[Checksums] Error for some datasets

[Checksums] Error for some datasets
The checksums command works very nicely for `squad`. But for `crime_and_punish` and `xnli`,
the same bug happens:

When running:
```
python nlp-cli nlp-cli test xnli --save_checksums
```

leads to:

```
  File "nlp-cli", line 33, in <module>
    service.run()
  File "/home/patrick/python_bin/nlp/commands/test.py", line 61, in run
    ignore_checksums=self._ignore_checksums,
  File "/home/patrick/python_bin/nlp/builder.py", line 383, in download_and_prepare
    self._download_and_prepare(dl_manager=dl_manager, download_config=download_config)
  File "/home/patrick/python_bin/nlp/builder.py", line 627, in _download_and_prepare
    dl_manager=dl_manager, max_examples_per_split=download_config.max_examples_per_split,
  File "/home/patrick/python_bin/nlp/builder.py", line 431, in _download_and_prepare
    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
  File "/home/patrick/python_bin/nlp/datasets/xnli/8bf4185a2da1ef2a523186dd660d9adcf0946189e7fa5942ea31c63c07b68a7f/xnli.py", line 95, in _split_generators
    dl_dir = dl_manager.download_and_extract(_DATA_URL)
  File "/home/patrick/python_bin/nlp/utils/download_manager.py", line 246, in download_and_extract
    return self.extract(self.download(url_or_urls))
  File "/home/patrick/python_bin/nlp/utils/download_manager.py", line 186, in download
    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
  File "/home/patrick/python_bin/nlp/utils/download_manager.py", line 166, in _record_sizes_checksums
    self._recorded_sizes_checksums[url] = get_size_checksum(path)
  File "/home/patrick/python_bin/nlp/utils/checksums_utils.py", line 81, in get_size_checksum
    with open(path, "rb") as f:
TypeError: expected str, bytes or os.PathLike object, not tuple
```

Awesome!


Error when citation is not given in the DatasetInfo
The following error is raised when the `citation` parameter is missing when we instantiate a `DatasetInfo`:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/jplu/dev/jplu/datasets/src/nlp/info.py", line 338, in __repr__
    citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
AttributeError: 'NoneType' object has no attribute 'strip'
```

I propose to do the following change in the `info.py` file. The method:
```python
def __repr__(self):
        splits_pprint = _indent("\n".join(["{"] + [
                "    '{}': {},".format(k, split.num_examples)
                for k, split in sorted(self.splits.items())
        ] + ["}"]))
        features_pprint = _indent(repr(self.features))
        citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
        return INFO_STR.format(
                name=self.name,
                version=self.version,
                description=self.description,
                total_num_examples=self.splits.total_num_examples,
                features=features_pprint,
                splits=splits_pprint,
                citation=citation_pprint,
                homepage=self.homepage,
                supervised_keys=self.supervised_keys,
                # Proto add a \n that we strip.
                license=str(self.license).strip())
```
Becomes:
```python
def __repr__(self):
        splits_pprint = _indent("\n".join(["{"] + [
                "    '{}': {},".format(k, split.num_examples)
                for k, split in sorted(self.splits.items())
        ] + ["}"]))
        features_pprint = _indent(repr(self.features))
        ## the strip is done only is the citation is given
        citation_pprint = self.citation

        if self.citation:
            citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
        return INFO_STR.format(
                name=self.name,
                version=self.version,
                description=self.description,
                total_num_examples=self.splits.total_num_examples,
                features=features_pprint,
                splits=splits_pprint,
                citation=citation_pprint,
                homepage=self.homepage,
                supervised_keys=self.supervised_keys,
                # Proto add a \n that we strip.
                license=str(self.license).strip())
```
And now it is ok. @thomwolf are you ok with this fix?
Yes looks good to me.
Note that we may refactor quite strongly the `info.py` to make it a lot simpler (it's very complicated for basically a dictionary of info I think)


Error when citation is not given in the DatasetInfo
The following error is raised when the `citation` parameter is missing when we instantiate a `DatasetInfo`:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/jplu/dev/jplu/datasets/src/nlp/info.py", line 338, in __repr__
    citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
AttributeError: 'NoneType' object has no attribute 'strip'
```

I propose to do the following change in the `info.py` file. The method:
```python
def __repr__(self):
        splits_pprint = _indent("\n".join(["{"] + [
                "    '{}': {},".format(k, split.num_examples)
                for k, split in sorted(self.splits.items())
        ] + ["}"]))
        features_pprint = _indent(repr(self.features))
        citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
        return INFO_STR.format(
                name=self.name,
                version=self.version,
                description=self.description,
                total_num_examples=self.splits.total_num_examples,
                features=features_pprint,
                splits=splits_pprint,
                citation=citation_pprint,
                homepage=self.homepage,
                supervised_keys=self.supervised_keys,
                # Proto add a \n that we strip.
                license=str(self.license).strip())
```
Becomes:
```python
def __repr__(self):
        splits_pprint = _indent("\n".join(["{"] + [
                "    '{}': {},".format(k, split.num_examples)
                for k, split in sorted(self.splits.items())
        ] + ["}"]))
        features_pprint = _indent(repr(self.features))
        ## the strip is done only is the citation is given
        citation_pprint = self.citation

        if self.citation:
            citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
        return INFO_STR.format(
                name=self.name,
                version=self.version,
                description=self.description,
                total_num_examples=self.splits.total_num_examples,
                features=features_pprint,
                splits=splits_pprint,
                citation=citation_pprint,
                homepage=self.homepage,
                supervised_keys=self.supervised_keys,
                # Proto add a \n that we strip.
                license=str(self.license).strip())
```
And now it is ok. @thomwolf are you ok with this fix?
No, problem ^^ It might just be a temporary fix :)


Error when citation is not given in the DatasetInfo
The following error is raised when the `citation` parameter is missing when we instantiate a `DatasetInfo`:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/jplu/dev/jplu/datasets/src/nlp/info.py", line 338, in __repr__
    citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
AttributeError: 'NoneType' object has no attribute 'strip'
```

I propose to do the following change in the `info.py` file. The method:
```python
def __repr__(self):
        splits_pprint = _indent("\n".join(["{"] + [
                "    '{}': {},".format(k, split.num_examples)
                for k, split in sorted(self.splits.items())
        ] + ["}"]))
        features_pprint = _indent(repr(self.features))
        citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
        return INFO_STR.format(
                name=self.name,
                version=self.version,
                description=self.description,
                total_num_examples=self.splits.total_num_examples,
                features=features_pprint,
                splits=splits_pprint,
                citation=citation_pprint,
                homepage=self.homepage,
                supervised_keys=self.supervised_keys,
                # Proto add a \n that we strip.
                license=str(self.license).strip())
```
Becomes:
```python
def __repr__(self):
        splits_pprint = _indent("\n".join(["{"] + [
                "    '{}': {},".format(k, split.num_examples)
                for k, split in sorted(self.splits.items())
        ] + ["}"]))
        features_pprint = _indent(repr(self.features))
        ## the strip is done only is the citation is given
        citation_pprint = self.citation

        if self.citation:
            citation_pprint = _indent('"""{}"""'.format(self.citation.strip()))
        return INFO_STR.format(
                name=self.name,
                version=self.version,
                description=self.description,
                total_num_examples=self.splits.total_num_examples,
                features=features_pprint,
                splits=splits_pprint,
                citation=citation_pprint,
                homepage=self.homepage,
                supervised_keys=self.supervised_keys,
                # Proto add a \n that we strip.
                license=str(self.license).strip())
```
And now it is ok. @thomwolf are you ok with this fix?
Fixed.


ValueError when a split is empty

ValueError when a split is empty
When a split is empty either TEST, VALIDATION or TRAIN I get the following error:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/jplu/dev/jplu/datasets/src/nlp/load.py", line 295, in load
    ds = dbuilder.as_dataset(**as_dataset_kwargs)
  File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 587, in as_dataset
    datasets = utils.map_nested(build_single_dataset, split, map_tuple=True)
  File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 158, in map_nested
    for k, v in data_struct.items()
  File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 158, in <dictcomp>
    for k, v in data_struct.items()
  File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 172, in map_nested
    return function(data_struct)
  File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 601, in _build_single_dataset
    split=split,
  File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 625, in _as_dataset
    split_infos=self.info.splits.values(),
  File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 200, in read
    return py_utils.map_nested(_read_instruction_to_ds, instructions)
  File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 172, in map_nested
    return function(data_struct)
  File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 191, in _read_instruction_to_ds
    file_instructions = make_file_instructions(name, split_infos, instruction)
  File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 104, in make_file_instructions
    absolute_instructions=absolute_instructions,
  File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 122, in _make_file_instructions_from_absolutes
    'Split empty. This might means that dataset hasn\'t been generated '
ValueError: Split empty. This might means that dataset hasn't been generated yet and info not restored from GCS, or that legacy dataset is used.
```

How to reproduce:
```python
import csv

import nlp

class Bbc(nlp.GeneratorBasedBuilder):
    VERSION = nlp.Version("1.0.0")

    def __init__(self, **config):
        self.train = config.pop("train", None)
        self.validation = config.pop("validation", None)
        super(Bbc, self).__init__(**config)

    def _info(self):
        return nlp.DatasetInfo(builder=self, description="bla", features=nlp.features.FeaturesDict({"id": nlp.int32, "text": nlp.string, "label": nlp.string}))

    def _split_generators(self, dl_manager):
        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": self.train}),
                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": self.validation}),
                nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={"filepath": None})]

    def _generate_examples(self, filepath):
        if not filepath:
            return None, {}

        with open(filepath) as f:
            reader = csv.reader(f, delimiter=',', quotechar="\"")
            lines = list(reader)[1:]

            for idx, line in enumerate(lines):
                yield idx, {"id": idx, "text": line[1], "label": line[0]}
```

```python
import nlp
dataset = nlp.load("bbc", builder_kwargs={"train": "bbc/data/train.csv", "validation": "bbc/data/test.csv"})
```
To fix this I propose to modify only the file `arrow_reader.py` with few updates. First update, the following method:
```python
def _make_file_instructions_from_absolutes(
        name,
        name2len,
        absolute_instructions,
):
    """Returns the files instructions from the absolute instructions list."""
    # For each split, return the files instruction (skip/take)
    file_instructions = []
    num_examples = 0
    for abs_instr in absolute_instructions:
        length = name2len[abs_instr.splitname]
        if not length:
            raise ValueError(
                    'Split empty. This might means that dataset hasn\'t been generated '
                    'yet and info not restored from GCS, or that legacy dataset is used.')
        filename = filename_for_dataset_split(
                dataset_name=name,
                split=abs_instr.splitname,
                filetype_suffix='arrow')
        from_ = 0 if abs_instr.from_ is None else abs_instr.from_
        to = length if abs_instr.to is None else abs_instr.to
        num_examples += to - from_
        single_file_instructions = [{"filename": filename, "skip": from_, "take": to - from_}]
        file_instructions.extend(single_file_instructions)
    return FileInstructions(
            num_examples=num_examples,
            file_instructions=file_instructions,
    )
```
Becomes:
```python
def _make_file_instructions_from_absolutes(
        name,
        name2len,
        absolute_instructions,
):
    """Returns the files instructions from the absolute instructions list."""
    # For each split, return the files instruction (skip/take)
    file_instructions = []
    num_examples = 0
    for abs_instr in absolute_instructions:
        length = name2len[abs_instr.splitname]
        ## Delete the if not length and the raise
        filename = filename_for_dataset_split(
                dataset_name=name,
                split=abs_instr.splitname,
                filetype_suffix='arrow')
        from_ = 0 if abs_instr.from_ is None else abs_instr.from_
        to = length if abs_instr.to is None else abs_instr.to
        num_examples += to - from_
        single_file_instructions = [{"filename": filename, "skip": from_, "take": to - from_}]
        file_instructions.extend(single_file_instructions)
    return FileInstructions(
            num_examples=num_examples,
            file_instructions=file_instructions,
    )
```

Second update the following method:
```python
def _read_files(files, info):
    """Returns Dataset for given file instructions.

    Args:
        files: List[dict(filename, skip, take)], the files information.
            The filenames contain the absolute path, not relative.
            skip/take indicates which example read in the file: `ds.slice(skip, take)`
    """
    pa_batches = []
    for f_dict in files:
        pa_table: pa.Table = _get_dataset_from_filename(f_dict)
        pa_batches.extend(pa_table.to_batches())
    pa_table = pa.Table.from_batches(pa_batches)
    ds = Dataset(arrow_table=pa_table, data_files=files, info=info)
    return ds
```
Becomes:
```python
def _read_files(files, info):
    """Returns Dataset for given file instructions.

    Args:
        files: List[dict(filename, skip, take)], the files information.
            The filenames contain the absolute path, not relative.
            skip/take indicates which example read in the file: `ds.slice(skip, take)`
    """
    pa_batches = []
    for f_dict in files:
        pa_table: pa.Table = _get_dataset_from_filename(f_dict)
        pa_batches.extend(pa_table.to_batches())
    ## we modify the table only if there are some batches
    if pa_batches:
        pa_table = pa.Table.from_batches(pa_batches)
    ds = Dataset(arrow_table=pa_table, data_files=files, info=info)
    return ds
```

With these two updates it works now. @thomwolf are you ok with this changes?


ValueError when a split is empty

ValueError when a split is empty
When a split is empty either TEST, VALIDATION or TRAIN I get the following error:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/jplu/dev/jplu/datasets/src/nlp/load.py", line 295, in load
    ds = dbuilder.as_dataset(**as_dataset_kwargs)
  File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 587, in as_dataset
    datasets = utils.map_nested(build_single_dataset, split, map_tuple=True)
  File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 158, in map_nested
    for k, v in data_struct.items()
  File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 158, in <dictcomp>
    for k, v in data_struct.items()
  File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 172, in map_nested
    return function(data_struct)
  File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 601, in _build_single_dataset
    split=split,
  File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 625, in _as_dataset
    split_infos=self.info.splits.values(),
  File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 200, in read
    return py_utils.map_nested(_read_instruction_to_ds, instructions)
  File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 172, in map_nested
    return function(data_struct)
  File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 191, in _read_instruction_to_ds
    file_instructions = make_file_instructions(name, split_infos, instruction)
  File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 104, in make_file_instructions
    absolute_instructions=absolute_instructions,
  File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 122, in _make_file_instructions_from_absolutes
    'Split empty. This might means that dataset hasn\'t been generated '
ValueError: Split empty. This might means that dataset hasn't been generated yet and info not restored from GCS, or that legacy dataset is used.
```

How to reproduce:
```python
import csv

import nlp

class Bbc(nlp.GeneratorBasedBuilder):
    VERSION = nlp.Version("1.0.0")

    def __init__(self, **config):
        self.train = config.pop("train", None)
        self.validation = config.pop("validation", None)
        super(Bbc, self).__init__(**config)

    def _info(self):
        return nlp.DatasetInfo(builder=self, description="bla", features=nlp.features.FeaturesDict({"id": nlp.int32, "text": nlp.string, "label": nlp.string}))

    def _split_generators(self, dl_manager):
        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": self.train}),
                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": self.validation}),
                nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={"filepath": None})]

    def _generate_examples(self, filepath):
        if not filepath:
            return None, {}

        with open(filepath) as f:
            reader = csv.reader(f, delimiter=',', quotechar="\"")
            lines = list(reader)[1:]

            for idx, line in enumerate(lines):
                yield idx, {"id": idx, "text": line[1], "label": line[0]}
```

```python
import nlp
dataset = nlp.load("bbc", builder_kwargs={"train": "bbc/data/train.csv", "validation": "bbc/data/test.csv"})
```
Yes sounds good to me!
Do you want to make a PR? or I can do it as well


ValueError when a split is empty

ValueError when a split is empty
When a split is empty either TEST, VALIDATION or TRAIN I get the following error:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/jplu/dev/jplu/datasets/src/nlp/load.py", line 295, in load
    ds = dbuilder.as_dataset(**as_dataset_kwargs)
  File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 587, in as_dataset
    datasets = utils.map_nested(build_single_dataset, split, map_tuple=True)
  File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 158, in map_nested
    for k, v in data_struct.items()
  File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 158, in <dictcomp>
    for k, v in data_struct.items()
  File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 172, in map_nested
    return function(data_struct)
  File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 601, in _build_single_dataset
    split=split,
  File "/home/jplu/dev/jplu/datasets/src/nlp/builder.py", line 625, in _as_dataset
    split_infos=self.info.splits.values(),
  File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 200, in read
    return py_utils.map_nested(_read_instruction_to_ds, instructions)
  File "/home/jplu/dev/jplu/datasets/src/nlp/utils/py_utils.py", line 172, in map_nested
    return function(data_struct)
  File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 191, in _read_instruction_to_ds
    file_instructions = make_file_instructions(name, split_infos, instruction)
  File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 104, in make_file_instructions
    absolute_instructions=absolute_instructions,
  File "/home/jplu/dev/jplu/datasets/src/nlp/arrow_reader.py", line 122, in _make_file_instructions_from_absolutes
    'Split empty. This might means that dataset hasn\'t been generated '
ValueError: Split empty. This might means that dataset hasn't been generated yet and info not restored from GCS, or that legacy dataset is used.
```

How to reproduce:
```python
import csv

import nlp

class Bbc(nlp.GeneratorBasedBuilder):
    VERSION = nlp.Version("1.0.0")

    def __init__(self, **config):
        self.train = config.pop("train", None)
        self.validation = config.pop("validation", None)
        super(Bbc, self).__init__(**config)

    def _info(self):
        return nlp.DatasetInfo(builder=self, description="bla", features=nlp.features.FeaturesDict({"id": nlp.int32, "text": nlp.string, "label": nlp.string}))

    def _split_generators(self, dl_manager):
        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": self.train}),
                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": self.validation}),
                nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={"filepath": None})]

    def _generate_examples(self, filepath):
        if not filepath:
            return None, {}

        with open(filepath) as f:
            reader = csv.reader(f, delimiter=',', quotechar="\"")
            lines = list(reader)[1:]

            for idx, line in enumerate(lines):
                yield idx, {"id": idx, "text": line[1], "label": line[0]}
```

```python
import nlp
dataset = nlp.load("bbc", builder_kwargs={"train": "bbc/data/train.csv", "validation": "bbc/data/test.csv"})
```
Fixed.


[Feature] Keep the list of labels of a dataset as metadata
It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata.
Yes! I see mostly two options for this:
- a `Feature` approach like currently (but we might deprecate features)
- wrapping in a smart way the Dictionary arrays of Arrow: https://arrow.apache.org/docs/python/data.html?highlight=dictionary%20encode#dictionary-arrays


[Feature] Keep the list of labels of a dataset as metadata
It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata.
I would have a preference for the second bullet point.


[Feature] Keep the list of labels of a dataset as metadata
It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata.
This should be accessible now as a feature in dataset.info.features (and even have the mapping methods).


[Feature] Keep the list of labels of a dataset as metadata
It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata.
Perfect! Well done!!


[Feature] Keep the list of labels of a dataset as metadata
It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata.
Hi,
I hope we could get a better documentation.
It took me more than 1 hour to found this way to get the label information.


[Feature] Keep the list of labels of a dataset as metadata
It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata.
Yes we are working on the doc right now, should be in the next release quite soon.


[Feature] More dataset outputs

[Feature] More dataset outputs
Add the following dataset outputs:

- Spark
- Pandas
Yes!
- pandas will be a one-liner in `arrow_dataset`: https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.to_pandas
- for Spark I have no idea. let's investigate that at some point


[Feature] More dataset outputs

[Feature] More dataset outputs
Add the following dataset outputs:

- Spark
- Pandas
For Spark it looks to be pretty straightforward as well https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html but looks to be having a dependency to Spark is necessary, then nevermind we can skip it


[Feature] More dataset outputs

[Feature] More dataset outputs
Add the following dataset outputs:

- Spark
- Pandas
Now Pandas is available.


Issue to read a local dataset

Issue to read a local dataset
Hello,

As proposed by @thomwolf, I open an issue to explain what I'm trying to do without success. What I want to do is to create and load a local dataset, the script I have done is the following:
```python
import os
import csv

import nlp

class BbcConfig(nlp.BuilderConfig):
    def __init__(self, **kwargs):
        super(BbcConfig, self).__init__(**kwargs)

class Bbc(nlp.GeneratorBasedBuilder):
    _DIR = "./data"
    _DEV_FILE = "test.csv"
    _TRAINING_FILE = "train.csv"

    BUILDER_CONFIGS = [BbcConfig(name="bbc", version=nlp.Version("1.0.0"))]

    def _info(self):
        return nlp.DatasetInfo(builder=self, features=nlp.features.FeaturesDict({"id": nlp.string, "text": nlp.string, "label": nlp.string}))

    def _split_generators(self, dl_manager):
        files = {"train": os.path.join(self._DIR, self._TRAINING_FILE), "dev": os.path.join(self._DIR, self._DEV_FILE)}

        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": files["train"]}),
                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": files["dev"]})]

    def _generate_examples(self, filepath):
        with open(filepath) as f:
            reader = csv.reader(f, delimiter=',', quotechar="\"")
            lines = list(reader)[1:]

            for idx, line in enumerate(lines):
                yield idx, {"idx": idx, "text": line[1], "label": line[0]}

```

The dataset is attached to this issue as well:
[data.zip](https://github.com/huggingface/datasets/files/4476928/data.zip)

Now the steps to reproduce what I would like to do:
1. unzip data locally (I know the nlp lib can detect and extract archives but I want to reduce and facilitate the reproduction as much as possible)
2. create the `bbc.py` script as above at the same location than the unziped `data` folder.

Now I try to load the dataset in three different ways and none works, the first one with the name of the dataset like I would do with TFDS:
```python
import nlp
from bbc import Bbc
dataset = nlp.load("bbc")
```

I get:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
    builder_cls = load_dataset(path, name=name, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 88, in load_dataset
    local_files_only=local_files_only,
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/utils/file_utils.py", line 214, in cached_path
    if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
  File "/opt/anaconda3/envs/transformers/lib/python3.7/zipfile.py", line 203, in is_zipfile
    with open(filename, "rb") as fp:
TypeError: expected str, bytes or os.PathLike object, not NoneType
```

But @thomwolf told me that no need to import the script, just put the path of it, then I tried three different way to do:
```python
import nlp
dataset = nlp.load("bbc.py")
```
And
```python
import nlp
dataset = nlp.load("./bbc.py")
```
And
```python
import nlp
dataset = nlp.load("/absolute/path/to/bbc.py")
```

These three ways gives me:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
    builder_cls = load_dataset(path, name=name, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 124, in load_dataset
    dataset_module = importlib.import_module(module_path)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
  File "<frozen importlib._bootstrap>", line 983, in _find_and_load
  File "<frozen importlib._bootstrap>", line 965, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'nlp.datasets.2fd72627d92c328b3e9c4a3bf7ec932c48083caca09230cebe4c618da6e93688.bbc'
```
Any idea of what I'm missing? or I might have spot a bug :)
My first bug report ❤
Looking into this right now!


Issue to read a local dataset

Issue to read a local dataset
Hello,

As proposed by @thomwolf, I open an issue to explain what I'm trying to do without success. What I want to do is to create and load a local dataset, the script I have done is the following:
```python
import os
import csv

import nlp

class BbcConfig(nlp.BuilderConfig):
    def __init__(self, **kwargs):
        super(BbcConfig, self).__init__(**kwargs)

class Bbc(nlp.GeneratorBasedBuilder):
    _DIR = "./data"
    _DEV_FILE = "test.csv"
    _TRAINING_FILE = "train.csv"

    BUILDER_CONFIGS = [BbcConfig(name="bbc", version=nlp.Version("1.0.0"))]

    def _info(self):
        return nlp.DatasetInfo(builder=self, features=nlp.features.FeaturesDict({"id": nlp.string, "text": nlp.string, "label": nlp.string}))

    def _split_generators(self, dl_manager):
        files = {"train": os.path.join(self._DIR, self._TRAINING_FILE), "dev": os.path.join(self._DIR, self._DEV_FILE)}

        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": files["train"]}),
                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": files["dev"]})]

    def _generate_examples(self, filepath):
        with open(filepath) as f:
            reader = csv.reader(f, delimiter=',', quotechar="\"")
            lines = list(reader)[1:]

            for idx, line in enumerate(lines):
                yield idx, {"idx": idx, "text": line[1], "label": line[0]}

```

The dataset is attached to this issue as well:
[data.zip](https://github.com/huggingface/datasets/files/4476928/data.zip)

Now the steps to reproduce what I would like to do:
1. unzip data locally (I know the nlp lib can detect and extract archives but I want to reduce and facilitate the reproduction as much as possible)
2. create the `bbc.py` script as above at the same location than the unziped `data` folder.

Now I try to load the dataset in three different ways and none works, the first one with the name of the dataset like I would do with TFDS:
```python
import nlp
from bbc import Bbc
dataset = nlp.load("bbc")
```

I get:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
    builder_cls = load_dataset(path, name=name, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 88, in load_dataset
    local_files_only=local_files_only,
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/utils/file_utils.py", line 214, in cached_path
    if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
  File "/opt/anaconda3/envs/transformers/lib/python3.7/zipfile.py", line 203, in is_zipfile
    with open(filename, "rb") as fp:
TypeError: expected str, bytes or os.PathLike object, not NoneType
```

But @thomwolf told me that no need to import the script, just put the path of it, then I tried three different way to do:
```python
import nlp
dataset = nlp.load("bbc.py")
```
And
```python
import nlp
dataset = nlp.load("./bbc.py")
```
And
```python
import nlp
dataset = nlp.load("/absolute/path/to/bbc.py")
```

These three ways gives me:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
    builder_cls = load_dataset(path, name=name, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 124, in load_dataset
    dataset_module = importlib.import_module(module_path)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
  File "<frozen importlib._bootstrap>", line 983, in _find_and_load
  File "<frozen importlib._bootstrap>", line 965, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'nlp.datasets.2fd72627d92c328b3e9c4a3bf7ec932c48083caca09230cebe4c618da6e93688.bbc'
```
Any idea of what I'm missing? or I might have spot a bug :)
Ok, there are some news, most good than bad :laughing:

The dataset script now became:
```python
import csv

import nlp

class Bbc(nlp.GeneratorBasedBuilder):
    VERSION = nlp.Version("1.0.0")

    def __init__(self, **config):
        self.train = config.pop("train", None)
        self.validation = config.pop("validation", None)
        super(Bbc, self).__init__(**config)

    def _info(self):
        return nlp.DatasetInfo(builder=self, description="bla", features=nlp.features.FeaturesDict({"id": nlp.int32, "text": nlp.string, "label": nlp.string}))

    def _split_generators(self, dl_manager):
        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": self.train}),
                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": self.validation})]

    def _generate_examples(self, filepath):
        with open(filepath) as f:
            reader = csv.reader(f, delimiter=',', quotechar="\"")
            lines = list(reader)[1:]

            for idx, line in enumerate(lines):
                yield idx, {"id": idx, "text": line[1], "label": line[0]}

```

And the dataset folder becomes:
```
.
├── bbc
│   ├── bbc.py
│   └── data
│       ├── test.csv
│       └── train.csv
```
I can load the dataset by using the keywords arguments like this:
```python
import nlp
dataset = nlp.load("bbc", builder_kwargs={"train": "bbc/data/train.csv", "validation": "bbc/data/test.csv"})
```

That was the good part ^^ Because it took me some time to understand that the script itself is put in cache in `datasets/src/nlp/datasets/some-hash/bbc.py` which is very difficult to discover without checking the source code. It means that doesn't matter the changes you do to your original script it is taken into account. I think instead of doing a hash on the name (I suppose it is the name), a hash on the content of the script itself should be a better solution.

Then by diving a bit in the code I found the `force_reload` parameter [here](https://github.com/huggingface/datasets/blob/master/src/nlp/load.py#L50) but the call of this `load_dataset` method is done with the `builder_kwargs` as seen [here](https://github.com/huggingface/datasets/blob/master/src/nlp/load.py#L166) which is ok until the call to the builder is done as the builder do not have this `force_reload` parameter. To show as example, the previous load becomes:
```python
import nlp
dataset = nlp.load("bbc", builder_kwargs={"train": "bbc/data/train.csv", "validation": "bbc/data/test.csv", "force_reload": True})
```
Raises
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/jplu/dev/jplu/datasets/src/nlp/load.py", line 283, in load
    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
  File "/home/jplu/dev/jplu/datasets/src/nlp/load.py", line 170, in builder
    builder_instance = builder_cls(**builder_kwargs)
  File "/home/jplu/dev/jplu/datasets/src/nlp/datasets/84d638d2a8ca919d1021a554e741766f50679dc6553d5a0612b6094311babd39/bbc.py", line 12, in __init__
    super(Bbc, self).__init__(**config)
TypeError: __init__() got an unexpected keyword argument 'force_reload'
```
So yes the cache is refreshed with the new script but then raises this error.


Issue to read a local dataset

Issue to read a local dataset
Hello,

As proposed by @thomwolf, I open an issue to explain what I'm trying to do without success. What I want to do is to create and load a local dataset, the script I have done is the following:
```python
import os
import csv

import nlp

class BbcConfig(nlp.BuilderConfig):
    def __init__(self, **kwargs):
        super(BbcConfig, self).__init__(**kwargs)

class Bbc(nlp.GeneratorBasedBuilder):
    _DIR = "./data"
    _DEV_FILE = "test.csv"
    _TRAINING_FILE = "train.csv"

    BUILDER_CONFIGS = [BbcConfig(name="bbc", version=nlp.Version("1.0.0"))]

    def _info(self):
        return nlp.DatasetInfo(builder=self, features=nlp.features.FeaturesDict({"id": nlp.string, "text": nlp.string, "label": nlp.string}))

    def _split_generators(self, dl_manager):
        files = {"train": os.path.join(self._DIR, self._TRAINING_FILE), "dev": os.path.join(self._DIR, self._DEV_FILE)}

        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": files["train"]}),
                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": files["dev"]})]

    def _generate_examples(self, filepath):
        with open(filepath) as f:
            reader = csv.reader(f, delimiter=',', quotechar="\"")
            lines = list(reader)[1:]

            for idx, line in enumerate(lines):
                yield idx, {"idx": idx, "text": line[1], "label": line[0]}

```

The dataset is attached to this issue as well:
[data.zip](https://github.com/huggingface/datasets/files/4476928/data.zip)

Now the steps to reproduce what I would like to do:
1. unzip data locally (I know the nlp lib can detect and extract archives but I want to reduce and facilitate the reproduction as much as possible)
2. create the `bbc.py` script as above at the same location than the unziped `data` folder.

Now I try to load the dataset in three different ways and none works, the first one with the name of the dataset like I would do with TFDS:
```python
import nlp
from bbc import Bbc
dataset = nlp.load("bbc")
```

I get:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
    builder_cls = load_dataset(path, name=name, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 88, in load_dataset
    local_files_only=local_files_only,
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/utils/file_utils.py", line 214, in cached_path
    if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
  File "/opt/anaconda3/envs/transformers/lib/python3.7/zipfile.py", line 203, in is_zipfile
    with open(filename, "rb") as fp:
TypeError: expected str, bytes or os.PathLike object, not NoneType
```

But @thomwolf told me that no need to import the script, just put the path of it, then I tried three different way to do:
```python
import nlp
dataset = nlp.load("bbc.py")
```
And
```python
import nlp
dataset = nlp.load("./bbc.py")
```
And
```python
import nlp
dataset = nlp.load("/absolute/path/to/bbc.py")
```

These three ways gives me:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
    builder_cls = load_dataset(path, name=name, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 124, in load_dataset
    dataset_module = importlib.import_module(module_path)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
  File "<frozen importlib._bootstrap>", line 983, in _find_and_load
  File "<frozen importlib._bootstrap>", line 965, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'nlp.datasets.2fd72627d92c328b3e9c4a3bf7ec932c48083caca09230cebe4c618da6e93688.bbc'
```
Any idea of what I'm missing? or I might have spot a bug :)
Ok great, so as discussed today, let's:
- have a main dataset directory inside the lib with sub-directories hashed by the content of the file
- keep a cache for downloading the scripts from S3 for now
- later: add methods to list and clean the local versions of the datasets (and the distant versions on S3 as well)

Side question: do you often use `builder_kwargs` for other things than supplying file paths? I was thinking about having a more easy to read and remember `data_files` argument maybe.


Issue to read a local dataset

Issue to read a local dataset
Hello,

As proposed by @thomwolf, I open an issue to explain what I'm trying to do without success. What I want to do is to create and load a local dataset, the script I have done is the following:
```python
import os
import csv

import nlp

class BbcConfig(nlp.BuilderConfig):
    def __init__(self, **kwargs):
        super(BbcConfig, self).__init__(**kwargs)

class Bbc(nlp.GeneratorBasedBuilder):
    _DIR = "./data"
    _DEV_FILE = "test.csv"
    _TRAINING_FILE = "train.csv"

    BUILDER_CONFIGS = [BbcConfig(name="bbc", version=nlp.Version("1.0.0"))]

    def _info(self):
        return nlp.DatasetInfo(builder=self, features=nlp.features.FeaturesDict({"id": nlp.string, "text": nlp.string, "label": nlp.string}))

    def _split_generators(self, dl_manager):
        files = {"train": os.path.join(self._DIR, self._TRAINING_FILE), "dev": os.path.join(self._DIR, self._DEV_FILE)}

        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": files["train"]}),
                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": files["dev"]})]

    def _generate_examples(self, filepath):
        with open(filepath) as f:
            reader = csv.reader(f, delimiter=',', quotechar="\"")
            lines = list(reader)[1:]

            for idx, line in enumerate(lines):
                yield idx, {"idx": idx, "text": line[1], "label": line[0]}

```

The dataset is attached to this issue as well:
[data.zip](https://github.com/huggingface/datasets/files/4476928/data.zip)

Now the steps to reproduce what I would like to do:
1. unzip data locally (I know the nlp lib can detect and extract archives but I want to reduce and facilitate the reproduction as much as possible)
2. create the `bbc.py` script as above at the same location than the unziped `data` folder.

Now I try to load the dataset in three different ways and none works, the first one with the name of the dataset like I would do with TFDS:
```python
import nlp
from bbc import Bbc
dataset = nlp.load("bbc")
```

I get:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
    builder_cls = load_dataset(path, name=name, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 88, in load_dataset
    local_files_only=local_files_only,
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/utils/file_utils.py", line 214, in cached_path
    if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
  File "/opt/anaconda3/envs/transformers/lib/python3.7/zipfile.py", line 203, in is_zipfile
    with open(filename, "rb") as fp:
TypeError: expected str, bytes or os.PathLike object, not NoneType
```

But @thomwolf told me that no need to import the script, just put the path of it, then I tried three different way to do:
```python
import nlp
dataset = nlp.load("bbc.py")
```
And
```python
import nlp
dataset = nlp.load("./bbc.py")
```
And
```python
import nlp
dataset = nlp.load("/absolute/path/to/bbc.py")
```

These three ways gives me:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
    builder_cls = load_dataset(path, name=name, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 124, in load_dataset
    dataset_module = importlib.import_module(module_path)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
  File "<frozen importlib._bootstrap>", line 983, in _find_and_load
  File "<frozen importlib._bootstrap>", line 965, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'nlp.datasets.2fd72627d92c328b3e9c4a3bf7ec932c48083caca09230cebe4c618da6e93688.bbc'
```
Any idea of what I'm missing? or I might have spot a bug :)
Good plan!

Yes I do use `builder_kwargs` for other things such as:
- dataset name
- properties to know how to properly read a CSV file: do I have to skip the first line in a CSV, which delimiter is used, and the columns ids to use.
- properties to know how to properly read a JSON file: which properties in a JSON object to read


Issue to read a local dataset

Issue to read a local dataset
Hello,

As proposed by @thomwolf, I open an issue to explain what I'm trying to do without success. What I want to do is to create and load a local dataset, the script I have done is the following:
```python
import os
import csv

import nlp

class BbcConfig(nlp.BuilderConfig):
    def __init__(self, **kwargs):
        super(BbcConfig, self).__init__(**kwargs)

class Bbc(nlp.GeneratorBasedBuilder):
    _DIR = "./data"
    _DEV_FILE = "test.csv"
    _TRAINING_FILE = "train.csv"

    BUILDER_CONFIGS = [BbcConfig(name="bbc", version=nlp.Version("1.0.0"))]

    def _info(self):
        return nlp.DatasetInfo(builder=self, features=nlp.features.FeaturesDict({"id": nlp.string, "text": nlp.string, "label": nlp.string}))

    def _split_generators(self, dl_manager):
        files = {"train": os.path.join(self._DIR, self._TRAINING_FILE), "dev": os.path.join(self._DIR, self._DEV_FILE)}

        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": files["train"]}),
                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={"filepath": files["dev"]})]

    def _generate_examples(self, filepath):
        with open(filepath) as f:
            reader = csv.reader(f, delimiter=',', quotechar="\"")
            lines = list(reader)[1:]

            for idx, line in enumerate(lines):
                yield idx, {"idx": idx, "text": line[1], "label": line[0]}

```

The dataset is attached to this issue as well:
[data.zip](https://github.com/huggingface/datasets/files/4476928/data.zip)

Now the steps to reproduce what I would like to do:
1. unzip data locally (I know the nlp lib can detect and extract archives but I want to reduce and facilitate the reproduction as much as possible)
2. create the `bbc.py` script as above at the same location than the unziped `data` folder.

Now I try to load the dataset in three different ways and none works, the first one with the name of the dataset like I would do with TFDS:
```python
import nlp
from bbc import Bbc
dataset = nlp.load("bbc")
```

I get:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
    builder_cls = load_dataset(path, name=name, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 88, in load_dataset
    local_files_only=local_files_only,
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/utils/file_utils.py", line 214, in cached_path
    if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
  File "/opt/anaconda3/envs/transformers/lib/python3.7/zipfile.py", line 203, in is_zipfile
    with open(filename, "rb") as fp:
TypeError: expected str, bytes or os.PathLike object, not NoneType
```

But @thomwolf told me that no need to import the script, just put the path of it, then I tried three different way to do:
```python
import nlp
dataset = nlp.load("bbc.py")
```
And
```python
import nlp
dataset = nlp.load("./bbc.py")
```
And
```python
import nlp
dataset = nlp.load("/absolute/path/to/bbc.py")
```

These three ways gives me:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 280, in load
    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 166, in builder
    builder_cls = load_dataset(path, name=name, **builder_kwargs)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/site-packages/nlp/load.py", line 124, in load_dataset
    dataset_module = importlib.import_module(module_path)
  File "/opt/anaconda3/envs/transformers/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
  File "<frozen importlib._bootstrap>", line 983, in _find_and_load
  File "<frozen importlib._bootstrap>", line 965, in _find_and_load_unlocked
ModuleNotFoundError: No module named 'nlp.datasets.2fd72627d92c328b3e9c4a3bf7ec932c48083caca09230cebe4c618da6e93688.bbc'
```
Any idea of what I'm missing? or I might have spot a bug :)
Done!