





'''simple docstring'''







import numpy as np



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     snake_case :			Tuple					   = np.array(__lowercase							)
     snake_case :			Optional[int]					   = np.array(__lowercase							)

     snake_case :			Any					   = abs(predict - actual							)
     snake_case :			Any					   = difference.mean()

     return score



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     snake_case :			int					   = np.array(__lowercase							)
     snake_case :			Any					   = np.array(__lowercase							)

     snake_case :			Dict					   = predict - actual
     snake_case :			Optional[int]					   = np.square(__lowercase							)

     snake_case :			Dict					   = square_diff.mean()
     return score



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     snake_case :			List[str]					   = np.array(__lowercase							)
     snake_case :			int					   = np.array(__lowercase							)

     snake_case :			List[str]					   = predict - actual
     snake_case :			Optional[Any]					   = np.square(__lowercase							)
     snake_case :			str					   = square_diff.mean()
     snake_case :			Dict					   = np.sqrt(__lowercase							)
     return score



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     snake_case :			Optional[int]					   = np.array(__lowercase							)
     snake_case :			Union[str, Any]					   = np.array(__lowercase							)

     snake_case :			List[str]					   = np.log(predict + 1							)
     snake_case :			Tuple					   = np.log(actual + 1							)

     snake_case :			Any					   = log_predict - log_actual
     snake_case :			str					   = np.square(__lowercase							)
     snake_case :			Optional[Any]					   = square_diff.mean()

     snake_case :			Any					   = np.sqrt(__lowercase							)

     return score



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     snake_case :			List[Any]					   = np.array(__lowercase							)
     snake_case :			Union[str, Any]					   = np.array(__lowercase							)

     snake_case :			Tuple					   = predict - actual
     snake_case :			Dict					   = np.sum(__lowercase							) / len(__lowercase							)
     snake_case :			List[Any]					   = np.sum(__lowercase							) / len(__lowercase							)
     # print(numerator, denumerator)
     snake_case :			Optional[Any]					   = float(__lowercase							) / denumerator * 100

     return score







def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     return np.mean(np.array(__lowercase							) == np.array(__lowercase							)							)









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowercase			:      Tuple			=					{"""configuration_bert_generation""": ["""BertGenerationConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:      Tuple			=					["""BertGenerationTokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:      List[Any]			=					[
          """BertGenerationDecoder""",
          """BertGenerationEncoder""",
          """BertGenerationPreTrainedModel""",
          """load_tf_weights_in_bert_generation""",
      ]


if TYPE_CHECKING:
      from .configuration_bert_generation import BertGenerationConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_generation import BertGenerationTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bert_generation import (
                BertGenerationDecoder,
                BertGenerationEncoder,
                BertGenerationPreTrainedModel,
                load_tf_weights_in_bert_generation,
            )

else:
      import sys

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



