





'''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		)    ->							Dict:
     _lowerCAmelCase				=      AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"			,     return_dict=_lowerCAmelCase		).to(_lowerCAmelCase		)
     _lowerCAmelCase				=      AutoTokenizer.from_pretrained("google/mt5-small"		)

     _lowerCAmelCase				=      tokenizer("Hello there"			,     return_tensors="pt"		).input_ids
     _lowerCAmelCase				=      tokenizer("Hi I am"			,     return_tensors="pt"		).input_ids

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

     _lowerCAmelCase				=      -84.9127
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)









'''simple docstring'''


from __future__ import annotations


def 	__a(SCREAMING_SNAKE_CASE_  :					float		,       SCREAMING_SNAKE_CASE_  :					float		,       SCREAMING_SNAKE_CASE_  :					float    ):



    '''simple docstring'''





    if (voltage, current, resistance).count(0    ) != 1:
        raise ValueError("One and only one argument must be 0"    )
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"    )
    if voltage == 0:
        return {"voltage": float(current * resistance    )}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


