



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


_snake_case  :   List[Any]						   =  get_tests_dir('fixtures/test_sentencepiece.model')



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

  """simple docstring"""


  a_      =			XLMRobertaTokenizer
  a_      =			XLMRobertaTokenizerFast
  a_      =			True
  a_      =			True






  def     lowercase		(    self			:		List[Any]					)							->						Dict:
     super().setUp()

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






  def     lowercase		(    self			:		Optional[Any]					)							->						int:
     __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     lowercase		(    self			:		Any					)							->						List[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					) ,  1_0_0_2					)






  def     lowercase		(    self			:		Optional[Any]					)							->						Tuple:
     self.assertEqual(self.get_tokenizer().vocab_size ,  1_0_0_2					)






  def     lowercase		(    self			:		str					)							->						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 [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] ,  )

     __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, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 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     lowercase		(    self			:		Union[str, Any]					)							->						Dict:

     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     lowercase		(    self			:		Tuple					)							->						Union[str, Any]:
     return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'					)






  def     lowercase		(    self			:		Tuple					)							->						Optional[int]:

     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     lowercase		(    self			:		List[str]					)							->						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     lowercase		(    self			:		List[str]					)							->						Any:
     __lowerCAmelCase							     =     'Hello World!'
     __lowerCAmelCase							     =     [0, 3_5_3_7_8, 6_6_6_1, 3_8, 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     lowercase		(    self			:		List[str]					)							->						Dict:
     __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,
         3_2_9_3,
         8_3,
         1_0,
         4_5_5_2,
         4_9_8_9,
         7_9_8_6,
         6_7_8,
         1_0,
         5_9_1_5,
         1_1_1,
         1_7_9_4_5_9,
         1_2_4_8_5_0,
         4,
         6_0_4_4,
         2_3_7,
         1_2,
         6,
         5,
         6,
         4,
         6_7_8_0,
         7_0_5,
         1_5,
         1_3_8_8,
         4_4,
         3_7_8,
         1_0_1_1_4,
         7_1_1,
         1_5_2,
         2_0,
         6,
         5,
         2_2_3_7_6,
         6_4_2,
         1_2_2_1,
         1_5_1_9_0,
         3_4_1_5_3,
         4_5_0,
         5_6_0_8,
         9_5_9,
         1_1_1_9,
         5_7_7_0_2,
         1_3_6,
         1_8_6,
         4_7,
         1_0_9_8,
         2_9_3_6_7,
         4_7,
         # 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_0_4_4,
         2_3_7,
         6_2_8_4,
         5_0_9_0_1,
         5_2_8,
         3_1,
         9_0,
         3_4,
         9_2_7,
         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     lowercase		(    self			:		Optional[int]					)							->						Any:
     __lowerCAmelCase							     =     {'input_ids': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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' ,  )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case  :   Optional[Any]						   =  {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   int						   =  ['FNetTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   Tuple						   =  ['FNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   List[str]						   =  [
         'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'FNetForMaskedLM',
         'FNetForMultipleChoice',
         'FNetForNextSentencePrediction',
         'FNetForPreTraining',
         'FNetForQuestionAnswering',
         'FNetForSequenceClassification',
         'FNetForTokenClassification',
         'FNetLayer',
         'FNetModel',
         'FNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     _snake_case  :   str						   =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

