

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      A__					(			__A  :						str     ,				__A  :						Union[str, Any]			)					->Any:
      __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(__A			)
      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      A__					(			__A  :						Dict     ,				__A  :						DatasetInfo			)					->Any:
      __A    =str(__A			)
      dataset_info.write_to_directory(__A			)
      __A    =DatasetInfo.from_directory(__A			)
      assert dataset_info == reloaded
      assert os.path.exists(os.path.join(__A     ,				'''dataset_info.json'''			)			)





def      A__					(			)					->Union[str, Any]:
      __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''': 42}]     ,				download_checksums={}     ,				download_size=13_37     ,				post_processing_size=4_42     ,				dataset_size=12_34     ,				size_in_bytes=13_37 + 4_42 + 12_34     ,				)
      __A    =dataset_info._to_yaml_dict()
      assert sorted(__A			) == 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(__A			)
      __A    =yaml.safe_load(__A			)
      assert dataset_info_yaml_dict == reloaded





def      A__					(			)					->Dict:
      __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=42     ,				)
            }			),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=42			),
                '''v2''': DatasetInfo(dataset_size=13_37			),
            }			),
    ]     ,				)
def      A__					(			__A  :						Any     ,				__A  :						DatasetInfosDict			)					->int:
      __A    =str(__A			)
      dataset_infos_dict.write_to_directory(__A			)
      __A    =DatasetInfosDict.from_directory(__A			)

      # 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(__A     ,				'''README.md'''			)			)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase      :					Tuple					    =      {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					List[str]					    =      ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase      :					List[str]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


