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https://api.github.com/repos/huggingface/datasets/issues/5872 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5872/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5872/comments | https://api.github.com/repos/huggingface/datasets/issues/5872/events | https://github.com/huggingface/datasets/pull/5872 | 1,713,174,662 | PR_kwDODunzps5QrQ5o | 5,872 | Fix infer module for uppercase extensions | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-17T05:56:45 | 2023-05-17T14:26:59 | 2023-05-17T14:19:18 | MEMBER | null | false | {
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} | Fix the `infer_module_for_data_files` and `infer_module_for_data_files_in_archives` functions when passed a data file name with uppercase extension, e.g. `filename.TXT`.
Before, `None` module was returned. | {
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https://api.github.com/repos/huggingface/datasets/issues/5868 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5868/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5868/comments | https://api.github.com/repos/huggingface/datasets/issues/5868/events | https://github.com/huggingface/datasets/issues/5868 | 1,711,173,098 | I_kwDODunzps5l_m3q | 5,868 | Is it possible to change a cached file and 're-cache' it instead of re-generating? | {
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"Arrow files/primitives (tables and arrays) are immutable, so re-generating them is the only option, I'm afraid.",
"> \r\n\r\nGot it, thanks for your reply"
] | 2023-05-16T03:45:42 | 2023-05-17T11:21:36 | 2023-05-17T11:21:36 | NONE | null | null | null | ### Feature request
Hi,
I have a huge cached file using `map`(over 500GB), and I want to change an attribution of each element, is there possible to do it using some method instead of re-generating, because `map` takes over 24 hours
### Motivation
For large datasets, I think it is very important because we always face the problem which is changing something in the original cache without re-generating it.
### Your contribution
For now, I can't help, sorry. | {
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https://api.github.com/repos/huggingface/datasets/issues/5861 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5861/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5861/comments | https://api.github.com/repos/huggingface/datasets/issues/5861/events | https://github.com/huggingface/datasets/pull/5861 | 1,709,807,340 | PR_kwDODunzps5Qf55q | 5,861 | Better error message when combining dataset dicts instead of datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/5860 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5860/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5860/comments | https://api.github.com/repos/huggingface/datasets/issues/5860/events | https://github.com/huggingface/datasets/pull/5860 | 1,709,727,460 | PR_kwDODunzps5QfojD | 5,860 | Minor tqdm optim | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-15T09:49:37 | 2023-05-17T18:46:46 | 2023-05-17T18:39:35 | MEMBER | null | false | {
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} | Don't create a tqdm progress bar when `disable_tqdm` is passed to `map_nested`.
On my side it sped up some iterable datasets by ~30% when `map_nested` is used extensively to recursively tensorize python dicts. | {
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https://api.github.com/repos/huggingface/datasets/issues/5853 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5853/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5853/comments | https://api.github.com/repos/huggingface/datasets/issues/5853/events | https://github.com/huggingface/datasets/pull/5853 | 1,708,092,786 | PR_kwDODunzps5QaZLP | 5,853 | [docs] Redirects, migrated from nginx | {
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"@mishig25 note that it's not exactly the same behavior as in nginx as here it interacts a bit with the `version` and the `language`\r\n\r\nShould be close enough, though.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow=... | 2023-05-12T19:19:27 | 2023-05-15T10:37:19 | 2023-05-15T10:30:14 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/5848 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5848/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5848/comments | https://api.github.com/repos/huggingface/datasets/issues/5848/events | https://github.com/huggingface/datasets/pull/5848 | 1,707,506,734 | PR_kwDODunzps5QYa1B | 5,848 | Add `accelerate` as metric's test dependency to fix CI error | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-12T12:01:01 | 2023-05-12T13:48:47 | 2023-05-12T13:39:06 | CONTRIBUTOR | null | false | {
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} | The `frugalscore` metric uses Transformers' Trainer, which requires `accelerate` (as of recently).
Fixes the following [CI error](https://github.com/huggingface/datasets/actions/runs/4950900048/jobs/8855148703?pr=5845). | {
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https://api.github.com/repos/huggingface/datasets/issues/5851 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5851/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5851/comments | https://api.github.com/repos/huggingface/datasets/issues/5851/events | https://github.com/huggingface/datasets/issues/5851 | 1,707,907,048 | I_kwDODunzps5lzJfo | 5,851 | Error message not clear in interleaving datasets | {
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standard env
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)
### Reproduction
I'm trying to interleave 'sciq', 'wiki' and the 'pile-enron' dataset. I think the error I made was that I loaded the train split of one, but for the other but the error is not too helpful-
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[/home/suryahari/Vornoi/save_model_ops.py](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/Vornoi/save_model_ops.py) in line 3
[41](file:///home/suryahari/Vornoi/save_model_ops.py?line=40) # %%
----> [43](file:///home/suryahari/Vornoi/save_model_ops.py?line=42) dataset = interleave_datasets(datasets, stopping_strategy="all_exhausted")
File [~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124), in interleave_datasets(datasets, probabilities, seed, info, split, stopping_strategy)
[122](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=121) for dataset in datasets[1:]:
[123](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=122) if (map_style and not isinstance(dataset, Dataset)) or (iterable and not isinstance(dataset, IterableDataset)):
--> [124](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=123) raise ValueError(
[125](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=124) f"Unable to interleave a {type(datasets[0])} with a {type(dataset)}. Expected a list of Dataset objects or a list of IterableDataset objects."
[126](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=125) )
[127](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=126) if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
[128](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=127) raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy.")
ValueError: Unable to interleave a with a . Expected a list of Dataset objects or a list of IterableDataset objects.
```
### Expected behavior
the error message should hopefully be more clear | {
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https://api.github.com/repos/huggingface/datasets/issues/5845 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5845/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5845/comments | https://api.github.com/repos/huggingface/datasets/issues/5845/events | https://github.com/huggingface/datasets/pull/5845 | 1,706,253,251 | PR_kwDODunzps5QUMjS | 5,845 | Add `date_format` param to the CSV reader | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-11T17:29:57 | 2023-05-15T07:39:13 | 2023-05-12T15:14:48 | CONTRIBUTOR | null | false | {
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} | Adds the `date_format` param introduced in Pandas 2.0 to the CSV reader and improves its type hints. | {
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https://api.github.com/repos/huggingface/datasets/issues/5841 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5841/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5841/comments | https://api.github.com/repos/huggingface/datasets/issues/5841/events | https://github.com/huggingface/datasets/issues/5841 | 1,705,286,639 | I_kwDODunzps5lpJvv | 5,841 | Abusurdly slow on iteration | {
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"Hi ! You can try to use the [Image](https://huggingface.co/docs/datasets/v2.12.0/en/package_reference/main_classes#datasets.Image) type which [decodes images on-the-fly](https://huggingface.co/docs/datasets/v2.12.0/en/about_dataset_features#image-feature) into pytorch tensors :)\r\n\r\n```python\r\nds = Dataset.fr... | 2023-05-11T08:04:09 | 2023-05-15T15:38:13 | 2023-05-15T15:38:13 | NONE | null | null | null | ### Describe the bug
I am attempting to iterate through an image dataset, but I am encountering a significant slowdown in the iteration speed. In order to investigate this issue, I conducted the following experiment:
```python
a=torch.randn(100,224)
a=torch.stack([a] * 10000)
a.shape
# %%
ds=Dataset.from_dict({"tensor":a})
for i in tqdm(ds.with_format("numpy")):
pass
for i in tqdm(ds.with_format("torch")):
pass
```
I noticed that the dataset in numpy format performs significantly faster than the one in torch format. My hypothesis is that the dataset undergoes a transformation process of torch->python->numpy(torch) in the background, which might be causing the slowdown. Is there any way to expedite the process by bypassing such transformations?
Furthermore, if I increase the size of a to an image shape, like:
```python
a=torch.randn(3,224,224)
```
the iteration speed becomes absurdly slow, around 100 iterations per second, whereas the speed with numpy format is approximately 250 iterations per second. This level of speed would be unacceptable for large image datasets, as it could take several hours just to iterate through a single epoch.
### Steps to reproduce the bug
```python
a=torch.randn(100,224)
a=torch.stack([a] * 10000)
a.shape
# %%
ds=Dataset.from_dict({"tensor":a})
for i in tqdm(ds.with_format("numpy")):
pass
for i in tqdm(ds.with_format("torch")):
pass
```
### Expected behavior
iteration faster
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.4.0-148-generic-x86_64-with-glibc2.10
- Python version: 3.8.16
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5840 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5840/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5840/comments | https://api.github.com/repos/huggingface/datasets/issues/5840/events | https://github.com/huggingface/datasets/issues/5840 | 1,705,212,085 | I_kwDODunzps5lo3i1 | 5,840 | load model error. | {
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"Please report this in the `transformers` repo, as it's not related to `datasets`"
] | 2023-05-11T07:12:38 | 2023-05-12T13:44:07 | 2023-05-12T13:44:06 | NONE | null | null | null | ### Describe the bug
I had trained one model use deepspeed, when I load the final load I get the follow error:
OSError: Can't load tokenizer for '/XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure '/home/fm001/hzl/Project/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor' is the correct path to a directory containing all relevant files for a BloomTokenizerFast tokenizer.
my load code is : python chat.py --path /XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor/
### Steps to reproduce the bug
。。。
### Expected behavior
。。。
### Environment info
。。。 | {
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https://api.github.com/repos/huggingface/datasets/issues/5838 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5838/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5838/comments | https://api.github.com/repos/huggingface/datasets/issues/5838/events | https://github.com/huggingface/datasets/issues/5838 | 1,703,210,848 | I_kwDODunzps5lhO9g | 5,838 | Streaming support for `load_from_disk` | {
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"As the name says, `load_from_disk` load the data from your disk. If the data is hosted on S3, it is first downloaded locally and then loaded from your disk.\r\n\r\nThere is a discussion on streaming data from S3 here though: #5281 ",
"@lhoestq \r\nThanks for your comment. I have checked out the discussion before... | 2023-05-10T06:25:22 | 2023-05-12T09:37:45 | 2023-05-12T09:37:45 | NONE | null | null | null | ### Feature request
Support for streaming datasets stored in object stores in `load_from_disk`.
### Motivation
The `load_from_disk` function supports fetching datasets stored in object stores such as `s3`. In many cases, the datasets that are stored in object stores are very large and being able to stream the data from the buckets becomes essential.
### Your contribution
I'd be happy to contribute this feature if I could get the guidance on how to do so. | {
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https://api.github.com/repos/huggingface/datasets/issues/5836 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5836/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5836/comments | https://api.github.com/repos/huggingface/datasets/issues/5836/events | https://github.com/huggingface/datasets/pull/5836 | 1,702,773,316 | PR_kwDODunzps5QIgzu | 5,836 | [docs] Custom decoding transforms | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5836). All of your documentation changes will be reflected on that endpoint.",
"The error seems unrelated to the changes, so feel free to merge.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\... | 2023-05-09T21:21:41 | 2023-05-15T07:36:12 | 2023-05-10T20:23:03 | MEMBER | null | false | {
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} | Adds custom decoding transform solution to the docs to fix #5782. | {
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https://api.github.com/repos/huggingface/datasets/issues/5835 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5835/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5835/comments | https://api.github.com/repos/huggingface/datasets/issues/5835/events | https://github.com/huggingface/datasets/pull/5835 | 1,702,522,620 | PR_kwDODunzps5QHquR | 5,835 | Always set nullable fields in the writer | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-09T18:16:59 | 2023-05-23T16:10:29 | 2023-05-19T13:04:30 | MEMBER | null | false | {
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} | This fixes loading of e.g. parquet data with non-nullable fields.
Indeed `datasets.Features` doesn't support non-nullable fields, which can lead to data not concatenable due to arrow schema mismatch. | {
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https://api.github.com/repos/huggingface/datasets/issues/5834 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5834/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5834/comments | https://api.github.com/repos/huggingface/datasets/issues/5834/events | https://github.com/huggingface/datasets/issues/5834 | 1,702,448,892 | I_kwDODunzps5leU78 | 5,834 | Is uint8 supported? | {
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"Hi ! The numpy formatting detaults to int64 and float32 - but you can use uint8 using\r\n```python\r\nds = ds.with_format(\"numpy\", dtype=np.uint8)\r\n```",
"Related to https://github.com/huggingface/datasets/issues/5517.",
"Thank you!\r\nBy setting `ds.with_format(\"numpy\", dtype=np.uint8)`, the dataset ret... | 2023-05-09T17:31:13 | 2023-05-13T05:04:21 | 2023-05-13T05:04:21 | NONE | null | null | null | ### Describe the bug
I expect the dataset to store the data in the `uint8` data type, but it's returning `int64` instead.
While I've found that `datasets` doesn't yet support float16 (https://github.com/huggingface/datasets/issues/4981), I'm wondering if this is the case for other data types as well.
Is there a way to store vector data as `uint8` and then upload it to the hub?
### Steps to reproduce the bug
```python
from datasets import Features, Dataset, Sequence, Value
import numpy as np
dataset = Dataset.from_dict(
{"vector": [np.array([0, 1, 2], dtype=np.uint8)]}, features=Features({"vector": Sequence(Value("uint8"))})
).with_format("numpy")
print(dataset[0]["vector"].dtype)
```
### Expected behavior
Expected: `uint8`
Actual: `int64`
### Environment info
- `datasets` version: 2.12.0
- Platform: macOS-12.1-x86_64-i386-64bit
- Python version: 3.8.12
- Huggingface_hub version: 0.12.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5832 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5832/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5832/comments | https://api.github.com/repos/huggingface/datasets/issues/5832/events | https://github.com/huggingface/datasets/issues/5832 | 1,702,135,336 | I_kwDODunzps5ldIYo | 5,832 | 404 Client Error: Not Found for url: https://huggingface.co/api/models/bert-large-cased | {
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"moved to https://github.com/huggingface/transformers/issues/23233"
] | 2023-05-09T14:14:59 | 2023-05-09T14:25:59 | 2023-05-09T14:25:59 | NONE | null | null | null | ### Describe the bug
Running [Bert-Large-Cased](https://huggingface.co/bert-large-cased) model causes `HTTPError`, with the following traceback-
```
HTTPError Traceback (most recent call last)
<ipython-input-6-5c580443a1ad> in <module>
----> 1 tokenizer = BertTokenizer.from_pretrained('bert-large-cased')
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/transformers/tokenization_utils_base.py in from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs)
1646 # At this point pretrained_model_name_or_path is either a directory or a model identifier name
1647 fast_tokenizer_file = get_fast_tokenizer_file(
-> 1648 pretrained_model_name_or_path, revision=revision, use_auth_token=use_auth_token
1649 )
1650 additional_files_names = {
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/transformers/tokenization_utils_base.py in get_fast_tokenizer_file(path_or_repo, revision, use_auth_token)
3406 """
3407 # Inspect all files from the repo/folder.
-> 3408 all_files = get_list_of_files(path_or_repo, revision=revision, use_auth_token=use_auth_token)
3409 tokenizer_files_map = {}
3410 for file_name in all_files:
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/transformers/file_utils.py in get_list_of_files(path_or_repo, revision, use_auth_token)
1685 token = None
1686 model_info = HfApi(endpoint=HUGGINGFACE_CO_RESOLVE_ENDPOINT).model_info(
-> 1687 path_or_repo, revision=revision, token=token
1688 )
1689 return [f.rfilename for f in model_info.siblings]
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/huggingface_hub/hf_api.py in model_info(self, repo_id, revision, token)
246 )
247 r = requests.get(path, headers=headers)
--> 248 r.raise_for_status()
249 d = r.json()
250 return ModelInfo(**d)
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/requests/models.py in raise_for_status(self)
951
952 if http_error_msg:
--> 953 raise HTTPError(http_error_msg, response=self)
954
955 def close(self):
HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/models/bert-large-cased
```
I have also tried running in offline mode, as [discussed here](https://huggingface.co/docs/transformers/installation#offline-mode)
```
HF_DATASETS_OFFLINE=1
TRANSFORMERS_OFFLINE=1
```
### Steps to reproduce the bug
1. `from transformers import BertTokenizer, BertModel`
2. `tokenizer = BertTokenizer.from_pretrained('bert-large-cased')`
### Expected behavior
Run without the HTTP error.
### Environment info
| # Name | Version | Build | Channel | |
|--------------------|------------|-----------------------------|---------|---|
| _libgcc_mutex | 0.1 | main | | |
| _openmp_mutex | 4.5 | 1_gnu | | |
| _pytorch_select | 0.1 | cpu_0 | | |
| appdirs | 1.4.4 | pypi_0 | pypi | |
| backcall | 0.2.0 | pypi_0 | pypi | |
| blas | 1.0 | mkl | | |
| bzip2 | 1.0.8 | h7b6447c_0 | | |
| ca-certificates | 2021.7.5 | h06a4308_1 | | |
| certifi | 2021.5.30 | py37h06a4308_0 | | |
| cffi | 1.14.6 | py37h400218f_0 | | |
| charset-normalizer | 2.0.3 | pypi_0 | pypi | |
| click | 8.0.1 | pypi_0 | pypi | |
| colorama | 0.4.4 | pypi_0 | pypi | |
| cudatoolkit | 11.1.74 | h6bb024c_0 | nvidia | |
| cycler | 0.11.0 | pypi_0 | pypi | |
| decorator | 5.0.9 | pypi_0 | pypi | |
| docker-pycreds | 0.4.0 | pypi_0 | pypi | |
| docopt | 0.6.2 | pypi_0 | pypi | |
| dominate | 2.6.0 | pypi_0 | pypi | |
| ffmpeg | 4.3 | hf484d3e_0 | pytorch | |
| filelock | 3.0.12 | pypi_0 | pypi | |
| fonttools | 4.38.0 | pypi_0 | pypi | |
| freetype | 2.10.4 | h5ab3b9f_0 | | |
| gitdb | 4.0.7 | pypi_0 | pypi | |
| gitpython | 3.1.18 | pypi_0 | pypi | |
| gmp | 6.2.1 | h2531618_2 | | |
| gnutls | 3.6.15 | he1e5248_0 | | |
| huggingface-hub | 0.0.12 | pypi_0 | pypi | |
| humanize | 3.10.0 | pypi_0 | pypi | |
| idna | 3.2 | pypi_0 | pypi | |
| importlib-metadata | 4.6.1 | pypi_0 | pypi | |
| intel-openmp | 2019.4 | 243 | | |
| ipdb | 0.13.9 | pypi_0 | pypi | |
| ipython | 7.25.0 | pypi_0 | pypi | |
| ipython-genutils | 0.2.0 | pypi_0 | pypi | |
| jedi | 0.18.0 | pypi_0 | pypi | |
| joblib | 1.0.1 | pypi_0 | pypi | |
| jpeg | 9b | h024ee3a_2 | | |
| jsonpickle | 1.5.2 | pypi_0 | pypi | |
| kiwisolver | 1.4.4 | pypi_0 | pypi | |
| 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 | | |
| libiconv | 1.15 | h63c8f33_5 | | |
| libidn2 | 2.3.2 | h7f8727e_0 | | |
| libmklml | 2019.0.5 | 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 | | |
| libwebp-base | 1.2.0 | h27cfd23_0 | | |
| lz4-c | 1.9.3 | h2531618_0 | | |
| matplotlib | 3.5.3 | pypi_0 | pypi | |
| matplotlib-inline | 0.1.2 | pypi_0 | pypi | |
| mergedeep | 1.3.4 | pypi_0 | pypi | |
| mkl | 2020.2 | 256 | | |
| mkl-service | 2.3.0 | py37he8ac12f_0 | | |
| mkl_fft | 1.3.0 | py37h54f3939_0 | | |
| mkl_random | 1.1.1 | py37h0573a6f_0 | | |
| msgpack | 1.0.2 | pypi_0 | pypi | |
| munch | 2.5.0 | pypi_0 | pypi | |
| ncurses | 6.2 | he6710b0_1 | | |
| nettle | 3.7.3 | hbbd107a_1 | | |
| ninja | 1.10.2 | hff7bd54_1 | | |
| nltk | 3.8.1 | pypi_0 | pypi | |
| numpy | 1.19.2 | py37h54aff64_0 | | |
| numpy-base | 1.19.2 | py37hfa32c7d_0 | | |
| olefile | 0.46 | py37_0 | | |
| openh264 | 2.1.0 | hd408876_0 | | |
| openjpeg | 2.3.0 | h05c96fa_1 | | |
| openssl | 1.1.1k | h27cfd23_0 | | |
| packaging | 21.0 | pypi_0 | pypi | |
| pandas | 1.3.1 | pypi_0 | pypi | |
| parso | 0.8.2 | pypi_0 | pypi | |
| pathtools | 0.1.2 | pypi_0 | pypi | |
| pexpect | 4.8.0 | pypi_0 | pypi | |
| pickleshare | 0.7.5 | pypi_0 | pypi | |
| pillow | 8.3.1 | py37h2c7a002_0 | | |
| pip | 21.1.3 | py37h06a4308_0 | | |
| prompt-toolkit | 3.0.19 | pypi_0 | pypi | |
| protobuf | 4.21.12 | pypi_0 | pypi | |
| psutil | 5.8.0 | pypi_0 | pypi | |
| ptyprocess | 0.7.0 | pypi_0 | pypi | |
| py-cpuinfo | 8.0.0 | pypi_0 | pypi | |
| pycparser | 2.20 | py_2 | | |
| pygments | 2.9.0 | pypi_0 | pypi | |
| pyparsing | 2.4.7 | pypi_0 | pypi | |
| python | 3.7.10 | h12debd9_4 | | |
| python-dateutil | 2.8.2 | pypi_0 | pypi | |
| pytorch | 1.9.0 | py3.7_cuda11.1_cudnn8.0.5_0 | pytorch | |
| pytz | 2021.1 | pypi_0 | pypi | |
| pyyaml | 5.4.1 | pypi_0 | pypi | |
| readline | 8.1 | h27cfd23_0 | | |
| regex | 2022.10.31 | pypi_0 | pypi | |
| requests | 2.26.0 | pypi_0 | pypi | |
| sacred | 0.8.2 | pypi_0 | pypi | |
| sacremoses | 0.0.45 | pypi_0 | pypi | |
| scikit-learn | 0.24.2 | pypi_0 | pypi | |
| scipy | 1.7.0 | pypi_0 | pypi | |
| sentry-sdk | 1.15.0 | pypi_0 | pypi | |
| setproctitle | 1.3.2 | pypi_0 | pypi | |
| setuptools | 52.0.0 | py37h06a4308_0 | | |
| six | 1.16.0 | pyhd3eb1b0_0 | | |
| smmap | 4.0.0 | pypi_0 | pypi | |
| sqlite | 3.36.0 | hc218d9a_0 | | |
| threadpoolctl | 2.2.0 | pypi_0 | pypi | |
| tk | 8.6.10 | hbc83047_0 | | |
| tokenizers | 0.10.3 | pypi_0 | pypi | |
| toml | 0.10.2 | pypi_0 | pypi | |
| torchaudio | 0.9.0 | py37 | pytorch | |
| torchvision | 0.10.0 | py37_cu111 | pytorch | |
| tqdm | 4.61.2 | pypi_0 | pypi | |
| traitlets | 5.0.5 | pypi_0 | pypi | |
| transformers | 4.9.1 | pypi_0 | pypi | |
| typing-extensions | 3.10.0.0 | hd3eb1b0_0 | | |
| typing_extensions | 3.10.0.0 | pyh06a4308_0 | | |
| urllib3 | 1.26.14 | pypi_0 | pypi | |
| wandb | 0.13.10 | pypi_0 | pypi | |
| wcwidth | 0.2.5 | pypi_0 | pypi | |
| wheel | 0.36.2 | pyhd3eb1b0_0 | | |
| wrapt | 1.12.1 | pypi_0 | pypi | |
| xz | 5.2.5 | h7b6447c_0 | | |
| zipp | 3.5.0 | pypi_0 | pypi | |
| zlib | 1.2.11 | h7b6447c_3 | | |
| zstd | 1.4.9 | haebb681_0 | | | | {
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"Can you paste the error stack trace?",
"That is weird. I can't reproduce it again after reboot.\r\n```python\r\nIn [2]: import platform\r\n\r\nIn [3]: platform.platform()\r\nOut[3]: 'macOS-13.2-arm64-arm-64bit'\r\n\r\nIn [4]: from datasets import load_dataset\r\n ...:\r\n ...: jazzy = load_dataset(\"nomic-ai... | 2023-05-08T10:07:14 | 2023-05-09T00:46:42 | 2023-05-09T00:46:42 | NONE | null | null | null | ### Describe the bug
M2 MBP can't run
```python
from datasets import load_dataset
jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
### Steps to reproduce the bug
1. Use M2 MBP
2. Python 3.10.10 from pyenv
3. Run
```
from datasets import load_dataset
jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
### Expected behavior
Be able to run normally
### Environment info
```
from datasets import load_dataset
jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
OSX: 13.2
CPU: M2
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"Hi! \r\n\r\nYou can call `map` as follows to avoid the error:\r\n```python\r\naugmented_dataset_cln = dataset_cln['train'].map(augment_dataset, features=dataset_cln['train'].features)\r\n```",
"Thanks it is solved"
] | 2023-05-07T21:02:54 | 2023-05-10T05:06:58 | 2023-05-10T05:05:47 | NONE | null | null | null | ### Describe the bug
I am not able to concatenate the augmentation of the stream data. I am using the latest version of dataset.
ValueError: The features can't be aligned because the key audio of features {'audio_id': Value(dtype='string',
id=None), 'audio': {'array': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'path':
Value(dtype='null', id=None), 'sampling_rate': Value(dtype='int64', id=None)}, 'transcript': Value(dtype='string',
id=None)} has unexpected type - {'array': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None),
'path': Value(dtype='null', id=None), 'sampling_rate': Value(dtype='int64', id=None)} (expected either
Audio(sampling_rate=16000, mono=True, decode=True, id=None) or Value("null").
### Steps to reproduce the bug
dataset = load_dataset("tobiolatunji/afrispeech-200", "all", streaming=True).shuffle(seed=42)
dataset_cln = dataset.remove_columns(['speaker_id', 'path', 'age_group', 'gender', 'accent', 'domain', 'country', 'duration'])
dataset_cln = dataset_cln.cast_column("audio", Audio(sampling_rate=16000))
from audiomentations import AddGaussianNoise,Compose,Gain,OneOf,PitchShift,PolarityInversion,TimeStretch
augmentation = Compose([
AddGaussianNoise(min_amplitude=0.005, max_amplitude=0.015, p=0.2)
])
def augment_dataset(batch):
audio = batch["audio"]
audio["array"] = augmentation(audio["array"], sample_rate=audio["sampling_rate"])
return batch
augmented_dataset_cln = dataset_cln['train'].map(augment_dataset)
dataset_cln['train'] = interleave_datasets([dataset_cln['train'], augmented_dataset_cln])
dataset_cln['train'] = dataset_cln['train'].shuffle(seed=42)
### Expected behavior
I should be able to merge as sampling rate is same.
### Environment info
import datasets
import transformers
import accelerate
print(datasets.__version__)
print(transformers.__version__)
print(torch.__version__)
print(evaluate.__version__)
print(accelerate.__version__)
2.12.0
4.28.1
2.0.0
0.4.0
0.18.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5824 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5824/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5824/comments | https://api.github.com/repos/huggingface/datasets/issues/5824/events | https://github.com/huggingface/datasets/pull/5824 | 1,697,152,148 | PR_kwDODunzps5P1rIZ | 5,824 | Fix incomplete docstring for `BuilderConfig` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-05T07:34:28 | 2023-05-05T12:39:14 | 2023-05-05T12:31:54 | CONTRIBUTOR | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5824",
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"patch_url": "https://github.com/huggingface/datasets/pull/5824.patch",
"merged_at": "2023-05-05T12:31:54"
} | Fixes #5820
Also fixed a couple of typos I spotted | {
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https://api.github.com/repos/huggingface/datasets/issues/5823 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5823/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5823/comments | https://api.github.com/repos/huggingface/datasets/issues/5823/events | https://github.com/huggingface/datasets/issues/5823 | 1,697,024,789 | I_kwDODunzps5lJosV | 5,823 | [2.12.0] DatasetDict.save_to_disk not saving to S3 | {
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"Hi ! Can you try adding the `s3://` prefix ?\r\n```python\r\nf\"s3://{s3_bucket}/{s3_dir}/{dataset_name}\"\r\n```",
"Ugh, yeah that was it. Thank you!"
] | 2023-05-05T05:22:59 | 2023-05-05T15:01:18 | 2023-05-05T15:01:17 | NONE | null | null | null | ### Describe the bug
When trying to save a `DatasetDict` to a private S3 bucket using `save_to_disk`, the artifacts are instead saved locally, and not in the S3 bucket.
I have tried using the deprecated `fs` as well as the `storage_options` arguments and I get the same results.
### Steps to reproduce the bug
1. Create a DatsetDict `dataset`
2. Create a S3FileSystem object
`s3 = datasets.filesystems.S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key)`
3. Save using `dataset_dict.save_to_disk(f"{s3_bucket}/{s3_dir}/{dataset_name}", storage_options=s3.storage_options)` or `dataset_dict.save_to_disk(f"{s3_bucket}/{s3_dir}/{dataset_name}", fs=s3)`
4. Check the corresponding S3 bucket and verify nothing has been uploaded
5. Check the path at f"{s3_bucket}/{s3_dir}/{dataset_name}" and verify that files have been saved there
### Expected behavior
Artifacts are uploaded at the f"{s3_bucket}/{s3_dir}/{dataset_name}" S3 location.
### Environment info
- `datasets` version: 2.12.0
- Platform: macOS-13.3.1-x86_64-i386-64bit
- Python version: 3.11.2
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5822 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5822/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5822/comments | https://api.github.com/repos/huggingface/datasets/issues/5822/events | https://github.com/huggingface/datasets/issues/5822 | 1,696,627,308 | I_kwDODunzps5lIHps | 5,822 | Audio Dataset with_format torch problem | {
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"Hi ! Can you try with a more recent version of `datasets` ?",
"Ok, yes it worked with the most recent version. Thanks"
] | 2023-05-04T20:07:51 | 2023-05-11T20:45:53 | 2023-05-11T20:45:53 | NONE | null | null | null | ### Describe the bug
Common Voice v10 Delta (German) Dataset from here https://commonvoice.mozilla.org/de/datasets
```
audio_dataset = \
(Dataset
.from_dict({"audio": ('/tmp/cv-corpus-10.0-delta-2022-07-04/de/clips/' + df.path).to_list()})
.cast_column("audio", Audio(sampling_rate=16_000))
.with_format('numpy'))
audio_dataset[0]["audio"]
```
works, but
```
audio_dataset = \
(Dataset
.from_dict({"audio": ('/tmp/cv-corpus-10.0-delta-2022-07-04/de/clips/' + df.path).to_list()})
.cast_column("audio", Audio(sampling_rate=16_000))
.with_format('torch'))
audio_dataset[0]["audio"]
```
does not instead I get
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[54], line 1
----> 1 audio_dataset[0]["audio"]
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/arrow_dataset.py:2154, in Dataset.__getitem__(self, key)
2152 def __getitem__(self, key): # noqa: F811
2153 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2154 return self._getitem(
2155 key,
2156 )
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/arrow_dataset.py:2139, in Dataset._getitem(self, key, decoded, **kwargs)
2137 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
2138 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 2139 formatted_output = format_table(
2140 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
2141 )
2142 return formatted_output
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
--> 532 return formatter(pa_table, query_type=query_type)
533 elif query_type == "column":
534 if key in format_columns:
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type)
279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
280 if query_type == "row":
--> 281 return self.format_row(pa_table)
282 elif query_type == "column":
283 return self.format_column(pa_table)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:58, in TorchFormatter.format_row(self, pa_table)
56 def format_row(self, pa_table: pa.Table) -> dict:
57 row = self.numpy_arrow_extractor().extract_row(pa_table)
---> 58 return self.recursive_tensorize(row)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:54, in TorchFormatter.recursive_tensorize(self, data_struct)
53 def recursive_tensorize(self, data_struct: dict):
---> 54 return map_nested(self._recursive_tensorize, data_struct, map_list=False)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:356, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
354 num_proc = 1
355 if num_proc <= 1 or len(iterable) <= num_proc:
--> 356 mapped = [
357 _single_map_nested((function, obj, types, None, True, None))
358 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
359 ]
360 else:
361 split_kwds = [] # We organize the splits ourselve (contiguous splits)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:357, in <listcomp>(.0)
354 num_proc = 1
355 if num_proc <= 1 or len(iterable) <= num_proc:
356 mapped = [
--> 357 _single_map_nested((function, obj, types, None, True, None))
358 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
359 ]
360 else:
361 split_kwds = [] # We organize the splits ourselve (contiguous splits)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:309, in _single_map_nested(args)
306 pbar = logging.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit="obj", desc=pbar_desc)
308 if isinstance(data_struct, dict):
--> 309 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
310 else:
311 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:309, in <dictcomp>(.0)
306 pbar = logging.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit="obj", desc=pbar_desc)
308 if isinstance(data_struct, dict):
--> 309 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
310 else:
311 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:293, in _single_map_nested(args)
291 # Singleton first to spare some computation
292 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 293 return function(data_struct)
295 # Reduce logging to keep things readable in multiprocessing with tqdm
296 if rank is not None and logging.get_verbosity() < logging.WARNING:
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:51, in TorchFormatter._recursive_tensorize(self, data_struct)
49 if data_struct.dtype == np.object: # pytorch tensors cannot be instantied from an array of objects
50 return [self.recursive_tensorize(substruct) for substruct in data_struct]
---> 51 return self._tensorize(data_struct)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:38, in TorchFormatter._tensorize(self, value)
35 import torch
37 default_dtype = {}
---> 38 if np.issubdtype(value.dtype, np.integer):
39 default_dtype = {"dtype": torch.int64}
40 elif np.issubdtype(value.dtype, np.floating):
AttributeError: 'NoneType' object has no attribute 'dtype'
```
### Steps to reproduce the bug
1. Download some audio dataset in this case I used Common Voice v10 Delta (German) Dataset from here https://commonvoice.mozilla.org/de/datasets
2. Try the Code from above
### Expected behavior
It should work for torch
### Environment info
pytorch: 2.0.0
datasets: 2.3.2
numpy: 1.21.6
Python: 3.8
Linux | {
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https://api.github.com/repos/huggingface/datasets/issues/5820 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5820/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5820/comments | https://api.github.com/repos/huggingface/datasets/issues/5820/events | https://github.com/huggingface/datasets/issues/5820 | 1,695,892,811 | I_kwDODunzps5lFUVL | 5,820 | Incomplete docstring for `BuilderConfig` | {
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"Thanks for reporting! You are more than welcome to improve `BuilderConfig`'s docstring.\r\n\r\nThis class serves an identical purpose as `tensorflow_datasets`'s `BuilderConfig`, and its docstring is [here](https://github.com/tensorflow/datasets/blob/a95e38b5bb018312c3d3720619c2a8ef83ebf57f/tensorflow_datasets/core... | 2023-05-04T12:14:34 | 2023-05-05T12:31:56 | 2023-05-05T12:31:56 | CONTRIBUTOR | null | null | null | Hi guys !
I stumbled upon this docstring while working on a project.
Some of the attributes have missing descriptions.
https://github.com/huggingface/datasets/blob/bc5fef5b6d91f009e4101684adcb374df2c170f6/src/datasets/builder.py#L104-L117 | {
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https://api.github.com/repos/huggingface/datasets/issues/5819 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5819/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5819/comments | https://api.github.com/repos/huggingface/datasets/issues/5819/events | https://github.com/huggingface/datasets/issues/5819 | 1,695,536,738 | I_kwDODunzps5lD9Zi | 5,819 | Cannot pickle error in Dataset.from_generator() | {
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"Hi! It should work if you put `model = torch.compile(model)` inside the `generate_data` function. If a referenced object is outside, it needs to be pickable, and that's not the case for the compiled models (or functions). ",
"> Hi! It should work if you put `model = torch.compile(model)` inside the `generate_da... | 2023-05-04T08:39:09 | 2023-05-05T19:20:59 | 2023-05-05T19:20:58 | NONE | null | null | null | ### Describe the bug
I'm trying to use Dataset.from_generator() to generate a large dataset.
### Steps to reproduce the bug
Code to reproduce:
```
from transformers import T5Tokenizer, T5ForConditionalGeneration, GenerationConfig
import torch
from tqdm import tqdm
from datasets import load_dataset
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small", device_map="auto")
model = torch.compile(model)
def generate_data(data_loader):
model.eval()
for batch in tqdm(data_loader):
input_ids = tokenizer(batch['instruction'], return_tensors='pt', padding=True, truncation=True).input_ids.to("cuda:0")
with torch.no_grad():
outputs = model.generate(input_ids, generation_config=generation_config)
decoder_hidden_states = outputs.decoder_hidden_states
for i, h in zip(batch['instruction'], decoder_hidden_states):
yield {"instruction": i, "decoder_hidden_states": h}
generation_config = GenerationConfig(
temperature=1,
max_new_tokens=1024,
do_sample=False,
num_return_sequences=1,
return_dict_in_generate=True,
output_scores=True,
output_hidden_states=True,
)
from datasets import Dataset, load_dataset
from torch.utils.data import DataLoader
dataset = load_dataset("HuggingFaceH4/databricks_dolly_15k")
train_loader = DataLoader(dataset['train'], batch_size=2, shuffle=True)
dataset = Dataset.from_generator(generator=generate_data, gen_kwargs={"data_loader": train_loader})
dataset.save_to_disk("data/flant5_small_generation")
```
### Expected behavior
The dataset should be generated and saved.
But the following error occurred:
```
Traceback (most recent call last):
File "/remote-home/xhwang/alpaca-lora/data_collection_t5.py", line 46, in <module>
dataset = Dataset.from_generator(generator=generate_data, gen_kwargs={"data_loader": train_loader})
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1035, in from_generator
return GeneratorDatasetInputStream(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/io/generator.py", line 28, in __init__
self.builder = Generator(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/builder.py", line 336, in __init__
self.config, self.config_id = self._create_builder_config(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/builder.py", line 505, in _create_builder_config
config_id = builder_config.create_config_id(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/builder.py", line 179, in create_config_id
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/fingerprint.py", line 236, in hash
return cls.hash_default(value)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/fingerprint.py", line 229, in hash_default
return cls.hash_bytes(dumps(value))
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 726, in dumps
dump(obj, file)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 701, in dump
Pickler(file, recurse=True).dump(obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 394, in dump
StockPickler.dump(self, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 487, in dump
self.save(obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1084, in _save_with_postproc
pickler._batch_setitems(iter(source.items()))
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 603, in save
self.save_reduce(obj=obj, *rv)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 603, in save
self.save_reduce(obj=obj, *rv)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1070, in _save_with_postproc
pickler.save_reduce(*reduction, obj=obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 887, in save_tuple
save(element)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1070, in _save_with_postproc
pickler.save_reduce(*reduction, obj=obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 887, in save_tuple
save(element)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 1003, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1084, in _save_with_postproc
pickler._batch_setitems(iter(source.items()))
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 578, in save
rv = reduce(self.proto)
TypeError: cannot pickle 'ConfigModuleInstance' object
```
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-4.15.0-156-generic-x86_64-with-glibc2.31
- Python version: 3.10.10
- Huggingface_hub version: 0.13.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5817 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5817/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5817/comments | https://api.github.com/repos/huggingface/datasets/issues/5817/events | https://github.com/huggingface/datasets/issues/5817 | 1,694,891,866 | I_kwDODunzps5lBf9a | 5,817 | Setting `num_proc` errors when `.map` returns additional items. | {
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"Hi ! Unfortunately I couldn't reproduce on my side locally and with datasets 2.11 and python 3.10.11 on colab.\r\nWhat version of `multiprocess` are you using ?",
"I've got `multiprocess` version `0.70.14`.\r\n\r\nI've done some more testing and the error only occurs in PyCharm's Python Console. It seems to be [... | 2023-05-03T21:46:53 | 2023-05-04T21:14:21 | 2023-05-04T20:22:25 | NONE | null | null | null | ### Describe the bug
I'm using a map function that returns more rows than are passed in.
If I try to use `num_proc` I get:
```
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 563, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 528, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3097, in map
for rank, done, content in iflatmap_unordered(
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1372, in iflatmap_unordered
yield queue.get(timeout=0.05)
File "<string>", line 2, in get
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/managers.py", line 818, in _callmethod
kind, result = conn.recv()
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/connection.py", line 258, in recv
buf = self._recv_bytes()
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/connection.py", line 422, in _recv_bytes
buf = self._recv(4)
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/connection.py", line 391, in _recv
raise EOFError
EOFError
```
### Steps to reproduce the bug
This is copied from the [Datasets docs](https://huggingface.co/docs/datasets/v2.12.0/en/process#batch-processing), with `num_proc` added, and will error.
```py
import datasets
dataset = ... # any old dataset
def chunk_examples(examples):
chunks = []
for sentence in examples["text"]:
chunks += [sentence[i : i + 50] for i in range(0, len(sentence), 50)]
return {"chunks": chunks}
chunked_dataset = dataset.map(
chunk_examples,
batched=True,
remove_columns=dataset.column_names,
num_proc=2, # Remove and it works
)
```
### Expected behavior
Should work fine. On a related note, multi-processing also fails if there is a Meta class anywhere in scope (and there are plenty in the standard library). This is the fault of `dill` and is a long standing issue.
Have you considered using Loky for multiprocessing? I've found that the built-in `datasets` multi-processing breaks more than it works so have written my own function using `loky`, for reference:
```py
import datasets
import loky
def fast_loop(dataset: datasets.Dataset, func, num_proc=None):
if num_proc is None:
import os
num_proc = len(os.sched_getaffinity(0))
shards = [
dataset.shard(num_shards=num_proc, index=i, contiguous=True)
for i in range(num_proc)
]
executor = loky.get_reusable_executor(max_workers=num_proc)
results = executor.map(func, shards)
return datasets.combine.concatenate_datasets(list(results))
```
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- Huggingface_hub version: 0.12.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5816 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5816/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5816/comments | https://api.github.com/repos/huggingface/datasets/issues/5816/events | https://github.com/huggingface/datasets/pull/5816 | 1,694,590,856 | PR_kwDODunzps5Ps4t9 | 5,816 | Preserve `stopping_strategy` of shuffled interleaved dataset (random cycling case) | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... | 2023-05-03T18:34:18 | 2023-05-04T14:31:55 | 2023-05-04T14:24:49 | CONTRIBUTOR | null | false | {
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} | Preserve the `stopping_strategy` in the `RandomlyCyclingMultiSourcesExamplesIterable.shard_data_sources` to fix shuffling a dataset interleaved (from multiple sources) with probabilities.
Fix #5812
| {
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https://api.github.com/repos/huggingface/datasets/issues/5813 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5813/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5813/comments | https://api.github.com/repos/huggingface/datasets/issues/5813/events | https://github.com/huggingface/datasets/pull/5813 | 1,691,908,535 | PR_kwDODunzps5Pj0_E | 5,813 | [DO-NOT-MERGE] Debug Windows issue at #3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5812 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5812/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5812/comments | https://api.github.com/repos/huggingface/datasets/issues/5812/events | https://github.com/huggingface/datasets/issues/5812 | 1,691,798,169 | I_kwDODunzps5k1sqZ | 5,812 | Cannot shuffle interleaved IterableDataset with "all_exhausted" stopping strategy | {
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Shuffling interleaved `IterableDataset` with "all_exhausted" strategy yields non-exhaustive sampling.
### Steps to reproduce the bug
```py
from datasets import IterableDataset, interleave_datasets
def gen(bias, length):
for i in range(length):
yield dict(a=bias+i)
seed = 42
probabilities = [0.2, 0.6, 0.2]
d1 = IterableDataset.from_generator(lambda: gen(0, 3))
d2 = IterableDataset.from_generator(lambda: gen(10, 4))
d3 = IterableDataset.from_generator(lambda: gen(20, 3))
ds = interleave_datasets([d1, d2, d3], probabilities=probabilities, seed=seed, stopping_strategy='all_exhausted')
ds = ds.shuffle(buffer_size=1000)
for x in ds:
print(x)
```
This code produces
```
{'a': 0}
{'a': 22}
{'a': 20}
{'a': 21}
{'a': 10}
{'a': 1}
```
### Expected behavior
It should produce a longer list of examples to exhaust all the datasets.
If you comment out the shuffle line, it will exhaust all the datasets properly.
Here is the output if you comment out shuffling:
```
{'a': 10}
{'a': 11}
{'a': 20}
{'a': 12}
{'a': 0}
{'a': 21}
{'a': 13}
{'a': 10}
{'a': 1}
{'a': 11}
{'a': 12}
{'a': 22}
{'a': 13}
{'a': 20}
{'a': 10}
{'a': 11}
{'a': 12}
{'a': 2}
```
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.31
- Python version: 3.10.11
- Huggingface_hub version: 0.14.1
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
This was run on Google Colab. | {
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https://api.github.com/repos/huggingface/datasets/issues/5810 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5810/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5810/comments | https://api.github.com/repos/huggingface/datasets/issues/5810/events | https://github.com/huggingface/datasets/pull/5810 | 1,689,917,822 | PR_kwDODunzps5PdJHI | 5,810 | Add `fn_kwargs` to `map` and `filter` of `IterableDataset` and `IterableDatasetDict` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Sorry, the local test passed because it was inadvertently testing the main branch. I am currently fixing where the test failed.",
"- I have fixed the bug and addressed the above two points.\r\n- I have tested locally and confirmed ... | 2023-04-30T13:23:01 | 2023-05-22T08:12:39 | 2023-05-22T08:05:31 | CONTRIBUTOR | null | false | {
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} | # Overview
I've added an argument`fn_kwargs` for map and filter methods of `IterableDataset` and `IterableDatasetDict` classes.
# Details
Currently, the map and filter methods of some classes related to `IterableDataset` do not allow specifing the arguments passed to the function. This pull request adds `fn_kwargs` to pass arguments to the mapping function. This allows users to preprocess data more flexibly.
Added `fn_kwargs` to the following classes and methods (description of the argument is also added).
1. class `FilteredExamplesIterable`
2. method `filter` of class `IterableDataset`
3. method `map` of class `IterableDatasetDict`
4. method `filter` of class `IterableDatasetDict`
# Example of changes
Here's an example of how to use the new functionality:
```python
from datasets import IterableDatasetDict
def preprocess_function(example, a=None, b=None):
# do something
return example
dataset = IterableDatasetDict(...)
dataset = dataset.map(preprocess_function, fn_kwargs={"a": 1, "b": 2})
```
# Related Issues
This pull request is related to the following issue:
https://github.com/huggingface/datasets/issues/3444 .
# Testing
I have added unit tests to test the new functionality.
In test_iterable_dataset.py
- Added `test_filtered_examples_iterable_with_fn_kwargs` for [1](#details).
- Added `test_iterable_dataset_filter` for [2](#details).
- Added `test_iterable_dataset_map_with_fn_kwargs`. This is not a newly added feature, but was added because it was not tested.
In test_dataset_dict.py
- Added `_create_dummy_iterable_dataset` for [3](#details) and [4](#details).
- Added `_create_dummy_iterable_dataset_dict` for [3](#details) and [4](#details).
- Added `test_iterable_map` for [3](#details).
- Added `test_iterable_filter` for [4](#details).
Note that, there is no test for `IterableDatasetDict` at the current main branch. I thought about writing tests for `IterableDatasetDict` in a new file, but I decided to add them in the test file for `DatasetDict` (test_dataset_dict.py).
# Checklist
- [x] Format the code.
- [x] Added tests.
- [x] Passed tests locally. | {
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https://api.github.com/repos/huggingface/datasets/issues/5804 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5804/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5804/comments | https://api.github.com/repos/huggingface/datasets/issues/5804/events | https://github.com/huggingface/datasets/pull/5804 | 1,688,285,666 | PR_kwDODunzps5PX0Dk | 5,804 | Set dev version | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5804). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma... | 2023-04-28T10:10:01 | 2023-04-28T10:18:51 | 2023-04-28T10:10:29 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/5803 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5803/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5803/comments | https://api.github.com/repos/huggingface/datasets/issues/5803/events | https://github.com/huggingface/datasets/pull/5803 | 1,688,256,290 | PR_kwDODunzps5PXtte | 5,803 | Release: 2.12.0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5803). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma... | 2023-04-28T09:52:11 | 2023-04-28T10:18:56 | 2023-04-28T09:54:43 | MEMBER | null | false | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-04-27T09:51:36 | 2023-04-27T14:59:47 | 2023-04-27T14:51:40 | MEMBER | null | false | {
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} | This PR adds validation of `data_files`, so that they are non-empty (str, list, or dict) or `None` (default).
See: https://github.com/huggingface/datasets/pull/5787#discussion_r1178862327 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
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Related to:
- #2157
Fix #5799.
CC: @stas00 | {
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"followers_... | null | [] | 2023-04-27T08:06:05 | 2023-04-27T09:30:17 | 2023-04-27T09:30:17 | MEMBER | null | null | null | As reported by @stas00, files downloaded to the cache do not respect umask:
```bash
$ ls -l /path/to/cache/datasets/downloads/
-rw------- 1 uername username 150M Apr 25 16:41 5e646c1d600f065adaeb134e536f6f2f296a6d804bd1f0e1fdcd20ee28c185c6
```
Related to:
- #2065 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-04-26T17:39:43 | 2023-04-27T16:41:50 | 2023-04-27T16:34:45 | MEMBER | null | false | {
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} | Added a "Use with Spark" doc page to document `Dataset.from_spark` following https://github.com/huggingface/datasets/pull/5701
cc @maddiedawson | {
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"_The documentation is not available anymore as the PR was closed or merged._",
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} | This PR allows to run the CI on push to a branch named "ci-*", without needing to open a PR.
- This will allow to make CI tests without opening a PR, e.g., for future `huggingface-hub` releases, future dependency releases (like `fsspec`, `pandas`,...)
Note that to build the documentation, we already allow it on push to a branch named "doc-builder*".
See:
- #5788
CC: @Wauplin | {
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"merged_at": "2023-04-25T14:25:30"
} | Related to the coming release of `huggingface_hub==0.14.0`. It will break some internal tests. The PR fixes these tests. Let's double-check the CI but I expect the fixed tests to be running fine with both `hfh<=0.13.4` and `hfh==0.14`. Worth case scenario, existing PRs will have to be rebased once this fix is merged.
See related [discussion](https://huggingface.slack.com/archives/C02V5EA0A95/p1682337463368609?thread_ts=1681994202.635609&cid=C02V5EA0A95) (private slack).
cc @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/5872 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5872/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5872/comments | https://api.github.com/repos/huggingface/datasets/issues/5872/events | https://github.com/huggingface/datasets/pull/5872 | 1,713,174,662 | PR_kwDODunzps5QrQ5o | 5,872 | Fix infer module for uppercase extensions | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-17T05:56:45 | 2023-05-17T14:26:59 | 2023-05-17T14:19:18 | MEMBER | null | false | {
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} | Fix the `infer_module_for_data_files` and `infer_module_for_data_files_in_archives` functions when passed a data file name with uppercase extension, e.g. `filename.TXT`.
Before, `None` module was returned. | {
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https://api.github.com/repos/huggingface/datasets/issues/5868 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5868/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5868/comments | https://api.github.com/repos/huggingface/datasets/issues/5868/events | https://github.com/huggingface/datasets/issues/5868 | 1,711,173,098 | I_kwDODunzps5l_m3q | 5,868 | Is it possible to change a cached file and 're-cache' it instead of re-generating? | {
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"Arrow files/primitives (tables and arrays) are immutable, so re-generating them is the only option, I'm afraid.",
"> \r\n\r\nGot it, thanks for your reply"
] | 2023-05-16T03:45:42 | 2023-05-17T11:21:36 | 2023-05-17T11:21:36 | NONE | null | null | null | ### Feature request
Hi,
I have a huge cached file using `map`(over 500GB), and I want to change an attribution of each element, is there possible to do it using some method instead of re-generating, because `map` takes over 24 hours
### Motivation
For large datasets, I think it is very important because we always face the problem which is changing something in the original cache without re-generating it.
### Your contribution
For now, I can't help, sorry. | {
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https://api.github.com/repos/huggingface/datasets/issues/5861 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5861/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5861/comments | https://api.github.com/repos/huggingface/datasets/issues/5861/events | https://github.com/huggingface/datasets/pull/5861 | 1,709,807,340 | PR_kwDODunzps5Qf55q | 5,861 | Better error message when combining dataset dicts instead of datasets | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-15T10:36:24 | 2023-05-23T10:40:13 | 2023-05-23T10:32:58 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/5860 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5860/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5860/comments | https://api.github.com/repos/huggingface/datasets/issues/5860/events | https://github.com/huggingface/datasets/pull/5860 | 1,709,727,460 | PR_kwDODunzps5QfojD | 5,860 | Minor tqdm optim | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-15T09:49:37 | 2023-05-17T18:46:46 | 2023-05-17T18:39:35 | MEMBER | null | false | {
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} | Don't create a tqdm progress bar when `disable_tqdm` is passed to `map_nested`.
On my side it sped up some iterable datasets by ~30% when `map_nested` is used extensively to recursively tensorize python dicts. | {
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https://api.github.com/repos/huggingface/datasets/issues/5853 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5853/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5853/comments | https://api.github.com/repos/huggingface/datasets/issues/5853/events | https://github.com/huggingface/datasets/pull/5853 | 1,708,092,786 | PR_kwDODunzps5QaZLP | 5,853 | [docs] Redirects, migrated from nginx | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@mishig25 note that it's not exactly the same behavior as in nginx as here it interacts a bit with the `version` and the `language`\r\n\r\nShould be close enough, though.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow=... | 2023-05-12T19:19:27 | 2023-05-15T10:37:19 | 2023-05-15T10:30:14 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/5848 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5848/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5848/comments | https://api.github.com/repos/huggingface/datasets/issues/5848/events | https://github.com/huggingface/datasets/pull/5848 | 1,707,506,734 | PR_kwDODunzps5QYa1B | 5,848 | Add `accelerate` as metric's test dependency to fix CI error | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-12T12:01:01 | 2023-05-12T13:48:47 | 2023-05-12T13:39:06 | CONTRIBUTOR | null | false | {
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} | The `frugalscore` metric uses Transformers' Trainer, which requires `accelerate` (as of recently).
Fixes the following [CI error](https://github.com/huggingface/datasets/actions/runs/4950900048/jobs/8855148703?pr=5845). | {
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https://api.github.com/repos/huggingface/datasets/issues/5851 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5851/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5851/comments | https://api.github.com/repos/huggingface/datasets/issues/5851/events | https://github.com/huggingface/datasets/issues/5851 | 1,707,907,048 | I_kwDODunzps5lzJfo | 5,851 | Error message not clear in interleaving datasets | {
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standard env
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)
### Reproduction
I'm trying to interleave 'sciq', 'wiki' and the 'pile-enron' dataset. I think the error I made was that I loaded the train split of one, but for the other but the error is not too helpful-
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[/home/suryahari/Vornoi/save_model_ops.py](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/Vornoi/save_model_ops.py) in line 3
[41](file:///home/suryahari/Vornoi/save_model_ops.py?line=40) # %%
----> [43](file:///home/suryahari/Vornoi/save_model_ops.py?line=42) dataset = interleave_datasets(datasets, stopping_strategy="all_exhausted")
File [~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124](https://vscode-remote+ssh-002dremote-002bthomsonlab-002d2-002ejamesgornet-002ecom.vscode-resource.vscode-cdn.net/home/suryahari/~/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py:124), in interleave_datasets(datasets, probabilities, seed, info, split, stopping_strategy)
[122](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=121) for dataset in datasets[1:]:
[123](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=122) if (map_style and not isinstance(dataset, Dataset)) or (iterable and not isinstance(dataset, IterableDataset)):
--> [124](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=123) raise ValueError(
[125](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=124) f"Unable to interleave a {type(datasets[0])} with a {type(dataset)}. Expected a list of Dataset objects or a list of IterableDataset objects."
[126](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=125) )
[127](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=126) if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
[128](file:///home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py?line=127) raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy.")
ValueError: Unable to interleave a with a . Expected a list of Dataset objects or a list of IterableDataset objects.
```
### Expected behavior
the error message should hopefully be more clear | {
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https://api.github.com/repos/huggingface/datasets/issues/5845 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5845/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5845/comments | https://api.github.com/repos/huggingface/datasets/issues/5845/events | https://github.com/huggingface/datasets/pull/5845 | 1,706,253,251 | PR_kwDODunzps5QUMjS | 5,845 | Add `date_format` param to the CSV reader | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-11T17:29:57 | 2023-05-15T07:39:13 | 2023-05-12T15:14:48 | CONTRIBUTOR | null | false | {
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} | Adds the `date_format` param introduced in Pandas 2.0 to the CSV reader and improves its type hints. | {
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https://api.github.com/repos/huggingface/datasets/issues/5841 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5841/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5841/comments | https://api.github.com/repos/huggingface/datasets/issues/5841/events | https://github.com/huggingface/datasets/issues/5841 | 1,705,286,639 | I_kwDODunzps5lpJvv | 5,841 | Abusurdly slow on iteration | {
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"Hi ! You can try to use the [Image](https://huggingface.co/docs/datasets/v2.12.0/en/package_reference/main_classes#datasets.Image) type which [decodes images on-the-fly](https://huggingface.co/docs/datasets/v2.12.0/en/about_dataset_features#image-feature) into pytorch tensors :)\r\n\r\n```python\r\nds = Dataset.fr... | 2023-05-11T08:04:09 | 2023-05-15T15:38:13 | 2023-05-15T15:38:13 | NONE | null | null | null | ### Describe the bug
I am attempting to iterate through an image dataset, but I am encountering a significant slowdown in the iteration speed. In order to investigate this issue, I conducted the following experiment:
```python
a=torch.randn(100,224)
a=torch.stack([a] * 10000)
a.shape
# %%
ds=Dataset.from_dict({"tensor":a})
for i in tqdm(ds.with_format("numpy")):
pass
for i in tqdm(ds.with_format("torch")):
pass
```
I noticed that the dataset in numpy format performs significantly faster than the one in torch format. My hypothesis is that the dataset undergoes a transformation process of torch->python->numpy(torch) in the background, which might be causing the slowdown. Is there any way to expedite the process by bypassing such transformations?
Furthermore, if I increase the size of a to an image shape, like:
```python
a=torch.randn(3,224,224)
```
the iteration speed becomes absurdly slow, around 100 iterations per second, whereas the speed with numpy format is approximately 250 iterations per second. This level of speed would be unacceptable for large image datasets, as it could take several hours just to iterate through a single epoch.
### Steps to reproduce the bug
```python
a=torch.randn(100,224)
a=torch.stack([a] * 10000)
a.shape
# %%
ds=Dataset.from_dict({"tensor":a})
for i in tqdm(ds.with_format("numpy")):
pass
for i in tqdm(ds.with_format("torch")):
pass
```
### Expected behavior
iteration faster
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.4.0-148-generic-x86_64-with-glibc2.10
- Python version: 3.8.16
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5840 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5840/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5840/comments | https://api.github.com/repos/huggingface/datasets/issues/5840/events | https://github.com/huggingface/datasets/issues/5840 | 1,705,212,085 | I_kwDODunzps5lo3i1 | 5,840 | load model error. | {
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"Please report this in the `transformers` repo, as it's not related to `datasets`"
] | 2023-05-11T07:12:38 | 2023-05-12T13:44:07 | 2023-05-12T13:44:06 | NONE | null | null | null | ### Describe the bug
I had trained one model use deepspeed, when I load the final load I get the follow error:
OSError: Can't load tokenizer for '/XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure '/home/fm001/hzl/Project/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor' is the correct path to a directory containing all relevant files for a BloomTokenizerFast tokenizer.
my load code is : python chat.py --path /XXX/DeepSpeedExamples/applications/DeepSpeed-Chat/output/step3-models/1.3b/actor/
### Steps to reproduce the bug
。。。
### Expected behavior
。。。
### Environment info
。。。 | {
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https://api.github.com/repos/huggingface/datasets/issues/5838 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5838/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5838/comments | https://api.github.com/repos/huggingface/datasets/issues/5838/events | https://github.com/huggingface/datasets/issues/5838 | 1,703,210,848 | I_kwDODunzps5lhO9g | 5,838 | Streaming support for `load_from_disk` | {
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"As the name says, `load_from_disk` load the data from your disk. If the data is hosted on S3, it is first downloaded locally and then loaded from your disk.\r\n\r\nThere is a discussion on streaming data from S3 here though: #5281 ",
"@lhoestq \r\nThanks for your comment. I have checked out the discussion before... | 2023-05-10T06:25:22 | 2023-05-12T09:37:45 | 2023-05-12T09:37:45 | NONE | null | null | null | ### Feature request
Support for streaming datasets stored in object stores in `load_from_disk`.
### Motivation
The `load_from_disk` function supports fetching datasets stored in object stores such as `s3`. In many cases, the datasets that are stored in object stores are very large and being able to stream the data from the buckets becomes essential.
### Your contribution
I'd be happy to contribute this feature if I could get the guidance on how to do so. | {
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https://api.github.com/repos/huggingface/datasets/issues/5836 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5836/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5836/comments | https://api.github.com/repos/huggingface/datasets/issues/5836/events | https://github.com/huggingface/datasets/pull/5836 | 1,702,773,316 | PR_kwDODunzps5QIgzu | 5,836 | [docs] Custom decoding transforms | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5836). All of your documentation changes will be reflected on that endpoint.",
"The error seems unrelated to the changes, so feel free to merge.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\... | 2023-05-09T21:21:41 | 2023-05-15T07:36:12 | 2023-05-10T20:23:03 | MEMBER | null | false | {
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} | Adds custom decoding transform solution to the docs to fix #5782. | {
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https://api.github.com/repos/huggingface/datasets/issues/5835 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5835/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5835/comments | https://api.github.com/repos/huggingface/datasets/issues/5835/events | https://github.com/huggingface/datasets/pull/5835 | 1,702,522,620 | PR_kwDODunzps5QHquR | 5,835 | Always set nullable fields in the writer | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-09T18:16:59 | 2023-05-23T16:10:29 | 2023-05-19T13:04:30 | MEMBER | null | false | {
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} | This fixes loading of e.g. parquet data with non-nullable fields.
Indeed `datasets.Features` doesn't support non-nullable fields, which can lead to data not concatenable due to arrow schema mismatch. | {
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https://api.github.com/repos/huggingface/datasets/issues/5834 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5834/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5834/comments | https://api.github.com/repos/huggingface/datasets/issues/5834/events | https://github.com/huggingface/datasets/issues/5834 | 1,702,448,892 | I_kwDODunzps5leU78 | 5,834 | Is uint8 supported? | {
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"Hi ! The numpy formatting detaults to int64 and float32 - but you can use uint8 using\r\n```python\r\nds = ds.with_format(\"numpy\", dtype=np.uint8)\r\n```",
"Related to https://github.com/huggingface/datasets/issues/5517.",
"Thank you!\r\nBy setting `ds.with_format(\"numpy\", dtype=np.uint8)`, the dataset ret... | 2023-05-09T17:31:13 | 2023-05-13T05:04:21 | 2023-05-13T05:04:21 | NONE | null | null | null | ### Describe the bug
I expect the dataset to store the data in the `uint8` data type, but it's returning `int64` instead.
While I've found that `datasets` doesn't yet support float16 (https://github.com/huggingface/datasets/issues/4981), I'm wondering if this is the case for other data types as well.
Is there a way to store vector data as `uint8` and then upload it to the hub?
### Steps to reproduce the bug
```python
from datasets import Features, Dataset, Sequence, Value
import numpy as np
dataset = Dataset.from_dict(
{"vector": [np.array([0, 1, 2], dtype=np.uint8)]}, features=Features({"vector": Sequence(Value("uint8"))})
).with_format("numpy")
print(dataset[0]["vector"].dtype)
```
### Expected behavior
Expected: `uint8`
Actual: `int64`
### Environment info
- `datasets` version: 2.12.0
- Platform: macOS-12.1-x86_64-i386-64bit
- Python version: 3.8.12
- Huggingface_hub version: 0.12.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5832 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5832/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5832/comments | https://api.github.com/repos/huggingface/datasets/issues/5832/events | https://github.com/huggingface/datasets/issues/5832 | 1,702,135,336 | I_kwDODunzps5ldIYo | 5,832 | 404 Client Error: Not Found for url: https://huggingface.co/api/models/bert-large-cased | {
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"moved to https://github.com/huggingface/transformers/issues/23233"
] | 2023-05-09T14:14:59 | 2023-05-09T14:25:59 | 2023-05-09T14:25:59 | NONE | null | null | null | ### Describe the bug
Running [Bert-Large-Cased](https://huggingface.co/bert-large-cased) model causes `HTTPError`, with the following traceback-
```
HTTPError Traceback (most recent call last)
<ipython-input-6-5c580443a1ad> in <module>
----> 1 tokenizer = BertTokenizer.from_pretrained('bert-large-cased')
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/transformers/tokenization_utils_base.py in from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs)
1646 # At this point pretrained_model_name_or_path is either a directory or a model identifier name
1647 fast_tokenizer_file = get_fast_tokenizer_file(
-> 1648 pretrained_model_name_or_path, revision=revision, use_auth_token=use_auth_token
1649 )
1650 additional_files_names = {
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/transformers/tokenization_utils_base.py in get_fast_tokenizer_file(path_or_repo, revision, use_auth_token)
3406 """
3407 # Inspect all files from the repo/folder.
-> 3408 all_files = get_list_of_files(path_or_repo, revision=revision, use_auth_token=use_auth_token)
3409 tokenizer_files_map = {}
3410 for file_name in all_files:
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/transformers/file_utils.py in get_list_of_files(path_or_repo, revision, use_auth_token)
1685 token = None
1686 model_info = HfApi(endpoint=HUGGINGFACE_CO_RESOLVE_ENDPOINT).model_info(
-> 1687 path_or_repo, revision=revision, token=token
1688 )
1689 return [f.rfilename for f in model_info.siblings]
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/huggingface_hub/hf_api.py in model_info(self, repo_id, revision, token)
246 )
247 r = requests.get(path, headers=headers)
--> 248 r.raise_for_status()
249 d = r.json()
250 return ModelInfo(**d)
~/miniconda3/envs/cmd-chall/lib/python3.7/site-packages/requests/models.py in raise_for_status(self)
951
952 if http_error_msg:
--> 953 raise HTTPError(http_error_msg, response=self)
954
955 def close(self):
HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/models/bert-large-cased
```
I have also tried running in offline mode, as [discussed here](https://huggingface.co/docs/transformers/installation#offline-mode)
```
HF_DATASETS_OFFLINE=1
TRANSFORMERS_OFFLINE=1
```
### Steps to reproduce the bug
1. `from transformers import BertTokenizer, BertModel`
2. `tokenizer = BertTokenizer.from_pretrained('bert-large-cased')`
### Expected behavior
Run without the HTTP error.
### Environment info
| # Name | Version | Build | Channel | |
|--------------------|------------|-----------------------------|---------|---|
| _libgcc_mutex | 0.1 | main | | |
| _openmp_mutex | 4.5 | 1_gnu | | |
| _pytorch_select | 0.1 | cpu_0 | | |
| appdirs | 1.4.4 | pypi_0 | pypi | |
| backcall | 0.2.0 | pypi_0 | pypi | |
| blas | 1.0 | mkl | | |
| bzip2 | 1.0.8 | h7b6447c_0 | | |
| ca-certificates | 2021.7.5 | h06a4308_1 | | |
| certifi | 2021.5.30 | py37h06a4308_0 | | |
| cffi | 1.14.6 | py37h400218f_0 | | |
| charset-normalizer | 2.0.3 | pypi_0 | pypi | |
| click | 8.0.1 | pypi_0 | pypi | |
| colorama | 0.4.4 | pypi_0 | pypi | |
| cudatoolkit | 11.1.74 | h6bb024c_0 | nvidia | |
| cycler | 0.11.0 | pypi_0 | pypi | |
| decorator | 5.0.9 | pypi_0 | pypi | |
| docker-pycreds | 0.4.0 | pypi_0 | pypi | |
| docopt | 0.6.2 | pypi_0 | pypi | |
| dominate | 2.6.0 | pypi_0 | pypi | |
| ffmpeg | 4.3 | hf484d3e_0 | pytorch | |
| filelock | 3.0.12 | pypi_0 | pypi | |
| fonttools | 4.38.0 | pypi_0 | pypi | |
| freetype | 2.10.4 | h5ab3b9f_0 | | |
| gitdb | 4.0.7 | pypi_0 | pypi | |
| gitpython | 3.1.18 | pypi_0 | pypi | |
| gmp | 6.2.1 | h2531618_2 | | |
| gnutls | 3.6.15 | he1e5248_0 | | |
| huggingface-hub | 0.0.12 | pypi_0 | pypi | |
| humanize | 3.10.0 | pypi_0 | pypi | |
| idna | 3.2 | pypi_0 | pypi | |
| importlib-metadata | 4.6.1 | pypi_0 | pypi | |
| intel-openmp | 2019.4 | 243 | | |
| ipdb | 0.13.9 | pypi_0 | pypi | |
| ipython | 7.25.0 | pypi_0 | pypi | |
| ipython-genutils | 0.2.0 | pypi_0 | pypi | |
| jedi | 0.18.0 | pypi_0 | pypi | |
| joblib | 1.0.1 | pypi_0 | pypi | |
| jpeg | 9b | h024ee3a_2 | | |
| jsonpickle | 1.5.2 | pypi_0 | pypi | |
| kiwisolver | 1.4.4 | pypi_0 | pypi | |
| 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 | | |
| libiconv | 1.15 | h63c8f33_5 | | |
| libidn2 | 2.3.2 | h7f8727e_0 | | |
| libmklml | 2019.0.5 | 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 | | |
| libwebp-base | 1.2.0 | h27cfd23_0 | | |
| lz4-c | 1.9.3 | h2531618_0 | | |
| matplotlib | 3.5.3 | pypi_0 | pypi | |
| matplotlib-inline | 0.1.2 | pypi_0 | pypi | |
| mergedeep | 1.3.4 | pypi_0 | pypi | |
| mkl | 2020.2 | 256 | | |
| mkl-service | 2.3.0 | py37he8ac12f_0 | | |
| mkl_fft | 1.3.0 | py37h54f3939_0 | | |
| mkl_random | 1.1.1 | py37h0573a6f_0 | | |
| msgpack | 1.0.2 | pypi_0 | pypi | |
| munch | 2.5.0 | pypi_0 | pypi | |
| ncurses | 6.2 | he6710b0_1 | | |
| nettle | 3.7.3 | hbbd107a_1 | | |
| ninja | 1.10.2 | hff7bd54_1 | | |
| nltk | 3.8.1 | pypi_0 | pypi | |
| numpy | 1.19.2 | py37h54aff64_0 | | |
| numpy-base | 1.19.2 | py37hfa32c7d_0 | | |
| olefile | 0.46 | py37_0 | | |
| openh264 | 2.1.0 | hd408876_0 | | |
| openjpeg | 2.3.0 | h05c96fa_1 | | |
| openssl | 1.1.1k | h27cfd23_0 | | |
| packaging | 21.0 | pypi_0 | pypi | |
| pandas | 1.3.1 | pypi_0 | pypi | |
| parso | 0.8.2 | pypi_0 | pypi | |
| pathtools | 0.1.2 | pypi_0 | pypi | |
| pexpect | 4.8.0 | pypi_0 | pypi | |
| pickleshare | 0.7.5 | pypi_0 | pypi | |
| pillow | 8.3.1 | py37h2c7a002_0 | | |
| pip | 21.1.3 | py37h06a4308_0 | | |
| prompt-toolkit | 3.0.19 | pypi_0 | pypi | |
| protobuf | 4.21.12 | pypi_0 | pypi | |
| psutil | 5.8.0 | pypi_0 | pypi | |
| ptyprocess | 0.7.0 | pypi_0 | pypi | |
| py-cpuinfo | 8.0.0 | pypi_0 | pypi | |
| pycparser | 2.20 | py_2 | | |
| pygments | 2.9.0 | pypi_0 | pypi | |
| pyparsing | 2.4.7 | pypi_0 | pypi | |
| python | 3.7.10 | h12debd9_4 | | |
| python-dateutil | 2.8.2 | pypi_0 | pypi | |
| pytorch | 1.9.0 | py3.7_cuda11.1_cudnn8.0.5_0 | pytorch | |
| pytz | 2021.1 | pypi_0 | pypi | |
| pyyaml | 5.4.1 | pypi_0 | pypi | |
| readline | 8.1 | h27cfd23_0 | | |
| regex | 2022.10.31 | pypi_0 | pypi | |
| requests | 2.26.0 | pypi_0 | pypi | |
| sacred | 0.8.2 | pypi_0 | pypi | |
| sacremoses | 0.0.45 | pypi_0 | pypi | |
| scikit-learn | 0.24.2 | pypi_0 | pypi | |
| scipy | 1.7.0 | pypi_0 | pypi | |
| sentry-sdk | 1.15.0 | pypi_0 | pypi | |
| setproctitle | 1.3.2 | pypi_0 | pypi | |
| setuptools | 52.0.0 | py37h06a4308_0 | | |
| six | 1.16.0 | pyhd3eb1b0_0 | | |
| smmap | 4.0.0 | pypi_0 | pypi | |
| sqlite | 3.36.0 | hc218d9a_0 | | |
| threadpoolctl | 2.2.0 | pypi_0 | pypi | |
| tk | 8.6.10 | hbc83047_0 | | |
| tokenizers | 0.10.3 | pypi_0 | pypi | |
| toml | 0.10.2 | pypi_0 | pypi | |
| torchaudio | 0.9.0 | py37 | pytorch | |
| torchvision | 0.10.0 | py37_cu111 | pytorch | |
| tqdm | 4.61.2 | pypi_0 | pypi | |
| traitlets | 5.0.5 | pypi_0 | pypi | |
| transformers | 4.9.1 | pypi_0 | pypi | |
| typing-extensions | 3.10.0.0 | hd3eb1b0_0 | | |
| typing_extensions | 3.10.0.0 | pyh06a4308_0 | | |
| urllib3 | 1.26.14 | pypi_0 | pypi | |
| wandb | 0.13.10 | pypi_0 | pypi | |
| wcwidth | 0.2.5 | pypi_0 | pypi | |
| wheel | 0.36.2 | pyhd3eb1b0_0 | | |
| wrapt | 1.12.1 | pypi_0 | pypi | |
| xz | 5.2.5 | h7b6447c_0 | | |
| zipp | 3.5.0 | pypi_0 | pypi | |
| zlib | 1.2.11 | h7b6447c_3 | | |
| zstd | 1.4.9 | haebb681_0 | | | | {
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"Can you paste the error stack trace?",
"That is weird. I can't reproduce it again after reboot.\r\n```python\r\nIn [2]: import platform\r\n\r\nIn [3]: platform.platform()\r\nOut[3]: 'macOS-13.2-arm64-arm-64bit'\r\n\r\nIn [4]: from datasets import load_dataset\r\n ...:\r\n ...: jazzy = load_dataset(\"nomic-ai... | 2023-05-08T10:07:14 | 2023-05-09T00:46:42 | 2023-05-09T00:46:42 | NONE | null | null | null | ### Describe the bug
M2 MBP can't run
```python
from datasets import load_dataset
jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
### Steps to reproduce the bug
1. Use M2 MBP
2. Python 3.10.10 from pyenv
3. Run
```
from datasets import load_dataset
jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
### Expected behavior
Be able to run normally
### Environment info
```
from datasets import load_dataset
jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
OSX: 13.2
CPU: M2
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"Hi! \r\n\r\nYou can call `map` as follows to avoid the error:\r\n```python\r\naugmented_dataset_cln = dataset_cln['train'].map(augment_dataset, features=dataset_cln['train'].features)\r\n```",
"Thanks it is solved"
] | 2023-05-07T21:02:54 | 2023-05-10T05:06:58 | 2023-05-10T05:05:47 | NONE | null | null | null | ### Describe the bug
I am not able to concatenate the augmentation of the stream data. I am using the latest version of dataset.
ValueError: The features can't be aligned because the key audio of features {'audio_id': Value(dtype='string',
id=None), 'audio': {'array': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'path':
Value(dtype='null', id=None), 'sampling_rate': Value(dtype='int64', id=None)}, 'transcript': Value(dtype='string',
id=None)} has unexpected type - {'array': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None),
'path': Value(dtype='null', id=None), 'sampling_rate': Value(dtype='int64', id=None)} (expected either
Audio(sampling_rate=16000, mono=True, decode=True, id=None) or Value("null").
### Steps to reproduce the bug
dataset = load_dataset("tobiolatunji/afrispeech-200", "all", streaming=True).shuffle(seed=42)
dataset_cln = dataset.remove_columns(['speaker_id', 'path', 'age_group', 'gender', 'accent', 'domain', 'country', 'duration'])
dataset_cln = dataset_cln.cast_column("audio", Audio(sampling_rate=16000))
from audiomentations import AddGaussianNoise,Compose,Gain,OneOf,PitchShift,PolarityInversion,TimeStretch
augmentation = Compose([
AddGaussianNoise(min_amplitude=0.005, max_amplitude=0.015, p=0.2)
])
def augment_dataset(batch):
audio = batch["audio"]
audio["array"] = augmentation(audio["array"], sample_rate=audio["sampling_rate"])
return batch
augmented_dataset_cln = dataset_cln['train'].map(augment_dataset)
dataset_cln['train'] = interleave_datasets([dataset_cln['train'], augmented_dataset_cln])
dataset_cln['train'] = dataset_cln['train'].shuffle(seed=42)
### Expected behavior
I should be able to merge as sampling rate is same.
### Environment info
import datasets
import transformers
import accelerate
print(datasets.__version__)
print(transformers.__version__)
print(torch.__version__)
print(evaluate.__version__)
print(accelerate.__version__)
2.12.0
4.28.1
2.0.0
0.4.0
0.18.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5824 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5824/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5824/comments | https://api.github.com/repos/huggingface/datasets/issues/5824/events | https://github.com/huggingface/datasets/pull/5824 | 1,697,152,148 | PR_kwDODunzps5P1rIZ | 5,824 | Fix incomplete docstring for `BuilderConfig` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-05-05T07:34:28 | 2023-05-05T12:39:14 | 2023-05-05T12:31:54 | CONTRIBUTOR | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5824",
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"patch_url": "https://github.com/huggingface/datasets/pull/5824.patch",
"merged_at": "2023-05-05T12:31:54"
} | Fixes #5820
Also fixed a couple of typos I spotted | {
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https://api.github.com/repos/huggingface/datasets/issues/5823 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5823/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5823/comments | https://api.github.com/repos/huggingface/datasets/issues/5823/events | https://github.com/huggingface/datasets/issues/5823 | 1,697,024,789 | I_kwDODunzps5lJosV | 5,823 | [2.12.0] DatasetDict.save_to_disk not saving to S3 | {
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"Hi ! Can you try adding the `s3://` prefix ?\r\n```python\r\nf\"s3://{s3_bucket}/{s3_dir}/{dataset_name}\"\r\n```",
"Ugh, yeah that was it. Thank you!"
] | 2023-05-05T05:22:59 | 2023-05-05T15:01:18 | 2023-05-05T15:01:17 | NONE | null | null | null | ### Describe the bug
When trying to save a `DatasetDict` to a private S3 bucket using `save_to_disk`, the artifacts are instead saved locally, and not in the S3 bucket.
I have tried using the deprecated `fs` as well as the `storage_options` arguments and I get the same results.
### Steps to reproduce the bug
1. Create a DatsetDict `dataset`
2. Create a S3FileSystem object
`s3 = datasets.filesystems.S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key)`
3. Save using `dataset_dict.save_to_disk(f"{s3_bucket}/{s3_dir}/{dataset_name}", storage_options=s3.storage_options)` or `dataset_dict.save_to_disk(f"{s3_bucket}/{s3_dir}/{dataset_name}", fs=s3)`
4. Check the corresponding S3 bucket and verify nothing has been uploaded
5. Check the path at f"{s3_bucket}/{s3_dir}/{dataset_name}" and verify that files have been saved there
### Expected behavior
Artifacts are uploaded at the f"{s3_bucket}/{s3_dir}/{dataset_name}" S3 location.
### Environment info
- `datasets` version: 2.12.0
- Platform: macOS-13.3.1-x86_64-i386-64bit
- Python version: 3.11.2
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5822 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5822/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5822/comments | https://api.github.com/repos/huggingface/datasets/issues/5822/events | https://github.com/huggingface/datasets/issues/5822 | 1,696,627,308 | I_kwDODunzps5lIHps | 5,822 | Audio Dataset with_format torch problem | {
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"Hi ! Can you try with a more recent version of `datasets` ?",
"Ok, yes it worked with the most recent version. Thanks"
] | 2023-05-04T20:07:51 | 2023-05-11T20:45:53 | 2023-05-11T20:45:53 | NONE | null | null | null | ### Describe the bug
Common Voice v10 Delta (German) Dataset from here https://commonvoice.mozilla.org/de/datasets
```
audio_dataset = \
(Dataset
.from_dict({"audio": ('/tmp/cv-corpus-10.0-delta-2022-07-04/de/clips/' + df.path).to_list()})
.cast_column("audio", Audio(sampling_rate=16_000))
.with_format('numpy'))
audio_dataset[0]["audio"]
```
works, but
```
audio_dataset = \
(Dataset
.from_dict({"audio": ('/tmp/cv-corpus-10.0-delta-2022-07-04/de/clips/' + df.path).to_list()})
.cast_column("audio", Audio(sampling_rate=16_000))
.with_format('torch'))
audio_dataset[0]["audio"]
```
does not instead I get
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[54], line 1
----> 1 audio_dataset[0]["audio"]
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/arrow_dataset.py:2154, in Dataset.__getitem__(self, key)
2152 def __getitem__(self, key): # noqa: F811
2153 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2154 return self._getitem(
2155 key,
2156 )
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/arrow_dataset.py:2139, in Dataset._getitem(self, key, decoded, **kwargs)
2137 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
2138 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 2139 formatted_output = format_table(
2140 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
2141 )
2142 return formatted_output
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
--> 532 return formatter(pa_table, query_type=query_type)
533 elif query_type == "column":
534 if key in format_columns:
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type)
279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
280 if query_type == "row":
--> 281 return self.format_row(pa_table)
282 elif query_type == "column":
283 return self.format_column(pa_table)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:58, in TorchFormatter.format_row(self, pa_table)
56 def format_row(self, pa_table: pa.Table) -> dict:
57 row = self.numpy_arrow_extractor().extract_row(pa_table)
---> 58 return self.recursive_tensorize(row)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:54, in TorchFormatter.recursive_tensorize(self, data_struct)
53 def recursive_tensorize(self, data_struct: dict):
---> 54 return map_nested(self._recursive_tensorize, data_struct, map_list=False)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:356, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
354 num_proc = 1
355 if num_proc <= 1 or len(iterable) <= num_proc:
--> 356 mapped = [
357 _single_map_nested((function, obj, types, None, True, None))
358 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
359 ]
360 else:
361 split_kwds = [] # We organize the splits ourselve (contiguous splits)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:357, in <listcomp>(.0)
354 num_proc = 1
355 if num_proc <= 1 or len(iterable) <= num_proc:
356 mapped = [
--> 357 _single_map_nested((function, obj, types, None, True, None))
358 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
359 ]
360 else:
361 split_kwds = [] # We organize the splits ourselve (contiguous splits)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:309, in _single_map_nested(args)
306 pbar = logging.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit="obj", desc=pbar_desc)
308 if isinstance(data_struct, dict):
--> 309 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
310 else:
311 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:309, in <dictcomp>(.0)
306 pbar = logging.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit="obj", desc=pbar_desc)
308 if isinstance(data_struct, dict):
--> 309 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
310 else:
311 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:293, in _single_map_nested(args)
291 # Singleton first to spare some computation
292 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 293 return function(data_struct)
295 # Reduce logging to keep things readable in multiprocessing with tqdm
296 if rank is not None and logging.get_verbosity() < logging.WARNING:
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:51, in TorchFormatter._recursive_tensorize(self, data_struct)
49 if data_struct.dtype == np.object: # pytorch tensors cannot be instantied from an array of objects
50 return [self.recursive_tensorize(substruct) for substruct in data_struct]
---> 51 return self._tensorize(data_struct)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:38, in TorchFormatter._tensorize(self, value)
35 import torch
37 default_dtype = {}
---> 38 if np.issubdtype(value.dtype, np.integer):
39 default_dtype = {"dtype": torch.int64}
40 elif np.issubdtype(value.dtype, np.floating):
AttributeError: 'NoneType' object has no attribute 'dtype'
```
### Steps to reproduce the bug
1. Download some audio dataset in this case I used Common Voice v10 Delta (German) Dataset from here https://commonvoice.mozilla.org/de/datasets
2. Try the Code from above
### Expected behavior
It should work for torch
### Environment info
pytorch: 2.0.0
datasets: 2.3.2
numpy: 1.21.6
Python: 3.8
Linux | {
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https://api.github.com/repos/huggingface/datasets/issues/5820 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5820/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5820/comments | https://api.github.com/repos/huggingface/datasets/issues/5820/events | https://github.com/huggingface/datasets/issues/5820 | 1,695,892,811 | I_kwDODunzps5lFUVL | 5,820 | Incomplete docstring for `BuilderConfig` | {
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"Thanks for reporting! You are more than welcome to improve `BuilderConfig`'s docstring.\r\n\r\nThis class serves an identical purpose as `tensorflow_datasets`'s `BuilderConfig`, and its docstring is [here](https://github.com/tensorflow/datasets/blob/a95e38b5bb018312c3d3720619c2a8ef83ebf57f/tensorflow_datasets/core... | 2023-05-04T12:14:34 | 2023-05-05T12:31:56 | 2023-05-05T12:31:56 | CONTRIBUTOR | null | null | null | Hi guys !
I stumbled upon this docstring while working on a project.
Some of the attributes have missing descriptions.
https://github.com/huggingface/datasets/blob/bc5fef5b6d91f009e4101684adcb374df2c170f6/src/datasets/builder.py#L104-L117 | {
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https://api.github.com/repos/huggingface/datasets/issues/5819 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5819/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5819/comments | https://api.github.com/repos/huggingface/datasets/issues/5819/events | https://github.com/huggingface/datasets/issues/5819 | 1,695,536,738 | I_kwDODunzps5lD9Zi | 5,819 | Cannot pickle error in Dataset.from_generator() | {
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"Hi! It should work if you put `model = torch.compile(model)` inside the `generate_data` function. If a referenced object is outside, it needs to be pickable, and that's not the case for the compiled models (or functions). ",
"> Hi! It should work if you put `model = torch.compile(model)` inside the `generate_da... | 2023-05-04T08:39:09 | 2023-05-05T19:20:59 | 2023-05-05T19:20:58 | NONE | null | null | null | ### Describe the bug
I'm trying to use Dataset.from_generator() to generate a large dataset.
### Steps to reproduce the bug
Code to reproduce:
```
from transformers import T5Tokenizer, T5ForConditionalGeneration, GenerationConfig
import torch
from tqdm import tqdm
from datasets import load_dataset
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small", device_map="auto")
model = torch.compile(model)
def generate_data(data_loader):
model.eval()
for batch in tqdm(data_loader):
input_ids = tokenizer(batch['instruction'], return_tensors='pt', padding=True, truncation=True).input_ids.to("cuda:0")
with torch.no_grad():
outputs = model.generate(input_ids, generation_config=generation_config)
decoder_hidden_states = outputs.decoder_hidden_states
for i, h in zip(batch['instruction'], decoder_hidden_states):
yield {"instruction": i, "decoder_hidden_states": h}
generation_config = GenerationConfig(
temperature=1,
max_new_tokens=1024,
do_sample=False,
num_return_sequences=1,
return_dict_in_generate=True,
output_scores=True,
output_hidden_states=True,
)
from datasets import Dataset, load_dataset
from torch.utils.data import DataLoader
dataset = load_dataset("HuggingFaceH4/databricks_dolly_15k")
train_loader = DataLoader(dataset['train'], batch_size=2, shuffle=True)
dataset = Dataset.from_generator(generator=generate_data, gen_kwargs={"data_loader": train_loader})
dataset.save_to_disk("data/flant5_small_generation")
```
### Expected behavior
The dataset should be generated and saved.
But the following error occurred:
```
Traceback (most recent call last):
File "/remote-home/xhwang/alpaca-lora/data_collection_t5.py", line 46, in <module>
dataset = Dataset.from_generator(generator=generate_data, gen_kwargs={"data_loader": train_loader})
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1035, in from_generator
return GeneratorDatasetInputStream(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/io/generator.py", line 28, in __init__
self.builder = Generator(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/builder.py", line 336, in __init__
self.config, self.config_id = self._create_builder_config(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/builder.py", line 505, in _create_builder_config
config_id = builder_config.create_config_id(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/builder.py", line 179, in create_config_id
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/fingerprint.py", line 236, in hash
return cls.hash_default(value)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/fingerprint.py", line 229, in hash_default
return cls.hash_bytes(dumps(value))
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 726, in dumps
dump(obj, file)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 701, in dump
Pickler(file, recurse=True).dump(obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 394, in dump
StockPickler.dump(self, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 487, in dump
self.save(obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1084, in _save_with_postproc
pickler._batch_setitems(iter(source.items()))
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 603, in save
self.save_reduce(obj=obj, *rv)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 603, in save
self.save_reduce(obj=obj, *rv)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1070, in _save_with_postproc
pickler.save_reduce(*reduction, obj=obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 887, in save_tuple
save(element)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1070, in _save_with_postproc
pickler.save_reduce(*reduction, obj=obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 887, in save_tuple
save(element)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 1003, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1084, in _save_with_postproc
pickler._batch_setitems(iter(source.items()))
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 578, in save
rv = reduce(self.proto)
TypeError: cannot pickle 'ConfigModuleInstance' object
```
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-4.15.0-156-generic-x86_64-with-glibc2.31
- Python version: 3.10.10
- Huggingface_hub version: 0.13.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5817 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5817/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5817/comments | https://api.github.com/repos/huggingface/datasets/issues/5817/events | https://github.com/huggingface/datasets/issues/5817 | 1,694,891,866 | I_kwDODunzps5lBf9a | 5,817 | Setting `num_proc` errors when `.map` returns additional items. | {
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"Hi ! Unfortunately I couldn't reproduce on my side locally and with datasets 2.11 and python 3.10.11 on colab.\r\nWhat version of `multiprocess` are you using ?",
"I've got `multiprocess` version `0.70.14`.\r\n\r\nI've done some more testing and the error only occurs in PyCharm's Python Console. It seems to be [... | 2023-05-03T21:46:53 | 2023-05-04T21:14:21 | 2023-05-04T20:22:25 | NONE | null | null | null | ### Describe the bug
I'm using a map function that returns more rows than are passed in.
If I try to use `num_proc` I get:
```
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 563, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 528, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3097, in map
for rank, done, content in iflatmap_unordered(
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1372, in iflatmap_unordered
yield queue.get(timeout=0.05)
File "<string>", line 2, in get
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/managers.py", line 818, in _callmethod
kind, result = conn.recv()
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/connection.py", line 258, in recv
buf = self._recv_bytes()
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/connection.py", line 422, in _recv_bytes
buf = self._recv(4)
File "/home/davidg/.virtualenvs/learning/lib/python3.10/site-packages/multiprocess/connection.py", line 391, in _recv
raise EOFError
EOFError
```
### Steps to reproduce the bug
This is copied from the [Datasets docs](https://huggingface.co/docs/datasets/v2.12.0/en/process#batch-processing), with `num_proc` added, and will error.
```py
import datasets
dataset = ... # any old dataset
def chunk_examples(examples):
chunks = []
for sentence in examples["text"]:
chunks += [sentence[i : i + 50] for i in range(0, len(sentence), 50)]
return {"chunks": chunks}
chunked_dataset = dataset.map(
chunk_examples,
batched=True,
remove_columns=dataset.column_names,
num_proc=2, # Remove and it works
)
```
### Expected behavior
Should work fine. On a related note, multi-processing also fails if there is a Meta class anywhere in scope (and there are plenty in the standard library). This is the fault of `dill` and is a long standing issue.
Have you considered using Loky for multiprocessing? I've found that the built-in `datasets` multi-processing breaks more than it works so have written my own function using `loky`, for reference:
```py
import datasets
import loky
def fast_loop(dataset: datasets.Dataset, func, num_proc=None):
if num_proc is None:
import os
num_proc = len(os.sched_getaffinity(0))
shards = [
dataset.shard(num_shards=num_proc, index=i, contiguous=True)
for i in range(num_proc)
]
executor = loky.get_reusable_executor(max_workers=num_proc)
results = executor.map(func, shards)
return datasets.combine.concatenate_datasets(list(results))
```
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- Huggingface_hub version: 0.12.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5816 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5816/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5816/comments | https://api.github.com/repos/huggingface/datasets/issues/5816/events | https://github.com/huggingface/datasets/pull/5816 | 1,694,590,856 | PR_kwDODunzps5Ps4t9 | 5,816 | Preserve `stopping_strategy` of shuffled interleaved dataset (random cycling case) | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a... | 2023-05-03T18:34:18 | 2023-05-04T14:31:55 | 2023-05-04T14:24:49 | CONTRIBUTOR | null | false | {
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} | Preserve the `stopping_strategy` in the `RandomlyCyclingMultiSourcesExamplesIterable.shard_data_sources` to fix shuffling a dataset interleaved (from multiple sources) with probabilities.
Fix #5812
| {
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https://api.github.com/repos/huggingface/datasets/issues/5813 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5813/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5813/comments | https://api.github.com/repos/huggingface/datasets/issues/5813/events | https://github.com/huggingface/datasets/pull/5813 | 1,691,908,535 | PR_kwDODunzps5Pj0_E | 5,813 | [DO-NOT-MERGE] Debug Windows issue at #3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5812 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5812/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5812/comments | https://api.github.com/repos/huggingface/datasets/issues/5812/events | https://github.com/huggingface/datasets/issues/5812 | 1,691,798,169 | I_kwDODunzps5k1sqZ | 5,812 | Cannot shuffle interleaved IterableDataset with "all_exhausted" stopping strategy | {
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Shuffling interleaved `IterableDataset` with "all_exhausted" strategy yields non-exhaustive sampling.
### Steps to reproduce the bug
```py
from datasets import IterableDataset, interleave_datasets
def gen(bias, length):
for i in range(length):
yield dict(a=bias+i)
seed = 42
probabilities = [0.2, 0.6, 0.2]
d1 = IterableDataset.from_generator(lambda: gen(0, 3))
d2 = IterableDataset.from_generator(lambda: gen(10, 4))
d3 = IterableDataset.from_generator(lambda: gen(20, 3))
ds = interleave_datasets([d1, d2, d3], probabilities=probabilities, seed=seed, stopping_strategy='all_exhausted')
ds = ds.shuffle(buffer_size=1000)
for x in ds:
print(x)
```
This code produces
```
{'a': 0}
{'a': 22}
{'a': 20}
{'a': 21}
{'a': 10}
{'a': 1}
```
### Expected behavior
It should produce a longer list of examples to exhaust all the datasets.
If you comment out the shuffle line, it will exhaust all the datasets properly.
Here is the output if you comment out shuffling:
```
{'a': 10}
{'a': 11}
{'a': 20}
{'a': 12}
{'a': 0}
{'a': 21}
{'a': 13}
{'a': 10}
{'a': 1}
{'a': 11}
{'a': 12}
{'a': 22}
{'a': 13}
{'a': 20}
{'a': 10}
{'a': 11}
{'a': 12}
{'a': 2}
```
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.31
- Python version: 3.10.11
- Huggingface_hub version: 0.14.1
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
This was run on Google Colab. | {
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https://api.github.com/repos/huggingface/datasets/issues/5810 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5810/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5810/comments | https://api.github.com/repos/huggingface/datasets/issues/5810/events | https://github.com/huggingface/datasets/pull/5810 | 1,689,917,822 | PR_kwDODunzps5PdJHI | 5,810 | Add `fn_kwargs` to `map` and `filter` of `IterableDataset` and `IterableDatasetDict` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Sorry, the local test passed because it was inadvertently testing the main branch. I am currently fixing where the test failed.",
"- I have fixed the bug and addressed the above two points.\r\n- I have tested locally and confirmed ... | 2023-04-30T13:23:01 | 2023-05-22T08:12:39 | 2023-05-22T08:05:31 | CONTRIBUTOR | null | false | {
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} | # Overview
I've added an argument`fn_kwargs` for map and filter methods of `IterableDataset` and `IterableDatasetDict` classes.
# Details
Currently, the map and filter methods of some classes related to `IterableDataset` do not allow specifing the arguments passed to the function. This pull request adds `fn_kwargs` to pass arguments to the mapping function. This allows users to preprocess data more flexibly.
Added `fn_kwargs` to the following classes and methods (description of the argument is also added).
1. class `FilteredExamplesIterable`
2. method `filter` of class `IterableDataset`
3. method `map` of class `IterableDatasetDict`
4. method `filter` of class `IterableDatasetDict`
# Example of changes
Here's an example of how to use the new functionality:
```python
from datasets import IterableDatasetDict
def preprocess_function(example, a=None, b=None):
# do something
return example
dataset = IterableDatasetDict(...)
dataset = dataset.map(preprocess_function, fn_kwargs={"a": 1, "b": 2})
```
# Related Issues
This pull request is related to the following issue:
https://github.com/huggingface/datasets/issues/3444 .
# Testing
I have added unit tests to test the new functionality.
In test_iterable_dataset.py
- Added `test_filtered_examples_iterable_with_fn_kwargs` for [1](#details).
- Added `test_iterable_dataset_filter` for [2](#details).
- Added `test_iterable_dataset_map_with_fn_kwargs`. This is not a newly added feature, but was added because it was not tested.
In test_dataset_dict.py
- Added `_create_dummy_iterable_dataset` for [3](#details) and [4](#details).
- Added `_create_dummy_iterable_dataset_dict` for [3](#details) and [4](#details).
- Added `test_iterable_map` for [3](#details).
- Added `test_iterable_filter` for [4](#details).
Note that, there is no test for `IterableDatasetDict` at the current main branch. I thought about writing tests for `IterableDatasetDict` in a new file, but I decided to add them in the test file for `DatasetDict` (test_dataset_dict.py).
# Checklist
- [x] Format the code.
- [x] Added tests.
- [x] Passed tests locally. | {
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https://api.github.com/repos/huggingface/datasets/issues/5804 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5804/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5804/comments | https://api.github.com/repos/huggingface/datasets/issues/5804/events | https://github.com/huggingface/datasets/pull/5804 | 1,688,285,666 | PR_kwDODunzps5PX0Dk | 5,804 | Set dev version | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5804). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma... | 2023-04-28T10:10:01 | 2023-04-28T10:18:51 | 2023-04-28T10:10:29 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/5803 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5803/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5803/comments | https://api.github.com/repos/huggingface/datasets/issues/5803/events | https://github.com/huggingface/datasets/pull/5803 | 1,688,256,290 | PR_kwDODunzps5PXtte | 5,803 | Release: 2.12.0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5803). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchma... | 2023-04-28T09:52:11 | 2023-04-28T10:18:56 | 2023-04-28T09:54:43 | MEMBER | null | false | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-04-27T09:51:36 | 2023-04-27T14:59:47 | 2023-04-27T14:51:40 | MEMBER | null | false | {
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} | This PR adds validation of `data_files`, so that they are non-empty (str, list, or dict) or `None` (default).
See: https://github.com/huggingface/datasets/pull/5787#discussion_r1178862327 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
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Related to:
- #2157
Fix #5799.
CC: @stas00 | {
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"followers_... | null | [] | 2023-04-27T08:06:05 | 2023-04-27T09:30:17 | 2023-04-27T09:30:17 | MEMBER | null | null | null | As reported by @stas00, files downloaded to the cache do not respect umask:
```bash
$ ls -l /path/to/cache/datasets/downloads/
-rw------- 1 uername username 150M Apr 25 16:41 5e646c1d600f065adaeb134e536f6f2f296a6d804bd1f0e1fdcd20ee28c185c6
```
Related to:
- #2065 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-04-26T17:39:43 | 2023-04-27T16:41:50 | 2023-04-27T16:34:45 | MEMBER | null | false | {
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} | Added a "Use with Spark" doc page to document `Dataset.from_spark` following https://github.com/huggingface/datasets/pull/5701
cc @maddiedawson | {
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"_The documentation is not available anymore as the PR was closed or merged._",
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} | This PR allows to run the CI on push to a branch named "ci-*", without needing to open a PR.
- This will allow to make CI tests without opening a PR, e.g., for future `huggingface-hub` releases, future dependency releases (like `fsspec`, `pandas`,...)
Note that to build the documentation, we already allow it on push to a branch named "doc-builder*".
See:
- #5788
CC: @Wauplin | {
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} | Related to the coming release of `huggingface_hub==0.14.0`. It will break some internal tests. The PR fixes these tests. Let's double-check the CI but I expect the fixed tests to be running fine with both `hfh<=0.13.4` and `hfh==0.14`. Worth case scenario, existing PRs will have to be rebased once this fix is merged.
See related [discussion](https://huggingface.slack.com/archives/C02V5EA0A95/p1682337463368609?thread_ts=1681994202.635609&cid=C02V5EA0A95) (private slack).
cc @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/5787 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5787/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5787/comments | https://api.github.com/repos/huggingface/datasets/issues/5787/events | https://github.com/huggingface/datasets/pull/5787 | 1,680,965,959 | PR_kwDODunzps5O_KNU | 5,787 | Fix inferring module for unsupported data files | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I think you can revert the last commit - it should fail if data_files={} IMO",
"The validation of non-empty data_files is addressed in this PR:\r\n- #5802",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<det... | 2023-04-24T10:44:50 | 2023-04-27T13:06:01 | 2023-04-27T12:57:28 | MEMBER | null | false | {
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} | This PR raises a FileNotFoundError instead:
```
FileNotFoundError: No (supported) data files or dataset script found in <dataset_name>
```
Fix #5785. | {
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"Hi ! PyTorch may hang when calling `load_state_dict()` in a subprocess. To fix that, set the multiprocessing start method to \"spawn\". Since `datasets` uses `multiprocess`, you should do:\r\n\r\n```python\r\n# Required to avoid issues with pytorch (otherwise hangs during load_state_dict in multiprocessing)\r\nimp... | 2023-04-24T10:38:07 | 2023-04-24T10:43:58 | 2023-04-24T10:43:58 | MEMBER | null | null | null | ### Describe the bug
I am trying to use a Pytorch model loaded on CPUs with multiple processes with a `.map` or a `.filter` method.
Usually, when dealing with models that are non-pickable, creating a class such that the `map` function is the method `__call__`, and adding `reduce` helps to solve the problem.
However, here, the command hangs without throwing an error.
### Steps to reproduce the bug
```
from datasets import Dataset
import torch
from torch import nn
from torchvision import models
class FilterFunction:
#__slots__ = ("path_model", "model") # Doesn't change anything uncommented
def __init__(self, path_model):
self.path_model = path_model
model = models.resnet50()
model.fc = nn.Sequential(
nn.Linear(2048, 512),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(512, 10),
nn.LogSoftmax(dim=1)
)
model.load_state_dict(torch.load(path_model, map_location=torch.device("cpu")))
model.eval()
self.model = model
def __call__(self, batch):
return [True] * len(batch["id"])
# Comment this to have an error
def __reduce__(self):
return (self.__class__, (self.path_model,))
dataset = Dataset.from_dict({"id": [0, 1, 2, 4]})
# Download (100 MB) at https://github.com/emiliantolo/pytorch_nsfw_model/raw/master/ResNet50_nsfw_model.pth
path_model = "/fsx/hugo/nsfw_image/ResNet50_nsfw_model.pth"
filter_function = FilterFunction(path_model=path_model)
# Works
filtered_dataset = dataset.filter(filter_function, num_proc=1, batched=True, batch_size=2)
# Doesn't work
filtered_dataset = dataset.filter(filter_function, num_proc=2, batched=True, batch_size=2)
```
### Expected behavior
The command `filtered_dataset = dataset.filter(filter_function, num_proc=2, batched=True, batch_size=2)` should work and not hang.
### Environment info
Datasets: 2.11.0
Pyarrow: 11.0.0
Ubuntu | {
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"followers_... | null | [] | 2023-04-24T10:38:03 | 2023-04-27T12:57:30 | 2023-04-27T12:57:30 | MEMBER | null | null | null | Currently, we raise a TypeError for unsupported data files:
```
TypeError: 'NoneType' object is not iterable
```
See:
- https://github.com/huggingface/datasets-server/issues/1073
We should give a more informative error message. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-04-24T10:34:03 | 2023-04-26T16:04:42 | 2023-04-26T15:54:44 | MEMBER | null | false | {
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} | When a subprocess hangs in `filter` or `map`, one should be able to get the subprocess' traceback when interrupting the main process. Right now it shows nothing.
To do so I `.get()` the subprocesses async results even the main process is stopped with e.g. `KeyboardInterrupt`. I added a timeout in case the subprocess is hanging or crashed. | {
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https://api.github.com/repos/huggingface/datasets/issues/5782 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5782/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5782/comments | https://api.github.com/repos/huggingface/datasets/issues/5782/events | https://github.com/huggingface/datasets/issues/5782 | 1,679,622,367 | I_kwDODunzps5kHQDf | 5,782 | Support for various audio-loading backends instead of always relying on SoundFile | {
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"Hi! \r\n\r\nYou can use `set_transform`/`with_transform` to define a custom decoding for audio formats not supported by `soundfile`:\r\n```python\r\naudio_dataset_amr = Dataset.from_dict({\"audio\": [\"audio_samples/audio.amr\"]})\r\n\r\ndef decode_audio(batch):\r\n batch[\"audio\"] = [read_ffmpeg(audio_path) f... | 2023-04-22T17:09:25 | 2023-05-10T20:23:04 | 2023-05-10T20:23:04 | NONE | null | null | null | ### Feature request
Introduce an option to select from a variety of audio-loading backends rather than solely relying on the SoundFile library. For instance, if the ffmpeg library is installed, it can serve as a fallback loading option.
### Motivation
- The SoundFile library, used in [features/audio.py](https://github.com/huggingface/datasets/blob/649d5a3315f9e7666713b6affe318ee00c7163a0/src/datasets/features/audio.py#L185), supports only a [limited number of audio formats](https://pysoundfile.readthedocs.io/en/latest/index.html?highlight=supported#soundfile.available_formats).
- However, current methods for creating audio datasets permit the inclusion of audio files in formats not supported by SoundFile.
- As a result, developers may potentially create a dataset they cannot read back.
In my most recent project, I dealt with phone call recordings in `.amr` or `.gsm` formats and was genuinely surprised when I couldn't read the dataset I had just packaged a minute prior. Nonetheless, I can still accurately read these files using the librosa library, which employs the audioread library that internally leverages ffmpeg to read such files.
Example:
```python
audio_dataset_amr = Dataset.from_dict({"audio": ["audio_samples/audio.amr"]}).cast_column("audio", Audio())
audio_dataset_amr.save_to_disk("audio_dataset_amr")
audio_dataset_amr = Dataset.load_from_disk("audio_dataset_amr")
print(audio_dataset_amr[0])
```
Results in:
```
Traceback (most recent call last):
...
raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x7f316323e4d0>: Format not recognised.
```
While I acknowledge that support for these rare file types may not be a priority, I believe it's quite unfortunate that it's possible to create an unreadable dataset in this manner.
### Your contribution
I've created a [simple demo repository](https://github.com/BoringDonut/hf-datasets-ffmpeg-audio) that highlights the mentioned issue. It demonstrates how to create an .amr dataset that results in an error when attempting to read it just a few lines later.
Additionally, I've made a [fork with a rudimentary solution](https://github.com/BoringDonut/datasets/blob/fea73a8fbbc8876467c7e6422c9360546c6372d8/src/datasets/features/audio.py#L189) that utilizes ffmpeg to load files not supported by SoundFile.
Here you may see github actions fails to read `.amr` dataset using the version of the current dataset, but will work with the patched version:
- https://github.com/BoringDonut/hf-datasets-ffmpeg-audio/actions/runs/4773780420/jobs/8487063785
- https://github.com/BoringDonut/hf-datasets-ffmpeg-audio/actions/runs/4773780420/jobs/8487063829
As evident from the GitHub action above, this solution resolves the previously mentioned problem.
I'd be happy to create a proper pull request, provide runtime benchmarks and tests if you could offer some guidance on the following:
- Where should I incorporate the ffmpeg (or other backends) code? For example, should I create a new file or simply add a function within the Audio class?
- Is it feasible to pass the audio-loading function as an argument within the current architecture? This would be useful if I know in advance that I'll be reading files not supported by SoundFile.
A few more notes:
- In theory, it's possible to load audio using librosa/audioread since librosa is already expected to be installed. However, librosa [will soon discontinue audioread support](https://github.com/librosa/librosa/blob/aacb4c134002903ae56bbd4b4a330519a5abacc0/librosa/core/audio.py#L227). Moreover, using audioread on its own seems inconvenient because it requires a file [path as input](https://github.com/beetbox/audioread/blob/ff9535df934c48038af7be9617fdebb12078cc07/audioread/__init__.py#L108) and cannot work with bytes already loaded into memory or an open file descriptor (as mentioned in [librosa docs](https://librosa.org/doc/main/generated/librosa.load.html#librosa.load), only SoundFile backend supports an open file descriptor as an input). | {
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"It looks like an issue with your installation of scipy, can you try reinstalling it ?",
"Sorry for the late reply, but that worked @lhoestq . Thanks for the assist."
] | 2023-04-22T15:10:44 | 2023-05-02T23:41:25 | 2023-05-02T23:41:25 | NONE | null | null | null | ### Describe the bug
I tried to load a dataset using the `datasets` library in a conda jupyter notebook and got the below error.
```
ImportError: dlopen(/Users/gilbertyoung/miniforge3/envs/review_sense/lib/python3.8/site-packages/scipy/sparse/linalg/_isolve/_iterative.cpython-38-darwin.so, 0x0002): Library not loaded: @rpath/liblapack.3.dylib
Referenced from: <65B094A2-59D7-31AC-A966-4DB9E11D2A15> /Users/gilbertyoung/miniforge3/envs/review_sense/lib/python3.8/site-packages/scipy/sparse/linalg/_isolve/_iterative.cpython-38-darwin.so
Reason: tried: '/Users/gilbertyoung/miniforge3/envs/review_sense/lib/python3.8/site-packages/scipy/sparse/linalg/_isolve/liblapack.3.dylib' (no such file), '/Users/gilbertyoung/miniforge3/envs/review_sense/lib/python3.8/site-packages/scipy/sparse/linalg/_isolve/../../../../../../liblapack.3.dylib' (no such file), '/Users/gilbertyoung/miniforge3/envs/review_sense/lib/python3.8/site-packages/scipy/sparse/linalg/_isolve/liblapack.3.dylib' (no such file), '/Users/gilbertyoung/miniforge3/envs/review_sense/lib/python3.8/site-packages/scipy/sparse/linalg/_isolve/../../../../../../liblapack.3.dylib' (no such file), '/Users/gilbertyoung/miniforge3/envs/review_sense/bin/../lib/liblapack.3.dylib' (no such file), '/Users/gilbertyoung/miniforge3/envs/review_sense/bin/../lib/liblapack.3.dylib' (no such file), '/usr/local/lib/liblapack.3.dylib' (no such file), '/usr/lib/liblapack.3.dylib' (no such file, not in dyld cache)
```
### Steps to reproduce the bug
Run the `load_datasets` function
### Expected behavior
I expected the dataset to be loaded into my notebook.
### Environment info
name: review_sense
channels:
- apple
- conda-forge
dependencies:
- python=3.8
- pip>=19.0
- jupyter
- tensorflow-deps
#- scikit-learn
#- scipy
- pandas
- pandas-datareader
- matplotlib
- pillow
- tqdm
- requests
- h5py
- pyyaml
- flask
- boto3
- ipykernel
- seaborn
- pip:
- tensorflow-macos==2.9
- tensorflow-metal==0.5.0
- bayesian-optimization
- gym
- kaggle
- huggingface_hub
- datasets
- numpy
- huggingface
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https://api.github.com/repos/huggingface/datasets/issues/5780 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5780/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5780/comments | https://api.github.com/repos/huggingface/datasets/issues/5780/events | https://github.com/huggingface/datasets/issues/5780 | 1,679,367,149 | I_kwDODunzps5kGRvt | 5,780 | TypeError: 'NoneType' object does not support item assignment | {
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```
def load_datasets(formats, data_dir=datadir, data_files=datafile）：
dataset = load_dataset(formats, data_dir=datadir, data_files=datafile, split=split, streaming=True, **kwargs)
return dataset
raw_datasets = DatasetDict()
raw_datasets["train"] = load_datasets(“csv”, args.datadir, "train.csv", split=train_split)
raw_datasets["test"] = load_datasets(“csv”, args.datadir, "dev.csv", split=test_split)
raw_datasets = raw_datasets.cast_column("audio", Audio(sampling_rate=16000))
```
error：
```
main()
File "peft_adalora_whisper_large_training.py", line 502, in main
raw_datasets = raw_datasets.cast_column("audio", Audio(sampling_rate=16000))
File "/home/ybZhang/miniconda3/envs/whister/lib/python3.8/site-packages/datasets/dataset_dict.py", line 2015, in cast_column
info.features[column] = feature
TypeError: 'NoneType' object does not support item assignment
```
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https://api.github.com/repos/huggingface/datasets/issues/5779 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5779/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5779/comments | https://api.github.com/repos/huggingface/datasets/issues/5779/events | https://github.com/huggingface/datasets/pull/5779 | 1,678,669,865 | PR_kwDODunzps5O3sHp | 5,779 | Call fs.makedirs in save_to_disk | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-04-21T15:04:28 | 2023-04-26T12:20:01 | 2023-04-26T12:11:15 | MEMBER | null | false | {
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"merged_at": "2023-04-26T12:11:15"
} | We need to call `fs.makedirs` when saving a dataset using `save_to_disk`, because some fs implementations have actual directories (S3 and others don't)
Close https://github.com/huggingface/datasets/issues/5775 | {
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https://api.github.com/repos/huggingface/datasets/issues/5777 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5777/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5777/comments | https://api.github.com/repos/huggingface/datasets/issues/5777/events | https://github.com/huggingface/datasets/issues/5777 | 1,677,655,969 | I_kwDODunzps5j_v-h | 5,777 | datasets.load_dataset("code_search_net", "python") : NotADirectoryError: [Errno 20] Not a directory | {
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"Note:\r\nI listed the datasets and grepped around to find what appears to be an alternative source for this:\r\n\r\nraw_datasets = load_dataset(\"espejelomar/code_search_net_python_10000_examples\", \"python\")",
"Thanks for reporting, @jason-brian-anderson.\r\n\r\nYes, this is a known issue: the [CodeSearchNet]... | 2023-04-21T02:08:07 | 2023-05-11T11:51:56 | 2023-05-11T11:51:56 | NONE | null | null | null | ### Describe the bug
While checking out the [tokenizer tutorial](https://huggingface.co/course/chapter6/2?fw=pt), i noticed getting an error while initially downloading the python dataset used in the examples.
The [collab with the error is here](https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter6/section2.ipynb#scrollTo=hGb69Yo3eV8S)
```
from datasets import load_dataset
import os
os.environ["HF_DATASETS_CACHE"] = "/workspace"
# This can take a few minutes to load, so grab a coffee or tea while you wait!
raw_datasets = load_dataset("code_search_net", "python")
```
yeilds:
```
ile /opt/conda/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:524, in xlistdir(path, use_auth_token)
522 main_hop, *rest_hops = _as_str(path).split("::")
523 if is_local_path(main_hop):
--> 524 return os.listdir(path)
525 else:
526 # globbing inside a zip in a private repo requires authentication
527 if not rest_hops and (main_hop.startswith("http://") or main_hop.startswith("https://")):
NotADirectoryError: [Errno 20] Not a directory: '/workspace/downloads/25ceeb4c25ab737d688bd56ea92bfbb1f199fe572470456cf2d675479f342ac7/python/final/jsonl/train'
```
I was able to reproduce this erro both in the collab and on my own pytorch/pytorch container pulled from the dockerhub official pytorch image, so i think it may be a server side thing.
### Steps to reproduce the bug
Steps to reproduce the issue:
1. run `raw_datasets = load_dataset("code_search_net", "python")`
### Expected behavior
expect the code to not exception during dataset pull.
### Environment info
i tried both the default HF_DATASETS_CACHE on Collab, and on my local container. i then pointed to the HF_DATASETS_CACHE to a large capacity local storage and the problem was consisten across all 3 scenarios. | {
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"We just fixed this on `main` and will do a new release soon :)"
] | 2023-04-20T16:58:01 | 2023-04-26T12:11:36 | 2023-04-26T12:11:17 | NONE | null | null | null | ### Describe the bug
https://github.com/huggingface/datasets/blob/e7ce0ac60c7efc10886471932854903a7c19f172/src/datasets/arrow_dataset.py#L1371
Here is the bug point, when I want to save from a `DatasetDict` class and the items of the instance is like `[('train', Dataset({features: ..., num_rows: ...}))]` , there is no guarantee that there exists a directory name `train` under `dataset_dict_path`.
### Steps to reproduce the bug
1. Mock a DatasetDict with items like what I said.
2. using save_to_disk with storage_options, u can use local sftp. code may like below
```python
from datasets import load_dataset
dataset = load_dataset(...)
dataset.save_to_disk('sftp:///tmp', storage_options={'host': 'localhost', 'username': 'admin'})
```
I suppose u can reproduce the bug by these steps.
### Expected behavior
Should create the folder if it does not exists, just like we do locally.
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-6.2.10-arch1-1-x86_64-with-glibc2.35
- Python version: 3.10.9
- Huggingface_hub version: 0.13.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-04-20T13:21:32 | 2023-04-20T13:34:26 | 2023-04-20T13:24:28 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/5772 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5772/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5772/comments | https://api.github.com/repos/huggingface/datasets/issues/5772/events | https://github.com/huggingface/datasets/pull/5772 | 1,675,033,510 | PR_kwDODunzps5OreXV | 5,772 | Fix JSON builder when missing keys in first row | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-04-19T14:32:57 | 2023-04-21T06:45:13 | 2023-04-21T06:35:27 | MEMBER | null | false | {
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} | Until now, the JSON builder only considered the keys present in the first element of the list:
- Either explicitly: by passing index 0 in `dataset[0].keys()`
- Or implicitly: `pa.Table.from_pylist(dataset)`, where "schema (default None): If not passed, will be inferred from the first row of the mapping values"
This PR fixes the bug by considering the union of the keys present in all the rows.
Fix #5726. | {
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https://api.github.com/repos/huggingface/datasets/issues/5771 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5771/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5771/comments | https://api.github.com/repos/huggingface/datasets/issues/5771/events | https://github.com/huggingface/datasets/issues/5771 | 1,674,828,380 | I_kwDODunzps5j09pc | 5,771 | Support cloud storage for loading datasets | {
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"A duplicate of https://github.com/huggingface/datasets/issues/5281"
] | 2023-04-19T12:43:53 | 2023-05-07T17:47:41 | 2023-05-07T17:47:41 | CONTRIBUTOR | null | null | null | ### Feature request
It seems that the the current implementation supports cloud storage only for `load_from_disk`. It would be nice if a similar functionality existed in `load_dataset`.
### Motivation
Motivation is pretty clear -- let users work with datasets located in the cloud.
### Your contribution
I can help implementing this. | {
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https://api.github.com/repos/huggingface/datasets/issues/5770 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5770/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5770/comments | https://api.github.com/repos/huggingface/datasets/issues/5770/events | https://github.com/huggingface/datasets/pull/5770 | 1,673,581,555 | PR_kwDODunzps5OmntV | 5,770 | Add IterableDataset.from_spark | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi again @lhoestq this is ready for review! Not sure I have permission to add people to the reviewers list...",
"Cool ! I think you can define `IterableDataset.from_spark` instead of adding `streaming=` in `Dataset.from_spark`, it ... | 2023-04-18T17:47:53 | 2023-05-17T14:07:32 | 2023-05-17T14:00:38 | CONTRIBUTOR | null | false | {
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} | Follow-up from https://github.com/huggingface/datasets/pull/5701
Related issue: https://github.com/huggingface/datasets/issues/5678 | {
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https://api.github.com/repos/huggingface/datasets/issues/5769 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5769/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5769/comments | https://api.github.com/repos/huggingface/datasets/issues/5769/events | https://github.com/huggingface/datasets/issues/5769 | 1,673,441,182 | I_kwDODunzps5jvq-e | 5,769 | Tiktoken tokenizers are not pickable | {
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"Thanks for reporting, @markovalexander.\r\n\r\nUnfortunately, I'm not able to reproduce the issue: the `tiktoken` tokenizer can be used within `Dataset.map`, both in my local machine and in a Colab notebook: https://colab.research.google.com/drive/1DhJroZgk0sNFJ2Mrz-jYgrmh9jblXaCG?usp=sharing\r\n\r\nAre you sure y... | 2023-04-18T16:07:40 | 2023-05-04T18:55:57 | 2023-05-04T18:55:57 | NONE | null | null | null | ### Describe the bug
Since tiktoken tokenizer is not pickable, it is not possible to use it inside `dataset.map()` with multiprocessing enabled. However, you [made](https://github.com/huggingface/datasets/issues/5536) tiktoken's tokenizers pickable in `datasets==2.10.0` for caching. For some reason, this logic does not work in dataset processing and raises `TypeError: cannot pickle 'builtins.CoreBPE' object`
### Steps to reproduce the bug
```
from datasets import load_dataset
import tiktoken
dataset = load_dataset("stas/openwebtext-10k")
enc = tiktoken.get_encoding("gpt2")
tokenized = dataset.map(
process,
remove_columns=['text'],
desc="tokenizing the OWT splits",
num_proc=2,
)
def process(example):
ids = enc.encode(example['text'])
ids.append(enc.eot_token)
out = {'ids': ids, 'len': len(ids)}
return out
```
### Expected behavior
starts processing dataset
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.0-1021-oracle-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.13.4
- PyArrow version: 9.0.0
- Pandas version: 2.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5768 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5768/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5768/comments | https://api.github.com/repos/huggingface/datasets/issues/5768/events | https://github.com/huggingface/datasets/issues/5768 | 1,672,494,561 | I_kwDODunzps5jsD3h | 5,768 | load_dataset("squad") doesn't work in 2.7.1 and 2.10.1 | {
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"Thanks for reporting, @yaseen157.\r\n\r\nCould you please give the complete error stack trace?",
"I am not able to reproduce your issue: the dataset loads perfectly on my local machine and on a Colab notebook: https://colab.research.google.com/drive/1Fbdoa1JdNz8DOdX6gmIsOK1nCT8Abj4O?usp=sharing\r\n```python\r\nI... | 2023-04-18T07:10:56 | 2023-04-20T10:27:23 | 2023-04-20T10:27:22 | NONE | null | null | null | ### Describe the bug
There is an issue that seems to be unique to the "squad" dataset, in which it cannot be loaded using standard methods. This issue is most quickly reproduced from the command line, using the HF examples to verify a dataset is loaded properly.
This is not a problem with "squad_v2" dataset for example.
### Steps to reproduce the bug
cmd line
> $ python -c "from datasets import load_dataset; print(load_dataset('squad', split='train')[0])"
OR
Python IDE
> from datasets import load_dataset
> load_dataset("squad")
### Expected behavior
I expected to either see the output described here from running the very same command in command line ([https://huggingface.co/docs/datasets/installation]), or any output that does not raise Python's TypeError.
There is some funky behaviour in the dataset builder portion of the codebase that means it is trying to import the squad dataset with an incorrect path, or the squad dataset couldn't be downloaded. I'm not really sure what the problem is beyond that. Messing around with caching I did manage to get it to load the dataset once, and then couldn't repeat this.
### Environment info
datasets=2.7.1 **or** 2.10.1, python=3.10.8, Linux 3.10.0-1160.36.2.el7.x86_64 **or** Windows 10-64
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https://api.github.com/repos/huggingface/datasets/issues/5767 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5767/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5767/comments | https://api.github.com/repos/huggingface/datasets/issues/5767/events | https://github.com/huggingface/datasets/issues/5767 | 1,672,433,979 | I_kwDODunzps5jr1E7 | 5,767 | How to use Distill-BERT with different datasets? | {
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"Closing this one in favor of the same issue opened in the `transformers` repo."
] | 2023-04-18T06:25:12 | 2023-04-20T16:52:05 | 2023-04-20T16:52:05 | NONE | null | null | null | ### Describe the bug
- `transformers` version: 4.11.3
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyTorch version (GPU?): 1.12.0+cu102 (True)
- Tensorflow version (GPU?): 2.10.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
### Steps to reproduce the bug
I recently read [this](https://huggingface.co/docs/transformers/quicktour#train-with-tensorflow:~:text=The%20most%20important%20thing%20to%20remember%20is%20you%20need%20to%20instantiate%20a%20tokenizer%20with%20the%20same%20model%20name%20to%20ensure%20you%E2%80%99re%20using%20the%20same%20tokenization%20rules%20a%20model%20was%20pretrained%20with.) and was wondering how to use distill-BERT (which is pre-trained with imdb dataset) with a different dataset (for eg. [this](https://huggingface.co/datasets/yhavinga/imdb_dutch) dataset)?
### Expected behavior
Distill-BERT should work with different datasets.
### Environment info
- `datasets` version: 1.12.1
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 11.0.0 | {
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"Thanks for reporting, @sauravtii.\r\n\r\nUnfortunately, I'm not able to reproduce the issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"josianem/imdb\")\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text', 'label'],\r... | 2023-04-17T09:08:18 | 2023-04-18T07:18:20 | 2023-04-18T07:18:20 | NONE | null | null | null | ### Describe the bug
I want to use this (https://huggingface.co/datasets/josianem/imdb) dataset therefore I am trying to load it using the following code:
```
dataset = load_dataset("josianem/imdb")
```
The dataset is not getting loaded and gives the error message as the following:
```
Traceback (most recent call last):
File "sample.py", line 3, in <module>
dataset = load_dataset("josianem/imdb")
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/load.py", line 1112, in load_dataset
builder_instance.download_and_prepare(
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py", line 636, in download_and_prepare
self._download_and_prepare(
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py", line 704, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/saurav/.cache/huggingface/modules/datasets_modules/datasets/imdb/cc6ab4acab2799be15d5d217c24548b856156dafdc850165fdc4f2031f27ff2f/imdb.py", line 79, in _split_generators
archive = dl_manager.download(_DOWNLOAD_URL)
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 197, in map_nested
return function(data_struct)
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 289, in cached_path
output_path = get_from_cache(
File "/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 606, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://www.dropbox.com/s/zts98j4vkqtsns6/aclImdb_v2.tar?dl=1
```
### Steps to reproduce the bug
You can reproduce the error by using the following code:
```
from datasets import load_dataset, load_metric
dataset = load_dataset("josianem/imdb")
```
### Expected behavior
The dataset should get loaded (I am using this dataset for the first time so not much aware of the exact behavior).
### Environment info
- `datasets` version: 1.12.1
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 11.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5763 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5763/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5763/comments | https://api.github.com/repos/huggingface/datasets/issues/5763/events | https://github.com/huggingface/datasets/pull/5763 | 1,670,476,302 | PR_kwDODunzps5OcMI7 | 5,763 | fix typo: "mow" -> "now" | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-04-17T06:03:44 | 2023-04-17T15:01:53 | 2023-04-17T14:54:46 | CONTRIBUTOR | null | false | {
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"merged_at": "2023-04-17T14:54:46"
} | I noticed a typo as I was reading the datasets documentation. This PR contains a trivial fix changing "mow" to "now." | {
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https://api.github.com/repos/huggingface/datasets/issues/5762 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5762/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5762/comments | https://api.github.com/repos/huggingface/datasets/issues/5762/events | https://github.com/huggingface/datasets/issues/5762 | 1,670,326,470 | I_kwDODunzps5jjyjG | 5,762 | Not able to load the pile | {
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"Thanks for reporting, @surya-narayanan.\r\n\r\nI see you already started a discussion about this on the Community tab of the corresponding dataset: https://huggingface.co/datasets/EleutherAI/the_pile/discussions/10\r\nLet's continue the discussion there!"
] | 2023-04-17T03:09:10 | 2023-04-17T09:37:27 | 2023-04-17T09:37:27 | NONE | null | null | null | ### Describe the bug
Got this error when I am trying to load the pile dataset
```
TypeError: Couldn't cast array of type
struct<file: string, id: string>
to
{'id': Value(dtype='string', id=None)}
```
### Steps to reproduce the bug
Please visit the following sample notebook
https://colab.research.google.com/drive/1JHcjawcHL6QHhi5VcqYd07W2QCEj2nWK#scrollTo=ulJP3eJCI-tB
### Expected behavior
The pile should work
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.31
- Python version: 3.9.16
- Huggingface_hub version: 0.13.4
- PyArrow version: 9.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5758 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5758/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5758/comments | https://api.github.com/repos/huggingface/datasets/issues/5758/events | https://github.com/huggingface/datasets/pull/5758 | 1,669,920,923 | PR_kwDODunzps5OaY9S | 5,758 | Fixes #5757 | {
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"The CI can be fixed by merging `main` into your branch. Can you do that before we merge ?",
"_The documentation is not available anymore as the PR was closed or merged._",
"Done.\n\nOn Thu, Apr 20, 2023 at 6:01 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> The CI can be fixed by merging main into your branch. Ca... | 2023-04-16T11:56:01 | 2023-04-20T15:37:49 | 2023-04-20T15:30:48 | CONTRIBUTOR | null | false | {
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} | Fixes the bug #5757 | {
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It seems that `~` is not recognized correctly in local paths. Whenever I try to use it I get an exception
### Steps to reproduce the bug
```python
load_dataset("imagefolder", data_dir="~/data/my_dataset")
```
Will generate the following error:
```
EmptyDatasetError: The directory at /path/to/cwd/~/data/datasets/clementine_tagged_per_cam doesn't contain any data files
```
### Expected behavior
Load the dataset.
### Environment info
datasets==2.11.0 | {
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"Hi! I've merged a PR on the Hub with a fix: https://huggingface.co/datasets/fashion_mnist/discussions/3",
"Thanks, this appears to have fixed the issue.\r\n\r\nI've created a PR for the same change in the mnist dataset: https://huggingface.co/datasets/mnist/discussions/3/files"
] | 2023-04-16T04:59:47 | 2023-04-18T03:40:56 | 2023-04-18T03:40:56 | NONE | null | null | null | ### Describe the bug
When calling shuffle on a IterableDataset with streaming=True, I get the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/administrator/Documents/Projects/huggingface/jax-diffusers-sprint-consistency-models/virtualenv/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 937, in __iter__
for key, example in ex_iterable:
File "/home/administrator/Documents/Projects/huggingface/jax-diffusers-sprint-consistency-models/virtualenv/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 627, in __iter__
for x in self.ex_iterable:
File "/home/administrator/Documents/Projects/huggingface/jax-diffusers-sprint-consistency-models/virtualenv/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 138, in __iter__
yield from self.generate_examples_fn(**kwargs_with_shuffled_shards)
File "/home/administrator/.cache/huggingface/modules/datasets_modules/datasets/mnist/fda16c03c4ecfb13f165ba7e29cf38129ce035011519968cdaf74894ce91c9d4/mnist.py", line 111, in _generate_examples
images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)
ValueError: cannot reshape array of size 59992 into shape (60000,28,28)
```
Tested with the fashion_mnist and mnist datasets
### Steps to reproduce the bug
Code to reproduce
```python
from datasets import load_dataset
SHUFFLE_SEED = 42
SHUFFLE_BUFFER_SIZE = 10_000
dataset = load_dataset('fashion_mnist', streaming=True).shuffle(seed=SHUFFLE_SEED, buffer_size=SHUFFLE_BUFFER_SIZE)
next(iter(dataset['train']))
```
### Expected behavior
A random item from the dataset and no error
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.0-69-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.13.4
- PyArrow version: 11.0.0
- Pandas version: 2.0.0
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https://api.github.com/repos/huggingface/datasets/issues/5755 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5755/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5755/comments | https://api.github.com/repos/huggingface/datasets/issues/5755/events | https://github.com/huggingface/datasets/issues/5755 | 1,669,048,438 | I_kwDODunzps5je6h2 | 5,755 | ImportError: cannot import name 'DeprecatedEnum' from 'datasets.utils.deprecation_utils' | {
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"update the version. fix"
] | 2023-04-14T23:28:54 | 2023-04-14T23:36:19 | 2023-04-14T23:36:19 | NONE | null | null | null | ### Describe the bug
The module moved to new place?
### Steps to reproduce the bug
in the import step,
```python
from datasets.utils.deprecation_utils import DeprecatedEnum
```
error:
```
ImportError: cannot import name 'DeprecatedEnum' from 'datasets.utils.deprecation_utils'
```
### Expected behavior
import successfully
### Environment info
python==3.9.16
datasets==1.18.3 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-04-14T18:15:14 | 2023-04-20T15:27:58 | 2023-04-20T15:21:00 | CONTRIBUTOR | null | false | {
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"patch_url": "https://github.com/huggingface/datasets/pull/5754.patch",
"merged_at": "2023-04-20T15:21:00"
} | `GeneratorBasedBuilder`'s TQDM bars were not used as context managers. This PR fixes that (missed these bars in https://github.com/huggingface/datasets/pull/5560).
Also, this PR modifies the single-proc `save_to_disk` to fix the issue with the TQDM bar not accumulating the progress in the multi-shard setting (again, this bug was introduced by me in the linked PR 😎) | {
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https://api.github.com/repos/huggingface/datasets/issues/5753 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5753/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5753/comments | https://api.github.com/repos/huggingface/datasets/issues/5753/events | https://github.com/huggingface/datasets/issues/5753 | 1,668,659,536 | I_kwDODunzps5jdblQ | 5,753 | [IterableDatasets] Add column followed by interleave datasets gives bogus outputs | {
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"Problem with the code snippet! Using global vars and functions was not a good idea with iterable datasets!\r\n\r\nIf we update to:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal_dataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# now add a new co... | 2023-04-14T17:32:31 | 2023-04-14T17:45:52 | 2023-04-14T17:36:37 | CONTRIBUTOR | null | null | null | ### Describe the bug
If we add a new column to our iterable dataset using the hack described in #5752, when we then interleave datasets the new column is pinned to one value.
### Steps to reproduce the bug
What we're going to do here is:
1. Load an iterable dataset in streaming mode (`original_dataset`)
2. Add a new column to this dataset using the hack in #5752 (`modified_dataset_1`)
3. Create another new dataset by adding a column with the same key but different values (`modified_dataset_2`)
4. Interleave our new datasets (`modified_dataset_1` + `modified_dataset_2`)
5. Check the value of our newly added column (`new_column`)
```python
from datasets import load_dataset
# load an iterable dataset
original_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
# now add a new column to our streaming dataset using our hack from 5752
name = "new_column"
column = [f"new dataset 1, row {i}" for i in range(50)]
new_features = original_dataset.features.copy()
new_features[name] = new_features["file"] # I know that "file" has the right column type to match our new feature
def add_column_fn(example, idx):
if name in example:
raise ValueError(f"Error when adding {name}: column {name} is already in the dataset.")
return {name: column[idx]}
modified_dataset_1 = original_dataset.map(add_column_fn, with_indices=True, features=new_features)
# now create a second modified dataset using the same trick
column = [f"new dataset 2, row {i}" for i in range(50)]
def add_column_fn(example, idx):
if name in example:
raise ValueError(f"Error when adding {name}: column {name} is already in the dataset.")
return {name: column[idx]}
modified_dataset_2 = original_dataset.map(add_column_fn, with_indices=True, features=new_features)
# interleave these datasets
interleaved_dataset = interleave_datasets([modified_dataset_1, modified_dataset_2])
# now check what the value of the added column is
for i, sample in enumerate(interleaved_dataset):
print(sample["new_column"])
if i == 10:
break
```
**Print Output:**
```
new dataset 2, row 0
new dataset 2, row 0
new dataset 2, row 1
new dataset 2, row 1
new dataset 2, row 2
new dataset 2, row 2
new dataset 2, row 3
new dataset 2, row 3
new dataset 2, row 4
new dataset 2, row 4
new dataset 2, row 5
```
We see that we only get outputs from our second dataset.
### Expected behavior
We should interleave between dataset 1 and 2 and increase in row value:
```
new dataset 1, row 0
new dataset 2, row 0
new dataset 1, row 1
new dataset 2, row 1
new dataset 1, row 2
new dataset 2, row 2
...
```
### Environment info
- datasets version: 2.10.2.dev0
- Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.16
- Huggingface_hub version: 0.13.3
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | {
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