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https://github.com/huggingface/datasets/issues/5650

> @WiNE-iNEFF Probably they are named inconsistently so that the correct pattern for which files should correspond to which split cannot be inferred. You can make it clearer to the loader by removing split names from filenames and putting files in separate folder for each split (you can take a look at the [documentation for imagefolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder)): > ``` > Fuaimeanna2/ > ├─ test > │   ├─ label_0 > │   │   ├── filename_0.jpg > │   │   └── filename_1.jpg > │   │   └── ... > │   ├─ label_1 > │   │   └── ... > │   ├─ label_2 > │   │   └── ... > │   └─ label_3 > │   └── ... > ├─ train > │   ├─ label_0 > │   │   └── ... > │   ├─ label_1 > │   │   └── ... > │   ├─ label_2 > │   │   └── ... > │   └─ label_3 > │   └── ... > └── validation >    ├─ label_0 >    │   └── ... >    ├─ label_1 >    │   └── ... >    ├─ label_2 >    │   └── ... > └─ label_3 > └── ... > ``` I have read this documentation more than once. It just wasn't a problem before.

load_dataset can't work correct with my image data

I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked

load_dataset can't work correct with my image data I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked > @WiNE-iNEFF Probably they are named inconsistently so that the correct pattern for which files should correspond to which split cannot be inferred. You can make it clearer to the loader by removing split names from filenames and putting files in separate folder for each split (you can take a look at the [documentation for imagefolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder)): > ``` > Fuaimeanna2/ > ├─ test > │   ├─ label_0 > │   │   ├── filename_0.jpg > │   │   └── filename_1.jpg > │   │   └── ... > │   ├─ label_1 > │   │   └── ... > │   ├─ label_2 > │   │   └── ... > │   └─ label_3 > │   └── ... > ├─ train > │   ├─ label_0 > │   │   └── ... > │   ├─ label_1 > │   │   └── ... > │   ├─ label_2 > │   │   └── ... > │   └─ label_3 > │   └── ... > └── validation >    ├─ label_0 >    │   └── ... >    ├─ label_1 >    │   └── ... >    ├─ label_2 >    │   └── ... > └─ label_3 > └── ... > ``` I have read this documentation more than once. It just wasn't a problem before.

https://github.com/huggingface/datasets/issues/5650

Hi, You need to use: ``` from datasets import load_dataset dataset = load_dataset("imagefolder", split="train", data_dir="path_to_your_folder") ``` instead of ``` from datasets import load_dataset dataset = load_dataset("my_folder_name", split="train") ``` To create an image dataset from your local folders.

load_dataset can't work correct with my image data

I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked

load_dataset can't work correct with my image data I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked Hi, You need to use: ``` from datasets import load_dataset dataset = load_dataset("imagefolder", split="train", data_dir="path_to_your_folder") ``` instead of ``` from datasets import load_dataset dataset = load_dataset("my_folder_name", split="train") ``` To create an image dataset from your local folders.

https://github.com/huggingface/datasets/issues/5650

> Hi, > > You need to use: > > ``` > from datasets import load_dataset > > dataset = load_dataset("imagefolder", split="train", data_dir="path_to_your_folder") > ``` > > instead of > > ``` > from datasets import load_dataset > > dataset = load_dataset("my_folder_name", split="train") > ``` > > To create an image dataset from your local folders. Thank you, but even using the method that you wrote above absolutely nothing changes, especially without using data_dir on my other data everything works fine

load_dataset can't work correct with my image data

I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked

load_dataset can't work correct with my image data I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked > Hi, > > You need to use: > > ``` > from datasets import load_dataset > > dataset = load_dataset("imagefolder", split="train", data_dir="path_to_your_folder") > ``` > > instead of > > ``` > from datasets import load_dataset > > dataset = load_dataset("my_folder_name", split="train") > ``` > > To create an image dataset from your local folders. Thank you, but even using the method that you wrote above absolutely nothing changes, especially without using data_dir on my other data everything works fine

https://github.com/huggingface/datasets/issues/5650

@WiNE-iNEFF have you tried the suggestion I posted above? with removing split names from filenames and structuring files in folders? > even using the method that you wrote above absolutely nothing changes fyi - nothing changed because these two approaches are basically the same. it's just that when you pass your data directory as a dataset name (`load_dataset("my_folder_name"`), not as `data_dir` (`load_dataset("imagefolder", data_dir="my_folder_name"`), `datasets` infers what module to use (`imagefolder` in your case) automatically, by file extensions.

load_dataset can't work correct with my image data

I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked

load_dataset can't work correct with my image data I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked @WiNE-iNEFF have you tried the suggestion I posted above? with removing split names from filenames and structuring files in folders? > even using the method that you wrote above absolutely nothing changes fyi - nothing changed because these two approaches are basically the same. it's just that when you pass your data directory as a dataset name (`load_dataset("my_folder_name"`), not as `data_dir` (`load_dataset("imagefolder", data_dir="my_folder_name"`), `datasets` infers what module to use (`imagefolder` in your case) automatically, by file extensions.

https://github.com/huggingface/datasets/issues/5650

Oh I didn't know that! OK but in any case, not sure why the image builder isn't working for @WiNE-iNEFF. But it's hard for us to help if we can't reproduce. I'd just check the structure of the folders, see if the splits are correctly set up, etc.

load_dataset can't work correct with my image data

I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked

load_dataset can't work correct with my image data I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked Oh I didn't know that! OK but in any case, not sure why the image builder isn't working for @WiNE-iNEFF. But it's hard for us to help if we can't reproduce. I'd just check the structure of the folders, see if the splits are correctly set up, etc.

https://github.com/huggingface/datasets/issues/5650

> @WiNE-iNEFF have you tried the suggestion I posted above? with removing split names from filenames and structuring files in folders? > > > > even using the method that you wrote above absolutely nothing changes > > fyi - nothing changed because these two approaches are basically the same. it's just that when you pass your data directory as a dataset name (`load_dataset("my_folder_name"`), not as `data_dir` (`load_dataset("imagefolder", data_dir="my_folder_name"`), `datasets` infers what module to use (`imagefolder` in your case) automatically, by file extensions. I'll try to try your method over the next few days, then I'll write it turned out

load_dataset can't work correct with my image data

I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked

load_dataset can't work correct with my image data I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked > @WiNE-iNEFF have you tried the suggestion I posted above? with removing split names from filenames and structuring files in folders? > > > > even using the method that you wrote above absolutely nothing changes > > fyi - nothing changed because these two approaches are basically the same. it's just that when you pass your data directory as a dataset name (`load_dataset("my_folder_name"`), not as `data_dir` (`load_dataset("imagefolder", data_dir="my_folder_name"`), `datasets` infers what module to use (`imagefolder` in your case) automatically, by file extensions. I'll try to try your method over the next few days, then I'll write it turned out

https://github.com/huggingface/datasets/issues/5650

> @WiNE-iNEFF have you tried the suggestion I posted above? with removing split names from filenames and structuring files in folders? > > > > even using the method that you wrote above absolutely nothing changes > > fyi - nothing changed because these two approaches are basically the same. it's just that when you pass your data directory as a dataset name (`load_dataset("my_folder_name"`), not as `data_dir` (`load_dataset("imagefolder", data_dir="my_folder_name"`), `datasets` infers what module to use (`imagefolder` in your case) automatically, by file extensions. I tried creating a `train` folder and put my image folders in it. As a result, all 18,000 images were loaded.

load_dataset can't work correct with my image data

I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked

load_dataset can't work correct with my image data I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked > @WiNE-iNEFF have you tried the suggestion I posted above? with removing split names from filenames and structuring files in folders? > > > > even using the method that you wrote above absolutely nothing changes > > fyi - nothing changed because these two approaches are basically the same. it's just that when you pass your data directory as a dataset name (`load_dataset("my_folder_name"`), not as `data_dir` (`load_dataset("imagefolder", data_dir="my_folder_name"`), `datasets` infers what module to use (`imagefolder` in your case) automatically, by file extensions. I tried creating a `train` folder and put my image folders in it. As a result, all 18,000 images were loaded.

https://github.com/huggingface/datasets/issues/5650

@WiNE-iNEFF great! So to explain what happened according to my assumptions: When you use a standard packaged loader (like `imagefolder`, `csv`, `jsonl`, and so on) and load your data like `load_dataset("my_folder_name")` or `load_dataset("imagefolder", data_dir="my_folder_name"`, the library searches for patterns to divide files into splits. This is described a bit in [this doc](https://huggingface.co/docs/datasets/v2.10.0/en/repository_structure#splits-and-file-names). And the order to search for patterns is the following: 1. first it checks for [pattern like `data/<split_name>-xxxxx-of-xxxxx`](https://huggingface.co/docs/datasets/v2.10.0/en/repository_structure#custom-split-names) (which allows to pass custom split names) 2. then for directories named as splits (if you have directories named `train`, `test` etc.) 3. then for [splits in filenames](https://huggingface.co/docs/datasets/v2.10.0/en/repository_structure#splits-and-file-names) (like if you have files named `train-image.jpg`, `test_0.jpg`, ...) 4. then if no pattern was found, it treats all files as belonging to a single `train` split The code is [here](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L215). So I assume that in your case, since you didn't have directories for splits (pattern 2), some files that included split keywords (pattern 3) were included and others were ignored as not matching the pattern. And when you added `train` directory, the pattern for directories (pattern 2) was triggered first and everything worked as expected. Everything worked in your previous cases probably because you didn't have split names keywords in filenames, so all the files ended up being a part of a single train split (pattern 4). Another way to mitigate this apart from structuring your data according to the patterns is to explicitly state with files belong to which splits by passing them with `data_files` parameter: ```python load_dataset("my_folder_name", data_files={"train": "**"}) # to tell that all files should be included ``` Now I see that this order should be explained in documentation and also referenced in sections for packaged modules like `imagefolder`, thank you for pointing this out.

load_dataset can't work correct with my image data

I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked

load_dataset can't work correct with my image data I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked @WiNE-iNEFF great! So to explain what happened according to my assumptions: When you use a standard packaged loader (like `imagefolder`, `csv`, `jsonl`, and so on) and load your data like `load_dataset("my_folder_name")` or `load_dataset("imagefolder", data_dir="my_folder_name"`, the library searches for patterns to divide files into splits. This is described a bit in [this doc](https://huggingface.co/docs/datasets/v2.10.0/en/repository_structure#splits-and-file-names). And the order to search for patterns is the following: 1. first it checks for [pattern like `data/<split_name>-xxxxx-of-xxxxx`](https://huggingface.co/docs/datasets/v2.10.0/en/repository_structure#custom-split-names) (which allows to pass custom split names) 2. then for directories named as splits (if you have directories named `train`, `test` etc.) 3. then for [splits in filenames](https://huggingface.co/docs/datasets/v2.10.0/en/repository_structure#splits-and-file-names) (like if you have files named `train-image.jpg`, `test_0.jpg`, ...) 4. then if no pattern was found, it treats all files as belonging to a single `train` split The code is [here](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L215). So I assume that in your case, since you didn't have directories for splits (pattern 2), some files that included split keywords (pattern 3) were included and others were ignored as not matching the pattern. And when you added `train` directory, the pattern for directories (pattern 2) was triggered first and everything worked as expected. Everything worked in your previous cases probably because you didn't have split names keywords in filenames, so all the files ended up being a part of a single train split (pattern 4). Another way to mitigate this apart from structuring your data according to the patterns is to explicitly state with files belong to which splits by passing them with `data_files` parameter: ```python load_dataset("my_folder_name", data_files={"train": "**"}) # to tell that all files should be included ``` Now I see that this order should be explained in documentation and also referenced in sections for packaged modules like `imagefolder`, thank you for pointing this out.

https://github.com/huggingface/datasets/issues/5650

@NielsRogge @polinaeterna I have a similar problem when reading my dataset. I want to use DETR for object detection, but my data is in YOLO format. With a dataset of 10k images, yolo format involves having 10k labels. As far as I read regarding [COCO format](https://auto.gluon.ai/stable/tutorials/multimodal/object_detection/data_preparation/convert_data_to_coco_format.html), there must be one JSON per split. However, as I post in the [Hugging Face forum](https://discuss.huggingface.co/t/prepare-dataset-from-yolo-format-to-coco-for-detr/34894), when it is read, the number of rows is 1, which does not make sense. The instruction to read the train-val-test splits are: ```python from datasets import load_dataset data_files = { "train": './train_labels.json', "validation": './val_labels.json', "test": './test_labels.json' } dataset = load_dataset("json", data_files=data_files) ``` An example of the short version of the json file I read, to reproduce my error, is the following: ``` json { "info": {}, "licenses": [], "images": [ { "id": 1, "file_name": "aceca_100.mp4frame21.png", "width": 1280, "height": 720, "pixel_values": null, "pixel_mask": null }, { "id": 2, "file_name": "aceca_100.mp4frame24.png", "width": 1280, "height": 720, "pixel_values": null, "pixel_mask": null }, { "id": 3, "file_name": "aceca_100.mp4frame25.png", "width": 1280, "height": 720, "pixel_values": null, "pixel_mask": null}], "annotations": [ { "id": 1, "image_id": 1, "category_id": 0, "bbox": [0.0, 278.21896388398557, 86.94096523844935, 156.0293445072134], "area": 13565.341816979679, "iscrowd": 0 }, { "id": 2, "image_id": 2, "category_id": 0, "bbox": [149.28851295721816, 297.6359759754418, 34.76802347007475, 98.03908698442889], "area": 3408.625277259324, "iscrowd": 0 }, { "id": 3, "image_id": 3, "category_id": 0, "bbox": [153.3817197549372, 300.168969412891, 31.787555842913775, 89.69583163436312], "area": 2851.2112569539095, "iscrowd": 0 } ], "categories": [ { "id": 0, "name": "person" } ] } ``` If full files required, my email is aruigui98@gmail.com

load_dataset can't work correct with my image data

I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked

load_dataset can't work correct with my image data I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked @NielsRogge @polinaeterna I have a similar problem when reading my dataset. I want to use DETR for object detection, but my data is in YOLO format. With a dataset of 10k images, yolo format involves having 10k labels. As far as I read regarding [COCO format](https://auto.gluon.ai/stable/tutorials/multimodal/object_detection/data_preparation/convert_data_to_coco_format.html), there must be one JSON per split. However, as I post in the [Hugging Face forum](https://discuss.huggingface.co/t/prepare-dataset-from-yolo-format-to-coco-for-detr/34894), when it is read, the number of rows is 1, which does not make sense. The instruction to read the train-val-test splits are: ```python from datasets import load_dataset data_files = { "train": './train_labels.json', "validation": './val_labels.json', "test": './test_labels.json' } dataset = load_dataset("json", data_files=data_files) ``` An example of the short version of the json file I read, to reproduce my error, is the following: ``` json { "info": {}, "licenses": [], "images": [ { "id": 1, "file_name": "aceca_100.mp4frame21.png", "width": 1280, "height": 720, "pixel_values": null, "pixel_mask": null }, { "id": 2, "file_name": "aceca_100.mp4frame24.png", "width": 1280, "height": 720, "pixel_values": null, "pixel_mask": null }, { "id": 3, "file_name": "aceca_100.mp4frame25.png", "width": 1280, "height": 720, "pixel_values": null, "pixel_mask": null}], "annotations": [ { "id": 1, "image_id": 1, "category_id": 0, "bbox": [0.0, 278.21896388398557, 86.94096523844935, 156.0293445072134], "area": 13565.341816979679, "iscrowd": 0 }, { "id": 2, "image_id": 2, "category_id": 0, "bbox": [149.28851295721816, 297.6359759754418, 34.76802347007475, 98.03908698442889], "area": 3408.625277259324, "iscrowd": 0 }, { "id": 3, "image_id": 3, "category_id": 0, "bbox": [153.3817197549372, 300.168969412891, 31.787555842913775, 89.69583163436312], "area": 2851.2112569539095, "iscrowd": 0 } ], "categories": [ { "id": 0, "name": "person" } ] } ``` If full files required, my email is aruigui98@gmail.com

https://github.com/huggingface/datasets/issues/5650

Hi @Alberto1404, to load an object detection dataset it's recommended to make use of the metadata feature as explained [here](https://huggingface.co/docs/datasets/image_dataset#object-detection).

load_dataset can't work correct with my image data

I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked

load_dataset can't work correct with my image data I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked Hi @Alberto1404, to load an object detection dataset it's recommended to make use of the metadata feature as explained [here](https://huggingface.co/docs/datasets/image_dataset#object-detection).

https://github.com/huggingface/datasets/issues/5650

You can now refer to https://huggingface.co/docs/datasets/repository_structure to learn about the `datasets`' data files inference, so I'm closing this issue.

load_dataset can't work correct with my image data

I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked

load_dataset can't work correct with my image data I have about 20000 images in my folder which divided into 4 folders with class names. When i use load_dataset("my_folder_name", split="train") this function create dataset in which there are only 4 images, the remaining 19000 images were not added there. What is the problem and did not understand. Tried converting images and the like but absolutely nothing worked You can now refer to https://huggingface.co/docs/datasets/repository_structure to learn about the `datasets`' data files inference, so I'm closing this issue.

https://github.com/huggingface/datasets/issues/5649

Thanks for reporting, @lsb. We are investigating it. On the other hand, please note that in the next `datasets` release, the index will not be created by default (see #5583). If you would like to have it, you will need to explicitly pass `index=True`.

The index column created with .to_sql() is dependent on the batch_size when writing

### Describe the bug It seems like the "index" column is designed to be unique? The values are only unique per batch. The SQL index is not a unique index. This can be a problem, for instance, when building a faiss index on a dataset and then trying to match up ids with a sql export. ### Steps to reproduce the bug ``` from datasets import Dataset import sqlite3 db = sqlite3.connect(":memory:") nice_numbers = Dataset.from_dict({"nice_number": range(101,106)}) nice_numbers.to_sql("nice1", db, batch_size=1) nice_numbers.to_sql("nice2", db, batch_size=2) print(db.execute("select * from nice1").fetchall()) # [(0, 101), (0, 102), (0, 103), (0, 104), (0, 105)] print(db.execute("select * from nice2").fetchall()) # [(0, 101), (1, 102), (0, 103), (1, 104), (0, 105)] ``` ### Expected behavior I expected the "index" column to be unique ### Environment info ``` % datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.9.6 - PyArrow version: 7.0.0 - Pandas version: 1.5.2 zsh: segmentation fault datasets-cli env ```

The index column created with .to_sql() is dependent on the batch_size when writing ### Describe the bug It seems like the "index" column is designed to be unique? The values are only unique per batch. The SQL index is not a unique index. This can be a problem, for instance, when building a faiss index on a dataset and then trying to match up ids with a sql export. ### Steps to reproduce the bug ``` from datasets import Dataset import sqlite3 db = sqlite3.connect(":memory:") nice_numbers = Dataset.from_dict({"nice_number": range(101,106)}) nice_numbers.to_sql("nice1", db, batch_size=1) nice_numbers.to_sql("nice2", db, batch_size=2) print(db.execute("select * from nice1").fetchall()) # [(0, 101), (0, 102), (0, 103), (0, 104), (0, 105)] print(db.execute("select * from nice2").fetchall()) # [(0, 101), (1, 102), (0, 103), (1, 104), (0, 105)] ``` ### Expected behavior I expected the "index" column to be unique ### Environment info ``` % datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.9.6 - PyArrow version: 7.0.0 - Pandas version: 1.5.2 zsh: segmentation fault datasets-cli env ``` Thanks for reporting, @lsb. We are investigating it. On the other hand, please note that in the next `datasets` release, the index will not be created by default (see #5583). If you would like to have it, you will need to explicitly pass `index=True`.

https://github.com/huggingface/datasets/issues/5649

I think this is low enough priority for me to close this as Won't Fix. If I need any primary keys I can generate them beforehand. Feel free to reopen.

The index column created with .to_sql() is dependent on the batch_size when writing

### Describe the bug It seems like the "index" column is designed to be unique? The values are only unique per batch. The SQL index is not a unique index. This can be a problem, for instance, when building a faiss index on a dataset and then trying to match up ids with a sql export. ### Steps to reproduce the bug ``` from datasets import Dataset import sqlite3 db = sqlite3.connect(":memory:") nice_numbers = Dataset.from_dict({"nice_number": range(101,106)}) nice_numbers.to_sql("nice1", db, batch_size=1) nice_numbers.to_sql("nice2", db, batch_size=2) print(db.execute("select * from nice1").fetchall()) # [(0, 101), (0, 102), (0, 103), (0, 104), (0, 105)] print(db.execute("select * from nice2").fetchall()) # [(0, 101), (1, 102), (0, 103), (1, 104), (0, 105)] ``` ### Expected behavior I expected the "index" column to be unique ### Environment info ``` % datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.9.6 - PyArrow version: 7.0.0 - Pandas version: 1.5.2 zsh: segmentation fault datasets-cli env ```

The index column created with .to_sql() is dependent on the batch_size when writing ### Describe the bug It seems like the "index" column is designed to be unique? The values are only unique per batch. The SQL index is not a unique index. This can be a problem, for instance, when building a faiss index on a dataset and then trying to match up ids with a sql export. ### Steps to reproduce the bug ``` from datasets import Dataset import sqlite3 db = sqlite3.connect(":memory:") nice_numbers = Dataset.from_dict({"nice_number": range(101,106)}) nice_numbers.to_sql("nice1", db, batch_size=1) nice_numbers.to_sql("nice2", db, batch_size=2) print(db.execute("select * from nice1").fetchall()) # [(0, 101), (0, 102), (0, 103), (0, 104), (0, 105)] print(db.execute("select * from nice2").fetchall()) # [(0, 101), (1, 102), (0, 103), (1, 104), (0, 105)] ``` ### Expected behavior I expected the "index" column to be unique ### Environment info ``` % datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.9.6 - PyArrow version: 7.0.0 - Pandas version: 1.5.2 zsh: segmentation fault datasets-cli env ``` I think this is low enough priority for me to close this as Won't Fix. If I need any primary keys I can generate them beforehand. Feel free to reopen.

https://github.com/huggingface/datasets/issues/5648

Thanks for reporting! This can be fixed by setting the format to `arrow` in `flatten_indices` and restoring the original format after the flattening. I'm working on a PR that reduces the number of the `flatten_indices` calls in our codebase and makes `flatten_indices` a no-op when a dataset does not have an indices mapping, so I'll incorporate the fix in that PR.

flatten_indices doesn't work with pandas format

### Describe the bug Hi, I noticed that `flatten_indices` throws an error when the batch format is `pandas`. This is probably due to the fact that flatten_indices uses map internally which doesn't accept dataframes as the transformation function output ### Steps to reproduce the bug tabular_data = pd.DataFrame(np.random.randn(10,10)) tabular_data = datasets.arrow_dataset.Dataset.from_pandas(tabular_data) tabular_data.with_format("pandas").select([0,1,2,3]).flatten_indices() ### Expected behavior No error thrown ### Environment info - `datasets` version: 2.10.1 - Python version: 3.9.5 - PyArrow version: 11.0.0 - Pandas version: 1.4.1

flatten_indices doesn't work with pandas format ### Describe the bug Hi, I noticed that `flatten_indices` throws an error when the batch format is `pandas`. This is probably due to the fact that flatten_indices uses map internally which doesn't accept dataframes as the transformation function output ### Steps to reproduce the bug tabular_data = pd.DataFrame(np.random.randn(10,10)) tabular_data = datasets.arrow_dataset.Dataset.from_pandas(tabular_data) tabular_data.with_format("pandas").select([0,1,2,3]).flatten_indices() ### Expected behavior No error thrown ### Environment info - `datasets` version: 2.10.1 - Python version: 3.9.5 - PyArrow version: 11.0.0 - Pandas version: 1.4.1 Thanks for reporting! This can be fixed by setting the format to `arrow` in `flatten_indices` and restoring the original format after the flattening. I'm working on a PR that reduces the number of the `flatten_indices` calls in our codebase and makes `flatten_indices` a no-op when a dataset does not have an indices mapping, so I'll incorporate the fix in that PR.

https://github.com/huggingface/datasets/issues/5647

We now log these messages instead of printing them (addressed in #6019), so I'm closing this issue.

Make all print statements optional

### Feature request Make all print statements optional to speed up the development ### Motivation Im loading multiple tiny datasets and all the print statements make the loading slower ### Your contribution I can help contribute

Make all print statements optional ### Feature request Make all print statements optional to speed up the development ### Motivation Im loading multiple tiny datasets and all the print statements make the loading slower ### Your contribution I can help contribute We now log these messages instead of printing them (addressed in #6019), so I'm closing this issue.

https://github.com/huggingface/datasets/issues/5645

It looks like an error that we observed once in https://github.com/huggingface/datasets/pull/5166 Can you try to update `datasets` ? ``` pip install -U datasets ``` if it doesn't work, can you make sure you don't have packages installed that may modify `dill`'s behavior, such as `apache-beam` ?

Datasets map and select(range()) is giving dill error

### Describe the bug I'm using Huggingface Datasets library to load the dataset in google colab When I do, > data = train_dataset.select(range(10)) or > train_datasets = train_dataset.map( > process_data_to_model_inputs, > batched=True, > batch_size=batch_size, > remove_columns=["article", "abstract"], > ) I get following error: `module 'dill._dill' has no attribute 'log'` I've tried downgrading the dill version from latest to 0.2.8, but no luck. Stack trace: > --------------------------------------------------------------------------- > ModuleNotFoundError Traceback (most recent call last) > /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in _no_cache_fields(obj) > 367 try: > --> 368 import transformers as tr > 369 > > ModuleNotFoundError: No module named 'transformers' > > During handling of the above exception, another exception occurred: > > AttributeError Traceback (most recent call last) > 17 frames > <ipython-input-13-dd14813880a6> in <module> > ----> 1 test = train_dataset.select(range(10)) > > /usr/local/lib/python3.9/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) > 155 } > 156 # apply actual function > --> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) > 158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] > 159 # re-apply format to the output > > /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) > 155 if kwargs.get(fingerprint_name) is None: > 156 kwargs_for_fingerprint["fingerprint_name"] = fingerprint_name > --> 157 kwargs[fingerprint_name] = update_fingerprint( > 158 self._fingerprint, transform, kwargs_for_fingerprint > 159 ) > > /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args) > 103 for key in sorted(transform_args): > 104 hasher.update(key) > --> 105 hasher.update(transform_args[key]) > 106 return hasher.hexdigest() > 107 > > /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in update(self, value) > 55 def update(self, value): > 56 self.m.update(f"=={type(value)}==".encode("utf8")) > ---> 57 self.m.update(self.hash(value).encode("utf-8")) > 58 > 59 def hexdigest(self): > > /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in hash(cls, value) > 51 return cls.dispatch[type(value)](cls, value) > 52 else: > ---> 53 return cls.hash_default(value) > 54 > 55 def update(self, value): > > /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in hash_default(cls, value) > 44 @classmethod > 45 def hash_default(cls, value): > ---> 46 return cls.hash_bytes(dumps(value)) > 47 > 48 @classmethod > > /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in dumps(obj) > 387 file = StringIO() > 388 with _no_cache_fields(obj): > --> 389 dump(obj, file) > 390 return file.getvalue() > 391 > > /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in dump(obj, file) > 359 def dump(obj, file): > 360 """pickle an object to a file""" > --> 361 Pickler(file, recurse=True).dump(obj) > 362 return > 363 > > /usr/local/lib/python3.9/dist-packages/dill/_dill.py in dump(self, obj) > 392 return > 393 > --> 394 def load_session(filename='/tmp/session.pkl', main=None): > 395 """update the __main__ module with the state from the session file""" > 396 if main is None: main = _main_module > > /usr/lib/python3.9/pickle.py in dump(self, obj) > 485 if self.proto >= 4: > 486 self.framer.start_framing() > --> 487 self.save(obj) > 488 self.write(STOP) > 489 self.framer.end_framing() > > /usr/local/lib/python3.9/dist-packages/dill/_dill.py in save(self, obj, save_persistent_id) > 386 pickler._byref = False # disable pickling by name reference > 387 pickler._recurse = False # disable pickling recursion for globals > --> 388 pickler._session = True # is best indicator of when pickling a session > 389 pickler.dump(main) > 390 finally: > > /usr/lib/python3.9/pickle.py in save(self, obj, save_persistent_id) > 558 f = self.dispatch.get(t) > 559 if f is not None: > --> 560 f(self, obj) # Call unbound method with explicit self > 561 return > 562 > > /usr/local/lib/python3.9/dist-packages/dill/_dill.py in save_singleton(pickler, obj) > > /usr/lib/python3.9/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj) > 689 write(NEWOBJ) > 690 else: > --> 691 save(func) > 692 save(args) > 693 write(REDUCE) > > /usr/local/lib/python3.9/dist-packages/dill/_dill.py in save(self, obj, save_persistent_id) > 386 pickler._byref = False # disable pickling by name reference > 387 pickler._recurse = False # disable pickling recursion for globals > --> 388 pickler._session = True # is best indicator of when pickling a session > 389 pickler.dump(main) > 390 finally: > > /usr/lib/python3.9/pickle.py in save(self, obj, save_persistent_id) > 558 f = self.dispatch.get(t) > 559 if f is not None: > --> 560 f(self, obj) # Call unbound method with explicit self > 561 return > 562 > > /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in save_function(pickler, obj) > 583 dill._dill.log.info("# F1") > 584 else: > --> 585 dill._dill.log.info("F2: %s" % obj) > 586 name = getattr(obj, "__qualname__", getattr(obj, "__name__", None)) > 587 dill._dill.StockPickler.save_global(pickler, obj, name=name) > > AttributeError: module 'dill._dill' has no attribute 'log' ### Steps to reproduce the bug After loading the dataset(eg: https://huggingface.co/datasets/scientific_papers) in google colab do either > data = train_dataset.select(range(10)) or > train_datasets = train_dataset.map( > process_data_to_model_inputs, > batched=True, > batch_size=batch_size, > remove_columns=["article", "abstract"], > ) ### Expected behavior The map and select function should work ### Environment info dataset: https://huggingface.co/datasets/scientific_papers dill = 0.3.6 python= 3.9.16 transformer = 4.2.0

Datasets map and select(range()) is giving dill error ### Describe the bug I'm using Huggingface Datasets library to load the dataset in google colab When I do, > data = train_dataset.select(range(10)) or > train_datasets = train_dataset.map( > process_data_to_model_inputs, > batched=True, > batch_size=batch_size, > remove_columns=["article", "abstract"], > ) I get following error: `module 'dill._dill' has no attribute 'log'` I've tried downgrading the dill version from latest to 0.2.8, but no luck. Stack trace: > --------------------------------------------------------------------------- > ModuleNotFoundError Traceback (most recent call last) > /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in _no_cache_fields(obj) > 367 try: > --> 368 import transformers as tr > 369 > > ModuleNotFoundError: No module named 'transformers' > > During handling of the above exception, another exception occurred: > > AttributeError Traceback (most recent call last) > 17 frames > <ipython-input-13-dd14813880a6> in <module> > ----> 1 test = train_dataset.select(range(10)) > > /usr/local/lib/python3.9/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) > 155 } > 156 # apply actual function > --> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) > 158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] > 159 # re-apply format to the output > > /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) > 155 if kwargs.get(fingerprint_name) is None: > 156 kwargs_for_fingerprint["fingerprint_name"] = fingerprint_name > --> 157 kwargs[fingerprint_name] = update_fingerprint( > 158 self._fingerprint, transform, kwargs_for_fingerprint > 159 ) > > /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args) > 103 for key in sorted(transform_args): > 104 hasher.update(key) > --> 105 hasher.update(transform_args[key]) > 106 return hasher.hexdigest() > 107 > > /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in update(self, value) > 55 def update(self, value): > 56 self.m.update(f"=={type(value)}==".encode("utf8")) > ---> 57 self.m.update(self.hash(value).encode("utf-8")) > 58 > 59 def hexdigest(self): > > /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in hash(cls, value) > 51 return cls.dispatch[type(value)](cls, value) > 52 else: > ---> 53 return cls.hash_default(value) > 54 > 55 def update(self, value): > > /usr/local/lib/python3.9/dist-packages/datasets/fingerprint.py in hash_default(cls, value) > 44 @classmethod > 45 def hash_default(cls, value): > ---> 46 return cls.hash_bytes(dumps(value)) > 47 > 48 @classmethod > > /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in dumps(obj) > 387 file = StringIO() > 388 with _no_cache_fields(obj): > --> 389 dump(obj, file) > 390 return file.getvalue() > 391 > > /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in dump(obj, file) > 359 def dump(obj, file): > 360 """pickle an object to a file""" > --> 361 Pickler(file, recurse=True).dump(obj) > 362 return > 363 > > /usr/local/lib/python3.9/dist-packages/dill/_dill.py in dump(self, obj) > 392 return > 393 > --> 394 def load_session(filename='/tmp/session.pkl', main=None): > 395 """update the __main__ module with the state from the session file""" > 396 if main is None: main = _main_module > > /usr/lib/python3.9/pickle.py in dump(self, obj) > 485 if self.proto >= 4: > 486 self.framer.start_framing() > --> 487 self.save(obj) > 488 self.write(STOP) > 489 self.framer.end_framing() > > /usr/local/lib/python3.9/dist-packages/dill/_dill.py in save(self, obj, save_persistent_id) > 386 pickler._byref = False # disable pickling by name reference > 387 pickler._recurse = False # disable pickling recursion for globals > --> 388 pickler._session = True # is best indicator of when pickling a session > 389 pickler.dump(main) > 390 finally: > > /usr/lib/python3.9/pickle.py in save(self, obj, save_persistent_id) > 558 f = self.dispatch.get(t) > 559 if f is not None: > --> 560 f(self, obj) # Call unbound method with explicit self > 561 return > 562 > > /usr/local/lib/python3.9/dist-packages/dill/_dill.py in save_singleton(pickler, obj) > > /usr/lib/python3.9/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, state_setter, obj) > 689 write(NEWOBJ) > 690 else: > --> 691 save(func) > 692 save(args) > 693 write(REDUCE) > > /usr/local/lib/python3.9/dist-packages/dill/_dill.py in save(self, obj, save_persistent_id) > 386 pickler._byref = False # disable pickling by name reference > 387 pickler._recurse = False # disable pickling recursion for globals > --> 388 pickler._session = True # is best indicator of when pickling a session > 389 pickler.dump(main) > 390 finally: > > /usr/lib/python3.9/pickle.py in save(self, obj, save_persistent_id) > 558 f = self.dispatch.get(t) > 559 if f is not None: > --> 560 f(self, obj) # Call unbound method with explicit self > 561 return > 562 > > /usr/local/lib/python3.9/dist-packages/datasets/utils/py_utils.py in save_function(pickler, obj) > 583 dill._dill.log.info("# F1") > 584 else: > --> 585 dill._dill.log.info("F2: %s" % obj) > 586 name = getattr(obj, "__qualname__", getattr(obj, "__name__", None)) > 587 dill._dill.StockPickler.save_global(pickler, obj, name=name) > > AttributeError: module 'dill._dill' has no attribute 'log' ### Steps to reproduce the bug After loading the dataset(eg: https://huggingface.co/datasets/scientific_papers) in google colab do either > data = train_dataset.select(range(10)) or > train_datasets = train_dataset.map( > process_data_to_model_inputs, > batched=True, > batch_size=batch_size, > remove_columns=["article", "abstract"], > ) ### Expected behavior The map and select function should work ### Environment info dataset: https://huggingface.co/datasets/scientific_papers dill = 0.3.6 python= 3.9.16 transformer = 4.2.0 It looks like an error that we observed once in https://github.com/huggingface/datasets/pull/5166 Can you try to update `datasets` ? ``` pip install -U datasets ``` if it doesn't work, can you make sure you don't have packages installed that may modify `dill`'s behavior, such as `apache-beam` ?

https://github.com/huggingface/datasets/issues/5638

I think https://github.com/fsspec/universal_pathlib is the project you are looking for. `xPath` has the methods often used in dataset scripts, and `mkdir` is not one of them (`dl_manager`'s role is to "interact" with the file system, so using `mkdir` is discouraged).

xPath to implement all operations for Path

### Feature request Current xPath implementation is a great extension of Path in order to work with remote objects. However some methods such as `mkdir` are not implemented correctly. It should instead rely on `fsspec` methods, instead of defaulting do `Path` methods which only work locally. ### Motivation I'm using xPath to interact with remote objects. ### Your contribution I could try to make a PR. I'm a bit unfamiliar with chaining right now.

xPath to implement all operations for Path ### Feature request Current xPath implementation is a great extension of Path in order to work with remote objects. However some methods such as `mkdir` are not implemented correctly. It should instead rely on `fsspec` methods, instead of defaulting do `Path` methods which only work locally. ### Motivation I'm using xPath to interact with remote objects. ### Your contribution I could try to make a PR. I'm a bit unfamiliar with chaining right now. I think https://github.com/fsspec/universal_pathlib is the project you are looking for. `xPath` has the methods often used in dataset scripts, and `mkdir` is not one of them (`dl_manager`'s role is to "interact" with the file system, so using `mkdir` is discouraged).

https://github.com/huggingface/datasets/issues/5638

Right is there a difference between UPath and xPath? Typically is xPath less well implemented compared to Upath, ie missing some implementations of some methods? Or are there methods in xPath that are not implemented with UPath?

xPath to implement all operations for Path

### Feature request Current xPath implementation is a great extension of Path in order to work with remote objects. However some methods such as `mkdir` are not implemented correctly. It should instead rely on `fsspec` methods, instead of defaulting do `Path` methods which only work locally. ### Motivation I'm using xPath to interact with remote objects. ### Your contribution I could try to make a PR. I'm a bit unfamiliar with chaining right now.

xPath to implement all operations for Path ### Feature request Current xPath implementation is a great extension of Path in order to work with remote objects. However some methods such as `mkdir` are not implemented correctly. It should instead rely on `fsspec` methods, instead of defaulting do `Path` methods which only work locally. ### Motivation I'm using xPath to interact with remote objects. ### Your contribution I could try to make a PR. I'm a bit unfamiliar with chaining right now. Right is there a difference between UPath and xPath? Typically is xPath less well implemented compared to Upath, ie missing some implementations of some methods? Or are there methods in xPath that are not implemented with UPath?

https://github.com/huggingface/datasets/issues/5638

`xPath` is an internal component (it doesn't have a leading underscore in the name, but it should) not meant to be used outside of `datasets`, and it's only tested on HTTP URLs, not S3.

xPath to implement all operations for Path

### Feature request Current xPath implementation is a great extension of Path in order to work with remote objects. However some methods such as `mkdir` are not implemented correctly. It should instead rely on `fsspec` methods, instead of defaulting do `Path` methods which only work locally. ### Motivation I'm using xPath to interact with remote objects. ### Your contribution I could try to make a PR. I'm a bit unfamiliar with chaining right now.

xPath to implement all operations for Path ### Feature request Current xPath implementation is a great extension of Path in order to work with remote objects. However some methods such as `mkdir` are not implemented correctly. It should instead rely on `fsspec` methods, instead of defaulting do `Path` methods which only work locally. ### Motivation I'm using xPath to interact with remote objects. ### Your contribution I could try to make a PR. I'm a bit unfamiliar with chaining right now. `xPath` is an internal component (it doesn't have a leading underscore in the name, but it should) not meant to be used outside of `datasets`, and it's only tested on HTTP URLs, not S3.

https://github.com/huggingface/datasets/issues/5638

Okay I understand that xPath won't support my usecase. What I was perhaps getting to is why not use UPath in `datasets` instead of `xPath` if UPath seems to have strictly more robust implementations.

xPath to implement all operations for Path

### Feature request Current xPath implementation is a great extension of Path in order to work with remote objects. However some methods such as `mkdir` are not implemented correctly. It should instead rely on `fsspec` methods, instead of defaulting do `Path` methods which only work locally. ### Motivation I'm using xPath to interact with remote objects. ### Your contribution I could try to make a PR. I'm a bit unfamiliar with chaining right now.

xPath to implement all operations for Path ### Feature request Current xPath implementation is a great extension of Path in order to work with remote objects. However some methods such as `mkdir` are not implemented correctly. It should instead rely on `fsspec` methods, instead of defaulting do `Path` methods which only work locally. ### Motivation I'm using xPath to interact with remote objects. ### Your contribution I could try to make a PR. I'm a bit unfamiliar with chaining right now. Okay I understand that xPath won't support my usecase. What I was perhaps getting to is why not use UPath in `datasets` instead of `xPath` if UPath seems to have strictly more robust implementations.

https://github.com/huggingface/datasets/issues/5638

It seems like `universal_pathlib` does not support `fsspec` URL chaining (`::` is the chaining symbol) and "compression" filesystems (e.g., `zip`), but this is what we need to access and stream files from within an archive (e.g., we want to stream URLs such as this one: `zip://data.parquet::https://www.dummyurl.com/archive.zip`)

xPath to implement all operations for Path

### Feature request Current xPath implementation is a great extension of Path in order to work with remote objects. However some methods such as `mkdir` are not implemented correctly. It should instead rely on `fsspec` methods, instead of defaulting do `Path` methods which only work locally. ### Motivation I'm using xPath to interact with remote objects. ### Your contribution I could try to make a PR. I'm a bit unfamiliar with chaining right now.

xPath to implement all operations for Path ### Feature request Current xPath implementation is a great extension of Path in order to work with remote objects. However some methods such as `mkdir` are not implemented correctly. It should instead rely on `fsspec` methods, instead of defaulting do `Path` methods which only work locally. ### Motivation I'm using xPath to interact with remote objects. ### Your contribution I could try to make a PR. I'm a bit unfamiliar with chaining right now. It seems like `universal_pathlib` does not support `fsspec` URL chaining (`::` is the chaining symbol) and "compression" filesystems (e.g., `zip`), but this is what we need to access and stream files from within an archive (e.g., we want to stream URLs such as this one: `zip://data.parquet::https://www.dummyurl.com/archive.zip`)

https://github.com/huggingface/datasets/issues/5637

Hi! Yes, only `torch` is currently supported. Unlike `Dataset`, `IterableDataset` is not PyArrow-backed, so we cannot simply call `to_numpy` on the underlying subtables to format them numerically. Instead, we must manually convert examples to (numeric) arrays while preserving consistency with `Dataset`, which is not trivial, so this is still a to-do.

IterableDataset with_format does not support 'device' keyword for jax

### Describe the bug As seen here: https://huggingface.co/docs/datasets/use_with_jax dataset.with_format() supports the keyword 'device', to put data on a specific device when loaded as jax. However, when called on an IterableDataset, I got the error `TypeError: with_format() got an unexpected keyword argument 'device'` Looking over the code, it seems IterableDataset support only pytorch and no support for jax device keyword? https://github.com/huggingface/datasets/blob/fc5c84f36684343bff3e424cb0fd1ac5ecdd66da/src/datasets/iterable_dataset.py#L1029 ### Steps to reproduce the bug 1. Load an IterableDataset (tested in streaming mode) 2. Call with_format('jax',device=device) ### Expected behavior I expect to call `with_format('jax', device=device)` as per [documentation](https://huggingface.co/docs/datasets/use_with_jax) without error ### Environment info Tested with installing newest (dev) and also pip release (2.10.1). - `datasets` version: 2.10.2.dev0 - Platform: Linux-5.15.89+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - Huggingface_hub version: 0.12.1 - PyArrow version: 11.0.0 - Pandas version: 1.3.5

IterableDataset with_format does not support 'device' keyword for jax ### Describe the bug As seen here: https://huggingface.co/docs/datasets/use_with_jax dataset.with_format() supports the keyword 'device', to put data on a specific device when loaded as jax. However, when called on an IterableDataset, I got the error `TypeError: with_format() got an unexpected keyword argument 'device'` Looking over the code, it seems IterableDataset support only pytorch and no support for jax device keyword? https://github.com/huggingface/datasets/blob/fc5c84f36684343bff3e424cb0fd1ac5ecdd66da/src/datasets/iterable_dataset.py#L1029 ### Steps to reproduce the bug 1. Load an IterableDataset (tested in streaming mode) 2. Call with_format('jax',device=device) ### Expected behavior I expect to call `with_format('jax', device=device)` as per [documentation](https://huggingface.co/docs/datasets/use_with_jax) without error ### Environment info Tested with installing newest (dev) and also pip release (2.10.1). - `datasets` version: 2.10.2.dev0 - Platform: Linux-5.15.89+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - Huggingface_hub version: 0.12.1 - PyArrow version: 11.0.0 - Pandas version: 1.3.5 Hi! Yes, only `torch` is currently supported. Unlike `Dataset`, `IterableDataset` is not PyArrow-backed, so we cannot simply call `to_numpy` on the underlying subtables to format them numerically. Instead, we must manually convert examples to (numeric) arrays while preserving consistency with `Dataset`, which is not trivial, so this is still a to-do.

https://github.com/huggingface/datasets/issues/5637

Any plans to support it in the future? Or would streaming dataset be left without support for jax and tensorflow?

IterableDataset with_format does not support 'device' keyword for jax

### Describe the bug As seen here: https://huggingface.co/docs/datasets/use_with_jax dataset.with_format() supports the keyword 'device', to put data on a specific device when loaded as jax. However, when called on an IterableDataset, I got the error `TypeError: with_format() got an unexpected keyword argument 'device'` Looking over the code, it seems IterableDataset support only pytorch and no support for jax device keyword? https://github.com/huggingface/datasets/blob/fc5c84f36684343bff3e424cb0fd1ac5ecdd66da/src/datasets/iterable_dataset.py#L1029 ### Steps to reproduce the bug 1. Load an IterableDataset (tested in streaming mode) 2. Call with_format('jax',device=device) ### Expected behavior I expect to call `with_format('jax', device=device)` as per [documentation](https://huggingface.co/docs/datasets/use_with_jax) without error ### Environment info Tested with installing newest (dev) and also pip release (2.10.1). - `datasets` version: 2.10.2.dev0 - Platform: Linux-5.15.89+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - Huggingface_hub version: 0.12.1 - PyArrow version: 11.0.0 - Pandas version: 1.3.5

IterableDataset with_format does not support 'device' keyword for jax ### Describe the bug As seen here: https://huggingface.co/docs/datasets/use_with_jax dataset.with_format() supports the keyword 'device', to put data on a specific device when loaded as jax. However, when called on an IterableDataset, I got the error `TypeError: with_format() got an unexpected keyword argument 'device'` Looking over the code, it seems IterableDataset support only pytorch and no support for jax device keyword? https://github.com/huggingface/datasets/blob/fc5c84f36684343bff3e424cb0fd1ac5ecdd66da/src/datasets/iterable_dataset.py#L1029 ### Steps to reproduce the bug 1. Load an IterableDataset (tested in streaming mode) 2. Call with_format('jax',device=device) ### Expected behavior I expect to call `with_format('jax', device=device)` as per [documentation](https://huggingface.co/docs/datasets/use_with_jax) without error ### Environment info Tested with installing newest (dev) and also pip release (2.10.1). - `datasets` version: 2.10.2.dev0 - Platform: Linux-5.15.89+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - Huggingface_hub version: 0.12.1 - PyArrow version: 11.0.0 - Pandas version: 1.3.5 Any plans to support it in the future? Or would streaming dataset be left without support for jax and tensorflow?

https://github.com/huggingface/datasets/issues/5634

Hi! By default, tqdm has `leave=True` to "keep all traces of the progress bar upon the termination of iteration". However, we use `leave=False` in some places (as of recently), which removes the bar once the iteration is over. I feel like our TQDM bars are noisy, so I think we should always set `leave=False` and also use the `delay` parameter to display progress bars only for tasks that take time (e.g., more than 3s). What do you think about this? Do you find these bars useful (after the dataset generation is over)?

Not all progress bars are showing up when they should for downloading dataset

### Describe the bug During downloading the rotten tomatoes dataset, not all progress bars are displayed properly. This might be related to [this ticket](https://github.com/huggingface/datasets/issues/5117) as it raised the same concern but its not clear if the fix solves this issue too. ipywidgets <img width="1243" alt="image" src="https://user-images.githubusercontent.com/110427462/224851138-13fee5b7-ab51-4883-b96f-1b9808782e3b.png"> tqdm <img width="1251" alt="Screen Shot 2023-03-13 at 3 58 59 PM" src="https://user-images.githubusercontent.com/110427462/224851180-5feb7825-9250-4b1e-ad0c-f3172ac1eb78.png"> ### Steps to reproduce the bug 1. Run this line ``` from datasets import load_dataset rotten_tomatoes = load_dataset("rotten_tomatoes", split="train") ``` ### Expected behavior all progress bars for builder script, metadata, readme, training, validation, and test set ### Environment info requirements.txt ``` aiofiles==22.1.0 aiohttp==3.8.4 aiosignal==1.3.1 aiosqlite==0.18.0 anyio==3.6.2 appnope==0.1.3 argon2-cffi==21.3.0 argon2-cffi-bindings==21.2.0 arrow==1.2.3 asttokens==2.2.1 async-generator==1.10 async-timeout==4.0.2 attrs==22.2.0 Babel==2.12.1 backcall==0.2.0 beautifulsoup4==4.11.2 bleach==6.0.0 brotlipy @ file:///Users/runner/miniforge3/conda-bld/brotlipy_1666764961872/work certifi==2022.12.7 cffi @ file:///Users/runner/miniforge3/conda-bld/cffi_1671179414629/work cfgv==3.3.1 charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1661170624537/work comm==0.1.2 conda==22.9.0 conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1669907009957/work conda_package_streaming @ file:///home/conda/feedstock_root/build_artifacts/conda-package-streaming_1669733752472/work coverage==7.2.1 cryptography @ file:///Users/runner/miniforge3/conda-bld/cryptography_1669592251328/work datasets==2.1.0 debugpy==1.6.6 decorator==5.1.1 defusedxml==0.7.1 dill==0.3.6 distlib==0.3.6 distro==1.4.0 entrypoints==0.4 exceptiongroup==1.1.0 executing==1.2.0 fastjsonschema==2.16.3 filelock==3.9.0 flaky==3.7.0 fqdn==1.5.1 frozenlist==1.3.3 fsspec==2023.3.0 huggingface-hub==0.10.1 identify==2.5.18 idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work iniconfig==2.0.0 ipykernel==6.12.1 ipyparallel==8.4.1 ipython==7.32.0 ipython-genutils==0.2.0 ipywidgets==8.0.4 isoduration==20.11.0 jedi==0.18.2 Jinja2==3.1.2 json5==0.9.11 jsonpointer==2.3 jsonschema==4.17.3 jupyter-events==0.6.3 jupyter-ydoc==0.2.2 jupyter_client==8.0.3 jupyter_core==5.2.0 jupyter_server==2.4.0 jupyter_server_fileid==0.8.0 jupyter_server_terminals==0.4.4 jupyter_server_ydoc==0.6.1 jupyterlab==3.6.1 jupyterlab-pygments==0.2.2 jupyterlab-widgets==3.0.5 jupyterlab_server==2.20.0 libmambapy @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/libmambapy mamba @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/mamba MarkupSafe==2.1.2 matplotlib-inline==0.1.6 mistune==2.0.5 multidict==6.0.4 multiprocess==0.70.14 nbclassic==0.5.3 nbclient==0.7.2 nbconvert==7.2.9 nbformat==5.7.3 nest-asyncio==1.5.6 nodeenv==1.7.0 notebook==6.5.3 notebook_shim==0.2.2 numpy==1.24.2 outcome==1.2.0 packaging==23.0 pandas==1.5.3 pandocfilters==1.5.0 parso==0.8.3 pexpect==4.8.0 pickleshare==0.7.5 platformdirs==3.0.0 plotly==5.13.1 pluggy==1.0.0 pre-commit==3.1.0 prometheus-client==0.16.0 prompt-toolkit==3.0.38 psutil==5.9.4 ptyprocess==0.7.0 pure-eval==0.2.2 pyarrow==11.0.0 pycosat @ file:///Users/runner/miniforge3/conda-bld/pycosat_1666836580084/work pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work Pygments==2.14.0 pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1665350324128/work pyrsistent==0.19.3 PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work pytest==7.2.1 pytest-asyncio==0.20.3 pytest-cov==4.0.0 pytest-timeout==2.1.0 python-dateutil==2.8.2 python-json-logger==2.0.7 pytz==2022.7.1 PyYAML==6.0 pyzmq==25.0.0 requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1661872987712/work responses==0.18.0 rfc3339-validator==0.1.4 rfc3986-validator==0.1.1 ruamel-yaml-conda @ file:///Users/runner/miniforge3/conda-bld/ruamel_yaml_1666819760545/work Send2Trash==1.8.0 simplegeneric==0.8.1 six==1.16.0 sniffio==1.3.0 sortedcontainers==2.4.0 soupsieve==2.4 stack-data==0.6.2 tenacity==8.2.2 terminado==0.17.1 tinycss2==1.2.1 tomli==2.0.1 toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1657485559105/work tornado==6.2 tqdm==4.64.1 traitlets==5.8.1 trio==0.22.0 typing_extensions==4.5.0 uri-template==1.2.0 urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1669259737463/work virtualenv==20.19.0 wcwidth==0.2.6 webcolors==1.12 webencodings==0.5.1 websocket-client==1.5.1 widgetsnbextension==4.0.5 xxhash==3.2.0 y-py==0.5.9 yarl==1.8.2 ypy-websocket==0.8.2 zstandard==0.19.0 ```

Not all progress bars are showing up when they should for downloading dataset ### Describe the bug During downloading the rotten tomatoes dataset, not all progress bars are displayed properly. This might be related to [this ticket](https://github.com/huggingface/datasets/issues/5117) as it raised the same concern but its not clear if the fix solves this issue too. ipywidgets <img width="1243" alt="image" src="https://user-images.githubusercontent.com/110427462/224851138-13fee5b7-ab51-4883-b96f-1b9808782e3b.png"> tqdm <img width="1251" alt="Screen Shot 2023-03-13 at 3 58 59 PM" src="https://user-images.githubusercontent.com/110427462/224851180-5feb7825-9250-4b1e-ad0c-f3172ac1eb78.png"> ### Steps to reproduce the bug 1. Run this line ``` from datasets import load_dataset rotten_tomatoes = load_dataset("rotten_tomatoes", split="train") ``` ### Expected behavior all progress bars for builder script, metadata, readme, training, validation, and test set ### Environment info requirements.txt ``` aiofiles==22.1.0 aiohttp==3.8.4 aiosignal==1.3.1 aiosqlite==0.18.0 anyio==3.6.2 appnope==0.1.3 argon2-cffi==21.3.0 argon2-cffi-bindings==21.2.0 arrow==1.2.3 asttokens==2.2.1 async-generator==1.10 async-timeout==4.0.2 attrs==22.2.0 Babel==2.12.1 backcall==0.2.0 beautifulsoup4==4.11.2 bleach==6.0.0 brotlipy @ file:///Users/runner/miniforge3/conda-bld/brotlipy_1666764961872/work certifi==2022.12.7 cffi @ file:///Users/runner/miniforge3/conda-bld/cffi_1671179414629/work cfgv==3.3.1 charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1661170624537/work comm==0.1.2 conda==22.9.0 conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1669907009957/work conda_package_streaming @ file:///home/conda/feedstock_root/build_artifacts/conda-package-streaming_1669733752472/work coverage==7.2.1 cryptography @ file:///Users/runner/miniforge3/conda-bld/cryptography_1669592251328/work datasets==2.1.0 debugpy==1.6.6 decorator==5.1.1 defusedxml==0.7.1 dill==0.3.6 distlib==0.3.6 distro==1.4.0 entrypoints==0.4 exceptiongroup==1.1.0 executing==1.2.0 fastjsonschema==2.16.3 filelock==3.9.0 flaky==3.7.0 fqdn==1.5.1 frozenlist==1.3.3 fsspec==2023.3.0 huggingface-hub==0.10.1 identify==2.5.18 idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work iniconfig==2.0.0 ipykernel==6.12.1 ipyparallel==8.4.1 ipython==7.32.0 ipython-genutils==0.2.0 ipywidgets==8.0.4 isoduration==20.11.0 jedi==0.18.2 Jinja2==3.1.2 json5==0.9.11 jsonpointer==2.3 jsonschema==4.17.3 jupyter-events==0.6.3 jupyter-ydoc==0.2.2 jupyter_client==8.0.3 jupyter_core==5.2.0 jupyter_server==2.4.0 jupyter_server_fileid==0.8.0 jupyter_server_terminals==0.4.4 jupyter_server_ydoc==0.6.1 jupyterlab==3.6.1 jupyterlab-pygments==0.2.2 jupyterlab-widgets==3.0.5 jupyterlab_server==2.20.0 libmambapy @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/libmambapy mamba @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/mamba MarkupSafe==2.1.2 matplotlib-inline==0.1.6 mistune==2.0.5 multidict==6.0.4 multiprocess==0.70.14 nbclassic==0.5.3 nbclient==0.7.2 nbconvert==7.2.9 nbformat==5.7.3 nest-asyncio==1.5.6 nodeenv==1.7.0 notebook==6.5.3 notebook_shim==0.2.2 numpy==1.24.2 outcome==1.2.0 packaging==23.0 pandas==1.5.3 pandocfilters==1.5.0 parso==0.8.3 pexpect==4.8.0 pickleshare==0.7.5 platformdirs==3.0.0 plotly==5.13.1 pluggy==1.0.0 pre-commit==3.1.0 prometheus-client==0.16.0 prompt-toolkit==3.0.38 psutil==5.9.4 ptyprocess==0.7.0 pure-eval==0.2.2 pyarrow==11.0.0 pycosat @ file:///Users/runner/miniforge3/conda-bld/pycosat_1666836580084/work pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work Pygments==2.14.0 pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1665350324128/work pyrsistent==0.19.3 PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work pytest==7.2.1 pytest-asyncio==0.20.3 pytest-cov==4.0.0 pytest-timeout==2.1.0 python-dateutil==2.8.2 python-json-logger==2.0.7 pytz==2022.7.1 PyYAML==6.0 pyzmq==25.0.0 requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1661872987712/work responses==0.18.0 rfc3339-validator==0.1.4 rfc3986-validator==0.1.1 ruamel-yaml-conda @ file:///Users/runner/miniforge3/conda-bld/ruamel_yaml_1666819760545/work Send2Trash==1.8.0 simplegeneric==0.8.1 six==1.16.0 sniffio==1.3.0 sortedcontainers==2.4.0 soupsieve==2.4 stack-data==0.6.2 tenacity==8.2.2 terminado==0.17.1 tinycss2==1.2.1 tomli==2.0.1 toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1657485559105/work tornado==6.2 tqdm==4.64.1 traitlets==5.8.1 trio==0.22.0 typing_extensions==4.5.0 uri-template==1.2.0 urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1669259737463/work virtualenv==20.19.0 wcwidth==0.2.6 webcolors==1.12 webencodings==0.5.1 websocket-client==1.5.1 widgetsnbextension==4.0.5 xxhash==3.2.0 y-py==0.5.9 yarl==1.8.2 ypy-websocket==0.8.2 zstandard==0.19.0 ``` Hi! By default, tqdm has `leave=True` to "keep all traces of the progress bar upon the termination of iteration". However, we use `leave=False` in some places (as of recently), which removes the bar once the iteration is over. I feel like our TQDM bars are noisy, so I think we should always set `leave=False` and also use the `delay` parameter to display progress bars only for tasks that take time (e.g., more than 3s). What do you think about this? Do you find these bars useful (after the dataset generation is over)?

https://github.com/huggingface/datasets/issues/5634

Hi sorry for the late update. I think the problem still exists despite the `leave` flag <img width="1105" alt="image" src="https://user-images.githubusercontent.com/110427462/226501615-5b02fb02-fd5f-4eda-b1f7-a7ed6570892d.png"> ``` Package Version ------------------------ --------- aiofiles 22.1.0 aiohttp 3.8.4 aiosignal 1.3.1 aiosqlite 0.18.0 anyio 3.6.2 appnope 0.1.3 argon2-cffi 21.3.0 argon2-cffi-bindings 21.2.0 arrow 1.2.3 asttokens 2.2.1 async-generator 1.10 async-timeout 4.0.2 attrs 22.2.0 Babel 2.12.1 backcall 0.2.0 beautifulsoup4 4.11.2 bleach 6.0.0 brotlipy 0.7.0 certifi 2022.12.7 cffi 1.15.1 cfgv 3.3.1 charset-normalizer 2.1.1 comm 0.1.2 conda 22.9.0 conda-package-handling 2.0.2 conda_package_streaming 0.7.0 coverage 7.2.1 cryptography 38.0.4 datasets 2.8.0 debugpy 1.6.6 decorator 5.1.1 defusedxml 0.7.1 dill 0.3.6 distlib 0.3.6 distro 1.4.0 entrypoints 0.4 exceptiongroup 1.1.0 executing 1.2.0 fastjsonschema 2.16.3 filelock 3.9.0 flaky 3.7.0 fqdn 1.5.1 frozenlist 1.3.3 fsspec 2023.3.0 huggingface-hub 0.10.1 identify 2.5.18 idna 3.4 iniconfig 2.0.0 ipykernel 6.12.1 ipyparallel 8.4.1 ipython 7.32.0 ipython-genutils 0.2.0 ipywidgets 8.0.4 isoduration 20.11.0 jedi 0.18.2 Jinja2 3.1.2 json5 0.9.11 jsonpointer 2.3 jsonschema 4.17.3 jupyter_client 8.0.3 jupyter_core 5.2.0 jupyter-events 0.6.3 jupyter_server 2.4.0 jupyter_server_fileid 0.8.0 jupyter_server_terminals 0.4.4 jupyter_server_ydoc 0.6.1 jupyter-ydoc 0.2.2 jupyterlab 3.6.1 jupyterlab-pygments 0.2.2 jupyterlab_server 2.20.0 jupyterlab-widgets 3.0.5 libmambapy 1.1.0 mamba 1.1.0 MarkupSafe 2.1.2 matplotlib-inline 0.1.6 mistune 2.0.5 multidict 6.0.4 multiprocess 0.70.14 nbclassic 0.5.3 nbclient 0.7.2 nbconvert 7.2.9 nbformat 5.7.3 nest-asyncio 1.5.6 nodeenv 1.7.0 notebook 6.5.3 notebook_shim 0.2.2 numpy 1.24.2 outcome 1.2.0 packaging 23.0 pandas 1.5.3 pandocfilters 1.5.0 parso 0.8.3 pexpect 4.8.0 pickleshare 0.7.5 pip 22.3.1 platformdirs 3.0.0 plotly 5.13.1 pluggy 1.0.0 pre-commit 3.1.0 prometheus-client 0.16.0 prompt-toolkit 3.0.38 psutil 5.9.4 ptyprocess 0.7.0 pure-eval 0.2.2 pyarrow 11.0.0 pycosat 0.6.4 pycparser 2.21 Pygments 2.14.0 pyOpenSSL 22.1.0 pyrsistent 0.19.3 PySocks 1.7.1 pytest 7.2.1 pytest-asyncio 0.20.3 pytest-cov 4.0.0 pytest-timeout 2.1.0 python-dateutil 2.8.2 python-json-logger 2.0.7 pytz 2022.7.1 PyYAML 6.0 pyzmq 25.0.0 requests 2.28.1 responses 0.18.0 rfc3339-validator 0.1.4 rfc3986-validator 0.1.1 ruamel-yaml-conda 0.15.80 Send2Trash 1.8.0 setuptools 65.6.3 simplegeneric 0.8.1 six 1.16.0 sniffio 1.3.0 sortedcontainers 2.4.0 soupsieve 2.4 stack-data 0.6.2 tenacity 8.2.2 terminado 0.17.1 tinycss2 1.2.1 tomli 2.0.1 toolz 0.12.0 tornado 6.2 tqdm 4.65.0 traitlets 5.8.1 trio 0.22.0 typing_extensions 4.5.0 uri-template 1.2.0 urllib3 1.26.13 virtualenv 20.19.0 wcwidth 0.2.6 webcolors 1.12 webencodings 0.5.1 websocket-client 1.5.1 wheel 0.38.4 widgetsnbextension 4.0.5 xxhash 3.2.0 y-py 0.5.9 yarl 1.8.2 ypy-websocket 0.8.2 zstandard 0.19.0 ``` Any idea why this is happening? I debugged this to know the tqdm.pbar value is not being updated properly and its not the kernel not sending the comm messages to the IProgress bar

Not all progress bars are showing up when they should for downloading dataset

### Describe the bug During downloading the rotten tomatoes dataset, not all progress bars are displayed properly. This might be related to [this ticket](https://github.com/huggingface/datasets/issues/5117) as it raised the same concern but its not clear if the fix solves this issue too. ipywidgets <img width="1243" alt="image" src="https://user-images.githubusercontent.com/110427462/224851138-13fee5b7-ab51-4883-b96f-1b9808782e3b.png"> tqdm <img width="1251" alt="Screen Shot 2023-03-13 at 3 58 59 PM" src="https://user-images.githubusercontent.com/110427462/224851180-5feb7825-9250-4b1e-ad0c-f3172ac1eb78.png"> ### Steps to reproduce the bug 1. Run this line ``` from datasets import load_dataset rotten_tomatoes = load_dataset("rotten_tomatoes", split="train") ``` ### Expected behavior all progress bars for builder script, metadata, readme, training, validation, and test set ### Environment info requirements.txt ``` aiofiles==22.1.0 aiohttp==3.8.4 aiosignal==1.3.1 aiosqlite==0.18.0 anyio==3.6.2 appnope==0.1.3 argon2-cffi==21.3.0 argon2-cffi-bindings==21.2.0 arrow==1.2.3 asttokens==2.2.1 async-generator==1.10 async-timeout==4.0.2 attrs==22.2.0 Babel==2.12.1 backcall==0.2.0 beautifulsoup4==4.11.2 bleach==6.0.0 brotlipy @ file:///Users/runner/miniforge3/conda-bld/brotlipy_1666764961872/work certifi==2022.12.7 cffi @ file:///Users/runner/miniforge3/conda-bld/cffi_1671179414629/work cfgv==3.3.1 charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1661170624537/work comm==0.1.2 conda==22.9.0 conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1669907009957/work conda_package_streaming @ file:///home/conda/feedstock_root/build_artifacts/conda-package-streaming_1669733752472/work coverage==7.2.1 cryptography @ file:///Users/runner/miniforge3/conda-bld/cryptography_1669592251328/work datasets==2.1.0 debugpy==1.6.6 decorator==5.1.1 defusedxml==0.7.1 dill==0.3.6 distlib==0.3.6 distro==1.4.0 entrypoints==0.4 exceptiongroup==1.1.0 executing==1.2.0 fastjsonschema==2.16.3 filelock==3.9.0 flaky==3.7.0 fqdn==1.5.1 frozenlist==1.3.3 fsspec==2023.3.0 huggingface-hub==0.10.1 identify==2.5.18 idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work iniconfig==2.0.0 ipykernel==6.12.1 ipyparallel==8.4.1 ipython==7.32.0 ipython-genutils==0.2.0 ipywidgets==8.0.4 isoduration==20.11.0 jedi==0.18.2 Jinja2==3.1.2 json5==0.9.11 jsonpointer==2.3 jsonschema==4.17.3 jupyter-events==0.6.3 jupyter-ydoc==0.2.2 jupyter_client==8.0.3 jupyter_core==5.2.0 jupyter_server==2.4.0 jupyter_server_fileid==0.8.0 jupyter_server_terminals==0.4.4 jupyter_server_ydoc==0.6.1 jupyterlab==3.6.1 jupyterlab-pygments==0.2.2 jupyterlab-widgets==3.0.5 jupyterlab_server==2.20.0 libmambapy @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/libmambapy mamba @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/mamba MarkupSafe==2.1.2 matplotlib-inline==0.1.6 mistune==2.0.5 multidict==6.0.4 multiprocess==0.70.14 nbclassic==0.5.3 nbclient==0.7.2 nbconvert==7.2.9 nbformat==5.7.3 nest-asyncio==1.5.6 nodeenv==1.7.0 notebook==6.5.3 notebook_shim==0.2.2 numpy==1.24.2 outcome==1.2.0 packaging==23.0 pandas==1.5.3 pandocfilters==1.5.0 parso==0.8.3 pexpect==4.8.0 pickleshare==0.7.5 platformdirs==3.0.0 plotly==5.13.1 pluggy==1.0.0 pre-commit==3.1.0 prometheus-client==0.16.0 prompt-toolkit==3.0.38 psutil==5.9.4 ptyprocess==0.7.0 pure-eval==0.2.2 pyarrow==11.0.0 pycosat @ file:///Users/runner/miniforge3/conda-bld/pycosat_1666836580084/work pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work Pygments==2.14.0 pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1665350324128/work pyrsistent==0.19.3 PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work pytest==7.2.1 pytest-asyncio==0.20.3 pytest-cov==4.0.0 pytest-timeout==2.1.0 python-dateutil==2.8.2 python-json-logger==2.0.7 pytz==2022.7.1 PyYAML==6.0 pyzmq==25.0.0 requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1661872987712/work responses==0.18.0 rfc3339-validator==0.1.4 rfc3986-validator==0.1.1 ruamel-yaml-conda @ file:///Users/runner/miniforge3/conda-bld/ruamel_yaml_1666819760545/work Send2Trash==1.8.0 simplegeneric==0.8.1 six==1.16.0 sniffio==1.3.0 sortedcontainers==2.4.0 soupsieve==2.4 stack-data==0.6.2 tenacity==8.2.2 terminado==0.17.1 tinycss2==1.2.1 tomli==2.0.1 toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1657485559105/work tornado==6.2 tqdm==4.64.1 traitlets==5.8.1 trio==0.22.0 typing_extensions==4.5.0 uri-template==1.2.0 urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1669259737463/work virtualenv==20.19.0 wcwidth==0.2.6 webcolors==1.12 webencodings==0.5.1 websocket-client==1.5.1 widgetsnbextension==4.0.5 xxhash==3.2.0 y-py==0.5.9 yarl==1.8.2 ypy-websocket==0.8.2 zstandard==0.19.0 ```

Not all progress bars are showing up when they should for downloading dataset ### Describe the bug During downloading the rotten tomatoes dataset, not all progress bars are displayed properly. This might be related to [this ticket](https://github.com/huggingface/datasets/issues/5117) as it raised the same concern but its not clear if the fix solves this issue too. ipywidgets <img width="1243" alt="image" src="https://user-images.githubusercontent.com/110427462/224851138-13fee5b7-ab51-4883-b96f-1b9808782e3b.png"> tqdm <img width="1251" alt="Screen Shot 2023-03-13 at 3 58 59 PM" src="https://user-images.githubusercontent.com/110427462/224851180-5feb7825-9250-4b1e-ad0c-f3172ac1eb78.png"> ### Steps to reproduce the bug 1. Run this line ``` from datasets import load_dataset rotten_tomatoes = load_dataset("rotten_tomatoes", split="train") ``` ### Expected behavior all progress bars for builder script, metadata, readme, training, validation, and test set ### Environment info requirements.txt ``` aiofiles==22.1.0 aiohttp==3.8.4 aiosignal==1.3.1 aiosqlite==0.18.0 anyio==3.6.2 appnope==0.1.3 argon2-cffi==21.3.0 argon2-cffi-bindings==21.2.0 arrow==1.2.3 asttokens==2.2.1 async-generator==1.10 async-timeout==4.0.2 attrs==22.2.0 Babel==2.12.1 backcall==0.2.0 beautifulsoup4==4.11.2 bleach==6.0.0 brotlipy @ file:///Users/runner/miniforge3/conda-bld/brotlipy_1666764961872/work certifi==2022.12.7 cffi @ file:///Users/runner/miniforge3/conda-bld/cffi_1671179414629/work cfgv==3.3.1 charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1661170624537/work comm==0.1.2 conda==22.9.0 conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1669907009957/work conda_package_streaming @ file:///home/conda/feedstock_root/build_artifacts/conda-package-streaming_1669733752472/work coverage==7.2.1 cryptography @ file:///Users/runner/miniforge3/conda-bld/cryptography_1669592251328/work datasets==2.1.0 debugpy==1.6.6 decorator==5.1.1 defusedxml==0.7.1 dill==0.3.6 distlib==0.3.6 distro==1.4.0 entrypoints==0.4 exceptiongroup==1.1.0 executing==1.2.0 fastjsonschema==2.16.3 filelock==3.9.0 flaky==3.7.0 fqdn==1.5.1 frozenlist==1.3.3 fsspec==2023.3.0 huggingface-hub==0.10.1 identify==2.5.18 idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work iniconfig==2.0.0 ipykernel==6.12.1 ipyparallel==8.4.1 ipython==7.32.0 ipython-genutils==0.2.0 ipywidgets==8.0.4 isoduration==20.11.0 jedi==0.18.2 Jinja2==3.1.2 json5==0.9.11 jsonpointer==2.3 jsonschema==4.17.3 jupyter-events==0.6.3 jupyter-ydoc==0.2.2 jupyter_client==8.0.3 jupyter_core==5.2.0 jupyter_server==2.4.0 jupyter_server_fileid==0.8.0 jupyter_server_terminals==0.4.4 jupyter_server_ydoc==0.6.1 jupyterlab==3.6.1 jupyterlab-pygments==0.2.2 jupyterlab-widgets==3.0.5 jupyterlab_server==2.20.0 libmambapy @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/libmambapy mamba @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/mamba MarkupSafe==2.1.2 matplotlib-inline==0.1.6 mistune==2.0.5 multidict==6.0.4 multiprocess==0.70.14 nbclassic==0.5.3 nbclient==0.7.2 nbconvert==7.2.9 nbformat==5.7.3 nest-asyncio==1.5.6 nodeenv==1.7.0 notebook==6.5.3 notebook_shim==0.2.2 numpy==1.24.2 outcome==1.2.0 packaging==23.0 pandas==1.5.3 pandocfilters==1.5.0 parso==0.8.3 pexpect==4.8.0 pickleshare==0.7.5 platformdirs==3.0.0 plotly==5.13.1 pluggy==1.0.0 pre-commit==3.1.0 prometheus-client==0.16.0 prompt-toolkit==3.0.38 psutil==5.9.4 ptyprocess==0.7.0 pure-eval==0.2.2 pyarrow==11.0.0 pycosat @ file:///Users/runner/miniforge3/conda-bld/pycosat_1666836580084/work pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work Pygments==2.14.0 pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1665350324128/work pyrsistent==0.19.3 PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work pytest==7.2.1 pytest-asyncio==0.20.3 pytest-cov==4.0.0 pytest-timeout==2.1.0 python-dateutil==2.8.2 python-json-logger==2.0.7 pytz==2022.7.1 PyYAML==6.0 pyzmq==25.0.0 requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1661872987712/work responses==0.18.0 rfc3339-validator==0.1.4 rfc3986-validator==0.1.1 ruamel-yaml-conda @ file:///Users/runner/miniforge3/conda-bld/ruamel_yaml_1666819760545/work Send2Trash==1.8.0 simplegeneric==0.8.1 six==1.16.0 sniffio==1.3.0 sortedcontainers==2.4.0 soupsieve==2.4 stack-data==0.6.2 tenacity==8.2.2 terminado==0.17.1 tinycss2==1.2.1 tomli==2.0.1 toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1657485559105/work tornado==6.2 tqdm==4.64.1 traitlets==5.8.1 trio==0.22.0 typing_extensions==4.5.0 uri-template==1.2.0 urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1669259737463/work virtualenv==20.19.0 wcwidth==0.2.6 webcolors==1.12 webencodings==0.5.1 websocket-client==1.5.1 widgetsnbextension==4.0.5 xxhash==3.2.0 y-py==0.5.9 yarl==1.8.2 ypy-websocket==0.8.2 zstandard==0.19.0 ``` Hi sorry for the late update. I think the problem still exists despite the `leave` flag <img width="1105" alt="image" src="https://user-images.githubusercontent.com/110427462/226501615-5b02fb02-fd5f-4eda-b1f7-a7ed6570892d.png"> ``` Package Version ------------------------ --------- aiofiles 22.1.0 aiohttp 3.8.4 aiosignal 1.3.1 aiosqlite 0.18.0 anyio 3.6.2 appnope 0.1.3 argon2-cffi 21.3.0 argon2-cffi-bindings 21.2.0 arrow 1.2.3 asttokens 2.2.1 async-generator 1.10 async-timeout 4.0.2 attrs 22.2.0 Babel 2.12.1 backcall 0.2.0 beautifulsoup4 4.11.2 bleach 6.0.0 brotlipy 0.7.0 certifi 2022.12.7 cffi 1.15.1 cfgv 3.3.1 charset-normalizer 2.1.1 comm 0.1.2 conda 22.9.0 conda-package-handling 2.0.2 conda_package_streaming 0.7.0 coverage 7.2.1 cryptography 38.0.4 datasets 2.8.0 debugpy 1.6.6 decorator 5.1.1 defusedxml 0.7.1 dill 0.3.6 distlib 0.3.6 distro 1.4.0 entrypoints 0.4 exceptiongroup 1.1.0 executing 1.2.0 fastjsonschema 2.16.3 filelock 3.9.0 flaky 3.7.0 fqdn 1.5.1 frozenlist 1.3.3 fsspec 2023.3.0 huggingface-hub 0.10.1 identify 2.5.18 idna 3.4 iniconfig 2.0.0 ipykernel 6.12.1 ipyparallel 8.4.1 ipython 7.32.0 ipython-genutils 0.2.0 ipywidgets 8.0.4 isoduration 20.11.0 jedi 0.18.2 Jinja2 3.1.2 json5 0.9.11 jsonpointer 2.3 jsonschema 4.17.3 jupyter_client 8.0.3 jupyter_core 5.2.0 jupyter-events 0.6.3 jupyter_server 2.4.0 jupyter_server_fileid 0.8.0 jupyter_server_terminals 0.4.4 jupyter_server_ydoc 0.6.1 jupyter-ydoc 0.2.2 jupyterlab 3.6.1 jupyterlab-pygments 0.2.2 jupyterlab_server 2.20.0 jupyterlab-widgets 3.0.5 libmambapy 1.1.0 mamba 1.1.0 MarkupSafe 2.1.2 matplotlib-inline 0.1.6 mistune 2.0.5 multidict 6.0.4 multiprocess 0.70.14 nbclassic 0.5.3 nbclient 0.7.2 nbconvert 7.2.9 nbformat 5.7.3 nest-asyncio 1.5.6 nodeenv 1.7.0 notebook 6.5.3 notebook_shim 0.2.2 numpy 1.24.2 outcome 1.2.0 packaging 23.0 pandas 1.5.3 pandocfilters 1.5.0 parso 0.8.3 pexpect 4.8.0 pickleshare 0.7.5 pip 22.3.1 platformdirs 3.0.0 plotly 5.13.1 pluggy 1.0.0 pre-commit 3.1.0 prometheus-client 0.16.0 prompt-toolkit 3.0.38 psutil 5.9.4 ptyprocess 0.7.0 pure-eval 0.2.2 pyarrow 11.0.0 pycosat 0.6.4 pycparser 2.21 Pygments 2.14.0 pyOpenSSL 22.1.0 pyrsistent 0.19.3 PySocks 1.7.1 pytest 7.2.1 pytest-asyncio 0.20.3 pytest-cov 4.0.0 pytest-timeout 2.1.0 python-dateutil 2.8.2 python-json-logger 2.0.7 pytz 2022.7.1 PyYAML 6.0 pyzmq 25.0.0 requests 2.28.1 responses 0.18.0 rfc3339-validator 0.1.4 rfc3986-validator 0.1.1 ruamel-yaml-conda 0.15.80 Send2Trash 1.8.0 setuptools 65.6.3 simplegeneric 0.8.1 six 1.16.0 sniffio 1.3.0 sortedcontainers 2.4.0 soupsieve 2.4 stack-data 0.6.2 tenacity 8.2.2 terminado 0.17.1 tinycss2 1.2.1 tomli 2.0.1 toolz 0.12.0 tornado 6.2 tqdm 4.65.0 traitlets 5.8.1 trio 0.22.0 typing_extensions 4.5.0 uri-template 1.2.0 urllib3 1.26.13 virtualenv 20.19.0 wcwidth 0.2.6 webcolors 1.12 webencodings 0.5.1 websocket-client 1.5.1 wheel 0.38.4 widgetsnbextension 4.0.5 xxhash 3.2.0 y-py 0.5.9 yarl 1.8.2 ypy-websocket 0.8.2 zstandard 0.19.0 ``` Any idea why this is happening? I debugged this to know the tqdm.pbar value is not being updated properly and its not the kernel not sending the comm messages to the IProgress bar

https://github.com/huggingface/datasets/issues/5633

Okay, the issue was likely caused by mixing `conda` and `pip` usage - I forgot that I have already used `pip` in this environment previously and that it was 'spoiled' because of it. Creating another environment and installing `datasets` by pip with other packages from the `requirements.txt` file solved the problem.

Cannot import datasets

### Describe the bug Hi, I cannot even import the library :( I installed it by running: ``` $ conda install datasets ``` Then I realized I should maybe use the huggingface channel, because I encountered the error below, so I ran: ``` $ conda remove datasets $ conda install -c huggingface datasets ``` Please see 'steps to reproduce the bug' for the specific error, as steps to reproduce is just importing the library ### Steps to reproduce the bug ``` $ python3 Python 3.8.15 (default, Nov 24 2022, 15:19:38) [GCC 11.2.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> import datasets Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/datasets/__init__.py", line 33, in <module> from .arrow_dataset import Dataset, concatenate_datasets File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 59, in <module> from .arrow_reader import ArrowReader File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/datasets/arrow_reader.py", line 27, in <module> import pyarrow.parquet as pq File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/pyarrow/parquet/__init__.py", line 20, in <module> from .core import * File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/pyarrow/parquet/core.py", line 37, in <module> from pyarrow._parquet import (ParquetReader, Statistics, # noqa ImportError: cannot import name 'FileEncryptionProperties' from 'pyarrow._parquet' (/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/pyarrow/_parquet.cpython-38-x86_64-linux-gnu.so) ``` ### Expected behavior I would expect for the statement `import datasets` to cause no error ### Environment info Output of `conda list`: ``` # packages in environment at /home/jack/.conda/envs/pbalawender_zpp: # # Name Version Build Channel _libgcc_mutex 0.1 main _openmp_mutex 5.1 1_gnu abseil-cpp 20210324.2 h2531618_0 advertools 0.13.2 pypi_0 pypi aiofiles 0.8.0 pypi_0 pypi aiohttp 3.8.3 py38h5eee18b_0 aiosignal 1.2.0 pyhd3eb1b0_0 aiosqlite 0.17.0 pypi_0 pypi anyio 3.6.2 pypi_0 pypi aquirdturtle-collapsible-headings 3.1.0 pypi_0 pypi argon2-cffi 21.3.0 pypi_0 pypi argon2-cffi-bindings 21.2.0 pypi_0 pypi arrow 1.2.3 pypi_0 pypi arrow-cpp 3.0.0 py38h6b21186_4 asttokens 2.2.0 pypi_0 pypi async-timeout 4.0.2 py38h06a4308_0 attrs 22.1.0 py38h06a4308_0 automat 22.10.0 pypi_0 pypi aws-c-common 0.4.57 he6710b0_1 aws-c-event-stream 0.1.6 h2531618_5 aws-checksums 0.1.9 he6710b0_0 aws-sdk-cpp 1.8.185 hce553d0_0 babel 2.11.0 pypi_0 pypi backcall 0.2.0 pyhd3eb1b0_0 beautifulsoup4 4.11.1 pypi_0 pypi blas 1.0 mkl bleach 5.0.1 pypi_0 pypi boost-cpp 1.73.0 h27cfd23_11 bottleneck 1.3.5 py38h7deecbd_0 brotli 1.0.9 h5eee18b_7 brotli-bin 1.0.9 h5eee18b_7 brotlipy 0.7.0 py38h27cfd23_1003 bzip2 1.0.8 h7b6447c_0 c-ares 1.18.1 h7f8727e_0 ca-certificates 2023.01.10 h06a4308_0 certifi 2022.9.24 pypi_0 pypi cffi 1.15.1 py38h5eee18b_3 charset-normalizer 2.1.1 pypi_0 pypi click 8.1.3 pypi_0 pypi constantly 15.1.0 pypi_0 pypi contourpy 1.0.6 pypi_0 pypi cryptography 38.0.4 pypi_0 pypi cssselect 1.2.0 pypi_0 pypi cudatoolkit 10.1.243 h8cb64d8_10 conda-forge cycler 0.11.0 pypi_0 pypi dacite 1.6.0 pypi_0 pypi dataclasses 0.8 pyh6d0b6a4_7 datasets 1.18.4 py_0 huggingface datetime 4.7 pypi_0 pypi debugpy 1.6.4 pypi_0 pypi decorator 5.1.1 pyhd3eb1b0_0 defusedxml 0.7.1 pypi_0 pypi dill 0.3.6 py38h06a4308_0 docker-pycreds 0.4.0 pypi_0 pypi double-conversion 3.1.5 he6710b0_1 entrypoints 0.4 py38h06a4308_0 executing 0.8.3 pyhd3eb1b0_0 filelock 3.8.0 pypi_0 pypi flake8 6.0.0 pypi_0 pypi flask 2.1.3 py38h06a4308_0 flit-core 3.6.0 pyhd3eb1b0_0 fonttools 4.38.0 pypi_0 pypi fqdn 1.5.1 pypi_0 pypi freetype 2.12.1 h4a9f257_0 frozenlist 1.3.3 py38h5eee18b_0 fsspec 2022.11.0 py38h06a4308_0 gensim 4.2.0 pypi_0 pypi gflags 2.2.2 he6710b0_0 giflib 5.2.1 h5eee18b_3 gitdb 4.0.10 pypi_0 pypi gitpython 3.1.30 pypi_0 pypi glog 0.5.0 h2531618_0 grpc-cpp 1.39.0 hae934f6_5 huggingface-hub 0.11.1 pypi_0 pypi huggingface_hub 0.13.1 py_0 huggingface hyperlink 21.0.0 pypi_0 pypi icu 58.2 he6710b0_3 idna 3.4 py38h06a4308_0 importlib-metadata 5.1.0 pypi_0 pypi importlib_metadata 4.11.3 hd3eb1b0_0 importlib_resources 5.2.0 pyhd3eb1b0_1 incremental 22.10.0 pypi_0 pypi intel-openmp 2021.4.0 h06a4308_3561 ipykernel 6.17.1 pyh210e3f2_0 conda-forge ipython 8.7.0 pypi_0 pypi ipython-genutils 0.2.0 pypi_0 pypi ipywidgets 8.0.2 pyhd8ed1ab_1 conda-forge isoduration 20.11.0 pypi_0 pypi itemadapter 0.7.0 pypi_0 pypi itemloaders 1.0.6 pypi_0 pypi itsdangerous 2.0.1 pyhd3eb1b0_0 jedi 0.18.2 pypi_0 pypi jinja2 3.1.2 py38h06a4308_0 jmespath 1.0.1 pypi_0 pypi joblib 1.2.0 pypi_0 pypi jpeg 9b h024ee3a_2 json5 0.9.10 pypi_0 pypi jsonpickle 3.0.0 pypi_0 pypi jsonpointer 2.3 pypi_0 pypi jsonschema 4.17.3 py38h06a4308_0 jupyter-core 5.1.0 pypi_0 pypi jupyter-events 0.5.0 pypi_0 pypi jupyter-server 1.23.3 pypi_0 pypi jupyter-server-fileid 0.6.0 pypi_0 pypi jupyter-server-ydoc 0.4.0 pypi_0 pypi jupyter-ydoc 0.2.2 pypi_0 pypi jupyter_client 7.4.9 py38h06a4308_0 jupyter_core 5.2.0 py38h06a4308_0 jupyterlab 3.6.0a4 pypi_0 pypi jupyterlab-pygments 0.2.2 pypi_0 pypi jupyterlab-server 2.16.3 pypi_0 pypi jupyterlab_widgets 3.0.3 pyhd8ed1ab_0 conda-forge kiwisolver 1.4.4 pypi_0 pypi krb5 1.19.4 h568e23c_0 lcms2 2.12 h3be6417_0 ld_impl_linux-64 2.38 h1181459_1 libboost 1.73.0 h3ff78a5_11 libbrotlicommon 1.0.9 h5eee18b_7 libbrotlidec 1.0.9 h5eee18b_7 libbrotlienc 1.0.9 h5eee18b_7 libcurl 7.88.1 h91b91d3_0 libedit 3.1.20221030 h5eee18b_0 libev 4.33 h7f8727e_1 libevent 2.1.12 h8f2d780_0 libffi 3.4.2 h6a678d5_6 libgcc-ng 11.2.0 h1234567_1 libgomp 11.2.0 h1234567_1 libnghttp2 1.46.0 hce63b2e_0 libpng 1.6.39 h5eee18b_0 libprotobuf 3.17.2 h4ff587b_1 libsodium 1.0.18 h7b6447c_0 libssh2 1.10.0 h8f2d780_0 libstdcxx-ng 11.2.0 h1234567_1 libthrift 0.14.2 hcc01f38_0 libtiff 4.1.0 h2733197_1 libuv 1.44.2 h5eee18b_0 libwebp 1.2.0 h89dd481_0 lz4-c 1.9.4 h6a678d5_0 markupsafe 2.1.1 py38h7f8727e_0 matplotlib 3.6.2 pypi_0 pypi matplotlib-inline 0.1.6 py38h06a4308_0 mccabe 0.7.0 pypi_0 pypi mistune 2.0.4 pypi_0 pypi mkl 2021.4.0 h06a4308_640 mkl-service 2.4.0 py38h7f8727e_0 mkl_fft 1.3.1 py38hd3c417c_0 mkl_random 1.2.2 py38h51133e4_0 morfeusz2 1.99.6 pypi_0 pypi multidict 6.0.2 py38h5eee18b_0 multiprocess 0.70.14 py38h06a4308_0 nbclassic 0.4.8 pypi_0 pypi nbclient 0.7.2 pypi_0 pypi nbconvert 7.2.5 pypi_0 pypi nbformat 5.7.0 py38h06a4308_0 ncurses 6.4 h6a678d5_0 nest-asyncio 1.5.6 py38h06a4308_0 ninja 1.10.2 h06a4308_5 ninja-base 1.10.2 hd09550d_5 notebook 6.5.2 pypi_0 pypi notebook-shim 0.2.2 pypi_0 pypi numexpr 2.8.4 py38he184ba9_0 numpy 1.23.5 py38h14f4228_0 numpy-base 1.23.5 py38h31eccc5_0 oauthlib 3.2.2 pypi_0 pypi opencv-python 4.6.0.66 pypi_0 pypi openssl 1.1.1t h7f8727e_0 orc 1.6.9 ha97a36c_3 packaging 22.0 py38h06a4308_0 pandas 1.5.2 pypi_0 pypi pandocfilters 1.5.0 pypi_0 pypi parsel 1.7.0 pypi_0 pypi parso 0.8.3 pyhd3eb1b0_0 pathlib 1.0.1 pypi_0 pypi pathtools 0.1.2 pypi_0 pypi pexpect 4.8.0 pyhd3eb1b0_3 pickleshare 0.7.5 pyhd3eb1b0_1003 pillow 9.3.0 pypi_0 pypi pip 22.2.2 py38h06a4308_0 pkgutil-resolve-name 1.3.10 py38h06a4308_0 platformdirs 2.5.4 pypi_0 pypi prometheus-client 0.15.0 pypi_0 pypi promise 2.3 pypi_0 pypi prompt-toolkit 3.0.33 pypi_0 pypi protego 0.2.1 pypi_0 pypi protobuf 4.21.12 pypi_0 pypi psutil 5.9.0 py38h5eee18b_0 ptyprocess 0.7.0 pyhd3eb1b0_2 pure_eval 0.2.2 pyhd3eb1b0_0 pyarrow 10.0.1 pypi_0 pypi pyasn1 0.4.8 pypi_0 pypi pyasn1-modules 0.2.8 pypi_0 pypi pycodestyle 2.10.0 pypi_0 pypi pycparser 2.21 pyhd3eb1b0_0 pydispatcher 2.0.6 pypi_0 pypi pyflakes 3.0.1 pypi_0 pypi pygments 2.11.2 pyhd3eb1b0_0 pyopenssl 22.1.0 pypi_0 pypi pyrsistent 0.18.0 py38heee7806_0 pysocks 1.7.1 py38h06a4308_0 python 3.8.15 h7a1cb2a_2 python-dateutil 2.8.2 pyhd3eb1b0_0 python-dotenv 0.21.0 pypi_0 pypi python-fastjsonschema 2.16.2 py38h06a4308_0 python-json-logger 2.0.4 pypi_0 pypi python-xxhash 2.0.2 py38h5eee18b_1 pytorch 1.7.1 py3.8_cuda10.1.243_cudnn7.6.3_0 pytorch pytz 2022.6 pypi_0 pypi pyyaml 6.0 py38h5eee18b_1 pyzmq 23.2.0 py38h6a678d5_0 queuelib 1.6.2 pypi_0 pypi re2 2022.04.01 h295c915_0 readline 8.2 h5eee18b_0 regex 2022.10.31 pypi_0 pypi requests 2.28.1 py38h06a4308_0 requests-file 1.5.1 pypi_0 pypi requests-oauthlib 1.3.1 pypi_0 pypi rfc3339-validator 0.1.4 pypi_0 pypi rfc3986-validator 0.1.1 pypi_0 pypi scikit-learn 1.1.3 pypi_0 pypi scipy 1.9.3 pypi_0 pypi scrapy 2.7.1 pypi_0 pypi seaborn 0.12.1 pypi_0 pypi send2trash 1.8.0 pypi_0 pypi sentry-sdk 1.12.1 pypi_0 pypi service-identity 21.1.0 pypi_0 pypi setproctitle 1.3.2 pypi_0 pypi setuptools 65.6.3 pypi_0 pypi shortuuid 1.0.11 pypi_0 pypi six 1.16.0 pyhd3eb1b0_1 smart-open 6.2.0 pypi_0 pypi smmap 5.0.0 pypi_0 pypi snappy 1.1.9 h295c915_0 sniffio 1.3.0 pypi_0 pypi soupsieve 2.3.2.post1 pypi_0 pypi sqlite 3.40.1 h5082296_0 stack-data 0.6.2 pypi_0 pypi stack_data 0.2.0 pyhd3eb1b0_0 terminado 0.17.0 pypi_0 pypi threadpoolctl 3.1.0 pypi_0 pypi tinycss2 1.2.1 pypi_0 pypi tk 8.6.12 h1ccaba5_0 tldextract 3.4.0 pypi_0 pypi tokenizers 0.13.2 pypi_0 pypi tomli 2.0.1 pypi_0 pypi torchvision 0.8.2 py38_cu101 pytorch tornado 6.2 py38h5eee18b_0 tqdm 4.64.1 py38h06a4308_0 traitlets 5.6.0 pypi_0 pypi transformers 4.25.1 pypi_0 pypi tweepy 4.12.1 pypi_0 pypi twisted 22.10.0 pypi_0 pypi twython 3.9.1 pypi_0 pypi typing-extensions 4.4.0 py38h06a4308_0 typing_extensions 4.4.0 py38h06a4308_0 uri-template 1.2.0 pypi_0 pypi uriparser 0.9.3 he6710b0_1 urllib3 1.26.13 pypi_0 pypi utf8proc 2.6.1 h27cfd23_0 w3lib 2.1.0 pypi_0 pypi wandb 0.13.7 pypi_0 pypi wcwidth 0.2.5 pyhd3eb1b0_0 webcolors 1.12 pypi_0 pypi webencodings 0.5.1 pypi_0 pypi websocket-client 1.4.2 pypi_0 pypi werkzeug 2.2.2 py38h06a4308_0 wheel 0.38.4 py38h06a4308_0 widgetsnbextension 4.0.3 py38h06a4308_0 xxhash 0.8.0 h7f8727e_3 xz 5.2.10 h5eee18b_1 y-py 0.5.4 pypi_0 pypi yaml 0.2.5 h7b6447c_0 yarl 1.8.1 py38h5eee18b_0 ypy-websocket 0.5.0 pypi_0 pypi zeromq 4.3.4 h2531618_0 zipp 3.11.0 py38h06a4308_0 zlib 1.2.13 h5eee18b_0 zope-interface 5.5.2 pypi_0 pypi zstd 1.4.9 haebb681_0 ```

Cannot import datasets ### Describe the bug Hi, I cannot even import the library :( I installed it by running: ``` $ conda install datasets ``` Then I realized I should maybe use the huggingface channel, because I encountered the error below, so I ran: ``` $ conda remove datasets $ conda install -c huggingface datasets ``` Please see 'steps to reproduce the bug' for the specific error, as steps to reproduce is just importing the library ### Steps to reproduce the bug ``` $ python3 Python 3.8.15 (default, Nov 24 2022, 15:19:38) [GCC 11.2.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> import datasets Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/datasets/__init__.py", line 33, in <module> from .arrow_dataset import Dataset, concatenate_datasets File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 59, in <module> from .arrow_reader import ArrowReader File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/datasets/arrow_reader.py", line 27, in <module> import pyarrow.parquet as pq File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/pyarrow/parquet/__init__.py", line 20, in <module> from .core import * File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/pyarrow/parquet/core.py", line 37, in <module> from pyarrow._parquet import (ParquetReader, Statistics, # noqa ImportError: cannot import name 'FileEncryptionProperties' from 'pyarrow._parquet' (/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/pyarrow/_parquet.cpython-38-x86_64-linux-gnu.so) ``` ### Expected behavior I would expect for the statement `import datasets` to cause no error ### Environment info Output of `conda list`: ``` # packages in environment at /home/jack/.conda/envs/pbalawender_zpp: # # Name Version Build Channel _libgcc_mutex 0.1 main _openmp_mutex 5.1 1_gnu abseil-cpp 20210324.2 h2531618_0 advertools 0.13.2 pypi_0 pypi aiofiles 0.8.0 pypi_0 pypi aiohttp 3.8.3 py38h5eee18b_0 aiosignal 1.2.0 pyhd3eb1b0_0 aiosqlite 0.17.0 pypi_0 pypi anyio 3.6.2 pypi_0 pypi aquirdturtle-collapsible-headings 3.1.0 pypi_0 pypi argon2-cffi 21.3.0 pypi_0 pypi argon2-cffi-bindings 21.2.0 pypi_0 pypi arrow 1.2.3 pypi_0 pypi arrow-cpp 3.0.0 py38h6b21186_4 asttokens 2.2.0 pypi_0 pypi async-timeout 4.0.2 py38h06a4308_0 attrs 22.1.0 py38h06a4308_0 automat 22.10.0 pypi_0 pypi aws-c-common 0.4.57 he6710b0_1 aws-c-event-stream 0.1.6 h2531618_5 aws-checksums 0.1.9 he6710b0_0 aws-sdk-cpp 1.8.185 hce553d0_0 babel 2.11.0 pypi_0 pypi backcall 0.2.0 pyhd3eb1b0_0 beautifulsoup4 4.11.1 pypi_0 pypi blas 1.0 mkl bleach 5.0.1 pypi_0 pypi boost-cpp 1.73.0 h27cfd23_11 bottleneck 1.3.5 py38h7deecbd_0 brotli 1.0.9 h5eee18b_7 brotli-bin 1.0.9 h5eee18b_7 brotlipy 0.7.0 py38h27cfd23_1003 bzip2 1.0.8 h7b6447c_0 c-ares 1.18.1 h7f8727e_0 ca-certificates 2023.01.10 h06a4308_0 certifi 2022.9.24 pypi_0 pypi cffi 1.15.1 py38h5eee18b_3 charset-normalizer 2.1.1 pypi_0 pypi click 8.1.3 pypi_0 pypi constantly 15.1.0 pypi_0 pypi contourpy 1.0.6 pypi_0 pypi cryptography 38.0.4 pypi_0 pypi cssselect 1.2.0 pypi_0 pypi cudatoolkit 10.1.243 h8cb64d8_10 conda-forge cycler 0.11.0 pypi_0 pypi dacite 1.6.0 pypi_0 pypi dataclasses 0.8 pyh6d0b6a4_7 datasets 1.18.4 py_0 huggingface datetime 4.7 pypi_0 pypi debugpy 1.6.4 pypi_0 pypi decorator 5.1.1 pyhd3eb1b0_0 defusedxml 0.7.1 pypi_0 pypi dill 0.3.6 py38h06a4308_0 docker-pycreds 0.4.0 pypi_0 pypi double-conversion 3.1.5 he6710b0_1 entrypoints 0.4 py38h06a4308_0 executing 0.8.3 pyhd3eb1b0_0 filelock 3.8.0 pypi_0 pypi flake8 6.0.0 pypi_0 pypi flask 2.1.3 py38h06a4308_0 flit-core 3.6.0 pyhd3eb1b0_0 fonttools 4.38.0 pypi_0 pypi fqdn 1.5.1 pypi_0 pypi freetype 2.12.1 h4a9f257_0 frozenlist 1.3.3 py38h5eee18b_0 fsspec 2022.11.0 py38h06a4308_0 gensim 4.2.0 pypi_0 pypi gflags 2.2.2 he6710b0_0 giflib 5.2.1 h5eee18b_3 gitdb 4.0.10 pypi_0 pypi gitpython 3.1.30 pypi_0 pypi glog 0.5.0 h2531618_0 grpc-cpp 1.39.0 hae934f6_5 huggingface-hub 0.11.1 pypi_0 pypi huggingface_hub 0.13.1 py_0 huggingface hyperlink 21.0.0 pypi_0 pypi icu 58.2 he6710b0_3 idna 3.4 py38h06a4308_0 importlib-metadata 5.1.0 pypi_0 pypi importlib_metadata 4.11.3 hd3eb1b0_0 importlib_resources 5.2.0 pyhd3eb1b0_1 incremental 22.10.0 pypi_0 pypi intel-openmp 2021.4.0 h06a4308_3561 ipykernel 6.17.1 pyh210e3f2_0 conda-forge ipython 8.7.0 pypi_0 pypi ipython-genutils 0.2.0 pypi_0 pypi ipywidgets 8.0.2 pyhd8ed1ab_1 conda-forge isoduration 20.11.0 pypi_0 pypi itemadapter 0.7.0 pypi_0 pypi itemloaders 1.0.6 pypi_0 pypi itsdangerous 2.0.1 pyhd3eb1b0_0 jedi 0.18.2 pypi_0 pypi jinja2 3.1.2 py38h06a4308_0 jmespath 1.0.1 pypi_0 pypi joblib 1.2.0 pypi_0 pypi jpeg 9b h024ee3a_2 json5 0.9.10 pypi_0 pypi jsonpickle 3.0.0 pypi_0 pypi jsonpointer 2.3 pypi_0 pypi jsonschema 4.17.3 py38h06a4308_0 jupyter-core 5.1.0 pypi_0 pypi jupyter-events 0.5.0 pypi_0 pypi jupyter-server 1.23.3 pypi_0 pypi jupyter-server-fileid 0.6.0 pypi_0 pypi jupyter-server-ydoc 0.4.0 pypi_0 pypi jupyter-ydoc 0.2.2 pypi_0 pypi jupyter_client 7.4.9 py38h06a4308_0 jupyter_core 5.2.0 py38h06a4308_0 jupyterlab 3.6.0a4 pypi_0 pypi jupyterlab-pygments 0.2.2 pypi_0 pypi jupyterlab-server 2.16.3 pypi_0 pypi jupyterlab_widgets 3.0.3 pyhd8ed1ab_0 conda-forge kiwisolver 1.4.4 pypi_0 pypi krb5 1.19.4 h568e23c_0 lcms2 2.12 h3be6417_0 ld_impl_linux-64 2.38 h1181459_1 libboost 1.73.0 h3ff78a5_11 libbrotlicommon 1.0.9 h5eee18b_7 libbrotlidec 1.0.9 h5eee18b_7 libbrotlienc 1.0.9 h5eee18b_7 libcurl 7.88.1 h91b91d3_0 libedit 3.1.20221030 h5eee18b_0 libev 4.33 h7f8727e_1 libevent 2.1.12 h8f2d780_0 libffi 3.4.2 h6a678d5_6 libgcc-ng 11.2.0 h1234567_1 libgomp 11.2.0 h1234567_1 libnghttp2 1.46.0 hce63b2e_0 libpng 1.6.39 h5eee18b_0 libprotobuf 3.17.2 h4ff587b_1 libsodium 1.0.18 h7b6447c_0 libssh2 1.10.0 h8f2d780_0 libstdcxx-ng 11.2.0 h1234567_1 libthrift 0.14.2 hcc01f38_0 libtiff 4.1.0 h2733197_1 libuv 1.44.2 h5eee18b_0 libwebp 1.2.0 h89dd481_0 lz4-c 1.9.4 h6a678d5_0 markupsafe 2.1.1 py38h7f8727e_0 matplotlib 3.6.2 pypi_0 pypi matplotlib-inline 0.1.6 py38h06a4308_0 mccabe 0.7.0 pypi_0 pypi mistune 2.0.4 pypi_0 pypi mkl 2021.4.0 h06a4308_640 mkl-service 2.4.0 py38h7f8727e_0 mkl_fft 1.3.1 py38hd3c417c_0 mkl_random 1.2.2 py38h51133e4_0 morfeusz2 1.99.6 pypi_0 pypi multidict 6.0.2 py38h5eee18b_0 multiprocess 0.70.14 py38h06a4308_0 nbclassic 0.4.8 pypi_0 pypi nbclient 0.7.2 pypi_0 pypi nbconvert 7.2.5 pypi_0 pypi nbformat 5.7.0 py38h06a4308_0 ncurses 6.4 h6a678d5_0 nest-asyncio 1.5.6 py38h06a4308_0 ninja 1.10.2 h06a4308_5 ninja-base 1.10.2 hd09550d_5 notebook 6.5.2 pypi_0 pypi notebook-shim 0.2.2 pypi_0 pypi numexpr 2.8.4 py38he184ba9_0 numpy 1.23.5 py38h14f4228_0 numpy-base 1.23.5 py38h31eccc5_0 oauthlib 3.2.2 pypi_0 pypi opencv-python 4.6.0.66 pypi_0 pypi openssl 1.1.1t h7f8727e_0 orc 1.6.9 ha97a36c_3 packaging 22.0 py38h06a4308_0 pandas 1.5.2 pypi_0 pypi pandocfilters 1.5.0 pypi_0 pypi parsel 1.7.0 pypi_0 pypi parso 0.8.3 pyhd3eb1b0_0 pathlib 1.0.1 pypi_0 pypi pathtools 0.1.2 pypi_0 pypi pexpect 4.8.0 pyhd3eb1b0_3 pickleshare 0.7.5 pyhd3eb1b0_1003 pillow 9.3.0 pypi_0 pypi pip 22.2.2 py38h06a4308_0 pkgutil-resolve-name 1.3.10 py38h06a4308_0 platformdirs 2.5.4 pypi_0 pypi prometheus-client 0.15.0 pypi_0 pypi promise 2.3 pypi_0 pypi prompt-toolkit 3.0.33 pypi_0 pypi protego 0.2.1 pypi_0 pypi protobuf 4.21.12 pypi_0 pypi psutil 5.9.0 py38h5eee18b_0 ptyprocess 0.7.0 pyhd3eb1b0_2 pure_eval 0.2.2 pyhd3eb1b0_0 pyarrow 10.0.1 pypi_0 pypi pyasn1 0.4.8 pypi_0 pypi pyasn1-modules 0.2.8 pypi_0 pypi pycodestyle 2.10.0 pypi_0 pypi pycparser 2.21 pyhd3eb1b0_0 pydispatcher 2.0.6 pypi_0 pypi pyflakes 3.0.1 pypi_0 pypi pygments 2.11.2 pyhd3eb1b0_0 pyopenssl 22.1.0 pypi_0 pypi pyrsistent 0.18.0 py38heee7806_0 pysocks 1.7.1 py38h06a4308_0 python 3.8.15 h7a1cb2a_2 python-dateutil 2.8.2 pyhd3eb1b0_0 python-dotenv 0.21.0 pypi_0 pypi python-fastjsonschema 2.16.2 py38h06a4308_0 python-json-logger 2.0.4 pypi_0 pypi python-xxhash 2.0.2 py38h5eee18b_1 pytorch 1.7.1 py3.8_cuda10.1.243_cudnn7.6.3_0 pytorch pytz 2022.6 pypi_0 pypi pyyaml 6.0 py38h5eee18b_1 pyzmq 23.2.0 py38h6a678d5_0 queuelib 1.6.2 pypi_0 pypi re2 2022.04.01 h295c915_0 readline 8.2 h5eee18b_0 regex 2022.10.31 pypi_0 pypi requests 2.28.1 py38h06a4308_0 requests-file 1.5.1 pypi_0 pypi requests-oauthlib 1.3.1 pypi_0 pypi rfc3339-validator 0.1.4 pypi_0 pypi rfc3986-validator 0.1.1 pypi_0 pypi scikit-learn 1.1.3 pypi_0 pypi scipy 1.9.3 pypi_0 pypi scrapy 2.7.1 pypi_0 pypi seaborn 0.12.1 pypi_0 pypi send2trash 1.8.0 pypi_0 pypi sentry-sdk 1.12.1 pypi_0 pypi service-identity 21.1.0 pypi_0 pypi setproctitle 1.3.2 pypi_0 pypi setuptools 65.6.3 pypi_0 pypi shortuuid 1.0.11 pypi_0 pypi six 1.16.0 pyhd3eb1b0_1 smart-open 6.2.0 pypi_0 pypi smmap 5.0.0 pypi_0 pypi snappy 1.1.9 h295c915_0 sniffio 1.3.0 pypi_0 pypi soupsieve 2.3.2.post1 pypi_0 pypi sqlite 3.40.1 h5082296_0 stack-data 0.6.2 pypi_0 pypi stack_data 0.2.0 pyhd3eb1b0_0 terminado 0.17.0 pypi_0 pypi threadpoolctl 3.1.0 pypi_0 pypi tinycss2 1.2.1 pypi_0 pypi tk 8.6.12 h1ccaba5_0 tldextract 3.4.0 pypi_0 pypi tokenizers 0.13.2 pypi_0 pypi tomli 2.0.1 pypi_0 pypi torchvision 0.8.2 py38_cu101 pytorch tornado 6.2 py38h5eee18b_0 tqdm 4.64.1 py38h06a4308_0 traitlets 5.6.0 pypi_0 pypi transformers 4.25.1 pypi_0 pypi tweepy 4.12.1 pypi_0 pypi twisted 22.10.0 pypi_0 pypi twython 3.9.1 pypi_0 pypi typing-extensions 4.4.0 py38h06a4308_0 typing_extensions 4.4.0 py38h06a4308_0 uri-template 1.2.0 pypi_0 pypi uriparser 0.9.3 he6710b0_1 urllib3 1.26.13 pypi_0 pypi utf8proc 2.6.1 h27cfd23_0 w3lib 2.1.0 pypi_0 pypi wandb 0.13.7 pypi_0 pypi wcwidth 0.2.5 pyhd3eb1b0_0 webcolors 1.12 pypi_0 pypi webencodings 0.5.1 pypi_0 pypi websocket-client 1.4.2 pypi_0 pypi werkzeug 2.2.2 py38h06a4308_0 wheel 0.38.4 py38h06a4308_0 widgetsnbextension 4.0.3 py38h06a4308_0 xxhash 0.8.0 h7f8727e_3 xz 5.2.10 h5eee18b_1 y-py 0.5.4 pypi_0 pypi yaml 0.2.5 h7b6447c_0 yarl 1.8.1 py38h5eee18b_0 ypy-websocket 0.5.0 pypi_0 pypi zeromq 4.3.4 h2531618_0 zipp 3.11.0 py38h06a4308_0 zlib 1.2.13 h5eee18b_0 zope-interface 5.5.2 pypi_0 pypi zstd 1.4.9 haebb681_0 ``` Okay, the issue was likely caused by mixing `conda` and `pip` usage - I forgot that I have already used `pip` in this environment previously and that it was 'spoiled' because of it. Creating another environment and installing `datasets` by pip with other packages from the `requirements.txt` file solved the problem.

https://github.com/huggingface/datasets/issues/5632

Answered on the forum: > To fix the overflow error, we need to merge [support LargeListArray in pyarrow by xwwwwww · Pull Request #4800 · huggingface/datasets · GitHub](https://github.com/huggingface/datasets/pull/4800), which adds support for the large lists. However, before merging it, we need to come up with a cleaner API for large lists. I hope to find some time to address this before Datasets 3.0.

Dataset cannot convert too large dictionnary

### Describe the bug Hello everyone! I tried to build a new dataset with the command "dict_valid = datasets.Dataset.from_dict({'input_values': values_array})". However, I have a very large dataset (~400Go) and it seems that dataset cannot handle this. Indeed, I can create the dataset until a certain size of my dictionnary, and then I have the error "OverflowError: Python int too large to convert to C long". Do you know how to solve this problem? Unfortunately I cannot give a reproductible code because I cannot share a so large file, but you can find the code below (it's a test on only a part of the validation data ~10Go, but it's already the case). Thank you! ### Steps to reproduce the bug SAVE_DIR = './data/' features = h5py.File(SAVE_DIR+'features.hdf5','r') valid_data = features["validation"]["data/features"] v_array_values = [np.float32(item[()]) for item in valid_data.values()] for i in range(len(v_array_values)): v_array_values[i] = v_array_values[i].round(decimals=5) dict_valid = datasets.Dataset.from_dict({'input_values': v_array_values}) ### Expected behavior The code is expected to give me a Huggingface dataset. ### Environment info python: 3.8.15 numpy: 1.22.3 datasets: 2.3.2 pyarrow: 8.0.0

Dataset cannot convert too large dictionnary ### Describe the bug Hello everyone! I tried to build a new dataset with the command "dict_valid = datasets.Dataset.from_dict({'input_values': values_array})". However, I have a very large dataset (~400Go) and it seems that dataset cannot handle this. Indeed, I can create the dataset until a certain size of my dictionnary, and then I have the error "OverflowError: Python int too large to convert to C long". Do you know how to solve this problem? Unfortunately I cannot give a reproductible code because I cannot share a so large file, but you can find the code below (it's a test on only a part of the validation data ~10Go, but it's already the case). Thank you! ### Steps to reproduce the bug SAVE_DIR = './data/' features = h5py.File(SAVE_DIR+'features.hdf5','r') valid_data = features["validation"]["data/features"] v_array_values = [np.float32(item[()]) for item in valid_data.values()] for i in range(len(v_array_values)): v_array_values[i] = v_array_values[i].round(decimals=5) dict_valid = datasets.Dataset.from_dict({'input_values': v_array_values}) ### Expected behavior The code is expected to give me a Huggingface dataset. ### Environment info python: 3.8.15 numpy: 1.22.3 datasets: 2.3.2 pyarrow: 8.0.0 Answered on the forum: > To fix the overflow error, we need to merge [support LargeListArray in pyarrow by xwwwwww · Pull Request #4800 · huggingface/datasets · GitHub](https://github.com/huggingface/datasets/pull/4800), which adds support for the large lists. However, before merging it, we need to come up with a cleaner API for large lists. I hope to find some time to address this before Datasets 3.0.

https://github.com/huggingface/datasets/issues/5631

Hi! You can also use names other than "train", "validation" and "test". As an example, check the [script](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/blob/e095840f23f3dffc1056c078c2f9320dad9ca74d/common_voice_11_0.py#L139) of the Common Voice 11 dataset.

Custom split names

### Feature request Hi, I participated in multiple NLP tasks where there are more than just train, test, validation splits, there could be multiple validation sets or test sets. But it seems currently only those mentioned three splits supported. It would be nice to have the support for more splits on the hub. (currently i can have more splits when I am loading datasets from urls, but not hub) ### Motivation Easier access to more splits ### Your contribution No

Custom split names ### Feature request Hi, I participated in multiple NLP tasks where there are more than just train, test, validation splits, there could be multiple validation sets or test sets. But it seems currently only those mentioned three splits supported. It would be nice to have the support for more splits on the hub. (currently i can have more splits when I am loading datasets from urls, but not hub) ### Motivation Easier access to more splits ### Your contribution No Hi! You can also use names other than "train", "validation" and "test". As an example, check the [script](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/blob/e095840f23f3dffc1056c078c2f9320dad9ca74d/common_voice_11_0.py#L139) of the Common Voice 11 dataset.

https://github.com/huggingface/datasets/issues/5629

Hi! You seem to be using an outdated version of `datasets` that downloads the older script version. To avoid the error, you can either pass `revision="main"` to `load_dataset` (this can fail if a script uses newer features of the lib) or update your installation with `pip install -U datasets` (better solution).

load_dataset gives "403" error when using Financial phrasebank

When I try to load this dataset, I receive the following error: ConnectionError: Couldn't reach https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip (error 403) Has this been seen before? Thanks. The website loads when I try to access it manually.

load_dataset gives "403" error when using Financial phrasebank When I try to load this dataset, I receive the following error: ConnectionError: Couldn't reach https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip (error 403) Has this been seen before? Thanks. The website loads when I try to access it manually. Hi! You seem to be using an outdated version of `datasets` that downloads the older script version. To avoid the error, you can either pass `revision="main"` to `load_dataset` (this can fail if a script uses newer features of the lib) or update your installation with `pip install -U datasets` (better solution).

https://github.com/huggingface/datasets/issues/5627

The AutoTrain format is not supported right now. I think it would require a dedicated dataset builder

Unable to load AutoTrain-generated dataset from the hub

### Describe the bug DatasetGenerationError: An error occurred while generating the dataset -> ValueError: Couldn't cast ... because column names don't match ``` ValueError: Couldn't cast _data_files: list<item: struct<filename: string>> child 0, item: struct<filename: string> child 0, filename: string _fingerprint: string _format_columns: list<item: string> child 0, item: string _format_kwargs: struct<> _format_type: null _indexes: struct<> _output_all_columns: bool _split: null to {'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}} because column names don't match ``` ### Steps to reproduce the bug Steps to reproduce: 1. `pip install datasets==2.10.1` 2. Attempt to load (private dataset). Note that I'm authenticated via ` huggingface-cli login` ``` from datasets import load_dataset # load dataset dataset = "ijmiller2/autotrain-data-betterbin-vision-10000" dataset = load_dataset(dataset) ``` Here's the full traceback: ```Downloading and preparing dataset json/ijmiller2--autotrain-data-betterbin-vision-10000 to /Users/ian/.cache/huggingface/datasets/ijmiller2___json/ijmiller2--autotrain-data-betterbin-vision-10000-2eae034a9ff8a1a9/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51... Downloading data files: 100%|███████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2383.80it/s] Extracting data files: 100%|█████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 505.95it/s] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1874, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1868 writer = writer_class( 1869 features=writer._features, 1870 path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"), 1871 storage_options=self._fs.storage_options, 1872 embed_local_files=embed_local_files, 1873 ) -> 1874 writer.write_table(table) 1875 num_examples_progress_update += len(table) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/arrow_writer.py:568, in ArrowWriter.write_table(self, pa_table, writer_batch_size) 567 pa_table = pa_table.combine_chunks() --> 568 pa_table = table_cast(pa_table, self._schema) 569 if self.embed_local_files: File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2312, in table_cast(table, schema) 2311 if table.schema != schema: -> 2312 return cast_table_to_schema(table, schema) 2313 elif table.schema.metadata != schema.metadata: File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2270, in cast_table_to_schema(table, schema) 2269 if sorted(table.column_names) != sorted(features): -> 2270 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") 2271 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] ValueError: Couldn't cast _data_files: list<item: struct<filename: string>> child 0, item: struct<filename: string> child 0, filename: string _fingerprint: string _format_columns: list<item: string> child 0, item: string _format_kwargs: struct<> _format_type: null _indexes: struct<> _output_all_columns: bool _split: null to {'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}} because column names don't match The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Input In [8], in <cell line: 6>() 4 # load dataset 5 dataset = "ijmiller2/autotrain-data-betterbin-vision-10000" ----> 6 dataset = load_dataset(dataset) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/load.py:1782, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs) 1779 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1781 # Download and prepare data -> 1782 builder_instance.download_and_prepare( 1783 download_config=download_config, 1784 download_mode=download_mode, 1785 verification_mode=verification_mode, 1786 try_from_hf_gcs=try_from_hf_gcs, 1787 num_proc=num_proc, 1788 ) 1790 # Build dataset for splits 1791 keep_in_memory = ( 1792 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1793 ) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:872, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 870 if num_proc is not None: 871 prepare_split_kwargs["num_proc"] = num_proc --> 872 self._download_and_prepare( 873 dl_manager=dl_manager, 874 verification_mode=verification_mode, 875 **prepare_split_kwargs, 876 **download_and_prepare_kwargs, 877 ) 878 # Sync info 879 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:967, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 963 split_dict.add(split_generator.split_info) 965 try: 966 # Prepare split will record examples associated to the split --> 967 self._prepare_split(split_generator, **prepare_split_kwargs) 968 except OSError as e: 969 raise OSError( 970 "Cannot find data file. " 971 + (self.manual_download_instructions or "") 972 + "\nOriginal error:\n" 973 + str(e) 974 ) from None File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1749, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1747 job_id = 0 1748 with pbar: -> 1749 for job_id, done, content in self._prepare_split_single( 1750 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1751 ): 1752 if done: 1753 result = content File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1892, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1891 e = e.__context__ -> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1894 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior I'm ultimately trying to generate my own performance metrics on validation data (before putting an endpoint into production) and so was hoping to load all or at least the validation subset from the hub. I'm expecting the `load_dataset()` function to work as shown in the documentation [here](https://huggingface.co/docs/datasets/loading#hugging-face-hub): ```python dataset = load_dataset( "lhoestq/custom_squad", revision="main" # tag name, or branch name, or commit hash ) ``` ### Environment info - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.8.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.4

Unable to load AutoTrain-generated dataset from the hub ### Describe the bug DatasetGenerationError: An error occurred while generating the dataset -> ValueError: Couldn't cast ... because column names don't match ``` ValueError: Couldn't cast _data_files: list<item: struct<filename: string>> child 0, item: struct<filename: string> child 0, filename: string _fingerprint: string _format_columns: list<item: string> child 0, item: string _format_kwargs: struct<> _format_type: null _indexes: struct<> _output_all_columns: bool _split: null to {'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}} because column names don't match ``` ### Steps to reproduce the bug Steps to reproduce: 1. `pip install datasets==2.10.1` 2. Attempt to load (private dataset). Note that I'm authenticated via ` huggingface-cli login` ``` from datasets import load_dataset # load dataset dataset = "ijmiller2/autotrain-data-betterbin-vision-10000" dataset = load_dataset(dataset) ``` Here's the full traceback: ```Downloading and preparing dataset json/ijmiller2--autotrain-data-betterbin-vision-10000 to /Users/ian/.cache/huggingface/datasets/ijmiller2___json/ijmiller2--autotrain-data-betterbin-vision-10000-2eae034a9ff8a1a9/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51... Downloading data files: 100%|███████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2383.80it/s] Extracting data files: 100%|█████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 505.95it/s] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1874, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1868 writer = writer_class( 1869 features=writer._features, 1870 path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"), 1871 storage_options=self._fs.storage_options, 1872 embed_local_files=embed_local_files, 1873 ) -> 1874 writer.write_table(table) 1875 num_examples_progress_update += len(table) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/arrow_writer.py:568, in ArrowWriter.write_table(self, pa_table, writer_batch_size) 567 pa_table = pa_table.combine_chunks() --> 568 pa_table = table_cast(pa_table, self._schema) 569 if self.embed_local_files: File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2312, in table_cast(table, schema) 2311 if table.schema != schema: -> 2312 return cast_table_to_schema(table, schema) 2313 elif table.schema.metadata != schema.metadata: File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2270, in cast_table_to_schema(table, schema) 2269 if sorted(table.column_names) != sorted(features): -> 2270 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") 2271 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] ValueError: Couldn't cast _data_files: list<item: struct<filename: string>> child 0, item: struct<filename: string> child 0, filename: string _fingerprint: string _format_columns: list<item: string> child 0, item: string _format_kwargs: struct<> _format_type: null _indexes: struct<> _output_all_columns: bool _split: null to {'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}} because column names don't match The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Input In [8], in <cell line: 6>() 4 # load dataset 5 dataset = "ijmiller2/autotrain-data-betterbin-vision-10000" ----> 6 dataset = load_dataset(dataset) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/load.py:1782, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs) 1779 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1781 # Download and prepare data -> 1782 builder_instance.download_and_prepare( 1783 download_config=download_config, 1784 download_mode=download_mode, 1785 verification_mode=verification_mode, 1786 try_from_hf_gcs=try_from_hf_gcs, 1787 num_proc=num_proc, 1788 ) 1790 # Build dataset for splits 1791 keep_in_memory = ( 1792 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1793 ) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:872, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 870 if num_proc is not None: 871 prepare_split_kwargs["num_proc"] = num_proc --> 872 self._download_and_prepare( 873 dl_manager=dl_manager, 874 verification_mode=verification_mode, 875 **prepare_split_kwargs, 876 **download_and_prepare_kwargs, 877 ) 878 # Sync info 879 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:967, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 963 split_dict.add(split_generator.split_info) 965 try: 966 # Prepare split will record examples associated to the split --> 967 self._prepare_split(split_generator, **prepare_split_kwargs) 968 except OSError as e: 969 raise OSError( 970 "Cannot find data file. " 971 + (self.manual_download_instructions or "") 972 + "\nOriginal error:\n" 973 + str(e) 974 ) from None File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1749, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1747 job_id = 0 1748 with pbar: -> 1749 for job_id, done, content in self._prepare_split_single( 1750 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1751 ): 1752 if done: 1753 result = content File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1892, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1891 e = e.__context__ -> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1894 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior I'm ultimately trying to generate my own performance metrics on validation data (before putting an endpoint into production) and so was hoping to load all or at least the validation subset from the hub. I'm expecting the `load_dataset()` function to work as shown in the documentation [here](https://huggingface.co/docs/datasets/loading#hugging-face-hub): ```python dataset = load_dataset( "lhoestq/custom_squad", revision="main" # tag name, or branch name, or commit hash ) ``` ### Environment info - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.8.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.4 The AutoTrain format is not supported right now. I think it would require a dedicated dataset builder

https://github.com/huggingface/datasets/issues/5627

Okay, good to know. Thanks for the reply. For now I will just have to manage the split manually before training, because I can’t find any way of pulling out file indices or file names from the autogenerated split. The file names field of the image dataset (loaded directly from arrow file) is missing, just fyi (for anyone else this might be relevant too). On Fri, Mar 10, 2023 at 7:02 PM Quentin Lhoest ***@***.***> wrote: > The AutoTrain format is not supported right now. I think it would require > a dedicated dataset builder > > — > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/5627#issuecomment-1464734308>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/ACBJ4F5A353MCZ76OGRJ6CTW3PFI7ANCNFSM6AAAAAAVWXNUTE> > . > You are receiving this because you authored the thread.Message ID: > ***@***.***> >

Unable to load AutoTrain-generated dataset from the hub

### Describe the bug DatasetGenerationError: An error occurred while generating the dataset -> ValueError: Couldn't cast ... because column names don't match ``` ValueError: Couldn't cast _data_files: list<item: struct<filename: string>> child 0, item: struct<filename: string> child 0, filename: string _fingerprint: string _format_columns: list<item: string> child 0, item: string _format_kwargs: struct<> _format_type: null _indexes: struct<> _output_all_columns: bool _split: null to {'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}} because column names don't match ``` ### Steps to reproduce the bug Steps to reproduce: 1. `pip install datasets==2.10.1` 2. Attempt to load (private dataset). Note that I'm authenticated via ` huggingface-cli login` ``` from datasets import load_dataset # load dataset dataset = "ijmiller2/autotrain-data-betterbin-vision-10000" dataset = load_dataset(dataset) ``` Here's the full traceback: ```Downloading and preparing dataset json/ijmiller2--autotrain-data-betterbin-vision-10000 to /Users/ian/.cache/huggingface/datasets/ijmiller2___json/ijmiller2--autotrain-data-betterbin-vision-10000-2eae034a9ff8a1a9/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51... Downloading data files: 100%|███████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2383.80it/s] Extracting data files: 100%|█████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 505.95it/s] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1874, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1868 writer = writer_class( 1869 features=writer._features, 1870 path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"), 1871 storage_options=self._fs.storage_options, 1872 embed_local_files=embed_local_files, 1873 ) -> 1874 writer.write_table(table) 1875 num_examples_progress_update += len(table) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/arrow_writer.py:568, in ArrowWriter.write_table(self, pa_table, writer_batch_size) 567 pa_table = pa_table.combine_chunks() --> 568 pa_table = table_cast(pa_table, self._schema) 569 if self.embed_local_files: File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2312, in table_cast(table, schema) 2311 if table.schema != schema: -> 2312 return cast_table_to_schema(table, schema) 2313 elif table.schema.metadata != schema.metadata: File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2270, in cast_table_to_schema(table, schema) 2269 if sorted(table.column_names) != sorted(features): -> 2270 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") 2271 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] ValueError: Couldn't cast _data_files: list<item: struct<filename: string>> child 0, item: struct<filename: string> child 0, filename: string _fingerprint: string _format_columns: list<item: string> child 0, item: string _format_kwargs: struct<> _format_type: null _indexes: struct<> _output_all_columns: bool _split: null to {'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}} because column names don't match The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Input In [8], in <cell line: 6>() 4 # load dataset 5 dataset = "ijmiller2/autotrain-data-betterbin-vision-10000" ----> 6 dataset = load_dataset(dataset) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/load.py:1782, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs) 1779 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1781 # Download and prepare data -> 1782 builder_instance.download_and_prepare( 1783 download_config=download_config, 1784 download_mode=download_mode, 1785 verification_mode=verification_mode, 1786 try_from_hf_gcs=try_from_hf_gcs, 1787 num_proc=num_proc, 1788 ) 1790 # Build dataset for splits 1791 keep_in_memory = ( 1792 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1793 ) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:872, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 870 if num_proc is not None: 871 prepare_split_kwargs["num_proc"] = num_proc --> 872 self._download_and_prepare( 873 dl_manager=dl_manager, 874 verification_mode=verification_mode, 875 **prepare_split_kwargs, 876 **download_and_prepare_kwargs, 877 ) 878 # Sync info 879 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:967, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 963 split_dict.add(split_generator.split_info) 965 try: 966 # Prepare split will record examples associated to the split --> 967 self._prepare_split(split_generator, **prepare_split_kwargs) 968 except OSError as e: 969 raise OSError( 970 "Cannot find data file. " 971 + (self.manual_download_instructions or "") 972 + "\nOriginal error:\n" 973 + str(e) 974 ) from None File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1749, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1747 job_id = 0 1748 with pbar: -> 1749 for job_id, done, content in self._prepare_split_single( 1750 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1751 ): 1752 if done: 1753 result = content File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1892, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1891 e = e.__context__ -> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1894 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior I'm ultimately trying to generate my own performance metrics on validation data (before putting an endpoint into production) and so was hoping to load all or at least the validation subset from the hub. I'm expecting the `load_dataset()` function to work as shown in the documentation [here](https://huggingface.co/docs/datasets/loading#hugging-face-hub): ```python dataset = load_dataset( "lhoestq/custom_squad", revision="main" # tag name, or branch name, or commit hash ) ``` ### Environment info - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.8.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.4

Unable to load AutoTrain-generated dataset from the hub ### Describe the bug DatasetGenerationError: An error occurred while generating the dataset -> ValueError: Couldn't cast ... because column names don't match ``` ValueError: Couldn't cast _data_files: list<item: struct<filename: string>> child 0, item: struct<filename: string> child 0, filename: string _fingerprint: string _format_columns: list<item: string> child 0, item: string _format_kwargs: struct<> _format_type: null _indexes: struct<> _output_all_columns: bool _split: null to {'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}} because column names don't match ``` ### Steps to reproduce the bug Steps to reproduce: 1. `pip install datasets==2.10.1` 2. Attempt to load (private dataset). Note that I'm authenticated via ` huggingface-cli login` ``` from datasets import load_dataset # load dataset dataset = "ijmiller2/autotrain-data-betterbin-vision-10000" dataset = load_dataset(dataset) ``` Here's the full traceback: ```Downloading and preparing dataset json/ijmiller2--autotrain-data-betterbin-vision-10000 to /Users/ian/.cache/huggingface/datasets/ijmiller2___json/ijmiller2--autotrain-data-betterbin-vision-10000-2eae034a9ff8a1a9/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51... Downloading data files: 100%|███████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2383.80it/s] Extracting data files: 100%|█████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 505.95it/s] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1874, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1868 writer = writer_class( 1869 features=writer._features, 1870 path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"), 1871 storage_options=self._fs.storage_options, 1872 embed_local_files=embed_local_files, 1873 ) -> 1874 writer.write_table(table) 1875 num_examples_progress_update += len(table) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/arrow_writer.py:568, in ArrowWriter.write_table(self, pa_table, writer_batch_size) 567 pa_table = pa_table.combine_chunks() --> 568 pa_table = table_cast(pa_table, self._schema) 569 if self.embed_local_files: File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2312, in table_cast(table, schema) 2311 if table.schema != schema: -> 2312 return cast_table_to_schema(table, schema) 2313 elif table.schema.metadata != schema.metadata: File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2270, in cast_table_to_schema(table, schema) 2269 if sorted(table.column_names) != sorted(features): -> 2270 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") 2271 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] ValueError: Couldn't cast _data_files: list<item: struct<filename: string>> child 0, item: struct<filename: string> child 0, filename: string _fingerprint: string _format_columns: list<item: string> child 0, item: string _format_kwargs: struct<> _format_type: null _indexes: struct<> _output_all_columns: bool _split: null to {'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}} because column names don't match The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Input In [8], in <cell line: 6>() 4 # load dataset 5 dataset = "ijmiller2/autotrain-data-betterbin-vision-10000" ----> 6 dataset = load_dataset(dataset) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/load.py:1782, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs) 1779 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1781 # Download and prepare data -> 1782 builder_instance.download_and_prepare( 1783 download_config=download_config, 1784 download_mode=download_mode, 1785 verification_mode=verification_mode, 1786 try_from_hf_gcs=try_from_hf_gcs, 1787 num_proc=num_proc, 1788 ) 1790 # Build dataset for splits 1791 keep_in_memory = ( 1792 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1793 ) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:872, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 870 if num_proc is not None: 871 prepare_split_kwargs["num_proc"] = num_proc --> 872 self._download_and_prepare( 873 dl_manager=dl_manager, 874 verification_mode=verification_mode, 875 **prepare_split_kwargs, 876 **download_and_prepare_kwargs, 877 ) 878 # Sync info 879 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:967, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 963 split_dict.add(split_generator.split_info) 965 try: 966 # Prepare split will record examples associated to the split --> 967 self._prepare_split(split_generator, **prepare_split_kwargs) 968 except OSError as e: 969 raise OSError( 970 "Cannot find data file. " 971 + (self.manual_download_instructions or "") 972 + "\nOriginal error:\n" 973 + str(e) 974 ) from None File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1749, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1747 job_id = 0 1748 with pbar: -> 1749 for job_id, done, content in self._prepare_split_single( 1750 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1751 ): 1752 if done: 1753 result = content File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1892, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1891 e = e.__context__ -> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1894 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior I'm ultimately trying to generate my own performance metrics on validation data (before putting an endpoint into production) and so was hoping to load all or at least the validation subset from the hub. I'm expecting the `load_dataset()` function to work as shown in the documentation [here](https://huggingface.co/docs/datasets/loading#hugging-face-hub): ```python dataset = load_dataset( "lhoestq/custom_squad", revision="main" # tag name, or branch name, or commit hash ) ``` ### Environment info - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.8.13 - PyArrow version: 9.0.0 - Pandas version: 1.4.4 Okay, good to know. Thanks for the reply. For now I will just have to manage the split manually before training, because I can’t find any way of pulling out file indices or file names from the autogenerated split. The file names field of the image dataset (loaded directly from arrow file) is missing, just fyi (for anyone else this might be relevant too). On Fri, Mar 10, 2023 at 7:02 PM Quentin Lhoest ***@***.***> wrote: > The AutoTrain format is not supported right now. I think it would require > a dedicated dataset builder > > — > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/5627#issuecomment-1464734308>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/ACBJ4F5A353MCZ76OGRJ6CTW3PFI7ANCNFSM6AAAAAAVWXNUTE> > . > You are receiving this because you authored the thread.Message ID: > ***@***.***> >

https://github.com/huggingface/datasets/issues/5625

You can use "json" instead. It doesn't work by extension names, but rather by dataset builder names, e.g. "text", "imagefolder", etc. I don't think the example in `transformers` is correct because of that

Allow "jsonl" data type signifier

### Feature request `load_dataset` currently does not accept `jsonl` as type but only `json`. ### Motivation I was working with one of the `run_translation` scripts and used my own datasets (`.jsonl`) as train_dataset. But the default code did not work because ``` FileNotFoundError: Couldn't find a dataset script at jsonl\jsonl.py or any data file in the same directory. Couldn't find 'jsonl' on the Hugging Face Hub either: FileNotFoundError: Dataset 'jsonl' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`. ``` The reason is because the script has these lines to extract the data type by its extension. Therefore, the derived type is `jsonl` which is not recognized by datasets as the error above shows. https://github.com/huggingface/transformers/blob/ade26bf9912f69e2110137443e4406d7dbe253e7/examples/pytorch/translation/run_translation.py#L342-L356 I suppose you could argue that this is the script's fault (in which case I'll do a PR over at `transformers`) but it makes sense to me to add `jsonl` as an alias to `json` in `datasets`. ### Your contribution At the moment I cannot work on this. I think it can be as "easy" as having an alias for json, namely jsonl.

Allow "jsonl" data type signifier ### Feature request `load_dataset` currently does not accept `jsonl` as type but only `json`. ### Motivation I was working with one of the `run_translation` scripts and used my own datasets (`.jsonl`) as train_dataset. But the default code did not work because ``` FileNotFoundError: Couldn't find a dataset script at jsonl\jsonl.py or any data file in the same directory. Couldn't find 'jsonl' on the Hugging Face Hub either: FileNotFoundError: Dataset 'jsonl' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`. ``` The reason is because the script has these lines to extract the data type by its extension. Therefore, the derived type is `jsonl` which is not recognized by datasets as the error above shows. https://github.com/huggingface/transformers/blob/ade26bf9912f69e2110137443e4406d7dbe253e7/examples/pytorch/translation/run_translation.py#L342-L356 I suppose you could argue that this is the script's fault (in which case I'll do a PR over at `transformers`) but it makes sense to me to add `jsonl` as an alias to `json` in `datasets`. ### Your contribution At the moment I cannot work on this. I think it can be as "easy" as having an alias for json, namely jsonl. You can use "json" instead. It doesn't work by extension names, but rather by dataset builder names, e.g. "text", "imagefolder", etc. I don't think the example in `transformers` is correct because of that

https://github.com/huggingface/datasets/issues/5625

Yes, I understand the reasoning but this issue is to propose that the example in transformers (while incorrect) "makes sense" in terms of user expectation. So the question is whether it would be possible to add "aliases" for common types (like "json" and "text") based on common extensions (like jsonl and txt)?

Allow "jsonl" data type signifier

### Feature request `load_dataset` currently does not accept `jsonl` as type but only `json`. ### Motivation I was working with one of the `run_translation` scripts and used my own datasets (`.jsonl`) as train_dataset. But the default code did not work because ``` FileNotFoundError: Couldn't find a dataset script at jsonl\jsonl.py or any data file in the same directory. Couldn't find 'jsonl' on the Hugging Face Hub either: FileNotFoundError: Dataset 'jsonl' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`. ``` The reason is because the script has these lines to extract the data type by its extension. Therefore, the derived type is `jsonl` which is not recognized by datasets as the error above shows. https://github.com/huggingface/transformers/blob/ade26bf9912f69e2110137443e4406d7dbe253e7/examples/pytorch/translation/run_translation.py#L342-L356 I suppose you could argue that this is the script's fault (in which case I'll do a PR over at `transformers`) but it makes sense to me to add `jsonl` as an alias to `json` in `datasets`. ### Your contribution At the moment I cannot work on this. I think it can be as "easy" as having an alias for json, namely jsonl.

Allow "jsonl" data type signifier ### Feature request `load_dataset` currently does not accept `jsonl` as type but only `json`. ### Motivation I was working with one of the `run_translation` scripts and used my own datasets (`.jsonl`) as train_dataset. But the default code did not work because ``` FileNotFoundError: Couldn't find a dataset script at jsonl\jsonl.py or any data file in the same directory. Couldn't find 'jsonl' on the Hugging Face Hub either: FileNotFoundError: Dataset 'jsonl' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`. ``` The reason is because the script has these lines to extract the data type by its extension. Therefore, the derived type is `jsonl` which is not recognized by datasets as the error above shows. https://github.com/huggingface/transformers/blob/ade26bf9912f69e2110137443e4406d7dbe253e7/examples/pytorch/translation/run_translation.py#L342-L356 I suppose you could argue that this is the script's fault (in which case I'll do a PR over at `transformers`) but it makes sense to me to add `jsonl` as an alias to `json` in `datasets`. ### Your contribution At the moment I cannot work on this. I think it can be as "easy" as having an alias for json, namely jsonl. Yes, I understand the reasoning but this issue is to propose that the example in transformers (while incorrect) "makes sense" in terms of user expectation. So the question is whether it would be possible to add "aliases" for common types (like "json" and "text") based on common extensions (like jsonl and txt)?

https://github.com/huggingface/datasets/issues/5624

Hi @lithafnium, thanks for reporting. Please note that you can use the "Community" tab in the corresponding dataset page to start any discussion: https://huggingface.co/datasets/glue/discussions Indeed this issue was already raised there (https://huggingface.co/datasets/glue/discussions/5) and answered: https://huggingface.co/datasets/glue/discussions/5#63907885937867f0cb3cde31 > The test labels are not public. > > Note this dataset belongs to a benchmark: people send their predictions for the test split to GLUE (https://gluebenchmark.com/) and then they get a score in their leaderboard...

glue datasets returning -1 for test split

### Describe the bug Downloading any dataset from GLUE has -1 as class labels for test split. Train and validation have regular 0/1 class labels. This is also present in the dataset card online. ### Steps to reproduce the bug ``` dataset = load_dataset("glue", "sst2") for d in dataset: # prints out -1 print(d["label"] ``` ### Expected behavior Expected behavior should be 0/1 instead of -1. ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.15.0-46-generic-x86_64-with-glibc2.17 - Python version: 3.8.16 - PyArrow version: 8.0.0 - Pandas version: 1.5.3

glue datasets returning -1 for test split ### Describe the bug Downloading any dataset from GLUE has -1 as class labels for test split. Train and validation have regular 0/1 class labels. This is also present in the dataset card online. ### Steps to reproduce the bug ``` dataset = load_dataset("glue", "sst2") for d in dataset: # prints out -1 print(d["label"] ``` ### Expected behavior Expected behavior should be 0/1 instead of -1. ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.15.0-46-generic-x86_64-with-glibc2.17 - Python version: 3.8.16 - PyArrow version: 8.0.0 - Pandas version: 1.5.3 Hi @lithafnium, thanks for reporting. Please note that you can use the "Community" tab in the corresponding dataset page to start any discussion: https://huggingface.co/datasets/glue/discussions Indeed this issue was already raised there (https://huggingface.co/datasets/glue/discussions/5) and answered: https://huggingface.co/datasets/glue/discussions/5#63907885937867f0cb3cde31 > The test labels are not public. > > Note this dataset belongs to a benchmark: people send their predictions for the test split to GLUE (https://gluebenchmark.com/) and then they get a score in their leaderboard...

https://github.com/huggingface/datasets/issues/5613

Reopening, since I think the docs should inform the user of this problem. For example, [this page](https://huggingface.co/docs/datasets/installation) says > Datasets is tested on Python 3.7+. but it should probably say that Beam Datasets do not work with Python 3.10 (or link to a known issues page).

Version mismatch with multiprocess and dill on Python 3.10

### Describe the bug Grabbing the latest version of `datasets` and `apache-beam` with `poetry` using Python 3.10 gives a crash at runtime. The crash is ``` File "/Users/adpauls/sc/git/DSI-transformers/data/NQ/create_NQ_train_vali.py", line 1, in <module> import datasets File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/__init__.py", line 43, in <module> from .arrow_dataset import Dataset File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 65, in <module> from .arrow_reader import ArrowReader File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 30, in <module> from .download.download_config import DownloadConfig File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/__init__.py", line 9, in <module> from .download_manager import DownloadManager, DownloadMode File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/download_manager.py", line 35, in <module> from ..utils.py_utils import NestedDataStructure, map_nested, size_str File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 40, in <module> import multiprocess.pool File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 609, in <module> class ThreadPool(Pool): File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 611, in ThreadPool from .dummy import Process File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/dummy/__init__.py", line 87, in <module> class Condition(threading._Condition): AttributeError: module 'threading' has no attribute '_Condition'. Did you mean: 'Condition'? ``` I think this is a bad interaction of versions from `dill`, `multiprocess`, `apache-beam`, and `threading` from the Python (3.10) standard lib. Upgrading `multiprocess` to a version that does not crash like this is not possible because `apache-beam` pins `dill` to and old version: ``` Because multiprocess (0.70.10) depends on dill (>=0.3.2) and apache-beam (2.45.0) depends on dill (>=0.3.1.1,<0.3.2), multiprocess (0.70.10) is incompatible with apache-beam (2.45.0). And because no versions of apache-beam match >2.45.0,<3.0.0, multiprocess (0.70.10) is incompatible with apache-beam (>=2.45.0,<3.0.0). So, because yyy depends on both apache-beam (^2.45.0) and multiprocess (0.70.10), version solving failed. ``` Perhaps it is not right to file a bug here, but I'm not totally sure whose fault it is. And in any case, this is an immediate blocker to using `datasets` out of the box. Possibly related to https://github.com/huggingface/datasets/issues/5232. ### Steps to reproduce the bug Steps to reproduce: 1. Make a poetry project with this configuration ``` [tool.poetry] name = "yyy" version = "0.1.0" description = "" authors = ["Adam Pauls <adpauls@gmail.com>"] readme = "README.md" packages = [{ include = "xxx" }] [tool.poetry.dependencies] python = ">=3.10,<3.11" datasets = "^2.10.1" apache-beam = "^2.45.0" [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" ``` 2. `poetry install`. 3. `poetry run python -c "import datasets"`. ### Expected behavior Script runs. ### Environment info Python 3.10. Here are the versions installed by `poetry`: ``` •• Installing frozenlist (1.3.3) • Installing idna (3.4) • Installing multidict (6.0.4) • Installing aiosignal (1.3.1) • Installing async-timeout (4.0.2) • Installing attrs (22.2.0) • Installing certifi (2022.12.7) • Installing charset-normalizer (3.1.0) • Installing six (1.16.0) • Installing urllib3 (1.26.14) • Installing yarl (1.8.2) • Installing aiohttp (3.8.4) • Installing dill (0.3.1.1) • Installing docopt (0.6.2) • Installing filelock (3.9.0) • Installing numpy (1.22.4) • Installing pyparsing (3.0.9) • Installing protobuf (3.19.4) • Installing packaging (23.0) • Installing python-dateutil (2.8.2) • Installing pytz (2022.7.1) • Installing pyyaml (6.0) • Installing requests (2.28.2) • Installing tqdm (4.65.0) • Installing typing-extensions (4.5.0) • Installing cloudpickle (2.2.1) • Installing crcmod (1.7) • Installing fastavro (1.7.2) • Installing fasteners (0.18) • Installing fsspec (2023.3.0) • Installing grpcio (1.51.3) • Installing hdfs (2.7.0) • Installing httplib2 (0.20.4) • Installing huggingface-hub (0.12.1) • Installing multiprocess (0.70.9) • Installing objsize (0.6.1) • Installing orjson (3.8.7) • Installing pandas (1.5.3) • Installing proto-plus (1.22.2) • Installing pyarrow (9.0.0) • Installing pydot (1.4.2) • Installing pymongo (3.13.0) • Installing regex (2022.10.31) • Installing responses (0.18.0) • Installing xxhash (3.2.0) • Installing zstandard (0.20.0) • Installing apache-beam (2.45.0) • Installing datasets (2.10.1) ```

Version mismatch with multiprocess and dill on Python 3.10 ### Describe the bug Grabbing the latest version of `datasets` and `apache-beam` with `poetry` using Python 3.10 gives a crash at runtime. The crash is ``` File "/Users/adpauls/sc/git/DSI-transformers/data/NQ/create_NQ_train_vali.py", line 1, in <module> import datasets File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/__init__.py", line 43, in <module> from .arrow_dataset import Dataset File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 65, in <module> from .arrow_reader import ArrowReader File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 30, in <module> from .download.download_config import DownloadConfig File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/__init__.py", line 9, in <module> from .download_manager import DownloadManager, DownloadMode File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/download_manager.py", line 35, in <module> from ..utils.py_utils import NestedDataStructure, map_nested, size_str File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 40, in <module> import multiprocess.pool File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 609, in <module> class ThreadPool(Pool): File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 611, in ThreadPool from .dummy import Process File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/dummy/__init__.py", line 87, in <module> class Condition(threading._Condition): AttributeError: module 'threading' has no attribute '_Condition'. Did you mean: 'Condition'? ``` I think this is a bad interaction of versions from `dill`, `multiprocess`, `apache-beam`, and `threading` from the Python (3.10) standard lib. Upgrading `multiprocess` to a version that does not crash like this is not possible because `apache-beam` pins `dill` to and old version: ``` Because multiprocess (0.70.10) depends on dill (>=0.3.2) and apache-beam (2.45.0) depends on dill (>=0.3.1.1,<0.3.2), multiprocess (0.70.10) is incompatible with apache-beam (2.45.0). And because no versions of apache-beam match >2.45.0,<3.0.0, multiprocess (0.70.10) is incompatible with apache-beam (>=2.45.0,<3.0.0). So, because yyy depends on both apache-beam (^2.45.0) and multiprocess (0.70.10), version solving failed. ``` Perhaps it is not right to file a bug here, but I'm not totally sure whose fault it is. And in any case, this is an immediate blocker to using `datasets` out of the box. Possibly related to https://github.com/huggingface/datasets/issues/5232. ### Steps to reproduce the bug Steps to reproduce: 1. Make a poetry project with this configuration ``` [tool.poetry] name = "yyy" version = "0.1.0" description = "" authors = ["Adam Pauls <adpauls@gmail.com>"] readme = "README.md" packages = [{ include = "xxx" }] [tool.poetry.dependencies] python = ">=3.10,<3.11" datasets = "^2.10.1" apache-beam = "^2.45.0" [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" ``` 2. `poetry install`. 3. `poetry run python -c "import datasets"`. ### Expected behavior Script runs. ### Environment info Python 3.10. Here are the versions installed by `poetry`: ``` •• Installing frozenlist (1.3.3) • Installing idna (3.4) • Installing multidict (6.0.4) • Installing aiosignal (1.3.1) • Installing async-timeout (4.0.2) • Installing attrs (22.2.0) • Installing certifi (2022.12.7) • Installing charset-normalizer (3.1.0) • Installing six (1.16.0) • Installing urllib3 (1.26.14) • Installing yarl (1.8.2) • Installing aiohttp (3.8.4) • Installing dill (0.3.1.1) • Installing docopt (0.6.2) • Installing filelock (3.9.0) • Installing numpy (1.22.4) • Installing pyparsing (3.0.9) • Installing protobuf (3.19.4) • Installing packaging (23.0) • Installing python-dateutil (2.8.2) • Installing pytz (2022.7.1) • Installing pyyaml (6.0) • Installing requests (2.28.2) • Installing tqdm (4.65.0) • Installing typing-extensions (4.5.0) • Installing cloudpickle (2.2.1) • Installing crcmod (1.7) • Installing fastavro (1.7.2) • Installing fasteners (0.18) • Installing fsspec (2023.3.0) • Installing grpcio (1.51.3) • Installing hdfs (2.7.0) • Installing httplib2 (0.20.4) • Installing huggingface-hub (0.12.1) • Installing multiprocess (0.70.9) • Installing objsize (0.6.1) • Installing orjson (3.8.7) • Installing pandas (1.5.3) • Installing proto-plus (1.22.2) • Installing pyarrow (9.0.0) • Installing pydot (1.4.2) • Installing pymongo (3.13.0) • Installing regex (2022.10.31) • Installing responses (0.18.0) • Installing xxhash (3.2.0) • Installing zstandard (0.20.0) • Installing apache-beam (2.45.0) • Installing datasets (2.10.1) ``` Reopening, since I think the docs should inform the user of this problem. For example, [this page](https://huggingface.co/docs/datasets/installation) says > Datasets is tested on Python 3.7+. but it should probably say that Beam Datasets do not work with Python 3.10 (or link to a known issues page).

https://github.com/huggingface/datasets/issues/5613

Same problem on Colab using a vanilla setup running : Python 3.10.11 apache-beam 2.47.0 datasets 2.12.0

Version mismatch with multiprocess and dill on Python 3.10

### Describe the bug Grabbing the latest version of `datasets` and `apache-beam` with `poetry` using Python 3.10 gives a crash at runtime. The crash is ``` File "/Users/adpauls/sc/git/DSI-transformers/data/NQ/create_NQ_train_vali.py", line 1, in <module> import datasets File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/__init__.py", line 43, in <module> from .arrow_dataset import Dataset File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 65, in <module> from .arrow_reader import ArrowReader File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 30, in <module> from .download.download_config import DownloadConfig File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/__init__.py", line 9, in <module> from .download_manager import DownloadManager, DownloadMode File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/download_manager.py", line 35, in <module> from ..utils.py_utils import NestedDataStructure, map_nested, size_str File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 40, in <module> import multiprocess.pool File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 609, in <module> class ThreadPool(Pool): File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 611, in ThreadPool from .dummy import Process File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/dummy/__init__.py", line 87, in <module> class Condition(threading._Condition): AttributeError: module 'threading' has no attribute '_Condition'. Did you mean: 'Condition'? ``` I think this is a bad interaction of versions from `dill`, `multiprocess`, `apache-beam`, and `threading` from the Python (3.10) standard lib. Upgrading `multiprocess` to a version that does not crash like this is not possible because `apache-beam` pins `dill` to and old version: ``` Because multiprocess (0.70.10) depends on dill (>=0.3.2) and apache-beam (2.45.0) depends on dill (>=0.3.1.1,<0.3.2), multiprocess (0.70.10) is incompatible with apache-beam (2.45.0). And because no versions of apache-beam match >2.45.0,<3.0.0, multiprocess (0.70.10) is incompatible with apache-beam (>=2.45.0,<3.0.0). So, because yyy depends on both apache-beam (^2.45.0) and multiprocess (0.70.10), version solving failed. ``` Perhaps it is not right to file a bug here, but I'm not totally sure whose fault it is. And in any case, this is an immediate blocker to using `datasets` out of the box. Possibly related to https://github.com/huggingface/datasets/issues/5232. ### Steps to reproduce the bug Steps to reproduce: 1. Make a poetry project with this configuration ``` [tool.poetry] name = "yyy" version = "0.1.0" description = "" authors = ["Adam Pauls <adpauls@gmail.com>"] readme = "README.md" packages = [{ include = "xxx" }] [tool.poetry.dependencies] python = ">=3.10,<3.11" datasets = "^2.10.1" apache-beam = "^2.45.0" [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" ``` 2. `poetry install`. 3. `poetry run python -c "import datasets"`. ### Expected behavior Script runs. ### Environment info Python 3.10. Here are the versions installed by `poetry`: ``` •• Installing frozenlist (1.3.3) • Installing idna (3.4) • Installing multidict (6.0.4) • Installing aiosignal (1.3.1) • Installing async-timeout (4.0.2) • Installing attrs (22.2.0) • Installing certifi (2022.12.7) • Installing charset-normalizer (3.1.0) • Installing six (1.16.0) • Installing urllib3 (1.26.14) • Installing yarl (1.8.2) • Installing aiohttp (3.8.4) • Installing dill (0.3.1.1) • Installing docopt (0.6.2) • Installing filelock (3.9.0) • Installing numpy (1.22.4) • Installing pyparsing (3.0.9) • Installing protobuf (3.19.4) • Installing packaging (23.0) • Installing python-dateutil (2.8.2) • Installing pytz (2022.7.1) • Installing pyyaml (6.0) • Installing requests (2.28.2) • Installing tqdm (4.65.0) • Installing typing-extensions (4.5.0) • Installing cloudpickle (2.2.1) • Installing crcmod (1.7) • Installing fastavro (1.7.2) • Installing fasteners (0.18) • Installing fsspec (2023.3.0) • Installing grpcio (1.51.3) • Installing hdfs (2.7.0) • Installing httplib2 (0.20.4) • Installing huggingface-hub (0.12.1) • Installing multiprocess (0.70.9) • Installing objsize (0.6.1) • Installing orjson (3.8.7) • Installing pandas (1.5.3) • Installing proto-plus (1.22.2) • Installing pyarrow (9.0.0) • Installing pydot (1.4.2) • Installing pymongo (3.13.0) • Installing regex (2022.10.31) • Installing responses (0.18.0) • Installing xxhash (3.2.0) • Installing zstandard (0.20.0) • Installing apache-beam (2.45.0) • Installing datasets (2.10.1) ```

Version mismatch with multiprocess and dill on Python 3.10 ### Describe the bug Grabbing the latest version of `datasets` and `apache-beam` with `poetry` using Python 3.10 gives a crash at runtime. The crash is ``` File "/Users/adpauls/sc/git/DSI-transformers/data/NQ/create_NQ_train_vali.py", line 1, in <module> import datasets File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/__init__.py", line 43, in <module> from .arrow_dataset import Dataset File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 65, in <module> from .arrow_reader import ArrowReader File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 30, in <module> from .download.download_config import DownloadConfig File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/__init__.py", line 9, in <module> from .download_manager import DownloadManager, DownloadMode File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/download_manager.py", line 35, in <module> from ..utils.py_utils import NestedDataStructure, map_nested, size_str File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 40, in <module> import multiprocess.pool File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 609, in <module> class ThreadPool(Pool): File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 611, in ThreadPool from .dummy import Process File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/dummy/__init__.py", line 87, in <module> class Condition(threading._Condition): AttributeError: module 'threading' has no attribute '_Condition'. Did you mean: 'Condition'? ``` I think this is a bad interaction of versions from `dill`, `multiprocess`, `apache-beam`, and `threading` from the Python (3.10) standard lib. Upgrading `multiprocess` to a version that does not crash like this is not possible because `apache-beam` pins `dill` to and old version: ``` Because multiprocess (0.70.10) depends on dill (>=0.3.2) and apache-beam (2.45.0) depends on dill (>=0.3.1.1,<0.3.2), multiprocess (0.70.10) is incompatible with apache-beam (2.45.0). And because no versions of apache-beam match >2.45.0,<3.0.0, multiprocess (0.70.10) is incompatible with apache-beam (>=2.45.0,<3.0.0). So, because yyy depends on both apache-beam (^2.45.0) and multiprocess (0.70.10), version solving failed. ``` Perhaps it is not right to file a bug here, but I'm not totally sure whose fault it is. And in any case, this is an immediate blocker to using `datasets` out of the box. Possibly related to https://github.com/huggingface/datasets/issues/5232. ### Steps to reproduce the bug Steps to reproduce: 1. Make a poetry project with this configuration ``` [tool.poetry] name = "yyy" version = "0.1.0" description = "" authors = ["Adam Pauls <adpauls@gmail.com>"] readme = "README.md" packages = [{ include = "xxx" }] [tool.poetry.dependencies] python = ">=3.10,<3.11" datasets = "^2.10.1" apache-beam = "^2.45.0" [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" ``` 2. `poetry install`. 3. `poetry run python -c "import datasets"`. ### Expected behavior Script runs. ### Environment info Python 3.10. Here are the versions installed by `poetry`: ``` •• Installing frozenlist (1.3.3) • Installing idna (3.4) • Installing multidict (6.0.4) • Installing aiosignal (1.3.1) • Installing async-timeout (4.0.2) • Installing attrs (22.2.0) • Installing certifi (2022.12.7) • Installing charset-normalizer (3.1.0) • Installing six (1.16.0) • Installing urllib3 (1.26.14) • Installing yarl (1.8.2) • Installing aiohttp (3.8.4) • Installing dill (0.3.1.1) • Installing docopt (0.6.2) • Installing filelock (3.9.0) • Installing numpy (1.22.4) • Installing pyparsing (3.0.9) • Installing protobuf (3.19.4) • Installing packaging (23.0) • Installing python-dateutil (2.8.2) • Installing pytz (2022.7.1) • Installing pyyaml (6.0) • Installing requests (2.28.2) • Installing tqdm (4.65.0) • Installing typing-extensions (4.5.0) • Installing cloudpickle (2.2.1) • Installing crcmod (1.7) • Installing fastavro (1.7.2) • Installing fasteners (0.18) • Installing fsspec (2023.3.0) • Installing grpcio (1.51.3) • Installing hdfs (2.7.0) • Installing httplib2 (0.20.4) • Installing huggingface-hub (0.12.1) • Installing multiprocess (0.70.9) • Installing objsize (0.6.1) • Installing orjson (3.8.7) • Installing pandas (1.5.3) • Installing proto-plus (1.22.2) • Installing pyarrow (9.0.0) • Installing pydot (1.4.2) • Installing pymongo (3.13.0) • Installing regex (2022.10.31) • Installing responses (0.18.0) • Installing xxhash (3.2.0) • Installing zstandard (0.20.0) • Installing apache-beam (2.45.0) • Installing datasets (2.10.1) ``` Same problem on Colab using a vanilla setup running : Python 3.10.11 apache-beam 2.47.0 datasets 2.12.0

https://github.com/huggingface/datasets/issues/5613

I have made a workaround by forcing an install of the version of `multiprocess` version `0.70.15` (after installing `datasets` and `apache-beam`). I can confirm that (on Python 3.10 in [this colab notebook](https://colab.research.google.com/drive/1PTeGlshamFcJZix_GiS3vMXX_YzAhGv0?usp=sharing)) `datasets` can download pre-processed Wikipedia dumps and can download non-pre-processed dumps using `beam_runner="DirectRunner"`. I don't know if/how other `beam_runner`s can be made compatible.

Version mismatch with multiprocess and dill on Python 3.10

### Describe the bug Grabbing the latest version of `datasets` and `apache-beam` with `poetry` using Python 3.10 gives a crash at runtime. The crash is ``` File "/Users/adpauls/sc/git/DSI-transformers/data/NQ/create_NQ_train_vali.py", line 1, in <module> import datasets File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/__init__.py", line 43, in <module> from .arrow_dataset import Dataset File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 65, in <module> from .arrow_reader import ArrowReader File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 30, in <module> from .download.download_config import DownloadConfig File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/__init__.py", line 9, in <module> from .download_manager import DownloadManager, DownloadMode File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/download_manager.py", line 35, in <module> from ..utils.py_utils import NestedDataStructure, map_nested, size_str File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 40, in <module> import multiprocess.pool File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 609, in <module> class ThreadPool(Pool): File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 611, in ThreadPool from .dummy import Process File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/dummy/__init__.py", line 87, in <module> class Condition(threading._Condition): AttributeError: module 'threading' has no attribute '_Condition'. Did you mean: 'Condition'? ``` I think this is a bad interaction of versions from `dill`, `multiprocess`, `apache-beam`, and `threading` from the Python (3.10) standard lib. Upgrading `multiprocess` to a version that does not crash like this is not possible because `apache-beam` pins `dill` to and old version: ``` Because multiprocess (0.70.10) depends on dill (>=0.3.2) and apache-beam (2.45.0) depends on dill (>=0.3.1.1,<0.3.2), multiprocess (0.70.10) is incompatible with apache-beam (2.45.0). And because no versions of apache-beam match >2.45.0,<3.0.0, multiprocess (0.70.10) is incompatible with apache-beam (>=2.45.0,<3.0.0). So, because yyy depends on both apache-beam (^2.45.0) and multiprocess (0.70.10), version solving failed. ``` Perhaps it is not right to file a bug here, but I'm not totally sure whose fault it is. And in any case, this is an immediate blocker to using `datasets` out of the box. Possibly related to https://github.com/huggingface/datasets/issues/5232. ### Steps to reproduce the bug Steps to reproduce: 1. Make a poetry project with this configuration ``` [tool.poetry] name = "yyy" version = "0.1.0" description = "" authors = ["Adam Pauls <adpauls@gmail.com>"] readme = "README.md" packages = [{ include = "xxx" }] [tool.poetry.dependencies] python = ">=3.10,<3.11" datasets = "^2.10.1" apache-beam = "^2.45.0" [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" ``` 2. `poetry install`. 3. `poetry run python -c "import datasets"`. ### Expected behavior Script runs. ### Environment info Python 3.10. Here are the versions installed by `poetry`: ``` •• Installing frozenlist (1.3.3) • Installing idna (3.4) • Installing multidict (6.0.4) • Installing aiosignal (1.3.1) • Installing async-timeout (4.0.2) • Installing attrs (22.2.0) • Installing certifi (2022.12.7) • Installing charset-normalizer (3.1.0) • Installing six (1.16.0) • Installing urllib3 (1.26.14) • Installing yarl (1.8.2) • Installing aiohttp (3.8.4) • Installing dill (0.3.1.1) • Installing docopt (0.6.2) • Installing filelock (3.9.0) • Installing numpy (1.22.4) • Installing pyparsing (3.0.9) • Installing protobuf (3.19.4) • Installing packaging (23.0) • Installing python-dateutil (2.8.2) • Installing pytz (2022.7.1) • Installing pyyaml (6.0) • Installing requests (2.28.2) • Installing tqdm (4.65.0) • Installing typing-extensions (4.5.0) • Installing cloudpickle (2.2.1) • Installing crcmod (1.7) • Installing fastavro (1.7.2) • Installing fasteners (0.18) • Installing fsspec (2023.3.0) • Installing grpcio (1.51.3) • Installing hdfs (2.7.0) • Installing httplib2 (0.20.4) • Installing huggingface-hub (0.12.1) • Installing multiprocess (0.70.9) • Installing objsize (0.6.1) • Installing orjson (3.8.7) • Installing pandas (1.5.3) • Installing proto-plus (1.22.2) • Installing pyarrow (9.0.0) • Installing pydot (1.4.2) • Installing pymongo (3.13.0) • Installing regex (2022.10.31) • Installing responses (0.18.0) • Installing xxhash (3.2.0) • Installing zstandard (0.20.0) • Installing apache-beam (2.45.0) • Installing datasets (2.10.1) ```

Version mismatch with multiprocess and dill on Python 3.10 ### Describe the bug Grabbing the latest version of `datasets` and `apache-beam` with `poetry` using Python 3.10 gives a crash at runtime. The crash is ``` File "/Users/adpauls/sc/git/DSI-transformers/data/NQ/create_NQ_train_vali.py", line 1, in <module> import datasets File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/__init__.py", line 43, in <module> from .arrow_dataset import Dataset File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 65, in <module> from .arrow_reader import ArrowReader File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 30, in <module> from .download.download_config import DownloadConfig File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/__init__.py", line 9, in <module> from .download_manager import DownloadManager, DownloadMode File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/download_manager.py", line 35, in <module> from ..utils.py_utils import NestedDataStructure, map_nested, size_str File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 40, in <module> import multiprocess.pool File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 609, in <module> class ThreadPool(Pool): File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 611, in ThreadPool from .dummy import Process File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/dummy/__init__.py", line 87, in <module> class Condition(threading._Condition): AttributeError: module 'threading' has no attribute '_Condition'. Did you mean: 'Condition'? ``` I think this is a bad interaction of versions from `dill`, `multiprocess`, `apache-beam`, and `threading` from the Python (3.10) standard lib. Upgrading `multiprocess` to a version that does not crash like this is not possible because `apache-beam` pins `dill` to and old version: ``` Because multiprocess (0.70.10) depends on dill (>=0.3.2) and apache-beam (2.45.0) depends on dill (>=0.3.1.1,<0.3.2), multiprocess (0.70.10) is incompatible with apache-beam (2.45.0). And because no versions of apache-beam match >2.45.0,<3.0.0, multiprocess (0.70.10) is incompatible with apache-beam (>=2.45.0,<3.0.0). So, because yyy depends on both apache-beam (^2.45.0) and multiprocess (0.70.10), version solving failed. ``` Perhaps it is not right to file a bug here, but I'm not totally sure whose fault it is. And in any case, this is an immediate blocker to using `datasets` out of the box. Possibly related to https://github.com/huggingface/datasets/issues/5232. ### Steps to reproduce the bug Steps to reproduce: 1. Make a poetry project with this configuration ``` [tool.poetry] name = "yyy" version = "0.1.0" description = "" authors = ["Adam Pauls <adpauls@gmail.com>"] readme = "README.md" packages = [{ include = "xxx" }] [tool.poetry.dependencies] python = ">=3.10,<3.11" datasets = "^2.10.1" apache-beam = "^2.45.0" [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" ``` 2. `poetry install`. 3. `poetry run python -c "import datasets"`. ### Expected behavior Script runs. ### Environment info Python 3.10. Here are the versions installed by `poetry`: ``` •• Installing frozenlist (1.3.3) • Installing idna (3.4) • Installing multidict (6.0.4) • Installing aiosignal (1.3.1) • Installing async-timeout (4.0.2) • Installing attrs (22.2.0) • Installing certifi (2022.12.7) • Installing charset-normalizer (3.1.0) • Installing six (1.16.0) • Installing urllib3 (1.26.14) • Installing yarl (1.8.2) • Installing aiohttp (3.8.4) • Installing dill (0.3.1.1) • Installing docopt (0.6.2) • Installing filelock (3.9.0) • Installing numpy (1.22.4) • Installing pyparsing (3.0.9) • Installing protobuf (3.19.4) • Installing packaging (23.0) • Installing python-dateutil (2.8.2) • Installing pytz (2022.7.1) • Installing pyyaml (6.0) • Installing requests (2.28.2) • Installing tqdm (4.65.0) • Installing typing-extensions (4.5.0) • Installing cloudpickle (2.2.1) • Installing crcmod (1.7) • Installing fastavro (1.7.2) • Installing fasteners (0.18) • Installing fsspec (2023.3.0) • Installing grpcio (1.51.3) • Installing hdfs (2.7.0) • Installing httplib2 (0.20.4) • Installing huggingface-hub (0.12.1) • Installing multiprocess (0.70.9) • Installing objsize (0.6.1) • Installing orjson (3.8.7) • Installing pandas (1.5.3) • Installing proto-plus (1.22.2) • Installing pyarrow (9.0.0) • Installing pydot (1.4.2) • Installing pymongo (3.13.0) • Installing regex (2022.10.31) • Installing responses (0.18.0) • Installing xxhash (3.2.0) • Installing zstandard (0.20.0) • Installing apache-beam (2.45.0) • Installing datasets (2.10.1) ``` I have made a workaround by forcing an install of the version of `multiprocess` version `0.70.15` (after installing `datasets` and `apache-beam`). I can confirm that (on Python 3.10 in [this colab notebook](https://colab.research.google.com/drive/1PTeGlshamFcJZix_GiS3vMXX_YzAhGv0?usp=sharing)) `datasets` can download pre-processed Wikipedia dumps and can download non-pre-processed dumps using `beam_runner="DirectRunner"`. I don't know if/how other `beam_runner`s can be made compatible.

https://github.com/huggingface/datasets/issues/5612

I'm attaching a minimal reproducible example: ```python from datasets import load_dataset import pyarrow as pa import pyarrow.parquet as pq table_with_map = pa.Table.from_pydict( {"a": [1, 2], "b": [[("a", 2)], [("b", 4)]]}, schema=pa.schema({"a": pa.int32(), "b": pa.map_(pa.string(), pa.int32())}) ) pq.write_table(table_with_map, "parquet_with_map.parquet") dset = load_dataset("parquet", data_files="parquet_with_map.parquet", split="train") # error unless streaming=True ``` For a dataset generated with the packaged loaders (CSV, JSON, Parquet), `streaming=True` sets the dataset's features to `None` (unless explicitly provided in `load_dataset`), hence no error will be thrown as long as the features stay "unresolved" (resolving the features with `_resolve_features` will lead to an error).

Arrow map type in parquet files unsupported

### Describe the bug When I try to load parquet files that were processed with Spark, I get the following issue: `ValueError: Arrow type map<string, string ('warc_headers')> does not have a datasets dtype equivalent.` Strangely, loading the dataset with `streaming=True` solves the issue. ### Steps to reproduce the bug The dataset is private, but this can be reproduced with any dataset that has Arrow maps. ### Expected behavior Loading the dataset no matter whether streaming is True or not. ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-5.15.0-1029-gcp-x86_64-with-glibc2.31 - Python version: 3.10.7 - PyArrow version: 8.0.0 - Pandas version: 1.4.2

Arrow map type in parquet files unsupported ### Describe the bug When I try to load parquet files that were processed with Spark, I get the following issue: `ValueError: Arrow type map<string, string ('warc_headers')> does not have a datasets dtype equivalent.` Strangely, loading the dataset with `streaming=True` solves the issue. ### Steps to reproduce the bug The dataset is private, but this can be reproduced with any dataset that has Arrow maps. ### Expected behavior Loading the dataset no matter whether streaming is True or not. ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-5.15.0-1029-gcp-x86_64-with-glibc2.31 - Python version: 3.10.7 - PyArrow version: 8.0.0 - Pandas version: 1.4.2 I'm attaching a minimal reproducible example: ```python from datasets import load_dataset import pyarrow as pa import pyarrow.parquet as pq table_with_map = pa.Table.from_pydict( {"a": [1, 2], "b": [[("a", 2)], [("b", 4)]]}, schema=pa.schema({"a": pa.int32(), "b": pa.map_(pa.string(), pa.int32())}) ) pq.write_table(table_with_map, "parquet_with_map.parquet") dset = load_dataset("parquet", data_files="parquet_with_map.parquet", split="train") # error unless streaming=True ``` For a dataset generated with the packaged loaders (CSV, JSON, Parquet), `streaming=True` sets the dataset's features to `None` (unless explicitly provided in `load_dataset`), hence no error will be thrown as long as the features stay "unresolved" (resolving the features with `_resolve_features` will lead to an error).

https://github.com/huggingface/datasets/issues/5612

I've also been wondering about datasets support for Arrow Map datatypes. I had a situation where I had a pandas series of dict[str, float] with hundreds of different possible key values (ie. not bounded), and this got converted to a sequence of structs where every single struct had the entire set of keys. I worked around it, by explicitly creating a sequence of [str, float], but given that pyarrow has an explicit Map datatype, it would be good to be able to explicitly cast/force this data type combination.

Arrow map type in parquet files unsupported

### Describe the bug When I try to load parquet files that were processed with Spark, I get the following issue: `ValueError: Arrow type map<string, string ('warc_headers')> does not have a datasets dtype equivalent.` Strangely, loading the dataset with `streaming=True` solves the issue. ### Steps to reproduce the bug The dataset is private, but this can be reproduced with any dataset that has Arrow maps. ### Expected behavior Loading the dataset no matter whether streaming is True or not. ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-5.15.0-1029-gcp-x86_64-with-glibc2.31 - Python version: 3.10.7 - PyArrow version: 8.0.0 - Pandas version: 1.4.2

Arrow map type in parquet files unsupported ### Describe the bug When I try to load parquet files that were processed with Spark, I get the following issue: `ValueError: Arrow type map<string, string ('warc_headers')> does not have a datasets dtype equivalent.` Strangely, loading the dataset with `streaming=True` solves the issue. ### Steps to reproduce the bug The dataset is private, but this can be reproduced with any dataset that has Arrow maps. ### Expected behavior Loading the dataset no matter whether streaming is True or not. ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-5.15.0-1029-gcp-x86_64-with-glibc2.31 - Python version: 3.10.7 - PyArrow version: 8.0.0 - Pandas version: 1.4.2 I've also been wondering about datasets support for Arrow Map datatypes. I had a situation where I had a pandas series of dict[str, float] with hundreds of different possible key values (ie. not bounded), and this got converted to a sequence of structs where every single struct had the entire set of keys. I worked around it, by explicitly creating a sequence of [str, float], but given that pyarrow has an explicit Map datatype, it would be good to be able to explicitly cast/force this data type combination.

https://github.com/huggingface/datasets/issues/5610

Same problem, transformers 4.28.1 datasets 2.12.0 leak around 100Mb per 10 seconds when use dataloader_num_werker > 0 in training argumennts for transformer train, possile bug in transformers repo, but still not found solution :(

use datasets streaming mode in trainer ddp mode cause memory leak

### Describe the bug use datasets streaming mode in trainer ddp mode cause memory leak ### Steps to reproduce the bug import os import time import datetime import sys import numpy as np import random import torch from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler,DistributedSampler,BatchSampler torch.manual_seed(42) from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, GPT2Model,DataCollatorForLanguageModeling,AutoModelForCausalLM from transformers import AdamW, get_linear_schedule_with_warmup hf_model_path ='./Wenzhong-GPT2-110M' tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path) tokenizer.add_special_tokens({'pad_token': '<|pad|>'}) from datasets import load_dataset gpus=8 max_len = 576 batch_size_node = 17 save_step = 5000 gradient_accumulation = 2 dataloader_num = 4 max_step = 351000*1000//batch_size_node//gradient_accumulation//gpus #max_step = -1 print("total_step:%d"%(max_step)) import datasets datasets.version dataset = load_dataset("text", data_files="./gpt_data_v1/*",split='train',cache_dir='./dataset_cache',streaming=True) print('load over') shuffled_dataset = dataset.shuffle(seed=42) print('shuffle over') def dataset_tokener(example,max_lenth=max_len): example['text'] = list(map(lambda x : x.strip()+'<|endoftext|>',example['text'] )) return tokenizer(example['text'], truncation=True, max_length=max_lenth, padding="longest") new_new_dataset = shuffled_dataset.map(dataset_tokener, batched=True, remove_columns=["text"]) print('map over') configuration = GPT2Config.from_pretrained(hf_model_path, output_hidden_states=False) model = AutoModelForCausalLM.from_pretrained(hf_model_path) model.resize_token_embeddings(len(tokenizer)) seed_val = 42 random.seed(seed_val) np.random.seed(seed_val) torch.manual_seed(seed_val) torch.cuda.manual_seed_all(seed_val) from transformers import Trainer,TrainingArguments import os print("strat train") training_args = TrainingArguments(output_dir="./test_trainer", num_train_epochs=1.0, report_to="none", do_train=True, dataloader_num_workers=dataloader_num, local_rank=int(os.environ.get('LOCAL_RANK', -1)), overwrite_output_dir=True, logging_strategy='steps', logging_first_step=True, logging_dir="./logs", log_on_each_node=False, per_device_train_batch_size=batch_size_node, warmup_ratio=0.03, save_steps=save_step, save_total_limit=5, gradient_accumulation_steps=gradient_accumulation, max_steps=max_step, disable_tqdm=False, data_seed=42 ) trainer = Trainer( model=model, args=training_args, train_dataset=new_new_dataset, eval_dataset=None, tokenizer=tokenizer, data_collator=DataCollatorForLanguageModeling(tokenizer,mlm=False), #compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None, #preprocess_logits_for_metrics=preprocess_logits_for_metrics #if training_args.do_eval and not is_torch_tpu_available() #else None, ) trainer.train(resume_from_checkpoint=True) ### Expected behavior use the train code uppper my dataset ./gpt_data_v1 have 1000 files, each file size is 120mb start cmd is : python -m torch.distributed.launch --nproc_per_node=8 my_train.py here is result:  here is memory usage monitor in 12 hours  every dataloader work allocate over 24gb cpu memory according to memory usage monitor in 12 hours,sometime small memory releases, but total memory usage is increase. i think datasets streaming mode should not used so much memery,so maybe somewhere has memory leak. ### Environment info pytorch 1.11.0 py 3.8 cuda 11.3 transformers 4.26.1 datasets 2.9.0

use datasets streaming mode in trainer ddp mode cause memory leak ### Describe the bug use datasets streaming mode in trainer ddp mode cause memory leak ### Steps to reproduce the bug import os import time import datetime import sys import numpy as np import random import torch from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler,DistributedSampler,BatchSampler torch.manual_seed(42) from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, GPT2Model,DataCollatorForLanguageModeling,AutoModelForCausalLM from transformers import AdamW, get_linear_schedule_with_warmup hf_model_path ='./Wenzhong-GPT2-110M' tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path) tokenizer.add_special_tokens({'pad_token': '<|pad|>'}) from datasets import load_dataset gpus=8 max_len = 576 batch_size_node = 17 save_step = 5000 gradient_accumulation = 2 dataloader_num = 4 max_step = 351000*1000//batch_size_node//gradient_accumulation//gpus #max_step = -1 print("total_step:%d"%(max_step)) import datasets datasets.version dataset = load_dataset("text", data_files="./gpt_data_v1/*",split='train',cache_dir='./dataset_cache',streaming=True) print('load over') shuffled_dataset = dataset.shuffle(seed=42) print('shuffle over') def dataset_tokener(example,max_lenth=max_len): example['text'] = list(map(lambda x : x.strip()+'<|endoftext|>',example['text'] )) return tokenizer(example['text'], truncation=True, max_length=max_lenth, padding="longest") new_new_dataset = shuffled_dataset.map(dataset_tokener, batched=True, remove_columns=["text"]) print('map over') configuration = GPT2Config.from_pretrained(hf_model_path, output_hidden_states=False) model = AutoModelForCausalLM.from_pretrained(hf_model_path) model.resize_token_embeddings(len(tokenizer)) seed_val = 42 random.seed(seed_val) np.random.seed(seed_val) torch.manual_seed(seed_val) torch.cuda.manual_seed_all(seed_val) from transformers import Trainer,TrainingArguments import os print("strat train") training_args = TrainingArguments(output_dir="./test_trainer", num_train_epochs=1.0, report_to="none", do_train=True, dataloader_num_workers=dataloader_num, local_rank=int(os.environ.get('LOCAL_RANK', -1)), overwrite_output_dir=True, logging_strategy='steps', logging_first_step=True, logging_dir="./logs", log_on_each_node=False, per_device_train_batch_size=batch_size_node, warmup_ratio=0.03, save_steps=save_step, save_total_limit=5, gradient_accumulation_steps=gradient_accumulation, max_steps=max_step, disable_tqdm=False, data_seed=42 ) trainer = Trainer( model=model, args=training_args, train_dataset=new_new_dataset, eval_dataset=None, tokenizer=tokenizer, data_collator=DataCollatorForLanguageModeling(tokenizer,mlm=False), #compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None, #preprocess_logits_for_metrics=preprocess_logits_for_metrics #if training_args.do_eval and not is_torch_tpu_available() #else None, ) trainer.train(resume_from_checkpoint=True) ### Expected behavior use the train code uppper my dataset ./gpt_data_v1 have 1000 files, each file size is 120mb start cmd is : python -m torch.distributed.launch --nproc_per_node=8 my_train.py here is result:  here is memory usage monitor in 12 hours  every dataloader work allocate over 24gb cpu memory according to memory usage monitor in 12 hours,sometime small memory releases, but total memory usage is increase. i think datasets streaming mode should not used so much memery,so maybe somewhere has memory leak. ### Environment info pytorch 1.11.0 py 3.8 cuda 11.3 transformers 4.26.1 datasets 2.9.0 Same problem, transformers 4.28.1 datasets 2.12.0 leak around 100Mb per 10 seconds when use dataloader_num_werker > 0 in training argumennts for transformer train, possile bug in transformers repo, but still not found solution :(

https://github.com/huggingface/datasets/issues/5610

found an article described a problem, may be helpful for somebody: https://ppwwyyxx.com/blog/2022/Demystify-RAM-Usage-in-Multiprocess-DataLoader/ I confirm, it`s not memory leak, after some time memory growing has stopped

use datasets streaming mode in trainer ddp mode cause memory leak

### Describe the bug use datasets streaming mode in trainer ddp mode cause memory leak ### Steps to reproduce the bug import os import time import datetime import sys import numpy as np import random import torch from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler,DistributedSampler,BatchSampler torch.manual_seed(42) from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, GPT2Model,DataCollatorForLanguageModeling,AutoModelForCausalLM from transformers import AdamW, get_linear_schedule_with_warmup hf_model_path ='./Wenzhong-GPT2-110M' tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path) tokenizer.add_special_tokens({'pad_token': '<|pad|>'}) from datasets import load_dataset gpus=8 max_len = 576 batch_size_node = 17 save_step = 5000 gradient_accumulation = 2 dataloader_num = 4 max_step = 351000*1000//batch_size_node//gradient_accumulation//gpus #max_step = -1 print("total_step:%d"%(max_step)) import datasets datasets.version dataset = load_dataset("text", data_files="./gpt_data_v1/*",split='train',cache_dir='./dataset_cache',streaming=True) print('load over') shuffled_dataset = dataset.shuffle(seed=42) print('shuffle over') def dataset_tokener(example,max_lenth=max_len): example['text'] = list(map(lambda x : x.strip()+'<|endoftext|>',example['text'] )) return tokenizer(example['text'], truncation=True, max_length=max_lenth, padding="longest") new_new_dataset = shuffled_dataset.map(dataset_tokener, batched=True, remove_columns=["text"]) print('map over') configuration = GPT2Config.from_pretrained(hf_model_path, output_hidden_states=False) model = AutoModelForCausalLM.from_pretrained(hf_model_path) model.resize_token_embeddings(len(tokenizer)) seed_val = 42 random.seed(seed_val) np.random.seed(seed_val) torch.manual_seed(seed_val) torch.cuda.manual_seed_all(seed_val) from transformers import Trainer,TrainingArguments import os print("strat train") training_args = TrainingArguments(output_dir="./test_trainer", num_train_epochs=1.0, report_to="none", do_train=True, dataloader_num_workers=dataloader_num, local_rank=int(os.environ.get('LOCAL_RANK', -1)), overwrite_output_dir=True, logging_strategy='steps', logging_first_step=True, logging_dir="./logs", log_on_each_node=False, per_device_train_batch_size=batch_size_node, warmup_ratio=0.03, save_steps=save_step, save_total_limit=5, gradient_accumulation_steps=gradient_accumulation, max_steps=max_step, disable_tqdm=False, data_seed=42 ) trainer = Trainer( model=model, args=training_args, train_dataset=new_new_dataset, eval_dataset=None, tokenizer=tokenizer, data_collator=DataCollatorForLanguageModeling(tokenizer,mlm=False), #compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None, #preprocess_logits_for_metrics=preprocess_logits_for_metrics #if training_args.do_eval and not is_torch_tpu_available() #else None, ) trainer.train(resume_from_checkpoint=True) ### Expected behavior use the train code uppper my dataset ./gpt_data_v1 have 1000 files, each file size is 120mb start cmd is : python -m torch.distributed.launch --nproc_per_node=8 my_train.py here is result:  here is memory usage monitor in 12 hours  every dataloader work allocate over 24gb cpu memory according to memory usage monitor in 12 hours,sometime small memory releases, but total memory usage is increase. i think datasets streaming mode should not used so much memery,so maybe somewhere has memory leak. ### Environment info pytorch 1.11.0 py 3.8 cuda 11.3 transformers 4.26.1 datasets 2.9.0

use datasets streaming mode in trainer ddp mode cause memory leak ### Describe the bug use datasets streaming mode in trainer ddp mode cause memory leak ### Steps to reproduce the bug import os import time import datetime import sys import numpy as np import random import torch from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler,DistributedSampler,BatchSampler torch.manual_seed(42) from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, GPT2Model,DataCollatorForLanguageModeling,AutoModelForCausalLM from transformers import AdamW, get_linear_schedule_with_warmup hf_model_path ='./Wenzhong-GPT2-110M' tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path) tokenizer.add_special_tokens({'pad_token': '<|pad|>'}) from datasets import load_dataset gpus=8 max_len = 576 batch_size_node = 17 save_step = 5000 gradient_accumulation = 2 dataloader_num = 4 max_step = 351000*1000//batch_size_node//gradient_accumulation//gpus #max_step = -1 print("total_step:%d"%(max_step)) import datasets datasets.version dataset = load_dataset("text", data_files="./gpt_data_v1/*",split='train',cache_dir='./dataset_cache',streaming=True) print('load over') shuffled_dataset = dataset.shuffle(seed=42) print('shuffle over') def dataset_tokener(example,max_lenth=max_len): example['text'] = list(map(lambda x : x.strip()+'<|endoftext|>',example['text'] )) return tokenizer(example['text'], truncation=True, max_length=max_lenth, padding="longest") new_new_dataset = shuffled_dataset.map(dataset_tokener, batched=True, remove_columns=["text"]) print('map over') configuration = GPT2Config.from_pretrained(hf_model_path, output_hidden_states=False) model = AutoModelForCausalLM.from_pretrained(hf_model_path) model.resize_token_embeddings(len(tokenizer)) seed_val = 42 random.seed(seed_val) np.random.seed(seed_val) torch.manual_seed(seed_val) torch.cuda.manual_seed_all(seed_val) from transformers import Trainer,TrainingArguments import os print("strat train") training_args = TrainingArguments(output_dir="./test_trainer", num_train_epochs=1.0, report_to="none", do_train=True, dataloader_num_workers=dataloader_num, local_rank=int(os.environ.get('LOCAL_RANK', -1)), overwrite_output_dir=True, logging_strategy='steps', logging_first_step=True, logging_dir="./logs", log_on_each_node=False, per_device_train_batch_size=batch_size_node, warmup_ratio=0.03, save_steps=save_step, save_total_limit=5, gradient_accumulation_steps=gradient_accumulation, max_steps=max_step, disable_tqdm=False, data_seed=42 ) trainer = Trainer( model=model, args=training_args, train_dataset=new_new_dataset, eval_dataset=None, tokenizer=tokenizer, data_collator=DataCollatorForLanguageModeling(tokenizer,mlm=False), #compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None, #preprocess_logits_for_metrics=preprocess_logits_for_metrics #if training_args.do_eval and not is_torch_tpu_available() #else None, ) trainer.train(resume_from_checkpoint=True) ### Expected behavior use the train code uppper my dataset ./gpt_data_v1 have 1000 files, each file size is 120mb start cmd is : python -m torch.distributed.launch --nproc_per_node=8 my_train.py here is result:  here is memory usage monitor in 12 hours  every dataloader work allocate over 24gb cpu memory according to memory usage monitor in 12 hours,sometime small memory releases, but total memory usage is increase. i think datasets streaming mode should not used so much memery,so maybe somewhere has memory leak. ### Environment info pytorch 1.11.0 py 3.8 cuda 11.3 transformers 4.26.1 datasets 2.9.0 found an article described a problem, may be helpful for somebody: https://ppwwyyxx.com/blog/2022/Demystify-RAM-Usage-in-Multiprocess-DataLoader/ I confirm, it`s not memory leak, after some time memory growing has stopped

https://github.com/huggingface/datasets/issues/5609

Hi! We've recently made some improvements to `save_to_disk`/`list_to_disk` (100x faster in some scenarios), so it would help if you could install `datasets` directly from `main` (`pip install git+https://github.com/huggingface/datasets.git`) and re-run the "benchmark".

`load_from_disk` vs `load_dataset` performance.

### Describe the bug I have downloaded `openwebtext` (~12GB) and filtered out a small amount of junk (it's still huge). Now, I would like to use this filtered version for future work. It seems I have two choices: 1. Use `load_dataset` each time, relying on the cache mechanism, and re-run my filtering. 2. `save_to_disk` and then use `load_from_disk` to load the filtered version. The performance of these two approaches is wildly different: * Using `load_dataset` takes about 20 seconds to load the dataset, and a few seconds to re-filter (thanks to the brilliant filter/map caching) * Using `load_from_disk` takes 14 minutes! And the second time I tried, the session just crashed (on a machine with 32GB of RAM) I don't know if you'd call this a bug, but it seems like there shouldn't need to be two methods to load from disk, or that they should not take such wildly different amounts of time, or that one should not crash. Or maybe that the docs could offer some guidance about when to pick which method and why two methods exist, or just how do most people do it? Something I couldn't work out from reading the docs was this: can I modify a dataset from the hub, save it (locally) and use `load_dataset` to load it? This [post seemed to suggest that the answer is no](https://discuss.huggingface.co/t/save-and-load-datasets/9260). ### Steps to reproduce the bug See above ### Expected behavior Load times should be about the same. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

`load_from_disk` vs `load_dataset` performance. ### Describe the bug I have downloaded `openwebtext` (~12GB) and filtered out a small amount of junk (it's still huge). Now, I would like to use this filtered version for future work. It seems I have two choices: 1. Use `load_dataset` each time, relying on the cache mechanism, and re-run my filtering. 2. `save_to_disk` and then use `load_from_disk` to load the filtered version. The performance of these two approaches is wildly different: * Using `load_dataset` takes about 20 seconds to load the dataset, and a few seconds to re-filter (thanks to the brilliant filter/map caching) * Using `load_from_disk` takes 14 minutes! And the second time I tried, the session just crashed (on a machine with 32GB of RAM) I don't know if you'd call this a bug, but it seems like there shouldn't need to be two methods to load from disk, or that they should not take such wildly different amounts of time, or that one should not crash. Or maybe that the docs could offer some guidance about when to pick which method and why two methods exist, or just how do most people do it? Something I couldn't work out from reading the docs was this: can I modify a dataset from the hub, save it (locally) and use `load_dataset` to load it? This [post seemed to suggest that the answer is no](https://discuss.huggingface.co/t/save-and-load-datasets/9260). ### Steps to reproduce the bug See above ### Expected behavior Load times should be about the same. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 Hi! We've recently made some improvements to `save_to_disk`/`list_to_disk` (100x faster in some scenarios), so it would help if you could install `datasets` directly from `main` (`pip install git+https://github.com/huggingface/datasets.git`) and re-run the "benchmark".

https://github.com/huggingface/datasets/issues/5609

@mariosasko is that fix released to pip in the meantime? Asking cause im facing still the same issue (regarding loading images from local paths): ``` dataset = load_dataset("csv", cache_dir="cache", data_files=["/STORAGE/DATA/mijam/vit/code/list_filtered.csv"], num_proc=16, split="train").cast_column("image", Image()) dataset = dataset.class_encode_column("label") ``` quite fast. Then I do `save_to_disk()` and some time later: ``` dataset = load_from_disk('/STORAGE/DATA/mijam/accel/saved_arrow_big') ``` really slow. In theory it should be quicked since it only loads arrow files, no conversions and so on.

`load_from_disk` vs `load_dataset` performance.

### Describe the bug I have downloaded `openwebtext` (~12GB) and filtered out a small amount of junk (it's still huge). Now, I would like to use this filtered version for future work. It seems I have two choices: 1. Use `load_dataset` each time, relying on the cache mechanism, and re-run my filtering. 2. `save_to_disk` and then use `load_from_disk` to load the filtered version. The performance of these two approaches is wildly different: * Using `load_dataset` takes about 20 seconds to load the dataset, and a few seconds to re-filter (thanks to the brilliant filter/map caching) * Using `load_from_disk` takes 14 minutes! And the second time I tried, the session just crashed (on a machine with 32GB of RAM) I don't know if you'd call this a bug, but it seems like there shouldn't need to be two methods to load from disk, or that they should not take such wildly different amounts of time, or that one should not crash. Or maybe that the docs could offer some guidance about when to pick which method and why two methods exist, or just how do most people do it? Something I couldn't work out from reading the docs was this: can I modify a dataset from the hub, save it (locally) and use `load_dataset` to load it? This [post seemed to suggest that the answer is no](https://discuss.huggingface.co/t/save-and-load-datasets/9260). ### Steps to reproduce the bug See above ### Expected behavior Load times should be about the same. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

`load_from_disk` vs `load_dataset` performance. ### Describe the bug I have downloaded `openwebtext` (~12GB) and filtered out a small amount of junk (it's still huge). Now, I would like to use this filtered version for future work. It seems I have two choices: 1. Use `load_dataset` each time, relying on the cache mechanism, and re-run my filtering. 2. `save_to_disk` and then use `load_from_disk` to load the filtered version. The performance of these two approaches is wildly different: * Using `load_dataset` takes about 20 seconds to load the dataset, and a few seconds to re-filter (thanks to the brilliant filter/map caching) * Using `load_from_disk` takes 14 minutes! And the second time I tried, the session just crashed (on a machine with 32GB of RAM) I don't know if you'd call this a bug, but it seems like there shouldn't need to be two methods to load from disk, or that they should not take such wildly different amounts of time, or that one should not crash. Or maybe that the docs could offer some guidance about when to pick which method and why two methods exist, or just how do most people do it? Something I couldn't work out from reading the docs was this: can I modify a dataset from the hub, save it (locally) and use `load_dataset` to load it? This [post seemed to suggest that the answer is no](https://discuss.huggingface.co/t/save-and-load-datasets/9260). ### Steps to reproduce the bug See above ### Expected behavior Load times should be about the same. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 @mariosasko is that fix released to pip in the meantime? Asking cause im facing still the same issue (regarding loading images from local paths): ``` dataset = load_dataset("csv", cache_dir="cache", data_files=["/STORAGE/DATA/mijam/vit/code/list_filtered.csv"], num_proc=16, split="train").cast_column("image", Image()) dataset = dataset.class_encode_column("label") ``` quite fast. Then I do `save_to_disk()` and some time later: ``` dataset = load_from_disk('/STORAGE/DATA/mijam/accel/saved_arrow_big') ``` really slow. In theory it should be quicked since it only loads arrow files, no conversions and so on.

https://github.com/huggingface/datasets/issues/5609

@mjamroz I assume your CSV file stores image file paths. This means `save_to_disk` needs to embed the image bytes resulting in a much bigger Arrow file (than the initial one). Maybe specifying `num_shards` to make the Arrow files smaller can help (large Arrow files on some systems take a long time to load).

`load_from_disk` vs `load_dataset` performance.

### Describe the bug I have downloaded `openwebtext` (~12GB) and filtered out a small amount of junk (it's still huge). Now, I would like to use this filtered version for future work. It seems I have two choices: 1. Use `load_dataset` each time, relying on the cache mechanism, and re-run my filtering. 2. `save_to_disk` and then use `load_from_disk` to load the filtered version. The performance of these two approaches is wildly different: * Using `load_dataset` takes about 20 seconds to load the dataset, and a few seconds to re-filter (thanks to the brilliant filter/map caching) * Using `load_from_disk` takes 14 minutes! And the second time I tried, the session just crashed (on a machine with 32GB of RAM) I don't know if you'd call this a bug, but it seems like there shouldn't need to be two methods to load from disk, or that they should not take such wildly different amounts of time, or that one should not crash. Or maybe that the docs could offer some guidance about when to pick which method and why two methods exist, or just how do most people do it? Something I couldn't work out from reading the docs was this: can I modify a dataset from the hub, save it (locally) and use `load_dataset` to load it? This [post seemed to suggest that the answer is no](https://discuss.huggingface.co/t/save-and-load-datasets/9260). ### Steps to reproduce the bug See above ### Expected behavior Load times should be about the same. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

`load_from_disk` vs `load_dataset` performance. ### Describe the bug I have downloaded `openwebtext` (~12GB) and filtered out a small amount of junk (it's still huge). Now, I would like to use this filtered version for future work. It seems I have two choices: 1. Use `load_dataset` each time, relying on the cache mechanism, and re-run my filtering. 2. `save_to_disk` and then use `load_from_disk` to load the filtered version. The performance of these two approaches is wildly different: * Using `load_dataset` takes about 20 seconds to load the dataset, and a few seconds to re-filter (thanks to the brilliant filter/map caching) * Using `load_from_disk` takes 14 minutes! And the second time I tried, the session just crashed (on a machine with 32GB of RAM) I don't know if you'd call this a bug, but it seems like there shouldn't need to be two methods to load from disk, or that they should not take such wildly different amounts of time, or that one should not crash. Or maybe that the docs could offer some guidance about when to pick which method and why two methods exist, or just how do most people do it? Something I couldn't work out from reading the docs was this: can I modify a dataset from the hub, save it (locally) and use `load_dataset` to load it? This [post seemed to suggest that the answer is no](https://discuss.huggingface.co/t/save-and-load-datasets/9260). ### Steps to reproduce the bug See above ### Expected behavior Load times should be about the same. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 @mjamroz I assume your CSV file stores image file paths. This means `save_to_disk` needs to embed the image bytes resulting in a much bigger Arrow file (than the initial one). Maybe specifying `num_shards` to make the Arrow files smaller can help (large Arrow files on some systems take a long time to load).

https://github.com/huggingface/datasets/issues/5608

Hi! > naming convention of mp3 files Yes, this could be the problem. MP3 files should end with `.mp3`/`.MP3` to be recognized as audio files. If the file names are not the culprit, can you paste the audio folder's directory structure to help us reproduce the error (e.g., by running the `tree "x"` command)?

audiofolder only creates dataset of 13 rows (files) when the data folder it's reading from has 20,000 mp3 files.

### Describe the bug x = load_dataset("audiofolder", data_dir="x") When running this, x is a dataset of 13 rows (files) when it should be 20,000 rows (files) as the data_dir "x" has 20,000 mp3 files. Does anyone know what could possibly cause this (naming convention of mp3 files, etc.) ### Steps to reproduce the bug x = load_dataset("audiofolder", data_dir="x") ### Expected behavior x = load_dataset("audiofolder", data_dir="x") should create a dataset of 20,000 rows (files). ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

audiofolder only creates dataset of 13 rows (files) when the data folder it's reading from has 20,000 mp3 files. ### Describe the bug x = load_dataset("audiofolder", data_dir="x") When running this, x is a dataset of 13 rows (files) when it should be 20,000 rows (files) as the data_dir "x" has 20,000 mp3 files. Does anyone know what could possibly cause this (naming convention of mp3 files, etc.) ### Steps to reproduce the bug x = load_dataset("audiofolder", data_dir="x") ### Expected behavior x = load_dataset("audiofolder", data_dir="x") should create a dataset of 20,000 rows (files). ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 Hi! > naming convention of mp3 files Yes, this could be the problem. MP3 files should end with `.mp3`/`.MP3` to be recognized as audio files. If the file names are not the culprit, can you paste the audio folder's directory structure to help us reproduce the error (e.g., by running the `tree "x"` command)?

https://github.com/huggingface/datasets/issues/5608

Hi! I'm sorry, I don't want to reveal my entire dataset, but here's a snippet (all of the mp3 files below are some of the ones not being recognized by audiofolder. Also, for another dataset, audiofolder loaded zero mp3 files because "train" was in the name of one of the mp3 files. my_dataset ├── data │   ├── VHA_Innovation_Stories_-_Day_2-123.mp3 │   ├── VHA_Innovation_Stories_-_Day_2-124.mp3 │   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-93.mp3 │   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-94.mp3 │   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-95.mp3 │   ├── Your_Impact\357\274\232_Neurosurgery_equipment-5.mp3 │   └── Your_Impact\357\274\232_Neurosurgery_equipment-6.mp3 └── metadata.csv Here's a few of the 13 files recognized by the dataset: British_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-1.mp3 British_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-2.mp3 British_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-3.mp3 IVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-1.mp3 IVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-2.mp3

audiofolder only creates dataset of 13 rows (files) when the data folder it's reading from has 20,000 mp3 files.

### Describe the bug x = load_dataset("audiofolder", data_dir="x") When running this, x is a dataset of 13 rows (files) when it should be 20,000 rows (files) as the data_dir "x" has 20,000 mp3 files. Does anyone know what could possibly cause this (naming convention of mp3 files, etc.) ### Steps to reproduce the bug x = load_dataset("audiofolder", data_dir="x") ### Expected behavior x = load_dataset("audiofolder", data_dir="x") should create a dataset of 20,000 rows (files). ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

audiofolder only creates dataset of 13 rows (files) when the data folder it's reading from has 20,000 mp3 files. ### Describe the bug x = load_dataset("audiofolder", data_dir="x") When running this, x is a dataset of 13 rows (files) when it should be 20,000 rows (files) as the data_dir "x" has 20,000 mp3 files. Does anyone know what could possibly cause this (naming convention of mp3 files, etc.) ### Steps to reproduce the bug x = load_dataset("audiofolder", data_dir="x") ### Expected behavior x = load_dataset("audiofolder", data_dir="x") should create a dataset of 20,000 rows (files). ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 Hi! I'm sorry, I don't want to reveal my entire dataset, but here's a snippet (all of the mp3 files below are some of the ones not being recognized by audiofolder. Also, for another dataset, audiofolder loaded zero mp3 files because "train" was in the name of one of the mp3 files. my_dataset ├── data │   ├── VHA_Innovation_Stories_-_Day_2-123.mp3 │   ├── VHA_Innovation_Stories_-_Day_2-124.mp3 │   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-93.mp3 │   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-94.mp3 │   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-95.mp3 │   ├── Your_Impact\357\274\232_Neurosurgery_equipment-5.mp3 │   └── Your_Impact\357\274\232_Neurosurgery_equipment-6.mp3 └── metadata.csv Here's a few of the 13 files recognized by the dataset: British_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-1.mp3 British_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-2.mp3 British_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-3.mp3 IVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-1.mp3 IVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-2.mp3

https://github.com/huggingface/datasets/issues/5606

Hello, I have an interest in this issue. Is the `Dataset.to_dict` you are describing correct in the code here? https://github.com/huggingface/datasets/blob/35b789e8f6826b6b5a6b48fcc2416c890a1f326a/src/datasets/arrow_dataset.py#L4633-L4667

Add `Dataset.to_list` to the API

Since there is `Dataset.from_list` in the API, we should also add `Dataset.to_list` to be consistent. Regarding the implementation, we can re-use `Dataset.to_dict`'s code and replace the `to_pydict` calls with `to_pylist`.

Add `Dataset.to_list` to the API Since there is `Dataset.from_list` in the API, we should also add `Dataset.to_list` to be consistent. Regarding the implementation, we can re-use `Dataset.to_dict`'s code and replace the `to_pydict` calls with `to_pylist`. Hello, I have an interest in this issue. Is the `Dataset.to_dict` you are describing correct in the code here? https://github.com/huggingface/datasets/blob/35b789e8f6826b6b5a6b48fcc2416c890a1f326a/src/datasets/arrow_dataset.py#L4633-L4667

https://github.com/huggingface/datasets/issues/5604

Hi! You can specify `download_config=DownloadConfig(resume_download=True))` in `load_dataset` to resume the download when re-running the code after the timeout error: ```python from datasets import load_dataset, DownloadConfig dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets', download_config=DownloadConfig(resume_download=True)) ```

Problems with downloading The Pile

### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2

Problems with downloading The Pile ### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2 Hi! You can specify `download_config=DownloadConfig(resume_download=True))` in `load_dataset` to resume the download when re-running the code after the timeout error: ```python from datasets import load_dataset, DownloadConfig dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets', download_config=DownloadConfig(resume_download=True)) ```

https://github.com/huggingface/datasets/issues/5604

@mariosasko , I used your suggestion but its not saving anything , just stops and runs from the same point . below is the script to download and save on disk . ``` from datasets import load_dataset, DownloadConfig #load the Pile dataset from Hugging Face Datasets #dataset = load_dataset('the_pile') dataset = load_dataset('the_pile', split='train', cache_dir='datasets', download_config=DownloadConfig(resume_download=True)) # save each file in the dataset to disk for i, example in enumerate(dataset['train']): filename = f'pile_file_{i}.json' with open(filename, 'w') as f: f.write(str(example)) print("Finished saving Pile dataset files to disk.") ```

Problems with downloading The Pile

### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2

Problems with downloading The Pile ### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2 @mariosasko , I used your suggestion but its not saving anything , just stops and runs from the same point . below is the script to download and save on disk . ``` from datasets import load_dataset, DownloadConfig #load the Pile dataset from Hugging Face Datasets #dataset = load_dataset('the_pile') dataset = load_dataset('the_pile', split='train', cache_dir='datasets', download_config=DownloadConfig(resume_download=True)) # save each file in the dataset to disk for i, example in enumerate(dataset['train']): filename = f'pile_file_{i}.json' with open(filename, 'w') as f: f.write(str(example)) print("Finished saving Pile dataset files to disk.") ```

https://github.com/huggingface/datasets/issues/5604

@mariosasko , it shows nothing in dataset folder ``` du -sh /mnt/nlp/hugging_face/* 20K /mnt/nlp/hugging_face/datasets 4.0K /mnt/nlp/hugging_face/download_pile.py ```

Problems with downloading The Pile

### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2

Problems with downloading The Pile ### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2 @mariosasko , it shows nothing in dataset folder ``` du -sh /mnt/nlp/hugging_face/* 20K /mnt/nlp/hugging_face/datasets 4.0K /mnt/nlp/hugging_face/download_pile.py ```

https://github.com/huggingface/datasets/issues/5604

@mariosasko ``` root@d20f0ab8f4f8:/mnt/hugging_face# python3 download_pile.py No config specified, defaulting to: the_pile/all Downloading and preparing dataset the_pile/all to /mnt/hugging_face/datasets/the_pile/all/0.0.0/6fadc480ecb32470826cbf5900a9558b791ce55d5e9a0fdc8ad653e7b64bb349... Downloading data files: 0%| | 0/3 [00:00<?, ?it/s] Downloading data: 70%|████████████████████████████████████████████████████████████████████▊ | 10.7G/15.2G [12:09<11:53, 6.36MB/s] Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 15.2G/15.2G [22:15<00:00, 7.25MB/s] Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 15.2G/15.2G [46:17<00:00, 5.48MB/s] Downloading data: 40%|██████████████████████████████████████▏ | 6.07G/15.3G [50:49<1:17:02, 1.99MB/s] Traceback (most recent call last):██████████████████████████▊ | 6.07G/15.3G [50:49<25:35:23, 99.9kB/s] File "/usr/local/lib/python3.8/dist-packages/urllib3/response.py", line 444, in _error_catcher yield File "/usr/local/lib/python3.8/dist-packages/urllib3/response.py", line 567, in read data = self._fp_read(amt) if not fp_closed else b"" File "/usr/local/lib/python3.8/dist-packages/urllib3/response.py", line 525, in _fp_read data = self._fp.read(chunk_amt) File "/usr/lib/python3.8/http/client.py", line 459, in read n = self.readinto(b) File "/usr/lib/python3.8/http/client.py", line 503, in readinto n = self.fp.readinto(b) File "/usr/lib/python3.8/socket.py", line 669, in readinto return self._sock.recv_into(b) File "/usr/lib/python3.8/ssl.py", line 1241, in recv_into return self.read(nbytes, buffer) File "/usr/lib/python3.8/ssl.py", line 1099, in read return self._sslobj.read(len, buffer) ConnectionResetError: [Errno 104] Connection reset by peer During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 816, in generate yield from self.raw.stream(chunk_size, decode_content=True) File "/usr/local/lib/python3.8/dist-packages/urllib3/response.py", line 628, in stream data = self.read(amt=amt, decode_content=decode_content) File "/usr/local/lib/python3.8/dist-packages/urllib3/response.py", line 593, in read raise IncompleteRead(self._fp_bytes_read, self.length_remaining) File "/usr/lib/python3.8/contextlib.py", line 131, in __exit__ self.gen.throw(type, value, traceback) File "/usr/local/lib/python3.8/dist-packages/urllib3/response.py", line 461, in _error_catcher raise ProtocolError("Connection broken: %r" % e, e) urllib3.exceptions.ProtocolError: ("Connection broken: ConnectionResetError(104, 'Connection reset by peer')", ConnectionResetError(104, 'Connection reset by peer')) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "download_pile.py", line 6, in <module> dataset = load_dataset('the_pile', split='train', cache_dir='datasets', download_config=DownloadConfig(resume_download=True)) File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 945, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/root/.cache/huggingface/modules/datasets_modules/datasets/the_pile/6fadc480ecb32470826cbf5900a9558b791ce55d5e9a0fdc8ad653e7b64bb349/the_pile.py", line 192, in _split_generators data_dir = dl_manager.download(_DATA_URLS[self.config.name]) File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 427, in download downloaded_path_or_paths = map_nested( File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 443, in map_nested mapped = [ File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 444, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 363, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 363, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 346, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 453, in _download return cached_path(url_or_filename, download_config=download_config) File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 182, in cached_path output_path = get_from_cache( File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 575, in get_from_cache http_get( File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 379, in http_get for chunk in response.iter_content(chunk_size=1024): File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 818, in generate raise ChunkedEncodingError(e) requests.exceptions.ChunkedEncodingError: ("Connection broken: ConnectionResetError(104, 'Connection reset by peer')", ConnectionResetError(104, 'Connection reset by peer')) ```

Problems with downloading The Pile

### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2

Problems with downloading The Pile ### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2 @mariosasko ``` root@d20f0ab8f4f8:/mnt/hugging_face# python3 download_pile.py No config specified, defaulting to: the_pile/all Downloading and preparing dataset the_pile/all to /mnt/hugging_face/datasets/the_pile/all/0.0.0/6fadc480ecb32470826cbf5900a9558b791ce55d5e9a0fdc8ad653e7b64bb349... Downloading data files: 0%| | 0/3 [00:00<?, ?it/s] Downloading data: 70%|████████████████████████████████████████████████████████████████████▊ | 10.7G/15.2G [12:09<11:53, 6.36MB/s] Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 15.2G/15.2G [22:15<00:00, 7.25MB/s] Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 15.2G/15.2G [46:17<00:00, 5.48MB/s] Downloading data: 40%|██████████████████████████████████████▏ | 6.07G/15.3G [50:49<1:17:02, 1.99MB/s] Traceback (most recent call last):██████████████████████████▊ | 6.07G/15.3G [50:49<25:35:23, 99.9kB/s] File "/usr/local/lib/python3.8/dist-packages/urllib3/response.py", line 444, in _error_catcher yield File "/usr/local/lib/python3.8/dist-packages/urllib3/response.py", line 567, in read data = self._fp_read(amt) if not fp_closed else b"" File "/usr/local/lib/python3.8/dist-packages/urllib3/response.py", line 525, in _fp_read data = self._fp.read(chunk_amt) File "/usr/lib/python3.8/http/client.py", line 459, in read n = self.readinto(b) File "/usr/lib/python3.8/http/client.py", line 503, in readinto n = self.fp.readinto(b) File "/usr/lib/python3.8/socket.py", line 669, in readinto return self._sock.recv_into(b) File "/usr/lib/python3.8/ssl.py", line 1241, in recv_into return self.read(nbytes, buffer) File "/usr/lib/python3.8/ssl.py", line 1099, in read return self._sslobj.read(len, buffer) ConnectionResetError: [Errno 104] Connection reset by peer During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 816, in generate yield from self.raw.stream(chunk_size, decode_content=True) File "/usr/local/lib/python3.8/dist-packages/urllib3/response.py", line 628, in stream data = self.read(amt=amt, decode_content=decode_content) File "/usr/local/lib/python3.8/dist-packages/urllib3/response.py", line 593, in read raise IncompleteRead(self._fp_bytes_read, self.length_remaining) File "/usr/lib/python3.8/contextlib.py", line 131, in __exit__ self.gen.throw(type, value, traceback) File "/usr/local/lib/python3.8/dist-packages/urllib3/response.py", line 461, in _error_catcher raise ProtocolError("Connection broken: %r" % e, e) urllib3.exceptions.ProtocolError: ("Connection broken: ConnectionResetError(104, 'Connection reset by peer')", ConnectionResetError(104, 'Connection reset by peer')) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "download_pile.py", line 6, in <module> dataset = load_dataset('the_pile', split='train', cache_dir='datasets', download_config=DownloadConfig(resume_download=True)) File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 945, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/root/.cache/huggingface/modules/datasets_modules/datasets/the_pile/6fadc480ecb32470826cbf5900a9558b791ce55d5e9a0fdc8ad653e7b64bb349/the_pile.py", line 192, in _split_generators data_dir = dl_manager.download(_DATA_URLS[self.config.name]) File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 427, in download downloaded_path_or_paths = map_nested( File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 443, in map_nested mapped = [ File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 444, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 363, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 363, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 346, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 453, in _download return cached_path(url_or_filename, download_config=download_config) File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 182, in cached_path output_path = get_from_cache( File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 575, in get_from_cache http_get( File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 379, in http_get for chunk in response.iter_content(chunk_size=1024): File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 818, in generate raise ChunkedEncodingError(e) requests.exceptions.ChunkedEncodingError: ("Connection broken: ConnectionResetError(104, 'Connection reset by peer')", ConnectionResetError(104, 'Connection reset by peer')) ```

https://github.com/huggingface/datasets/issues/5604

Users with slow internet speed are doomed (4MB/s). The dataset downloads fine at minimum speed 10MB/s. Also, when the train splits were generated and then I removed the downloads folder to save up disk space, it started redownloading the whole dataset. Is there any way to use the already generated splits instead?

Problems with downloading The Pile

### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2

Problems with downloading The Pile ### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2 Users with slow internet speed are doomed (4MB/s). The dataset downloads fine at minimum speed 10MB/s. Also, when the train splits were generated and then I removed the downloads folder to save up disk space, it started redownloading the whole dataset. Is there any way to use the already generated splits instead?

https://github.com/huggingface/datasets/issues/5604

@sentialx @mariosasko , anytime on my above script , am I downloading and saving dataset correctly . Please suggest :)

Problems with downloading The Pile

### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2

Problems with downloading The Pile ### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2 @sentialx @mariosasko , anytime on my above script , am I downloading and saving dataset correctly . Please suggest :)

https://github.com/huggingface/datasets/issues/5604

@sentialx probably worth noting that `resume_download=True` doesn't directly save the dataset to disk, but instead just helps in resuming the dataset resume on interruption as @mariosasko mentions. resolving resumptions after a crash is [an open issue](https://github.com/huggingface/datasets/issues/5380) at the moment.

Problems with downloading The Pile

### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2

Problems with downloading The Pile ### Describe the bug The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.  Here are the downloaded files:  They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/). Alternatively, can I somehow download the files by myself and use the datasets preparing script? ### Steps to reproduce the bug dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets') ### Expected behavior The files should be downloaded correctly. ### Environment info - `datasets` version: 2.10.1 - Platform: Windows-10-10.0.22623-SP0 - Python version: 3.10.5 - PyArrow version: 9.0.0 - Pandas version: 1.4.2 @sentialx probably worth noting that `resume_download=True` doesn't directly save the dataset to disk, but instead just helps in resuming the dataset resume on interruption as @mariosasko mentions. resolving resumptions after a crash is [an open issue](https://github.com/huggingface/datasets/issues/5380) at the moment.

https://github.com/huggingface/datasets/issues/5601

Hi! It's better to report this kind of issue in the `huggingface_hub` repo, so if you still haven't resolved it, I suggest you open an issue there.

Authorization error

### Describe the bug Get `Authorization error` when try to push data into hugginface datasets hub. ### Steps to reproduce the bug I did all steps in the [tutorial](https://huggingface.co/docs/datasets/share), 1. `huggingface-cli login` with WRITE token 2. `git lfs install` 3. `git clone https://huggingface.co/datasets/namespace/your_dataset_name` 4. ``` cp /somewhere/data/*.json . git lfs track *.json git add .gitattributes git add *.json git commit -m "add json files" ``` but when I execute `git push` I got the error: ``` Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done. batch response: Authorization error. error: failed to push some refs to 'https://huggingface.co/datasets/zeusfsx/ukrainian-news' ``` Size of data ~100Gb. I have five json files - different parts. ### Expected behavior All my data pushed into hub ### Environment info - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.10.10 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

Authorization error ### Describe the bug Get `Authorization error` when try to push data into hugginface datasets hub. ### Steps to reproduce the bug I did all steps in the [tutorial](https://huggingface.co/docs/datasets/share), 1. `huggingface-cli login` with WRITE token 2. `git lfs install` 3. `git clone https://huggingface.co/datasets/namespace/your_dataset_name` 4. ``` cp /somewhere/data/*.json . git lfs track *.json git add .gitattributes git add *.json git commit -m "add json files" ``` but when I execute `git push` I got the error: ``` Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done. batch response: Authorization error. error: failed to push some refs to 'https://huggingface.co/datasets/zeusfsx/ukrainian-news' ``` Size of data ~100Gb. I have five json files - different parts. ### Expected behavior All my data pushed into hub ### Environment info - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.10.10 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 Hi! It's better to report this kind of issue in the `huggingface_hub` repo, so if you still haven't resolved it, I suggest you open an issue there.

https://github.com/huggingface/datasets/issues/5601

Yeah, I solved it. Problem was in osxkeychain. When I do `hugginface-cli login` it's add token with default account (username)`hg_user` but my repo contain other username. When I changed username in keychain - it works now.

Authorization error

### Describe the bug Get `Authorization error` when try to push data into hugginface datasets hub. ### Steps to reproduce the bug I did all steps in the [tutorial](https://huggingface.co/docs/datasets/share), 1. `huggingface-cli login` with WRITE token 2. `git lfs install` 3. `git clone https://huggingface.co/datasets/namespace/your_dataset_name` 4. ``` cp /somewhere/data/*.json . git lfs track *.json git add .gitattributes git add *.json git commit -m "add json files" ``` but when I execute `git push` I got the error: ``` Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done. batch response: Authorization error. error: failed to push some refs to 'https://huggingface.co/datasets/zeusfsx/ukrainian-news' ``` Size of data ~100Gb. I have five json files - different parts. ### Expected behavior All my data pushed into hub ### Environment info - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.10.10 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

Authorization error ### Describe the bug Get `Authorization error` when try to push data into hugginface datasets hub. ### Steps to reproduce the bug I did all steps in the [tutorial](https://huggingface.co/docs/datasets/share), 1. `huggingface-cli login` with WRITE token 2. `git lfs install` 3. `git clone https://huggingface.co/datasets/namespace/your_dataset_name` 4. ``` cp /somewhere/data/*.json . git lfs track *.json git add .gitattributes git add *.json git commit -m "add json files" ``` but when I execute `git push` I got the error: ``` Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done. batch response: Authorization error. error: failed to push some refs to 'https://huggingface.co/datasets/zeusfsx/ukrainian-news' ``` Size of data ~100Gb. I have five json files - different parts. ### Expected behavior All my data pushed into hub ### Environment info - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.10.10 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 Yeah, I solved it. Problem was in osxkeychain. When I do `hugginface-cli login` it's add token with default account (username)`hg_user` but my repo contain other username. When I changed username in keychain - it works now.

https://github.com/huggingface/datasets/issues/5600

Hi! > (see example of DreamboothDatasets) Could you please provide a link to it? If you are referring to the example in the `diffusers` repo, your issue is unrelated to `datasets` as that example uses `Dataset` from PyTorch to load data.

Dataloader getitem not working for DreamboothDatasets

### Describe the bug Dataloader getitem is not working as before (see example of [DreamboothDatasets](https://github.com/huggingface/peft/blob/main/examples/lora_dreambooth/train_dreambooth.py#L451C14-L529)) moving Datasets to 2.8.0 solved the issue. ### Steps to reproduce the bug 1- using DreamBoothDataset to load some images 2- error after loading when trying to visualise the images ### Expected behavior I was expecting a numpy array of the image ### Environment info - Platform: Linux-5.10.147+-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.3.5

Dataloader getitem not working for DreamboothDatasets ### Describe the bug Dataloader getitem is not working as before (see example of [DreamboothDatasets](https://github.com/huggingface/peft/blob/main/examples/lora_dreambooth/train_dreambooth.py#L451C14-L529)) moving Datasets to 2.8.0 solved the issue. ### Steps to reproduce the bug 1- using DreamBoothDataset to load some images 2- error after loading when trying to visualise the images ### Expected behavior I was expecting a numpy array of the image ### Environment info - Platform: Linux-5.10.147+-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 9.0.0 - Pandas version: 1.3.5 Hi! > (see example of DreamboothDatasets) Could you please provide a link to it? If you are referring to the example in the `diffusers` repo, your issue is unrelated to `datasets` as that example uses `Dataset` from PyTorch to load data.

https://github.com/huggingface/datasets/issues/5597

We won't support in-place modifications since `datasets` is based on the Apache Arrow format which doesn't support in-place modifications. In your case the old dataset is garbage collected pretty quickly so you won't have memory issues. Note that datasets loaded from disk (memory mapped) are not loaded in memory, and therefore the new dataset actually use the same buffers as the old one.

in-place dataset update

### Motivation For the circumstance that I creat an empty `Dataset` and keep appending new rows into it, I found that it leads to creating a new dataset at each call. It looks quite memory-consuming. I just wonder if there is any more efficient way to do this. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds = ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Feature request Call for in-place dataset update functions, that update the existing `Dataset` in place without creating a new copy. The interface is supposed to keep the same style as PyTorch, such as the in-place version of a `function` is named `function_`. For example, the in-pace version of `add_item`, i.e., `add_item_`, immediately updates the `Dataset`. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds.add_item_({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Related Functions * `.map` * `.filter` * `.add_item`

in-place dataset update ### Motivation For the circumstance that I creat an empty `Dataset` and keep appending new rows into it, I found that it leads to creating a new dataset at each call. It looks quite memory-consuming. I just wonder if there is any more efficient way to do this. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds = ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Feature request Call for in-place dataset update functions, that update the existing `Dataset` in place without creating a new copy. The interface is supposed to keep the same style as PyTorch, such as the in-place version of a `function` is named `function_`. For example, the in-pace version of `add_item`, i.e., `add_item_`, immediately updates the `Dataset`. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds.add_item_({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Related Functions * `.map` * `.filter` * `.add_item` We won't support in-place modifications since `datasets` is based on the Apache Arrow format which doesn't support in-place modifications. In your case the old dataset is garbage collected pretty quickly so you won't have memory issues. Note that datasets loaded from disk (memory mapped) are not loaded in memory, and therefore the new dataset actually use the same buffers as the old one.

https://github.com/huggingface/datasets/issues/5597

Thank you for your detailed reply. > In your case the old dataset is garbage collected pretty quickly so you won't have memory issues. I understand this, but it still copies the old dataset to create the new one, is this correct? So maybe it is not memory-consuming, but time-consuming?

in-place dataset update

### Motivation For the circumstance that I creat an empty `Dataset` and keep appending new rows into it, I found that it leads to creating a new dataset at each call. It looks quite memory-consuming. I just wonder if there is any more efficient way to do this. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds = ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Feature request Call for in-place dataset update functions, that update the existing `Dataset` in place without creating a new copy. The interface is supposed to keep the same style as PyTorch, such as the in-place version of a `function` is named `function_`. For example, the in-pace version of `add_item`, i.e., `add_item_`, immediately updates the `Dataset`. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds.add_item_({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Related Functions * `.map` * `.filter` * `.add_item`

in-place dataset update ### Motivation For the circumstance that I creat an empty `Dataset` and keep appending new rows into it, I found that it leads to creating a new dataset at each call. It looks quite memory-consuming. I just wonder if there is any more efficient way to do this. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds = ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Feature request Call for in-place dataset update functions, that update the existing `Dataset` in place without creating a new copy. The interface is supposed to keep the same style as PyTorch, such as the in-place version of a `function` is named `function_`. For example, the in-pace version of `add_item`, i.e., `add_item_`, immediately updates the `Dataset`. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds.add_item_({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Related Functions * `.map` * `.filter` * `.add_item` Thank you for your detailed reply. > In your case the old dataset is garbage collected pretty quickly so you won't have memory issues. I understand this, but it still copies the old dataset to create the new one, is this correct? So maybe it is not memory-consuming, but time-consuming?

https://github.com/huggingface/datasets/issues/5597

Indeed, and because of that it is more efficient to add multiple rows at once instead of one by one, using `concatenate_datasets` for example.

in-place dataset update

### Motivation For the circumstance that I creat an empty `Dataset` and keep appending new rows into it, I found that it leads to creating a new dataset at each call. It looks quite memory-consuming. I just wonder if there is any more efficient way to do this. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds = ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Feature request Call for in-place dataset update functions, that update the existing `Dataset` in place without creating a new copy. The interface is supposed to keep the same style as PyTorch, such as the in-place version of a `function` is named `function_`. For example, the in-pace version of `add_item`, i.e., `add_item_`, immediately updates the `Dataset`. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds.add_item_({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Related Functions * `.map` * `.filter` * `.add_item`

in-place dataset update ### Motivation For the circumstance that I creat an empty `Dataset` and keep appending new rows into it, I found that it leads to creating a new dataset at each call. It looks quite memory-consuming. I just wonder if there is any more efficient way to do this. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds = ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Feature request Call for in-place dataset update functions, that update the existing `Dataset` in place without creating a new copy. The interface is supposed to keep the same style as PyTorch, such as the in-place version of a `function` is named `function_`. For example, the in-pace version of `add_item`, i.e., `add_item_`, immediately updates the `Dataset`. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds.add_item_({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Related Functions * `.map` * `.filter` * `.add_item` Indeed, and because of that it is more efficient to add multiple rows at once instead of one by one, using `concatenate_datasets` for example.

https://github.com/huggingface/datasets/issues/5596

Apparently some JSON objects have a `"labels"` field. Since this field is not present in every object, you must specify all the fields types in the README.md EDIT: actually specifying the feature types doesn’t solve the issue, it raises an error because “labels” is missing in the data

[TypeError: Couldn't cast array of type] Can only load a subset of the dataset

### Describe the bug I'm trying to load this [dataset](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues) which consists of jsonl files and I get the following error: ``` casted_values = _c(array.values, feature[0]) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 1839, in wrapper return func(array, *args, **kwargs) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 2132, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type struct<type: string, action: string, datetime: timestamp[s], author: string, title: string, description: string, comment_id: int64, comment: string, labels: list<item: string>> to {'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)} ``` But I can succesfully load a subset of the dataset, for example this works: ```python ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train", data_files=[f"data/data-{x}.jsonl" for x in range(10)]) ``` and `ds.features` returns: ``` {'repo': Value(dtype='string', id=None), 'org': Value(dtype='string', id=None), 'issue_id': Value(dtype='int64', id=None), 'issue_number': Value(dtype='int64', id=None), 'pull_request': {'user_login': Value(dtype='string', id=None), 'repo': Value(dtype='string', id=None), 'number': Value(dtype='int64', id=None)}, 'events': [{'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)}]} ``` So I'm not sure if there's an issue with just some of the files. Grateful if you have any suggestions to fix the issue. Side note: I saw this related [issue](https://github.com/huggingface/datasets/issues/3637) and tried to write a loading script to have `events` as a `Sequence` and not `list` [here](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/blob/main/loading.py) (the script was renamed). It worked with a subset locally but doesn't for the remote dataset it can't find https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/resolve/main/data. ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train") ``` ### Expected behavior Load the entire dataset succesfully. ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-debian-10.13 - Python version: 3.7.12 - PyArrow version: 9.0.0 - Pandas version: 1.3.4

[TypeError: Couldn't cast array of type] Can only load a subset of the dataset ### Describe the bug I'm trying to load this [dataset](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues) which consists of jsonl files and I get the following error: ``` casted_values = _c(array.values, feature[0]) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 1839, in wrapper return func(array, *args, **kwargs) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 2132, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type struct<type: string, action: string, datetime: timestamp[s], author: string, title: string, description: string, comment_id: int64, comment: string, labels: list<item: string>> to {'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)} ``` But I can succesfully load a subset of the dataset, for example this works: ```python ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train", data_files=[f"data/data-{x}.jsonl" for x in range(10)]) ``` and `ds.features` returns: ``` {'repo': Value(dtype='string', id=None), 'org': Value(dtype='string', id=None), 'issue_id': Value(dtype='int64', id=None), 'issue_number': Value(dtype='int64', id=None), 'pull_request': {'user_login': Value(dtype='string', id=None), 'repo': Value(dtype='string', id=None), 'number': Value(dtype='int64', id=None)}, 'events': [{'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)}]} ``` So I'm not sure if there's an issue with just some of the files. Grateful if you have any suggestions to fix the issue. Side note: I saw this related [issue](https://github.com/huggingface/datasets/issues/3637) and tried to write a loading script to have `events` as a `Sequence` and not `list` [here](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/blob/main/loading.py) (the script was renamed). It worked with a subset locally but doesn't for the remote dataset it can't find https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/resolve/main/data. ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train") ``` ### Expected behavior Load the entire dataset succesfully. ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-debian-10.13 - Python version: 3.7.12 - PyArrow version: 9.0.0 - Pandas version: 1.3.4 Apparently some JSON objects have a `"labels"` field. Since this field is not present in every object, you must specify all the fields types in the README.md EDIT: actually specifying the feature types doesn’t solve the issue, it raises an error because “labels” is missing in the data

https://github.com/huggingface/datasets/issues/5596

We've updated the dataset to remove the extra `labels` field from some files, closing this issue. Thanks!

[TypeError: Couldn't cast array of type] Can only load a subset of the dataset

### Describe the bug I'm trying to load this [dataset](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues) which consists of jsonl files and I get the following error: ``` casted_values = _c(array.values, feature[0]) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 1839, in wrapper return func(array, *args, **kwargs) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 2132, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type struct<type: string, action: string, datetime: timestamp[s], author: string, title: string, description: string, comment_id: int64, comment: string, labels: list<item: string>> to {'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)} ``` But I can succesfully load a subset of the dataset, for example this works: ```python ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train", data_files=[f"data/data-{x}.jsonl" for x in range(10)]) ``` and `ds.features` returns: ``` {'repo': Value(dtype='string', id=None), 'org': Value(dtype='string', id=None), 'issue_id': Value(dtype='int64', id=None), 'issue_number': Value(dtype='int64', id=None), 'pull_request': {'user_login': Value(dtype='string', id=None), 'repo': Value(dtype='string', id=None), 'number': Value(dtype='int64', id=None)}, 'events': [{'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)}]} ``` So I'm not sure if there's an issue with just some of the files. Grateful if you have any suggestions to fix the issue. Side note: I saw this related [issue](https://github.com/huggingface/datasets/issues/3637) and tried to write a loading script to have `events` as a `Sequence` and not `list` [here](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/blob/main/loading.py) (the script was renamed). It worked with a subset locally but doesn't for the remote dataset it can't find https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/resolve/main/data. ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train") ``` ### Expected behavior Load the entire dataset succesfully. ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-debian-10.13 - Python version: 3.7.12 - PyArrow version: 9.0.0 - Pandas version: 1.3.4

[TypeError: Couldn't cast array of type] Can only load a subset of the dataset ### Describe the bug I'm trying to load this [dataset](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues) which consists of jsonl files and I get the following error: ``` casted_values = _c(array.values, feature[0]) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 1839, in wrapper return func(array, *args, **kwargs) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 2132, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type struct<type: string, action: string, datetime: timestamp[s], author: string, title: string, description: string, comment_id: int64, comment: string, labels: list<item: string>> to {'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)} ``` But I can succesfully load a subset of the dataset, for example this works: ```python ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train", data_files=[f"data/data-{x}.jsonl" for x in range(10)]) ``` and `ds.features` returns: ``` {'repo': Value(dtype='string', id=None), 'org': Value(dtype='string', id=None), 'issue_id': Value(dtype='int64', id=None), 'issue_number': Value(dtype='int64', id=None), 'pull_request': {'user_login': Value(dtype='string', id=None), 'repo': Value(dtype='string', id=None), 'number': Value(dtype='int64', id=None)}, 'events': [{'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)}]} ``` So I'm not sure if there's an issue with just some of the files. Grateful if you have any suggestions to fix the issue. Side note: I saw this related [issue](https://github.com/huggingface/datasets/issues/3637) and tried to write a loading script to have `events` as a `Sequence` and not `list` [here](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/blob/main/loading.py) (the script was renamed). It worked with a subset locally but doesn't for the remote dataset it can't find https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/resolve/main/data. ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train") ``` ### Expected behavior Load the entire dataset succesfully. ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-debian-10.13 - Python version: 3.7.12 - PyArrow version: 9.0.0 - Pandas version: 1.3.4 We've updated the dataset to remove the extra `labels` field from some files, closing this issue. Thanks!

https://github.com/huggingface/datasets/issues/5596

A similar error occurs in the Pile dataset (EleutherAI/the_pile) Loading the dataset produces the following error. ``` TypeError: Couldn't cast array of type struct<file: string, id: string> to {'id': Value(dtype='string', id=None)} ```

[TypeError: Couldn't cast array of type] Can only load a subset of the dataset

### Describe the bug I'm trying to load this [dataset](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues) which consists of jsonl files and I get the following error: ``` casted_values = _c(array.values, feature[0]) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 1839, in wrapper return func(array, *args, **kwargs) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 2132, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type struct<type: string, action: string, datetime: timestamp[s], author: string, title: string, description: string, comment_id: int64, comment: string, labels: list<item: string>> to {'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)} ``` But I can succesfully load a subset of the dataset, for example this works: ```python ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train", data_files=[f"data/data-{x}.jsonl" for x in range(10)]) ``` and `ds.features` returns: ``` {'repo': Value(dtype='string', id=None), 'org': Value(dtype='string', id=None), 'issue_id': Value(dtype='int64', id=None), 'issue_number': Value(dtype='int64', id=None), 'pull_request': {'user_login': Value(dtype='string', id=None), 'repo': Value(dtype='string', id=None), 'number': Value(dtype='int64', id=None)}, 'events': [{'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)}]} ``` So I'm not sure if there's an issue with just some of the files. Grateful if you have any suggestions to fix the issue. Side note: I saw this related [issue](https://github.com/huggingface/datasets/issues/3637) and tried to write a loading script to have `events` as a `Sequence` and not `list` [here](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/blob/main/loading.py) (the script was renamed). It worked with a subset locally but doesn't for the remote dataset it can't find https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/resolve/main/data. ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train") ``` ### Expected behavior Load the entire dataset succesfully. ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-debian-10.13 - Python version: 3.7.12 - PyArrow version: 9.0.0 - Pandas version: 1.3.4

[TypeError: Couldn't cast array of type] Can only load a subset of the dataset ### Describe the bug I'm trying to load this [dataset](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues) which consists of jsonl files and I get the following error: ``` casted_values = _c(array.values, feature[0]) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 1839, in wrapper return func(array, *args, **kwargs) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 2132, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type struct<type: string, action: string, datetime: timestamp[s], author: string, title: string, description: string, comment_id: int64, comment: string, labels: list<item: string>> to {'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)} ``` But I can succesfully load a subset of the dataset, for example this works: ```python ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train", data_files=[f"data/data-{x}.jsonl" for x in range(10)]) ``` and `ds.features` returns: ``` {'repo': Value(dtype='string', id=None), 'org': Value(dtype='string', id=None), 'issue_id': Value(dtype='int64', id=None), 'issue_number': Value(dtype='int64', id=None), 'pull_request': {'user_login': Value(dtype='string', id=None), 'repo': Value(dtype='string', id=None), 'number': Value(dtype='int64', id=None)}, 'events': [{'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)}]} ``` So I'm not sure if there's an issue with just some of the files. Grateful if you have any suggestions to fix the issue. Side note: I saw this related [issue](https://github.com/huggingface/datasets/issues/3637) and tried to write a loading script to have `events` as a `Sequence` and not `list` [here](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/blob/main/loading.py) (the script was renamed). It worked with a subset locally but doesn't for the remote dataset it can't find https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/resolve/main/data. ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train") ``` ### Expected behavior Load the entire dataset succesfully. ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-debian-10.13 - Python version: 3.7.12 - PyArrow version: 9.0.0 - Pandas version: 1.3.4 A similar error occurs in the Pile dataset (EleutherAI/the_pile) Loading the dataset produces the following error. ``` TypeError: Couldn't cast array of type struct<file: string, id: string> to {'id': Value(dtype='string', id=None)} ```

https://github.com/huggingface/datasets/issues/5596

i have the same problem ，how to solve : raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type list<item: string> to {'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None)}

[TypeError: Couldn't cast array of type] Can only load a subset of the dataset

### Describe the bug I'm trying to load this [dataset](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues) which consists of jsonl files and I get the following error: ``` casted_values = _c(array.values, feature[0]) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 1839, in wrapper return func(array, *args, **kwargs) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 2132, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type struct<type: string, action: string, datetime: timestamp[s], author: string, title: string, description: string, comment_id: int64, comment: string, labels: list<item: string>> to {'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)} ``` But I can succesfully load a subset of the dataset, for example this works: ```python ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train", data_files=[f"data/data-{x}.jsonl" for x in range(10)]) ``` and `ds.features` returns: ``` {'repo': Value(dtype='string', id=None), 'org': Value(dtype='string', id=None), 'issue_id': Value(dtype='int64', id=None), 'issue_number': Value(dtype='int64', id=None), 'pull_request': {'user_login': Value(dtype='string', id=None), 'repo': Value(dtype='string', id=None), 'number': Value(dtype='int64', id=None)}, 'events': [{'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)}]} ``` So I'm not sure if there's an issue with just some of the files. Grateful if you have any suggestions to fix the issue. Side note: I saw this related [issue](https://github.com/huggingface/datasets/issues/3637) and tried to write a loading script to have `events` as a `Sequence` and not `list` [here](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/blob/main/loading.py) (the script was renamed). It worked with a subset locally but doesn't for the remote dataset it can't find https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/resolve/main/data. ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train") ``` ### Expected behavior Load the entire dataset succesfully. ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-debian-10.13 - Python version: 3.7.12 - PyArrow version: 9.0.0 - Pandas version: 1.3.4

[TypeError: Couldn't cast array of type] Can only load a subset of the dataset ### Describe the bug I'm trying to load this [dataset](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues) which consists of jsonl files and I get the following error: ``` casted_values = _c(array.values, feature[0]) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 1839, in wrapper return func(array, *args, **kwargs) File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 2132, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type struct<type: string, action: string, datetime: timestamp[s], author: string, title: string, description: string, comment_id: int64, comment: string, labels: list<item: string>> to {'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)} ``` But I can succesfully load a subset of the dataset, for example this works: ```python ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train", data_files=[f"data/data-{x}.jsonl" for x in range(10)]) ``` and `ds.features` returns: ``` {'repo': Value(dtype='string', id=None), 'org': Value(dtype='string', id=None), 'issue_id': Value(dtype='int64', id=None), 'issue_number': Value(dtype='int64', id=None), 'pull_request': {'user_login': Value(dtype='string', id=None), 'repo': Value(dtype='string', id=None), 'number': Value(dtype='int64', id=None)}, 'events': [{'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)}]} ``` So I'm not sure if there's an issue with just some of the files. Grateful if you have any suggestions to fix the issue. Side note: I saw this related [issue](https://github.com/huggingface/datasets/issues/3637) and tried to write a loading script to have `events` as a `Sequence` and not `list` [here](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/blob/main/loading.py) (the script was renamed). It worked with a subset locally but doesn't for the remote dataset it can't find https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/resolve/main/data. ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train") ``` ### Expected behavior Load the entire dataset succesfully. ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-debian-10.13 - Python version: 3.7.12 - PyArrow version: 9.0.0 - Pandas version: 1.3.4 i have the same problem ，how to solve : raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type list<item: string> to {'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None)}

https://github.com/huggingface/datasets/issues/5594

Hi! I cannot reproduce this error on my machine. The raised error could mean that one of the downloaded files is corrupted. To verify this is not the case, you can run `load_dataset` as follows: ```python train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload", verification_mode="all_checks") ```

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 Hi! I cannot reproduce this error on my machine. The raised error could mean that one of the downloaded files is corrupted. To verify this is not the case, you can run `load_dataset` as follows: ```python train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload", verification_mode="all_checks") ```

https://github.com/huggingface/datasets/issues/5594

Hi! Apologies for the delayed response! I tried the above and it doesn't solve the issue. Actually, the dataset gets downloaded most times, but sometimes this error occurs (at random afaik). Is it possible that there is a server issue for this particular dataset? I am able to download other datasets using the same code on the same machine with no issues :( I get this error now : ``` Downloading data: 16%|███████████████▌ | 55.9M/355M [04:45<25:25, 196kB/s] Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 1107, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 439, in main en_dataset = load_dataset("xtreme", "udpos.English", split="train", download_mode="force_redownload", verification_mode="all_checks") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 949, in _download_and_prepare verify_checksums( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/utils/info_utils.py", line 62, in verify_checksums raise NonMatchingChecksumError( datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3105/ud-treebanks-v2.5.tgz'] Set `verification_mode='no_checks'` to skip checksums verification and ignore this error ```

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 Hi! Apologies for the delayed response! I tried the above and it doesn't solve the issue. Actually, the dataset gets downloaded most times, but sometimes this error occurs (at random afaik). Is it possible that there is a server issue for this particular dataset? I am able to download other datasets using the same code on the same machine with no issues :( I get this error now : ``` Downloading data: 16%|███████████████▌ | 55.9M/355M [04:45<25:25, 196kB/s] Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 1107, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 439, in main en_dataset = load_dataset("xtreme", "udpos.English", split="train", download_mode="force_redownload", verification_mode="all_checks") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 949, in _download_and_prepare verify_checksums( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/utils/info_utils.py", line 62, in verify_checksums raise NonMatchingChecksumError( datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3105/ud-treebanks-v2.5.tgz'] Set `verification_mode='no_checks'` to skip checksums verification and ignore this error ```

https://github.com/huggingface/datasets/issues/5594

If this happens randomly, then this means the data file from the error message is not always downloaded correctly. The only solution in this scenario is to download the dataset again by passing `download_mode="force_redownload"` to the `load_dataset` call.

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 If this happens randomly, then this means the data file from the error message is not always downloaded correctly. The only solution in this scenario is to download the dataset again by passing `download_mode="force_redownload"` to the `load_dataset` call.

https://github.com/huggingface/datasets/issues/5594

Wow. I effectively have to redownload a dataset of 1TB because of this now? Because 3% of its parts are broken? Why is this downloader library so sh*t and badly documented also? I found almost nothing on the net, at least finally this issue about the problem here. No words to express how disappointed I am by that dataset tool provided by Huggingface here, which I sadly have to use because HF is the only place where the Dataset I plan to work with is hosted.... I mean... checksum check after download... or hitting timeout of a part... and redownload if not matching... that's content of every junior developer training session. I added `verification_mode="all_checks"`. And it really calculated checksums for 4096 parts of ~350 MB... But then did nothing and tried to extract still, hitting the error again. EDIT: Apparently it is able to fix it by getting a little help: Just delete the broken parts and associated files from `~/.cache/huggingface/datasets/downloads`

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 Wow. I effectively have to redownload a dataset of 1TB because of this now? Because 3% of its parts are broken? Why is this downloader library so sh*t and badly documented also? I found almost nothing on the net, at least finally this issue about the problem here. No words to express how disappointed I am by that dataset tool provided by Huggingface here, which I sadly have to use because HF is the only place where the Dataset I plan to work with is hosted.... I mean... checksum check after download... or hitting timeout of a part... and redownload if not matching... that's content of every junior developer training session. I added `verification_mode="all_checks"`. And it really calculated checksums for 4096 parts of ~350 MB... But then did nothing and tried to extract still, hitting the error again. EDIT: Apparently it is able to fix it by getting a little help: Just delete the broken parts and associated files from `~/.cache/huggingface/datasets/downloads`

https://github.com/huggingface/datasets/issues/5594

I'm getting it too, although just retrying fixed it. Nevertheless, the dataset is too large to have re-downloaded the whole thing, for it's probably just one file with an issue. It would be good to know if there's a way people could manually examine the files (first for sizes, then possibly checksums)... going to the web or elsewhere to compare and correct it by hand, if ever needed.

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 I'm getting it too, although just retrying fixed it. Nevertheless, the dataset is too large to have re-downloaded the whole thing, for it's probably just one file with an issue. It would be good to know if there's a way people could manually examine the files (first for sizes, then possibly checksums)... going to the web or elsewhere to compare and correct it by hand, if ever needed.

https://github.com/huggingface/datasets/issues/5594

Okay, no, it got further but it is repeatedly giving me: ```/home/jaggz/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/3f27acf10f303eac5b6fbbbe02495aeddb46ecffdb0a2fe3507fcfbf89094631/common_voice_11_0.py", line 195, in _generate_examples result["audio"] = {"path": path, "bytes": file.read()} ^^^^^^^^^^^ File "/usr/lib/python3.11/tarfile.py", line 687, in read raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/jaggz/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", line 625, in <module> main() File "/home/jaggz/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", line 360, in main raw_datasets["train"] = load_dataset( ^^^^^^^^^^^^^ File "/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _download_and_prepare super()._download_and_prepare( File "/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py", line 1712, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the datase

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 Okay, no, it got further but it is repeatedly giving me: ```/home/jaggz/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/3f27acf10f303eac5b6fbbbe02495aeddb46ecffdb0a2fe3507fcfbf89094631/common_voice_11_0.py", line 195, in _generate_examples result["audio"] = {"path": path, "bytes": file.read()} ^^^^^^^^^^^ File "/usr/lib/python3.11/tarfile.py", line 687, in read raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/jaggz/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", line 625, in <module> main() File "/home/jaggz/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", line 360, in main raw_datasets["train"] = load_dataset( ^^^^^^^^^^^^^ File "/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _download_and_prepare super()._download_and_prepare( File "/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py", line 1712, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the datase

https://github.com/huggingface/datasets/issues/5594

@RuntimeRacer > EDIT: Apparently it is able to fix it by getting a little help: Just delete the broken parts and associated files from `~/.cache/huggingface/datasets/downloads` How do you know the broken parts? Mine's consistently erroring and.. yeah, really this thing should be able to check the files (but where's that even done)... 2023-11-02 00:14:09.846055: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. /home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py:299: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead. warnings.warn( 11/02/2023 00:14:37 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: True 11/02/2023 00:14:37 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, ... logging_dir=./whisper-tiny-en/runs/Nov02_00-14-28_jsys, ... run_name=./whisper-tiny-en, ... weight_decay=0.0, ) 11/02/2023 00:14:37 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, ... logging_dir=./whisper-tiny-en/runs/Nov02_00-14-28_jsys, ... weight_decay=0.0, ) Downloading data files: 0%| | 0/5 [00:00<?, ?it/s] Downloading data files: 100%|██████████| 5/5 [00:00<00:00, 2426.42it/s] Extracting data files: 0%| | 0/5 [00:00<?, ?it/s] Extracting data files: 100%|██████████| 5/5 [00:00<00:00, 421.16it/s] Downloading data files: 0%| | 0/5 [00:00<?, ?it/s] Downloading data files: 100%|██████████| 5/5 [00:00<00:00, 18707.87it/s] Extracting data files: 0%| | 0/5 [00:00<?, ?it/s] Extracting data files: 100%|██████████| 5/5 [00:00<00:00, 3754.97it/s] Generating train split: 0 examples [00:00, ? examples/s] Reading metadata...: 0it [00:00, ?it/s] ... Reading metadata...: 948736it [00:23, 40632.92it/s] Generating train split: 1 examples [00:23, 23.37s/ examples] ... Generating train split: 948736 examples [08:28, 1866.15 examples/s] Generating validation split: 0 examples [00:00, ? examples/s] Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 16089it [00:00, 157411.88it/s] Reading metadata...: 16354it [00:00, 158233.27it/s] Generating validation split: 1 examples [00:00, 7.60 examples/s] Generating validation split: 16354 examples [00:14, 1154.77 examples/s] Generating test split: 0 examples [00:00, ? examples/s] Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 16354it [00:00, 194855.03it/s] Generating test split: 1 examples [00:00, 4.53 examples/s] Generating test split: 16354 examples [00:07, 2105.43 examples/s] Generating other split: 0 examples [00:00, ? examples/s] Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 290846it [00:01, 235823.90it/s] Generating other split: 1 examples [00:01, 1.27s/ examples] ... Generating other split: 290846 examples [02:12, 2196.96 examples/s] Generating invalidated split: 0 examples [00:00, ? examples/s] Reading metadata...: 252599it [00:01, 241965.85it/s] Generating invalidated split: 1 examples [00:01, 1.08s/ examples] ... Generating invalidated split: 60130 examples [00:34, 1764.14 examples/s] Traceback (most recent call last): File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py", line 1676, in _prepare_split_single for key, record in generator: File "/home/j/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/3f27acf10f303eac5b6fbbbe02495aeddb46ecffdb0a2fe3507fcfbf89094631/common_voice_11_0.py", line 195, in _generate_examples result["audio"] = {"path": path, "bytes": file.read()} ^^^^^^^^^^^ File "/usr/lib/python3.11/tarfile.py", line 687, in read raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", line 625, in <module> main() File "/home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", line 360, in main raw_datasets["train"] = load_dataset( ^^^^^^^^^^^^^ File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _download_and_prepare super()._download_and_prepare( File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py", line 1712, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 @RuntimeRacer > EDIT: Apparently it is able to fix it by getting a little help: Just delete the broken parts and associated files from `~/.cache/huggingface/datasets/downloads` How do you know the broken parts? Mine's consistently erroring and.. yeah, really this thing should be able to check the files (but where's that even done)... 2023-11-02 00:14:09.846055: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. /home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py:299: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead. warnings.warn( 11/02/2023 00:14:37 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: True 11/02/2023 00:14:37 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, ... logging_dir=./whisper-tiny-en/runs/Nov02_00-14-28_jsys, ... run_name=./whisper-tiny-en, ... weight_decay=0.0, ) 11/02/2023 00:14:37 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, ... logging_dir=./whisper-tiny-en/runs/Nov02_00-14-28_jsys, ... weight_decay=0.0, ) Downloading data files: 0%| | 0/5 [00:00<?, ?it/s] Downloading data files: 100%|██████████| 5/5 [00:00<00:00, 2426.42it/s] Extracting data files: 0%| | 0/5 [00:00<?, ?it/s] Extracting data files: 100%|██████████| 5/5 [00:00<00:00, 421.16it/s] Downloading data files: 0%| | 0/5 [00:00<?, ?it/s] Downloading data files: 100%|██████████| 5/5 [00:00<00:00, 18707.87it/s] Extracting data files: 0%| | 0/5 [00:00<?, ?it/s] Extracting data files: 100%|██████████| 5/5 [00:00<00:00, 3754.97it/s] Generating train split: 0 examples [00:00, ? examples/s] Reading metadata...: 0it [00:00, ?it/s] ... Reading metadata...: 948736it [00:23, 40632.92it/s] Generating train split: 1 examples [00:23, 23.37s/ examples] ... Generating train split: 948736 examples [08:28, 1866.15 examples/s] Generating validation split: 0 examples [00:00, ? examples/s] Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 16089it [00:00, 157411.88it/s] Reading metadata...: 16354it [00:00, 158233.27it/s] Generating validation split: 1 examples [00:00, 7.60 examples/s] Generating validation split: 16354 examples [00:14, 1154.77 examples/s] Generating test split: 0 examples [00:00, ? examples/s] Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 16354it [00:00, 194855.03it/s] Generating test split: 1 examples [00:00, 4.53 examples/s] Generating test split: 16354 examples [00:07, 2105.43 examples/s] Generating other split: 0 examples [00:00, ? examples/s] Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 290846it [00:01, 235823.90it/s] Generating other split: 1 examples [00:01, 1.27s/ examples] ... Generating other split: 290846 examples [02:12, 2196.96 examples/s] Generating invalidated split: 0 examples [00:00, ? examples/s] Reading metadata...: 252599it [00:01, 241965.85it/s] Generating invalidated split: 1 examples [00:01, 1.08s/ examples] ... Generating invalidated split: 60130 examples [00:34, 1764.14 examples/s] Traceback (most recent call last): File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py", line 1676, in _prepare_split_single for key, record in generator: File "/home/j/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/3f27acf10f303eac5b6fbbbe02495aeddb46ecffdb0a2fe3507fcfbf89094631/common_voice_11_0.py", line 195, in _generate_examples result["audio"] = {"path": path, "bytes": file.read()} ^^^^^^^^^^^ File "/usr/lib/python3.11/tarfile.py", line 687, in read raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", line 625, in <module> main() File "/home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", line 360, in main raw_datasets["train"] = load_dataset( ^^^^^^^^^^^^^ File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _download_and_prepare super()._download_and_prepare( File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py", line 1712, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset

https://github.com/huggingface/datasets/issues/5594

@jaggzh Hi, I actually came around with a fix for this, wasn't that easy to solve since there were a lot of hidden pitfalls in the code, and it's quite hacky, but I was able to download the full dataset. I just didn't create a PR for it yet since I was too lazy to create a fork and change my local repo's origin. 😅 Let me try to do this tonight, I'll give you a ping once it's up. EDIT: And no, what I wrote above about adding a param to the download config does NOT solve it apparently. A code fix is required here.

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 @jaggzh Hi, I actually came around with a fix for this, wasn't that easy to solve since there were a lot of hidden pitfalls in the code, and it's quite hacky, but I was able to download the full dataset. I just didn't create a PR for it yet since I was too lazy to create a fork and change my local repo's origin. 😅 Let me try to do this tonight, I'll give you a ping once it's up. EDIT: And no, what I wrote above about adding a param to the download config does NOT solve it apparently. A code fix is required here.

https://github.com/huggingface/datasets/issues/5594

@mariosasko Can you re-open this? We really need some better diagnostics output, at the least, to locate which files are contributing, some checksum output, etc. I can't even tell if this is a mozilla...py issue or huggingface datasets or ....

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 @mariosasko Can you re-open this? We really need some better diagnostics output, at the least, to locate which files are contributing, some checksum output, etc. I can't even tell if this is a mozilla...py issue or huggingface datasets or ....

https://github.com/huggingface/datasets/issues/5594

@RuntimeRacer Beautiful, thank you so much. I patched with your PR and am re-running now. (I'm running this script: https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) Okay, actually it failed; so now I'm running with verification_mode='all_checks' added to the load_data() call and it's re-running now. Wish me luck. (Note: It's generating checksums; I don't see an option that handles anything between basic_checks and all_checks -- Something checking dl'ed files' lengths would be a good common fix I'd think; corruption is more rare nowadays than a short file (although maybe your patch helps prevent that in the first place.) :}

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 @RuntimeRacer Beautiful, thank you so much. I patched with your PR and am re-running now. (I'm running this script: https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) Okay, actually it failed; so now I'm running with verification_mode='all_checks' added to the load_data() call and it's re-running now. Wish me luck. (Note: It's generating checksums; I don't see an option that handles anything between basic_checks and all_checks -- Something checking dl'ed files' lengths would be a good common fix I'd think; corruption is more rare nowadays than a short file (although maybe your patch helps prevent that in the first place.) :}

https://github.com/huggingface/datasets/issues/5594

@RuntimeRacer No luck. Sigh. [Edit: My tmux copy didn't get some data. That was weird. I'm adding in the initial part of the output:] ``` Downloading data files: 100%|██████████| 5/5 [00:00<00:00, 2190.69it/s] Computing checksums: 100%|██████████| 41/41 [11:39<00:00, 17.05s/it] Extracting data files: 100%|██████████| 5/5 [00:00<00:00, 12.37it/s] Downloading data files: 100%|██████████| 5/5 [00:00<00:00, 107.64it/s] Extracting data files: 100%|██████████| 5/5 [00:00<00:00, 3149.82it/s] Reading metadata...: 948736it [00:03, 243227.36it/s]s/s] ... ``` ``` ... Reading metadata...: 252599it [00:01, 249267.71it/s]xamples/s] Generating invalidated split: 60130 examples [00:31, 1916.33 examples/s] Traceback (most recent call last): File "/home/j/src/py/datasets/src/datasets/builder.py", line 1676, in _prepare_split_single for key, record in generator: File "/home/j/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/3f27acf10f303eac5b6fbbbe02495aeddb46ecffdb0a2fe3507fcfbf89094631/common_voice_11_0.py", line 195, in _generate_examples result["audio"] = {"path": path, "bytes": file.read()} ^^^^^^^^^^^ File "/usr/lib/python3.11/tarfile.py", line 687, in read raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", line 627, in <module> main() File "/home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", line 360, in main raw_datasets["train"] = load_dataset( ^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/home/j/src/py/datasets/src/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/home/j/src/py/datasets/src/datasets/builder.py", line 1717, in _download_and_prepare super()._download_and_prepare( File "/home/j/src/py/datasets/src/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/j/src/py/datasets/src/datasets/builder.py", line 1555, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/j/src/py/datasets/src/datasets/builder.py", line 1712 ```

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 @RuntimeRacer No luck. Sigh. [Edit: My tmux copy didn't get some data. That was weird. I'm adding in the initial part of the output:] ``` Downloading data files: 100%|██████████| 5/5 [00:00<00:00, 2190.69it/s] Computing checksums: 100%|██████████| 41/41 [11:39<00:00, 17.05s/it] Extracting data files: 100%|██████████| 5/5 [00:00<00:00, 12.37it/s] Downloading data files: 100%|██████████| 5/5 [00:00<00:00, 107.64it/s] Extracting data files: 100%|██████████| 5/5 [00:00<00:00, 3149.82it/s] Reading metadata...: 948736it [00:03, 243227.36it/s]s/s] ... ``` ``` ... Reading metadata...: 252599it [00:01, 249267.71it/s]xamples/s] Generating invalidated split: 60130 examples [00:31, 1916.33 examples/s] Traceback (most recent call last): File "/home/j/src/py/datasets/src/datasets/builder.py", line 1676, in _prepare_split_single for key, record in generator: File "/home/j/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/3f27acf10f303eac5b6fbbbe02495aeddb46ecffdb0a2fe3507fcfbf89094631/common_voice_11_0.py", line 195, in _generate_examples result["audio"] = {"path": path, "bytes": file.read()} ^^^^^^^^^^^ File "/usr/lib/python3.11/tarfile.py", line 687, in read raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", line 627, in <module> main() File "/home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", line 360, in main raw_datasets["train"] = load_dataset( ^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/home/j/src/py/datasets/src/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/home/j/src/py/datasets/src/datasets/builder.py", line 1717, in _download_and_prepare super()._download_and_prepare( File "/home/j/src/py/datasets/src/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/j/src/py/datasets/src/datasets/builder.py", line 1555, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/j/src/py/datasets/src/datasets/builder.py", line 1712 ```

https://github.com/huggingface/datasets/issues/5594

I'm unable to reproduce this error. Based on https://github.com/psf/requests/issues/4956, newer releases of `urllib3` check the returned content length by default, so perhaps updating `requests` and `urllib3` to the latest versions (`pip install -U requests urllib3`) and loading the dataset with `datasets.load_dataset("xtreme", "udpos.English", download_config=datasets.DownloadConfig(resume_download=True))` (re-run when it fails to resume the download) can fix the issue.

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 I'm unable to reproduce this error. Based on https://github.com/psf/requests/issues/4956, newer releases of `urllib3` check the returned content length by default, so perhaps updating `requests` and `urllib3` to the latest versions (`pip install -U requests urllib3`) and loading the dataset with `datasets.load_dataset("xtreme", "udpos.English", download_config=datasets.DownloadConfig(resume_download=True))` (re-run when it fails to resume the download) can fix the issue.

https://github.com/huggingface/datasets/issues/5594

@jaggzh I think you will need to re-download the whole dataset with my patched code. Files which have already been downloaded and marked as complete by the broken downloader won't be detected even on re-run (I described that in the PR). I also had to download reazonspeech, which is over 1TB, twice. 🙈 For re-download, you need to manually delete the dataset files from your local machine's huggingface download cache. @mariosasko Not sure how you tested it, but it's not an issue in `requests` or `urllib`. The problem is the huggingface downloader, which generates a nested download thread for the actual download I think. The issue I had with the reazonspeech dataset (https://huggingface.co/datasets/reazon-research/reazonspeech/tree/main) basically was, that it started downloading a part, but sometimes the connection would 'starve' and only continue with a few kilobytes, and eventually stop receiving any data at all. Sometimes it would even recover during the download and finish properly. However, if it did not recover, the request would hit the really generous default timeout (which is 100 seconds I think), however the exception thrown by the failure inside `urllib`, isn't captured or handled by the upper level downloader code of the `datasets` library. `datasets` even has a retry mechanism, which would continue interrupted downloads if they have the `.incomplete` suffix, which isn't cleared if, for example, a manual `CTRL+C` is sent by the user to the python process. But: If it runs into that edge case I described above (TL;DR: connection starves after minutes + timeout exception which isn't captured), the cache downloader will consider the download as successful and remove the `.incomplete` suffix nevertheless, leaving the archive file in a corrupted state. Honestly, I spent hours on trying to figure out what was even going on and why the retry mechanics of the cache downloader didn't work at all. But it is indeed an issue caused by the download process itself not receiving any info about actual content size and filesize size on disk of the archive to be downloaded, thus, having no direct control in case something fails on the request level. IMHO, this requires a major refactor of the way this part of the downloader works. Yet I was able to quick-fix it by adding some synthetic Exception handling and explicit retry-handling in the code, als done in my PR.

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 @jaggzh I think you will need to re-download the whole dataset with my patched code. Files which have already been downloaded and marked as complete by the broken downloader won't be detected even on re-run (I described that in the PR). I also had to download reazonspeech, which is over 1TB, twice. 🙈 For re-download, you need to manually delete the dataset files from your local machine's huggingface download cache. @mariosasko Not sure how you tested it, but it's not an issue in `requests` or `urllib`. The problem is the huggingface downloader, which generates a nested download thread for the actual download I think. The issue I had with the reazonspeech dataset (https://huggingface.co/datasets/reazon-research/reazonspeech/tree/main) basically was, that it started downloading a part, but sometimes the connection would 'starve' and only continue with a few kilobytes, and eventually stop receiving any data at all. Sometimes it would even recover during the download and finish properly. However, if it did not recover, the request would hit the really generous default timeout (which is 100 seconds I think), however the exception thrown by the failure inside `urllib`, isn't captured or handled by the upper level downloader code of the `datasets` library. `datasets` even has a retry mechanism, which would continue interrupted downloads if they have the `.incomplete` suffix, which isn't cleared if, for example, a manual `CTRL+C` is sent by the user to the python process. But: If it runs into that edge case I described above (TL;DR: connection starves after minutes + timeout exception which isn't captured), the cache downloader will consider the download as successful and remove the `.incomplete` suffix nevertheless, leaving the archive file in a corrupted state. Honestly, I spent hours on trying to figure out what was even going on and why the retry mechanics of the cache downloader didn't work at all. But it is indeed an issue caused by the download process itself not receiving any info about actual content size and filesize size on disk of the archive to be downloaded, thus, having no direct control in case something fails on the request level. IMHO, this requires a major refactor of the way this part of the downloader works. Yet I was able to quick-fix it by adding some synthetic Exception handling and explicit retry-handling in the code, als done in my PR.

https://github.com/huggingface/datasets/issues/5594

@RuntimeRacer Ugh. It took a day. I'm seeing if I can get some debug code in here to examine the files myself. (I'm not sure why checksum tests would fail, so, yeah, I think you're right -- this stuff needs some work. Going through ipdb right now to try to get some idea of what's going on in the code).

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 @RuntimeRacer Ugh. It took a day. I'm seeing if I can get some debug code in here to examine the files myself. (I'm not sure why checksum tests would fail, so, yeah, I think you're right -- this stuff needs some work. Going through ipdb right now to try to get some idea of what's going on in the code).

https://github.com/huggingface/datasets/issues/5594

@RuntimeRacer Data can only be appended to the `.incomplete` files if `load_dataset` is called with `download_config=DownloadConfig(resume_download=True)`. Where exactly does this exception happen (in the code)? The error stack trace would help a lot.

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 @RuntimeRacer Data can only be appended to the `.incomplete` files if `load_dataset` is called with `download_config=DownloadConfig(resume_download=True)`. Where exactly does this exception happen (in the code)? The error stack trace would help a lot.

https://github.com/huggingface/datasets/issues/5594

@mariosasko I do not have a trace of this exception nor do I know which type it is. I am honestly not even sure if an exception is thrown, or the process just aborts without error. > @RuntimeRacer Data can only be appended to the .incomplete files if load_dataset is called with download_config=DownloadConfig(resume_download=True). Well, I think I did a very clear explaination of the issue in the PR I shared, and the description above, but maybe I wasn't precise enough. Let me try to explain once more: What you mention here is the "normal" case, if the process is aborted. In this case, there will be files with `.incomplete` suffix, which the cache downloader can continue to download. That is correct. BUT: What I am talking about all the time is an edge case: if the download step crashes / timeouts internally, the cache downloader will NOT be aware of this, and REMOVES the `.incomplete` suffix. It does NOT know that the file is incomplete when the `http_get` function returns and will remove the `.incomplete` suffix in any case once `http_get` returns. But the problem is that `http_get` returns without failure, even if the download failed. And this is still a problem even with latest `urllib` and `requests` library.

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 @mariosasko I do not have a trace of this exception nor do I know which type it is. I am honestly not even sure if an exception is thrown, or the process just aborts without error. > @RuntimeRacer Data can only be appended to the .incomplete files if load_dataset is called with download_config=DownloadConfig(resume_download=True). Well, I think I did a very clear explaination of the issue in the PR I shared, and the description above, but maybe I wasn't precise enough. Let me try to explain once more: What you mention here is the "normal" case, if the process is aborted. In this case, there will be files with `.incomplete` suffix, which the cache downloader can continue to download. That is correct. BUT: What I am talking about all the time is an edge case: if the download step crashes / timeouts internally, the cache downloader will NOT be aware of this, and REMOVES the `.incomplete` suffix. It does NOT know that the file is incomplete when the `http_get` function returns and will remove the `.incomplete` suffix in any case once `http_get` returns. But the problem is that `http_get` returns without failure, even if the download failed. And this is still a problem even with latest `urllib` and `requests` library.

https://github.com/huggingface/datasets/issues/5594

@RuntimeRacer Updating `urllib3` and `requests` to the latest versions fixes the issue explained in this [blog](https://blog.petrzemek.net/2018/04/22/on-incomplete-http-reads-and-the-requests-library-in-python/) post. However, the issue explained above seems more similar to [this](https://stackoverflow.com/questions/52731196/python-3-6-5-requests-with-streaming-getting-stuck-in-iter-content-even-if-chun) one. To address it, we can reduce the default timeout to 10 seconds (btw, this was the initial value, but it was causing problems for some users) and expose a config variable so that users can easily control it. Additionally, we can re-run `http_get` similarly to https://github.com/huggingface/huggingface_hub/pull/1766 when the connection/timeout error happens to make the logic even more robust. Would this work for you? The last part is what you did in the PR, right? @jaggzh From all the datasets mentioned in this issue, `xtreme` is the only one that stores the data file checksums in the metadata. So, the checksum check has no effect when enabled for the rest of the datasets.

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 @RuntimeRacer Updating `urllib3` and `requests` to the latest versions fixes the issue explained in this [blog](https://blog.petrzemek.net/2018/04/22/on-incomplete-http-reads-and-the-requests-library-in-python/) post. However, the issue explained above seems more similar to [this](https://stackoverflow.com/questions/52731196/python-3-6-5-requests-with-streaming-getting-stuck-in-iter-content-even-if-chun) one. To address it, we can reduce the default timeout to 10 seconds (btw, this was the initial value, but it was causing problems for some users) and expose a config variable so that users can easily control it. Additionally, we can re-run `http_get` similarly to https://github.com/huggingface/huggingface_hub/pull/1766 when the connection/timeout error happens to make the logic even more robust. Would this work for you? The last part is what you did in the PR, right? @jaggzh From all the datasets mentioned in this issue, `xtreme` is the only one that stores the data file checksums in the metadata. So, the checksum check has no effect when enabled for the rest of the datasets.

https://github.com/huggingface/datasets/issues/5594

(I don't have any .incomplete files, just the extraction errors.) I was going through the code to try to relate filenames to the hex/hash files, but realized I might not need to. So instead I coded up a script in bash to examine the tar files for validity (had an issue with bash subshells not adding to my array so I had cgpt recode it in perl). ```perl #!/usr/bin/perl use strict; use warnings; # Initialize the array to store tar files my @tars; # Open the current directory opendir(my $dh, '.') or die "Cannot open directory: $!"; # Read files in the current directory while (my $f = readdir($dh)) { # Skip files ending with lock, json, or py next if $f =~ /\.(lock|json|py)$/; # Use the `file` command to determine the type of file my $ft = `file "$f"`; # If it's a tar archive, add it to the list if ($ft =~ /tar archive/) { push @tars, $f; } } closedir($dh); print "Final Tars count: " . scalar(@tars) . "\n"; # Iterate over the tar files and check them foreach my $i (0 .. $#tars) { my $f = $tars[$i]; printf '%d/%d ', $i+1, scalar(@tars); # Use `ls -lgG` to list the files, similar to the original bash script system("ls -lgG '$f'"); # Check the integrity of the tar file my $errfn = "/tmp/$f.tarerr"; if (system("tar tf '$f' > /dev/null 2> '$errfn'") != 0) { print " BAD $f\n"; print " ERR: "; system("cat '$errfn'"); } # Remove the error file if it exists unlink $errfn if -e $errfn; } ``` This found one hash file that errored in the tar extraction, and one small tmp* file that also was supposedly a tar and was erroring. I removed those two and re-data loaded.. it grabbed just what it needed and I'm on my way. Yay! So... is there a way for the datasets api to get file sizes? That would be a very easy and fast test, leaving checksum slowdowns for extra-messed-up situations.

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 (I don't have any .incomplete files, just the extraction errors.) I was going through the code to try to relate filenames to the hex/hash files, but realized I might not need to. So instead I coded up a script in bash to examine the tar files for validity (had an issue with bash subshells not adding to my array so I had cgpt recode it in perl). ```perl #!/usr/bin/perl use strict; use warnings; # Initialize the array to store tar files my @tars; # Open the current directory opendir(my $dh, '.') or die "Cannot open directory: $!"; # Read files in the current directory while (my $f = readdir($dh)) { # Skip files ending with lock, json, or py next if $f =~ /\.(lock|json|py)$/; # Use the `file` command to determine the type of file my $ft = `file "$f"`; # If it's a tar archive, add it to the list if ($ft =~ /tar archive/) { push @tars, $f; } } closedir($dh); print "Final Tars count: " . scalar(@tars) . "\n"; # Iterate over the tar files and check them foreach my $i (0 .. $#tars) { my $f = $tars[$i]; printf '%d/%d ', $i+1, scalar(@tars); # Use `ls -lgG` to list the files, similar to the original bash script system("ls -lgG '$f'"); # Check the integrity of the tar file my $errfn = "/tmp/$f.tarerr"; if (system("tar tf '$f' > /dev/null 2> '$errfn'") != 0) { print " BAD $f\n"; print " ERR: "; system("cat '$errfn'"); } # Remove the error file if it exists unlink $errfn if -e $errfn; } ``` This found one hash file that errored in the tar extraction, and one small tmp* file that also was supposedly a tar and was erroring. I removed those two and re-data loaded.. it grabbed just what it needed and I'm on my way. Yay! So... is there a way for the datasets api to get file sizes? That would be a very easy and fast test, leaving checksum slowdowns for extra-messed-up situations.

https://github.com/huggingface/datasets/issues/5594

> @RuntimeRacer Updating `urllib3` and `requests` to the latest versions fixes the issue explained in this [blog](https://blog.petrzemek.net/2018/04/22/on-incomplete-http-reads-and-the-requests-library-in-python/) post. > > However, the issue explained above seems more similar to [this](https://stackoverflow.com/questions/52731196/python-3-6-5-requests-with-streaming-getting-stuck-in-iter-content-even-if-chun) one. To address it, we can reduce the default timeout to 10 seconds (btw, this was the initial value, but it was causing problems for some users) and expose a config variable so that users can easily control it. Additionally, we can re-run `http_get` similarly to [huggingface/huggingface_hub#1766](https://github.com/huggingface/huggingface_hub/pull/1766) when the connection/timeout error happens to make the logic even more robust. Would this work for you? The last part is what you did in the PR, right? > > @jaggzh From all the datasets mentioned in this issue, `xtreme` is the only one that stores the data file checksums in the metadata. So, the checksum check has no effect when enabled for the rest of the datasets. @mariosasko Well if you look at my commit date, you will see that I run into this problem still in October. The blog post you mention and the update in the pull request for `urllib` was from July: https://github.com/psf/requests/issues/4956#issuecomment-1648632935 But yeah the [issue on StackOverflow](https://stackoverflow.com/questions/52731196/python-3-6-5-requests-with-streaming-getting-stuck-in-iter-content-even-if-chun) you mentioned seems like that's the source issue I was running into there. I experimented with timeouts, but changing them didn't help to resolve the issue of the starving connection unfortunately. However, https://github.com/huggingface/huggingface_hub/pull/1766 seems like that could be working; it's very similar to my change. So yeah I think this would fix it probably. Also I can confirm the checksum option did not work for [reazonspeech](https://huggingface.co/datasets/reazon-research/reazonspeech/tree/main) as well. So maybe it's a double edge case that only occurs for some datasets. 🤷‍♂️

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 > @RuntimeRacer Updating `urllib3` and `requests` to the latest versions fixes the issue explained in this [blog](https://blog.petrzemek.net/2018/04/22/on-incomplete-http-reads-and-the-requests-library-in-python/) post. > > However, the issue explained above seems more similar to [this](https://stackoverflow.com/questions/52731196/python-3-6-5-requests-with-streaming-getting-stuck-in-iter-content-even-if-chun) one. To address it, we can reduce the default timeout to 10 seconds (btw, this was the initial value, but it was causing problems for some users) and expose a config variable so that users can easily control it. Additionally, we can re-run `http_get` similarly to [huggingface/huggingface_hub#1766](https://github.com/huggingface/huggingface_hub/pull/1766) when the connection/timeout error happens to make the logic even more robust. Would this work for you? The last part is what you did in the PR, right? > > @jaggzh From all the datasets mentioned in this issue, `xtreme` is the only one that stores the data file checksums in the metadata. So, the checksum check has no effect when enabled for the rest of the datasets. @mariosasko Well if you look at my commit date, you will see that I run into this problem still in October. The blog post you mention and the update in the pull request for `urllib` was from July: https://github.com/psf/requests/issues/4956#issuecomment-1648632935 But yeah the [issue on StackOverflow](https://stackoverflow.com/questions/52731196/python-3-6-5-requests-with-streaming-getting-stuck-in-iter-content-even-if-chun) you mentioned seems like that's the source issue I was running into there. I experimented with timeouts, but changing them didn't help to resolve the issue of the starving connection unfortunately. However, https://github.com/huggingface/huggingface_hub/pull/1766 seems like that could be working; it's very similar to my change. So yeah I think this would fix it probably. Also I can confirm the checksum option did not work for [reazonspeech](https://huggingface.co/datasets/reazon-research/reazonspeech/tree/main) as well. So maybe it's a double edge case that only occurs for some datasets. 🤷‍♂️

https://github.com/huggingface/datasets/issues/5594

Also, the hf urls to files -- while I can't see a way of getting a listing from the hf site side -- do include the file size in the http header response. So we do have a quick way of just verifying lengths for resume. (This message may not be interesting to you all). First, a json clip (mozilla-foundation___common_voice_11_0/en/11.0.0/3f27acf10f303eac5b6fbbbe02495aeddb46ecffdb0a2fe3507fcfbf89094631/dataset_info.json): * I don't know how specific this .json is to mozilla common voice * Note that *dataset_size* is not the dataset size :) DatasetInfo class docs indicate it might be their "combined size in bytes of the Arrow tables for all splits." * *num_bytes*: does match the individual file size though, and matches the http header (further down) ``` { "builder_name" : "common_voice_11_0", ... "config_name" : "en", "dataset_name" : "common_voice_11_0", "dataset_size" : 1680793952, ... "download_checksums" : { ... "https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/resolve/main/audio/en/invalidated/en_invalidated_3.tar" : { "checksum" : null, "num_bytes" : 2110853120 }, ... ``` ```bash ~/.cache/huggingface/datasets/downloads$ ls -lgG b45f82cb87bab2c35361857fcd46042ab658b42c37dc9a455248c2866c9b8f40* | cut -c 14- ``` ``` 2110853120 Nov 1 16:28 b45f82cb87bab2c35361857fcd46042ab658b42c37dc9a455248c2866c9b8f40 148 Nov 1 16:28 b45f82cb87bab2c35361857fcd46042ab658b42c37dc9a455248c2866c9b8f40.json 0 Nov 1 16:07 b45f82cb87bab2c35361857fcd46042ab658b42c37dc9a455248c2866c9b8f40.lock ``` * Note the -L to follow redirects. Two headers are below: ```bash $ curl -I -L https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/resolve/main/audio/en/invalidated/en_invalidated_3.tar ``` ``` HTTP/2 302 content-type: text/plain; charset=utf-8 content-length: 1215 location: https://cdn-lfs.huggingface.co/repos/00/ce/00ce867b4ae70bd23a10b60c32a8626d87b2666fc088ad03f86b94788faff554/984086fc250badece2992e8be4d7c4430f7c1208fb8bf37dc7c4aecdc803b220?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27en_invalidated_3.tar%3B+filename%3D%22en_invalidated_3.tar%22%3B&response-content-type=application%2Fx-tar&Expires=1699389040&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY5OTM4OTA0MH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8wMC9jZS8wMGNlODY3YjRhZTcwYmQyM2ExMGI2MGMzMmE4NjI2ZDg3YjI2NjZmYzA4OGFkMDNmODZiOTQ3ODhmYWZmNTU0Lzk4NDA4NmZjMjUwYmFkZWNlMjk5MmU4YmU0ZDdjNDQzMGY3YzEyMDhmYjhiZjM3ZGM3YzRhZWNkYzgwM2IyMjA%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=WYc32e75PqbKSAv3KTpG86ooFT6oOyDDQpCt1i2B8gVS10J3qvpZlDmxaBgnGlCCl7SRiAvhIQctgwooNtWbUeDqK3T4bAo0-OOrGCuVi-%7EKWUBcoHce7nHWpl%7Ex9ubHS%7EFoYcGB2SCEqh5fIgGjNV-VKRX6TSXkRto5bclQq4VCJKHufDsJ114A1V4Qu%7EYiRIWKG4Gi93Xv4OFhyWY0uqykvP5c0x02F%7ELX0m3WbW-eXBk6Fw2xnV1XLrEkdR-9Ax2vHqMYIIw6yV0wWEc1hxE393P9mMG1TNDj%7EXDuCoOaA7LbrwBCxai%7Ew2MopdPamTXyOia5-FnSqEdsV29v4Q__&Key-Pair-Id=KVTP0A1DKRTAX date: Sat, 04 Nov 2023 20:30:40 GMT x-powered-by: huggingface-moon x-request-id: Root=1-6546a9f0-5e7f729d09bdb38e35649a7e access-control-allow-origin: https://huggingface.co vary: Origin, Accept access-control-expose-headers: X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,ETag,Link,Accept-Ranges,Content-Range x-repo-commit: 23b4059922516c140711b91831aa3393a22e9b80 accept-ranges: bytes x-linked-size: 2110853120 x-linked-etag: "984086fc250badece2992e8be4d7c4430f7c1208fb8bf37dc7c4aecdc803b220" x-cache: Miss from cloudfront via: 1.1 f31a6426ebd75ce4393909b12f5cbdcc.cloudfront.net (CloudFront) x-amz-cf-pop: LAX53-P4 x-amz-cf-id: BcYMFcHVcxPome2IjAvx0ZU90G41QlNI_HEHDGDqCQaEPvrOsnsGXw== HTTP/2 200 content-type: application/x-tar content-length: 2110853120 date: Sat, 04 Nov 2023 20:19:35 GMT last-modified: Fri, 18 Nov 2022 15:08:22 GMT etag: "acac28988e2f7e73b68e865179fbd008" x-amz-storage-class: INTELLIGENT_TIERING x-amz-version-id: LgTuOcd9FGN4JnAXp26O.1v2VW42GPtF content-disposition: attachment; filename*=UTF-8''en_invalidated_3.tar; filename="en_invalidated_3.tar"; accept-ranges: bytes server: AmazonS3 x-cache: Hit from cloudfront via: 1.1 d07c8167eda81d307ca96358727f505e.cloudfront.net (CloudFront) x-amz-cf-pop: LAX50-P5 x-amz-cf-id: 6oNZg_V8U1M_JXsMHQAPuRmDfxbY2BnMUWcVH0nz3VnfEZCzF5lgkQ== age: 666 cache-control: public, max-age=604800, immutable, s-maxage=604800 vary: Origin ```

Error while downloading the xtreme udpos dataset

### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

Error while downloading the xtreme udpos dataset ### Describe the bug Hi, I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed ```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4... Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s] Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last): File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single for key, record in generator: File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs) File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples for path, file in filepath: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__ yield from self.generator(*self.args, **self.kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path yield from cls._iter_tar(f) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar for tarinfo in stream: File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__ tarinfo = self.next() File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next raise ReadError("unexpected end of data") tarfile.ReadError: unexpected end of data The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module> main() File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload") File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset builder_instance.download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare self._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare super()._download_and_prepare( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug ``` train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload") ``` ### Expected behavior Download the udpos dataset ### Environment info - `datasets` version: 2.10.1 - Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.8 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 Also, the hf urls to files -- while I can't see a way of getting a listing from the hf site side -- do include the file size in the http header response. So we do have a quick way of just verifying lengths for resume. (This message may not be interesting to you all). First, a json clip (mozilla-foundation___common_voice_11_0/en/11.0.0/3f27acf10f303eac5b6fbbbe02495aeddb46ecffdb0a2fe3507fcfbf89094631/dataset_info.json): * I don't know how specific this .json is to mozilla common voice * Note that *dataset_size* is not the dataset size :) DatasetInfo class docs indicate it might be their "combined size in bytes of the Arrow tables for all splits." * *num_bytes*: does match the individual file size though, and matches the http header (further down) ``` { "builder_name" : "common_voice_11_0", ... "config_name" : "en", "dataset_name" : "common_voice_11_0", "dataset_size" : 1680793952, ... "download_checksums" : { ... "https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/resolve/main/audio/en/invalidated/en_invalidated_3.tar" : { "checksum" : null, "num_bytes" : 2110853120 }, ... ``` ```bash ~/.cache/huggingface/datasets/downloads$ ls -lgG b45f82cb87bab2c35361857fcd46042ab658b42c37dc9a455248c2866c9b8f40* | cut -c 14- ``` ``` 2110853120 Nov 1 16:28 b45f82cb87bab2c35361857fcd46042ab658b42c37dc9a455248c2866c9b8f40 148 Nov 1 16:28 b45f82cb87bab2c35361857fcd46042ab658b42c37dc9a455248c2866c9b8f40.json 0 Nov 1 16:07 b45f82cb87bab2c35361857fcd46042ab658b42c37dc9a455248c2866c9b8f40.lock ``` * Note the -L to follow redirects. Two headers are below: ```bash $ curl -I -L https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/resolve/main/audio/en/invalidated/en_invalidated_3.tar ``` ``` HTTP/2 302 content-type: text/plain; charset=utf-8 content-length: 1215 location: https://cdn-lfs.huggingface.co/repos/00/ce/00ce867b4ae70bd23a10b60c32a8626d87b2666fc088ad03f86b94788faff554/984086fc250badece2992e8be4d7c4430f7c1208fb8bf37dc7c4aecdc803b220?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27en_invalidated_3.tar%3B+filename%3D%22en_invalidated_3.tar%22%3B&response-content-type=application%2Fx-tar&Expires=1699389040&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY5OTM4OTA0MH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8wMC9jZS8wMGNlODY3YjRhZTcwYmQyM2ExMGI2MGMzMmE4NjI2ZDg3YjI2NjZmYzA4OGFkMDNmODZiOTQ3ODhmYWZmNTU0Lzk4NDA4NmZjMjUwYmFkZWNlMjk5MmU4YmU0ZDdjNDQzMGY3YzEyMDhmYjhiZjM3ZGM3YzRhZWNkYzgwM2IyMjA%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=WYc32e75PqbKSAv3KTpG86ooFT6oOyDDQpCt1i2B8gVS10J3qvpZlDmxaBgnGlCCl7SRiAvhIQctgwooNtWbUeDqK3T4bAo0-OOrGCuVi-%7EKWUBcoHce7nHWpl%7Ex9ubHS%7EFoYcGB2SCEqh5fIgGjNV-VKRX6TSXkRto5bclQq4VCJKHufDsJ114A1V4Qu%7EYiRIWKG4Gi93Xv4OFhyWY0uqykvP5c0x02F%7ELX0m3WbW-eXBk6Fw2xnV1XLrEkdR-9Ax2vHqMYIIw6yV0wWEc1hxE393P9mMG1TNDj%7EXDuCoOaA7LbrwBCxai%7Ew2MopdPamTXyOia5-FnSqEdsV29v4Q__&Key-Pair-Id=KVTP0A1DKRTAX date: Sat, 04 Nov 2023 20:30:40 GMT x-powered-by: huggingface-moon x-request-id: Root=1-6546a9f0-5e7f729d09bdb38e35649a7e access-control-allow-origin: https://huggingface.co vary: Origin, Accept access-control-expose-headers: X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,ETag,Link,Accept-Ranges,Content-Range x-repo-commit: 23b4059922516c140711b91831aa3393a22e9b80 accept-ranges: bytes x-linked-size: 2110853120 x-linked-etag: "984086fc250badece2992e8be4d7c4430f7c1208fb8bf37dc7c4aecdc803b220" x-cache: Miss from cloudfront via: 1.1 f31a6426ebd75ce4393909b12f5cbdcc.cloudfront.net (CloudFront) x-amz-cf-pop: LAX53-P4 x-amz-cf-id: BcYMFcHVcxPome2IjAvx0ZU90G41QlNI_HEHDGDqCQaEPvrOsnsGXw== HTTP/2 200 content-type: application/x-tar content-length: 2110853120 date: Sat, 04 Nov 2023 20:19:35 GMT last-modified: Fri, 18 Nov 2022 15:08:22 GMT etag: "acac28988e2f7e73b68e865179fbd008" x-amz-storage-class: INTELLIGENT_TIERING x-amz-version-id: LgTuOcd9FGN4JnAXp26O.1v2VW42GPtF content-disposition: attachment; filename*=UTF-8''en_invalidated_3.tar; filename="en_invalidated_3.tar"; accept-ranges: bytes server: AmazonS3 x-cache: Hit from cloudfront via: 1.1 d07c8167eda81d307ca96358727f505e.cloudfront.net (CloudFront) x-amz-cf-pop: LAX50-P5 x-amz-cf-id: 6oNZg_V8U1M_JXsMHQAPuRmDfxbY2BnMUWcVH0nz3VnfEZCzF5lgkQ== age: 666 cache-control: public, max-age=604800, immutable, s-maxage=604800 vary: Origin ```

https://github.com/huggingface/datasets/issues/5586

Thanks for reporting and thanks @mariosasko for fixing ! We just did a patch release `2.10.1` with the fix

.sort() is broken when used after .filter(), only in 2.10.0

### Describe the bug Hi, thank you for your support! It seems like the addition of multiple key sort (#5502) in 2.10.0 broke the `.sort()` method. After filtering a dataset with `.filter()`, the `.sort()` seems to refer to the query_table index of the previous unfiltered dataset, resulting in an IndexError. This only happens with the 2.10.0 release. ### Steps to reproduce the bug ```Python from datasets import load_dataset # dataset with length of 1104 ds = load_dataset('glue', 'ax')['test'] ds = ds.filter(lambda x: x['idx'] > 1100) ds.sort('premise') print('Done') ``` File "/home/dongkeun/datasets_test/test.py", line 5, in <module> ds.sort('premise') File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 528, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 511, in wrapper out = func(dataset, *args, **kwargs) File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3959, in sort sort_table = query_table( File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 588, in query_table _check_valid_index_key(key, size) File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 537, in _check_valid_index_key _check_valid_index_key(max(key), size=size) File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 531, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 1103 is out of bounds for size 3 ### Expected behavior It should sort the dataset and print "Done". Which it does on 2.9.0. ### Environment info - `datasets` version: 2.10.0 - Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

.sort() is broken when used after .filter(), only in 2.10.0 ### Describe the bug Hi, thank you for your support! It seems like the addition of multiple key sort (#5502) in 2.10.0 broke the `.sort()` method. After filtering a dataset with `.filter()`, the `.sort()` seems to refer to the query_table index of the previous unfiltered dataset, resulting in an IndexError. This only happens with the 2.10.0 release. ### Steps to reproduce the bug ```Python from datasets import load_dataset # dataset with length of 1104 ds = load_dataset('glue', 'ax')['test'] ds = ds.filter(lambda x: x['idx'] > 1100) ds.sort('premise') print('Done') ``` File "/home/dongkeun/datasets_test/test.py", line 5, in <module> ds.sort('premise') File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 528, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 511, in wrapper out = func(dataset, *args, **kwargs) File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3959, in sort sort_table = query_table( File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 588, in query_table _check_valid_index_key(key, size) File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 537, in _check_valid_index_key _check_valid_index_key(max(key), size=size) File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 531, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 1103 is out of bounds for size 3 ### Expected behavior It should sort the dataset and print "Done". Which it does on 2.9.0. ### Environment info - `datasets` version: 2.10.0 - Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 Thanks for reporting and thanks @mariosasko for fixing ! We just did a patch release `2.10.1` with the fix

https://github.com/huggingface/datasets/issues/5585

Hi ! No the cache is not transportable in general. It will work on a shared filesystem if you use the same python environment, but not across machines/os/environments. In particular, reloading cached datasets does work, but reloading cached processed datasets (e.g. from `map`) may not work. This is because some hashes used by caching are based on pickle dumps of the function you pass to `map`. Finally you may copy the cache to another machine, but all the `cached-*.arrow` files are unlikely to be reloaded.

Cache is not transportable

### Describe the bug I would like to share cache between two machines (a Windows host machine and a WSL instance). I run most my code in WSL. I have just run out of space in the virtual drive. Rather than expand the drive size, I plan to move to cache to the host Windows machine, thereby sharing the downloads. I'm hoping that I can just copy/paste the cache files, but I notice that a lot of the file names start with the path name, e.g. `_home_davidg_.cache_huggingface_datasets_conll2003_default-451...98.lock` where `home/davidg` is where the cache is in WSL. This seems to suggest that the cache is not portable/cannot be centralised or shared. Is this the case, or are the files that start with path names not integral to the caching mechanism? Because copying the cache files _seems_ to work, but I'm not filled with confidence that something isn't going to break. A related issue, when trying to load a dataset that should come from cache (running in WSL, pointing to cache on the Windows host) it seemed to work fine, but it still uses a WSL directory for `.cache\huggingface\modules\datasets_modules`. I see nothing in the docs about this, or how to point it to a different place. I have asked a related question on the forum: https://discuss.huggingface.co/t/is-datasets-cache-operating-system-agnostic/32656 ### Steps to reproduce the bug View the cache directory in WSL/Windows. ### Expected behavior Cache can be shared between (virtual) machines and be transportable. It would be nice to have a simple way to say "Dear Hugging Face packages, please put ALL your cache in `blah/de/blah`" and have all the Hugging Face packages respect that single location. ### Environment info ``` - `datasets` version: 2.9.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 - ```

Cache is not transportable ### Describe the bug I would like to share cache between two machines (a Windows host machine and a WSL instance). I run most my code in WSL. I have just run out of space in the virtual drive. Rather than expand the drive size, I plan to move to cache to the host Windows machine, thereby sharing the downloads. I'm hoping that I can just copy/paste the cache files, but I notice that a lot of the file names start with the path name, e.g. `_home_davidg_.cache_huggingface_datasets_conll2003_default-451...98.lock` where `home/davidg` is where the cache is in WSL. This seems to suggest that the cache is not portable/cannot be centralised or shared. Is this the case, or are the files that start with path names not integral to the caching mechanism? Because copying the cache files _seems_ to work, but I'm not filled with confidence that something isn't going to break. A related issue, when trying to load a dataset that should come from cache (running in WSL, pointing to cache on the Windows host) it seemed to work fine, but it still uses a WSL directory for `.cache\huggingface\modules\datasets_modules`. I see nothing in the docs about this, or how to point it to a different place. I have asked a related question on the forum: https://discuss.huggingface.co/t/is-datasets-cache-operating-system-agnostic/32656 ### Steps to reproduce the bug View the cache directory in WSL/Windows. ### Expected behavior Cache can be shared between (virtual) machines and be transportable. It would be nice to have a simple way to say "Dear Hugging Face packages, please put ALL your cache in `blah/de/blah`" and have all the Hugging Face packages respect that single location. ### Environment info ``` - `datasets` version: 2.9.0 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 - Python version: 3.10.8 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 - ``` Hi ! No the cache is not transportable in general. It will work on a shared filesystem if you use the same python environment, but not across machines/os/environments. In particular, reloading cached datasets does work, but reloading cached processed datasets (e.g. from `map`) may not work. This is because some hashes used by caching are based on pickle dumps of the function you pass to `map`. Finally you may copy the cache to another machine, but all the `cached-*.arrow` files are unlikely to be reloaded.

https://github.com/huggingface/datasets/issues/5584

Hi @manuaero Thank you for your interest in the COYO dataset. Our dataset provides the img-url and alt-text in the form of a parquet, so to utilize the coyo dataset you will need to download it directly. We provide a [guide](https://github.com/kakaobrain/coyo-dataset/blob/main/download/README.md) to download, so check it out. Thank you.

Unable to load coyo700M dataset

### Describe the bug Seeing this error when downloading https://huggingface.co/datasets/kakaobrain/coyo-700m: ```ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.``` Full stack trace ```Downloading and preparing dataset parquet/kakaobrain--coyo-700m to /root/.cache/huggingface/datasets/kakaobrain___parquet/kakaobrain--coyo-700m-ae729692ae3e0073/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec... Downloading data files: 100% 1/1 [00:00<00:00, 63.35it/s] Extracting data files: 100% 1/1 [00:00<00:00, 5.00it/s] --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) [/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1859 _time = time.time() -> 1860 for _, table in generator: 1861 if max_shard_size is not None and writer._num_bytes > max_shard_size: 9 frames ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file. The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) [/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1891 e = e.__context__ -> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1893 1894 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset``` ### Steps to reproduce the bug ``` from datasets import load_dataset hf_dataset = load_dataset("kakaobrain/coyo-700m") ``` ### Expected behavior The above commands load the dataset successfully. Or handles exception and continue loading the remainder. ### Environment info colab. any

Unable to load coyo700M dataset ### Describe the bug Seeing this error when downloading https://huggingface.co/datasets/kakaobrain/coyo-700m: ```ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.``` Full stack trace ```Downloading and preparing dataset parquet/kakaobrain--coyo-700m to /root/.cache/huggingface/datasets/kakaobrain___parquet/kakaobrain--coyo-700m-ae729692ae3e0073/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec... Downloading data files: 100% 1/1 [00:00<00:00, 63.35it/s] Extracting data files: 100% 1/1 [00:00<00:00, 5.00it/s] --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) [/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1859 _time = time.time() -> 1860 for _, table in generator: 1861 if max_shard_size is not None and writer._num_bytes > max_shard_size: 9 frames ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file. The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) [/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1891 e = e.__context__ -> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1893 1894 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset``` ### Steps to reproduce the bug ``` from datasets import load_dataset hf_dataset = load_dataset("kakaobrain/coyo-700m") ``` ### Expected behavior The above commands load the dataset successfully. Or handles exception and continue loading the remainder. ### Environment info colab. any Hi @manuaero Thank you for your interest in the COYO dataset. Our dataset provides the img-url and alt-text in the form of a parquet, so to utilize the coyo dataset you will need to download it directly. We provide a [guide](https://github.com/kakaobrain/coyo-dataset/blob/main/download/README.md) to download, so check it out. Thank you.

https://github.com/huggingface/datasets/issues/5577

Hi! I've merged a PR to use `int32` instead of `int8` for `reddit_scores`, so it should work now.

Cannot load `the_pile_openwebtext2`

### Describe the bug I met the same bug mentioned in #3053 which is never fixed. Because several `reddit_scores` are larger than `int8` even `int16`. https://huggingface.co/datasets/the_pile_openwebtext2/blob/main/the_pile_openwebtext2.py#L62 ### Steps to reproduce the bug ```python3 from datasets import load_dataset dataset = load_dataset("the_pile_openwebtext2") ``` ### Expected behavior load as normal. ### Environment info - `datasets` version: 2.10.0 - Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31 - Python version: 3.9.2 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

Cannot load `the_pile_openwebtext2` ### Describe the bug I met the same bug mentioned in #3053 which is never fixed. Because several `reddit_scores` are larger than `int8` even `int16`. https://huggingface.co/datasets/the_pile_openwebtext2/blob/main/the_pile_openwebtext2.py#L62 ### Steps to reproduce the bug ```python3 from datasets import load_dataset dataset = load_dataset("the_pile_openwebtext2") ``` ### Expected behavior load as normal. ### Environment info - `datasets` version: 2.10.0 - Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31 - Python version: 3.9.2 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 Hi! I've merged a PR to use `int32` instead of `int8` for `reddit_scores`, so it should work now.

https://github.com/huggingface/datasets/issues/5575

Hi! Indeed it would be useful to support this. PyArrow natively supports schema-level and column-level metadata, so implementing this should be straightforward. The API I have in mind would work as follows: ```python col_feature = Value("string", metadata="Some column-level metadata") features = Features({"col": col_feature}, metadata="Some schema-level metadata") ``` WDYT?

Metadata for each column

### Feature request Being able to put some metadata for each column as a string or any other type. ### Motivation I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata ### Your contribution Maybe we could map another relational like database as the metadata?

Metadata for each column ### Feature request Being able to put some metadata for each column as a string or any other type. ### Motivation I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata ### Your contribution Maybe we could map another relational like database as the metadata? Hi! Indeed it would be useful to support this. PyArrow natively supports schema-level and column-level metadata, so implementing this should be straightforward. The API I have in mind would work as follows: ```python col_feature = Value("string", metadata="Some column-level metadata") features = Features({"col": col_feature}, metadata="Some schema-level metadata") ``` WDYT?

https://github.com/huggingface/datasets/issues/5575

Sorry for the late reply, Yes, I think this is the most straight-forward approach with the things that we already have.

Metadata for each column

### Feature request Being able to put some metadata for each column as a string or any other type. ### Motivation I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata ### Your contribution Maybe we could map another relational like database as the metadata?

Metadata for each column ### Feature request Being able to put some metadata for each column as a string or any other type. ### Motivation I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata ### Your contribution Maybe we could map another relational like database as the metadata? Sorry for the late reply, Yes, I think this is the most straight-forward approach with the things that we already have.

https://github.com/huggingface/datasets/issues/5575

Hi, is this feature to be implemented in the near future? It would be really nice if that would be the case!

Metadata for each column

### Feature request Being able to put some metadata for each column as a string or any other type. ### Motivation I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata ### Your contribution Maybe we could map another relational like database as the metadata?

Metadata for each column ### Feature request Being able to put some metadata for each column as a string or any other type. ### Motivation I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata ### Your contribution Maybe we could map another relational like database as the metadata? Hi, is this feature to be implemented in the near future? It would be really nice if that would be the case!

https://github.com/huggingface/datasets/issues/5575

Hi, I also need this feature for tell my customer if any of the feature is encrypted with a certain key.

Metadata for each column

### Feature request Being able to put some metadata for each column as a string or any other type. ### Motivation I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata ### Your contribution Maybe we could map another relational like database as the metadata?

Metadata for each column ### Feature request Being able to put some metadata for each column as a string or any other type. ### Motivation I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata ### Your contribution Maybe we could map another relational like database as the metadata? Hi, I also need this feature for tell my customer if any of the feature is encrypted with a certain key.

https://github.com/huggingface/datasets/issues/5574

Also encountering this issue for every dataset I try to stream! Installed datasets from main: ``` - `datasets` version: 2.10.1.dev0 - Platform: macOS-13.1-arm64-arm-64bit - Python version: 3.9.13 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 ``` Repro: ```python from datasets import load_dataset spigi = load_dataset("kensho/spgispeech", "dev", split="validation", streaming=True, use_auth_token=True) sample = next(iter(spigi)) ``` <details> <summary> Traceback </summary> ```python --------------------------------------------------------------------------- ClientResponseError Traceback (most recent call last) File ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:407, in HTTPFileSystem._info(self, url, **kwargs) 405 try: 406 info.update( --> 407 await _file_info( 408 self.encode_url(url), 409 size_policy=policy, 410 session=session, 411 **self.kwargs, 412 **kwargs, 413 ) 414 ) 415 if info.get("size") is not None: File ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:792, in _file_info(url, session, size_policy, **kwargs) 791 async with r: --> 792 r.raise_for_status() 794 # TODO: 795 # recognise lack of 'Accept-Ranges', 796 # or 'Accept-Ranges': 'none' (not 'bytes') 797 # to mean streaming only, no random access => return None File ~/venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py:1005, in ClientResponse.raise_for_status(self) 1004 self.release() -> 1005 raise ClientResponseError( 1006 self.request_info, 1007 self.history, 1008 status=self.status, 1009 message=self.reason, 1010 headers=self.headers, 1011 ) ClientResponseError: 403, message='Forbidden', url=URL('[https://cdn-lfs.huggingface.co/repos/e2/89/e28905247d6f48bb4edad5baf9b1bb4158e897a13fdf18bf3b8ee89ff8387ab8/46eca7431a7b6bad344bf451800e5b10cea1dd168f26d1027a6d9eb374b7fac3?response-content-disposition=attachment%3B+filename*%3DUTF-8''dev.csv%3B+filename%3D%22dev.csv%22%3B&response-content-type=text/csv&Expires=1677494732&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2UyLzg5L2UyODkwNTI0N2Q2ZjQ4YmI0ZWRhZDViYWY5YjFiYjQxNThlODk3YTEzZmRmMThiZjNiOGVlODlmZjgzODdhYjgvNDZlY2E3NDMxYTdiNmJhZDM0NGJmNDUxODAwZTViMTBjZWExZGQxNjhmMjZkMTAyN2E2ZDllYjM3NGI3ZmFjMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPXRleHQlMkZjc3YiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Nzc0OTQ3MzJ9fX1dfQ__&Signature=EzQB9f7xPckvqfFB6LzcyR-wzTnQCqtPDdWtQUzZ3QJ-gY-IHG5mxQITJgMr1nVTbJZrPmGAaDngMcPFUfSQa8RmCqYH~dZl-UGE8CO4neKNUT1DvA2WEvLDS4WaAJ3SN-9rX0uFb03~c1QS78cIgIRboYvf6ugKiJz86Bd7Vs~tcp201JFR0A6jIMseqApOnkb9d8dHMP3Ny~F6gO3Qf2QpEWM-QsDIyw2Kz2QV55nq8TsDpRYZCZo50~WwD~73Hej0PoDhEA1K37d19pa0CQhkaN-gjCrbT9xLabbvhJWa~ZkWcMdD0teCgjYqv1wKyvFXDAxukxLGEc7OBXVbYw__&Key-Pair-Id=KVTP0A1DKRTAX](https://cdn-lfs.huggingface.co/repos/e2/89/e28905247d6f48bb4edad5baf9b1bb4158e897a13fdf18bf3b8ee89ff8387ab8/46eca7431a7b6bad344bf451800e5b10cea1dd168f26d1027a6d9eb374b7fac3?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27dev.csv%3B+filename%3D%22dev.csv%22%3B&response-content-type=text/csv&Expires=1677494732&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2UyLzg5L2UyODkwNTI0N2Q2ZjQ4YmI0ZWRhZDViYWY5YjFiYjQxNThlODk3YTEzZmRmMThiZjNiOGVlODlmZjgzODdhYjgvNDZlY2E3NDMxYTdiNmJhZDM0NGJmNDUxODAwZTViMTBjZWExZGQxNjhmMjZkMTAyN2E2ZDllYjM3NGI3ZmFjMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPXRleHQlMkZjc3YiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Nzc0OTQ3MzJ9fX1dfQ__&Signature=EzQB9f7xPckvqfFB6LzcyR-wzTnQCqtPDdWtQUzZ3QJ-gY-IHG5mxQITJgMr1nVTbJZrPmGAaDngMcPFUfSQa8RmCqYH~dZl-UGE8CO4neKNUT1DvA2WEvLDS4WaAJ3SN-9rX0uFb03~c1QS78cIgIRboYvf6ugKiJz86Bd7Vs~tcp201JFR0A6jIMseqApOnkb9d8dHMP3Ny~F6gO3Qf2QpEWM-QsDIyw2Kz2QV55nq8TsDpRYZCZo50~WwD~73Hej0PoDhEA1K37d19pa0CQhkaN-gjCrbT9xLabbvhJWa~ZkWcMdD0teCgjYqv1wKyvFXDAxukxLGEc7OBXVbYw__&Key-Pair-Id=KVTP0A1DKRTAX)') The above exception was the direct cause of the following exception: FileNotFoundError Traceback (most recent call last) Cell In[5], line 4 1 from datasets import load_dataset 3 spigi = load_dataset("kensho/spgispeech", "dev", split="validation", streaming=True) ----> 4 sample = next(iter(spigi)) File ~/datasets/src/datasets/iterable_dataset.py:937, in IterableDataset.__iter__(self) 934 yield from self._iter_pytorch(ex_iterable) 935 return --> 937 for key, example in ex_iterable: 938 if self.features: 939 # `IterableDataset` automatically fills missing columns with None. 940 # This is done with `_apply_feature_types_on_example`. 941 yield _apply_feature_types_on_example( 942 example, self.features, token_per_repo_id=self._token_per_repo_id 943 ) File ~/datasets/src/datasets/iterable_dataset.py:113, in ExamplesIterable.__iter__(self) 112 def __iter__(self): --> 113 yield from self.generate_examples_fn(**self.kwargs) File ~/.cache/huggingface/modules/datasets_modules/datasets/kensho--spgispeech/5fbf75dd9ef795a9b5a673457d2cbaf0b8fa0de8fb62acbd1da338d83a41e2f0/spgispeech.py:186, in Spgispeech._generate_examples(self, local_extracted_archive_paths, archives, meta_path) 183 dict_keys = ["wav_filename", "wav_filesize", "transcript"] 185 logging.info("Reading metadata...") --> 186 with open(meta_path, encoding="utf-8") as f: 187 csvreader = csv.DictReader(f, delimiter="|") 188 metadata = {x["wav_filename"]: dict((k, x[k]) for k in dict_keys) for x in csvreader} File ~/datasets/src/datasets/streaming.py:70, in extend_module_for_streaming.<locals>.wrap_auth.<locals>.wrapper(*args, **kwargs) 68 @wraps(function) 69 def wrapper(*args, **kwargs): ---> 70 return function(*args, use_auth_token=use_auth_token, **kwargs) File ~/datasets/src/datasets/download/streaming_download_manager.py:495, in xopen(file, mode, use_auth_token, *args, **kwargs) 493 kwargs = {**kwargs, **new_kwargs} 494 try: --> 495 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() 496 except ValueError as e: 497 if str(e) == "Cannot seek streaming HTTP file": File ~/venv/lib/python3.9/site-packages/fsspec/core.py:135, in OpenFile.open(self) 128 def open(self): 129 """Materialise this as a real open file without context 130 131 The OpenFile object should be explicitly closed to avoid enclosed file 132 instances persisting. You must, therefore, keep a reference to the OpenFile 133 during the life of the file-like it generates. 134 """ --> 135 return self.__enter__() File ~/venv/lib/python3.9/site-packages/fsspec/core.py:103, in OpenFile.__enter__(self) 100 def __enter__(self): 101 mode = self.mode.replace("t", "").replace("b", "") + "b" --> 103 f = self.fs.open(self.path, mode=mode) 105 self.fobjects = [f] 107 if self.compression is not None: File ~/venv/lib/python3.9/site-packages/fsspec/spec.py:1106, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs) 1104 else: 1105 ac = kwargs.pop("autocommit", not self._intrans) -> 1106 f = self._open( 1107 path, 1108 mode=mode, 1109 block_size=block_size, 1110 autocommit=ac, 1111 cache_options=cache_options, 1112 **kwargs, 1113 ) 1114 if compression is not None: 1115 from fsspec.compression import compr File ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:346, in HTTPFileSystem._open(self, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs) 344 kw["asynchronous"] = self.asynchronous 345 kw.update(kwargs) --> 346 size = size or self.info(path, **kwargs)["size"] 347 session = sync(self.loop, self.set_session) 348 if block_size and size: File ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:113, in sync_wrapper.<locals>.wrapper(*args, **kwargs) 110 @functools.wraps(func) 111 def wrapper(*args, **kwargs): 112 self = obj or args[0] --> 113 return sync(self.loop, func, *args, **kwargs) File ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:98, in sync(loop, func, timeout, *args, **kwargs) 96 raise FSTimeoutError from return_result 97 elif isinstance(return_result, BaseException): ---> 98 raise return_result 99 else: 100 return return_result File ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:53, in _runner(event, coro, result, timeout) 51 coro = asyncio.wait_for(coro, timeout=timeout) 52 try: ---> 53 result[0] = await coro 54 except Exception as ex: 55 result[0] = ex File ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:420, in HTTPFileSystem._info(self, url, **kwargs) 417 except Exception as exc: 418 if policy == "get": 419 # If get failed, then raise a FileNotFoundError --> 420 raise FileNotFoundError(url) from exc 421 logger.debug(str(exc)) 423 return {"name": url, "size": None, **info, "type": "file"} FileNotFoundError: https://huggingface.co/datasets/kensho/spgispeech/resolve/main/data/meta/dev.csv ``` </details>

c4 dataset streaming fails with `FileNotFoundError`

### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

c4 dataset streaming fails with `FileNotFoundError` ### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 Also encountering this issue for every dataset I try to stream! Installed datasets from main: ``` - `datasets` version: 2.10.1.dev0 - Platform: macOS-13.1-arm64-arm-64bit - Python version: 3.9.13 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 ``` Repro: ```python from datasets import load_dataset spigi = load_dataset("kensho/spgispeech", "dev", split="validation", streaming=True, use_auth_token=True) sample = next(iter(spigi)) ``` <details> <summary> Traceback </summary> ```python --------------------------------------------------------------------------- ClientResponseError Traceback (most recent call last) File ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:407, in HTTPFileSystem._info(self, url, **kwargs) 405 try: 406 info.update( --> 407 await _file_info( 408 self.encode_url(url), 409 size_policy=policy, 410 session=session, 411 **self.kwargs, 412 **kwargs, 413 ) 414 ) 415 if info.get("size") is not None: File ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:792, in _file_info(url, session, size_policy, **kwargs) 791 async with r: --> 792 r.raise_for_status() 794 # TODO: 795 # recognise lack of 'Accept-Ranges', 796 # or 'Accept-Ranges': 'none' (not 'bytes') 797 # to mean streaming only, no random access => return None File ~/venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py:1005, in ClientResponse.raise_for_status(self) 1004 self.release() -> 1005 raise ClientResponseError( 1006 self.request_info, 1007 self.history, 1008 status=self.status, 1009 message=self.reason, 1010 headers=self.headers, 1011 ) ClientResponseError: 403, message='Forbidden', url=URL('[https://cdn-lfs.huggingface.co/repos/e2/89/e28905247d6f48bb4edad5baf9b1bb4158e897a13fdf18bf3b8ee89ff8387ab8/46eca7431a7b6bad344bf451800e5b10cea1dd168f26d1027a6d9eb374b7fac3?response-content-disposition=attachment%3B+filename*%3DUTF-8''dev.csv%3B+filename%3D%22dev.csv%22%3B&response-content-type=text/csv&Expires=1677494732&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2UyLzg5L2UyODkwNTI0N2Q2ZjQ4YmI0ZWRhZDViYWY5YjFiYjQxNThlODk3YTEzZmRmMThiZjNiOGVlODlmZjgzODdhYjgvNDZlY2E3NDMxYTdiNmJhZDM0NGJmNDUxODAwZTViMTBjZWExZGQxNjhmMjZkMTAyN2E2ZDllYjM3NGI3ZmFjMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPXRleHQlMkZjc3YiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Nzc0OTQ3MzJ9fX1dfQ__&Signature=EzQB9f7xPckvqfFB6LzcyR-wzTnQCqtPDdWtQUzZ3QJ-gY-IHG5mxQITJgMr1nVTbJZrPmGAaDngMcPFUfSQa8RmCqYH~dZl-UGE8CO4neKNUT1DvA2WEvLDS4WaAJ3SN-9rX0uFb03~c1QS78cIgIRboYvf6ugKiJz86Bd7Vs~tcp201JFR0A6jIMseqApOnkb9d8dHMP3Ny~F6gO3Qf2QpEWM-QsDIyw2Kz2QV55nq8TsDpRYZCZo50~WwD~73Hej0PoDhEA1K37d19pa0CQhkaN-gjCrbT9xLabbvhJWa~ZkWcMdD0teCgjYqv1wKyvFXDAxukxLGEc7OBXVbYw__&Key-Pair-Id=KVTP0A1DKRTAX](https://cdn-lfs.huggingface.co/repos/e2/89/e28905247d6f48bb4edad5baf9b1bb4158e897a13fdf18bf3b8ee89ff8387ab8/46eca7431a7b6bad344bf451800e5b10cea1dd168f26d1027a6d9eb374b7fac3?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27dev.csv%3B+filename%3D%22dev.csv%22%3B&response-content-type=text/csv&Expires=1677494732&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2UyLzg5L2UyODkwNTI0N2Q2ZjQ4YmI0ZWRhZDViYWY5YjFiYjQxNThlODk3YTEzZmRmMThiZjNiOGVlODlmZjgzODdhYjgvNDZlY2E3NDMxYTdiNmJhZDM0NGJmNDUxODAwZTViMTBjZWExZGQxNjhmMjZkMTAyN2E2ZDllYjM3NGI3ZmFjMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPXRleHQlMkZjc3YiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Nzc0OTQ3MzJ9fX1dfQ__&Signature=EzQB9f7xPckvqfFB6LzcyR-wzTnQCqtPDdWtQUzZ3QJ-gY-IHG5mxQITJgMr1nVTbJZrPmGAaDngMcPFUfSQa8RmCqYH~dZl-UGE8CO4neKNUT1DvA2WEvLDS4WaAJ3SN-9rX0uFb03~c1QS78cIgIRboYvf6ugKiJz86Bd7Vs~tcp201JFR0A6jIMseqApOnkb9d8dHMP3Ny~F6gO3Qf2QpEWM-QsDIyw2Kz2QV55nq8TsDpRYZCZo50~WwD~73Hej0PoDhEA1K37d19pa0CQhkaN-gjCrbT9xLabbvhJWa~ZkWcMdD0teCgjYqv1wKyvFXDAxukxLGEc7OBXVbYw__&Key-Pair-Id=KVTP0A1DKRTAX)') The above exception was the direct cause of the following exception: FileNotFoundError Traceback (most recent call last) Cell In[5], line 4 1 from datasets import load_dataset 3 spigi = load_dataset("kensho/spgispeech", "dev", split="validation", streaming=True) ----> 4 sample = next(iter(spigi)) File ~/datasets/src/datasets/iterable_dataset.py:937, in IterableDataset.__iter__(self) 934 yield from self._iter_pytorch(ex_iterable) 935 return --> 937 for key, example in ex_iterable: 938 if self.features: 939 # `IterableDataset` automatically fills missing columns with None. 940 # This is done with `_apply_feature_types_on_example`. 941 yield _apply_feature_types_on_example( 942 example, self.features, token_per_repo_id=self._token_per_repo_id 943 ) File ~/datasets/src/datasets/iterable_dataset.py:113, in ExamplesIterable.__iter__(self) 112 def __iter__(self): --> 113 yield from self.generate_examples_fn(**self.kwargs) File ~/.cache/huggingface/modules/datasets_modules/datasets/kensho--spgispeech/5fbf75dd9ef795a9b5a673457d2cbaf0b8fa0de8fb62acbd1da338d83a41e2f0/spgispeech.py:186, in Spgispeech._generate_examples(self, local_extracted_archive_paths, archives, meta_path) 183 dict_keys = ["wav_filename", "wav_filesize", "transcript"] 185 logging.info("Reading metadata...") --> 186 with open(meta_path, encoding="utf-8") as f: 187 csvreader = csv.DictReader(f, delimiter="|") 188 metadata = {x["wav_filename"]: dict((k, x[k]) for k in dict_keys) for x in csvreader} File ~/datasets/src/datasets/streaming.py:70, in extend_module_for_streaming.<locals>.wrap_auth.<locals>.wrapper(*args, **kwargs) 68 @wraps(function) 69 def wrapper(*args, **kwargs): ---> 70 return function(*args, use_auth_token=use_auth_token, **kwargs) File ~/datasets/src/datasets/download/streaming_download_manager.py:495, in xopen(file, mode, use_auth_token, *args, **kwargs) 493 kwargs = {**kwargs, **new_kwargs} 494 try: --> 495 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() 496 except ValueError as e: 497 if str(e) == "Cannot seek streaming HTTP file": File ~/venv/lib/python3.9/site-packages/fsspec/core.py:135, in OpenFile.open(self) 128 def open(self): 129 """Materialise this as a real open file without context 130 131 The OpenFile object should be explicitly closed to avoid enclosed file 132 instances persisting. You must, therefore, keep a reference to the OpenFile 133 during the life of the file-like it generates. 134 """ --> 135 return self.__enter__() File ~/venv/lib/python3.9/site-packages/fsspec/core.py:103, in OpenFile.__enter__(self) 100 def __enter__(self): 101 mode = self.mode.replace("t", "").replace("b", "") + "b" --> 103 f = self.fs.open(self.path, mode=mode) 105 self.fobjects = [f] 107 if self.compression is not None: File ~/venv/lib/python3.9/site-packages/fsspec/spec.py:1106, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs) 1104 else: 1105 ac = kwargs.pop("autocommit", not self._intrans) -> 1106 f = self._open( 1107 path, 1108 mode=mode, 1109 block_size=block_size, 1110 autocommit=ac, 1111 cache_options=cache_options, 1112 **kwargs, 1113 ) 1114 if compression is not None: 1115 from fsspec.compression import compr File ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:346, in HTTPFileSystem._open(self, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs) 344 kw["asynchronous"] = self.asynchronous 345 kw.update(kwargs) --> 346 size = size or self.info(path, **kwargs)["size"] 347 session = sync(self.loop, self.set_session) 348 if block_size and size: File ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:113, in sync_wrapper.<locals>.wrapper(*args, **kwargs) 110 @functools.wraps(func) 111 def wrapper(*args, **kwargs): 112 self = obj or args[0] --> 113 return sync(self.loop, func, *args, **kwargs) File ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:98, in sync(loop, func, timeout, *args, **kwargs) 96 raise FSTimeoutError from return_result 97 elif isinstance(return_result, BaseException): ---> 98 raise return_result 99 else: 100 return return_result File ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:53, in _runner(event, coro, result, timeout) 51 coro = asyncio.wait_for(coro, timeout=timeout) 52 try: ---> 53 result[0] = await coro 54 except Exception as ex: 55 result[0] = ex File ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:420, in HTTPFileSystem._info(self, url, **kwargs) 417 except Exception as exc: 418 if policy == "get": 419 # If get failed, then raise a FileNotFoundError --> 420 raise FileNotFoundError(url) from exc 421 logger.debug(str(exc)) 423 return {"name": url, "size": None, **info, "type": "file"} FileNotFoundError: https://huggingface.co/datasets/kensho/spgispeech/resolve/main/data/meta/dev.csv ``` </details>

https://github.com/huggingface/datasets/issues/5574

This problem now appears again, this time with an underlying HTTP 502 status code: ``` aiohttp.client_exceptions.ClientResponseError: 502, message='Bad Gateway', url=URL('https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-validation.00002-of-00008.json.gz') ```

c4 dataset streaming fails with `FileNotFoundError`

### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

c4 dataset streaming fails with `FileNotFoundError` ### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 This problem now appears again, this time with an underlying HTTP 502 status code: ``` aiohttp.client_exceptions.ClientResponseError: 502, message='Bad Gateway', url=URL('https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-validation.00002-of-00008.json.gz') ```

https://github.com/huggingface/datasets/issues/5574

Re-executing a minute later, the underlying cause is an HTTP 403 status code, as reported yesterday: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/4bf6b248b0f910dcde2cdf2118d6369d8208c8f9515ec29ab73e531f380b18e2?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-validation.00002-of-00008.json.gz%3B+filename%3D%22c4-validation.00002-of-00008.json.gz%22%3B&response-content-type=application/gzip&Expires=1677571273&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvNGJmNmIyNDhiMGY5MTBkY2RlMmNkZjIxMThkNjM2OWQ4MjA4YzhmOTUxNWVjMjlhYjczZTUzMWYzODBiMThlMj9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzU3MTI3M319fV19&Signature=WW42NOKkLuX~xVB1QfbkqzdvGo2AOXpgbF3PjTXy6iKd~ffilr1N9ScPXfvTXqy5yvdhJg1G0xJy1zYtUjGAL8GEx3Av-0vIhpWMGYTM8XKEU5gYA9qt30oVtNph6TkTYSABrsYTaj-hzQL9WCgyapmjvG69ETMh4wj44r2rcbk4T3j0l6l4u76Gh~lyRSll3aK4qycdUwcyL7FECDu~0W1mJIJwKkCrWHhSpHJSshb-0ElwG71pq4eyQ5g2uxHdK6JbRF7loxUpRQQJ1vlk0EHXdw0wTMaQ9tqHy6xcrQd8Ep0Yvx3tUD8MR0vWOcbQKnL6LwPQByc8tkChlpjnig__&Key-Pair-Id=KVTP0A1DKRTAX') ```

c4 dataset streaming fails with `FileNotFoundError`

### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

c4 dataset streaming fails with `FileNotFoundError` ### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 Re-executing a minute later, the underlying cause is an HTTP 403 status code, as reported yesterday: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/4bf6b248b0f910dcde2cdf2118d6369d8208c8f9515ec29ab73e531f380b18e2?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-validation.00002-of-00008.json.gz%3B+filename%3D%22c4-validation.00002-of-00008.json.gz%22%3B&response-content-type=application/gzip&Expires=1677571273&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvNGJmNmIyNDhiMGY5MTBkY2RlMmNkZjIxMThkNjM2OWQ4MjA4YzhmOTUxNWVjMjlhYjczZTUzMWYzODBiMThlMj9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzU3MTI3M319fV19&Signature=WW42NOKkLuX~xVB1QfbkqzdvGo2AOXpgbF3PjTXy6iKd~ffilr1N9ScPXfvTXqy5yvdhJg1G0xJy1zYtUjGAL8GEx3Av-0vIhpWMGYTM8XKEU5gYA9qt30oVtNph6TkTYSABrsYTaj-hzQL9WCgyapmjvG69ETMh4wj44r2rcbk4T3j0l6l4u76Gh~lyRSll3aK4qycdUwcyL7FECDu~0W1mJIJwKkCrWHhSpHJSshb-0ElwG71pq4eyQ5g2uxHdK6JbRF7loxUpRQQJ1vlk0EHXdw0wTMaQ9tqHy6xcrQd8Ep0Yvx3tUD8MR0vWOcbQKnL6LwPQByc8tkChlpjnig__&Key-Pair-Id=KVTP0A1DKRTAX') ```

https://github.com/huggingface/datasets/issues/5574

> It's been resolved again ;) I'm experiencing the same issue when trying to load this dataset, `FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/realnewslike/c4-train.00000-of-00512.json.gz`

c4 dataset streaming fails with `FileNotFoundError`

### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

c4 dataset streaming fails with `FileNotFoundError` ### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 > It's been resolved again ;) I'm experiencing the same issue when trying to load this dataset, `FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/realnewslike/c4-train.00000-of-00512.json.gz`

https://github.com/huggingface/datasets/issues/5574

Experiencing the same issues as above : `FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz If the repo is private or gated, make sure to log in with `huggingface-cli login`.` Have made sure to login as well, issue persists.

c4 dataset streaming fails with `FileNotFoundError`

### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

c4 dataset streaming fails with `FileNotFoundError` ### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 Experiencing the same issues as above : `FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz If the repo is private or gated, make sure to log in with `huggingface-cli login`.` Have made sure to login as well, issue persists.

https://github.com/huggingface/datasets/issues/5574

> Experiencing the same issues as above : `FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz If the repo is private or gated, make sure to log in with `huggingface-cli login`.` > > Have made sure to login as well, issue persists. I meet the same issue

c4 dataset streaming fails with `FileNotFoundError`

### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

c4 dataset streaming fails with `FileNotFoundError` ### Describe the bug Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`. ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("c4", "en", split="train", streaming=True) next(iter(dataset)) ``` causes a ``` FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz ``` I can download this file manually though e.g. by entering this URL in a browser. There is an underlying HTTP 403 status code: ``` aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX') ``` ### Expected behavior This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now. ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 > Experiencing the same issues as above : `FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz If the repo is private or gated, make sure to log in with `huggingface-cli login`.` > > Have made sure to login as well, issue persists. I meet the same issue

https://github.com/huggingface/datasets/issues/5571

Hi! You need to pass an input json file explicitly as `data_files` to `load_dataset` to avoid this error: ```python ds = load_dataset("json", data_files=args.input_json) ```

load_dataset fails for JSON in windows

### Describe the bug Steps: 1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method. 2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json" 3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON. 4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py. raise InvalidConfigName( f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. " f"They could create issues when creating a directory for this config on Windows filesystem." 6. When I bring the data to the current directory, it works fine. ### Steps to reproduce the bug Steps: 1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method. 2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json" 3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON. 4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py. raise InvalidConfigName( f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. " f"They could create issues when creating a directory for this config on Windows filesystem." 6. When I bring the data to the current directory, it works fine. ### Expected behavior Should be able to read from a path different than current directory in Windows machine. ### Environment info datasets version: 2.3.1 python version: 3.8 Windows OS

load_dataset fails for JSON in windows ### Describe the bug Steps: 1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method. 2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json" 3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON. 4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py. raise InvalidConfigName( f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. " f"They could create issues when creating a directory for this config on Windows filesystem." 6. When I bring the data to the current directory, it works fine. ### Steps to reproduce the bug Steps: 1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method. 2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json" 3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON. 4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py. raise InvalidConfigName( f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. " f"They could create issues when creating a directory for this config on Windows filesystem." 6. When I bring the data to the current directory, it works fine. ### Expected behavior Should be able to read from a path different than current directory in Windows machine. ### Environment info datasets version: 2.3.1 python version: 3.8 Windows OS Hi! You need to pass an input json file explicitly as `data_files` to `load_dataset` to avoid this error: ```python ds = load_dataset("json", data_files=args.input_json) ```

https://github.com/huggingface/datasets/issues/5570

Hi, thanks for the feedback! Would it help to add a tip or note saying the dataset is gated and you need to accept the license before downloading it?

load_dataset gives FileNotFoundError on imagenet-1k if license is not accepted on the hub

### Describe the bug When calling ```load_dataset('imagenet-1k')``` FileNotFoundError is raised, if not logged in and if logged in with huggingface-cli but not having accepted the licence on the hub. There is no error once accepting. ### Steps to reproduce the bug ``` from datasets import load_dataset imagenet = load_dataset("imagenet-1k", split="train", streaming=True) FileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub ``` tested on a colab notebook. ### Expected behavior I would expect a specific error indicating that I have to login then accept the dataset licence. I find this bug very relevant as this code is on a guide on the [Huggingface documentation for Datasets](https://huggingface.co/docs/datasets/about_mapstyle_vs_iterable) ### Environment info google colab cpu-only instance

load_dataset gives FileNotFoundError on imagenet-1k if license is not accepted on the hub ### Describe the bug When calling ```load_dataset('imagenet-1k')``` FileNotFoundError is raised, if not logged in and if logged in with huggingface-cli but not having accepted the licence on the hub. There is no error once accepting. ### Steps to reproduce the bug ``` from datasets import load_dataset imagenet = load_dataset("imagenet-1k", split="train", streaming=True) FileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub ``` tested on a colab notebook. ### Expected behavior I would expect a specific error indicating that I have to login then accept the dataset licence. I find this bug very relevant as this code is on a guide on the [Huggingface documentation for Datasets](https://huggingface.co/docs/datasets/about_mapstyle_vs_iterable) ### Environment info google colab cpu-only instance Hi, thanks for the feedback! Would it help to add a tip or note saying the dataset is gated and you need to accept the license before downloading it?