



"""simple docstring"""






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   List[str]     ) ->    str:
			"""simple docstring"""
			lowerCamelCase__		:		Dict		     =[False] * len(__UpperCAmelCase     )
			lowerCamelCase__		:		str		     =[]
			queue.append(__UpperCAmelCase     )
			lowerCamelCase__		:		Dict		     =True

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

			return visited[t]






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   str     ) ->    Tuple:
			"""simple docstring"""
			lowerCamelCase__		:		Optional[int]		     =[-1] * (len(__UpperCAmelCase     ))
			lowerCamelCase__		:		Optional[Any]		     =0
			while bfs(__UpperCAmelCase							,		__UpperCAmelCase							,		__UpperCAmelCase							,		__UpperCAmelCase     ):
						lowerCamelCase__		:		int		     =float('''Inf'''     )
						lowerCamelCase__		:		str		     =sink

						while s != source:
									# Find the minimum value in select path
									lowerCamelCase__		:		Optional[Any]		     =min(__UpperCAmelCase							,		graph[parent[s]][s]     )
									lowerCamelCase__		:		Optional[int]		     =parent[s]

						max_flow += path_flow
						lowerCamelCase__		:		Dict		     =sink

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


_lowercase			:			List[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],
]

_lowercase			:			Tuple										=						0, 5

print(ford_fulkerson(graph, source, sink))









"""simple docstring"""






import os
from collections import deque

import torch
from torch.utils.data import Dataset

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					def __init__(	self							:  List[Any],  lowerCamelCase							:  Dict="",  lowerCamelCase							:  Tuple="train"					)-> Dict:
								assert os.path.isdir(lowerCamelCase					)

								lowerCamelCase__		:		Optional[Any]		     =[]
								lowerCamelCase__		:		Dict		     =os.listdir(lowerCamelCase					)
								for story_filename in story_filenames_list:
											if "summary" in story_filename:
														continue
											lowerCamelCase__		:		Optional[int]		     =os.path.join(lowerCamelCase,  lowerCamelCase					)
											if not os.path.isfile(lowerCamelCase					):
														continue
											self.documents.append(lowerCamelCase					)



					def __len__(	self							:  Optional[Any]					)-> List[str]:
								return len(self.documents					)




					def __getitem__(	self							:  List[str],  lowerCamelCase							:  Dict					)-> str:
								lowerCamelCase__		:		int		     =self.documents[idx]
								lowerCamelCase__		:		List[Any]		     =document_path.split('''/'''					)[-1]
								with open(lowerCamelCase,  encoding='''utf-8'''					) as source:
											lowerCamelCase__		:		Optional[int]		     =source.read()
											lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =process_story(lowerCamelCase					)
								return document_name, story_lines, summary_lines






def 					snake_case__       (		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]		     =list(filter(lambda __lowerCamelCase     : len(__lowerCamelCase     ) != 0							,		[line.strip() for line in raw_story.split('''\n'''     )]     )     )

			# for some unknown reason some lines miss a period, add it
			lowerCamelCase__		:		Dict		     =[_add_missing_period(__lowerCamelCase     ) for line in nonempty_lines]

			# gather article lines
			lowerCamelCase__		:		Union[str, Any]		     =[]
			lowerCamelCase__		:		Optional[Any]		     =deque(__lowerCamelCase     )
			while True:
						try:
									lowerCamelCase__		:		Tuple		     =lines.popleft()
									if element.startswith('''@highlight'''     ):
												break
									story_lines.append(__lowerCamelCase     )
						except IndexError:
									# if "@highlight" is absent from the file we pop
									# all elements until there is None, raising an exception.
									return story_lines, []

    # gather summary lines
			lowerCamelCase__		:		Dict		     =list(filter(lambda __lowerCamelCase     : not t.startswith('''@highlight'''     )							,		__lowerCamelCase     )     )

			return story_lines, summary_lines






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
			if line.startswith('''@highlight'''     ):
						return line
			if line[-1] in END_TOKENS:
						return line
			return line + "."






def 					snake_case__       (		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   List[Any]     ):
			"""simple docstring"""
			if len(__lowerCamelCase     ) > block_size:
						return sequence[:block_size]
			else:
						sequence.extend([pad_token_id] * (block_size - len(__lowerCamelCase     ))     )
						return sequence






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			lowerCamelCase__		:		int		     =torch.ones_like(__lowerCamelCase     )
			lowerCamelCase__		:		Any		     =sequence == pad_token_id
			lowerCamelCase__		:		List[str]		     =0
			return mask






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Optional[Any]							,		__lowerCamelCase    :   Optional[Any]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Dict		     =[tokenizer.encode(__lowerCamelCase     ) for line in story_lines]
			lowerCamelCase__		:		List[Any]		     =[token for sentence in story_lines_token_ids for token in sentence]
			lowerCamelCase__		:		List[Any]		     =[tokenizer.encode(__lowerCamelCase     ) for line in summary_lines]
			lowerCamelCase__		:		Optional[int]		     =[token for sentence in summary_lines_token_ids for token in sentence]

			return story_token_ids, summary_token_ids






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Optional[int]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =[]
			for sequence in batch:
						lowerCamelCase__		:		Optional[int]		     =-1
						lowerCamelCase__		:		List[str]		     =[]
						for s in sequence:
									if s == separator_token_id:
												sentence_num += 1
									embeddings.append(sentence_num % 2     )
						batch_embeddings.append(__lowerCamelCase     )
			return torch.tensor(__lowerCamelCase     )




