

"""simple docstring"""


import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english

@require_tokenizers
class      __a							(UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = RoCBertTokenizer
     _SCREAMING_SNAKE_CASE						:Tuple       = None
     _SCREAMING_SNAKE_CASE						:Dict       = False
     _SCREAMING_SNAKE_CASE						:int       = True
     _SCREAMING_SNAKE_CASE						:Any       = filter_non_english

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           super().setUp()

           SCREAMING_SNAKE_CASE__   :      Any      		=	["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	{}
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	{}
           for i, value in enumerate(_a  ):
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	i
                 SCREAMING_SNAKE_CASE__   :      str      		=	i
           SCREAMING_SNAKE_CASE__   :      Dict      		=	os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""word_shape_file"""]  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""word_pronunciation_file"""]  )
           with open(self.vocab_file	,    """w"""	,    encoding="""utf-8"""  ) as vocab_writer:
                 vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]  )  )
           with open(self.word_shape_file	,    """w"""	,    encoding="""utf-8"""  ) as word_shape_writer:
                 json.dump(_a	,    _a	,    ensure_ascii=_a  )
           with open(self.word_pronunciation_file	,    """w"""	,    encoding="""utf-8"""  ) as word_pronunciation_writer:
                 json.dump(_a	,    _a	,    ensure_ascii=_a  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.tokenizer_class(self.vocab_file	,    self.word_shape_file	,    self.word_pronunciation_file  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tokenizer.tokenize("""你好[SEP]你是谁"""  )
           self.assertListEqual(_a	,    ["""你""", """好""", """[SEP]""", """你""", """是""", """谁"""]  )
           self.assertListEqual(tokenizer.convert_tokens_to_ids(_a  )	,    [5, 6, 2, 5, 7, 8]  )
           self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(_a  )	,    [5, 6, 2, 5, 7, 8]  )
           self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(_a  )	,    [5, 6, 2, 5, 7, 8]  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	RoCBertBasicTokenizer()

           self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""  )	,    ["""ah""", """\u535A""", """\u63A8""", """zz"""]  )

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	RoCBertBasicTokenizer(do_lower_case=_a  )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """  )	,    ["""hello""", """!""", """how""", """are""", """you""", """?"""]  )
           self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""  )	,    ["""hello"""]  )

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	RoCBertBasicTokenizer(do_lower_case=_a	,    strip_accents=_a  )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  )	,    ["""hällo""", """!""", """how""", """are""", """you""", """?"""]  )
           self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""  )	,    ["""h\u00E9llo"""]  )

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	RoCBertBasicTokenizer(do_lower_case=_a	,    strip_accents=_a  )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  )	,    ["""hallo""", """!""", """how""", """are""", """you""", """?"""]  )
           self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""  )	,    ["""hello"""]  )

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	RoCBertBasicTokenizer(do_lower_case=_a  )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  )	,    ["""hallo""", """!""", """how""", """are""", """you""", """?"""]  )
           self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""  )	,    ["""hello"""]  )

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	RoCBertBasicTokenizer(do_lower_case=_a  )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """  )	,    ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]  )

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	RoCBertBasicTokenizer(do_lower_case=_a	,    strip_accents=_a  )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  )	,    ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	RoCBertBasicTokenizer(do_lower_case=_a	,    strip_accents=_a  )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  )	,    ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]  )

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	RoCBertBasicTokenizer(do_lower_case=_a	,    never_split=["""[UNK]"""]  )

           self.assertListEqual(
               tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""  )	,    ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{}
           for i, token in enumerate(_a  ):
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	i
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	RoCBertWordpieceTokenizer(vocab=_a	,    unk_token="""[UNK]"""  )

           self.assertListEqual(tokenizer.tokenize(""""""  )	,    []  )

           self.assertListEqual(tokenizer.tokenize("""unwanted running"""  )	,    ["""un""", """##want""", """##ed""", """runn""", """##ing"""]  )

           self.assertListEqual(tokenizer.tokenize("""unwantedX running"""  )	,    ["""[UNK]""", """runn""", """##ing"""]  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           self.assertTrue(_is_whitespace(""" """  )  )
           self.assertTrue(_is_whitespace("""\t"""  )  )
           self.assertTrue(_is_whitespace("""\r"""  )  )
           self.assertTrue(_is_whitespace("""\n"""  )  )
           self.assertTrue(_is_whitespace("""\u00A0"""  )  )

           self.assertFalse(_is_whitespace("""A"""  )  )
           self.assertFalse(_is_whitespace("""-"""  )  )

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           self.assertTrue(_is_control("""\u0005"""  )  )

           self.assertFalse(_is_control("""A"""  )  )
           self.assertFalse(_is_control(""" """  )  )
           self.assertFalse(_is_control("""\t"""  )  )
           self.assertFalse(_is_control("""\r"""  )  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           self.assertTrue(_is_punctuation("""-"""  )  )
           self.assertTrue(_is_punctuation("""$"""  )  )
           self.assertTrue(_is_punctuation("""`"""  )  )
           self.assertTrue(_is_punctuation("""."""  )  )

           self.assertFalse(_is_punctuation("""A"""  )  )
           self.assertFalse(_is_punctuation(""" """  )  )

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.get_tokenizer()

           # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
           self.assertListEqual([tokenizer.tokenize(_a  ) for t in ["""Test""", """\xad""", """test"""]]	,    [["""[UNK]"""], [], ["""[UNK]"""]]  )

           if self.test_rust_tokenizer:
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.get_rust_tokenizer()
                 self.assertListEqual(
                     [rust_tokenizer.tokenize(_a  ) for t in ["""Test""", """\xad""", """test"""]]	,    [["""[UNK]"""], [], ["""[UNK]"""]]  )

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.rust_tokenizer_class.from_pretrained(_a	,    **_a  )

                       SCREAMING_SNAKE_CASE__   :      Dict      		=	f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer_r.encode_plus(
                           _a	,    return_attention_mask=_a	,    return_token_type_ids=_a	,    return_offsets_mapping=_a	,    add_special_tokens=_a	,    )

                       SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tokenizer_r.do_lower_case if hasattr(_a	,    """do_lower_case"""  ) else False
                       SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	(
                           [
                               ((0, 0), tokenizer_r.cls_token),
                               ((0, 1), """A"""),
                               ((1, 2), ""","""),
                               ((3, 5), """na"""),
                               ((5, 6), """##ï"""),
                               ((6, 8), """##ve"""),
                               ((9, 15), tokenizer_r.mask_token),
                               ((16, 21), """Allen"""),
                               ((21, 23), """##NL"""),
                               ((23, 24), """##P"""),
                               ((25, 33), """sentence"""),
                               ((33, 34), """."""),
                               ((0, 0), tokenizer_r.sep_token),
                           ]
                           if not do_lower_case
                           else [
                               ((0, 0), tokenizer_r.cls_token),
                               ((0, 1), """a"""),
                               ((1, 2), ""","""),
                               ((3, 8), """naive"""),
                               ((9, 15), tokenizer_r.mask_token),
                               ((16, 21), """allen"""),
                               ((21, 23), """##nl"""),
                               ((23, 24), """##p"""),
                               ((25, 33), """sentence"""),
                               ((33, 34), """."""),
                               ((0, 0), tokenizer_r.sep_token),
                           ]
                       )

                       self.assertEqual(
                           [e[1] for e in expected_results]	,    tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]  )  )
                       self.assertEqual([e[0] for e in expected_results]	,    tokens["""offset_mapping"""]  )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	["""的""", """人""", """有"""]
           SCREAMING_SNAKE_CASE__   :      Dict      		=	"""""".join(_a  )
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
                       SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	True
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.tokenizer_class.from_pretrained(_a	,    **_a  )
                       SCREAMING_SNAKE_CASE__   :      str      		=	self.rust_tokenizer_class.from_pretrained(_a	,    **_a  )

                       SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tokenizer_p.encode(_a	,    add_special_tokens=_a  )
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer_r.encode(_a	,    add_special_tokens=_a  )

                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	tokenizer_r.convert_ids_to_tokens(_a  )
                       SCREAMING_SNAKE_CASE__   :      Any      		=	tokenizer_p.convert_ids_to_tokens(_a  )

                       # it is expected that each Chinese character is not preceded by "##"
                       self.assertListEqual(_a	,    _a  )
                       self.assertListEqual(_a	,    _a  )

                       SCREAMING_SNAKE_CASE__   :      Any      		=	False
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.rust_tokenizer_class.from_pretrained(_a	,    **_a  )
                       SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.tokenizer_class.from_pretrained(_a	,    **_a  )

                       SCREAMING_SNAKE_CASE__   :      str      		=	tokenizer_r.encode(_a	,    add_special_tokens=_a  )
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	tokenizer_p.encode(_a	,    add_special_tokens=_a  )

                       SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tokenizer_r.convert_ids_to_tokens(_a  )
                       SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tokenizer_p.convert_ids_to_tokens(_a  )

                       # it is expected that only the first Chinese character is not preceded by "##".
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	[
                           f'''##{token}''' if idx != 0 else token for idx, token in enumerate(_a  )
                       ]
                       self.assertListEqual(_a	,    _a  )
                       self.assertListEqual(_a	,    _a  )

     @slow
     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.tokenizer_class(self.vocab_file	,    self.word_shape_file	,    self.word_pronunciation_file  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	tokenizer.encode("""你好"""	,    add_special_tokens=_a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	tokenizer.encode("""你是谁"""	,    add_special_tokens=_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	tokenizer.build_inputs_with_special_tokens(_a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	tokenizer.build_inputs_with_special_tokens(_a	,    _a  )

           assert encoded_sentence == [1] + text + [2]
           assert encoded_pair == [1] + text + [2] + text_a + [2]





     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_tokenizers(do_lower_case=_a  )
           for tokenizer in tokenizers:
                 with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
                       SCREAMING_SNAKE_CASE__   :      Any      		=	"""你好，你是谁"""
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	tokenizer.tokenize(_a  )
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer.convert_tokens_to_ids(_a  )
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer.convert_tokens_to_shape_ids(_a  )
                       SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer.convert_tokens_to_pronunciation_ids(_a  )
                       SCREAMING_SNAKE_CASE__   :      int      		=	tokenizer.prepare_for_model(
                           _a	,    _a	,    _a	,    add_special_tokens=_a  )

                       SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer.encode_plus(_a	,    add_special_tokens=_a  )

                       self.assertEqual(_a	,    _a  )





"""simple docstring"""


from typing import TYPE_CHECKING

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


a					:Any    = {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Union[str, Any]    = [
      "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RobertaPreLayerNormForCausalLM",
      "RobertaPreLayerNormForMaskedLM",
      "RobertaPreLayerNormForMultipleChoice",
      "RobertaPreLayerNormForQuestionAnswering",
      "RobertaPreLayerNormForSequenceClassification",
      "RobertaPreLayerNormForTokenClassification",
      "RobertaPreLayerNormModel",
      "RobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Optional[Any]    = [
      "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRobertaPreLayerNormForCausalLM",
      "TFRobertaPreLayerNormForMaskedLM",
      "TFRobertaPreLayerNormForMultipleChoice",
      "TFRobertaPreLayerNormForQuestionAnswering",
      "TFRobertaPreLayerNormForSequenceClassification",
      "TFRobertaPreLayerNormForTokenClassification",
      "TFRobertaPreLayerNormMainLayer",
      "TFRobertaPreLayerNormModel",
      "TFRobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:List[Any]    = [
      "FlaxRobertaPreLayerNormForCausalLM",
      "FlaxRobertaPreLayerNormForMaskedLM",
      "FlaxRobertaPreLayerNormForMultipleChoice",
      "FlaxRobertaPreLayerNormForQuestionAnswering",
      "FlaxRobertaPreLayerNormForSequenceClassification",
      "FlaxRobertaPreLayerNormForTokenClassification",
      "FlaxRobertaPreLayerNormModel",
      "FlaxRobertaPreLayerNormPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
      RobertaPreLayerNormConfig,
      RobertaPreLayerNormOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        RobertaPreLayerNormForCausalLM,
        RobertaPreLayerNormForMaskedLM,
        RobertaPreLayerNormForMultipleChoice,
        RobertaPreLayerNormForQuestionAnswering,
        RobertaPreLayerNormForSequenceClassification,
        RobertaPreLayerNormForTokenClassification,
        RobertaPreLayerNormModel,
        RobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roberta_prelayernorm import (
        TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRobertaPreLayerNormForCausalLM,
        TFRobertaPreLayerNormForMaskedLM,
        TFRobertaPreLayerNormForMultipleChoice,
        TFRobertaPreLayerNormForQuestionAnswering,
        TFRobertaPreLayerNormForSequenceClassification,
        TFRobertaPreLayerNormForTokenClassification,
        TFRobertaPreLayerNormMainLayer,
        TFRobertaPreLayerNormModel,
        TFRobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
        FlaxRobertaPreLayerNormPreTrainedModel,
    )

else:
  import sys

  a					:Optional[int]    = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




