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 lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	split_dict._to_yaml_list()
    assert len(SCREAMING_SNAKE_CASE			) == len(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[Any]    						=	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
        __UpperCamelCase  :List[Any]    						=	None
        # the split name of split_dict takes over the name of the split info object
        __UpperCamelCase  :Tuple    						=	split_name
    assert split_dict == reloaded



@pytest.mark.parametrize(
    '''split_info'''						,    [SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE			), SplitInfo(dataset_name='''my_dataset'''			)]			)
def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[int]    						=	asdict(SplitDict({'''train''': split_info}			)			)
    assert "dataset_name" in split_dict_asdict["train"]
    assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
    import optax
    from flax.training.common_utils import onehot

    from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
    from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    @slow
    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :List[str]    						=	FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''')
        __UpperCamelCase  :Dict    						=	AutoTokenizer.from_pretrained('''google/mt5-small''')

        __UpperCamelCase  :Optional[Any]    						=	tokenizer('''Hello there'''				,				return_tensors='''np''').input_ids
        __UpperCamelCase  :List[str]    						=	tokenizer('''Hi I am'''				,				return_tensors='''np''').input_ids

        __UpperCamelCase  :Optional[int]    						=	shift_tokens_right(__lowercase				,				model.config.pad_token_id				,				model.config.decoder_start_token_id)

        __UpperCamelCase  :Tuple    						=	model(__lowercase				,				decoder_input_ids=__lowercase).logits
        __UpperCamelCase  :Any    						=	optax.softmax_cross_entropy(__lowercase				,				onehot(__lowercase				,				logits.shape[-1])).mean()

        __UpperCamelCase  :str    						=	-(labels.shape[-1] * loss.item())

        __UpperCamelCase  :Optional[Any]    						=	-84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1E-4)





