




"""simple docstring"""






from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__     						=						logging.get_logger(__name__)

lowercase__     						=						{
    """snap-research/efficientformer-l1-300""": (
        """https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
    ),
}






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			"""efficientformer"""
			def __init__(  self  ,      lowercase = [3, 2, 6, 4]  ,      lowercase = [48, 96, 224, 448]  ,      lowercase = [True, True, True, True]  ,      lowercase = 448  ,      lowercase = 32  ,      lowercase = 4  ,      lowercase = 7  ,      lowercase = 5  ,      lowercase = 8  ,      lowercase = 4  ,      lowercase = 0.0  ,      lowercase = 16  ,      lowercase = 3  ,      lowercase = 3  ,      lowercase = 3  ,      lowercase = 2  ,      lowercase = 1  ,      lowercase = 0.0  ,      lowercase = 1  ,      lowercase = True  ,      lowercase = True  ,      lowercase = 1E-5  ,      lowercase = "gelu"  ,      lowercase = 0.02  ,      lowercase = 1E-12  ,      lowercase = 224  ,      lowercase = 1E-05  ,      **lowercase  ,      ):
							super().__init__(**lowercase  )

							_lowerCamelCase     :			Dict    =    hidden_act
							_lowerCamelCase     :			Optional[Any]    =    hidden_dropout_prob
							_lowerCamelCase     :			Optional[Any]    =    hidden_sizes
							_lowerCamelCase     :			Optional[Any]    =    num_hidden_layers
							_lowerCamelCase     :			int    =    num_attention_heads
							_lowerCamelCase     :			Tuple    =    initializer_range
							_lowerCamelCase     :			Tuple    =    layer_norm_eps
							_lowerCamelCase     :			Optional[Any]    =    patch_size
							_lowerCamelCase     :			List[str]    =    num_channels
							_lowerCamelCase     :			List[Any]    =    depths
							_lowerCamelCase     :			List[str]    =    mlp_expansion_ratio
							_lowerCamelCase     :			Union[str, Any]    =    downsamples
							_lowerCamelCase     :			List[str]    =    dim
							_lowerCamelCase     :			str    =    key_dim
							_lowerCamelCase     :			Tuple    =    attention_ratio
							_lowerCamelCase     :			Dict    =    resolution
							_lowerCamelCase     :			Any    =    pool_size
							_lowerCamelCase     :			Any    =    downsample_patch_size
							_lowerCamelCase     :			str    =    downsample_stride
							_lowerCamelCase     :			Tuple    =    downsample_pad
							_lowerCamelCase     :			List[str]    =    drop_path_rate
							_lowerCamelCase     :			Optional[Any]    =    num_metaad_blocks
							_lowerCamelCase     :			Optional[Any]    =    distillation
							_lowerCamelCase     :			int    =    use_layer_scale
							_lowerCamelCase     :			Optional[int]    =    layer_scale_init_value
							_lowerCamelCase     :			List[Any]    =    image_size
							_lowerCamelCase     :			Optional[Any]    =    batch_norm_eps





"""simple docstring"""






import functools
from typing import Any







def      _snake_case			(				lowercase__							,    lowercase__		):

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

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

				# Build trie
				_lowerCamelCase     :			dict[str, Any]    =    {}
				_lowerCamelCase     :			List[Any]    =    'WORD_KEEPER'

				for word in words:
								_lowerCamelCase     :			Dict    =    trie
								for c in word:
												if c not in trie_node:
																_lowerCamelCase     :			Any    =    {}

												_lowerCamelCase     :			str    =    trie_node[c]

								_lowerCamelCase     :			Optional[Any]    =    True

				_lowerCamelCase     :			Dict    =    len(lowercase__		)

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

								_lowerCamelCase     :			List[Any]    =    trie
								for i in range(lowercase__							,    lowercase__		):
												_lowerCamelCase     :			Any    =    trie_node.get(string[i]							,    lowercase__		)

												if trie_node is None:
																return False

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

								return False

				return is_breakable(0		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()