__lowerCamelCase	:					Optional[Any]			  =					tuple[float, float, float]
__lowerCamelCase	:					List[str]			  =					tuple[float, float, float]



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Vectorad:
 UpperCamelCase     : str	     =       end_pointa[0] - end_pointa[0]
 UpperCamelCase     : Any	     =       end_pointa[1] - end_pointa[1]
 UpperCamelCase     : Optional[Any]	     =       end_pointa[2] - end_pointa[2]
 return (x, y, z)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Vectorad:
 UpperCamelCase     : Optional[Any]	     =       ab[1] * ac[2] - ab[2] * ac[1]  # *i
 UpperCamelCase     : List[str]	     =       (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
 UpperCamelCase     : Union[str, Any]	     =       ab[0] * ac[1] - ab[1] * ac[0]  # *k
 return (x, y, z)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			bool:
 return tuple(round(_lowerCAmelCase							,       _lowerCAmelCase					) for x in vector					) == (0, 0, 0)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase = 10					)						->			bool:
 UpperCamelCase     : List[str]	     =       create_vector(_lowerCAmelCase							,       _lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       create_vector(_lowerCAmelCase							,       _lowerCAmelCase					)
 return is_zero_vector(get_ad_vectors_cross(_lowerCAmelCase							,       _lowerCAmelCase					)							,       _lowerCAmelCase					)







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import tensorflow as tf

    from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class  A__       (	unittest.TestCase      ):





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       )
       UpperCamelCase     : Tuple	     =       AutoTokenizer.from_pretrained("google/mt5-small"       )

       UpperCamelCase     : Dict	     =       tokenizer("Hello there"	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : int	     =       tokenizer("Hi I am"	,							return_tensors="tf"       ).input_ids

       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							labels=A_       ).loss
       UpperCamelCase     : List[str]	     =       -tf.math.reduce_mean(A_       ).numpy()

       UpperCamelCase     : Union[str, Any]	     =       -21.22_81_68
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 2e-4       )
