"""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


UpperCAmelCase__     						=  logging.get_logger(__name__)

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


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     str   							=							'align_text_model'


    def __init__(    self		:     str      , __lowerCAmelCase		:     List[str]=3_0522      , __lowerCAmelCase		:     List[str]=768      , __lowerCAmelCase		:     Any=12      , __lowerCAmelCase		:     int=12      , __lowerCAmelCase		:     Optional[Any]=3072      , __lowerCAmelCase		:     List[Any]="gelu"      , __lowerCAmelCase		:     Tuple=0.1      , __lowerCAmelCase		:     str=0.1      , __lowerCAmelCase		:     Dict=512      , __lowerCAmelCase		:     Dict=2      , __lowerCAmelCase		:     Any=0.02      , __lowerCAmelCase		:     Union[str, Any]=1e-1_2      , __lowerCAmelCase		:     Tuple=0      , __lowerCAmelCase		:     Optional[Any]="absolute"      , __lowerCAmelCase		:     Optional[int]=True      , **__lowerCAmelCase		:     str      , ):
      super().__init__(**__lowerCAmelCase    )

      _UpperCAmelCase         =					vocab_size
      _UpperCAmelCase         =					hidden_size
      _UpperCAmelCase         =					num_hidden_layers
      _UpperCAmelCase         =					num_attention_heads
      _UpperCAmelCase         =					hidden_act
      _UpperCAmelCase         =					intermediate_size
      _UpperCAmelCase         =					hidden_dropout_prob
      _UpperCAmelCase         =					attention_probs_dropout_prob
      _UpperCAmelCase         =					max_position_embeddings
      _UpperCAmelCase         =					type_vocab_size
      _UpperCAmelCase         =					initializer_range
      _UpperCAmelCase         =					layer_norm_eps
      _UpperCAmelCase         =					position_embedding_type
      _UpperCAmelCase         =					use_cache
      _UpperCAmelCase         =					pad_token_id


    @classmethod
    def    lowerCAmelCase_     (    cls		:     List[str]      , __lowerCAmelCase		:     Union[str, os.PathLike]      , **__lowerCAmelCase		:     List[str]    ):
      cls._set_token_in_kwargs(__lowerCAmelCase    )

      _UpperCAmelCase       , _UpperCAmelCase         =					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         =					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 				a (						lowerCAmelCase_      ):
    _snake_case   :     List[Any]   							=							'align_vision_model'


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

      _UpperCAmelCase         =					num_channels
      _UpperCAmelCase         =					image_size
      _UpperCAmelCase         =					width_coefficient
      _UpperCAmelCase         =					depth_coefficient
      _UpperCAmelCase         =					depth_divisor
      _UpperCAmelCase         =					kernel_sizes
      _UpperCAmelCase         =					in_channels
      _UpperCAmelCase         =					out_channels
      _UpperCAmelCase         =					depthwise_padding
      _UpperCAmelCase         =					strides
      _UpperCAmelCase         =					num_block_repeats
      _UpperCAmelCase         =					expand_ratios
      _UpperCAmelCase         =					squeeze_expansion_ratio
      _UpperCAmelCase         =					hidden_act
      _UpperCAmelCase         =					hidden_dim
      _UpperCAmelCase         =					pooling_type
      _UpperCAmelCase         =					initializer_range
      _UpperCAmelCase         =					batch_norm_eps
      _UpperCAmelCase         =					batch_norm_momentum
      _UpperCAmelCase         =					drop_connect_rate
      _UpperCAmelCase         =					sum(__lowerCAmelCase    ) * 4


    @classmethod
    def    lowerCAmelCase_     (    cls		:     Union[str, Any]      , __lowerCAmelCase		:     Union[str, os.PathLike]      , **__lowerCAmelCase		:     Any    ):
      cls._set_token_in_kwargs(__lowerCAmelCase    )

      _UpperCAmelCase       , _UpperCAmelCase         =					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         =					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 				a (						lowerCAmelCase_      ):
    _snake_case   :     List[Any]   							=							'align'
    _snake_case   :     Any   							=							True


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

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

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

      _UpperCAmelCase         =					AlignTextConfig(**__lowerCAmelCase    )
      _UpperCAmelCase         =					AlignVisionConfig(**__lowerCAmelCase    )

      _UpperCAmelCase         =					projection_dim
      _UpperCAmelCase         =					temperature_init_value
      _UpperCAmelCase         =					initializer_range


    @classmethod
    def    lowerCAmelCase_     (    cls		:     str      , __lowerCAmelCase		:     AlignTextConfig      , __lowerCAmelCase		:     AlignVisionConfig      , **__lowerCAmelCase		:     Any    ):
      return cls(text_config=text_config.to_dict()      , vision_config=vision_config.to_dict()      , **__lowerCAmelCase    )


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

"""simple docstring"""


UpperCAmelCase__     						=  [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  # Return True if there is node that has not iterated.
  _UpperCAmelCase         =					[False] * len(lowercase						)
  _UpperCAmelCase         =					[s]
  _UpperCAmelCase         =					True

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

  return visited[t]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[-1] * (len(lowercase						))
  _UpperCAmelCase         =					0
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					[i[:] for i in graph]  # Record original cut, copy.
  while bfs(lowercase      ,lowercase      ,lowercase      ,lowercase						):
    _UpperCAmelCase         =					float("""Inf"""						)
    _UpperCAmelCase         =					sink

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

    max_flow += path_flow
    _UpperCAmelCase         =					sink

    while v != source:
      _UpperCAmelCase         =					parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      _UpperCAmelCase         =					parent[v]

  for i in range(len(lowercase						)						):
    for j in range(len(graph[0]						)						):
      if graph[i][j] == 0 and temp[i][j] > 0:
        res.append((i, j)						)

  return res


if __name__ == "__main__":
    print(mincut(test_graph, source=0, sink=5))
