



'''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 				snake_case_  (      _lowerCAmelCase						:		SplitDict						)   ->     List[Any]:
							UpperCAmelCase				: str											=	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				: Optional[Any]											=	None
														# the split name of split_dict takes over the name of the split info object
														UpperCAmelCase				: Dict											=	split_name
							assert split_dict == reloaded




@pytest.mark.parametrize(
    '''split_info'''     ,   [SplitInfo(), SplitInfo(dataset_name=_lowerCAmelCase						), SplitInfo(dataset_name='''my_dataset'''						)]						)
def 				snake_case_  (      _lowerCAmelCase						:		List[Any]						)   ->     Optional[Any]:
							# 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				: 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 tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
						import torch

@require_torch
@require_optimum
@slow
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: int											=	AutoTokenizer.from_pretrained(__snake_case	)
								UpperCAmelCase				: Tuple											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	tokenizer('''This is me'''	,    return_tensors='''pt'''	)

								UpperCAmelCase				: Union[str, Any]											=	model.to_bettertransformer()

								self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								UpperCAmelCase				: Dict											=	model.generate(**__snake_case	)

								UpperCAmelCase				: Union[str, Any]											=	model.reverse_bettertransformer()

								self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

															self.assertFalse(
															    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()	)	)

															UpperCAmelCase				: Dict											=	model_reloaded.generate(**__snake_case	)
															self.assertTrue(torch.allclose(__snake_case	,    __snake_case	)	)







	def   A			(		self    :  Optional[int]	)    ->	List[Any]:
								UpperCAmelCase				: Dict											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: List[Any]											=	model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
															with self.assertRaises(__snake_case	):
																						model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Any											=	model.reverse_bettertransformer()
															model.save_pretrained(__snake_case	)





