





"""simple docstring"""






import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A 			=  get_tests_dir('''fixtures/test_sentencepiece.model''')




@require_sentencepiece
@require_tokenizers
class 				__lowercase			(			_UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  XLMRobertaTokenizer
					__lowerCAmelCase  =  XLMRobertaTokenizerFast
					__lowerCAmelCase  =  True
					__lowerCAmelCase  =  True







					def  _lowerCamelCase		(						self     ):
									super().setUp()

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







					def  _lowerCamelCase		(						self     ):
									__a :						Any			       =  '''<pad>'''
									__a :						Optional[int]			       =  1

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







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  list(self.get_tokenizer().get_vocab().keys()     )

									self.assertEqual(vocab_keys[0]	,			'''<s>'''     )
									self.assertEqual(vocab_keys[1]	,			'''<pad>'''     )
									self.assertEqual(vocab_keys[-1]	,			'''<mask>'''     )
									self.assertEqual(len(_UpperCAmelCase     )	,			1002     )







					def  _lowerCamelCase		(						self     ):
									self.assertEqual(self.get_tokenizer().vocab_size	,			1002     )







					def  _lowerCamelCase		(						self     ):
									__a :						Any			       =  XLMRobertaTokenizer(_UpperCAmelCase	,			keep_accents=_UpperCAmelCase     )

									__a :						Any			       =  tokenizer.tokenize('''This is a test'''     )
									self.assertListEqual(_UpperCAmelCase	,			['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']     )

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )	,			[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]	,			)

									__a :						Dict			       =  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''     )
									self.assertListEqual(
									    _UpperCAmelCase	,			[
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''9''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''é''',
									        '''.''',
									    ]	,			)
									__a :						Dict			       =  tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )
									self.assertListEqual(
									    _UpperCAmelCase	,			[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
									    ]	,			)

									__a :						Dict			       =  tokenizer.convert_ids_to_tokens(_UpperCAmelCase     )
									self.assertListEqual(
									    _UpperCAmelCase	,			[
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''<unk>''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''<unk>''',
									        '''.''',
									    ]	,			)







					def  _lowerCamelCase		(						self     ):
									if not self.test_slow_tokenizer:
													# as we don't have a slow version, we can't compare the outputs between slow and fast versions
													return

									__a :						Tuple			       =  (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
																	__a :						Any			       =  self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,			**_UpperCAmelCase     )
																	__a :						Optional[int]			       =  self.tokenizer_class.from_pretrained(_UpperCAmelCase	,			**_UpperCAmelCase     )

																	__a :						Union[str, Any]			       =  tempfile.mkdtemp()

																	__a :						Optional[int]			       =  tokenizer_r.save_pretrained(_UpperCAmelCase     )
																	__a :						Tuple			       =  tokenizer_p.save_pretrained(_UpperCAmelCase     )

																	# Checks it save with the same files + the tokenizer.json file for the fast one
																	self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files     )     )
																	__a :						Tuple			       =  tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f     )
																	self.assertSequenceEqual(_UpperCAmelCase	,			_UpperCAmelCase     )

																	# Checks everything loads correctly in the same way
																	__a :						Tuple			       =  tokenizer_r.from_pretrained(_UpperCAmelCase     )
																	__a :						Union[str, Any]			       =  tokenizer_p.from_pretrained(_UpperCAmelCase     )

																	# Check special tokens are set accordingly on Rust and Python
																	for key in tokenizer_pp.special_tokens_map:
																					self.assertTrue(hasattr(_UpperCAmelCase	,			_UpperCAmelCase     )     )
																					# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																					# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																	shutil.rmtree(_UpperCAmelCase     )

																	# Save tokenizer rust, legacy_format=True
																	__a :						Tuple			       =  tempfile.mkdtemp()

																	__a :						Dict			       =  tokenizer_r.save_pretrained(_UpperCAmelCase	,			legacy_format=_UpperCAmelCase     )
																	__a :						Any			       =  tokenizer_p.save_pretrained(_UpperCAmelCase     )

																	# Checks it save with the same files
																	self.assertSequenceEqual(_UpperCAmelCase	,			_UpperCAmelCase     )

																	# Checks everything loads correctly in the same way
																	__a :						int			       =  tokenizer_r.from_pretrained(_UpperCAmelCase     )
																	__a :						int			       =  tokenizer_p.from_pretrained(_UpperCAmelCase     )

																	# Check special tokens are set accordingly on Rust and Python
																	for key in tokenizer_pp.special_tokens_map:
																					self.assertTrue(hasattr(_UpperCAmelCase	,			_UpperCAmelCase     )     )

																	shutil.rmtree(_UpperCAmelCase     )

																	# Save tokenizer rust, legacy_format=False
																	__a :						List[Any]			       =  tempfile.mkdtemp()

																	__a :						Union[str, Any]			       =  tokenizer_r.save_pretrained(_UpperCAmelCase	,			legacy_format=_UpperCAmelCase     )
																	__a :						int			       =  tokenizer_p.save_pretrained(_UpperCAmelCase     )

																	# Checks it saved the tokenizer.json file
																	self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files     )     )

																	# Checks everything loads correctly in the same way
																	__a :						List[Any]			       =  tokenizer_r.from_pretrained(_UpperCAmelCase     )
																	__a :						List[str]			       =  tokenizer_p.from_pretrained(_UpperCAmelCase     )

																	# Check special tokens are set accordingly on Rust and Python
																	for key in tokenizer_pp.special_tokens_map:
																					self.assertTrue(hasattr(_UpperCAmelCase	,			_UpperCAmelCase     )     )

																	shutil.rmtree(_UpperCAmelCase     )







					@cached_property
					def  _lowerCamelCase		(						self     ):
									return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''     )







					def  _lowerCamelCase		(						self     ):
									with tempfile.NamedTemporaryFile() as f:
													shutil.copyfile(_UpperCAmelCase	,			f.name     )
													__a :						int			       =  XLMRobertaTokenizer(f.name	,			keep_accents=_UpperCAmelCase     )
													__a :						List[Any]			       =  pickle.dumps(_UpperCAmelCase     )
									pickle.loads(_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									if not self.test_rust_tokenizer:
													return

									__a :						str			       =  self.get_tokenizer()
									__a :						Union[str, Any]			       =  self.get_rust_tokenizer()

									__a :						int			       =  '''I was born in 92000, and this is falsé.'''

									__a :						Optional[int]			       =  tokenizer.tokenize(_UpperCAmelCase     )
									__a :						str			       =  rust_tokenizer.tokenize(_UpperCAmelCase     )
									self.assertListEqual(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						str			       =  tokenizer.encode(_UpperCAmelCase	,			add_special_tokens=_UpperCAmelCase     )
									__a :						Tuple			       =  rust_tokenizer.encode(_UpperCAmelCase	,			add_special_tokens=_UpperCAmelCase     )
									self.assertListEqual(_UpperCAmelCase	,			_UpperCAmelCase     )

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







					@slow
					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  '''Hello World!'''
									__a :						str			       =  [0, 35378, 6661, 38, 2]
									# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
									# xlmr.eval()
									# xlmr.encode(symbols)

									self.assertListEqual(_UpperCAmelCase	,			self.big_tokenizer.encode(_UpperCAmelCase     )     )







					@slow
					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  (
									    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
									    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
									)
									__a :						Optional[Any]			       =  [
									    0,
									    3293,
									    83,
									    10,
									    4552,
									    4989,
									    7986,
									    678,
									    10,
									    5915,
									    111,
									    179459,
									    124850,
									    4,
									    6044,
									    237,
									    12,
									    6,
									    5,
									    6,
									    4,
									    6780,
									    705,
									    15,
									    1388,
									    44,
									    378,
									    10114,
									    711,
									    152,
									    20,
									    6,
									    5,
									    22376,
									    642,
									    1221,
									    15190,
									    34153,
									    450,
									    5608,
									    959,
									    1119,
									    57702,
									    136,
									    186,
									    47,
									    1098,
									    29367,
									    47,
									    # 4426, # What fairseq tokenizes from "<unk>": "_<"
									    # 3678, # What fairseq tokenizes from "<unk>": "unk"
									    # 2740, # What fairseq tokenizes from "<unk>": ">"
									    3,  # What we tokenize from "<unk>": "<unk>"
									    6,  # Residue from the tokenization: an extra sentencepiece underline
									    4,
									    6044,
									    237,
									    6284,
									    50901,
									    528,
									    31,
									    90,
									    34,
									    927,
									    2,
									]
									# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
									# xlmr.eval()
									# xlmr.encode(symbols)

									self.assertListEqual(_UpperCAmelCase	,			self.big_tokenizer.encode(_UpperCAmelCase     )     )







					@slow
					def  _lowerCamelCase		(						self     ):
									# fmt: off
									__a :						List[str]			       =  {'''input_ids''': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 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, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 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]], '''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, 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], [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, 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=_UpperCAmelCase	,			model_name='''xlm-roberta-base'''	,			revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''	,			)






"""simple docstring"""






from __future__ import annotations

import os
from collections.abc import Mapping

A 			=  tuple[int, int]




class 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						set[int]			       =  vertices
									__a :						dict[EdgeT, int]			       =  {
									    (min(_UpperCAmelCase     ), max(_UpperCAmelCase     )): weight for edge, weight in edges.items()
									}







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									self.vertices.add(edge[0]     )
									self.vertices.add(edge[1]     )
									__a :						Any			       =  weight







					def  _lowerCamelCase		(						self     ):
									__a :						Graph			       =  Graph({min(self.vertices     )}	,			{}     )
									__a :						EdgeT
									__a :						int
									__a :						EdgeT
									__a :						int

									while len(subgraph.vertices     ) < len(self.vertices     ):
													__a :						List[Any]			       =  max(self.edges.values()     ) + 1
													for edge, weight in self.edges.items():
																	if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																					if weight < min_weight:
																									__a :						str			       =  edge
																									__a :						int			       =  weight

													subgraph.add_edge(_UpperCAmelCase	,			_UpperCAmelCase     )

									return subgraph






def     __A      (			a_						:str = "p107_network.txt")		->		int:
				__a :						str			       =  os.path.abspath(os.path.dirname(a_))
				__a :						str			       =  os.path.join(a_   ,       a_)
				__a :						dict[EdgeT, int]			       =  {}
				__a :						list[str]
				__a :						int
				__a :						int

				with open(a_) as f:
								__a :						Any			       =  f.read().strip().split('''\n''')

				__a :						Union[str, Any]			       =  [line.split(''',''') for line in data]

				for edgea in range(1   ,       len(a_)):
								for edgea in range(a_):
												if adjaceny_matrix[edgea][edgea] != "-":
																__a :						int			       =  int(adjaceny_matrix[edgea][edgea])

				__a :						Graph			       =  Graph(set(range(len(a_)))   ,       a_)

				__a :						Graph			       =  graph.prims_algorithm()

				__a :						int			       =  sum(graph.edges.values())
				__a :						int			       =  sum(subgraph.edges.values())

				return initial_total - optimal_total


if __name__ == "__main__":
		print(F'{solution() = }')