


"""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


lowerCamelCase_       :       Optional[int]      						=  get_tests_dir("""fixtures/test_sentencepiece.model""")






@require_sentencepiece
@require_tokenizers
class 				__A    (	SCREAMING_SNAKE_CASE__,   unittest.TestCase      ):






      """simple docstring"""




      __lowerCAmelCase			 =  XLMRobertaTokenizer
      __lowerCAmelCase			 =  XLMRobertaTokenizerFast
      __lowerCAmelCase			 =  True
      __lowerCAmelCase			 =  True


      def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
          super().setUp()

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


      def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
          a					="<pad>"
          a					=1

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


      def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
          a					=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(_SCREAMING_SNAKE_CASE						)			,   1002						)


      def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
          self.assertEqual(self.get_tokenizer().vocab_size			,   1002						)


      def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
          a					=XLMRobertaTokenizer(_SCREAMING_SNAKE_CASE			,   keep_accents=_SCREAMING_SNAKE_CASE						)

          a					=tokenizer.tokenize('''This is a test'''						)
          self.assertListEqual(_SCREAMING_SNAKE_CASE			,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']						)

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

          a					=tokenizer.tokenize('''I was born in 92000, and this is falsé.'''						)
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE			,   [
                  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''',
                  '''.''',
              ]			,   )
          a					=tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE						)
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE			,   [
                  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 ^
              ]			,   )

          a					=tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE						)
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE			,   [
                  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 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
          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

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

                  a					=tempfile.mkdtemp()

                  a					=tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE						)
                  a					=tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE						)

                  # 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						)						)
                  a					=tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f						)
                  self.assertSequenceEqual(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE						)

                  # Checks everything loads correctly in the same way
                  a					=tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE						)
                  a					=tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE						)

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

                  shutil.rmtree(_SCREAMING_SNAKE_CASE						)

                  # Save tokenizer rust, legacy_format=True
                  a					=tempfile.mkdtemp()

                  a					=tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE			,   legacy_format=_SCREAMING_SNAKE_CASE						)
                  a					=tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE						)

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

                  # Checks everything loads correctly in the same way
                  a					=tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE						)
                  a					=tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE						)

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

                  shutil.rmtree(_SCREAMING_SNAKE_CASE						)

                  # Save tokenizer rust, legacy_format=False
                  a					=tempfile.mkdtemp()

                  a					=tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE			,   legacy_format=_SCREAMING_SNAKE_CASE						)
                  a					=tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE						)

                  # 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
                  a					=tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE						)
                  a					=tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE						)

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

                  shutil.rmtree(_SCREAMING_SNAKE_CASE						)


      @cached_property
      def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
          return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''						)


      def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
          with tempfile.NamedTemporaryFile() as f:
              shutil.copyfile(_SCREAMING_SNAKE_CASE			,   f.name						)
              a					=XLMRobertaTokenizer(f.name			,   keep_accents=_SCREAMING_SNAKE_CASE						)
              a					=pickle.dumps(_SCREAMING_SNAKE_CASE						)
          pickle.loads(_SCREAMING_SNAKE_CASE						)


      def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
          if not self.test_rust_tokenizer:
              return

          a					=self.get_tokenizer()
          a					=self.get_rust_tokenizer()

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

          a					=tokenizer.tokenize(_SCREAMING_SNAKE_CASE						)
          a					=rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE						)
          self.assertListEqual(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE						)

          a					=tokenizer.encode(_SCREAMING_SNAKE_CASE			,   add_special_tokens=_SCREAMING_SNAKE_CASE						)
          a					=rust_tokenizer.encode(_SCREAMING_SNAKE_CASE			,   add_special_tokens=_SCREAMING_SNAKE_CASE						)
          self.assertListEqual(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE						)

          a					=self.get_rust_tokenizer()
          a					=tokenizer.encode(_SCREAMING_SNAKE_CASE						)
          a					=rust_tokenizer.encode(_SCREAMING_SNAKE_CASE						)
          self.assertListEqual(_SCREAMING_SNAKE_CASE			,   _SCREAMING_SNAKE_CASE						)


      @slow
      def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
          a					="Hello World!"
          a					=[0, 3_5378, 6661, 38, 2]
          # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
          # xlmr.eval()
          # xlmr.encode(symbols)

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


      @slow
      def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
          a					=(
              "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"
          )
          a					=[
              0,
              3293,
              83,
              10,
              4552,
              4989,
              7986,
              678,
              10,
              5915,
              111,
              17_9459,
              12_4850,
              4,
              6044,
              237,
              12,
              6,
              5,
              6,
              4,
              6780,
              705,
              15,
              1388,
              44,
              378,
              1_0114,
              711,
              152,
              20,
              6,
              5,
              2_2376,
              642,
              1221,
              1_5190,
              3_4153,
              450,
              5608,
              959,
              1119,
              5_7702,
              136,
              186,
              47,
              1098,
              2_9367,
              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,
              5_0901,
              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(_SCREAMING_SNAKE_CASE			,   self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE						)						)





      @slow
      def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
          # fmt: off
          a					={"input_ids": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 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, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 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=_SCREAMING_SNAKE_CASE			,   model_name='''xlm-roberta-base'''			,   revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''			,   )



"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCamelCase_       :       int      						=  {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Dict      						=  ["""MBartTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  ["""MBartTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[str]      						=  [
           """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """MBartForCausalLM""",
           """MBartForConditionalGeneration""",
           """MBartForQuestionAnswering""",
           """MBartForSequenceClassification""",
           """MBartModel""",
           """MBartPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[Any]      						=  [
           """TFMBartForConditionalGeneration""",
           """TFMBartModel""",
           """TFMBartPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  [
           """FlaxMBartForConditionalGeneration""",
           """FlaxMBartForQuestionAnswering""",
           """FlaxMBartForSequenceClassification""",
           """FlaxMBartModel""",
           """FlaxMBartPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart import MBartTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart_fast import MBartTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mbart import (
                  MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MBartForCausalLM,
                  MBartForConditionalGeneration,
                  MBartForQuestionAnswering,
                  MBartForSequenceClassification,
                  MBartModel,
                  MBartPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_mbart import (
                  FlaxMBartForConditionalGeneration,
                  FlaxMBartForQuestionAnswering,
                  FlaxMBartForSequenceClassification,
                  FlaxMBartModel,
                  FlaxMBartPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase_       :       List[Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)