


'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a_					:    Union[str, Any]          =    {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[Any]          =    [
     "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "RobertaPreLayerNormForCausalLM",
     "RobertaPreLayerNormForMaskedLM",
     "RobertaPreLayerNormForMultipleChoice",
     "RobertaPreLayerNormForQuestionAnswering",
     "RobertaPreLayerNormForSequenceClassification",
     "RobertaPreLayerNormForTokenClassification",
     "RobertaPreLayerNormModel",
     "RobertaPreLayerNormPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[str]          =    [
     "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFRobertaPreLayerNormForCausalLM",
     "TFRobertaPreLayerNormForMaskedLM",
     "TFRobertaPreLayerNormForMultipleChoice",
     "TFRobertaPreLayerNormForQuestionAnswering",
     "TFRobertaPreLayerNormForSequenceClassification",
     "TFRobertaPreLayerNormForTokenClassification",
     "TFRobertaPreLayerNormMainLayer",
     "TFRobertaPreLayerNormModel",
     "TFRobertaPreLayerNormPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[str]          =    [
     "FlaxRobertaPreLayerNormForCausalLM",
     "FlaxRobertaPreLayerNormForMaskedLM",
     "FlaxRobertaPreLayerNormForMultipleChoice",
     "FlaxRobertaPreLayerNormForQuestionAnswering",
     "FlaxRobertaPreLayerNormForSequenceClassification",
     "FlaxRobertaPreLayerNormForTokenClassification",
     "FlaxRobertaPreLayerNormModel",
     "FlaxRobertaPreLayerNormPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_roberta_prelayernorm import (
     ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
     RobertaPreLayerNormConfig,
     RobertaPreLayerNormOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
      RobertaPreLayerNormForCausalLM,
      RobertaPreLayerNormForMaskedLM,
      RobertaPreLayerNormForMultipleChoice,
      RobertaPreLayerNormForQuestionAnswering,
      RobertaPreLayerNormForSequenceClassification,
      RobertaPreLayerNormForTokenClassification,
      RobertaPreLayerNormModel,
      RobertaPreLayerNormPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roberta_prelayernorm import (
      TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRobertaPreLayerNormForCausalLM,
      TFRobertaPreLayerNormForMaskedLM,
      TFRobertaPreLayerNormForMultipleChoice,
      TFRobertaPreLayerNormForQuestionAnswering,
      TFRobertaPreLayerNormForSequenceClassification,
      TFRobertaPreLayerNormForTokenClassification,
      TFRobertaPreLayerNormMainLayer,
      TFRobertaPreLayerNormModel,
      TFRobertaPreLayerNormPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roberta_prelayernorm import (
      FlaxRobertaPreLayerNormForCausalLM,
      FlaxRobertaPreLayerNormForMaskedLM,
      FlaxRobertaPreLayerNormForMultipleChoice,
      FlaxRobertaPreLayerNormForQuestionAnswering,
      FlaxRobertaPreLayerNormForSequenceClassification,
      FlaxRobertaPreLayerNormForTokenClassification,
      FlaxRobertaPreLayerNormModel,
      FlaxRobertaPreLayerNormPreTrainedModel,
  )

else:
 import sys

 a_					:    Tuple          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





'''simple docstring'''






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=13_37				,						num_examples=42				,						dataset_name='my_dataset'		)}		),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42		)}		),
        SplitDict({'train': SplitInfo()}		),
    ]				,						)
def 	_A				(lowerCAmelCase__  :SplitDict		)   ->  Any:




     '''simple docstring'''



     _a			      =							split_dict._to_yaml_list()
     assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		)
     _a			      =							SplitDict._from_yaml_list(lowerCAmelCase__		)
     for split_name, split_info in split_dict.items():
          # dataset_name field is deprecated, and is therefore not part of the YAML dump
          _a			      =							None
          # the split name of split_dict takes over the name of the split info object
          _a			      =							split_name
     assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'				,						[SplitInfo(), SplitInfo(dataset_name=lowerCAmelCase__		), SplitInfo(dataset_name='my_dataset'		)]		)
def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							asdict(SplitDict({'train': split_info}		)		)
     assert "dataset_name" in split_dict_asdict["train"]
     assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

