




class 		lowercase							:

     def __init__(						self	,  _a	,  _a	,  _a       )		->							Optional[int]:
           _A							:  Optional[int]    =		None
           _A							:  Any    =		None
           _A							:  List[str]    =		graph

           self._normalize_graph(_a	,  _a       )
           _A							:  Tuple    =		len(_a       )
           _A							:  Dict    =		None

     def       a__			(						self	,  _a	,  _a       )		->							int:
           if sources is int:
                 _A							:  Optional[Any]    =		[sources]
           if sinks is int:
                 _A							:  str    =		[sinks]

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

           _A							:  Any    =		sources[0]
           _A							:  Any    =		sinks[0]

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

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

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

     def       a__			(						self       )		->							int:
           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	,  _a       )		->							Optional[Any]:
           _A							:  Union[str, Any]    =		algorithm(self       )







class 		lowercase							:

     def __init__(						self	,  _a       )		->							List[str]:
           _A							:  str    =		flow_network
           _A							:  int    =		flow_network.verticesCount
           _A							:  Optional[Any]    =		flow_network.sourceIndex
           _A							:  List[str]    =		flow_network.sinkIndex
           # it's just a reference, so you shouldn't change
           # it in your algorithms, use deep copy before doing that
           _A							:  List[str]    =		flow_network.graph
           _A							:  Tuple    =		False

     def       a__			(						self       )		->							Union[str, Any]:
           if not self.executed:
                 self._algorithm()
                 _A							:  List[Any]    =		True



     def       a__			(						self       )		->							Dict:
           pass







class 		lowercase							(     UpperCamelCase__	):

     def __init__(						self	,  _a       )		->							Dict:
           super().__init__(_a       )
           # use this to save your result
           _A							:  str    =		-1



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

           return self.maximum_flow







class 		lowercase							(     UpperCamelCase__	):

     def __init__(						self	,  _a       )		->							List[Any]:
           super().__init__(_a       )

           _A							:  int    =		[[0] * self.verticies_count for i in range(self.verticies_count       )]

           _A							:  str    =		[0] * self.verticies_count
           _A							:  Any    =		[0] * self.verticies_count

     def       a__			(						self       )		->							Any:
           _A							:  str    =		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							:  Tuple    =		[
               i
               for i in range(self.verticies_count       )
               if i != self.source_index and i != self.sink_index
           ]

           # move through list
           _A							:  List[Any]    =		0
           while i < len(_a       ):
                 _A							:  List[str]    =		vertices_list[i]
                 _A							:  str    =		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       )       )
                       _A							:  int    =		0
                 else:
                       i += 1

           _A							:  List[str]    =		sum(self.preflow[self.source_index]       )

     def       a__			(						self	,  _a       )		->							Tuple:
           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	,  _a	,  _a       )		->							Tuple:
           _A							:  str    =		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	,  _a       )		->							Dict:
           _A							:  Optional[Any]    =		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							:  Optional[int]    =		self.heights[to_index]

           if min_height is not None:
                 _A							:  str    =		min_height + 1


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

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

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







import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		CLIPTokenizer
     _a											=		CLIPTokenizerFast
     _a											=		True
     _a											=		{}
     _a											=		False

     def       a__			(						self       )		->							Optional[Any]:
           super().setUp()

           # fmt: off
           _A							:  int    =		["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
           # fmt: on
           _A							:  str    =		dict(zip(_a	,  range(len(_a       )       )       )       )
           _A							:  Optional[int]    =		["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>"""]
           _A							:  str    =		{"""unk_token""": """<unk>"""}

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

     def       a__			(						self	,  **_a       )		->							List[Any]:
           kwargs.update(self.special_tokens_map       )
           return CLIPTokenizer.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  **_a       )		->							Union[str, Any]:
           kwargs.update(self.special_tokens_map       )
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  _a       )		->							str:
           _A							:  Tuple    =		"""lower newer"""
           _A							:  Optional[Any]    =		"""lower newer"""
           return input_text, output_text

     def       a__			(						self       )		->							List[Any]:
           _A							:  Optional[int]    =		CLIPTokenizer(self.vocab_file	,  self.merges_file	,  **self.special_tokens_map       )
           _A							:  str    =		"""lower newer"""
           _A							:  List[str]    =		["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
           _A							:  int    =		tokenizer.tokenize(_a       )
           self.assertListEqual(_a	,  _a       )

           _A							:  str    =		tokens + [tokenizer.unk_token]
           _A							:  Dict    =		[10, 2, 16, 9, 3, 2, 16, 20]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(_a       )	,  _a       )

     @require_ftfy
     def       a__			(						self       )		->							Tuple:
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                       _A							:  Dict    =		self.tokenizer_class.from_pretrained(_a	,  **_a       )
                       _A							:  str    =		self.rust_tokenizer_class.from_pretrained(_a	,  **_a       )

                       _A							:  List[str]    =		"""A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."""
                       _A							:  Dict    =		tokenizer_s.tokenize(_a       )
                       _A							:  Dict    =		tokenizer_r.tokenize(_a       )

                       self.assertListEqual(_a	,  _a       )

                       # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                       # with Tilde) encoded in 2 different ways
                       _A							:  Tuple    =		"""xa\u0303y""" + """ """ + """x\xe3y"""
                       _A							:  Dict    =		tokenizer_s.tokenize(_a       )
                       _A							:  Dict    =		tokenizer_r.tokenize(_a       )

                       self.assertListEqual(_a	,  _a       )

                       # Test that the tokenization is identical on unicode of space type
                       _A							:  Any    =		[
                           """\u0009""",  # (horizontal tab, '\t')
                           """\u000B""",  # (vertical tab)
                           """\u000C""",  # (form feed)
                           """\u0020""",  # (space, ' ')
                           """\u200E""",  # (left-to-right mark):w
                           """\u200F""",  # (right-to-left mark)
                       ]
                       for unicode_seq in spaces_unicodes:
                             _A							:  Optional[int]    =		tokenizer_s.tokenize(_a       )
                             _A							:  List[Any]    =		tokenizer_r.tokenize(_a       )

                             self.assertListEqual(_a	,  _a       )

                       # Test that the tokenization is identical on unicode of line break type
                       _A							:  int    =		[
                           """\u000A""",  # (line feed, '\n')
                           """\r\n""",  # (carriage return and line feed, '\r\n')
                           """\u000D""",  # (carriage return, '\r')
                           """\r""",  # (carriage return, '\r')
                           """\u000D""",  # (carriage return, '\r')
                           """\u2028""",  # (line separator)
                           """\u2029""",  # (paragraph separator)
                           # "\u0085", # (next line)
                       ]

                       # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
                       # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
                       # space (and thus into an empty list).

                       for unicode_seq in line_break_unicodes:
                             _A							:  Optional[Any]    =		tokenizer_s.tokenize(_a       )
                             _A							:  Tuple    =		tokenizer_r.tokenize(_a       )

                             self.assertListEqual(_a	,  _a       )

     def       a__			(						self       )		->							Any:
           # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                       _A							:  List[Any]    =		"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                       _A							:  str    =		F'''{text_of_1_token} {text_of_1_token}'''

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

                       _A							:  Any    =		F''' {text}'''

                       _A							:  List[Any]    =		self.rust_tokenizer_class.from_pretrained(
                           _a	,  use_fast=_a	,  )
                       _A							:  Optional[int]    =		tokenizer_r(_a	,  return_offsets_mapping=_a	,  add_special_tokens=_a       )
                       self.assertEqual(encoding.offset_mapping[0]	,  (1, 1 + len(_a       ))       )
                       self.assertEqual(
                           encoding.offset_mapping[1]	,  (1 + len(_a       ) + 1, 1 + len(_a       ) + 1 + len(_a       ))	,  )

     def       a__			(						self       )		->							List[Any]:
           # Test related to the breaking change introduced in transformers v4.17.0
           # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
           with self.assertRaises(_a       ) as context:
                 self.rust_tokenizer_class.from_pretrained("""robot-test/old-clip-tokenizer"""       )

           self.assertTrue(
               context.exception.args[0].startswith(
                   """The `backend_tokenizer` provided does not match the expected format."""       )       )

     @require_ftfy
     def       a__			(						self       )		->							str:
           super().test_tokenization_python_rust_equals()



     def       a__			(						self       )		->							Union[str, Any]:
           # CLIP always lower cases letters
           pass

