


import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		int:
    assert isinstance(lowerCamelCase_							,				lowerCamelCase_				)
    assert dataset.num_rows == 4
    assert dataset.num_columns == 1
    assert dataset.column_names == ["text"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('keep_in_memory'							,				[False, True]				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		int:
    _lowercase :							str   	=	tmp_path / 'cache'
    _lowercase :							Any   	=	{'text': 'string'}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        _lowercase :							List[str]   	=	TextDatasetReader(lowerCamelCase_							,				cache_dir=lowerCamelCase_							,				keep_in_memory=lowerCamelCase_				).read()
    _check_text_dataset(lowerCamelCase_							,				lowerCamelCase_				)



@pytest.mark.parametrize(
    'features'							,				[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]							,				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		Any:
    _lowercase :							List[Any]   	=	tmp_path / 'cache'
    _lowercase :							int   	=	{'text': 'string'}
    _lowercase :							Any   	=	features.copy() if features else default_expected_features
    _lowercase :							Tuple   	=	(
        Features({feature: Value(lowerCamelCase_				) for feature, dtype in features.items()}				) if features is not None else None
    )
    _lowercase :							Any   	=	TextDatasetReader(lowerCamelCase_							,				features=lowerCamelCase_							,				cache_dir=lowerCamelCase_				).read()
    _check_text_dataset(lowerCamelCase_							,				lowerCamelCase_				)



@pytest.mark.parametrize('split'							,				[None, NamedSplit('train'				), 'train', 'test']				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		int:
    _lowercase :							List[Any]   	=	tmp_path / 'cache'
    _lowercase :							List[str]   	=	{'text': 'string'}
    _lowercase :							Optional[Any]   	=	TextDatasetReader(lowerCamelCase_							,				cache_dir=lowerCamelCase_							,				split=lowerCamelCase_				).read()
    _check_text_dataset(lowerCamelCase_							,				lowerCamelCase_				)
    assert dataset.split == split if split else "train"



@pytest.mark.parametrize('path_type'							,				[str, list]				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		Dict:
    if issubclass(lowerCamelCase_							,				lowerCamelCase_				):
        _lowercase :							Optional[Any]   	=	text_path
    elif issubclass(lowerCamelCase_							,				lowerCamelCase_				):
        _lowercase :							int   	=	[text_path]
    _lowercase :							List[Any]   	=	tmp_path / 'cache'
    _lowercase :							Optional[int]   	=	{'text': 'string'}
    _lowercase :							Optional[int]   	=	TextDatasetReader(lowerCamelCase_							,				cache_dir=lowerCamelCase_				).read()
    _check_text_dataset(lowerCamelCase_							,				lowerCamelCase_				)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_=("train",)				)					->		Union[str, Any]:
    assert isinstance(lowerCamelCase_							,				lowerCamelCase_				)
    for split in splits:
        _lowercase :							Tuple   	=	dataset_dict[split]
        assert dataset.num_rows == 4
        assert dataset.num_columns == 1
        assert dataset.column_names == ["text"]
        for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('keep_in_memory'							,				[False, True]				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		Optional[Any]:
    _lowercase :							int   	=	tmp_path / 'cache'
    _lowercase :							str   	=	{'text': 'string'}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        _lowercase :							str   	=	TextDatasetReader({'train': text_path}							,				cache_dir=lowerCamelCase_							,				keep_in_memory=lowerCamelCase_				).read()
    _check_text_datasetdict(lowerCamelCase_							,				lowerCamelCase_				)



@pytest.mark.parametrize(
    'features'							,				[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]							,				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		Any:
    _lowercase :							List[Any]   	=	tmp_path / 'cache'
    # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
    _lowercase :							Tuple   	=	{'text': 'string'}
    _lowercase :							Any   	=	features.copy() if features else default_expected_features
    _lowercase :							List[str]   	=	(
        Features({feature: Value(lowerCamelCase_				) for feature, dtype in features.items()}				) if features is not None else None
    )
    _lowercase :							Optional[Any]   	=	TextDatasetReader({'train': text_path}							,				features=lowerCamelCase_							,				cache_dir=lowerCamelCase_				).read()
    _check_text_datasetdict(lowerCamelCase_							,				lowerCamelCase_				)



@pytest.mark.parametrize('split'							,				[None, NamedSplit('train'				), 'train', 'test']				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		str:
    if split:
        _lowercase :							Union[str, Any]   	=	{split: text_path}
    else:
        _lowercase :							int   	=	'train'
        _lowercase :							List[str]   	=	{'train': text_path, 'test': text_path}
    _lowercase :							Optional[int]   	=	tmp_path / 'cache'
    _lowercase :							List[Any]   	=	{'text': 'string'}
    _lowercase :							List[str]   	=	TextDatasetReader(lowerCamelCase_							,				cache_dir=lowerCamelCase_				).read()
    _check_text_datasetdict(lowerCamelCase_							,				lowerCamelCase_							,				splits=list(path.keys()				)				)
    assert all(dataset[split].split == split for split in path.keys()				)






import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict



@pytest.mark.parametrize(
    'files'							,				[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]							,				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		List[str]:
    _lowercase :							Any   	=	tmp_path_factory.mktemp('dset_infos_dir'				)
    if "full:README.md" in files:
        with open(dataset_infos_dir / 'README.md'							,				'w'				) as f:
            f.write('---\ndataset_info:\n  dataset_size: 42\n---'				)
    if "empty:README.md" in files:
        with open(dataset_infos_dir / 'README.md'							,				'w'				) as f:
            f.write(''				)
    # we want to support dataset_infos.json for backward compatibility
    if "dataset_infos.json" in files:
        with open(dataset_infos_dir / 'dataset_infos.json'							,				'w'				) as f:
            f.write('{"default": {"dataset_size": 42}}'				)
    _lowercase :							Any   	=	DatasetInfosDict.from_directory(lowerCamelCase_				)
    assert dataset_infos
    assert dataset_infos["default"].dataset_size == 42



@pytest.mark.parametrize(
    'dataset_info'							,				[
        DatasetInfo(),
        DatasetInfo(
            description='foo'							,				features=Features({'a': Value('int32'				)}				)							,				builder_name='builder'							,				config_name='config'							,				version='1.0.0'							,				splits=[{'name': 'train'}]							,				download_size=42							,				),
    ]							,				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Optional[int]:
    _lowercase :							Union[str, Any]   	=	str(lowerCamelCase_				)
    dataset_info.write_to_directory(lowerCamelCase_				)
    _lowercase :							List[str]   	=	DatasetInfo.from_directory(lowerCamelCase_				)
    assert dataset_info == reloaded
    assert os.path.exists(os.path.join(lowerCamelCase_							,				'dataset_info.json'				)				)



def        UpperCamelCase_(   )					->		int:
    _lowercase :							Tuple   	=	DatasetInfo(
        description='foo'							,				citation='bar'							,				homepage='https://foo.bar'							,				license='CC0'							,				features=Features({'a': Value('int32'				)}				)							,				post_processed={}							,				supervised_keys=()							,				task_templates=[]							,				builder_name='builder'							,				config_name='config'							,				version='1.0.0'							,				splits=[{'name': 'train', 'num_examples': 42}]							,				download_checksums={}							,				download_size=1337							,				post_processing_size=442							,				dataset_size=1234							,				size_in_bytes=1337 + 442 + 1234							,				)
    _lowercase :							Optional[int]   	=	dataset_info._to_yaml_dict()
    assert sorted(lowerCamelCase_				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
    for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
        assert key in dataset_info_yaml_dict
        assert isinstance(dataset_info_yaml_dict[key]							,				(list, dict, int, str)				)
    _lowercase :							str   	=	yaml.safe_dump(lowerCamelCase_				)
    _lowercase :							str   	=	yaml.safe_load(lowerCamelCase_				)
    assert dataset_info_yaml_dict == reloaded



def        UpperCamelCase_(   )					->		int:
    _lowercase :							Tuple   	=	DatasetInfo()
    _lowercase :							Tuple   	=	dataset_info._to_yaml_dict()
    assert dataset_info_yaml_dict == {}



@pytest.mark.parametrize(
    'dataset_infos_dict'							,				[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}				),
        DatasetInfosDict({'my_config_name': DatasetInfo()}				),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'							,				features=Features({'a': Value('int32'				)}				)							,				builder_name='builder'							,				config_name='config'							,				version='1.0.0'							,				splits=[{'name': 'train'}]							,				download_size=42							,				)
            }				),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42				),
                'v2': DatasetInfo(dataset_size=1337				),
            }				),
    ]							,				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Dict:
    _lowercase :							Tuple   	=	str(lowerCamelCase_				)
    dataset_infos_dict.write_to_directory(lowerCamelCase_				)
    _lowercase :							Tuple   	=	DatasetInfosDict.from_directory(lowerCamelCase_				)

    # the config_name of the dataset_infos_dict take over the attribute
    for config_name, dataset_info in dataset_infos_dict.items():
        _lowercase :							Any   	=	config_name
        # the yaml representation doesn't include fields like description or citation
        # so we just test that we can recover what we can from the yaml
        _lowercase :							str   	=	DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
    assert dataset_infos_dict == reloaded

    if dataset_infos_dict:
        assert os.path.exists(os.path.join(lowerCamelCase_							,				'README.md'				)				)


