



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 :				Any							=logging.get_logger(__name__)

UpperCAmelCase :				List[str]							={
    """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    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    """vit-hybrid"""

				def __init__(			self  ,			snake_case__=None  ,			snake_case__=768  ,			snake_case__=12  ,			snake_case__=12  ,			snake_case__=3072  ,			snake_case__="gelu"  ,			snake_case__=0.0  ,			snake_case__=0.0  ,			snake_case__=0.02  ,			snake_case__=1e-12  ,			snake_case__=224  ,			snake_case__=1  ,			snake_case__=3  ,			snake_case__=[1, 1024, 24, 24]  ,			snake_case__=True  ,			**snake_case__  ,			):

									'''simple docstring'''


									super().__init__(**snake_case__     )

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

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

									UpperCamelCase_			= backbone_featmap_shape
									UpperCamelCase_			= backbone_config
									UpperCamelCase_			= hidden_size
									UpperCamelCase_			= num_hidden_layers
									UpperCamelCase_			= num_attention_heads
									UpperCamelCase_			= intermediate_size
									UpperCamelCase_			= hidden_act
									UpperCamelCase_			= hidden_dropout_prob
									UpperCamelCase_			= attention_probs_dropout_prob
									UpperCamelCase_			= initializer_range
									UpperCamelCase_			= layer_norm_eps
									UpperCamelCase_			= image_size
									UpperCamelCase_			= patch_size
									UpperCamelCase_			= num_channels
									UpperCamelCase_			= qkv_bias

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= copy.deepcopy(self.__dict__     )
									UpperCamelCase_			= self.backbone_config.to_dict()
									UpperCamelCase_			= self.__class__.model_type
									return output




'''simple docstring'''
from __future__ import annotations







class 					_A						:



   '''simple docstring'''






   def __init__(	self	:					Any					, lowerCamelCase	:					int						)->	None:
          snake_case__					:	Optional[int]          =					value
          snake_case__					:	Node | None          =					None
          snake_case__					:	Node | None          =					None







class 					_A						:



   '''simple docstring'''






   _lowercase    						= 42




   def __init__(	self	:					str						)->	None:
          snake_case__					:	int          =					0




   def   __lowerCAmelCase						(	self	:					Tuple					, lowerCamelCase	:					Node | None					, lowerCamelCase	:					int						)->	None:
          if node is None:
                 return

          if path_sum == self.target:
                 self.paths += 1

          if node.left:
                 self.depth_first_search(node.left					, path_sum + node.left.value						)
          if node.right:
                 self.depth_first_search(node.right					, path_sum + node.right.value						)




   def   __lowerCAmelCase						(	self	:					Tuple					, lowerCamelCase	:					Node | None					, lowerCamelCase	:					int | None = None						)->	int:
          if node is None:
                 return 0
          if target is not None:
                 snake_case__					:	Dict          =					target

          self.depth_first_search(lowerCamelCase					, node.value						)
          self.path_sum(node.left						)
          self.path_sum(node.right						)

          return self.paths


if __name__ == "__main__":
     import doctest

     doctest.testmod()




