'''simple docstring'''




import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
  pass

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


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     Tuple     =  {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  List[str]						=						"align_text_model"




      def __init__(       self						:				Any						,					A						:				Optional[int]=30522						,					A						:				List[Any]=768						,					A						:				List[str]=12						,					A						:				Optional[Any]=12						,					A						:				Dict=3072						,					A						:				Dict="gelu"						,					A						:				int=0.1						,					A						:				Optional[int]=0.1						,					A						:				Tuple=512						,					A						:				List[Any]=2						,					A						:				Union[str, Any]=0.02						,					A						:				Dict=1E-12						,					A						:				Any=0						,					A						:				List[str]="absolute"						,					A						:				int=True						,					**A						:				List[Any]						,					):
             super().__init__(**_lowerCAmelCase       )

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





      @classmethod
      def      _A	(       cls						:				Tuple						,					A						:				Union[str, os.PathLike]						,					**A						:				Dict       ):
             cls._set_token_in_kwargs(_lowerCAmelCase       )

             _UpperCAmelCase     :  Optional[int]							      =    cls.get_config_dict(_lowerCAmelCase						,					**_lowerCAmelCase       )

             # get the text config dict if we are loading from AlignConfig
             if config_dict.get("model_type"       ) == "align":
                    _UpperCAmelCase     :  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(_lowerCAmelCase						,					**_lowerCAmelCase       )

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Any						=						"align_vision_model"




      def __init__(       self						:				List[str]						,					A						:				int = 3						,					A						:				int = 600						,					A						:				float = 2.0						,					A						:				float = 3.1						,					A						:				int = 8						,					A						:				List[int] = [3, 3, 5, 3, 5, 5, 3]						,					A						:				List[int] = [32, 16, 24, 40, 80, 112, 192]						,					A						:				List[int] = [16, 24, 40, 80, 112, 192, 320]						,					A						:				List[int] = []						,					A						:				List[int] = [1, 2, 2, 2, 1, 2, 1]						,					A						:				List[int] = [1, 2, 2, 3, 3, 4, 1]						,					A						:				List[int] = [1, 6, 6, 6, 6, 6, 6]						,					A						:				float = 0.25						,					A						:				str = "swish"						,					A						:				int = 2560						,					A						:				str = "mean"						,					A						:				float = 0.02						,					A						:				float = 0.001						,					A						:				float = 0.99						,					A						:				float = 0.2						,					**A						:				List[Any]						,					):
             super().__init__(**_lowerCAmelCase       )

             _UpperCAmelCase     :  Optional[Any]							      =    num_channels
             _UpperCAmelCase     :  Optional[int]							      =    image_size
             _UpperCAmelCase     :  Optional[int]							      =    width_coefficient
             _UpperCAmelCase     :  Dict							      =    depth_coefficient
             _UpperCAmelCase     :  List[str]							      =    depth_divisor
             _UpperCAmelCase     :  List[Any]							      =    kernel_sizes
             _UpperCAmelCase     :  Any							      =    in_channels
             _UpperCAmelCase     :  int							      =    out_channels
             _UpperCAmelCase     :  Optional[int]							      =    depthwise_padding
             _UpperCAmelCase     :  str							      =    strides
             _UpperCAmelCase     :  Optional[int]							      =    num_block_repeats
             _UpperCAmelCase     :  Optional[Any]							      =    expand_ratios
             _UpperCAmelCase     :  Tuple							      =    squeeze_expansion_ratio
             _UpperCAmelCase     :  Union[str, Any]							      =    hidden_act
             _UpperCAmelCase     :  Union[str, Any]							      =    hidden_dim
             _UpperCAmelCase     :  Optional[int]							      =    pooling_type
             _UpperCAmelCase     :  List[str]							      =    initializer_range
             _UpperCAmelCase     :  int							      =    batch_norm_eps
             _UpperCAmelCase     :  Any							      =    batch_norm_momentum
             _UpperCAmelCase     :  Optional[Any]							      =    drop_connect_rate
             _UpperCAmelCase     :  Optional[Any]							      =    sum(_lowerCAmelCase       ) * 4





      @classmethod
      def      _A	(       cls						:				Optional[Any]						,					A						:				Union[str, os.PathLike]						,					**A						:				str       ):
             cls._set_token_in_kwargs(_lowerCAmelCase       )

             _UpperCAmelCase     :  List[str]							      =    cls.get_config_dict(_lowerCAmelCase						,					**_lowerCAmelCase       )

             # get the vision config dict if we are loading from AlignConfig
             if config_dict.get("model_type"       ) == "align":
                    _UpperCAmelCase     :  Tuple							      =    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(_lowerCAmelCase						,					**_lowerCAmelCase       )

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						"align"
      __UpperCamelCase:  Tuple						=						True




      def __init__(       self						:				List[Any]						,					A						:				int=None						,					A						:				str=None						,					A						:				Optional[int]=640						,					A						:				Optional[int]=1.0						,					A						:				str=0.02						,					**A						:				Any						,					):
             super().__init__(**_lowerCAmelCase       )

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

             if vision_config is None:
                    _UpperCAmelCase     :  List[Any]							      =    {}
                    logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."       )

             _UpperCAmelCase     :  List[Any]							      =    AlignTextConfig(**_lowerCAmelCase       )
             _UpperCAmelCase     :  str							      =    AlignVisionConfig(**_lowerCAmelCase       )

             _UpperCAmelCase     :  Optional[Any]							      =    projection_dim
             _UpperCAmelCase     :  Optional[int]							      =    temperature_init_value
             _UpperCAmelCase     :  List[str]							      =    initializer_range




      @classmethod
      def      _A	(       cls						:				int						,					A						:				AlignTextConfig						,					A						:				AlignVisionConfig						,					**A						:				Any       ):
             return cls(text_config=text_config.to_dict()						,					vision_config=vision_config.to_dict()						,					**_lowerCAmelCase       )





      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    copy.deepcopy(self.__dict__       )
             _UpperCAmelCase     :  List[Any]							      =    self.text_config.to_dict()
             _UpperCAmelCase     :  str							      =    self.vision_config.to_dict()
             _UpperCAmelCase     :  List[Any]							      =    self.__class__.model_type
             return output







def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  # Return True if there is node that has not iterated.
  lowercase			:Union[str, Any]							=  [False] * len(lowerCamelCase      )
  lowercase			:Union[str, Any]							=  []
  queue.append(lowerCamelCase      )
  lowercase			:Optional[Any]							=  True

  while queue:
    lowercase			:Optional[Any]							=  queue.pop(0      )
    for ind in range(len(graph[u]      )      ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowerCamelCase      )
        lowercase			:Dict							=  True
        lowercase			:Dict							=  u

  return visited[t]



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  # This array is filled by BFS and to store path
  lowercase			:Optional[int]							=  [-1] * (len(lowerCamelCase      ))
  lowercase			:int							=  0
  while bfs(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
    lowercase			:int							=  float("Inf"      )
    lowercase			:Any							=  sink

    while s != source:
      # Find the minimum value in select path
      lowercase			:Any							=  min(lowerCamelCase,	graph[parent[s]][s]      )
      lowercase			:Dict							=  parent[s]

    max_flow += path_flow
    lowercase			:Union[str, Any]							=  sink

    while v != source:
      lowercase			:List[Any]							=  parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      lowercase			:Any							=  parent[v]
  return max_flow


_UpperCAmelCase		:  Optional[Any]	=   [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

_UpperCAmelCase	,	_UpperCAmelCase		:  Any	=   0, 5







print(ford_fulkerson(graph, source, sink))



