




'''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_      (						lowerCAmelCase_						,       unittest.TestCase							):
     A_											=			XLMRobertaTokenizer
     A_											=			XLMRobertaTokenizerFast
     A_											=			True
     A_											=			True





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           super().setUp()

           # We have a SentencePiece fixture for testing
           snake_case__       :   List[Any]	      =    XLMRobertaTokenizer(__lowerCamelCase     ,							keep_accents=__lowerCamelCase			)
           tokenizer.save_pretrained(self.tmpdirname			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Tuple	      =    '<pad>'
           snake_case__       :   Any	      =    1

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





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   List[str]	      =    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(__lowerCamelCase			)     ,							1002			)





     def 			_lowerCAmelCase      (     self     :					int			):
           self.assertEqual(self.get_tokenizer().vocab_size     ,							1002			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   List[str]	      =    XLMRobertaTokenizer(__lowerCamelCase     ,							keep_accents=__lowerCamelCase			)

           snake_case__       :   Tuple	      =    tokenizer.tokenize('This is a test'			)
           self.assertListEqual(__lowerCamelCase     ,							['▁This', '▁is', '▁a', '▁t', 'est']			)

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

           snake_case__       :   str	      =    tokenizer.tokenize('I was born in 92000, and this is falsé.'			)
           self.assertListEqual(
               __lowerCamelCase     ,							[
                   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',
                   '.',
               ]     ,							)
           snake_case__       :   Any	      =    tokenizer.convert_tokens_to_ids(__lowerCamelCase			)
           self.assertListEqual(
               __lowerCamelCase     ,							[
                   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 ^
               ]     ,							)

           snake_case__       :   str	      =    tokenizer.convert_ids_to_tokens(__lowerCamelCase			)
           self.assertListEqual(
               __lowerCamelCase     ,							[
                   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     :					Optional[int]			):
           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

           snake_case__       :   List[str]	      =    (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})"			):
                       snake_case__       :   Optional[int]	      =    self.rust_tokenizer_class.from_pretrained(__lowerCamelCase     ,							**__lowerCamelCase			)
                       snake_case__       :   Union[str, Any]	      =    self.tokenizer_class.from_pretrained(__lowerCamelCase     ,							**__lowerCamelCase			)

                       snake_case__       :   Dict	      =    tempfile.mkdtemp()

                       snake_case__       :   List[Any]	      =    tokenizer_r.save_pretrained(__lowerCamelCase			)
                       snake_case__       :   Optional[int]	      =    tokenizer_p.save_pretrained(__lowerCamelCase			)

                       # 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			)			)
                       snake_case__       :   Optional[Any]	      =    tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f			)
                       self.assertSequenceEqual(__lowerCamelCase     ,							__lowerCamelCase			)

                       # Checks everything loads correctly in the same way
                       snake_case__       :   Optional[int]	      =    tokenizer_r.from_pretrained(__lowerCamelCase			)
                       snake_case__       :   Optional[int]	      =    tokenizer_p.from_pretrained(__lowerCamelCase			)

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

                       shutil.rmtree(__lowerCamelCase			)

                       # Save tokenizer rust, legacy_format=True
                       snake_case__       :   Optional[Any]	      =    tempfile.mkdtemp()

                       snake_case__       :   Tuple	      =    tokenizer_r.save_pretrained(__lowerCamelCase     ,							legacy_format=__lowerCamelCase			)
                       snake_case__       :   int	      =    tokenizer_p.save_pretrained(__lowerCamelCase			)

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

                       # Checks everything loads correctly in the same way
                       snake_case__       :   Any	      =    tokenizer_r.from_pretrained(__lowerCamelCase			)
                       snake_case__       :   Optional[int]	      =    tokenizer_p.from_pretrained(__lowerCamelCase			)

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

                       shutil.rmtree(__lowerCamelCase			)

                       # Save tokenizer rust, legacy_format=False
                       snake_case__       :   Optional[int]	      =    tempfile.mkdtemp()

                       snake_case__       :   Tuple	      =    tokenizer_r.save_pretrained(__lowerCamelCase     ,							legacy_format=__lowerCamelCase			)
                       snake_case__       :   Any	      =    tokenizer_p.save_pretrained(__lowerCamelCase			)

                       # 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
                       snake_case__       :   Optional[int]	      =    tokenizer_r.from_pretrained(__lowerCamelCase			)
                       snake_case__       :   Optional[int]	      =    tokenizer_p.from_pretrained(__lowerCamelCase			)

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

                       shutil.rmtree(__lowerCamelCase			)





     @cached_property
     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           with tempfile.NamedTemporaryFile() as f:
                 shutil.copyfile(__lowerCamelCase     ,							f.name			)
                 snake_case__       :   Tuple	      =    XLMRobertaTokenizer(f.name     ,							keep_accents=__lowerCamelCase			)
                 snake_case__       :   Optional[Any]	      =    pickle.dumps(__lowerCamelCase			)
           pickle.loads(__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           if not self.test_rust_tokenizer:
                 return

           snake_case__       :   Tuple	      =    self.get_tokenizer()
           snake_case__       :   Dict	      =    self.get_rust_tokenizer()

           snake_case__       :   List[Any]	      =    'I was born in 92000, and this is falsé.'

           snake_case__       :   Tuple	      =    tokenizer.tokenize(__lowerCamelCase			)
           snake_case__       :   Any	      =    rust_tokenizer.tokenize(__lowerCamelCase			)
           self.assertListEqual(__lowerCamelCase     ,							__lowerCamelCase			)

           snake_case__       :   Any	      =    tokenizer.encode(__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase			)
           snake_case__       :   List[str]	      =    rust_tokenizer.encode(__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase			)
           self.assertListEqual(__lowerCamelCase     ,							__lowerCamelCase			)

           snake_case__       :   List[str]	      =    self.get_rust_tokenizer()
           snake_case__       :   int	      =    tokenizer.encode(__lowerCamelCase			)
           snake_case__       :   Dict	      =    rust_tokenizer.encode(__lowerCamelCase			)
           self.assertListEqual(__lowerCamelCase     ,							__lowerCamelCase			)





     @slow
     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   str	      =    'Hello World!'
           snake_case__       :   Optional[int]	      =    [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(__lowerCamelCase     ,							self.big_tokenizer.encode(__lowerCamelCase			)			)





     @slow
     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   Dict	      =    (
               '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'
           )
           snake_case__       :   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(__lowerCamelCase     ,							self.big_tokenizer.encode(__lowerCamelCase			)			)





     @slow
     def 			_lowerCAmelCase      (     self     :					str			):
           # fmt: off
           snake_case__       :   int	      =    {'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=__lowerCamelCase     ,							model_name='xlm-roberta-base'     ,							revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'     ,							)






'''simple docstring'''






import requests
from bsa import BeautifulSoup






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__       :   List[Any]	      =    BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE				,      params=__SCREAMING_SNAKE_CASE		).content				,      'html.parser'		)
      snake_case__       :   Optional[int]	      =    soup.find('div'				,      attrs={'class': 'gs_ri'}		)
      snake_case__       :   Dict	      =    div.find('div'				,      attrs={'class': 'gs_fl'}		).find_all('a'		)
      return anchors[2].get_text()


if __name__ == "__main__":
      A_			      =		{
          "title": (
              "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
              "capacitance, volumetric capacitance, and energy density"
          ),
          "journal": "Chem. Mater.",
          "volume": 30,
          "pages": "3979-3990",
          "year": 2018,
          "hl": "en",
      }
      print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
