



'''simple docstring'''

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],
]






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Dict       ):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [False] * len(__SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Tuple      					= [s]
				SCREAMING_SNAKE_CASE  :						Tuple      					= True

				while queue:
								SCREAMING_SNAKE_CASE  :						int      					= queue.pop(0       )
								for ind in range(len(graph[u]       )       ):
												if visited[ind] is False and graph[u][ind] > 0:
																queue.append(__SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						str      					= True
																SCREAMING_SNAKE_CASE  :						Any      					= u

				return visited[t]






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Tuple       ):
				SCREAMING_SNAKE_CASE  :						List[Any]      					= [-1] * (len(__SCREAMING_SNAKE_CASE       ))
				SCREAMING_SNAKE_CASE  :						Any      					= 0
				SCREAMING_SNAKE_CASE  :						Any      					= []
				SCREAMING_SNAKE_CASE  :						Tuple      					= [i[:] for i in graph]  # Record original cut, copy.
				while bfs(__SCREAMING_SNAKE_CASE    ,	__SCREAMING_SNAKE_CASE    ,	__SCREAMING_SNAKE_CASE    ,	__SCREAMING_SNAKE_CASE       ):
								SCREAMING_SNAKE_CASE  :						Dict      					= float('''Inf'''       )
								SCREAMING_SNAKE_CASE  :						str      					= sink

								while s != source:
												# Find the minimum value in select path
												SCREAMING_SNAKE_CASE  :						str      					= min(__SCREAMING_SNAKE_CASE    ,	graph[parent[s]][s]       )
												SCREAMING_SNAKE_CASE  :						Any      					= parent[s]

								max_flow += path_flow
								SCREAMING_SNAKE_CASE  :						int      					= sink

								while v != source:
												SCREAMING_SNAKE_CASE  :						List[str]      					= parent[v]
												graph[u][v] -= path_flow
												graph[v][u] += path_flow
												SCREAMING_SNAKE_CASE  :						List[Any]      					= parent[v]

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









'''simple docstring'''

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


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}


class 	a__     (				_lowercase	):
	__magic_name__		:    Optional[Any]	    =     "nllb-moe"
	__magic_name__		:    Optional[Any]	    =     ["past_key_values"]
	__magic_name__		:    Optional[int]	    =     {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


	def __init__(self : int,					__UpperCAmelCase : Tuple=128112,					__UpperCAmelCase : Any=1024,					__UpperCAmelCase : Optional[Any]=12,					__UpperCAmelCase : Optional[int]=4096,					__UpperCAmelCase : Any=16,					__UpperCAmelCase : Any=12,					__UpperCAmelCase : Optional[Any]=4096,					__UpperCAmelCase : Optional[int]=16,					__UpperCAmelCase : List[Any]=0.05,					__UpperCAmelCase : Dict=0.05,					__UpperCAmelCase : Dict=True,					__UpperCAmelCase : List[Any]=True,					__UpperCAmelCase : Any="relu",					__UpperCAmelCase : Union[str, Any]=1024,					__UpperCAmelCase : Optional[int]=0.1,					__UpperCAmelCase : Tuple=0.1,					__UpperCAmelCase : List[Any]=0.0,					__UpperCAmelCase : Optional[int]=0.02,					__UpperCAmelCase : Tuple=2,					__UpperCAmelCase : int=True,					__UpperCAmelCase : int=False,					__UpperCAmelCase : int="float32",					__UpperCAmelCase : Optional[Any]=False,					__UpperCAmelCase : List[str]=128,					__UpperCAmelCase : Dict=64,					__UpperCAmelCase : Dict=4,					__UpperCAmelCase : Optional[Any]=4,					__UpperCAmelCase : Optional[Any]=0.001,					__UpperCAmelCase : Optional[Any]=0.001,					__UpperCAmelCase : Optional[Any]="all",					__UpperCAmelCase : List[str]=False,					__UpperCAmelCase : Dict=False,					__UpperCAmelCase : Any=1.0,					__UpperCAmelCase : Dict=0.2,					__UpperCAmelCase : int=1,					__UpperCAmelCase : Union[str, Any]=0,					__UpperCAmelCase : Any=2,					__UpperCAmelCase : Union[str, Any]=False,					**__UpperCAmelCase : Dict,					)     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= vocab_size
					SCREAMING_SNAKE_CASE  :						int      					= max_position_embeddings
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= d_model
					SCREAMING_SNAKE_CASE  :						Dict      					= encoder_ffn_dim
					SCREAMING_SNAKE_CASE  :						int      					= encoder_layers
					SCREAMING_SNAKE_CASE  :						str      					= encoder_attention_heads
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= decoder_ffn_dim
					SCREAMING_SNAKE_CASE  :						str      					= decoder_layers
					SCREAMING_SNAKE_CASE  :						Dict      					= decoder_attention_heads
					SCREAMING_SNAKE_CASE  :						Dict      					= dropout
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= attention_dropout
					SCREAMING_SNAKE_CASE  :						Dict      					= activation_dropout
					SCREAMING_SNAKE_CASE  :						List[str]      					= activation_function
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= init_std
					SCREAMING_SNAKE_CASE  :						Any      					= encoder_layerdrop
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= decoder_layerdrop
					SCREAMING_SNAKE_CASE  :						Dict      					= use_cache
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= encoder_layers
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= scale_embedding  # scale factor will be sqrt(d_model) if True
					SCREAMING_SNAKE_CASE  :						List[str]      					= router_z_loss_coef
					SCREAMING_SNAKE_CASE  :						List[str]      					= router_aux_loss_coef
					SCREAMING_SNAKE_CASE  :						int      					= decoder_sparse_step
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= encoder_sparse_step
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= num_experts
					SCREAMING_SNAKE_CASE  :						List[Any]      					= expert_capacity
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= router_bias
					if router_dtype not in ["float32", "float16", "bfloat16"]:
									raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= router_dtype

					SCREAMING_SNAKE_CASE  :						int      					= router_ignore_padding_tokens
					SCREAMING_SNAKE_CASE  :						Dict      					= batch_prioritized_routing
					SCREAMING_SNAKE_CASE  :						Dict      					= second_expert_policy
					SCREAMING_SNAKE_CASE  :						Tuple      					= normalize_router_prob_before_dropping
					SCREAMING_SNAKE_CASE  :						List[str]      					= moe_eval_capacity_token_fraction
					SCREAMING_SNAKE_CASE  :						List[Any]      					= moe_token_dropout
					SCREAMING_SNAKE_CASE  :						List[Any]      					= output_router_logits
					super().__init__(
					    pad_token_id=__UpperCAmelCase,					bos_token_id=__UpperCAmelCase,					eos_token_id=__UpperCAmelCase,					is_encoder_decoder=__UpperCAmelCase,					decoder_start_token_id=__UpperCAmelCase,					**__UpperCAmelCase,					)




