

class     a__  :
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase					,					lowercase				)     ->  str:
							'''simple docstring'''






							A__						=	None
							A__						=	None
							A__						=	graph

							self._normalize_graph(lowercase					,					lowercase				)
							A__						=	len(lowercase				)
							A__						=	None


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase				)     ->  Optional[int]:
							'''simple docstring'''






							if sources is int:
											A__						=	[sources]
							if sinks is int:
											A__						=	[sinks]

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

							A__						=	sources[0]
							A__						=	sinks[0]

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

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

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


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''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					,					lowercase				)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	algorithm(self				)

class     a__  :
			"""simple docstring"""







			def __init__(			self					,					lowercase				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	flow_network
							A__						=	flow_network.verticesCount
							A__						=	flow_network.sourceIndex
							A__						=	flow_network.sinkIndex
							# it's just a reference, so you shouldn't change
							# it in your algorithms, use deep copy before doing that
							A__						=	flow_network.graph
							A__						=	False


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							if not self.executed:
											self._algorithm()
											A__						=	True


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							pass

class     a__  (    snake_case	):
			"""simple docstring"""







			def __init__(			self					,					lowercase				)     ->  List[Any]:
							'''simple docstring'''






							super().__init__(lowercase				)
							# use this to save your result
							A__						=	-1


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






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

							return self.maximum_flow

class     a__  (    snake_case	):
			"""simple docstring"""







			def __init__(			self					,					lowercase				)     ->  Dict:
							'''simple docstring'''






							super().__init__(lowercase				)

							A__						=	[[0] * self.verticies_count for i in range(self.verticies_count				)]

							A__						=	[0] * self.verticies_count
							A__						=	[0] * self.verticies_count


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	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
							A__						=	[
							    i
							    for i in range(self.verticies_count				)
							    if i != self.source_index and i != self.sink_index
							]

							# move through list
							A__						=	0
							while i < len(lowercase				):
											A__						=	vertices_list[i]
											A__						=	self.heights[vertex_index]
											self.process_vertex(lowercase				)
											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(lowercase				)				)
															A__						=	0
											else:
															i += 1

							A__						=	sum(self.preflow[self.source_index]				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  str:
							'''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(lowercase					,					lowercase				)

											self.relabel(lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase				)     ->  Dict:
							'''simple docstring'''






							A__						=	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					,					lowercase				)     ->  Dict:
							'''simple docstring'''






							A__						=	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):
															A__						=	self.heights[to_index]

							if min_height is not None:
											A__						=	min_height + 1


if __name__ == "__main__":
	lowerCAmelCase__		      =       [0]
	lowerCAmelCase__		      =       [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],
	# ]
	lowerCAmelCase__		      =       [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

	# prepare our network
	lowerCAmelCase__		      =       FlowNetwork(graph, entrances, exits)
	# set algorithm
	flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
	# and calculate
	lowerCAmelCase__		      =       flow_network.find_maximum_flow()

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


import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  DebertaTokenizer
  lowerCamelCase__   =  True
  lowerCamelCase__   =  DebertaTokenizerFast







  def       __A   (							self       :				List[Any]      )     ->		Dict:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        SCREAMING_SNAKE_CASE_      =     [
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "[UNK]",
        ]
        SCREAMING_SNAKE_CASE_      =     dict(zip(__magic_name__     ,   range(len(__magic_name__      )      )      )      )
        SCREAMING_SNAKE_CASE_      =     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        SCREAMING_SNAKE_CASE_      =     {"unk_token": "[UNK]"}

        SCREAMING_SNAKE_CASE_      =     os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]      )
        SCREAMING_SNAKE_CASE_      =     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(__magic_name__      ) + "\n"      )
        with open(self.merges_file     ,   "w"     ,   encoding="utf-8"      ) as fp:
              fp.write("\n".join(__magic_name__      )      )







  def       __A   (							self       :				str     ,   **__magic_name__       :				int      )     ->		Union[str, Any]:
        kwargs.update(self.special_tokens_map      )
        return self.tokenizer_class.from_pretrained(self.tmpdirname     ,   **__magic_name__      )







  def       __A   (							self       :				str     ,   __magic_name__       :				List[Any]      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     "lower newer"
        SCREAMING_SNAKE_CASE_      =     "lower newer"
        return input_text, output_text







  def       __A   (							self       :				Union[str, Any]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()
        SCREAMING_SNAKE_CASE_      =     "lower newer"
        SCREAMING_SNAKE_CASE_      =     ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
        SCREAMING_SNAKE_CASE_      =     tokenizer.tokenize(__magic_name__      )
        self.assertListEqual(__magic_name__     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokens + [tokenizer.unk_token]
        SCREAMING_SNAKE_CASE_      =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__      )     ,   __magic_name__      )







  def       __A   (							self       :				Optional[int]      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()
        SCREAMING_SNAKE_CASE_      =     tokenizer("Hello"     ,   "World"      )
        SCREAMING_SNAKE_CASE_      =     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd["token_type_ids"]     ,   __magic_name__      )







  @slow
  def       __A   (							self       :				Any      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     self.tokenizer_class.from_pretrained("microsoft/deberta-base"      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.encode("sequence builders"     ,   add_special_tokens=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     tokenizer.encode("multi-sequence build"     ,   add_special_tokens=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.encode(
            "sequence builders"     ,   add_special_tokens=__magic_name__     ,   add_prefix_space=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     tokenizer.encode(
            "sequence builders"     ,   "multi-sequence build"     ,   add_special_tokens=__magic_name__     ,   add_prefix_space=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.build_inputs_with_special_tokens(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     tokenizer.build_inputs_with_special_tokens(__magic_name__     ,   __magic_name__      )

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode







  @slow
  def       __A   (							self       :				Tuple      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     [self.tokenizer_class]
        if self.test_rust_tokenizer:
              tokenizer_classes.append(self.rust_tokenizer_class      )

        for tokenizer_class in tokenizer_classes:
              SCREAMING_SNAKE_CASE_      =     tokenizer_class.from_pretrained("microsoft/deberta-base"      )

              SCREAMING_SNAKE_CASE_      =     [
                  "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                  "ALBERT incorporates two parameter reduction techniques",
                  "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                  " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                  " vocabulary embedding.",
              ]

              SCREAMING_SNAKE_CASE_      =     tokenizer(__magic_name__     ,   padding=__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     [tokenizer.decode(__magic_name__     ,   skip_special_tokens=__magic_name__      ) for seq in encoding["input_ids"]]

              # fmt: off
              SCREAMING_SNAKE_CASE_      =     {
                  "input_ids": [
                      [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
                  ],
                  "token_type_ids": [
                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                  ],
                  "attention_mask": [
                      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                  ]
              }
              # fmt: on

              SCREAMING_SNAKE_CASE_      =     [
                  "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                  "ALBERT incorporates two parameter reduction techniques",
                  "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                  " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                  " vocabulary embedding.",
              ]

              self.assertDictEqual(encoding.data     ,   __magic_name__      )

              for expected, decoded in zip(__magic_name__     ,   __magic_name__      ):
                    self.assertEqual(__magic_name__     ,   __magic_name__      )


