





from typing import TYPE_CHECKING

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


__A       : Union[str, Any]						    = {"configuration_bit": ["BIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BitConfig", "BitOnnxConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       : Union[str, Any]						    = [
     "BIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BitForImageClassification",
     "BitModel",
     "BitPreTrainedModel",
     "BitBackbone",
 ]


try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       : Any						    = ["BitImageProcessor"]


if TYPE_CHECKING:
 from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bit import (
      BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BitBackbone,
      BitForImageClassification,
      BitModel,
      BitPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bit import BitImageProcessor

else:
 import sys

 __A       : Dict						    = _LazyModule(__name__, globals()["__file__"], _import_structure)












import numpy as np





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

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

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)

    _A    =       square_diff.mean()
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()
    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

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

    _A    =       log_predict - log_actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()

    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    # print(numerator, denumerator)
    _A    =       float(_SCREAMING_SNAKE_CASE							) / denumerator * 100

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Dict:




    """simple docstring"""




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





