


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


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}



class   _a      (      _UpperCamelCase		,		_UpperCamelCase ):
    a_    :				Tuple             =    "nat"

    a_    :				Tuple             =    {
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }
    def __init__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					str=4							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=3							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=64							,					SCREAMING_SNAKE_CASE__							:					Any=[3, 4, 6, 5]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=[2, 4, 8, 16]							,					SCREAMING_SNAKE_CASE__							:					List[Any]=7							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=3.0							,					SCREAMING_SNAKE_CASE__							:					Any=True							,					SCREAMING_SNAKE_CASE__							:					int=0.0							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=0.0							,					SCREAMING_SNAKE_CASE__							:					Any=0.1							,					SCREAMING_SNAKE_CASE__							:					Dict="gelu"							,					SCREAMING_SNAKE_CASE__							:					List[str]=0.02							,					SCREAMING_SNAKE_CASE__							:					int=1e-5							,					SCREAMING_SNAKE_CASE__							:					int=0.0							,					SCREAMING_SNAKE_CASE__							:					str=None							,					SCREAMING_SNAKE_CASE__							:					List[Any]=None							,					**SCREAMING_SNAKE_CASE__							:					Dict							,					):
          super().__init__(**__a				)

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







"""simple docstring"""


def   snake_case						(				_a:     List[Any]		,   _a:     Any		,   _a:     str		,   _a:     List[Any]							)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[False] * len(_a							)
      lowerCamelCase__           =		[]
      queue.append(_a							)
      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(_a							)
                        lowerCamelCase__           =		True
                        lowerCamelCase__           =		u

      return visited[t]



def   snake_case						(				_a:     List[Any]		,   _a:     str		,   _a:     List[str]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		[-1] * (len(_a							))
      lowerCamelCase__           =		0
      while bfs(_a		,   _a		,   _a		,   _a							):
            lowerCamelCase__           =		float('Inf'							)
            lowerCamelCase__           =		sink

            while s != source:
                  # Find the minimum value in select path
                  lowerCamelCase__           =		min(_a		,   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


_snake_case          =							[
    [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],
]

_snake_case      ,					_snake_case          =							0, 5






print(ford_fulkerson(graph, source, sink))



