

"""simple docstring"""


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


UpperCAmelCase													=logging.get_logger(__name__)

UpperCAmelCase													={
    "tanreinama/GPTSAN-2.8B-spout_is_uniform": (
        "https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
    ),
}





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   '''gptsan-japanese'''
    _lowerCamelCase					=   [
        '''past_key_values''',
    ]
    _lowerCamelCase					=   {
        '''hidden_size''': '''d_model''',
        '''num_attention_heads''': '''num_heads''',
        '''num_hidden_layers''': '''num_layers''',
    }




    def __init__( self ,lowerCamelCase_=3_6_0_0_0 ,lowerCamelCase_=1_2_8_0 ,lowerCamelCase_=1_0_2_4 ,lowerCamelCase_=8_1_9_2 ,lowerCamelCase_=4_0_9_6 ,lowerCamelCase_=1_2_8 ,lowerCamelCase_=1_0 ,lowerCamelCase_=0 ,lowerCamelCase_=1_6 ,lowerCamelCase_=1_6 ,lowerCamelCase_=1_2_8 ,lowerCamelCase_=0.0 ,lowerCamelCase_=1E-5 ,lowerCamelCase_=False ,lowerCamelCase_=0.0 ,lowerCamelCase_="float32" ,lowerCamelCase_=False ,lowerCamelCase_=False ,lowerCamelCase_=False ,lowerCamelCase_=0.0_02 ,lowerCamelCase_=False ,lowerCamelCase_=True ,lowerCamelCase_=3_5_9_9_8 ,lowerCamelCase_=3_5_9_9_5 ,lowerCamelCase_=3_5_9_9_9 ,**lowerCamelCase_ ,)    ->      Dict:
          A				  =		vocab_size
          A				  =		max_position_embeddings
          A				  =		d_model
          A				  =		d_ff
          A				  =		d_ext
          A				  =		d_spout
          A				  =		num_switch_layers
          A				  =		num_ext_layers
          A				  =		num_switch_layers + num_ext_layers
          A				  =		num_heads
          A				  =		num_experts
          A				  =		expert_capacity
          A				  =		dropout_rate
          A				  =		layer_norm_epsilon
          A				  =		router_bias
          A				  =		router_jitter_noise
          A				  =		router_dtype
          A				  =		router_ignore_padding_tokens
          A				  =		output_hidden_states
          A				  =		output_attentions
          A				  =		initializer_factor
          A				  =		output_router_logits
          A				  =		use_cache

          super().__init__(
              separator_token_id=lowerCamelCase_ ,pad_token_id=lowerCamelCase_ ,eos_token_id=lowerCamelCase_ ,**lowerCamelCase_ ,)







"""simple docstring"""


UpperCAmelCase													=[
    [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],
]



def   _A					(	_a	:						Dict     ,   _a	:						int     ,   _a	:						Dict     ,   _a	:						List[Any]				):



      """simple docstring"""






      A				  =		[False] * len(_a				)
      A				  =		[s]
      A				  =		True

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

      return visited[t]



def   _A					(	_a	:						str     ,   _a	:						Optional[Any]     ,   _a	:						Optional[int]				):



      """simple docstring"""






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

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

            max_flow += path_flow
            A				  =		sink

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

      for i in range(len(_a				)				):
            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))




