


'''simple docstring'''






import copy
import os
from typing import Union

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


__A	:	Optional[int]     =     logging.get_logger(__name__)

__A	:	Dict     =     {
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}
class 						__UpperCamelCase	(						UpperCamelCase_							):
							lowercase			:		str   		=  'bridgetower_vision_model'





							def __init__(  self		:Dict  ,_UpperCamelCase		:Optional[int]=7_6_8  ,_UpperCamelCase		:Optional[int]=1_2  ,_UpperCamelCase		:Dict=3  ,_UpperCamelCase		:Optional[Any]=1_6  ,_UpperCamelCase		:Any=2_8_8  ,_UpperCamelCase		:str=1  ,_UpperCamelCase		:Any=1E-0_5  ,_UpperCamelCase		:Optional[int]=False  ,_UpperCamelCase		:Optional[Any]=True  ,_UpperCamelCase		:List[str]=False  ,**_UpperCamelCase		:Union[str, Any]  ,):
									super().__init__(**__A			)
									snake_case_		:							Any			       =						hidden_size
									snake_case_		:							List[Any]			       =						num_hidden_layers
									snake_case_		:							Any			       =						num_channels
									snake_case_		:							str			       =						patch_size
									snake_case_		:							Dict			       =						image_size
									snake_case_		:							Union[str, Any]			       =						initializer_factor
									snake_case_		:							Dict			       =						layer_norm_eps
									snake_case_		:							Tuple			       =						stop_gradient
									snake_case_		:							Any			       =						share_layernorm
									snake_case_		:							Tuple			       =						remove_last_layer





							@classmethod
							def 		a__   (  cls		:Dict  ,_UpperCamelCase		:Union[str, os.PathLike]  ,**_UpperCamelCase		:Tuple			):
									snake_case_		:							Tuple			       =						cls.get_config_dict(__A  ,**__A			)

									if config_dict.get("""model_type"""			) == "bridgetower":
											snake_case_		:							List[Any]			       =						config_dict["text_config"]

									if "model_type" in config_dict and hasattr(cls  ,"""model_type"""			) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
											    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''			)

									return cls.from_dict(__A  ,**__A			)





class 						__UpperCamelCase	(						UpperCamelCase_							):
							lowercase			:		Tuple   		=  'bridgetower_text_model'





							def __init__(  self		:Optional[int]  ,_UpperCamelCase		:str=5_0_2_6_5  ,_UpperCamelCase		:List[Any]=7_6_8  ,_UpperCamelCase		:int=1_2  ,_UpperCamelCase		:Optional[Any]=1_2  ,_UpperCamelCase		:str=1  ,_UpperCamelCase		:Dict=3_0_7_2  ,_UpperCamelCase		:Tuple="gelu"  ,_UpperCamelCase		:Optional[Any]=0.1  ,_UpperCamelCase		:Tuple=0.1  ,_UpperCamelCase		:Any=5_1_4  ,_UpperCamelCase		:List[str]=1  ,_UpperCamelCase		:List[Any]=1E-0_5  ,_UpperCamelCase		:int=1  ,_UpperCamelCase		:str=0  ,_UpperCamelCase		:str=2  ,_UpperCamelCase		:Union[str, Any]="absolute"  ,_UpperCamelCase		:Optional[Any]=True  ,**_UpperCamelCase		:Optional[Any]  ,):
									super().__init__(**__A			)

									snake_case_		:							List[str]			       =						vocab_size
									snake_case_		:							Optional[int]			       =						hidden_size
									snake_case_		:							Union[str, Any]			       =						num_hidden_layers
									snake_case_		:							Dict			       =						num_attention_heads
									snake_case_		:							Tuple			       =						hidden_act
									snake_case_		:							Any			       =						initializer_factor
									snake_case_		:							List[Any]			       =						intermediate_size
									snake_case_		:							Tuple			       =						hidden_dropout_prob
									snake_case_		:							Optional[int]			       =						attention_probs_dropout_prob
									snake_case_		:							Any			       =						max_position_embeddings
									snake_case_		:							Union[str, Any]			       =						type_vocab_size
									snake_case_		:							Optional[int]			       =						layer_norm_eps
									snake_case_		:							Optional[Any]			       =						position_embedding_type
									snake_case_		:							Union[str, Any]			       =						use_cache
									snake_case_		:							List[str]			       =						pad_token_id
									snake_case_		:							Tuple			       =						bos_token_id
									snake_case_		:							Dict			       =						eos_token_id





							@classmethod
							def 		a__   (  cls		:List[Any]  ,_UpperCamelCase		:Union[str, os.PathLike]  ,**_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							Any			       =						cls.get_config_dict(__A  ,**__A			)

									if config_dict.get("""model_type"""			) == "bridgetower":
											snake_case_		:							Optional[int]			       =						config_dict["text_config"]

									if "model_type" in config_dict and hasattr(cls  ,"""model_type"""			) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
											    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''			)

									return cls.from_dict(__A  ,**__A			)





class 						__UpperCamelCase	(						UpperCamelCase_							):
							lowercase			:		List[str]   		=  'bridgetower'





							def __init__(  self		:Optional[int]  ,_UpperCamelCase		:Dict=True  ,_UpperCamelCase		:Union[str, Any]="gelu"  ,_UpperCamelCase		:Tuple=7_6_8  ,_UpperCamelCase		:Dict=1  ,_UpperCamelCase		:Optional[Any]=1E-0_5  ,_UpperCamelCase		:Optional[int]=False  ,_UpperCamelCase		:int="add"  ,_UpperCamelCase		:List[Any]=1_2  ,_UpperCamelCase		:Any=6  ,_UpperCamelCase		:List[str]=False  ,_UpperCamelCase		:int=False  ,_UpperCamelCase		:List[Any]=None  ,_UpperCamelCase		:Union[str, Any]=None  ,**_UpperCamelCase		:str  ,):
									# TODO: remove this once the Hub files are updated.
									snake_case_		:							Optional[int]			       =						kwargs.pop("""text_config_dict"""  ,__A			)
									snake_case_		:							Tuple			       =						kwargs.pop("""vision_config_dict"""  ,__A			)

									super().__init__(**__A			)
									snake_case_		:							Dict			       =						share_cross_modal_transformer_layers
									snake_case_		:							Optional[int]			       =						hidden_act
									snake_case_		:							Optional[Any]			       =						hidden_size
									snake_case_		:							Any			       =						initializer_factor
									snake_case_		:							Any			       =						layer_norm_eps
									snake_case_		:							Optional[Any]			       =						share_link_tower_layers
									snake_case_		:							Any			       =						link_tower_type
									snake_case_		:							Optional[Any]			       =						num_attention_heads
									snake_case_		:							Dict			       =						num_hidden_layers
									snake_case_		:							Tuple			       =						tie_word_embeddings
									snake_case_		:							Any			       =						init_layernorm_from_vision_encoder

									if text_config is None:
											snake_case_		:							Optional[Any]			       =						{}
											logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""			)

									if vision_config is None:
											snake_case_		:							int			       =						{}
											logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""			)

									snake_case_		:							Optional[int]			       =						BridgeTowerTextConfig(**__A			)
									snake_case_		:							Optional[Any]			       =						BridgeTowerVisionConfig(**__A			)





							@classmethod
							def 		a__   (  cls		:Any  ,_UpperCamelCase		:BridgeTowerTextConfig  ,_UpperCamelCase		:BridgeTowerVisionConfig  ,**_UpperCamelCase		:List[str]			):
									return cls(text_config=text_config.to_dict()  ,vision_config=vision_config.to_dict()  ,**__A			)





							def 		a__   (  self		:Tuple			):
									snake_case_		:							Tuple			       =						copy.deepcopy(self.__dict__			)
									snake_case_		:							str			       =						self.text_config.to_dict()
									snake_case_		:							Dict			       =						self.vision_config.to_dict()
									snake_case_		:							str			       =						self.__class__.model_type
									return output



'''simple docstring'''






from __future__ import annotations

import os
from collections.abc import Mapping

__A	:	str     =     tuple[int, int]
class 						__UpperCamelCase	:





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:set[int]  ,_UpperCamelCase		:Mapping[EdgeT, int]			):
									snake_case_		:							set[int]			       =						vertices
									snake_case_		:							dict[EdgeT, int]			       =						{
									    (min(_UpperCamelCase			), max(_UpperCamelCase			)): weight for edge, weight in edges.items()
									}





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:EdgeT  ,_UpperCamelCase		:int			):
									self.vertices.add(edge[0]			)
									self.vertices.add(edge[1]			)
									snake_case_		:							str			       =						weight





							def 		a__   (  self		:Tuple			):
									snake_case_		:							Graph			       =						Graph({min(self.vertices			)}  ,{}			)
									snake_case_		:							EdgeT
									snake_case_		:							int
									snake_case_		:							EdgeT
									snake_case_		:							int

									while len(subgraph.vertices			) < len(self.vertices			):
											snake_case_		:							int			       =						max(self.edges.values()			) + 1
											for edge, weight in self.edges.items():
													if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
															if weight < min_weight:
																	snake_case_		:							Dict			       =						edge
																	snake_case_		:							Dict			       =						weight

											subgraph.add_edge(_UpperCamelCase  ,_UpperCamelCase			)

									return subgraph





def      UpperCAmelCase      (		lowerCamelCase_  :str = "p107_network.txt" ):







		'''simple docstring'''
		snake_case_		:							str			       =						os.path.abspath(os.path.dirname(lowerCamelCase_ ) )
		snake_case_		:							str			       =						os.path.join(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							dict[EdgeT, int]			       =						{}
		snake_case_		:							list[str]
		snake_case_		:							int
		snake_case_		:							int

		with open(lowerCamelCase_ ) as f:
				snake_case_		:							Optional[int]			       =						f.read().strip().split("""\n""" )

		snake_case_		:							Any			       =						[line.split(""",""" ) for line in data]

		for edgea in range(1				,      len(lowerCamelCase_ ) ):
				for edgea in range(lowerCamelCase_ ):
						if adjaceny_matrix[edgea][edgea] != "-":
								snake_case_		:							str			       =						int(adjaceny_matrix[edgea][edgea] )

		snake_case_		:							Graph			       =						Graph(set(range(len(lowerCamelCase_ ) ) )				,      lowerCamelCase_ )

		snake_case_		:							Graph			       =						graph.prims_algorithm()

		snake_case_		:							int			       =						sum(graph.edges.values() )
		snake_case_		:							int			       =						sum(subgraph.edges.values() )

		return initial_total - optimal_total


if __name__ == "__main__":
							print(F'{solution() = }')