




class 		__lowerCAmelCase	:
							def __init__(						self						:Any		,      __magic_name__						:str		,      __magic_name__						:Any		,      __magic_name__						:Union[str, Any]				):







								'''simple docstring'''







								a										=							None
								a										=							None
								a										=							graph

								self._normalize_graph(__magic_name__		,      __magic_name__				)
								a										=							len(__magic_name__				)
								a										=							None
							def       lowerCamelCase__							(						self						:Union[str, Any]		,      __magic_name__						:List[str]		,      __magic_name__						:List[str]				):







								'''simple docstring'''







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

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

								a										=							sources[0]
								a										=							sinks[0]

								# make fake vertex if there are more
								# than one source or sink
								if len(__magic_name__				) > 1 or len(__magic_name__				) > 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       lowerCamelCase__							(						self						:List[Any]				):







								'''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       lowerCamelCase__							(						self						:Tuple		,      __magic_name__						:Optional[Any]				):







								'''simple docstring'''







								a										=							algorithm(self				)







class 		__lowerCAmelCase	:
							def __init__(						self						:Optional[Any]		,      __magic_name__						:int				):







								'''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       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								pass







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:Any		,      __magic_name__						:Optional[int]				):







								'''simple docstring'''







								super().__init__(__magic_name__				)
								# use this to save your result
								a										=							-1





							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







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

								return self.maximum_flow







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:List[str]		,      __magic_name__						:Union[str, Any]				):







								'''simple docstring'''







								super().__init__(__magic_name__				)

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

								a										=							[0] * self.verticies_count
								a										=							[0] * self.verticies_count
							def       lowerCamelCase__							(						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(__magic_name__				):
									a										=							vertices_list[i]
									a										=							self.heights[vertex_index]
									self.process_vertex(__magic_name__				)
									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(__magic_name__				)				)
										a										=							0
									else:
										i += 1

								a										=							sum(self.preflow[self.source_index]				)
							def       lowerCamelCase__							(						self						:Dict		,      __magic_name__						:List[Any]				):







								'''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(__magic_name__		,      __magic_name__				)

									self.relabel(__magic_name__				)
							def       lowerCamelCase__							(						self						:str		,      __magic_name__						:Dict		,      __magic_name__						:List[Any]				):







								'''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       lowerCamelCase__							(						self						:Any		,      __magic_name__						: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__":
		__UpperCamelCase					:       int    					=      [0]
		__UpperCamelCase					:       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],
		# ]
		__UpperCamelCase					:       List[Any]    					=      [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

		# prepare our network
		__UpperCamelCase					:       Union[str, Any]    					=      FlowNetwork(graph, entrances, exits)
		# set algorithm
		flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
		# and calculate
		__UpperCamelCase					:       Dict    					=      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 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  CLIPTokenizer
							UpperCamelCase__					     =  CLIPTokenizerFast
							UpperCamelCase__					     =  True
							UpperCamelCase__					     =  {}
							UpperCamelCase__					     =  False
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								super().setUp()

								# fmt: off
								a										=							["""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										=							dict(zip(__magic_name__		,      range(len(__magic_name__				)				)				)				)
								a										=							["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>"""]
								a										=							{"""unk_token""": """<unk>"""}

								a										=							os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""vocab_file"""]				)
								a										=							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       lowerCamelCase__							(						self						:Optional[Any]		,      **__magic_name__						:Union[str, Any]				):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map				)
								return CLIPTokenizer.from_pretrained(self.tmpdirname		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:int		,      **__magic_name__						:Optional[Any]				):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map				)
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:int		,      __magic_name__						:List[str]				):







								'''simple docstring'''







								a										=							"""lower newer"""
								a										=							"""lower newer"""
								return input_text, output_text
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							CLIPTokenizer(self.vocab_file		,      self.merges_file		,      **self.special_tokens_map				)
								a										=							"""lower newer"""
								a										=							["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
								a										=							tokenizer.tokenize(__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								a										=							tokens + [tokenizer.unk_token]
								a										=							[10, 2, 16, 9, 3, 2, 16, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__				)		,      __magic_name__				)
							@require_ftfy
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
										a										=							self.tokenizer_class.from_pretrained(__magic_name__		,      **__magic_name__				)
										a										=							self.rust_tokenizer_class.from_pretrained(__magic_name__		,      **__magic_name__				)

										a										=							"""A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."""
										a										=							tokenizer_s.tokenize(__magic_name__				)
										a										=							tokenizer_r.tokenize(__magic_name__				)

										self.assertListEqual(__magic_name__		,      __magic_name__				)

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

										self.assertListEqual(__magic_name__		,      __magic_name__				)

										# Test that the tokenization is identical on unicode of space type
										a										=							[
										    """\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										=							tokenizer_s.tokenize(__magic_name__				)
											a										=							tokenizer_r.tokenize(__magic_name__				)

											self.assertListEqual(__magic_name__		,      __magic_name__				)

										# Test that the tokenization is identical on unicode of line break type
										a										=							[
										    """\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										=							tokenizer_s.tokenize(__magic_name__				)
											a										=							tokenizer_r.tokenize(__magic_name__				)

											self.assertListEqual(__magic_name__		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
										a										=							"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
										a										=							F'{text_of_1_token} {text_of_1_token}'

										a										=							self.rust_tokenizer_class.from_pretrained(
										    __magic_name__		,      use_fast=__magic_name__		,      )
										a										=							tokenizer_r(__magic_name__		,      return_offsets_mapping=__magic_name__		,      add_special_tokens=__magic_name__				)
										self.assertEqual(encoding.offset_mapping[0]		,      (0, len(__magic_name__				))				)
										self.assertEqual(
										    encoding.offset_mapping[1]		,      (len(__magic_name__				) + 1, len(__magic_name__				) + 1 + len(__magic_name__				))		,      )

										a										=							F' {text}'

										a										=							self.rust_tokenizer_class.from_pretrained(
										    __magic_name__		,      use_fast=__magic_name__		,      )
										a										=							tokenizer_r(__magic_name__		,      return_offsets_mapping=__magic_name__		,      add_special_tokens=__magic_name__				)
										self.assertEqual(encoding.offset_mapping[0]		,      (1, 1 + len(__magic_name__				))				)
										self.assertEqual(
										    encoding.offset_mapping[1]		,      (1 + len(__magic_name__				) + 1, 1 + len(__magic_name__				) + 1 + len(__magic_name__				))		,      )
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								with self.assertRaises(__magic_name__				) 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       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								super().test_tokenization_python_rust_equals()





							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								pass

