"""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=1337   , num_examples=42   , dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'   , num_bytes=1337   , num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]   , )
def 				a_     (      _lowerCAmelCase  : Tuple     ):





       '''simple docstring'''

       lowercase__			:		List[Any]       =	split_dict._to_yaml_list()
       assert len(_lowerCAmelCase     ) == len(_lowerCAmelCase     )
       lowercase__			:		List[Any]       =	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
              lowercase__			:		Optional[int]       =	None
              # the split name of split_dict takes over the name of the split info object
              lowercase__			:		List[Any]       =	split_name
       assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'   , [SplitInfo(), SplitInfo(dataset_name=_lowerCAmelCase     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 				a_     (      _lowerCAmelCase  : List[str]     ):





       '''simple docstring'''

       lowercase__			:		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






'''simple docstring'''





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__a          =		logging.getLogger(__name__)







@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		field(default=_a		,		metadata={"help": "Whether to use SortishSampler or not."}	)
    lowercase					=		field(
        default=_a		,		metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}	)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `max_length` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `num_beams` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
        }		,		)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    int    						=      super().to_dict()
          for k, v in d.items():
                if isinstance(snake_case_ ,     snake_case_						):
                      snake_case__  :    Optional[int]    						=      v.to_dict()
          return d
