


"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A	:  Any            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__			,lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       int							 ="""maskformer-swin"""

							__UpperCAmelCase		:       Optional[Any]							 ={
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}

							def __init__(	self       ,  __a=2_24       ,  __a=4       ,  __a=3       ,  __a=96       ,  __a=[2, 2, 6, 2]       ,  __a=[3, 6, 12, 24]       ,  __a=7       ,  __a=4.0       ,  __a=True       ,  __a=0.0       ,  __a=0.0       ,  __a=0.1       ,  __a="gelu"       ,  __a=False       ,  __a=0.0_2       ,  __a=1e-5       ,  __a=None       ,  __a=None       ,  **__a       ,  ):
										super().__init__(**__a      )

										__lowerCAmelCase							       =     image_size
										__lowerCAmelCase							       =     patch_size
										__lowerCAmelCase							       =     num_channels
										__lowerCAmelCase							       =     embed_dim
										__lowerCAmelCase							       =     depths
										__lowerCAmelCase							       =     len(__a      )
										__lowerCAmelCase							       =     num_heads
										__lowerCAmelCase							       =     window_size
										__lowerCAmelCase							       =     mlp_ratio
										__lowerCAmelCase							       =     qkv_bias
										__lowerCAmelCase							       =     hidden_dropout_prob
										__lowerCAmelCase							       =     attention_probs_dropout_prob
										__lowerCAmelCase							       =     drop_path_rate
										__lowerCAmelCase							       =     hidden_act
										__lowerCAmelCase							       =     use_absolute_embeddings
										__lowerCAmelCase							       =     layer_norm_eps
										__lowerCAmelCase							       =     initializer_range
										# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
										# this indicates the channel dimension after the last stage of the model
										__lowerCAmelCase							       =     int(embed_dim * 2 ** (len(__a      ) - 1)      )
										__lowerCAmelCase							       =     ["stem"] + [f"stage{idx}" for idx in range(1       ,  len(__a      ) + 1      )]
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     get_aligned_output_features_output_indices(
										    out_features=__a       ,  out_indices=__a       ,  stage_names=self.stage_names      )








"""simple docstring"""


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [False] * len(_UpperCamelCase )
			__lowerCAmelCase							       =     []
			queue.append(_UpperCamelCase )
			__lowerCAmelCase							       =     True

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

			return visited[t]


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [-1] * (len(_UpperCamelCase ))
			__lowerCAmelCase							       =     0
			while bfs(_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):
						__lowerCAmelCase							       =     float("Inf" )
						__lowerCAmelCase							       =     sink

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

						max_flow += path_flow
						__lowerCAmelCase							       =     sink

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


A	:  Union[str, Any]            =     [
    [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],
]

A       ,       A	:  Any            =     0, 5


print(ford_fulkerson(graph, source, sink))




