




"""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(     __a       ,			unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					XLMRobertaTokenizer
 lowercase__ 					=					XLMRobertaTokenizerFast
 lowercase__ 					=					True
 lowercase__ 					=					True


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   super().setUp()

   # We have a SentencePiece fixture for testing
   _snake_case							:		Any								=  XLMRobertaTokenizer(a_,      keep_accents=a_		)
   tokenizer.save_pretrained(self.tmpdirname		)


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




   '''simple docstring'''



   _snake_case							:		int								=  """<pad>"""
   _snake_case							:		str								=  1

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


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




   '''simple docstring'''



   _snake_case							:		Tuple								=  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(a_		),      1_002		)


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   self.assertEqual(self.get_tokenizer().vocab_size,      1_002		)


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		Dict								=  XLMRobertaTokenizer(a_,      keep_accents=a_		)

   _snake_case							:		int								=  tokenizer.tokenize("""This is a test"""		)
   self.assertListEqual(a_,      ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]		)

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

   _snake_case							:		Tuple								=  tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
   self.assertListEqual(
       a_,      [
           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							:		Union[str, Any]								=  tokenizer.convert_tokens_to_ids(a_		)
   self.assertListEqual(
       a_,      [
           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							:		Optional[Any]								=  tokenizer.convert_ids_to_tokens(a_		)
   self.assertListEqual(
       a_,      [
           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  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   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							:		Optional[int]								=  (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							:		Tuple								=  self.rust_tokenizer_class.from_pretrained(a_,      **a_		)
       _snake_case							:		int								=  self.tokenizer_class.from_pretrained(a_,      **a_		)

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

       _snake_case							:		str								=  tokenizer_r.save_pretrained(a_		)
       _snake_case							:		List[Any]								=  tokenizer_p.save_pretrained(a_		)

       # 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							:		List[Any]								=  tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f		)
       self.assertSequenceEqual(a_,      a_		)

       # Checks everything loads correctly in the same way
       _snake_case							:		List[str]								=  tokenizer_r.from_pretrained(a_		)
       _snake_case							:		Union[str, Any]								=  tokenizer_p.from_pretrained(a_		)

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

       shutil.rmtree(a_		)

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

       _snake_case							:		Tuple								=  tokenizer_r.save_pretrained(a_,      legacy_format=a_		)
       _snake_case							:		str								=  tokenizer_p.save_pretrained(a_		)

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

       # Checks everything loads correctly in the same way
       _snake_case							:		Any								=  tokenizer_r.from_pretrained(a_		)
       _snake_case							:		Any								=  tokenizer_p.from_pretrained(a_		)

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

       shutil.rmtree(a_		)

       # Save tokenizer rust, legacy_format=False
       _snake_case							:		Union[str, Any]								=  tempfile.mkdtemp()

       _snake_case							:		Dict								=  tokenizer_r.save_pretrained(a_,      legacy_format=a_		)
       _snake_case							:		List[str]								=  tokenizer_p.save_pretrained(a_		)

       # 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(a_		)
       _snake_case							:		Optional[int]								=  tokenizer_p.from_pretrained(a_		)

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

       shutil.rmtree(a_		)


 @cached_property
 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""		)


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




   '''simple docstring'''



   with tempfile.NamedTemporaryFile() as f:
     shutil.copyfile(a_,      f.name		)
     _snake_case							:		str								=  XLMRobertaTokenizer(f.name,      keep_accents=a_		)
     _snake_case							:		str								=  pickle.dumps(a_		)
   pickle.loads(a_		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   if not self.test_rust_tokenizer:
     return

   _snake_case							:		Tuple								=  self.get_tokenizer()
   _snake_case							:		int								=  self.get_rust_tokenizer()

   _snake_case							:		Optional[Any]								=  """I was born in 92000, and this is falsé."""

   _snake_case							:		List[str]								=  tokenizer.tokenize(a_		)
   _snake_case							:		str								=  rust_tokenizer.tokenize(a_		)
   self.assertListEqual(a_,      a_		)

   _snake_case							:		List[str]								=  tokenizer.encode(a_,      add_special_tokens=a_		)
   _snake_case							:		Optional[int]								=  rust_tokenizer.encode(a_,      add_special_tokens=a_		)
   self.assertListEqual(a_,      a_		)

   _snake_case							:		List[Any]								=  self.get_rust_tokenizer()
   _snake_case							:		Tuple								=  tokenizer.encode(a_		)
   _snake_case							:		Dict								=  rust_tokenizer.encode(a_		)
   self.assertListEqual(a_,      a_		)


 @slow
 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  """Hello World!"""
   _snake_case							:		str								=  [0, 35_378, 6_661, 38, 2]
   # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
   # xlmr.eval()
   # xlmr.encode(symbols)

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


 @slow
 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  (
       """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[int]								=  [
       0,
       3_293,
       83,
       10,
       4_552,
       4_989,
       7_986,
       678,
       10,
       5_915,
       111,
       179_459,
       124_850,
       4,
       6_044,
       237,
       12,
       6,
       5,
       6,
       4,
       6_780,
       705,
       15,
       1_388,
       44,
       378,
       10_114,
       711,
       152,
       20,
       6,
       5,
       22_376,
       642,
       1_221,
       15_190,
       34_153,
       450,
       5_608,
       959,
       1_119,
       57_702,
       136,
       186,
       47,
       1_098,
       29_367,
       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,
       6_044,
       237,
       6_284,
       50_901,
       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(a_,      self.big_tokenizer.encode(a_		)		)


 @slow
 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  {"""input_ids""": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 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, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 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=a_,      model_name="""xlm-roberta-base""",      revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3""",      )








"""simple docstring"""


from __future__ import annotations


def 					UpperCAmelCase__       (snake_case__							:					list[float]    ):
  """simple docstring"""

  _snake_case							:		int								=  0.00
  _snake_case							:		int								=  0
  for resistor in resistors:
    if resistor <= 0:
      _snake_case							:		Dict								=  F"Resistor at index {index} has a negative or zero value!"
      raise ValueError(snake_case__    )
    first_sum += 1 / float(snake_case__    )
    index += 1
  return 1 / first_sum


def 					UpperCAmelCase__       (snake_case__							:					list[float]    ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  0.00
  _snake_case							:		Any								=  0
  for resistor in resistors:
    sum_r += resistor
    if resistor < 0:
      _snake_case							:		Any								=  F"Resistor at index {index} has a negative value!"
      raise ValueError(snake_case__    )
    index += 1
  return sum_r


if __name__ == "__main__":
     import doctest

     doctest.testmod()


