

"""simple docstring"""






import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class       snake_case__  ( lowerCAmelCase_					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						BioGptTokenizer
			SCREAMING_SNAKE_CASE__     				=						False







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





								'''simple docstring'''



								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								UpperCAmelCase			:   Dict      =				[
								    "l",
								    "o",
								    "w",
								    "e",
								    "r",
								    "s",
								    "t",
								    "i",
								    "d",
								    "n",
								    "w</w>",
								    "r</w>",
								    "t</w>",
								    "lo",
								    "low",
								    "er</w>",
								    "low</w>",
								    "lowest</w>",
								    "newer</w>",
								    "wider</w>",
								    "<unk>",
								]
								UpperCAmelCase			:   Optional[Any]      =				dict(zip(lowercase			,  range(len(lowercase  )  )  )  )
								UpperCAmelCase			:   Optional[Any]      =				["l o 123", "lo w 1456", "e r</w> 1789", ""]

								UpperCAmelCase			:   Optional[int]      =				os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]  )
								UpperCAmelCase			:   Optional[int]      =				os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["merges_file"]  )
								with open(self.vocab_file			,  "w"  ) as fp:
													fp.write(json.dumps(lowercase  )  )
								with open(self.merges_file			,  "w"  ) as fp:
													fp.write("\n".join(lowercase  )  )







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				"lower newer"
								UpperCAmelCase			:   Tuple      =				"lower newer"
								return input_text, output_text







			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				BioGptTokenizer(self.vocab_file			,  self.merges_file  )

								UpperCAmelCase			:   List[str]      =				"lower"
								UpperCAmelCase			:   int      =				["low", "er</w>"]
								UpperCAmelCase			:   str      =				tokenizer.tokenize(lowercase  )
								self.assertListEqual(lowercase			,  lowercase  )

								UpperCAmelCase			:   str      =				tokens + ["<unk>"]
								UpperCAmelCase			:   Any      =				[14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase  )			,  lowercase  )







			@slow
			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				BioGptTokenizer.from_pretrained("microsoft/biogpt"  )

								UpperCAmelCase			:   Optional[Any]      =				tokenizer.encode("sequence builders"			,  add_special_tokens=lowercase  )
								UpperCAmelCase			:   Any      =				tokenizer.encode("multi-sequence build"			,  add_special_tokens=lowercase  )

								UpperCAmelCase			:   Union[str, Any]      =				tokenizer.build_inputs_with_special_tokens(lowercase  )
								UpperCAmelCase			:   Any      =				tokenizer.build_inputs_with_special_tokens(lowercase			,  lowercase  )

								self.assertTrue(encoded_sentence == [2] + text  )
								self.assertTrue(encoded_pair == [2] + text + [2] + text_a  )





"""simple docstring"""






def   lowercase_	(    _lowercase       :		List[str]							,      _lowercase       :		Tuple							,      _lowercase       :		int							,      _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				[False] * len(_lowercase	)
					UpperCAmelCase			:   List[str]      =				[]
					queue.append(_lowercase	)
					UpperCAmelCase			:   List[Any]      =				True

					while queue:
										UpperCAmelCase			:   Any      =				queue.pop(0	)
										for ind in range(len(graph[u]	)	):
															if visited[ind] is False and graph[u][ind] > 0:
																				queue.append(_lowercase	)
																				UpperCAmelCase			:   Optional[int]      =				True
																				UpperCAmelCase			:   int      =				u

					return visited[t]



def   lowercase_	(    _lowercase       :		Tuple							,      _lowercase       :		Dict							,      _lowercase       :		Optional[int]	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				[-1] * (len(_lowercase	))
					UpperCAmelCase			:   List[Any]      =				0
					while bfs(_lowercase							,      _lowercase							,      _lowercase							,      _lowercase	):
										UpperCAmelCase			:   List[str]      =				float("Inf"	)
										UpperCAmelCase			:   str      =				sink

										while s != source:
															# Find the minimum value in select path
															UpperCAmelCase			:   Tuple      =				min(_lowercase							,      graph[parent[s]][s]	)
															UpperCAmelCase			:   Optional[Any]      =				parent[s]

										max_flow += path_flow
										UpperCAmelCase			:   List[str]      =				sink

										while v != source:
															UpperCAmelCase			:   Dict      =				parent[v]
															graph[u][v] -= path_flow
															graph[v][u] += path_flow
															UpperCAmelCase			:   Dict      =				parent[v]
					return max_flow


snake_case_   :   Optional[Any]        =       [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

snake_case_     ,      snake_case_   :   str        =       0, 5




print(ford_fulkerson(graph, source, sink))


