





'''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


_SCREAMING_SNAKE_CASE	 =							get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class    lowerCAmelCase_		(		__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							Any         =					XLMRobertaTokenizer
 __lowerCamelCase						:							Optional[Any]         =					XLMRobertaTokenizerFast
 __lowerCamelCase						:							Dict         =					True
 __lowerCamelCase						:							int         =					True




 def 							_snake_case   (	self		)    ->							Optional[int]:
     super().setUp()

     # We have a SentencePiece fixture for testing
     _lowerCAmelCase				=      XLMRobertaTokenizer(_lowerCAmelCase			,     keep_accents=_lowerCAmelCase		)
     tokenizer.save_pretrained(self.tmpdirname		)




 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      "<pad>"
     _lowerCAmelCase				=      1

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




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							Dict:
     self.assertEqual(self.get_tokenizer().vocab_size			,     1002		)




 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      XLMRobertaTokenizer(_lowerCAmelCase			,     keep_accents=_lowerCAmelCase		)

     _lowerCAmelCase				=      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]]			,     )

     _lowerCAmelCase				=      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",
             ".",
         ]			,     )
     _lowerCAmelCase				=      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 ^
         ]			,     )

     _lowerCAmelCase				=      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 							_snake_case   (	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

     _lowerCAmelCase				=      (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})'''		):
             _lowerCAmelCase				=      self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase			,     **_lowerCAmelCase		)
             _lowerCAmelCase				=      self.tokenizer_class.from_pretrained(_lowerCAmelCase			,     **_lowerCAmelCase		)

             _lowerCAmelCase				=      tempfile.mkdtemp()

             _lowerCAmelCase				=      tokenizer_r.save_pretrained(_lowerCAmelCase		)
             _lowerCAmelCase				=      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		)		)
             _lowerCAmelCase				=      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
             _lowerCAmelCase				=      tokenizer_r.from_pretrained(_lowerCAmelCase		)
             _lowerCAmelCase				=      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
             _lowerCAmelCase				=      tempfile.mkdtemp()

             _lowerCAmelCase				=      tokenizer_r.save_pretrained(_lowerCAmelCase			,     legacy_format=_lowerCAmelCase		)
             _lowerCAmelCase				=      tokenizer_p.save_pretrained(_lowerCAmelCase		)

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

             # Checks everything loads correctly in the same way
             _lowerCAmelCase				=      tokenizer_r.from_pretrained(_lowerCAmelCase		)
             _lowerCAmelCase				=      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
             _lowerCAmelCase				=      tempfile.mkdtemp()

             _lowerCAmelCase				=      tokenizer_r.save_pretrained(_lowerCAmelCase			,     legacy_format=_lowerCAmelCase		)
             _lowerCAmelCase				=      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
             _lowerCAmelCase				=      tokenizer_r.from_pretrained(_lowerCAmelCase		)
             _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							Optional[int]:
     return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"		)




 def 							_snake_case   (	self		)    ->							Tuple:
     with tempfile.NamedTemporaryFile() as f:
         shutil.copyfile(_lowerCAmelCase			,     f.name		)
         _lowerCAmelCase				=      XLMRobertaTokenizer(f.name			,     keep_accents=_lowerCAmelCase		)
         _lowerCAmelCase				=      pickle.dumps(_lowerCAmelCase		)
     pickle.loads(_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[Any]:
     if not self.test_rust_tokenizer:
         return

     _lowerCAmelCase				=      self.get_tokenizer()
     _lowerCAmelCase				=      self.get_rust_tokenizer()

     _lowerCAmelCase				=      "I was born in 92000, and this is falsé."

     _lowerCAmelCase				=      tokenizer.tokenize(_lowerCAmelCase		)
     _lowerCAmelCase				=      rust_tokenizer.tokenize(_lowerCAmelCase		)
     self.assertListEqual(_lowerCAmelCase			,     _lowerCAmelCase		)

     _lowerCAmelCase				=      tokenizer.encode(_lowerCAmelCase			,     add_special_tokens=_lowerCAmelCase		)
     _lowerCAmelCase				=      rust_tokenizer.encode(_lowerCAmelCase			,     add_special_tokens=_lowerCAmelCase		)
     self.assertListEqual(_lowerCAmelCase			,     _lowerCAmelCase		)

     _lowerCAmelCase				=      self.get_rust_tokenizer()
     _lowerCAmelCase				=      tokenizer.encode(_lowerCAmelCase		)
     _lowerCAmelCase				=      rust_tokenizer.encode(_lowerCAmelCase		)
     self.assertListEqual(_lowerCAmelCase			,     _lowerCAmelCase		)




 @slow
 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      "Hello World!"
     _lowerCAmelCase				=      [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 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      (
         "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"
     )
     _lowerCAmelCase				=      [
         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 							_snake_case   (	self		)    ->							Optional[Any]:
     # fmt: off
     _lowerCAmelCase				=      {"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'''


from __future__ import annotations


def 	__a(SCREAMING_SNAKE_CASE_  :					float		,       SCREAMING_SNAKE_CASE_  :					float		,       SCREAMING_SNAKE_CASE_  :					float    ):



    '''simple docstring'''





    if (voltage, current, resistance).count(0    ) != 1:
        raise ValueError("One and only one argument must be 0"    )
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"    )
    if voltage == 0:
        return {"voltage": float(current * resistance    )}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


