


SCREAMING_SNAKE_CASE :   Tuple							      =				"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

SCREAMING_SNAKE_CASE :   Dict							      =				[{"type": "code", "content": INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE :   List[str]							      =				{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}






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'				)				)


