






import numpy as np





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  Optional[int]:


 """simple docstring"""



 a		=			np.array(a_  )
 a		=			np.array(a_  )

 a		=			abs(predict - actual  )
 a		=			difference.mean()

 return score





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


 """simple docstring"""



 a		=			np.array(a_  )
 a		=			np.array(a_  )

 a		=			predict - actual
 a		=			np.square(a_  )

 a		=			square_diff.mean()
 return score





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  Dict:


 """simple docstring"""



 a		=			np.array(a_  )
 a		=			np.array(a_  )

 a		=			predict - actual
 a		=			np.square(a_  )
 a		=			square_diff.mean()
 a		=			np.sqrt(a_  )
 return score





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  List[Any]:


 """simple docstring"""



 a		=			np.array(a_  )
 a		=			np.array(a_  )

 a		=			np.log(predict + 1  )
 a		=			np.log(actual + 1  )

 a		=			log_predict - log_actual
 a		=			np.square(a_  )
 a		=			square_diff.mean()

 a		=			np.sqrt(a_  )

 return score





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


 """simple docstring"""



 a		=			np.array(a_  )
 a		=			np.array(a_  )

 a		=			predict - actual
 a		=			np.sum(a_  ) / len(a_  )
 a		=			np.sum(a_  ) / len(a_  )
 # print(numerator, denumerator)
 a		=			float(a_  ) / denumerator * 1_0_0

 return score





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


 """simple docstring"""



 return np.mean(np.array(a_  ) == np.array(a_  )  )













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__						:      int								=					{
    """configuration_fsmt""": ["""FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FSMTConfig"""],
    """tokenization_fsmt""": ["""FSMTTokenizer"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						:      Any								=					["""FSMTForConditionalGeneration""", """FSMTModel""", """PretrainedFSMTModel"""]


if TYPE_CHECKING:
				from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
				from .tokenization_fsmt import FSMTTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
				import sys

				UpperCamelCase__						:      List[Any]								=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





