
'''simple docstring'''







import numpy as np

def 				_snake_case (		lowercase			,    lowercase						)       ->       List[Any]:
				__a       :     Optional[int]								=	np.array(lowercase						)
				__a       :     Dict								=	np.array(lowercase						)

				__a       :     Optional[int]								=	abs(predict - actual						)
				__a       :     int								=	difference.mean()

				return score

def 				_snake_case (		lowercase			,    lowercase						)       ->       Any:
				__a       :     Union[str, Any]								=	np.array(lowercase						)
				__a       :     Dict								=	np.array(lowercase						)

				__a       :     Any								=	predict - actual
				__a       :     List[Any]								=	np.square(lowercase						)

				__a       :     List[Any]								=	square_diff.mean()
				return score

def 				_snake_case (		lowercase			,    lowercase						)       ->       Union[str, Any]:
				__a       :     str								=	np.array(lowercase						)
				__a       :     Union[str, Any]								=	np.array(lowercase						)

				__a       :     Tuple								=	predict - actual
				__a       :     Optional[Any]								=	np.square(lowercase						)
				__a       :     List[Any]								=	square_diff.mean()
				__a       :     str								=	np.sqrt(lowercase						)
				return score

def 				_snake_case (		lowercase			,    lowercase						)       ->       Optional[int]:
				__a       :     Dict								=	np.array(lowercase						)
				__a       :     str								=	np.array(lowercase						)

				__a       :     Optional[int]								=	np.log(predict + 1						)
				__a       :     Optional[int]								=	np.log(actual + 1						)

				__a       :     Optional[Any]								=	log_predict - log_actual
				__a       :     List[Any]								=	np.square(lowercase						)
				__a       :     Optional[Any]								=	square_diff.mean()

				__a       :     Tuple								=	np.sqrt(lowercase						)

				return score

def 				_snake_case (		lowercase			,    lowercase						)       ->       Optional[int]:
				__a       :     int								=	np.array(lowercase						)
				__a       :     List[str]								=	np.array(lowercase						)

				__a       :     str								=	predict - actual
				__a       :     Any								=	np.sum(lowercase						) / len(lowercase						)
				__a       :     Tuple								=	np.sum(lowercase						) / len(lowercase						)
				# print(numerator, denumerator)
				__a       :     Tuple								=	float(lowercase						) / denumerator * 1_0_0

				return score

def 				_snake_case (		lowercase			,    lowercase						)       ->       int:
				return np.mean(np.array(lowercase						) == np.array(lowercase						)						)

'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__SCREAMING_SNAKE_CASE		:  Tuple        =      {
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  Dict        =      ['SegformerFeatureExtractor']
					__SCREAMING_SNAKE_CASE		:  str        =      ['SegformerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SegformerDecodeHead',
					    'SegformerForImageClassification',
					    'SegformerForSemanticSegmentation',
					    'SegformerLayer',
					    'SegformerModel',
					    'SegformerPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFSegformerDecodeHead',
					    'TFSegformerForImageClassification',
					    'TFSegformerForSemanticSegmentation',
					    'TFSegformerModel',
					    'TFSegformerPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_segformer import SegformerFeatureExtractor
										from .image_processing_segformer import SegformerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_segformer import (
										    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SegformerDecodeHead,
										    SegformerForImageClassification,
										    SegformerForSemanticSegmentation,
										    SegformerLayer,
										    SegformerModel,
										    SegformerPreTrainedModel,
										)
					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_segformer import (
										    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSegformerDecodeHead,
										    TFSegformerForImageClassification,
										    TFSegformerForSemanticSegmentation,
										    TFSegformerModel,
										    TFSegformerPreTrainedModel,
										)

else:
					import sys

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