exceeded while calling a Python object
```

## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 3.0.0

 Hi ! I think your RecursionError comes from a different issue @BitcoinNLPer , could you open a separate issue please ?

Also which dataset are you using ? I tried loading `CodedotAI/code_clippy` but I get a different error
```python
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/load.py", line 1615, in load_dataset
    **config_kwargs,
  File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/load.py", line 1446, in load_dataset_builder
    builder_cls = import_main_class(dataset_module.module_path)
  File "/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/load.py", line 101, in import_main_class
    module = importlib.import_module(module_path)
  File "/Users/quentinlhoest/.virtualenvs/hf-datasets/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
  File "<frozen importlib._bootstrap>", line 983, in _find_and_load
  File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 728, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/Users/quentinlhoest/.cache/huggingface/modules/datasets_modules/datasets/CodedotAI___code_clippy/d332f69d036e8c80f47bc9a96d676c3fa30cb50af7bb81e2d4d12e80b83efc4d/code_clippy.py", line 66, in <module>
    url_elements = results.find_all("a")
AttributeError: 'NoneType' object has no attribute 'find_all'
```


Support multi-worker with streaming dataset (IterableDataset).
 **Is your feature request related to a problem? Please describe.**
The current `.map` does not support multi-process, CPU can become bottleneck if the pre-processing is complex (e.g. t5 span masking).

**Describe the solution you'd like**
Ideally `.map` should support multi-worker like tfds, with `AUTOTUNE`.

**Describe alternatives you've considered**
A simpler solution is to shard the dataset and process it in parallel with pytorch dataloader. The shard does not need to be of equal size.
* https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset

**Additional context**

 Hi ! This is a great idea :)
I think we could have something similar to what we have in `datasets.Dataset.map`, i.e. a `num_proc` parameter that tells how many processes to spawn to parallelize the data processing.

Regarding AUTOTUNE, this could be a nice feature as well, we could see how to add it in a second step


load_dataset("financial_phrasebank") NonMatchingChecksumError
 ## Describe the bug
Attempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError

## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("financial_phrasebank", 'sentences_allagree')
```

## Expected results
I expect to see the financial_phrasebank dataset downloaded successfully

## Actual results
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip']

## Environment info
- `datasets` version: 1.9.0
- Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6
- Python version: 3.7.10
- PyArrow version: 4.0.1

 Hi! It's probably because this dataset is stored on google drive and it has a per day quota limit. It should work if you retry, I was able to initiate the download.

Similar issue [here](https://github.com/huggingface/datasets/issues/2646)


load_dataset("financial_phrasebank") NonMatchingChecksumError
 ## Describe the bug
Attempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError

## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("financial_phrasebank", 'sentences_allagree')
```

## Expected results
I expect to see the financial_phrasebank dataset downloaded successfully

## Actual results
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip']

## Environment info
- `datasets` version: 1.9.0
- Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6
- Python version: 3.7.10
- PyArrow version: 4.0.1

 Hi ! Loading the dataset works on my side as well.
Feel free to try again and let us know if it works for you know


load_dataset("financial_phrasebank") NonMatchingChecksumError
 ## Describe the bug
Attempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError

## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("financial_phrasebank", 'sentences_allagree')
```

## Expected results
I expect to see the financial_phrasebank dataset downloaded successfully

## Actual results
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip']

## Environment info
- `datasets` version: 1.9.0
- Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6
- Python version: 3.7.10
- PyArrow version: 4.0.1

 Thank you! I've been trying periodically for the past month, and no luck yet with this particular dataset. Just tried again and still hitting the checksum error.

Code:

`dataset = load_dataset("financial_phrasebank", "sentences_allagree") `

Traceback:

```
---------------------------------------------------------------------------
NonMatchingChecksumError                  Traceback (most recent call last)
<ipython-input-2-55cc2144f31e> in <module>
----> 1 dataset = load_dataset("financial_phrasebank", "sentences_allagree")

/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)
    859         ignore_verifications=ignore_verifications,
    860         try_from_hf_gcs=try_from_hf_gcs,
--> 861         use_auth_token=use_auth_token,
    862     )
    863

/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
    582                     if not downloaded_from_gcs:
    583                         self._download_and_prepare(
--> 584                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
    585                         )
    586                     # Sync info

/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
    642         if verify_infos:
    643             verify_checksums(
--> 644                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
    645             )
    646

/opt/conda/lib/python3.7/site-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
     38     if len(bad_urls) > 0:
     39         error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))
     41     logger.info("All the checksums matched successfully" + for_verification_name)
     42

NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip']
```


Progress bars are not properly rendered in Jupyter notebook
 ## Describe the bug
The progress bars are not Jupyter widgets; regular progress bars appear (like in a terminal).

## Steps to reproduce the bug
```python
ds.map(tokenize, num_proc=10)
```

## Expected results
Jupyter widgets displaying the progress bars.

## Actual results
Simple plane progress bars.

cc: Reported by @thomwolf
 To add my experience when trying to debug this issue:

Seems like previously the workaround given [here](https://github.com/tqdm/tqdm/issues/485#issuecomment-473338308) worked around this issue. But with the latest version of jupyter/tqdm I still get terminal warnings that IPython tried to send a message from a forked process.


Progress bars are not properly rendered in Jupyter notebook
 ## Describe the bug
The progress bars are not Jupyter widgets; regular progress bars appear (like in a terminal).

## Steps to reproduce the bug
```python
ds.map(tokenize, num_proc=10)
```

## Expected results
Jupyter widgets displaying the progress bars.

## Actual results
Simple plane progress bars.

cc: Reported by @thomwolf
 Hi @mludv, thanks for the hint!!! :)

We will definitely take it into account to try to fix this issue... It seems somehow related to `multiprocessing` and `tqdm`...


Load datasets from the Hub without requiring a dataset script
 As a user I would like to be able to upload my csv/json/text/parquet/etc. files in a dataset repository on the Hugging Face Hub and be able to load this dataset with `load_dataset` without having to implement a dataset script.

Moreover I would like to be able to specify which file goes into which split using the `data_files` argument.

This feature should be compatible with private repositories and dataset streaming.

This can be implemented by checking the extension of the files in the dataset repository and then by using the right dataset builder that is already packaged in the library (csv/json/text/parquet/etc.)
 This is so cool, let us know if we can help with anything on the hub side (@Pierrci @elishowk) 🎉


Integration with AugLy

Integration with AugLy
 **Is your feature request related to a problem? Please describe.**
Facebook recently launched a library, [AugLy](https://github.com/facebookresearch/AugLy) , that has a unified API for augmentations for image, video and text.

It would be pretty exciting to have it hooked up to HF libraries so that we can make NLP models robust to misspellings or to punctuation, or emojis etc. Plus, with Transformers supporting more CV use cases, having augmentations support becomes crucial.

**Describe the solution you'd like**
The biggest difference between augmentations and preprocessing is that preprocessing happens only once, but you are running augmentations once per epoch. AugLy operates on text directly, so this breaks the typical workflow where we would run the tokenizer once, set format to pt tensors and be ready for the Dataloader.

**Describe alternatives you've considered**

One possible way of implementing these is to make a custom Dataset class where getitem(i) runs the augmentation and the tokenizer every time, though this would slow training down considerably given we wouldn't even run the tokenizer in batches.

 Hi,

you can define your own custom formatting with `Dataset.set_transform()` and then run the tokenizer with the batches of augmented data as follows:
```python
dset = load_dataset("imdb", split="train")  # Let's say we are working with the IMDB dataset
dset.set_transform(lambda ex: {"text": augly_text_augmentation(ex["text"])}, columns="text", output_all_columns=True)
dataloader = torch.utils.data.DataLoader(dset, batch_size=32)
for epoch in range(5):
    for batch in dataloader:
       tokenizer_output = tokenizer(batch.pop("text"), padding=True, truncation=True, return_tensors="pt")
       batch.update(tokenizer_output)
       output = model(**batch)
       ...
```


`filelock.py` Error

`filelock.py` Error
 ## Describe the bug

It seems that the `filelock.py` went error.

```
>>> ds=load_dataset('xsum')

^CTraceback (most recent call last):
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
    fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
OSError: [Errno 37] No locks available
```

According to error log, it is OSError, but there is an `except` in the `_acquire` function.

```
    def _acquire(self):
        open_mode = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
        try:
            fd = os.open(self._lock_file, open_mode)
        except (IOError, OSError):
            pass
        else:
            self._lock_file_fd = fd
        return None
```

I don't know why it stucked rather than `pass` directly.

I am not quite familiar with filelock operation, so any help is highly appriciated.

## Steps to reproduce the bug
```python

ds = load_dataset('xsum')
```

## Expected results
A clear and concise description of the expected results.

## Actual results
```
>>> ds=load_dataset('xsum')

^CTraceback (most recent call last):
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
    fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
OSError: [Errno 37] No locks available

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 818, in load_dataset
    use_auth_token=use_auth_token,
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 470, in prepare_module
    with FileLock(lock_path):
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 323, in __enter__
    self.acquire()
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 272, in acquire
    self._acquire()
  File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
    fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
KeyboardInterrupt
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->

- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-135-generic-x86_64-with-debian-buster-sid
- Python version: 3.6.13
- PyArrow version: 4.0.1

 Hi @liyucheng09, thanks for reporting.

Apparently this issue has to do with your environment setup. One question: is your data in an NFS share? Some people have reported this error when using `fcntl` to write to an NFS share... If this is the case, then it might be that your NFS just may not be set up to provide file locks. You should ask your system administrator, or try these commands in the terminal:
```shell
sudo systemctl enable rpc-statd
sudo systemctl start rpc-statd
```


Jsonlines export error

Jsonlines export error
 ## Describe the bug
When exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default

## Steps to reproduce the bug
This what I'm running:

in python:

```
from datasets import load_dataset
ptb = load_dataset("ptb_text_only")
ptb["train"].to_json("ptb.jsonl")
```

then out of python:

```
head -10000 ptb.jsonl
```

## Expected results
Properly separated lines

## Actual results
The last line is a concatenation of two lines

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->

- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyArrow version: 4.0.1
 For some reason this happens (both `datasets` version are on master) only on Python 3.6 and not Python 3.8.


Jsonlines export error

Jsonlines export error
 ## Describe the bug
When exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default

## Steps to reproduce the bug
This what I'm running:

in python:

```
from datasets import load_dataset
ptb = load_dataset("ptb_text_only")
ptb["train"].to_json("ptb.jsonl")
```

then out of python:

```
head -10000 ptb.jsonl
```

## Expected results
Properly separated lines

## Actual results
The last line is a concatenation of two lines

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->

- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyArrow version: 4.0.1
 @TevenLeScao we are using `pandas` to serialize the dataset to JSON Lines. So it must be due to pandas. Could you please check the pandas version causing the issue?


Jsonlines export error

Jsonlines export error
 ## Describe the bug
When exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default

## Steps to reproduce the bug
This what I'm running:

in python:

```
from datasets import load_dataset
ptb = load_dataset("ptb_text_only")
ptb["train"].to_json("ptb.jsonl")
```

then out of python:

```
head -10000 ptb.jsonl
```

## Expected results
Properly separated lines

## Actual results
The last line is a concatenation of two lines

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->

- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyArrow version: 4.0.1
 @TevenLeScao I have just checked it: this was a bug in `pandas` and it was fixed in version 1.2: https://github.com/pandas-dev/pandas/pull/36898


Jsonlines export error

Jsonlines export error
 ## Describe the bug
When exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default

## Steps to reproduce the bug
This what I'm running:

in python:

```
from datasets import load_dataset
ptb = load_dataset("ptb_text_only")
ptb["train"].to_json("ptb.jsonl")
```

then out of python:

```
head -10000 ptb.jsonl
```

## Expected results
Properly separated lines

## Actual results
The last line is a concatenation of two lines

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->

- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyArrow version: 4.0.1
 Sorry, I was also talking to teven offline so I already had the PR ready before noticing x)


Jsonlines export error

Jsonlines export error
 ## Describe the bug
When exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default

## Steps to reproduce the bug
This what I'm running:

in python:

```
from datasets import load_dataset
ptb = load_dataset("ptb_text_only")
ptb["train"].to_json("ptb.jsonl")
```

then out of python:

```
head -10000 ptb.jsonl
```

## Expected results
Properly separated lines

## Actual results
The last line is a concatenation of two lines

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->

- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyArrow version: 4.0.1
 I was also already working in my PR... Nevermind. Next time we should pay attention if there is somebody (self-)assigned to an issue and if he/she is still working on it before overtaking it... 😄


Streaming local gzip compressed JSON line files is not working
 ## Describe the bug
Using streaming to iterate on local gzip compressed JSON files raise a file not exist error

## Steps to reproduce the bug
```python
from datasets import load_dataset

streamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)

next(iter(streamed_dataset))
```

## Actual results
```
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-6-27a664e29784> in <module>
----> 1 next(iter(streamed_dataset))

~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self)
    336
    337     def __iter__(self):
--> 338         for key, example in self._iter():
    339             if self.features:
    340                 # we encode the example for ClassLabel feature types for example

~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in _iter(self)
    333         else:
    334             ex_iterable = self._ex_iterable
--> 335         yield from ex_iterable
    336
    337     def __iter__(self):

~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self)
     76
     77     def __iter__(self):
---> 78         for key, example in self.generate_examples_fn(**self.kwargs):
     79             yield key, example
     80

~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in wrapper(**kwargs)
    282     def wrapper(**kwargs):
    283         python_formatter = PythonFormatter()
--> 284         for key, table in generate_tables_fn(**kwargs):
    285             batch = python_formatter.format_batch(table)
    286             for i, example in enumerate(_batch_to_examples(batch)):

~/Documents/GitHub/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files, original_files)
     85                         file,
     86                         read_options=self.config.pa_read_options,
---> 87                         parse_options=self.config.pa_parse_options,
     88                     )
     89                 except pa.ArrowInvalid as err:

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json._get_reader()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_input_stream()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_native_file()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile.__cinit__()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile._open_readable()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()

FileNotFoundError: [Errno 2] Failed to open local file 'gzip://file-000000000000.json::/Users/thomwolf/github-dataset/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory
```

## Environment info
- `datasets` version: 1.9.1.dev0
- Platform: Darwin-19.6.0-x86_64-i386-64bit
- Python version: 3.7.7
- PyArrow version: 1.0.0
 Hi @thomwolf, thanks for reporting.

It seems this might be due to the fact that the JSON Dataset builder uses `pyarrow.json` (`paj.read_json`) to read the data without using the Python standard `open(file,...` (which is the one patched with `xopen` to work in streaming mode).

This has to be fixed.


Streaming local gzip compressed JSON line files is not working
 ## Describe the bug
Using streaming to iterate on local gzip compressed JSON files raise a file not exist error

## Steps to reproduce the bug
```python
from datasets import load_dataset

streamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)

next(iter(streamed_dataset))
```

## Actual results
```
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-6-27a664e29784> in <module>
----> 1 next(iter(streamed_dataset))

~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self)
    336
    337     def __iter__(self):
--> 338         for key, example in self._iter():
    339             if self.features:
    340                 # we encode the example for ClassLabel feature types for example

~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in _iter(self)
    333         else:
    334             ex_iterable = self._ex_iterable
--> 335         yield from ex_iterable
    336
    337     def __iter__(self):

~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self)
     76
     77     def __iter__(self):
---> 78         for key, example in self.generate_examples_fn(**self.kwargs):
     79             yield key, example
     80

~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in wrapper(**kwargs)
    282     def wrapper(**kwargs):
    283         python_formatter = PythonFormatter()
--> 284         for key, table in generate_tables_fn(**kwargs):
    285             batch = python_formatter.format_batch(table)
    286             for i, example in enumerate(_batch_to_examples(batch)):

~/Documents/GitHub/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files, original_files)
     85                         file,
     86                         read_options=self.config.pa_read_options,
---> 87                         parse_options=self.config.pa_parse_options,
     88                     )
     89                 except pa.ArrowInvalid as err:

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json._get_reader()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_input_stream()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_native_file()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile.__cinit__()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile._open_readable()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()

FileNotFoundError: [Errno 2] Failed to open local file 'gzip://file-000000000000.json::/Users/thomwolf/github-dataset/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory
```

## Environment info
- `datasets` version: 1.9.1.dev0
- Platform: Darwin-19.6.0-x86_64-i386-64bit
- Python version: 3.7.7
- PyArrow version: 1.0.0
 Sorry for reopening this, but I'm having the same issue as @thomwolf when streaming a gzipped JSON Lines file from the hub. Or is that just not possible by definition?
I installed `datasets`in editable mode from source (so probably includes the fix from #2608 ?):
```
>>> datasets.__version__
'1.9.1.dev0'
```

```
>>> msmarco = datasets.load_dataset("webis/msmarco", "corpus", streaming=True)
Using custom data configuration corpus-174d3b7155eb68db
>>> msmarco_iter = iter(msmarco['train'])
>>> print(next(msmarco_iter))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/media/ssd/TREC/msmarco/datasets/src/datasets/iterable_dataset.py", line 338, in __iter__
    for key, example in self._iter():
  File "/media/ssd/TREC/msmarco/datasets/src/datasets/iterable_dataset.py", line 335, in _iter
    yield from ex_iterable
  File "/media/ssd/TREC/msmarco/datasets/src/datasets/iterable_dataset.py", line 78, in __iter__
    for key, example in self.generate_examples_fn(**self.kwargs):
  File "/home/christopher/.cache/huggingface/modules/datasets_modules/datasets/msmarco/eb63dff8d83107168e973c7a655a6082d37e08d71b4ac39a0afada479c138745/msmarco.py", line 96, in _generate_examples
    with gzip.open(file, "rt", encoding="utf-8") as f:
  File "/usr/lib/python3.6/gzip.py", line 53, in open
    binary_file = GzipFile(filename, gz_mode, compresslevel)
  File "/usr/lib/python3.6/gzip.py", line 163, in __init__
    fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
FileNotFoundError: [Errno 2] No such file or directory: 'https://huggingface.co/datasets/webis/msmarco/resolve/main/msmarco_doc_00.gz'
```

Loading the dataset without streaming set to True, works fine.


Streaming local gzip compressed JSON line files is not working
 ## Describe the bug
Using streaming to iterate on local gzip compressed JSON files raise a file not exist error

## Steps to reproduce the bug
```python
from datasets import load_dataset

streamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)

next(iter(streamed_dataset))
```

## Actual results
```
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-6-27a664e29784> in <module>
----> 1 next(iter(streamed_dataset))

~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self)
    336
    337     def __iter__(self):
--> 338         for key, example in self._iter():
    339             if self.features:
    340                 # we encode the example for ClassLabel feature types for example

~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in _iter(self)
    333         else:
    334             ex_iterable = self._ex_iterable
--> 335         yield from ex_iterable
    336
    337     def __iter__(self):

~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self)
     76
     77     def __iter__(self):
---> 78         for key, example in self.generate_examples_fn(**self.kwargs):
     79             yield key, example
     80

~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in wrapper(**kwargs)
    282     def wrapper(**kwargs):
    283         python_formatter = PythonFormatter()
--> 284         for key, table in generate_tables_fn(**kwargs):
    285             batch = python_formatter.format_batch(table)
    286             for i, example in enumerate(_batch_to_examples(batch)):

~/Documents/GitHub/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files, original_files)
     85                         file,
     86                         read_options=self.config.pa_read_options,
---> 87                         parse_options=self.config.pa_parse_options,
     88                     )
     89                 except pa.ArrowInvalid as err:

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json._get_reader()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_input_stream()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_native_file()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile.__cinit__()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile._open_readable()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()

~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()

FileNotFoundError: [Errno 2] Failed to open local file 'gzip://file-000000000000.json::/Users/thomwolf/github-dataset/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory
```

## Environment info
- `datasets` version: 1.9.1.dev0
- Platform: Darwin-19.6.0-x86_64-i386-64bit
- Python version: 3.7.7
- PyArrow version: 1.0.0
 Hi ! To make the streaming work, we extend `open` in the dataset builder to work with urls.

Therefore you just need to use `open` before using `gzip.open`:
```diff
- with gzip.open(file, "rt", encoding="utf-8") as f:
+ with gzip.open(open(file, "rb"), "rt", encoding="utf-8") as f:
```

You can see that it is the case for oscar.py and c4.py for example:

https://github.com/huggingface/datasets/blob/8814b393984c1c2e1800ba370de2a9f7c8644908/datasets/oscar/oscar.py#L358-L358

https://github.com/huggingface/datasets/blob/8814b393984c1c2e1800ba370de2a9f7c8644908/datasets/c4/c4.py#L88-L88


Add option to delete temporary files (e.g. extracted files) when loading dataset
 I'm loading a dataset constituted of 44 GB of compressed JSON files.

When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables

Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.

I can maybe tackle this one in the JSON script unless you want a more general solution.
 Hi !
If we want something more general, we could either
1. delete the extracted files after the arrow data generation automatically, or
2. delete each extracted file during the arrow generation right after it has been closed.

Solution 2 is better to save disk space during the arrow generation. Is it what you had in mind ?

The API could look like
```python
load_dataset(..., delete_extracted_files_after_usage=True)
```

In terms of implementation, here are some directions we could take for each solution:
1. get the list of the extracted files from the DownloadManager and then delete them after the dataset is processed. This can be implemented in `download_and_prepare` I guess
2. maybe wrap and mock `open` in the builder to make it delete the file when the file is closed.


Add option to delete temporary files (e.g. extracted files) when loading dataset
 I'm loading a dataset constituted of 44 GB of compressed JSON files.

When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables

Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.

I can maybe tackle this one in the JSON script unless you want a more general solution.
 Also, if I delete the extracted files they need to be re-extracted again instead of loading from the Arrow cache files


Add option to delete temporary files (e.g. extracted files) when loading dataset
 I'm loading a dataset constituted of 44 GB of compressed JSON files.

When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables

Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.

I can maybe tackle this one in the JSON script unless you want a more general solution.
 I think we already opened an issue about this topic (suggested by @stas00): duplicated of #2481?

This is in our TODO list... 😅


Add option to delete temporary files (e.g. extracted files) when loading dataset
 I'm loading a dataset constituted of 44 GB of compressed JSON files.

When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables

Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.

I can maybe tackle this one in the JSON script unless you want a more general solution.
 I think the deletion of each extracted file could be implemented in our CacheManager and ExtractManager (once merged to master: #2295, #2277). 😉


Add option to delete temporary files (e.g. extracted files) when loading dataset
 I'm loading a dataset constituted of 44 GB of compressed JSON files.

When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables

Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.

I can maybe tackle this one in the JSON script unless you want a more general solution.
 Nevermind @thomwolf, I just mentioned the other issue so that both appear linked in GitHub and we do not forget to close both once we make the corresponding Pull Request... That was the main reason! 😄


Add option to delete temporary files (e.g. extracted files) when loading dataset
 I'm loading a dataset constituted of 44 GB of compressed JSON files.

When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables

Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.

I can maybe tackle this one in the JSON script unless you want a more general solution.
 Ok yes. I think this is an important feature to be able to use large datasets which are pretty much always compressed files.

In particular now this requires to keep the extracted file on the drive if you want to avoid reprocessing the dataset so in my case, this require using always ~400GB of drive instead of just 200GB (which is already significant).

Two nice features would be to:
- allow to delete the extracted files without loosing the ability to load the dataset from the cached arrow-file
- streamlined decompression when only the currently read file is extracted - this might require to read the list of files from the extracted archives before processing them?


Add option to delete temporary files (e.g. extracted files) when loading dataset
 I'm loading a dataset constituted of 44 GB of compressed JSON files.

When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables

Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.

I can maybe tackle this one in the JSON script unless you want a more general solution.
 Note that I'm confirming that with the current master branch of dataset, deleting extracted files (without deleting the arrow cache file) lead to **re-extracting** these files when reloading the dataset instead of directly loading the arrow cache file.


Add option to delete temporary files (e.g. extracted files) when loading dataset
 I'm loading a dataset constituted of 44 GB of compressed JSON files.

When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables

Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.

I can maybe tackle this one in the JSON script unless you want a more general solution.
 Hi ! That's weird, it doesn't do that on my side (tested on master on my laptop by deleting the `extracted` folder in the download cache directory). You tested with one of the files at https://huggingface.co/datasets/thomwolf/github-python that you have locally ?


Add option to delete temporary files (e.g. extracted files) when loading dataset
 I'm loading a dataset constituted of 44 GB of compressed JSON files.

When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables

Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.

I can maybe tackle this one in the JSON script unless you want a more general solution.
 @thomwolf I'm sorry but I can't reproduce this problem. I'm also using:
```python
ds = load_dataset("json", split="train", data_files=data_files, cache_dir=cache_dir)
```
after having removed the extracted files:
```python
assert sorted((cache_dir / "downloads" / "extracted").iterdir()) == []
```

I get the logging message:
```shell
WARNING  datasets.builder:builder.py:531 Reusing dataset json ...
```


Add option to delete temporary files (e.g. extracted files) when loading dataset
 I'm loading a dataset constituted of 44 GB of compressed JSON files.

When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables

Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.

I can maybe tackle this one in the JSON script unless you want a more general solution.
 >
>
> Do you confirm the extracted folder stays empty after reloading?

Yes, I have the above mentioned assertion on the emptiness of the extracted folder:
```python
assert sorted((cache_dir / "downloads" / "extracted").iterdir()) == []
```


Unable to download omp dataset

Unable to download omp dataset
 ## Describe the bug
The omp dataset cannot be downloaded because of a DuplicatedKeysError

## Steps to reproduce the bug
from datasets import load_dataset
omp = load_dataset('omp', 'posts_labeled')
print(omp)

## Expected results
This code should download the omp dataset and print the dictionary

## Actual results
Downloading and preparing dataset omp/posts_labeled (download: 1.27 MiB, generated: 13.31 MiB, post-processed: Unknown size, total: 14.58 MiB) to /home/erika_distefano/.cache/huggingface/datasets/omp/posts_labeled/1.1.0/2fe5b067be3bff1d4588d5b0cbb9b5b22ae1b9d5b026a8ff572cd389f862735b...
0 examples [00:00, ? examples/s]2021-07-06 09:43:55.868815: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0
Traceback (most recent call last):
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 990, in _prepare_split
    writer.write(example, key)
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 338, in write
    self.check_duplicate_keys()
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys
    raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 3326
Keys should be unique and deterministic in nature

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "hf_datasets.py", line 32, in <module>
    omp = load_dataset('omp', 'posts_labeled')
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
    use_auth_token=use_auth_token,
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepare
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prepare
    self._prepare_split(split_generator, **prepare_split_kwargs)
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 992, in _prepare_split
    num_examples, num_bytes = writer.finalize()
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 409, in finalize
    self.check_duplicate_keys()
  File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys
    raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 3326
Keys should be unique and deterministic in nature

## Environment info
- `datasets` version: 1.8.0
- Platform: Ubuntu 18.04.4 LTS
- Python version: 3.6.9
- PyArrow version: 3.0.0

 Hi @erikadistefano , thanks for reporting the issue.

I have created a Pull Request that should fix it.

Once merged into master, feel free to update your installed `datasets` library (either by installing it from our GitHub master branch or waiting until our next release) to be able to load omp dataset.


Transformer Class on dataset

Transformer Class on dataset
 Just wondering if you have intenttion to create

TransformerClass :
    dataset --> dataset

and make determnistic transformation (ie not fit).

 Hi ! Do you have an example in mind that shows how this could be useful ?


Transformer Class on dataset

Transformer Class on dataset
 Just wondering if you have intenttion to create

TransformerClass :
    dataset --> dataset

and make determnistic transformation (ie not fit).

 Example:

Merge 2 datasets into one datasets

Label extraction from dataset

dataset(text, label)
   —> dataset(text, newlabel)

TextCleaning.

For image dataset,
Transformation are easier (ie linear algebra).

> On Jul 6, 2021, at 17:39, Quentin Lhoest ***@***.***> wrote:
>
>
> Hi ! Do you have an example in mind that shows how this could be useful ?
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub, or unsubscribe.


Transformer Class on dataset

Transformer Class on dataset
 Just wondering if you have intenttion to create

TransformerClass :
    dataset --> dataset

and make determnistic transformation (ie not fit).

 There are already a few transformations that you can apply on a dataset using methods like `dataset.map()`.
You can find examples in the documentation here:
https://huggingface.co/docs/datasets/processing.html

You can merge two datasets with `concatenate_datasets()` or do label extraction with `dataset.map()` for example


Transformer Class on dataset

Transformer Class on dataset
 Just wondering if you have intenttion to create

TransformerClass :
    dataset --> dataset

and make determnistic transformation (ie not fit).

 Ok, sure.

Thanks for pointing on functional part.
My question is more
“Philosophical”/Design perspective.

There are 2 perspetive:
  Add transformation methods to
     Dataset Class

 OR Create a Transformer Class
   which operates on Dataset Class.

T(Dataset) —> Dataset

datasetnew = MyTransform.transform(dataset)
datasetNew.save(path)

What would be the difficulty
of implementing a Transformer Class
operating at dataset level ?

thanks

> On Jul 6, 2021, at 22:00, Quentin Lhoest ***@***.***> wrote:
>
>
> There are already a few transformations that you can apply on a dataset using methods like dataset.map().
> You can find examples in the documentation here:
> https://huggingface.co/docs/datasets/processing.html
>
> You can merge two datasets with concatenate_datasets() or do label extraction with dataset.map() for example
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub, or unsubscribe.


Transformer Class on dataset

Transformer Class on dataset
 Just wondering if you have intenttion to create

TransformerClass :
    dataset --> dataset

and make determnistic transformation (ie not fit).

 I can imagine that this would be a useful API to implement processing pipelines as transforms. They could be used to perform higher level transforms compared to the atomic transforms allowed by methods like map, filter, etc.

I guess if you find any transform that could be useful for text dataset processing, image dataset processing etc. we could definitely start having such transforms :)


Transformer Class on dataset

Transformer Class on dataset
 Just wondering if you have intenttion to create

TransformerClass :
    dataset --> dataset

and make determnistic transformation (ie not fit).

 Thanks for reply.

What would be the constraints
to have
Dataset —> Dataset consistency ?

Main issue would be
larger than memory dataset and
serialization on disk.

Technically,
one still process at atomic level
and try to wrap the full results
into Dataset…. (!)

What would you think ?

> On Jul 7, 2021, at 16:51, Quentin Lhoest ***@***.***> wrote:
>
>
> I can imagine that this would be a useful API to implement processing pipelines as transforms. They could be used to perform higher level transforms compared to the atomic transforms allowed by methods like map, filter, etc.
>
> I guess if you find any transform that could be useful for text dataset processing, image dataset processing etc. we could definitely start having such transforms :)
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub, or unsubscribe.


Transformer Class on dataset

Transformer Class on dataset
 Just wondering if you have intenttion to create

TransformerClass :
    dataset --> dataset

and make determnistic transformation (ie not fit).

 We can be pretty flexible and not impose any constraints for transforms.

Moreover, this library is designed to support datasets bigger than memory. The datasets are loaded from the disk via memory mapping, without filling up RAM. Even processing functions like `map` work in a batched fashion to not fill up your RAM. So this shouldn't be an issue


Transformer Class on dataset

Transformer Class on dataset
 Just wondering if you have intenttion to create

TransformerClass :
    dataset --> dataset

and make determnistic transformation (ie not fit).

 Ok thanks.

But, Dataset has various flavors.
In current design of Dataset,
   how the serialization on disk is done (?)

The main issue is serialization
of   newdataset= Transform(Dataset)
 (ie thats why am referring to Out Of memory dataset…):

   Should be part of Transform or part of dataset ?

Maybe, not, since the output is aimed to feed model in memory (?)

> On Jul 7, 2021, at 18:04, Quentin Lhoest ***@***.***> wrote:
>
>
> We can be pretty flexible and not impose any constraints for transforms.
>
> Moreover, this library is designed to support datasets bigger than memory. The datasets are loaded from the disk via memory mapping, without filling up RAM. Even processing functions like map work in a batched fashion to not fill up your RAM. So this shouldn't be an issue
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub, or unsubscribe.


Transformer Class on dataset

Transformer Class on dataset
 Just wondering if you have intenttion to create

TransformerClass :
    dataset --> dataset

and make determnistic transformation (ie not fit).

 I'm not sure I understand, could you elaborate a bit more please ?

Each dataset is a wrapper of a PyArrow Table that contains all the data. The table is loaded from an arrow file on the disk.
We have an ArrowWriter and ArrowReader class to write/read arrow tables on disk or in in-memory buffers.


ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets
 Error traceback:
---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-8-a7b592d3bca0> in <module>()
      1 from datasets import load_dataset, load_metric
      2
----> 3 common_voice_train = load_dataset("common_voice", "pa-IN", split="train+validation")
      4 common_voice_test = load_dataset("common_voice", "pa-IN", split="test")

9 frames
/root/.cache/huggingface/modules/datasets_modules/datasets/common_voice/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b/common_voice.py in <module>()
     19
     20 import datasets
---> 21 from datasets.tasks import AutomaticSpeechRecognition
     22
     23

ModuleNotFoundError: No module named 'datasets.tasks'
 Hi @profsatwinder.

It looks like you are using an old version of `datasets`. Please update it with `pip install -U datasets` and indicate if the problem persists.


ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets
 Error traceback:
---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-8-a7b592d3bca0> in <module>()
      1 from datasets import load_dataset, load_metric
      2
----> 3 common_voice_train = load_dataset("common_voice", "pa-IN", split="train+validation")
      4 common_voice_test = load_dataset("common_voice", "pa-IN", split="test")

9 frames
/root/.cache/huggingface/modules/datasets_modules/datasets/common_voice/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b/common_voice.py in <module>()
     19
     20 import datasets
---> 21 from datasets.tasks import AutomaticSpeechRecognition
     22
     23

ModuleNotFoundError: No module named 'datasets.tasks'
 @albertvillanova Thanks for the information. I updated it to 1.9.0 and the issue is resolved. Thanks again.


Cached dataset overflowing disk space

Cached dataset overflowing disk space
 I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).

The cache folder is 500gb (and now my disk space is full).

Is there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files).

This might not technically be a bug, but I was unsure and I felt that the bug was the closest one.

Traceback (most recent call last):
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/multiprocess/pool.py", line 121, in worker
    result = (True, func(*args, **kwds))
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 186, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/fingerprint.py", line 397, in wrapper
    out = func(self, *args, **kwargs)
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1983, in _map_single
    writer.finalize()
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_writer.py", line 418, in finalize
    self.pa_writer.close()
  File "pyarrow/ipc.pxi", line 402, in pyarrow.lib._CRecordBatchWriter.close
  File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
OSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device
"""

The above exception was the direct cause of the following exception:

 I'm using the datasets concatenate dataset to combine the datasets and then train.
train_dataset = concatenate_datasets([dataset1, dataset2, common_voice_train])


Cached dataset overflowing disk space

Cached dataset overflowing disk space
 I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).

The cache folder is 500gb (and now my disk space is full).

Is there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files).

This might not technically be a bug, but I was unsure and I felt that the bug was the closest one.

Traceback (most recent call last):
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/multiprocess/pool.py", line 121, in worker
    result = (True, func(*args, **kwds))
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 186, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/fingerprint.py", line 397, in wrapper
    out = func(self, *args, **kwargs)
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1983, in _map_single
    writer.finalize()
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_writer.py", line 418, in finalize
    self.pa_writer.close()
  File "pyarrow/ipc.pxi", line 402, in pyarrow.lib._CRecordBatchWriter.close
  File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
OSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device
"""

The above exception was the direct cause of the following exception:

 Hi @BirgerMoell.

You have several options:
- to set caching to be stored on a different path location, other than the default one (`~/.cache/huggingface/datasets`):
  - either setting the environment variable `HF_DATASETS_CACHE` with the path to the new cache location
  - or by passing it with the parameter `cache_dir` when loading each of the datasets: `dataset = load_dataset(..., cache_dir=your_new_location)`

  You can get all the information in the docs: https://huggingface.co/docs/datasets/loading_datasets.html#cache-directory
- I wouldn't recommend disabling caching, because current implementation generates cache files anyway, although in a temporary directory and they are deleted when the session closes. See details here: https://huggingface.co/docs/datasets/processing.html#enable-or-disable-caching
- You could alternatively load the datasets in streaming mode. This is a new feature which allows loading the datasets without downloading the entire files. More information here: https://huggingface.co/docs/datasets/dataset_streaming.html


Cached dataset overflowing disk space

Cached dataset overflowing disk space
 I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).

The cache folder is 500gb (and now my disk space is full).

Is there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files).

This might not technically be a bug, but I was unsure and I felt that the bug was the closest one.

Traceback (most recent call last):
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/multiprocess/pool.py", line 121, in worker
    result = (True, func(*args, **kwds))
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 186, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/fingerprint.py", line 397, in wrapper
    out = func(self, *args, **kwargs)
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1983, in _map_single
    writer.finalize()
  File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_writer.py", line 418, in finalize
    self.pa_writer.close()
  File "pyarrow/ipc.pxi", line 402, in pyarrow.lib._CRecordBatchWriter.close
  File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
OSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device
"""

The above exception was the direct cause of the following exception:

 Hi @BirgerMoell,

We are planning to add a new feature to datasets, which could be interesting in your case: Add the option to delete temporary files (decompressed files) from the cache directory (see: #2481, #2604).

We will ping you once this feature is implemented, so that the size of your cache directory will be considerably reduced.


sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index
 ## Describe the bug
The built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start'].

For example:
id = '56d1f453e7d4791d009025bd'
answers = {'text': ['Pure Land'], 'answer_start': [146]}
However the actual text in context at location 146 is 'ure Land,'
Which is an off-by-one error from the correct answer.

## Steps to reproduce the bug
```python
import datasets

def check_context_answer_alignment(example):
    for a_idx in range(len(example['answers']['text'])):
        # check raw dataset for answer consistency between context and answer
        answer_text = example['answers']['text'][a_idx]
        a_st_idx = example['answers']['answer_start'][a_idx]
        a_end_idx = a_st_idx + len(example['answers']['text'][a_idx])
        answer_text_from_context = example['context'][a_st_idx:a_end_idx]
        if answer_text != answer_text_from_context:
            #print(example['id'])
            return False
    return True

dataset = datasets.load_dataset('squad_v2', split='train', keep_in_memory=True)

start_len = len(dataset)
dataset = dataset.filter(check_context_answer_alignment,
                                num_proc=1,
                                keep_in_memory=True)
end_len = len(dataset)
print('{} instances contain mis-alignment between the answer text and answer index.'.format(start_len - end_len))
```

## Expected results
This code should result in 0 rows being filtered out from the dataset.

## Actual results
This filter command results in 258 rows being flagged as containing a discrepancy between the text contained within answers['text'] and the text in example['context'] at the answers['answer_start'] location.

This code will reproduce the problem and produce the following count:
"258 instances contain mis-alignment between the answer text and answer index."

## Environment info
Steps to rebuilt the Conda environment:
```
# create a virtual environment to stuff all these packages into
conda create -n round8 python=3.8 -y

# activate the virtual environment
conda activate round8

# install pytorch (best done through conda to handle cuda dependencies)
conda install pytorch torchvision torchtext cudatoolkit=11.1 -c pytorch-lts -c nvidia

pip install jsonpickle transformers datasets matplotlib
```

OS: Ubuntu 20.04
Python 3.8

Result of `conda env export`:
```
name: round8
channels:
  - pytorch-lts
  - nvidia
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=4.5=1_gnu
  - blas=1.0=mkl
  - brotlipy=0.7.0=py38h27cfd23_1003
  - bzip2=1.0.8=h7b6447c_0
  - ca-certificates=2021.5.25=h06a4308_1
  - certifi=2021.5.30=py38h06a4308_0
  - cffi=1.14.5=py38h261ae71_0
  - chardet=4.0.0=py38h06a4308_1003
  - cryptography=3.4.7=py38hd23ed53_0
  - cudatoolkit=11.1.74=h6bb024c_0
  - ffmpeg=4.2.2=h20bf706_0
  - freetype=2.10.4=h5ab3b9f_0
  - gmp=6.2.1=h2531618_2
  - gnutls=3.6.15=he1e5248_0
  - idna=2.10=pyhd3eb1b0_0
  - intel-openmp=2021.2.0=h06a4308_610
  - jpeg=9b=h024ee3a_2
  - lame=3.100=h7b6447c_0
  - lcms2=2.12=h3be6417_0
  - ld_impl_linux-64=2.35.1=h7274673_9
  - libffi=3.3=he6710b0_2
  - libgcc-ng=9.3.0=h5101ec6_17
  - libgomp=9.3.0=h5101ec6_17
  - libidn2=2.3.1=h27cfd23_0
  - libopus=1.3.1=h7b6447c_0
  - libpng=1.6.37=hbc83047_0
  - libstdcxx-ng=9.3.0=hd4cf53a_17
  - libtasn1=4.16.0=h27cfd23_0
  - libtiff=4.2.0=h85742a9_0
  - libunistring=0.9.10=h27cfd23_0
  - libuv=1.40.0=h7b6447c_0
  - libvpx=1.7.0=h439df22_0
  - libwebp-base=1.2.0=h27cfd23_0
  - lz4-c=1.9.3=h2531618_0
  - mkl=2021.2.0=h06a4308_296
  - mkl-service=2.3.0=py38h27cfd23_1
  - mkl_fft=1.3.0=py38h42c9631_2
  - mkl_random=1.2.1=py38ha9443f7_2
  - ncurses=6.2=he6710b0_1
  - nettle=3.7.3=hbbd107a_1
  - ninja=1.10.2=hff7bd54_1
  - numpy=1.20.2=py38h2d18471_0
  - numpy-base=1.20.2=py38hfae3a4d_0
  - olefile=0.46=py_0
  - openh264=2.1.0=hd408876_0
  - openssl=1.1.1k=h27cfd23_0
  - pillow=8.2.0=py38he98fc37_0
  - pip=21.1.2=py38h06a4308_0
  - pycparser=2.20=py_2
  - pyopenssl=20.0.1=pyhd3eb1b0_1
  - pysocks=1.7.1=py38h06a4308_0
  - python=3.8.10=h12debd9_8
  - pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0
  - readline=8.1=h27cfd23_0
  - requests=2.25.1=pyhd3eb1b0_0
  - setuptools=52.0.0=py38h06a4308_0
  - six=1.16.0=pyhd3eb1b0_0
  - sqlite=3.35.4=hdfb4753_0
  - tk=8.6.10=hbc83047_0
  - torchtext=0.9.1=py38
  - torchvision=0.9.1=py38_cu111
  - typing_extensions=3.7.4.3=pyha847dfd_0
  - urllib3=1.26.4=pyhd3eb1b0_0
  - wheel=0.36.2=pyhd3eb1b0_0
  - x264=1!157.20191217=h7b6447c_0
  - xz=5.2.5=h7b6447c_0
  - zlib=1.2.11=h7b6447c_3
  - zstd=1.4.9=haebb681_0
  - pip:
    - click==8.0.1
    - cycler==0.10.0
    - datasets==1.8.0
    - dill==0.3.4
    - filelock==3.0.12
    - fsspec==2021.6.0
    - huggingface-hub==0.0.8
    - joblib==1.0.1
    - jsonpickle==2.0.0
    - kiwisolver==1.3.1
    - matplotlib==3.4.2
    - multiprocess==0.70.12.2
    - packaging==20.9
    - pandas==1.2.4
    - pyarrow==3.0.0
    - pyparsing==2.4.7
    - python-dateutil==2.8.1
    - pytz==2021.1
    - regex==2021.4.4
    - sacremoses==0.0.45
    - tokenizers==0.10.3
    - tqdm==4.49.0
    - transformers==4.6.1
    - xxhash==2.0.2
prefix: /home/mmajurski/anaconda3/envs/round8
```

 Hi @mmajurski, thanks for reporting this issue.

Indeed this misalignment arises because the source dataset context field contains leading blank spaces (and these are counted within the answer_start), while our datasets loading script removes these leading blank spaces.

I'm going to fix our script so that all leading blank spaces in the source dataset are kept, and there is no misalignment between the answer text and the answer_start within the context.


sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index
 ## Describe the bug
The built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start'].

For example:
id = '56d1f453e7d4791d009025bd'
answers = {'text': ['Pure Land'], 'answer_start': [146]}
However the actual text in context at location 146 is 'ure Land,'
Which is an off-by-one error from the correct answer.

## Steps to reproduce the bug
```python
import datasets

def check_context_answer_alignment(example):
    for a_idx in range(len(example['answers']['text'])):
        # check raw dataset for answer consistency between context and answer
        answer_text = example['answers']['text'][a_idx]
        a_st_idx = example['answers']['answer_start'][a_idx]
        a_end_idx = a_st_idx + len(example['answers']['text'][a_idx])
        answer_text_from_context = example['context'][a_st_idx:a_end_idx]
        if answer_text != answer_text_from_context:
            #print(example['id'])
            return False
    return True

dataset = datasets.load_dataset('squad_v2', split='train', keep_in_memory=True)

start_len = len(dataset)
dataset = dataset.filter(check_context_answer_alignment,
                                num_proc=1,
                                keep_in_memory=True)
end_len = len(dataset)
print('{} instances contain mis-alignment between the answer text and answer index.'.format(start_len - end_len))
```

## Expected results
This code should result in 0 rows being filtered out from the dataset.

## Actual results
This filter command results in 258 rows being flagged as containing a discrepancy between the text contained within answers['text'] and the text in example['context'] at the answers['answer_start'] location.

This code will reproduce the problem and produce the following count:
"258 instances contain mis-alignment between the answer text and answer index."

## Environment info
Steps to rebuilt the Conda environment:
```
# create a virtual environment to stuff all these packages into
conda create -n round8 python=3.8 -y

# activate the virtual environment
conda activate round8

# install pytorch (best done through conda to handle cuda dependencies)
conda install pytorch torchvision torchtext cudatoolkit=11.1 -c pytorch-lts -c nvidia

pip install jsonpickle transformers datasets matplotlib
```

OS: Ubuntu 20.04
Python 3.8

Result of `conda env export`:
```
name: round8
channels:
  - pytorch-lts
  - nvidia
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=4.5=1_gnu
  - blas=1.0=mkl
  - brotlipy=0.7.0=py38h27cfd23_1003
  - bzip2=1.0.8=h7b6447c_0
  - ca-certificates=2021.5.25=h06a4308_1
  - certifi=2021.5.30=py38h06a4308_0
  - cffi=1.14.5=py38h261ae71_0
  - chardet=4.0.0=py38h06a4308_1003
  - cryptography=3.4.7=py38hd23ed53_0
  - cudatoolkit=11.1.74=h6bb024c_0
  - ffmpeg=4.2.2=h20bf706_0
  - freetype=2.10.4=h5ab3b9f_0
  - gmp=6.2.1=h2531618_2
  - gnutls=3.6.15=he1e5248_0
  - idna=2.10=pyhd3eb1b0_0
  - intel-openmp=2021.2.0=h06a4308_610
  - jpeg=9b=h024ee3a_2
  - lame=3.100=h7b6447c_0
  - lcms2=2.12=h3be6417_0
  - ld_impl_linux-64=2.35.1=h7274673_9
  - libffi=3.3=he6710b0_2
  - libgcc-ng=9.3.0=h5101ec6_17
  - libgomp=9.3.0=h5101ec6_17
  - libidn2=2.3.1=h27cfd23_0
  - libopus=1.3.1=h7b6447c_0
  - libpng=1.6.37=hbc83047_0
  - libstdcxx-ng=9.3.0=hd4cf53a_17
  - libtasn1=4.16.0=h27cfd23_0
  - libtiff=4.2.0=h85742a9_0
  - libunistring=0.9.10=h27cfd23_0
  - libuv=1.40.0=h7b6447c_0
  - libvpx=1.7.0=h439df22_0
  - libwebp-base=1.2.0=h27cfd23_0
  - lz4-c=1.9.3=h2531618_0
  - mkl=2021.2.0=h06a4308_296
  - mkl-service=2.3.0=py38h27cfd23_1
  - mkl_fft=1.3.0=py38h42c9631_2
  - mkl_random=1.2.1=py38ha9443f7_2
  - ncurses=6.2=he6710b0_1
  - nettle=3.7.3=hbbd107a_1
  - ninja=1.10.2=hff7bd54_1
  - numpy=1.20.2=py38h2d18471_0
  - numpy-base=1.20.2=py38hfae3a4d_0
  - olefile=0.46=py_0
  - openh264=2.1.0=hd408876_0
  - openssl=1.1.1k=h27cfd23_0
  - pillow=8.2.0=py38he98fc37_0
  - pip=21.1.2=py38h06a4308_0
  - pycparser=2.20=py_2
  - pyopenssl=20.0.1=pyhd3eb1b0_1
  - pysocks=1.7.1=py38h06a4308_0
  - python=3.8.10=h12debd9_8
  - pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0
  - readline=8.1=h27cfd23_0
  - requests=2.25.1=pyhd3eb1b0_0
  - setuptools=52.0.0=py38h06a4308_0
  - six=1.16.0=pyhd3eb1b0_0
  - sqlite=3.35.4=hdfb4753_0
  - tk=8.6.10=hbc83047_0
  - torchtext=0.9.1=py38
  - torchvision=0.9.1=py38_cu111
  - typing_extensions=3.7.4.3=pyha847dfd_0
  - urllib3=1.26.4=pyhd3eb1b0_0
  - wheel=0.36.2=pyhd3eb1b0_0
  - x264=1!157.20191217=h7b6447c_0
  - xz=5.2.5=h7b6447c_0
  - zlib=1.2.11=h7b6447c_3
  - zstd=1.4.9=haebb681_0
  - pip:
    - click==8.0.1
    - cycler==0.10.0
    - datasets==1.8.0
    - dill==0.3.4
    - filelock==3.0.12
    - fsspec==2021.6.0
    - huggingface-hub==0.0.8
    - joblib==1.0.1
    - jsonpickle==2.0.0
    - kiwisolver==1.3.1
    - matplotlib==3.4.2
    - multiprocess==0.70.12.2
    - packaging==20.9
    - pandas==1.2.4
    - pyarrow==3.0.0
    - pyparsing==2.4.7
    - python-dateutil==2.8.1
    - pytz==2021.1
    - regex==2021.4.4
    - sacremoses==0.0.45
    - tokenizers==0.10.3
    - tqdm==4.49.0
    - transformers==4.6.1
    - xxhash==2.0.2
prefix: /home/mmajurski/anaconda3/envs/round8
```

 If you are going to be altering the data cleaning from the source Squad dataset, here is one thing to consider.
There are occasional double spaces separating words which it might be nice to get rid of.

Either way, thank you.


Error iteration over IterableDataset using Torch DataLoader
 ## Describe the bug
I have an IterableDataset (created using streaming=True) and I am trying to create batches using Torch DataLoader class by passing this IterableDataset to it. This throws error which is pasted below. I can do the same by using Torch IterableDataset. One thing I noticed is that in the former case when I look at the dataloader.sampler class I get torch.utils.data.sampler.SequentialSampler while the latter one gives torch.utils.data.dataloader._InfiniteConstantSampler.

I am not sure if this is how it is meant to be used, but that's what seemed reasonable to me.

## Steps to reproduce the bug

1. Does not work.
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
>>> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)
>>> dataloader.sampler
<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>
>>> for batch in dataloader:
...     print(batch)
```

2. Works.
```python
import torch
from torch.utils.data import Dataset, IterableDataset, DataLoader
class CustomIterableDataset(IterableDataset):
  'Characterizes a dataset for PyTorch'
  def __init__(self, data):
        'Initialization'
        self.data = data

  def __iter__(self):
        return iter(self.data)

data = list(range(12))
dataset = CustomIterableDataset(data)
dataloader = DataLoader(dataset, batch_size=4)
print("dataloader: ", dataloader.sampler)
for batch in dataloader:
    print(batch)
```

## Expected results
To get batches of data with the batch size as 4. Output from the latter one (2) though Datasource is different here so actual data is different.
dataloader:  <torch.utils.data.dataloader._InfiniteConstantSampler object at 0x7f1cc29e2c50>
tensor([0, 1, 2, 3])
tensor([4, 5, 6, 7])
tensor([ 8,  9, 10, 11])

## Actual results
<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>

...
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
    data = self._next_data()
  File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 474, in _next_data
    index = self._next_index()  # may raise StopIteration
  File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 427, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
  File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 227, in __iter__
    for idx in self.sampler:
  File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 67, in __iter__
    return iter(range(len(self.data_source)))
TypeError: object of type 'IterableDataset' has no len()

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: '1.8.1.dev0'
- Platform: Linux
- Python version: Python 3.6.8
- PyArrow version: '3.0.0'

 Hi ! This is because you first need to format the dataset for pytorch:

```python
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
>>> torch_iterable_dataset = dataset.with_format("torch")
>>> assert isinstance(torch_iterable_dataset, torch.utils.data.IterableDataset)
>>> dataloader = torch.utils.data.DataLoader(torch_iterable_dataset, batch_size=4)
>>> next(iter(dataloader))
{'id': tensor([0, 1, 2, 3]), 'text': ['Mtendere Village was inspired...]}
```

This is because the pytorch dataloader expects a subclass of `torch.utils.data.IterableDataset`. Since you can't pass an arbitrary iterable to a pytorch dataloader, you first need to build an object that inherits from `torch.utils.data.IterableDataset` using `with_format("torch")` for example.


Error iteration over IterableDataset using Torch DataLoader
 ## Describe the bug
I have an IterableDataset (created using streaming=True) and I am trying to create batches using Torch DataLoader class by passing this IterableDataset to it. This throws error which is pasted below. I can do the same by using Torch IterableDataset. One thing I noticed is that in the former case when I look at the dataloader.sampler class I get torch.utils.data.sampler.SequentialSampler while the latter one gives torch.utils.data.dataloader._InfiniteConstantSampler.

I am not sure if this is how it is meant to be used, but that's what seemed reasonable to me.

## Steps to reproduce the bug

1. Does not work.
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
>>> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)
>>> dataloader.sampler
<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>
>>> for batch in dataloader:
...     print(batch)
```

2. Works.
```python
import torch
from torch.utils.data import Dataset, IterableDataset, DataLoader
class CustomIterableDataset(IterableDataset):
  'Characterizes a dataset for PyTorch'
  def __init__(self, data):
        'Initialization'
        self.data = data

  def __iter__(self):
        return iter(self.data)

data = list(range(12))
dataset = CustomIterableDataset(data)
dataloader = DataLoader(dataset, batch_size=4)
print("dataloader: ", dataloader.sampler)
for batch in dataloader:
    print(batch)
```

## Expected results
To get batches of data with the batch size as 4. Output from the latter one (2) though Datasource is different here so actual data is different.
dataloader:  <torch.utils.data.dataloader._InfiniteConstantSampler object at 0x7f1cc29e2c50>
tensor([0, 1, 2, 3])
tensor([4, 5, 6, 7])
tensor([ 8,  9, 10, 11])

## Actual results
<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>

...
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
    data = self._next_data()
  File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 474, in _next_data
    index = self._next_index()  # may raise StopIteration
  File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 427, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
  File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 227, in __iter__
    for idx in self.sampler:
  File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 67, in __iter__
    return iter(range(len(self.data_source)))
TypeError: object of type 'IterableDataset' has no len()

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: '1.8.1.dev0'
- Platform: Linux
- Python version: Python 3.6.8
- PyArrow version: '3.0.0'

 Thank you for that and the example!

What you said makes total sense; I just somehow missed that and assumed HF IterableDataset was a subclass of Torch IterableDataset.


Finding right block-size with JSON loading difficult for user
 As reported by @thomwolf, while loading a JSON Lines file with "json" loading script, he gets
> json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 383)

 This was actually a second error arising from a too small block-size in the json reader.

Finding the right block size is difficult for the layman user


Weights of model checkpoint not initialized for RobertaModel for Bertscore
 When applying bertscore out of the box,

```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']```

Following the typical usage from https://huggingface.co/docs/datasets/loading_metrics.html

```
from datasets import load_metric
metric = load_metric('bertscore')

# Example of typical usage
for batch in dataset:
    inputs, references = batch
    predictions = model(inputs)
    metric.add_batch(predictions=predictions, references=references)
score = metric.compute(lang="en")
#score = metric.compute(model_type="roberta-large") # gives the same error
```

I am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https://github.com/Tiiiger/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo....

## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27
- Python version:  3.9.5
- PyArrow version: 3.0.0

 Hi @suzyahyah, thanks for reporting.

The message you get is indeed not an error message, but a warning coming from Hugging Face `transformers`. The complete warning message is:
```
Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.bias', 'lm_head.layer_norm.weight']
- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
```

In this case, this behavior IS expected and you can safely ignore the warning message.

The reason is that you are just using RoBERTa to get the contextual embeddings of the input sentences/tokens, thus leaving away its head layer, whose weights are ignored.

Feel free to reopen this issue if you need further explanations.


Weights of model checkpoint not initialized for RobertaModel for Bertscore
 When applying bertscore out of the box,

```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']```

Following the typical usage from https://huggingface.co/docs/datasets/loading_metrics.html

```
from datasets import load_metric
metric = load_metric('bertscore')

# Example of typical usage
for batch in dataset:
    inputs, references = batch
    predictions = model(inputs)
    metric.add_batch(predictions=predictions, references=references)
score = metric.compute(lang="en")
#score = metric.compute(model_type="roberta-large") # gives the same error
```

I am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https://github.com/Tiiiger/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo....

## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27
- Python version:  3.9.5
- PyArrow version: 3.0.0

 Hi @suzyahyah, I have created a Pull Request to filter out that warning message in this specific case, since the behavior is as expected and the warning message can only cause confusion for users (as in your case).


Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`
 ## Describe the bug
If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.

## Steps to reproduce the bug

- Create a local dataset builder class
- load the local builder class file using `load_dataset` and let the cache build
- update the file's content
- The cache should rebuilt.

## Expected results

With `ignore_verifications=True`, `load_dataset` should pick up existing cache.

## Actual results

Creates new cache.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.7
- PyArrow version: 3.0.0

 Hi ! I just tried to reproduce what you said:
- create a local builder class
- use `load_dataset`
- update the builder class code
- use `load_dataset` again (with or without `ignore_verifications=True`)
And it creates a new cache, as expected.

What modifications did you do to your builder's code ?


Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`
 ## Describe the bug
If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.

## Steps to reproduce the bug

- Create a local dataset builder class
- load the local builder class file using `load_dataset` and let the cache build
- update the file's content
- The cache should rebuilt.

## Expected results

With `ignore_verifications=True`, `load_dataset` should pick up existing cache.

## Actual results

Creates new cache.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.7
- PyArrow version: 3.0.0

 Hi @lhoestq. Thanks for your reply. I just did minor modifications for which it should not regenerate cache (for e.g. Adding a print statement). Overall, regardless of cache miss, there should be an explicit option to allow reuse of existing cache if author knows cache shouldn't be affected.


Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`
 ## Describe the bug
If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.

## Steps to reproduce the bug

- Create a local dataset builder class
- load the local builder class file using `load_dataset` and let the cache build
- update the file's content
- The cache should rebuilt.

## Expected results

With `ignore_verifications=True`, `load_dataset` should pick up existing cache.

## Actual results

Creates new cache.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.7
- PyArrow version: 3.0.0

 The cache is based on the hash of the dataset builder's code, so changing the code makes it recompute the cache.

You could still rename the cache directory of your previous computation to the new expected cache directory if you want to avoid having to recompute it and if you're sure that it would generate the exact same result.

The verifications are data integrity verifications: it checks the checksums of the downloaded files, as well as the size of the generated splits.


Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`
 ## Describe the bug
If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.

## Steps to reproduce the bug

- Create a local dataset builder class
- load the local builder class file using `load_dataset` and let the cache build
- update the file's content
- The cache should rebuilt.

## Expected results

With `ignore_verifications=True`, `load_dataset` should pick up existing cache.

## Actual results

Creates new cache.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.7
- PyArrow version: 3.0.0

 Hi @apsdehal,

If you decide to follow @lhoestq's suggestion to rename the cache directory of your previous computation to the new expected cache directory, you can do the following to get the name of the new expected cache directory once #2500 is merged:
```python
from datasets import load_dataset_builder
dataset_builder = load_dataset_builder("path/to/your/dataset")
print(dataset_builder.cache_dir)
```

This way, you don't have to recompute the hash of the dataset script yourself each time you modify the script.


Memory usage consistently increases when processing a dataset with `.map`
 ## Describe the bug

I have a HF dataset with image paths stored in it and I am trying to load those image paths using `.map` with `num_proc=80`. I am noticing that the memory usage consistently keeps on increasing with time. I tried using `DEFAULT_WRITER_BATCH_SIZE=10` in the builder to decrease arrow writer's batch size but that doesn't seem to help.

## Steps to reproduce the bug

Providing code as it is would be hard. I can provide a MVP if that helps.

## Expected results

Memory usage should become consistent after some time following the launch of processing.

## Actual results

Memory usage keeps on increasing.

## Environment info

- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.7
- PyArrow version: 3.0.0
 Hi ! Can you share the function you pass to `map` ?
I know you mentioned it would be hard to share some code but this would really help to understand what happened


Multilabel metrics not supported

Multilabel metrics not supported
 When I try to use a metric like F1 macro I get the following error:

```
TypeError: int() argument must be a string, a bytes-like object or a number, not 'list'
```
There is an explicit casting here:

https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/src/datasets/features.py#L274

And looks like this is because here

https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/metrics/f1/f1.py#L88

the features can only be integers, so we cannot use that F1 for multilabel. Instead, if I create the following F1 (ints replaced with sequence of ints), it will work:

```python
class F1(datasets.Metric):
    def _info(self):
        return datasets.MetricInfo(
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            features=datasets.Features(
                {
                    "predictions": datasets.Sequence(datasets.Value("int32")),
                    "references": datasets.Sequence(datasets.Value("int32")),
                }
            ),
            reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"],
        )

    def _compute(self, predictions, references, labels=None, pos_label=1, average="binary", sample_weight=None):
        return {
            "f1": f1_score(
                references,
                predictions,
                labels=labels,
                pos_label=pos_label,
                average=average,
                sample_weight=sample_weight,
            ),
        }
```

 Hi @GuillemGSubies, thanks for reporting.

I have made a PR to fix this issue and allow metrics to be computed also for multilabel classification problems.


Multilabel metrics not supported

Multilabel metrics not supported
 When I try to use a metric like F1 macro I get the following error:

```
TypeError: int() argument must be a string, a bytes-like object or a number, not 'list'
```
There is an explicit casting here:

https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/src/datasets/features.py#L274

And looks like this is because here

https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/metrics/f1/f1.py#L88

the features can only be integers, so we cannot use that F1 for multilabel. Instead, if I create the following F1 (ints replaced with sequence of ints), it will work:

```python
class F1(datasets.Metric):
    def _info(self):
        return datasets.MetricInfo(
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            features=datasets.Features(
                {
                    "predictions": datasets.Sequence(datasets.Value("int32")),
                    "references": datasets.Sequence(datasets.Value("int32")),
                }
            ),
            reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"],
        )

    def _compute(self, predictions, references, labels=None, pos_label=1, average="binary", sample_weight=None):
        return {
            "f1": f1_score(
                references,
                predictions,
                labels=labels,
                pos_label=pos_label,
                average=average,
                sample_weight=sample_weight,
            ),
        }
```

 Sorry for reopening but I just noticed that the `_compute` method for the F1 metric is still not good enough for multilabel problems:

https://github.com/huggingface/datasets/blob/92a3ee549705aa0a107c9fa5caf463b3b3da2616/metrics/f1/f1.py#L115

Somehow we should be able to change the parameter `average` at least


load_dataset("web_nlg") NonMatchingChecksumError
 Hi! It seems the WebNLG dataset gives a NonMatchingChecksumError.

## Steps to reproduce the bug

```python
from datasets import load_dataset
dataset = load_dataset('web_nlg', name="release_v3.0_en", split="dev")
```

Gives

```
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip']
```

## Environment info
- `datasets` version: 1.8.0
- Platform: macOS-11.3.1-x86_64-i386-64bit
- Python version: 3.9.4
- PyArrow version: 3.0.0

Also tested on Linux, with python 3.6.8
 Hi ! Thanks for reporting. This is due to the WebNLG repository that got updated today.
I just pushed a fix at #2558 - this shouldn't happen anymore in the future.