
"""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


_a		       =							logging.get_logger(__name__)

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


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Dict       =		"""align_text_model"""







  def __init__(		self				,   lowercase_=3_0522				,   lowercase_=768				,   lowercase_=12				,   lowercase_=12				,   lowercase_=3072				,   lowercase_="gelu"				,   lowercase_=0.1				,   lowercase_=0.1				,   lowercase_=512				,   lowercase_=2				,   lowercase_=0.02				,   lowercase_=1E-1_2				,   lowercase_=0				,   lowercase_="absolute"				,   lowercase_=True				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(**lowercase_  )

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







  @classmethod
  def        UpperCamelCase__					(		cls				,   lowercase_				,   **lowercase_  ):

     """simple docstring"""
     cls._set_token_in_kwargs(lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    cls.get_config_dict(lowercase_				,   **lowercase_  )

     # get the text config dict if we are loading from AlignConfig
     if config_dict.get("model_type"  ) == "align":
        UpperCAmelCase_					:					Union[str, 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(lowercase_				,   **lowercase_  )




class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						int       =		"""align_vision_model"""







  def __init__(		self				,   lowercase_ = 3				,   lowercase_ = 600				,   lowercase_ = 2.0				,   lowercase_ = 3.1				,   lowercase_ = 8				,   lowercase_ = [3, 3, 5, 3, 5, 5, 3]				,   lowercase_ = [32, 16, 24, 40, 80, 112, 192]				,   lowercase_ = [16, 24, 40, 80, 112, 192, 320]				,   lowercase_ = []				,   lowercase_ = [1, 2, 2, 2, 1, 2, 1]				,   lowercase_ = [1, 2, 2, 3, 3, 4, 1]				,   lowercase_ = [1, 6, 6, 6, 6, 6, 6]				,   lowercase_ = 0.25				,   lowercase_ = "swish"				,   lowercase_ = 2560				,   lowercase_ = "mean"				,   lowercase_ = 0.02				,   lowercase_ = 0.0_01				,   lowercase_ = 0.99				,   lowercase_ = 0.2				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(**lowercase_  )

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







  @classmethod
  def        UpperCamelCase__					(		cls				,   lowercase_				,   **lowercase_  ):

     """simple docstring"""
     cls._set_token_in_kwargs(lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    cls.get_config_dict(lowercase_				,   **lowercase_  )

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




class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						int       =		"""align"""
  SCREAMING_SNAKE_CASE__			:						str       =		True







  def __init__(		self				,   lowercase_=None				,   lowercase_=None				,   lowercase_=640				,   lowercase_=1.0				,   lowercase_=0.02				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(**lowercase_  )

     if text_config is None:
        UpperCAmelCase_					:					Union[str, Any]			     =    {}
        logger.info("text_config is None. Initializing the AlignTextConfig with default values."  )

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

     UpperCAmelCase_					:					Tuple			     =    AlignTextConfig(**lowercase_  )
     UpperCAmelCase_					:					str			     =    AlignVisionConfig(**lowercase_  )

     UpperCAmelCase_					:					List[Any]			     =    projection_dim
     UpperCAmelCase_					:					Union[str, Any]			     =    temperature_init_value
     UpperCAmelCase_					:					Union[str, Any]			     =    initializer_range







  @classmethod
  def        UpperCamelCase__					(		cls				,   lowercase_				,   lowercase_				,   **lowercase_  ):

     """simple docstring"""
     return cls(text_config=text_config.to_dict()				,   vision_config=vision_config.to_dict()				,   **lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    copy.deepcopy(self.__dict__  )
     UpperCAmelCase_					:					Dict			     =    self.text_config.to_dict()
     UpperCAmelCase_					:					str			     =    self.vision_config.to_dict()
     UpperCAmelCase_					:					Optional[Any]			     =    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))
