



'''simple docstring'''
import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 							lowercase__     (							lowercase						, unittest.TestCase       ):
	lowercase__      =      LongformerTokenizer
	lowercase__      =      True
	lowercase__      =      LongformerTokenizerFast
	lowercase__      =      True





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							_UpperCamelCase :		Tuple			    =       [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							_UpperCamelCase :		List[Any]			    =       dict(zip(lowerCamelCase__			,range(len(lowerCamelCase__			)			)			)			)
							_UpperCamelCase :		str			    =       ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							_UpperCamelCase :		List[Any]			    =       {'unk_token': '<unk>'}

							_UpperCamelCase :		Union[str, Any]			    =       os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['vocab_file']			)
							_UpperCamelCase :		List[Any]			    =       os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['merges_file']			)
							with open(self.vocab_file			,'w'			,encoding='utf-8'			) as fp:
													fp.write(json.dumps(lowerCamelCase__			) + '\n'			)
							with open(self.merges_file			,'w'			,encoding='utf-8'			) as fp:
													fp.write('\n'.join(lowerCamelCase__			)			)





	def      UpperCamelCase_  (			self :    Optional[int]			,**lowerCamelCase__ :    Tuple			):







							'''simple docstring'''
							kwargs.update(self.special_tokens_map			)
							return self.tokenizer_class.from_pretrained(self.tmpdirname			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Dict			,**lowerCamelCase__ :    int			):







							'''simple docstring'''
							kwargs.update(self.special_tokens_map			)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Optional[Any]			,lowerCamelCase__ :    Dict			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       'lower newer'
							_UpperCamelCase :		Any			    =       'lower newer'
							return input_text, output_text





	def      UpperCamelCase_  (			self :    Dict			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       self.tokenizer_class(self.vocab_file			,self.merges_file			,**self.special_tokens_map			)
							_UpperCamelCase :		Dict			    =       'lower newer'
							_UpperCamelCase :		str			    =       ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
							_UpperCamelCase :		int			    =       tokenizer.tokenize(lowerCamelCase__			)  # , add_prefix_space=True)
							self.assertListEqual(lowerCamelCase__			,lowerCamelCase__			)

							_UpperCamelCase :		List[str]			    =       tokens + [tokenizer.unk_token]
							_UpperCamelCase :		Union[str, Any]			    =       [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__			)			,lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       self.get_tokenizer()

							self.assertListEqual(tokenizer.encode('Hello world!'			,add_special_tokens=lowerCamelCase__			)			,[0, 31414, 232, 328, 2]			)
							self.assertListEqual(
							    tokenizer.encode('Hello world! cécé herlolip 418'			,add_special_tokens=lowerCamelCase__			)			,[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]			,)





	@slow
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'			)

							_UpperCamelCase :		Dict			    =       tokenizer.encode('sequence builders'			,add_special_tokens=lowerCamelCase__			)
							_UpperCamelCase :		int			    =       tokenizer.encode('multi-sequence build'			,add_special_tokens=lowerCamelCase__			)

							_UpperCamelCase :		Dict			    =       tokenizer.encode(
							    'sequence builders'			,add_special_tokens=lowerCamelCase__			,add_prefix_space=lowerCamelCase__			)
							_UpperCamelCase :		Dict			    =       tokenizer.encode(
							    'sequence builders'			,'multi-sequence build'			,add_special_tokens=lowerCamelCase__			,add_prefix_space=lowerCamelCase__			)

							_UpperCamelCase :		int			    =       tokenizer.build_inputs_with_special_tokens(lowerCamelCase__			)
							_UpperCamelCase :		Any			    =       tokenizer.build_inputs_with_special_tokens(lowerCamelCase__			,lowerCamelCase__			)

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode





	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       self.get_tokenizer()

							_UpperCamelCase :		Optional[int]			    =       'Encode this sequence.'
							_UpperCamelCase :		Optional[int]			    =       tokenizer.byte_encoder[' '.encode('utf-8'			)[0]]

							# Testing encoder arguments
							_UpperCamelCase :		Union[str, Any]			    =       tokenizer.encode(lowerCamelCase__			,add_special_tokens=lowerCamelCase__			,add_prefix_space=lowerCamelCase__			)
							_UpperCamelCase :		str			    =       tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
							self.assertNotEqual(lowerCamelCase__			,lowerCamelCase__			)

							_UpperCamelCase :		List[Any]			    =       tokenizer.encode(lowerCamelCase__			,add_special_tokens=lowerCamelCase__			,add_prefix_space=lowerCamelCase__			)
							_UpperCamelCase :		Any			    =       tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
							self.assertEqual(lowerCamelCase__			,lowerCamelCase__			)

							tokenizer.add_special_tokens({'bos_token': '<s>'}			)
							_UpperCamelCase :		Optional[int]			    =       tokenizer.encode(lowerCamelCase__			,add_special_tokens=lowerCamelCase__			)
							_UpperCamelCase :		Optional[Any]			    =       tokenizer.convert_ids_to_tokens(encoded[1]			)[0]
							self.assertNotEqual(lowerCamelCase__			,lowerCamelCase__			)

							# Testing spaces after special tokens
							_UpperCamelCase :		Optional[int]			    =       '<mask>'
							tokenizer.add_special_tokens(
							    {'mask_token': AddedToken(lowerCamelCase__			,lstrip=lowerCamelCase__			,rstrip=lowerCamelCase__			)}			)  # mask token has a left space
							_UpperCamelCase :		Tuple			    =       tokenizer.convert_tokens_to_ids(lowerCamelCase__			)

							_UpperCamelCase :		int			    =       'Encode <mask> sequence'
							_UpperCamelCase :		str			    =       'Encode <mask>sequence'

							_UpperCamelCase :		List[Any]			    =       tokenizer.encode(lowerCamelCase__			)
							_UpperCamelCase :		Optional[Any]			    =       encoded.index(lowerCamelCase__			)
							_UpperCamelCase :		Tuple			    =       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
							self.assertEqual(lowerCamelCase__			,lowerCamelCase__			)

							_UpperCamelCase :		Optional[Any]			    =       tokenizer.encode(lowerCamelCase__			)
							_UpperCamelCase :		Dict			    =       encoded.index(lowerCamelCase__			)
							_UpperCamelCase :		Dict			    =       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
							self.assertNotEqual(lowerCamelCase__			,lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							pass





	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'			):
																			_UpperCamelCase :		Optional[Any]			    =       self.rust_tokenizer_class.from_pretrained(lowerCamelCase__			,**lowerCamelCase__			)
																			_UpperCamelCase :		Union[str, Any]			    =       self.tokenizer_class.from_pretrained(lowerCamelCase__			,**lowerCamelCase__			)
																			_UpperCamelCase :		List[str]			    =       'A, <mask> AllenNLP sentence.'
																			_UpperCamelCase :		int			    =       tokenizer_r.encode_plus(lowerCamelCase__			,add_special_tokens=lowerCamelCase__			,return_token_type_ids=lowerCamelCase__			)
																			_UpperCamelCase :		Optional[Any]			    =       tokenizer_p.encode_plus(lowerCamelCase__			,add_special_tokens=lowerCamelCase__			,return_token_type_ids=lowerCamelCase__			)

																			# token_type_ids should put 0 everywhere
																			self.assertEqual(sum(tokens_r['token_type_ids']			)			,sum(tokens_p['token_type_ids']			)			)

																			# attention_mask should put 1 everywhere, so sum over length should be 1
																			self.assertEqual(
																			    sum(tokens_r['attention_mask']			) / len(tokens_r['attention_mask']			)			,sum(tokens_p['attention_mask']			) / len(tokens_p['attention_mask']			)			,)

																			_UpperCamelCase :		Optional[Any]			    =       tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']			)
																			_UpperCamelCase :		List[str]			    =       tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']			)

																			# Rust correctly handles the space before the mask while python doesnt
																			self.assertSequenceEqual(tokens_p['input_ids']			,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]			)
																			self.assertSequenceEqual(tokens_r['input_ids']			,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]			)

																			self.assertSequenceEqual(
																			    lowerCamelCase__			,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
																			self.assertSequenceEqual(
																			    lowerCamelCase__			,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)





	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							for trim_offsets, add_prefix_space in itertools.product([True, False]			,repeat=2			):
													_UpperCamelCase :		Optional[Any]			    =       self.rust_tokenizer_class.from_pretrained(
													    self.tmpdirname			,use_fast=lowerCamelCase__			,add_prefix_space=lowerCamelCase__			,trim_offsets=lowerCamelCase__			)

													_UpperCamelCase :		Tuple			    =       json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()			)
													_UpperCamelCase :		Optional[int]			    =       json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()			)

													self.assertEqual(pre_tokenizer_state['add_prefix_space']			,lowerCamelCase__			)

													self.assertEqual(post_processor_state['add_prefix_space']			,lowerCamelCase__			)
													self.assertEqual(post_processor_state['trim_offsets']			,lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
							# `trim_offsets`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'			):
																			_UpperCamelCase :		Optional[int]			    =       'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
																			_UpperCamelCase :		List[Any]			    =       F'{text_of_1_token} {text_of_1_token}'

																			_UpperCamelCase :		List[str]			    =       self.rust_tokenizer_class.from_pretrained(
																			    lowerCamelCase__			,use_fast=lowerCamelCase__			,add_prefix_space=lowerCamelCase__			,trim_offsets=lowerCamelCase__			)
																			_UpperCamelCase :		Optional[Any]			    =       tokenizer_r(lowerCamelCase__			,return_offsets_mapping=lowerCamelCase__			,add_special_tokens=lowerCamelCase__			)
																			self.assertEqual(encoding.offset_mapping[0]			,(0, len(lowerCamelCase__			))			)
																			self.assertEqual(
																			    encoding.offset_mapping[1]			,(len(lowerCamelCase__			) + 1, len(lowerCamelCase__			) + 1 + len(lowerCamelCase__			))			,)

																			_UpperCamelCase :		Any			    =       self.rust_tokenizer_class.from_pretrained(
																			    lowerCamelCase__			,use_fast=lowerCamelCase__			,add_prefix_space=lowerCamelCase__			,trim_offsets=lowerCamelCase__			)
																			_UpperCamelCase :		Dict			    =       tokenizer_r(lowerCamelCase__			,return_offsets_mapping=lowerCamelCase__			,add_special_tokens=lowerCamelCase__			)
																			self.assertEqual(encoding.offset_mapping[0]			,(0, len(lowerCamelCase__			))			)
																			self.assertEqual(
																			    encoding.offset_mapping[1]			,(len(lowerCamelCase__			) + 1, len(lowerCamelCase__			) + 1 + len(lowerCamelCase__			))			,)

																			_UpperCamelCase :		Optional[Any]			    =       self.rust_tokenizer_class.from_pretrained(
																			    lowerCamelCase__			,use_fast=lowerCamelCase__			,add_prefix_space=lowerCamelCase__			,trim_offsets=lowerCamelCase__			)
																			_UpperCamelCase :		Optional[Any]			    =       tokenizer_r(lowerCamelCase__			,return_offsets_mapping=lowerCamelCase__			,add_special_tokens=lowerCamelCase__			)
																			self.assertEqual(encoding.offset_mapping[0]			,(0, len(lowerCamelCase__			))			)
																			self.assertEqual(
																			    encoding.offset_mapping[1]			,(len(lowerCamelCase__			), len(lowerCamelCase__			) + 1 + len(lowerCamelCase__			))			,)

																			_UpperCamelCase :		Tuple			    =       self.rust_tokenizer_class.from_pretrained(
																			    lowerCamelCase__			,use_fast=lowerCamelCase__			,add_prefix_space=lowerCamelCase__			,trim_offsets=lowerCamelCase__			)
																			_UpperCamelCase :		List[Any]			    =       tokenizer_r(lowerCamelCase__			,return_offsets_mapping=lowerCamelCase__			,add_special_tokens=lowerCamelCase__			)
																			self.assertEqual(encoding.offset_mapping[0]			,(0, len(lowerCamelCase__			))			)
																			self.assertEqual(
																			    encoding.offset_mapping[1]			,(len(lowerCamelCase__			), len(lowerCamelCase__			) + 1 + len(lowerCamelCase__			))			,)

																			_UpperCamelCase :		List[Any]			    =       F' {text}'

																			# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																			#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																			# )
																			# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																			# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																			# self.assertEqual(
																			#     encoding.offset_mapping[1],
																			#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																			# )

																			_UpperCamelCase :		Dict			    =       self.rust_tokenizer_class.from_pretrained(
																			    lowerCamelCase__			,use_fast=lowerCamelCase__			,add_prefix_space=lowerCamelCase__			,trim_offsets=lowerCamelCase__			)
																			_UpperCamelCase :		Tuple			    =       tokenizer_r(lowerCamelCase__			,return_offsets_mapping=lowerCamelCase__			,add_special_tokens=lowerCamelCase__			)
																			self.assertEqual(encoding.offset_mapping[0]			,(1, 1 + len(lowerCamelCase__			))			)
																			self.assertEqual(
																			    encoding.offset_mapping[1]			,(1 + len(lowerCamelCase__			) + 1, 1 + len(lowerCamelCase__			) + 1 + len(lowerCamelCase__			))			,)

																			_UpperCamelCase :		str			    =       self.rust_tokenizer_class.from_pretrained(
																			    lowerCamelCase__			,use_fast=lowerCamelCase__			,add_prefix_space=lowerCamelCase__			,trim_offsets=lowerCamelCase__			)
																			_UpperCamelCase :		Dict			    =       tokenizer_r(lowerCamelCase__			,return_offsets_mapping=lowerCamelCase__			,add_special_tokens=lowerCamelCase__			)
																			self.assertEqual(encoding.offset_mapping[0]			,(0, 1 + len(lowerCamelCase__			))			)
																			self.assertEqual(
																			    encoding.offset_mapping[1]			,(1 + len(lowerCamelCase__			), 1 + len(lowerCamelCase__			) + 1 + len(lowerCamelCase__			))			,)

																			_UpperCamelCase :		List[Any]			    =       self.rust_tokenizer_class.from_pretrained(
																			    lowerCamelCase__			,use_fast=lowerCamelCase__			,add_prefix_space=lowerCamelCase__			,trim_offsets=lowerCamelCase__			)
																			_UpperCamelCase :		Dict			    =       tokenizer_r(lowerCamelCase__			,return_offsets_mapping=lowerCamelCase__			,add_special_tokens=lowerCamelCase__			)
																			self.assertEqual(encoding.offset_mapping[0]			,(0, 1 + len(lowerCamelCase__			))			)
																			self.assertEqual(
																			    encoding.offset_mapping[1]			,(1 + len(lowerCamelCase__			), 1 + len(lowerCamelCase__			) + 1 + len(lowerCamelCase__			))			,)







'''simple docstring'''
class 							lowercase__     :





	def __init__(			self :    List[str]			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    Optional[int]			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       None
							_UpperCamelCase :		List[Any]			    =       None
							_UpperCamelCase :		int			    =       graph

							self._normalize_graph(lowerCamelCase__			,lowerCamelCase__			)
							_UpperCamelCase :		List[str]			    =       len(lowerCamelCase__			)
							_UpperCamelCase :		Tuple			    =       None





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							if sources is int:
													_UpperCamelCase :		Optional[int]			    =       [sources]
							if sinks is int:
													_UpperCamelCase :		Union[str, Any]			    =       [sinks]

							if len(lowerCamelCase__			) == 0 or len(lowerCamelCase__			) == 0:
													return

							_UpperCamelCase :		List[str]			    =       sources[0]
							_UpperCamelCase :		str			    =       sinks[0]

							# make fake vertex if there are more
							# than one source or sink
							if len(lowerCamelCase__			) > 1 or len(lowerCamelCase__			) > 1:
													_UpperCamelCase :		Dict			    =       0
													for i in sources:
																			max_input_flow += sum(self.graph[i]			)

													_UpperCamelCase :		Tuple			    =       len(self.graph			) + 1
													for room in self.graph:
																			room.insert(0			,0			)
													self.graph.insert(0			,[0] * size			)
													for i in sources:
																			_UpperCamelCase :		List[Any]			    =       max_input_flow
													_UpperCamelCase :		Tuple			    =       0

													_UpperCamelCase :		int			    =       len(self.graph			) + 1
													for room in self.graph:
																			room.append(0			)
													self.graph.append([0] * size			)
													for i in sinks:
																			_UpperCamelCase :		Optional[int]			    =       max_input_flow
													_UpperCamelCase :		Optional[int]			    =       size - 1





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							if self.maximum_flow_algorithm is None:
													raise Exception('You need to set maximum flow algorithm before.'			)
							if self.source_index is None or self.sink_index is None:
													return 0

							self.maximum_flow_algorithm.execute()
							return self.maximum_flow_algorithm.getMaximumFlow()





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       algorithm(self			)






class 							lowercase__     :





	def __init__(			self :    List[str]			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       flow_network
							_UpperCamelCase :		List[str]			    =       flow_network.verticesCount
							_UpperCamelCase :		List[str]			    =       flow_network.sourceIndex
							_UpperCamelCase :		Any			    =       flow_network.sinkIndex
							# it's just a reference, so you shouldn't change
							# it in your algorithms, use deep copy before doing that
							_UpperCamelCase :		List[Any]			    =       flow_network.graph
							_UpperCamelCase :		Any			    =       False





	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							if not self.executed:
													self._algorithm()
													_UpperCamelCase :		Any			    =       True





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							pass






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Union[str, Any]			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			)
							# use this to save your result
							_UpperCamelCase :		Tuple			    =       -1





	def      UpperCamelCase_  (			self :    Dict			):







							'''simple docstring'''
							if not self.executed:
													raise Exception('You should execute algorithm before using its result!'			)

							return self.maximum_flow






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Optional[Any]			,lowerCamelCase__ :    Dict			):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			)

							_UpperCamelCase :		Dict			    =       [[0] * self.verticies_count for i in range(self.verticies_count			)]

							_UpperCamelCase :		Optional[Any]			    =       [0] * self.verticies_count
							_UpperCamelCase :		List[str]			    =       [0] * self.verticies_count





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       self.verticies_count

							# push some substance to graph
							for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]			):
													self.preflow[self.source_index][nextvertex_index] += bandwidth
													self.preflow[nextvertex_index][self.source_index] -= bandwidth
													self.excesses[nextvertex_index] += bandwidth

							# Relabel-to-front selection rule
							_UpperCamelCase :		List[str]			    =       [
							    i
							    for i in range(self.verticies_count			)
							    if i != self.source_index and i != self.sink_index
							]

							# move through list
							_UpperCamelCase :		int			    =       0
							while i < len(lowerCamelCase__			):
													_UpperCamelCase :		List[Any]			    =       vertices_list[i]
													_UpperCamelCase :		str			    =       self.heights[vertex_index]
													self.process_vertex(lowerCamelCase__			)
													if self.heights[vertex_index] > previous_height:
																			# if it was relabeled, swap elements
																			# and start from 0 index
																			vertices_list.insert(0			,vertices_list.pop(lowerCamelCase__			)			)
																			_UpperCamelCase :		Dict			    =       0
													else:
																			i += 1

							_UpperCamelCase :		Optional[Any]			    =       sum(self.preflow[self.source_index]			)





	def      UpperCamelCase_  (			self :    Union[str, Any]			,lowerCamelCase__ :    Dict			):







							'''simple docstring'''
							while self.excesses[vertex_index] > 0:
													for neighbour_index in range(self.verticies_count			):
																			# if it's neighbour and current vertex is higher
																			if (
																			    self.graph[vertex_index][neighbour_index]
																			    - self.preflow[vertex_index][neighbour_index]
																			    > 0
																			    and self.heights[vertex_index] > self.heights[neighbour_index]
																			):
																									self.push(lowerCamelCase__			,lowerCamelCase__			)

													self.relabel(lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       min(
							    self.excesses[from_index]			,self.graph[from_index][to_index] - self.preflow[from_index][to_index]			,)
							self.preflow[from_index][to_index] += preflow_delta
							self.preflow[to_index][from_index] -= preflow_delta
							self.excesses[from_index] -= preflow_delta
							self.excesses[to_index] += preflow_delta





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       None
							for to_index in range(self.verticies_count			):
													if (
													    self.graph[vertex_index][to_index]
													    - self.preflow[vertex_index][to_index]
													    > 0
													) and (min_height is None or self.heights[to_index] < min_height):
																			_UpperCamelCase :		List[Any]			    =       self.heights[to_index]

							if min_height is not None:
													_UpperCamelCase :		Any			    =       min_height + 1


if __name__ == "__main__":
					snake_case_			:     List[str]  	=  [0]
					snake_case_			:     int  	=  [3]
					# graph = [
					#     [0, 0, 4, 6, 0, 0],
					#     [0, 0, 5, 2, 0, 0],
					#     [0, 0, 0, 0, 4, 4],
					#     [0, 0, 0, 0, 6, 6],
					#     [0, 0, 0, 0, 0, 0],
					#     [0, 0, 0, 0, 0, 0],
					# ]
					snake_case_			:     int  	=  [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

					# prepare our network
					snake_case_			:     List[Any]  	=  FlowNetwork(graph, entrances, exits)
					# set algorithm
					flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
					# and calculate
					snake_case_			:     Tuple  	=  flow_network.find_maximum_flow()

					print(F"""maximum flow is {maximum_flow}""")


