



"""simple docstring"""
import copy
import os
from typing import Union

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


_lowerCAmelCase    :List[Any]     =					logging.get_logger(__name__)

_lowerCAmelCase    :Optional[Any]     =					{
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									='''blip_text_model'''





     def __init__(					self       ,	A=3_0_5_2_4       ,	A=7_6_8       ,	A=7_6_8       ,	A=3_0_7_2       ,	A=7_6_8       ,	A=1_2       ,	A=8       ,	A=5_1_2       ,	A="gelu"       ,	A=1E-12       ,	A=0.0       ,	A=0.0       ,	A=0.02       ,	A=3_0_5_2_2       ,	A=2       ,	A=0       ,	A=1_0_2       ,	A=True       ,	A=True       ,	**A       ,	)							->					List[str]:
         super().__init__(
             pad_token_id=A       ,	bos_token_id=A       ,	eos_token_id=A       ,	sep_token_id=A       ,	**A       ,	)

         _UpperCAmelCase							:  Optional[Any]											=    vocab_size
         _UpperCAmelCase							:  Any											=    hidden_size
         _UpperCAmelCase							:  List[Any]											=    encoder_hidden_size
         _UpperCAmelCase							:  int											=    intermediate_size
         _UpperCAmelCase							:  Dict											=    projection_dim
         _UpperCAmelCase							:  Optional[Any]											=    hidden_dropout_prob
         _UpperCAmelCase							:  List[str]											=    num_hidden_layers
         _UpperCAmelCase							:  int											=    num_attention_heads
         _UpperCAmelCase							:  int											=    max_position_embeddings
         _UpperCAmelCase							:  Optional[Any]											=    layer_norm_eps
         _UpperCAmelCase							:  Optional[Any]											=    hidden_act
         _UpperCAmelCase							:  List[Any]											=    initializer_range
         _UpperCAmelCase							:  Union[str, Any]											=    attention_probs_dropout_prob
         _UpperCAmelCase							:  List[Any]											=    is_decoder
         _UpperCAmelCase							:  Any											=    use_cache





     @classmethod
     def  __lowerCAmelCase	(					cls       ,	A       ,	**A		)							->					"PretrainedConfig":
         cls._set_token_in_kwargs(A		)

         _UpperCAmelCase							,				_UpperCAmelCase							:  int											=    cls.get_config_dict(A       ,	**A		)

         # get the text config dict if we are loading from BlipConfig
         if config_dict.get('''model_type'''		) == "blip":
             _UpperCAmelCase							:  str											=    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							( a      ):





     '''simple docstring'''



     a__									='''blip_vision_model'''





     def __init__(					self       ,	A=7_6_8       ,	A=3_0_7_2       ,	A=5_1_2       ,	A=1_2       ,	A=1_2       ,	A=3_8_4       ,	A=1_6       ,	A="gelu"       ,	A=1E-5       ,	A=0.0       ,	A=1E-10       ,	**A       ,	)							->					Union[str, Any]:
         super().__init__(**A		)

         _UpperCAmelCase							:  Union[str, Any]											=    hidden_size
         _UpperCAmelCase							:  str											=    intermediate_size
         _UpperCAmelCase							:  Optional[int]											=    projection_dim
         _UpperCAmelCase							:  Optional[int]											=    num_hidden_layers
         _UpperCAmelCase							:  Optional[Any]											=    num_attention_heads
         _UpperCAmelCase							:  Optional[int]											=    patch_size
         _UpperCAmelCase							:  str											=    image_size
         _UpperCAmelCase							:  Tuple											=    initializer_range
         _UpperCAmelCase							:  List[Any]											=    attention_dropout
         _UpperCAmelCase							:  Optional[Any]											=    layer_norm_eps
         _UpperCAmelCase							:  Dict											=    hidden_act





     @classmethod
     def  __lowerCAmelCase	(					cls       ,	A       ,	**A		)							->					"PretrainedConfig":
         cls._set_token_in_kwargs(A		)

         _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    cls.get_config_dict(A       ,	**A		)

         # get the vision config dict if we are loading from BlipConfig
         if config_dict.get('''model_type'''		) == "blip":
             _UpperCAmelCase							:  Union[str, Any]											=    config_dict['''vision_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							( a      ):





     '''simple docstring'''



     a__									='''blip'''
     a__									=True





     def __init__(					self       ,	A=None       ,	A=None       ,	A=5_1_2       ,	A=2.6_592       ,	A=2_5_6       ,	**A       ,	)							->					Dict:
         super().__init__(**A		)

         if text_config is None:
             _UpperCAmelCase							:  Union[str, Any]											=    {}
             logger.info('''`text_config` is `None`. Initializing the `BlipTextConfig` with default values.'''		)

         if vision_config is None:
             _UpperCAmelCase							:  Optional[int]											=    {}
             logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'''		)

         _UpperCAmelCase							:  Optional[Any]											=    BlipTextConfig(**A		)
         _UpperCAmelCase							:  List[Any]											=    BlipVisionConfig(**A		)

         _UpperCAmelCase							:  Tuple											=    self.vision_config.hidden_size

         _UpperCAmelCase							:  Any											=    projection_dim
         _UpperCAmelCase							:  List[Any]											=    logit_scale_init_value
         _UpperCAmelCase							:  Tuple											=    1.0
         _UpperCAmelCase							:  Any											=    0.02
         _UpperCAmelCase							:  List[Any]											=    image_text_hidden_size





     @classmethod
     def  __lowerCAmelCase	(					cls       ,	A       ,	A       ,	**A		)							->					List[Any]:
         return cls(text_config=text_config.to_dict()       ,	vision_config=vision_config.to_dict()       ,	**A		)





     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  int											=    copy.deepcopy(self.__dict__		)
         _UpperCAmelCase							:  Tuple											=    self.text_config.to_dict()
         _UpperCAmelCase							:  Optional[int]											=    self.vision_config.to_dict()
         _UpperCAmelCase							:  Any											=    self.__class__.model_type
         return output







"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCAmelCase    :Tuple     =					tuple[int, int]
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A		)							->					None:
         _UpperCAmelCase							:  set[int]											=    vertices
         _UpperCAmelCase							:  dict[EdgeT, int]											=    {
             (min(A		), max(A		)): weight for edge, weight in edges.items()
         }





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					None:
         self.vertices.add(edge[0]		)
         self.vertices.add(edge[1]		)
         _UpperCAmelCase							:  List[Any]											=    weight





     def  __lowerCAmelCase	(					self		)							->					Graph:
         _UpperCAmelCase							:  Graph											=    Graph({min(self.vertices		)}       ,	{}		)
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int

         while len(subgraph.vertices		) < len(self.vertices		):
             _UpperCAmelCase							:  Any											=    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:
                         _UpperCAmelCase							:  Tuple											=    edge
                         _UpperCAmelCase							:  Optional[int]											=    weight

             subgraph.add_edge(A       ,	A		)

         return subgraph





def  lowerCamelCase_						(UpperCamelCase__		:		str = "p107_network.txt"							):
    _UpperCAmelCase							:  str											=    os.path.abspath(os.path.dirname(UpperCamelCase__							)							)
    _UpperCAmelCase							:  str											=    os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  dict[EdgeT, int]											=    {}
    _UpperCAmelCase							:  list[str]
    _UpperCAmelCase							:  int
    _UpperCAmelCase							:  int

    with open(UpperCamelCase__							) as f:
        _UpperCAmelCase							:  str											=    f.read().strip().split('''\n'''							)

    _UpperCAmelCase							:  List[Any]											=    [line.split(''','''							) for line in data]

    for edgea in range(1     ,       len(UpperCamelCase__							)							):
        for edgea in range(UpperCamelCase__							):
            if adjaceny_matrix[edgea][edgea] != "-":
                _UpperCAmelCase							:  Optional[Any]											=    int(adjaceny_matrix[edgea][edgea]							)

    _UpperCAmelCase							:  Graph											=    Graph(set(range(len(UpperCamelCase__							)							)							)     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Graph											=    graph.prims_algorithm()

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

    return initial_total - optimal_total


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


