

"""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 	lowercase      (   __UpperCamelCase						) ->      List[str]:
 __magic_name__     =      split_dict._to_yaml_list()
 assert len(__UpperCamelCase						) == len(__UpperCamelCase						)
 __magic_name__     =      SplitDict._from_yaml_list(__UpperCamelCase						)
 for split_name, split_info in split_dict.items():
  # dataset_name field is deprecated, and is therefore not part of the YAML dump
  __magic_name__     =      None
  # the split name of split_dict takes over the name of the split info object
  __magic_name__     =      split_name
 assert split_dict == reloaded





@pytest.mark.parametrize(
    '''split_info'''      ,   [SplitInfo(), SplitInfo(dataset_name=__UpperCamelCase						), SplitInfo(dataset_name='''my_dataset'''						)]						)
def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 # 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
 __magic_name__     =      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        _lowercase					(     unittest.TestCase     ):







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,						return_dict=UpperCamelCase_       ).to(UpperCamelCase_       )
      __magic_name__     =      AutoTokenizer.from_pretrained('''google/mt5-small'''       )

      __magic_name__     =      tokenizer('''Hello there''' ,						return_tensors='''pt'''       ).input_ids
      __magic_name__     =      tokenizer('''Hi I am''' ,						return_tensors='''pt'''       ).input_ids

      __magic_name__     =      model(input_ids.to(UpperCamelCase_       ) ,						labels=labels.to(UpperCamelCase_       )       ).loss
      __magic_name__     =      -(labels.shape[-1] * loss.item())

      __magic_name__     =      -8_4.9_1_2_7
      self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )

