
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     _SCREAMING_SNAKE_CASE     (      snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = BioGptTokenizer
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					lowerCamelCase_						=			[
					    "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>",
					]
					lowerCamelCase_						=			dict(zip(lowercase						,  range(len(lowercase ) ) ) )
					lowerCamelCase_						=			["l o 123", "lo w 1456", "e r</w> 1789", ""]

					lowerCamelCase_						=			os.path.join(self.tmpdirname						,  VOCAB_FILES_NAMES["vocab_file"] )
					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Any:
					lowerCamelCase_						=			"lower newer"
					lowerCamelCase_						=			"lower newer"
					return input_text, output_text



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			BioGptTokenizer(self.vocab_file						,  self.merges_file )

					lowerCamelCase_						=			"lower"
					lowerCamelCase_						=			["low", "er</w>"]
					lowerCamelCase_						=			tokenizer.tokenize(lowercase )
					self.assertListEqual(lowercase						,  lowercase )

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



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					lowerCamelCase_						=			BioGptTokenizer.from_pretrained("microsoft/biogpt" )

					lowerCamelCase_						=			tokenizer.encode("sequence builders"						,  add_special_tokens=lowercase )
					lowerCamelCase_						=			tokenizer.encode("multi-sequence build"						,  add_special_tokens=lowercase )

					lowerCamelCase_						=			tokenizer.build_inputs_with_special_tokens(lowercase )
					lowerCamelCase_						=			tokenizer.build_inputs_with_special_tokens(lowercase						,  lowercase )

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




from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def 			lowerCamelCase_ (				):
				lowerCamelCase_	,    lowerCamelCase_						=			9, 1_4  # noqa: F841
				lowerCamelCase_						=			[
				    [0, 1, 4],
				    [0, 7, 8],
				    [1, 2, 8],
				    [7, 8, 7],
				    [7, 6, 1],
				    [2, 8, 2],
				    [8, 6, 6],
				    [2, 3, 7],
				    [2, 5, 4],
				    [6, 5, 2],
				    [3, 5, 1_4],
				    [3, 4, 9],
				    [5, 4, 1_0],
				    [1, 7, 1_1],
				]

				lowerCamelCase_						=			defaultdict(lowerCamelCase__    )
				for nodea, nodea, cost in edges:
								adjancency[nodea].append([nodea, cost]    )
								adjancency[nodea].append([nodea, cost]    )

				lowerCamelCase_						=			mst(lowerCamelCase__    )

				lowerCamelCase_						=			[
				    [7, 6, 1],
				    [2, 8, 2],
				    [6, 5, 2],
				    [0, 1, 4],
				    [2, 5, 4],
				    [2, 3, 7],
				    [0, 7, 8],
				    [3, 4, 9],
				]

				for answer in expected:
								lowerCamelCase_						=			tuple(answer[:2]    )
								lowerCamelCase_						=			tuple(edge[::-1]    )
								assert edge in result or reverse in result


