

"""simple docstring"""


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      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	AutoTokenizer.from_pretrained("""google/mt5-small"""  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	tokenizer("""Hello there"""	,    return_tensors="""np"""  ).input_ids
           SCREAMING_SNAKE_CASE__   :      Dict      		=	tokenizer("""Hi I am"""	,    return_tensors="""np"""  ).input_ids

           SCREAMING_SNAKE_CASE__   :      str      		=	shift_tokens_right(_a	,    model.config.pad_token_id	,    model.config.decoder_start_token_id  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(_a	,    decoder_input_ids=_a  ).logits
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	optax.softmax_cross_entropy(_a	,    onehot(_a	,    logits.shape[-1]  )  ).mean()

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

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







"""simple docstring"""


a					:List[str]    = [
    (1_000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]




def  _lowercase					(    __lowerCAmelCase							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	0
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	0
      while place < len(__lowerCAmelCase							):
            if (place + 1 < len(__lowerCAmelCase							)) and (vals[roman[place]] < vals[roman[place + 1]]):
                  total += vals[roman[place + 1]] - vals[roman[place]]
                  place += 2
            else:
                  total += vals[roman[place]]
                  place += 1
      return total




def  _lowercase					(    __lowerCAmelCase							)       ->				str:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for arabic, roman in ROMAN:
            ((SCREAMING_SNAKE_CASE__)		,		(SCREAMING_SNAKE_CASE__))   :      List[str]      		=	divmod(__lowerCAmelCase  ,					__lowerCAmelCase							)
            result.append(roman * factor							)
            if number == 0:
                  break
      return "".join(__lowerCAmelCase							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




