

import functools
from typing import Any





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							if not isinstance(UpperCamelCase__     ,							UpperCamelCase__   ) or len(UpperCamelCase__   ) == 0:
														raise ValueError('the string should be not empty string'   )

							if not isinstance(UpperCamelCase__     ,							UpperCamelCase__   ) or not all(
							    isinstance(UpperCamelCase__     ,							UpperCamelCase__   ) and len(UpperCamelCase__   ) > 0 for item in words   ):
														raise ValueError('the words should be a list of non-empty strings'   )

							# Build trie
							snake_case_           =					{}
							snake_case_           =					'WORD_KEEPER'

							for word in words:
														snake_case_           =					trie
														for c in word:
																					if c not in trie_node:
																												snake_case_           =					{}

																					snake_case_           =					trie_node[c]

														snake_case_           =					True

							snake_case_           =					len(UpperCamelCase__   )

							# Dynamic programming method
							@functools.cache
							def is_breakable(UpperCamelCase__   ) -> bool:
														if index == len_string:
																					return True

														snake_case_           =					trie
														for i in range(UpperCamelCase__     ,							UpperCamelCase__   ):
																					snake_case_           =					trie_node.get(string[i]     ,							UpperCamelCase__   )

																					if trie_node is None:
																												return False

																					if trie_node.get(UpperCamelCase__     ,							UpperCamelCase__   ) and is_breakable(i + 1   ):
																												return True

														return False

							return is_breakable(0   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_UpperCAmelCase	:     Dict		       = logging.get_logger(__name__)







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Any     =    '''upernet'''




				def __init__(      self   ,     snake_case=None   ,     snake_case=512   ,     snake_case=0.02   ,     snake_case=[1, 2, 3, 6]   ,     snake_case=True   ,     snake_case=0.4   ,     snake_case=384   ,     snake_case=256   ,     snake_case=1   ,     snake_case=False   ,     snake_case=255   ,     **snake_case   ,     ):
											super().__init__(**snake_case )

											if backbone_config is None:
																		logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
																		snake_case_           =					CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4'] )
											elif isinstance(snake_case   ,     snake_case ):
																		snake_case_           =					backbone_config.get('model_type' )
																		snake_case_           =					CONFIG_MAPPING[backbone_model_type]
																		snake_case_           =					config_class.from_dict(snake_case )

											snake_case_           =					backbone_config
											snake_case_           =					hidden_size
											snake_case_           =					initializer_range
											snake_case_           =					pool_scales
											snake_case_           =					use_auxiliary_head
											snake_case_           =					auxiliary_loss_weight
											snake_case_           =					auxiliary_in_channels
											snake_case_           =					auxiliary_channels
											snake_case_           =					auxiliary_num_convs
											snake_case_           =					auxiliary_concat_input
											snake_case_           =					loss_ignore_index




				def 					a							(      self ):
											snake_case_           =					copy.deepcopy(self.__dict__ )
											snake_case_           =					self.backbone_config.to_dict()
											snake_case_           =					self.__class__.model_type
											return output




