





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 	UpperCamelCase__(       UpperCamelCase__			:       SplitDict  )->Dict:
       A__											=						split_dict._to_yaml_list()
       assert len(__SCREAMING_SNAKE_CASE  ) == len(__SCREAMING_SNAKE_CASE  )
       A__											=						SplitDict._from_yaml_list(__SCREAMING_SNAKE_CASE  )
       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=__SCREAMING_SNAKE_CASE  ), SplitInfo(dataset_name='''my_dataset'''  )]  )
def 	UpperCamelCase__(       UpperCamelCase__			:       Tuple  )->Any:
       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







'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
							from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,							return_dict=__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''google/mt5-small'''		)

											lowercase_	:     int													=       tokenizer('''Hello there'''				,							return_tensors='''pt'''		).input_ids
											lowercase_	:     Union[str, Any]													=       tokenizer('''Hi I am'''				,							return_tensors='''pt'''		).input_ids

											lowercase_	:     Union[str, Any]													=       model(input_ids.to(__SCREAMING_SNAKE_CASE		)				,							labels=labels.to(__SCREAMING_SNAKE_CASE		)		).loss
											lowercase_	:     int													=       -(labels.shape[-1] * loss.item())

											lowercase_	:     Any													=       -84.9_127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)



