


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


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







@require_sentencepiece
@require_tokenizers
class       lowerCamelCase__   (       lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			XLMRobertaTokenizer
     SCREAMING_SNAKE_CASE__      			=			XLMRobertaTokenizerFast
     SCREAMING_SNAKE_CASE__      			=			True
     SCREAMING_SNAKE_CASE__      			=			True





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

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





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      ='''<pad>'''
      _lowercase							      =1

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





     def      __A					(self       )     ->   Tuple:
      _lowercase							      =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       )		,     1_0_0_2       )





     def      __A					(self       )     ->   int:
      self.assertEqual(self.get_tokenizer().vocab_size		,     1_0_0_2       )





     def      __A					(self       )     ->   Dict:
      _lowercase							      =XLMRobertaTokenizer(UpperCAmelCase		,     keep_accents=UpperCAmelCase       )

      _lowercase							      =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 [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]		,     )

      _lowercase							      =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''',
              '''.''',
          ]		,     )
      _lowercase							      =tokenizer.convert_tokens_to_ids(UpperCAmelCase       )
      self.assertListEqual(
          UpperCAmelCase		,     [
              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 ^
          ]		,     )

      _lowercase							      =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      __A					(self       )     ->   Optional[Any]:
      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

      _lowercase							      =(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})"       ):
        _lowercase							      =self.rust_tokenizer_class.from_pretrained(UpperCAmelCase		,     **UpperCAmelCase       )
        _lowercase							      =self.tokenizer_class.from_pretrained(UpperCAmelCase		,     **UpperCAmelCase       )

        _lowercase							      =tempfile.mkdtemp()

        _lowercase							      =tokenizer_r.save_pretrained(UpperCAmelCase       )
        _lowercase							      =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       )       )
        _lowercase							      =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
        _lowercase							      =tokenizer_r.from_pretrained(UpperCAmelCase       )
        _lowercase							      =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
        _lowercase							      =tempfile.mkdtemp()

        _lowercase							      =tokenizer_r.save_pretrained(UpperCAmelCase		,     legacy_format=UpperCAmelCase       )
        _lowercase							      =tokenizer_p.save_pretrained(UpperCAmelCase       )

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

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

        _lowercase							      =tokenizer_r.save_pretrained(UpperCAmelCase		,     legacy_format=UpperCAmelCase       )
        _lowercase							      =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
        _lowercase							      =tokenizer_r.from_pretrained(UpperCAmelCase       )
        _lowercase							      =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      __A					(self       )     ->   List[str]:
      return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''       )





     def      __A					(self       )     ->   Dict:
      with tempfile.NamedTemporaryFile() as f:
       shutil.copyfile(UpperCAmelCase		,     f.name       )
       _lowercase							      =XLMRobertaTokenizer(f.name		,     keep_accents=UpperCAmelCase       )
       _lowercase							      =pickle.dumps(UpperCAmelCase       )
      pickle.loads(UpperCAmelCase       )





     def      __A					(self       )     ->   str:
      if not self.test_rust_tokenizer:
       return

      _lowercase							      =self.get_tokenizer()
      _lowercase							      =self.get_rust_tokenizer()

      _lowercase							      ='''I was born in 92000, and this is falsé.'''

      _lowercase							      =tokenizer.tokenize(UpperCAmelCase       )
      _lowercase							      =rust_tokenizer.tokenize(UpperCAmelCase       )
      self.assertListEqual(UpperCAmelCase		,     UpperCAmelCase       )

      _lowercase							      =tokenizer.encode(UpperCAmelCase		,     add_special_tokens=UpperCAmelCase       )
      _lowercase							      =rust_tokenizer.encode(UpperCAmelCase		,     add_special_tokens=UpperCAmelCase       )
      self.assertListEqual(UpperCAmelCase		,     UpperCAmelCase       )

      _lowercase							      =self.get_rust_tokenizer()
      _lowercase							      =tokenizer.encode(UpperCAmelCase       )
      _lowercase							      =rust_tokenizer.encode(UpperCAmelCase       )
      self.assertListEqual(UpperCAmelCase		,     UpperCAmelCase       )





     @slow
     def      __A					(self       )     ->   List[Any]:
      _lowercase							      ='''Hello World!'''
      _lowercase							      =[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(UpperCAmelCase		,     self.big_tokenizer.encode(UpperCAmelCase       )       )





     @slow
     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =(
          '''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'''
      )
      _lowercase							      =[
          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(UpperCAmelCase		,     self.big_tokenizer.encode(UpperCAmelCase       )       )





     @slow
     def      __A					(self       )     ->   Union[str, Any]:
      # fmt: off
      _lowercase							      ={'''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=UpperCAmelCase		,     model_name='''xlm-roberta-base'''		,     revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''		,     )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLMForMultipleChoice''',
      '''XLMForQuestionAnswering''',
      '''XLMForQuestionAnsweringSimple''',
      '''XLMForSequenceClassification''',
      '''XLMForTokenClassification''',
      '''XLMModel''',
      '''XLMPreTrainedModel''',
      '''XLMWithLMHeadModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLMForMultipleChoice''',
      '''TFXLMForQuestionAnsweringSimple''',
      '''TFXLMForSequenceClassification''',
      '''TFXLMForTokenClassification''',
      '''TFXLMMainLayer''',
      '''TFXLMModel''',
      '''TFXLMPreTrainedModel''',
      '''TFXLMWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

