






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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]			,				__lowercase   :			Union[str, Any]    )						->      Dict:





    """simple docstring"""

    __A         =	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}}"""    )
    __A         =	DatasetInfosDict.from_directory(__lowercase    )
    assert dataset_infos
    assert dataset_infos["default"].dataset_size == 4_2



@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=4_2			,				),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any			,				__lowercase   :			DatasetInfo    )						->      str:





    """simple docstring"""

    __A         =	str(__lowercase    )
    dataset_info.write_to_directory(__lowercase    )
    __A         =	DatasetInfo.from_directory(__lowercase    )
    assert dataset_info == reloaded
    assert os.path.exists(os.path.join(__lowercase			,				"""dataset_info.json"""    )    )



def 			_SCREAMING_SNAKE_CASE						( )						->      Any:





    """simple docstring"""

    __A         =	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""": 4_2}]			,				download_checksums={}			,				download_size=1_3_3_7			,				post_processing_size=4_4_2			,				dataset_size=1_2_3_4			,				size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4			,				)
    __A         =	dataset_info._to_yaml_dict()
    assert sorted(__lowercase    ) == 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)    )
    __A         =	yaml.safe_dump(__lowercase    )
    __A         =	yaml.safe_load(__lowercase    )
    assert dataset_info_yaml_dict == reloaded



def 			_SCREAMING_SNAKE_CASE						( )						->      int:





    """simple docstring"""

    __A         =	DatasetInfo()
    __A         =	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=4_2			,				)
            }    ),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=4_2    ),
                """v2""": DatasetInfo(dataset_size=1_3_3_7    ),
            }    ),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			DatasetInfosDict    )						->      Dict:





    """simple docstring"""

    __A         =	str(__lowercase    )
    dataset_infos_dict.write_to_directory(__lowercase    )
    __A         =	DatasetInfosDict.from_directory(__lowercase    )

    # the config_name of the dataset_infos_dict take over the attribute
    for config_name, dataset_info in dataset_infos_dict.items():
        __A         =	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
        __A         =	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(__lowercase			,				"""README.md"""    )    )












from typing import TYPE_CHECKING

from ...utils import _LazyModule


__a      :       Optional[int]				  =   {"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
  from .tokenization_bertweet import BertweetTokenizer

else:
  import sys

  __a      :       int				  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




