




from typing import TYPE_CHECKING

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


__a			:	Optional[int]							     =		{"""configuration_levit""": ["""LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LevitConfig""", """LevitOnnxConfig"""]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__a			:	Dict							     =		["""LevitFeatureExtractor"""]
				__a			:	List[Any]							     =		["""LevitImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__a			:	Any							     =		[
				    """LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """LevitForImageClassification""",
				    """LevitForImageClassificationWithTeacher""",
				    """LevitModel""",
				    """LevitPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_levit import LevitFeatureExtractor
								from .image_processing_levit import LevitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_levit import (
								    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LevitForImageClassification,
								    LevitForImageClassificationWithTeacher,
								    LevitModel,
								    LevitPreTrainedModel,
								)
else:
				import sys

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






import numpy as np




def  a_		(							__snake_case     ,		__snake_case					)	->			List[str]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

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

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)

			UpperCamelCase_     				= square_diff.mean()
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()
			UpperCamelCase_     				= np.sqrt(__snake_case					)
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[int]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

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

			UpperCamelCase_     				= log_predict - log_actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()

			UpperCamelCase_     				= np.sqrt(__snake_case					)

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			int:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

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

			return score





def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


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