

from typing import TYPE_CHECKING

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


lowercase     :       Optional[Any]  	=       {
    "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase     :       Tuple  	=       ["SegformerFeatureExtractor"]
					lowercase     :       List[str]  	=       ["SegformerImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase     :       Tuple  	=       [
					    "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "SegformerDecodeHead",
					    "SegformerForImageClassification",
					    "SegformerForSemanticSegmentation",
					    "SegformerLayer",
					    "SegformerModel",
					    "SegformerPreTrainedModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase     :       Optional[int]  	=       [
					    "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

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


from __future__ import annotations







def  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		list[int]     ,			_lowerCAmelCase :		list[int]     ,			_lowerCAmelCase :		int		):

			# Base Case
			if counter == 0 or capacity == 0:
						return 0

			# If weight of the nth item is more than Knapsack of capacity,
			#   then this item cannot be included in the optimal solution,
			# else return the maximum of two cases:
			#   (1) nth item included
			#   (2) not included
			if weights[counter - 1] > capacity:
						return knapsack(_lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			counter - 1		)
			else:
						__UpperCamelCase    :    Tuple								=      capacity - weights[counter - 1]
						__UpperCamelCase    :    List[str]								=      values[counter - 1] + knapsack(
						    _lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			counter - 1		)
						__UpperCamelCase    :    Dict								=      knapsack(_lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			counter - 1		)
						return max(_lowerCAmelCase     ,			_lowerCAmelCase		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()