sunny2309/githug-issues huggingface dataset:
How do i get "amazon_us_reviews

### Feature request

I have been trying to load 'amazon_us_dataset" but unable to do so. 

`amazon_us_reviews = load_dataset('amazon_us_reviews')`
`print(amazon_us_reviews)`


> [ValueError: Config name is missing.

Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02']
Example of usage:
	`load_dataset('amazon_us_reviews', 'Wireless_v1_00')`]

__________________________________________________________________________
`amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00')
print(amazon_us_reviews)`

**ERROR**
`Generating` train split: 0%
0/960872 [00:00<?, ? examples/s]
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)
   1692                         )
-> 1693                     example = self.info.features.encode_example(record) if self.info.features is not None else record
   1694                     writer.write(example, key)

11 frames
KeyError: 'marketplace'

The above exception was the direct cause of the following exception:

DatasetGenerationError                    Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)
   1710             if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
   1711                 e = e.__context__
-> 1712             raise DatasetGenerationError("An error occurred while generating the dataset") from e
   1713 
   1714         yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)

DatasetGenerationError: An error occurred while generating the dataset

### Motivation

The dataset I'm using
https://huggingface.co/datasets/amazon_us_reviews

### Your contribution

What is the best way to load this data
Dataset.get_nearest_examples does not return all feature values for the k most similar datapoints - side effect of Dataset.set_format

### Describe the bug

Hi team!

I observe that there seems to be a side effect of `Dataset.set_format`: after setting a format and creating a FAISS index, the method `get_nearest_examples` from the `Dataset` class, fails to retrieve anything else but the embeddings themselves - not super useful. This is not the case if not using the `set_format` method: you can also retrieve any other feature value, such as an index/id/etc.

Are you able to reproduce what I observe?

### Steps to reproduce the bug

```python

from datasets import Dataset
import numpy as np

foo = {'vectors': np.random.random((100,1024)), 'ids': [str(u) for u in range(100)]}
foo = Dataset.from_dict(foo)
foo.set_format('numpy', ['vectors'])
foo.add_faiss_index('vectors')
new_vector = np.random.random(1024)
scores, res = foo.get_nearest_examples('vectors', new_vector, k=3)
```
This will return, for the resulting most similar vectors to `new_vector` - in particular it will not return the `ids` feature:
```
{'vectors': array([[random values ...]])}
```

### Expected behavior

The expected behavior happens when the `set_format` method is not called:
```python

from datasets import Dataset
import numpy as np

foo = {'vectors': np.random.random((100,1024)), 'ids': [str(u) for u in range(100)]}
foo = Dataset.from_dict(foo)
# foo.set_format('numpy', ['vectors'])
foo.add_faiss_index('vectors')
new_vector = np.random.random(1024)
scores, res = foo.get_nearest_examples('vectors', new_vector, k=3)
```
This *will*  return the `ids` of the similar vectors - with unfortunately a list of lists in lieu of the array I think for caching reasons - read it elsewhere
```
{'vectors': [[random values on multiple lines...]], 'ids': ['x', 'y', 'z']}
```

### Environment info

- `datasets` version: 2.12.0
- Platform: Linux-5.4.0-155-generic-x86_64-with-glibc2.31
- Python version: 3.10.6
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
Add `fsspec` support for `to_json`, `to_csv`, and `to_parquet`

Hi to whoever is reading this! 🤗 (Most likely @mariosasko)

## What's in this PR?

This PR replaces the `open` from Python with `fsspec.open` and adds the argument `storage_options` for the methods `to_json`, `to_csv`, and `to_parquet`, to allow users to export any 🤗`Dataset` into a file in a file-system as requested at #6086.

## What's missing in this PR?

As per `to_json`, `to_csv`, and `to_parquet` docstrings for the recently included `storage_options` arg, I've scoped it to 2.15.0, so we should check that before merging in case we want to scope that for 2.14.2 instead.

Additionally, should we also add `fsspec` support for the `from_csv`, `from_json`, and `from_parquet` methods? If you want me to do so @mariosasko just let me know and I'll create another PR to support that too!
Fix deprecation of errors in TextConfig

This PR fixes an issue with the deprecation of `errors` in `TextConfig` introduced by:
- #5974

```python
In [1]: ds = load_dataset("text", data_files="test.txt", errors="strict")
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-13-701c27131a5d> in <module>
----> 1 ds = load_dataset("text", data_files="test.txt", errors="strict")

~/huggingface/datasets/src/datasets/load.py 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, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
   2107 
   2108     # Create a dataset builder
-> 2109     builder_instance = load_dataset_builder(
   2110         path=path,
   2111         name=name,

~/huggingface/datasets/src/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs)
   1830     builder_cls = get_dataset_builder_class(dataset_module, dataset_name=dataset_name)
   1831     # Instantiate the dataset builder
-> 1832     builder_instance: DatasetBuilder = builder_cls(
   1833         cache_dir=cache_dir,
   1834         dataset_name=dataset_name,

~/huggingface/datasets/src/datasets/builder.py in __init__(self, cache_dir, dataset_name, config_name, hash, base_path, info, features, token, use_auth_token, repo_id, data_files, data_dir, storage_options, writer_batch_size, name, **config_kwargs)
    371         if data_dir is not None:
    372             config_kwargs["data_dir"] = data_dir
--> 373         self.config, self.config_id = self._create_builder_config(
    374             config_name=config_name,
    375             custom_features=features,

~/huggingface/datasets/src/datasets/builder.py in _create_builder_config(self, config_name, custom_features, **config_kwargs)
    550             if "version" not in config_kwargs and hasattr(self, "VERSION") and self.VERSION:
    551                 config_kwargs["version"] = self.VERSION
--> 552             builder_config = self.BUILDER_CONFIG_CLASS(**config_kwargs)
    553 
    554         # otherwise use the config_kwargs to overwrite the attributes

TypeError: __init__() got an unexpected keyword argument 'errors'
```

Similar to:
- #6094
Fix deprecation of use_auth_token in DownloadConfig

This PR fixes an issue with the deprecation of `use_auth_token` in `DownloadConfig` introduced by:
- #5996

```python
In [1]: from datasets import DownloadConfig

In [2]: DownloadConfig(use_auth_token=False)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-41927b449e72> in <module>
----> 1 DownloadConfig(use_auth_token=False)

TypeError: __init__() got an unexpected keyword argument 'use_auth_token'
```

```python
In [1]: from datasets import get_dataset_config_names
In [2]: get_dataset_config_names("squad", use_auth_token=False)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-22-4671992ead50> in <module>
----> 1 get_dataset_config_names("squad", use_auth_token=False)

~/huggingface/datasets/src/datasets/inspect.py in get_dataset_config_names(path, revision, download_config, download_mode, dynamic_modules_path, data_files, **download_kwargs)
    349     ```
    350     """
--> 351     dataset_module = dataset_module_factory(
    352         path,
    353         revision=revision,

~/huggingface/datasets/src/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
   1374     """
   1375     if download_config is None:
-> 1376         download_config = DownloadConfig(**download_kwargs)
   1377     download_mode = DownloadMode(download_mode or DownloadMode.REUSE_DATASET_IF_EXISTS)
   1378     download_config.extract_compressed_file = True

TypeError: __init__() got an unexpected keyword argument 'use_auth_token'
```
Deprecate `download_custom`

Deprecate `DownloadManager.download_custom`. Users should use `fsspec` URLs (cacheable) or make direct requests with `fsspec`/`requests` (not cacheable) instead.

We should deprecate this method as it's not compatible with streaming, and implementing the streaming version of it is hard/impossible. There have been requests to implement the streaming version of this method on the forum, but the reason for this seems to be a tip in the docs that "promotes" this method (this PR removes it).
FilesIterable skips all the files after a hidden file

### Describe the bug

When initializing `FilesIterable` with a list of file paths using `FilesIterable.from_paths`, it will discard all the files after a hidden file.
The problem is in [this line](https://github.com/huggingface/datasets/blob/88896a7b28610ace95e444b94f9a4bc332cc1ee3/src/datasets/download/download_manager.py#L233C26-L233C26) where `return` should be replaced by `continue`. 

### Steps to reproduce the bug

https://colab.research.google.com/drive/1SQlxs4y_LSo1Q89KnFoYDSyyKEISun_J#scrollTo=93K4_blkW-8-

### Expected behavior

The script should print all the files except the hidden one.

### Environment info

- `datasets` version: 2.14.1
- Platform: Linux-5.15.109+-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.16.4
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
AssertionError: daemonic processes are not allowed to have children

### Describe the bug

When I load_dataset with num_proc > 0 in a deamon process, I got an error:

```python
  File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 564, in download_and_extract
    return self.extract(self.download(url_or_urls))
    ^^^^^^^^^^^^^^^^^
  File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 427, in download
    downloaded_path_or_paths = map_nested(
    ^^^^^^^^^^^^^^^^^
  File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 468, in map_nested
    mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested)
    ^^^^^^^^^^^^^^^^^
  File "/Users/codingl2k1/Work/datasets/src/datasets/utils/experimental.py", line 40, in _inner_fn
    return fn(*args, **kwargs)
    ^^^^^^^^^^^^^^^^^
  File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 34, in parallel_map
    return _map_with_multiprocessing_pool(
    ^^^^^^^^^^^^^^^^^
  File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 64, in _map_with_multiprocessing_pool
    with Pool(num_proc, initargs=initargs, initializer=initializer) as pool:
      ^^^^^^^^^^^^^^^^^
  File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/context.py", line 119, in Pool
    return Pool(processes, initializer, initargs, maxtasksperchild,
    ^^^^^^^^^^^^^^^^^
  File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 215, in __init__
    self._repopulate_pool()
    ^^^^^^^^^^^^^^^^^
  File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 306, in _repopulate_pool
    return self._repopulate_pool_static(self._ctx, self.Process,
    ^^^^^^^^^^^^^^^^^
  File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 329, in _repopulate_pool_static
    w.start()
  File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/process.py", line 118, in start
    assert not _current_process._config.get('daemon'),     ^^^^^^^^^^^^^^^^^
AssertionError: daemonic processes are not allowed to have children
```

The download is io-intensive computing, may be datasets can replece the multi processing pool by a multi threading pool if in a deamon process.

### Steps to reproduce the bug

1. start a deamon process
2. run load_dataset with num_proc > 0

### Expected behavior

No error.

### Environment info

Python 3.11.4
datasets latest master
fsspec dependency is set too low

### Describe the bug

fsspec.callbacks.TqdmCallback (used in https://github.com/huggingface/datasets/blob/73bed12ecda17d1573fd3bf73ed5db24d3622f86/src/datasets/utils/file_utils.py#L338) was first released in fsspec [2022.3.0](https://github.com/fsspec/filesystem_spec/releases/tag/2022.3.0, commit where it was added: https://github.com/fsspec/filesystem_spec/commit/9577c8a482eb0a69092913b81580942a68d66a76#diff-906155c7e926a9ff58b9f23369bb513b09b445f5b0f41fa2a84015d0b471c68cR180), however the dependency is set to 2021.11.1 https://github.com/huggingface/datasets/blob/main/setup.py#L129



### Steps to reproduce the bug

1. Install fsspec==2021.11.1
2. Install latest datasets==2.14.1
3. Import datasets, import fails due to lack of `fsspec.callbacks.TqdmCallback`

### Expected behavior

No import issue

### Environment info

N/A
Fix `fsspec` download

Testing `ds = load_dataset("audiofolder", data_files="s3://datasets.huggingface.co/SpeechCommands/v0.01/v0.01_test.tar.gz", storage_options={"anon": True})` and trying to fix the issues raised by `fsspec` ...

TODO: fix
```
    self.session = aiobotocore.session.AioSession(**self.kwargs)
TypeError: __init__() got an unexpected keyword argument 'hf'
```
by "preparing `storage_options`" for the `fsspec` head/get
Deprecate `Dataset.export`

Deprecate `Dataset.export` that generates a TFRecord file from a dataset as this method is undocumented, and the usage seems low. Users should use [TFRecordWriter](https://www.tensorflow.org/api_docs/python/tf/io/TFRecordWriter#write) or the official [TFRecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) tutorial (on which this method is based) to write TFRecord files instead.
Iterating over DataLoader based on HF datasets is stuck forever

### Describe the bug

I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment.
I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here?

### Steps to reproduce the bug

```
train_dataset = load_dataset(
    "parquet", data_files = {'train': tr_data_path + '*.parquet'}, 
    split = 'train', 
    collate_fn = streaming_data_collate_fn,
    streaming = True
).with_format('torch')

train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0)

t = time.time()
iter_ = 0
for batch in train_dataloader:
    iter_ += 1
    
    if iter_ == 1000:
        break
        
print (time.time() - t) 
```

### Expected behavior

The snippet should work normally and load the next batch of data.

### Environment info

datasets: '2.14.0'
pyarrow: '12.0.0'
torch: '2.0.0'
Python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0]

!uname -r
5.10.178-162.673.amzn2.x86_64
resume_download with streaming=True

### Describe the bug

I used:
```
dataset = load_dataset(
    "oscar-corpus/OSCAR-2201",
    token=True,
    language="fr",
    streaming=True,
    split="train"
)
```
Unfortunately, the server had a problem during the training process. I saved the step my training stopped at.
But how can I resume download from step 1_000_´000 without re-streaming all the first 1 million docs of the dataset?

`download_config=DownloadConfig(resume_download=True)` seems to not work with streaming=True.

### Steps to reproduce the bug

```
from datasets import load_dataset, DownloadConfig
dataset = load_dataset(
    "oscar-corpus/OSCAR-2201",
    token=True,
    language="fr",
    streaming=True,  # optional
    split="train",
    download_config=DownloadConfig(resume_download=True)
)
# interupt the run and try to relaunch it => this restart from scratch
```

### Expected behavior

I would expect a parameter to start streaming from a given index in the dataset.

### Environment info

- `datasets` version: 2.14.0
- Platform: Linux-5.19.0-45-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.0
No gzip encoding from github

Don't accept gzip encoding from github, otherwise some files are not streamable + seekable.

fix https://huggingface.co/datasets/code_x_glue_cc_code_to_code_trans/discussions/2#64c0e0c1a04a514ba6303e84

and making sure https://github.com/huggingface/datasets/issues/2918 works as well
storage_options provided to load_dataset not fully piping through since datasets 2.14.0

### Describe the bug

Since the latest release of `datasets` (`2.14.0`), custom filesystem `storage_options` passed to `load_dataset()` do not seem to propagate through all the way - leading to problems if loading data files that need those options to be set.

I think this is because of the new `_prepare_path_and_storage_options()` (https://github.com/huggingface/datasets/pull/6028), which returns the right `storage_options` to use given a path and a `DownloadConfig` - but which might not be taking into account the extra `storage_options` explicitly provided e.g. through `load_dataset()`

### Steps to reproduce the bug

```python
import fsspec

import pandas as pd
import datasets

# Generate mock parquet file
data_files = "demo.parquet"
pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}).to_parquet(data_files)

_storage_options = {"x": 1, "y": 2}
fs = fsspec.filesystem("file", **_storage_options)

dataset = datasets.load_dataset(
    "parquet",
    data_files=data_files,
    storage_options=fs.storage_options
)
```

Looking at the `storage_options` resolved here:

https://github.com/huggingface/datasets/blob/b0177910b32712f28d147879395e511207e39958/src/datasets/data_files.py#L331

 they end up being `{}`, instead of propagating through the `storage_options` that were provided to `load_dataset` (`fs.storage_options`). As these then get used for the filesystem operation a few lines below

https://github.com/huggingface/datasets/blob/b0177910b32712f28d147879395e511207e39958/src/datasets/data_files.py#L339

the call will fail if the user-provided `storage_options` were needed.

---

A temporary workaround that seemed to work locally to bypass the problem was to bundle a duplicate of the `storage_options` into the `download_config`, so that they make their way all the way to `_prepare_path_and_storage_options()` and get extracted correctly:

```python
dataset = datasets.load_dataset(
        "parquet",
        data_files=data_files,
        storage_options=fs.storage_options,
        download_config=datasets.DownloadConfig(storage_options={fs.protocol: fs.storage_options}),
    )
```

### Expected behavior

`storage_options` provided to `load_dataset` take effect in all backend filesystem operations.

### Environment info

datasets==2.14.0
KeyError: dataset has no key "image"

### Describe the bug

I've loaded a local image dataset with:
`ds = laod_dataset("imagefolder", data_dir=path-to-data)`

And defined a transform to process the data, following the Datasets docs.

However, I get a keyError error, indicating there's no "image" key in my dataset. When I printed out the example_batch sent to the transformation function, it shows only the labels are being sent to the function.
For some reason, the images are not in the example batches.

### Steps to reproduce the bug

I'm using the latest stable version of datasets 

### Expected behavior

I expect the example_batches to contain both images and labels

### Environment info

I'm using the latest stable version of datasets
Add column type guessing from map return function

As discussed [here](https://github.com/huggingface/datasets/issues/5965), there are some cases where datasets is unable to automatically promote columns during mapping. The fix is to explicitly provide a `features` definition so pyarrow can configure itself with the right column types from the outset.

This PR provides an alternative approach, which is functionally equivalent to specifying features but a bit cleaner within a larger mapping pipeline. It allows clients to typehint the return variable coming from the mapper function - if we find one of these type annotations specified, and no explicit features have been passed in, we'll try to convert it into a Features map. If the map function runs and casting is unable to succeed, it will raise a DatasetTransformationNotAllowedError that indicates the typehint may be to blame. It works for batched and non-batched mapping functions.

Currently supported column types:
- builtins primitives: string, int, float, bool
- dictionaries, lists (nested and one-deep)
- Optional types and None-Unions (synonymous with optional types)

It's used like:

```python
class DatasetTyped(TypedDict):
  texts: list[str]

def dataset_typed_map(batch) -> DatasetTyped:
  return {"texts": [text.split() for text in batch["raw_text"]]}

dataset = {"raw_text": ["", "This is a test", "This is another test"]}

with Dataset.from_dict(dataset) as dset:
  new_dataset = dset.map(
    dataset_typed_map,
    batched=True,
    batch_size=1,
    num_proc=1,
)
```

Open questions:
- Should logging indicate we have automatically guessed these types? Or proceed quietly until we hit an error (as is the current implementation).