





"""simple docstring"""


def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						# Return True if there is node that has not iterated.
						UpperCamelCase				:		List[str]				   =			[False] * len(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Union[str, Any]				   =			[]
						queue.append(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		List[Any]				   =			True

						while queue:
												UpperCamelCase				:		Tuple				   =			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     )
																								UpperCamelCase				:		List[Any]				   =			True
																								UpperCamelCase				:		Tuple				   =			u

						return visited[t]

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						# This array is filled by BFS and to store path
						UpperCamelCase				:		int				   =			[-1] * (len(SCREAMING_SNAKE_CASE     ))
						UpperCamelCase				:		Any				   =			0
						while bfs(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		str				   =			float("""Inf"""     )
												UpperCamelCase				:		str				   =			sink

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

												max_flow += path_flow
												UpperCamelCase				:		List[str]				   =			sink

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


__magic_name__       :					Dict						=			[
    [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],
]

__magic_name__						,							__magic_name__       :					Union[str, Any]						=			0, 5
print(ford_fulkerson(graph, source, sink))










"""simple docstring"""


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__magic_name__       :					Dict						=			logging.getLogger(__name__)






@dataclass
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=0.0					,     metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to SortishSamler or not."""}	)
			__lowerCAmelCase     :				bool										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """whether to use adafactor"""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Attention dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[str]										=  field(
			    default="""linear"""					,     metadata={"""help""": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()	)}'''}					,     )



