




from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class  UpperCAmelCase						:






   def __init__(	self   :       Union[str, Any]						,					UpperCamelCase_   :       Any     )->			None:
     _lowercase: Any						     =    data
     _lowercase: Node | None						     =    None






   def __str__(	self   :       Dict     )->			str:
     return f'{self.data}'

class  UpperCAmelCase						:






   def __init__(	self   :       Optional[int]     )->			None:
     _lowercase: Node | None						     =    None
     _lowercase: Node | None						     =    None






   def __iter__(	self   :       Dict     )->			Iterator[Any]:
     _lowercase: Optional[int]						     =    self.front
     while node:
       yield node.data
       _lowercase: Optional[int]						     =    node.next






   def __len__(	self   :       Tuple     )->			int:
     return len(tuple(iter(self     )     )     )






   def __str__(	self   :       Union[str, Any]     )->			str:
     return " <- ".join(str(UpperCamelCase_     ) for item in self     )






   def  _a						(	self   :       Any     )->			bool:
     return len(self     ) == 0






   def  _a						(	self   :       Dict						,					UpperCamelCase_   :       Any     )->			None:
     _lowercase: List[Any]						     =    Node(UpperCamelCase_     )
     if self.is_empty():
       _lowercase: List[str]						     =    node
     else:
       assert isinstance(self.rear						,					UpperCamelCase_     )
       _lowercase: Union[str, Any]						     =    node
       _lowercase: Dict						     =    node






   def  _a						(	self   :       Optional[Any]     )->			Any:
     if self.is_empty():
       raise IndexError('dequeue from empty queue'     )
     assert isinstance(self.front						,					UpperCamelCase_     )
     _lowercase: str						     =    self.front
     _lowercase: str						     =    self.front.next
     if self.front is None:
       _lowercase: List[Any]						     =    None
     return node.data






   def  _a						(	self   :       Any     )->			None:
     _lowercase: List[Any]						     =    None


if __name__ == "__main__":
   from doctest import testmod

   testmod()






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


UpperCAmelCase__						:       Dict							=    logging.get_logger(__name__)

UpperCAmelCase__						:       Optional[Any]							=    {
    '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      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							int							=					"vit-hybrid"






   def __init__(	self   :       Optional[int]						,					UpperCamelCase_   :       Any=None						,					UpperCamelCase_   :       str=7_68						,					UpperCamelCase_   :       Optional[int]=12						,					UpperCamelCase_   :       str=12						,					UpperCamelCase_   :       Dict=30_72						,					UpperCamelCase_   :       Dict="gelu"						,					UpperCamelCase_   :       str=0.0						,					UpperCamelCase_   :       List[Any]=0.0						,					UpperCamelCase_   :       List[str]=0.02						,					UpperCamelCase_   :       Tuple=1E-12						,					UpperCamelCase_   :       Union[str, Any]=2_24						,					UpperCamelCase_   :       Any=1						,					UpperCamelCase_   :       Optional[Any]=3						,					UpperCamelCase_   :       Optional[Any]=[1, 10_24, 24, 24]						,					UpperCamelCase_   :       Any=True						,					**UpperCamelCase_   :       int						,					)->			Any:
     super().__init__(**UpperCamelCase_     )

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

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

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






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