
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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			SplitDict)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Tuple			   =					split_dict._to_yaml_list()
							assert len(_lowerCamelCase) == len(_lowerCamelCase)
							__UpperCamelCase	:   Optional[int]			   =					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
														__UpperCamelCase	:   Tuple			   =					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=_lowerCamelCase), SplitInfo(dataset_name="my_dataset")])
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str)					->    Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase	:   Dict			   =					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 torch import nn





class 		_lowercase						(					nn.Module       ):

 def __init__( self	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_					:						List[Any] =					class_size
  UpperCamelCase_					:						List[Any] =					embed_size
  # self.mlp1 = nn.Linear(embed_size, embed_size)
  # self.mlp2 = (nn.Linear(embed_size, class_size))
  UpperCamelCase_					:						int =					nn.Linear(snake_case   ,      snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Any			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.mlp(snake_case			)
  return logits



