import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict




@pytest.mark.parametrize(
    '''split_dict'''  ,					[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''  ,					num_bytes=1_337  ,					num_examples=42  ,					dataset_name='''my_dataset'''		)}		),
        SplitDict({'''train''': SplitInfo(name='''train'''  ,					num_bytes=1_337  ,					num_examples=42		)}		),
        SplitDict({'''train''': SplitInfo()}		),
    ]  ,					)
def       lowerCAmelCase_     (		lowercase:							SplitDict		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= split_dict._to_yaml_list()
 assert len(lowercase		) == len(lowercase		)
 _UpperCamelCase:     Tuple							= SplitDict._from_yaml_list(lowercase		)
 for split_name, split_info in split_dict.items():
  # dataset_name field is deprecated, and is therefore not part of the YAML dump
  _UpperCamelCase:     Optional[Any]							= None
  # the split name of split_dict takes over the name of the split info object
  _UpperCamelCase:     Any							= split_name
 assert split_dict == reloaded





@pytest.mark.parametrize(
    '''split_info'''  ,					[SplitInfo(), SplitInfo(dataset_name=lowercase		), SplitInfo(dataset_name='''my_dataset'''		)]		)
def       lowerCAmelCase_     (		lowercase:							str		)    ->				Tuple:







 '''simple docstring'''
 # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
 # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
 _UpperCamelCase:     Optional[Any]							= asdict(SplitDict({'''train''': split_info}		)		)
 assert "dataset_name" in split_dict_asdict["train"]
 assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			    =							{'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''XLNetTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''XLNetTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLNetForMultipleChoice''',
      '''XLNetForQuestionAnswering''',
      '''XLNetForQuestionAnsweringSimple''',
      '''XLNetForSequenceClassification''',
      '''XLNetForTokenClassification''',
      '''XLNetLMHeadModel''',
      '''XLNetModel''',
      '''XLNetPreTrainedModel''',
      '''load_tf_weights_in_xlnet''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLNetForMultipleChoice''',
      '''TFXLNetForQuestionAnsweringSimple''',
      '''TFXLNetForSequenceClassification''',
      '''TFXLNetForTokenClassification''',
      '''TFXLNetLMHeadModel''',
      '''TFXLNetMainLayer''',
      '''TFXLNetModel''',
      '''TFXLNetPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_			    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)