




from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
UpperCamelCase								=	1.0_5457_1817E-34  # unit of ℏ : J * s

UpperCamelCase								=	3E8  # unit of c : m * s^-1



def 				_A  (		lowerCAmelCase_	:   float  ,							lowerCAmelCase_	:   float  ,							lowerCAmelCase_	:   float     ):
     """simple docstring"""

     if (force, area, distance).count(0     ) != 1:
          raise ValueError("One and only one argument must be 0"     )
     if force < 0:
          raise ValueError("Magnitude of force can not be negative"     )
     if distance < 0:
          raise ValueError("Distance can not be negative"     )
     if area < 0:
          raise ValueError("Area can not be negative"     )
     if force == 0:
          lowerCAmelCase__            =					(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
              240 * (distance) ** 4
          )
          return {"force": force}
     elif area == 0:
          lowerCAmelCase__            =					(240 * force * (distance) ** 4) / (
              REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
          )
          return {"area": area}
     elif distance == 0:
          lowerCAmelCase__            =					(
              (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
          ) ** (1 / 4)
          return {"distance": distance}
     raise ValueError("One and only one argument must be 0"     )


# Run doctest
if __name__ == "__main__":
     import doctest

     doctest.testmod()







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   lowerCAmelCase				(   unittest.TestCase     ):
 @slow
 def 	A_      ( self    : int      )     ->      Optional[Any]:
     lowerCamelCase__       : Tuple   					=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
     lowerCamelCase__       : Any   					=						AutoTokenizer.from_pretrained('google/mt5-small'      )

     lowerCamelCase__       : int   					=						tokenizer('Hello there'     ,	return_tensors='tf'      ).input_ids
     lowerCamelCase__       : int   					=						tokenizer('Hi I am'     ,	return_tensors='tf'      ).input_ids

     lowerCamelCase__       : List[str]   					=						model(UpperCAmelCase     ,	labels=UpperCAmelCase      ).loss
     lowerCamelCase__       : List[Any]   					=						-tf.math.reduce_mean(UpperCAmelCase      ).numpy()

     lowerCamelCase__       : Dict   					=						-2_1.2_2_8_1_6_8
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 2e-4      )





