
import numpy as np


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  Tuple:
    __lowercase						:     List[Any]		       =    np.array(__lowerCAmelCase		)
    __lowercase						:     Union[str, Any]		       =    np.array(__lowerCAmelCase		)

    __lowercase						:     Dict		       =    abs(predict - actual		)
    __lowercase						:     Tuple		       =    difference.mean()

    return score


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  Optional[int]:
    __lowercase						:     Dict		       =    np.array(__lowerCAmelCase		)
    __lowercase						:     Optional[Any]		       =    np.array(__lowerCAmelCase		)

    __lowercase						:     str		       =    predict - actual
    __lowercase						:     str		       =    np.square(__lowerCAmelCase		)

    __lowercase						:     Optional[Any]		       =    square_diff.mean()
    return score


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  List[Any]:
    __lowercase						:     Tuple		       =    np.array(__lowerCAmelCase		)
    __lowercase						:     List[str]		       =    np.array(__lowerCAmelCase		)

    __lowercase						:     Any		       =    predict - actual
    __lowercase						:     Tuple		       =    np.square(__lowerCAmelCase		)
    __lowercase						:     Optional[int]		       =    square_diff.mean()
    __lowercase						:     Tuple		       =    np.sqrt(__lowerCAmelCase		)
    return score


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  Optional[Any]:
    __lowercase						:     Tuple		       =    np.array(__lowerCAmelCase		)
    __lowercase						:     Dict		       =    np.array(__lowerCAmelCase		)

    __lowercase						:     Optional[int]		       =    np.log(predict + 1		)
    __lowercase						:     Union[str, Any]		       =    np.log(actual + 1		)

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

    __lowercase						:     Union[str, Any]		       =    np.sqrt(__lowerCAmelCase		)

    return score


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  Optional[int]:
    __lowercase						:     str		       =    np.array(__lowerCAmelCase		)
    __lowercase						:     int		       =    np.array(__lowerCAmelCase		)

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

    return score





def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  Tuple:
    return np.mean(np.array(__lowerCAmelCase		) == np.array(__lowerCAmelCase		)		)




from typing import TYPE_CHECKING

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


__lowerCAmelCase   :     Optional[int]      =						{"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase   :     List[Any]      =						[
     "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTHybridForImageClassification",
     "ViTHybridModel",
     "ViTHybridPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase   :     Tuple      =						["ViTHybridImageProcessor"]


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

 __lowerCAmelCase   :     Tuple      =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


