

from hashlib import mda, shaaaa

__snake_case							:			Union[str, Any]      		= (shaaaa, mda)



class 					A__:
						"""simple docstring"""






						def __init__(						self     ,			_lowercase = 8						)    ->   None:
									a_	:	int          =   0B0
									a_	:	Dict          =   size





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   None:
									a_	:	str          =   self.hash_(_lowercase						)
									self.bitarray |= h





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   bool:
									a_	:	str          =   self.hash_(_lowercase						)
									return (h & self.bitarray) == h





						def __contains__(						self     ,			_lowercase						)    ->   bool:
									return self.exists(_lowercase						)





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   str:
									a_	:	List[str]          =   bin(_lowercase						)[2:]
									return res.zfill(self.size						)





						@property
						def  UpperCamelCase__				(						self						)    ->   str:
									return self.format_bin(self.bitarray						)





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   int:
									a_	:	Any          =   0B0
									for func in HASH_FUNCTIONS:
												a_	:	List[str]          =   (
												    int.from_bytes(func(value.encode()						).digest()     ,			"""little"""						) % self.size
												)
												res |= 2**position
									return res





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   str:
									return self.format_bin(self.hash_(_lowercase						)						)





						@property
						def  UpperCamelCase__				(						self						)    ->   float:
									a_	:	int          =   bin(self.bitarray						).count("""1"""						)
									return (n_ones / self.size) ** len(_lowercase						)





import copy
from typing import Dict

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


__snake_case							:			int      		= logging.get_logger(__name__)

__snake_case							:			Dict      		= {
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class 					A__(a_       ):
						"""simple docstring"""






						_A						:							Dict									= '''vit-hybrid'''





						def __init__(						self     ,			_lowercase=None     ,			_lowercase=768     ,			_lowercase=12     ,			_lowercase=12     ,			_lowercase=3_072     ,			_lowercase="gelu"     ,			_lowercase=0.0     ,			_lowercase=0.0     ,			_lowercase=0.0_2     ,			_lowercase=1e-12     ,			_lowercase=224     ,			_lowercase=1     ,			_lowercase=3     ,			_lowercase=[1, 1_024, 24, 24]     ,			_lowercase=True     ,			**_lowercase     ,			)    ->   Tuple:
									super().__init__(**_lowercase						)

									if backbone_config is None:
												logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""						)
												a_	:	Union[str, Any]          =   {
												    """global_padding""": """same""",
												    """layer_type""": """bottleneck""",
												    """depths""": [3, 4, 9],
												    """out_features""": ["""stage3"""],
												    """embedding_dynamic_padding""": True,
												}

									if isinstance(_lowercase     ,			_lowercase						):
												if "model_type" in backbone_config:
															a_	:	Any          =   CONFIG_MAPPING[backbone_config["""model_type"""]]
												else:
															logger.info(
															    """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""						)
															a_	:	Optional[int]          =   BitConfig
												a_	:	Dict          =   backbone_config_class(**_lowercase						)

									a_	:	List[str]          =   backbone_featmap_shape
									a_	:	int          =   backbone_config
									a_	:	Union[str, Any]          =   hidden_size
									a_	:	Optional[int]          =   num_hidden_layers
									a_	:	Dict          =   num_attention_heads
									a_	:	Optional[int]          =   intermediate_size
									a_	:	Dict          =   hidden_act
									a_	:	Optional[Any]          =   hidden_dropout_prob
									a_	:	List[str]          =   attention_probs_dropout_prob
									a_	:	Tuple          =   initializer_range
									a_	:	Any          =   layer_norm_eps
									a_	:	List[Any]          =   image_size
									a_	:	Optional[Any]          =   patch_size
									a_	:	Optional[Any]          =   num_channels
									a_	:	str          =   qkv_bias





						def  UpperCamelCase__				(						self						)    ->   Dict[str, any]:
									a_	:	List[str]          =   copy.deepcopy(self.__dict__						)
									a_	:	Optional[int]          =   self.backbone_config.to_dict()
									a_	:	List[str]          =   self.__class__.model_type
									return output


