'''simple docstring'''




import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__SCREAMING_SNAKE_CASE       :     Tuple     =  get_tests_dir("""fixtures/spiece.model""")

@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Tuple						=						AlbertTokenizer
      __UpperCamelCase:  Optional[Any]						=						AlbertTokenizerFast
      __UpperCamelCase:  str						=						True
      __UpperCamelCase:  Optional[int]						=						True
      __UpperCamelCase:  Any						=						True




      def      _A	(       self						:				int       ):
             super().setUp()

             # We have a SentencePiece fixture for testing
             _UpperCAmelCase     :  Union[str, Any]							      =    AlbertTokenizer(A       )
             tokenizer.save_pretrained(self.tmpdirname       )




      def      _A	(       self						:				Dict						,					A						:				int       ):
             _UpperCAmelCase     :  Tuple							      =    "this is a test"
             _UpperCAmelCase     :  List[str]							      =    "this is a test"
             return input_text, output_text




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  int							      =    "<pad>"
             _UpperCAmelCase     :  Any							      =    0

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(A       )						,					A       )
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(A       )						,					A       )




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Dict							      =    list(self.get_tokenizer().get_vocab().keys()       )

             self.assertEqual(vocab_keys[0]						,					"<pad>"       )
             self.assertEqual(vocab_keys[1]						,					"<unk>"       )
             self.assertEqual(vocab_keys[-1]						,					"▁eloquent"       )
             self.assertEqual(len(A       )						,					30000       )




      def      _A	(       self						:				Tuple       ):
             self.assertEqual(self.get_tokenizer().vocab_size						,					30000       )




      def      _A	(       self						:				int       ):
             if not self.test_rust_tokenizer:
                    return

             _UpperCAmelCase     :  int							      =    self.get_tokenizer()
             _UpperCAmelCase     :  str							      =    self.get_rust_tokenizer()

             _UpperCAmelCase     :  Optional[Any]							      =    "I was born in 92000, and this is falsé."

             _UpperCAmelCase     :  List[str]							      =    tokenizer.tokenize(A       )
             _UpperCAmelCase     :  str							      =    rust_tokenizer.tokenize(A       )
             self.assertListEqual(A						,					A       )

             _UpperCAmelCase     :  List[str]							      =    tokenizer.encode(A						,					add_special_tokens=A       )
             _UpperCAmelCase     :  Optional[int]							      =    rust_tokenizer.encode(A						,					add_special_tokens=A       )
             self.assertListEqual(A						,					A       )

             _UpperCAmelCase     :  Optional[int]							      =    self.get_rust_tokenizer()
             _UpperCAmelCase     :  Dict							      =    tokenizer.encode(A       )
             _UpperCAmelCase     :  str							      =    rust_tokenizer.encode(A       )
             self.assertListEqual(A						,					A       )




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  int							      =    AlbertTokenizer(A						,					keep_accents=A       )

             _UpperCAmelCase     :  Any							      =    tokenizer.tokenize("This is a test"       )
             self.assertListEqual(A						,					["▁this", "▁is", "▁a", "▁test"]       )

             self.assertListEqual(tokenizer.convert_tokens_to_ids(A       )						,					[48, 25, 21, 1289]       )

             _UpperCAmelCase     :  Any							      =    tokenizer.tokenize("I was born in 92000, and this is falsé."       )
             self.assertListEqual(
                 A						,					["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]       )
             _UpperCAmelCase     :  Optional[int]							      =    tokenizer.convert_tokens_to_ids(A       )
             self.assertListEqual(A						,					[31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]       )

             _UpperCAmelCase     :  List[str]							      =    tokenizer.convert_ids_to_tokens(A       )
             self.assertListEqual(
                 A						,					["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]						,					)




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  List[str]							      =    AlbertTokenizer(A       )

             _UpperCAmelCase     :  Optional[int]							      =    tokenizer.encode("sequence builders"       )
             _UpperCAmelCase     :  Dict							      =    tokenizer.encode("multi-sequence build"       )

             _UpperCAmelCase     :  str							      =    tokenizer.build_inputs_with_special_tokens(A       )
             _UpperCAmelCase     :  Optional[int]							      =    tokenizer.build_inputs_with_special_tokens(A						,					A       )

             assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
             assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
                 tokenizer.sep_token_id
             ]





      @slow
      def      _A	(       self						:				Dict       ):
             # fmt: off
             _UpperCAmelCase     :  int							      =    {"attention_mask": [[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, 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, 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, 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], [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, 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]], "input_ids": [[2, 21970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 12051, 18, 17, 7103, 2153, 673, 8, 3515, 18684, 8, 4461, 6, 1927, 297, 8, 12060, 2607, 18, 13, 5, 4461, 15, 10538, 38, 8, 135, 15, 822, 58, 15, 993, 10363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 10641, 6, 29, 84, 2512, 2430, 782, 18684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 11712, 15, 7103, 2153, 673, 17, 24883, 9990, 9, 3], [2, 11502, 25, 1006, 20, 782, 8, 11809, 855, 1732, 19393, 18667, 37, 367, 21018, 69, 1854, 34, 11860, 19124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 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], [2, 14, 2231, 886, 2385, 17659, 84, 14, 16792, 1952, 9, 3, 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]], "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, 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, 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]]}  # noqa: E501
             # fmt: on

             self.tokenizer_integration_test_util(
                 expected_encoding=A						,					model_name="albert-base-v2"						,					revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"						,					)


'''simple docstring'''




class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				Tuple						,					A						:				Any						,					A						:				str						,					A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[int]							      =    None
             _UpperCAmelCase     :  Optional[int]							      =    None
             _UpperCAmelCase     :  Any							      =    graph

             self._normalize_graph(A						,					A       )
             _UpperCAmelCase     :  List[str]							      =    len(A       )
             _UpperCAmelCase     :  Tuple							      =    None




      def      _A	(       self						:				Any						,					A						:				List[Any]						,					A						:				str       ):
             if sources is int:
                    _UpperCAmelCase     :  List[Any]							      =    [sources]
             if sinks is int:
                    _UpperCAmelCase     :  List[Any]							      =    [sinks]

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

             _UpperCAmelCase     :  str							      =    sources[0]
             _UpperCAmelCase     :  Union[str, Any]							      =    sinks[0]

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

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

                    _UpperCAmelCase     :  str							      =    len(self.graph       ) + 1
                    for room in self.graph:
                           room.append(0       )
                    self.graph.append([0] * size       )
                    for i in sinks:
                           _UpperCAmelCase     :  Dict							      =    max_input_flow
                    _UpperCAmelCase     :  List[Any]							      =    size - 1




      def      _A	(       self						:				Union[str, Any]       ):
             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      _A	(       self						:				Tuple						,					A						:				Dict       ):
             _UpperCAmelCase     :  str							      =    algorithm(self       )

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				Any						,					A						:				str       ):
             _UpperCAmelCase     :  Optional[int]							      =    flow_network
             _UpperCAmelCase     :  Any							      =    flow_network.verticesCount
             _UpperCAmelCase     :  List[str]							      =    flow_network.sourceIndex
             _UpperCAmelCase     :  Union[str, 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     :  Any							      =    flow_network.graph
             _UpperCAmelCase     :  Union[str, Any]							      =    False




      def      _A	(       self						:				List[str]       ):
             if not self.executed:
                    self._algorithm()
                    _UpperCAmelCase     :  int							      =    True





      def      _A	(       self						:				List[Any]       ):
             pass

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[int]						,					A						:				Union[str, Any]       ):
             super().__init__(A       )
             # use this to save your result
             _UpperCAmelCase     :  Any							      =    -1





      def      _A	(       self						:				Union[str, Any]       ):
             if not self.executed:
                    raise Exception("You should execute algorithm before using its result!"       )

             return self.maximum_flow

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Tuple						,					A						:				int       ):
             super().__init__(A       )

             _UpperCAmelCase     :  List[str]							      =    [[0] * self.verticies_count for i in range(self.verticies_count       )]

             _UpperCAmelCase     :  Union[str, Any]							      =    [0] * self.verticies_count
             _UpperCAmelCase     :  int							      =    [0] * self.verticies_count




      def      _A	(       self						:				Dict       ):
             _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     :  Optional[int]							      =    [
                 i
                 for i in range(self.verticies_count       )
                 if i != self.source_index and i != self.sink_index
             ]

             # move through list
             _UpperCAmelCase     :  Any							      =    0
             while i < len(A       ):
                    _UpperCAmelCase     :  int							      =    vertices_list[i]
                    _UpperCAmelCase     :  int							      =    self.heights[vertex_index]
                    self.process_vertex(A       )
                    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(A       )       )
                           _UpperCAmelCase     :  Union[str, Any]							      =    0
                    else:
                           i += 1

             _UpperCAmelCase     :  List[Any]							      =    sum(self.preflow[self.source_index]       )




      def      _A	(       self						:				Union[str, Any]						,					A						:				str       ):
             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(A						,					A       )

                    self.relabel(A       )




      def      _A	(       self						:				int						,					A						:				Dict						,					A						:				List[str]       ):
             _UpperCAmelCase     :  int							      =    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      _A	(       self						:				Optional[int]						,					A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  str							      =    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     :  Tuple							      =    self.heights[to_index]

             if min_height is not None:
                    _UpperCAmelCase     :  Optional[Any]							      =    min_height + 1


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Optional[int]     =  [0]
  __SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  [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],
  # ]
  __SCREAMING_SNAKE_CASE       :     List[Any]     =  [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

  # prepare our network
  __SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  FlowNetwork(graph, entrances, exits)
  # set algorithm
  flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
  # and calculate
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  flow_network.find_maximum_flow()

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

