




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=1337     ,	num_examples=42     ,	dataset_name="my_dataset"							)}							),
        SplitDict({"train": SplitInfo(name="train"     ,	num_bytes=1337     ,	num_examples=42							)}							),
        SplitDict({"train": SplitInfo()}							),
    ]     ,	)
def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     split_dict._to_yaml_list()
	assert len(_lowerCAmelCase							) == len(_lowerCAmelCase							)
	snake_case_		    =     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
		snake_case_		    =     None
		# the split name of split_dict takes over the name of the split info object
		snake_case_		    =     split_name
	assert split_dict == reloaded






@pytest.mark.parametrize(
    "split_info"     ,	[SplitInfo(), SplitInfo(dataset_name=_lowerCAmelCase							), SplitInfo(dataset_name="my_dataset"							)]							)
def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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





'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase											=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						[
       'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BioGptForCausalLM',
       'BioGptForTokenClassification',
       'BioGptForSequenceClassification',
       'BioGptModel',
       'BioGptPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

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






