'''simple docstring'''






import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class    a (       __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					TransfoXLTokenizer
  __lowerCAmelCase				      =					False
  __lowerCAmelCase				      =					False







  def    lowercase_  (							self					):

    '''simple docstring'''







    super().setUp()

    __UpperCAmelCase:					List[Any]		= [
        """<unk>""",
        """[CLS]""",
        """[SEP]""",
        """want""",
        """unwanted""",
        """wa""",
        """un""",
        """running""",
        """,""",
        """low""",
        """l""",
    ]
    __UpperCAmelCase:					List[Any]		= os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["""vocab_file"""]					)
    with open(self.vocab_file   ,    """w"""   ,    encoding="""utf-8"""					) as vocab_writer:
      vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)







  def    lowercase_  (							self   ,    **snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= True
    return TransfoXLTokenizer.from_pretrained(self.tmpdirname   ,    **snake_case_					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= """<unk> UNwanted , running"""
    __UpperCAmelCase:					Any		= """<unk> unwanted, running"""
    return input_text, output_text







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= TransfoXLTokenizer(vocab_file=self.vocab_file   ,    lower_case=snake_case_					)

    __UpperCAmelCase:					Optional[int]		= tokenizer.tokenize("""<unk> UNwanted , running"""					)
    self.assertListEqual(snake_case_   ,    ["""<unk>""", """unwanted""", """,""", """running"""]					)

    self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_					)   ,    [0, 4, 8, 7]					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= TransfoXLTokenizer(lower_case=snake_case_					)

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







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= TransfoXLTokenizer(lower_case=snake_case_					)

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







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= TransfoXLTokenizer(lower_case=snake_case_					)
    __UpperCAmelCase:					Tuple		= """Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
    __UpperCAmelCase:					Union[str, Any]		= [
        """Hello""",
        """(""",
        """bracket""",
        """)""",
        """and""",
        """side""",
        """@-@""",
        """scrolled""",
        """[""",
        """and""",
        """]""",
        """Henry""",
        """'s""",
        """$""",
        """5""",
        """@,@""",
        """000""",
        """with""",
        """3""",
        """@.@""",
        """34""",
        """m""",
        """.""",
        """What""",
        """'s""",
        """up""",
        """!""",
        """?""",
    ]

    self.assertListEqual(tokenizer.tokenize(snake_case_					)   ,    snake_case_					)

    self.assertEqual(tokenizer.convert_tokens_to_string(snake_case_					)   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= self.get_tokenizer()
    __UpperCAmelCase:					Union[str, Any]		= len(snake_case_					)

    tokenizer.add_tokens(["""new1""", """new2"""]					)
    tokenizer.move_added_token("""new1"""   ,    1					)

    # Check that moved token is not copied (duplicate)
    self.assertEqual(len(snake_case_					)   ,    original_len + 2					)
    # Check that token is moved to specified id
    self.assertEqual(tokenizer.encode("""new1"""					)   ,    [1]					)
    self.assertEqual(tokenizer.decode([1]					)   ,    """new1"""					)
'''simple docstring'''






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_	   =			{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			['RemBertTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			['RemBertTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'RemBertForCausalLM',
        'RemBertForMaskedLM',
        'RemBertForMultipleChoice',
        'RemBertForQuestionAnswering',
        'RemBertForSequenceClassification',
        'RemBertForTokenClassification',
        'RemBertLayer',
        'RemBertModel',
        'RemBertPreTrainedModel',
        'load_tf_weights_in_rembert',
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFRemBertForCausalLM',
        'TFRemBertForMaskedLM',
        'TFRemBertForMultipleChoice',
        'TFRemBertForQuestionAnswering',
        'TFRemBertForSequenceClassification',
        'TFRemBertForTokenClassification',
        'TFRemBertLayer',
        'TFRemBertModel',
        'TFRemBertPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_rembert import RemBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_rembert_fast import RemBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_rembert import (
            REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            RemBertForCausalLM,
            RemBertForMaskedLM,
            RemBertForMultipleChoice,
            RemBertForQuestionAnswering,
            RemBertForSequenceClassification,
            RemBertForTokenClassification,
            RemBertLayer,
            RemBertModel,
            RemBertPreTrainedModel,
            load_tf_weights_in_rembert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_rembert import (
            TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRemBertForCausalLM,
            TFRemBertForMaskedLM,
            TFRemBertForMultipleChoice,
            TFRemBertForQuestionAnswering,
            TFRemBertForSequenceClassification,
            TFRemBertForTokenClassification,
            TFRemBertLayer,
            TFRemBertModel,
            TFRemBertPreTrainedModel,
        )


else:
    import sys

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