code: 
  
  
  
  '''simple docstring'''
  
  
  
  import unittest
  
  from transformers import ErnieMTokenizer
  from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  a__						:			Union[str, Any]										=			get_tests_dir('fixtures/spiece.model')
  @require_sentencepiece
  @require_tokenizers
  class        UpperCAmelCase__  (    UpperCAmelCase_							,			unittest.TestCase):
   __SCREAMING_SNAKE_CASE					      =				ErnieMTokenizer
   __SCREAMING_SNAKE_CASE					      =				False
   __SCREAMING_SNAKE_CASE					      =				True
   __SCREAMING_SNAKE_CASE					      =				False
   __SCREAMING_SNAKE_CASE					      =				False
  
  
  
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    super().setUp()
  
    # We have a SentencePiece fixture for testing
    __UpperCamelCase				      =  ErnieMTokenizer(lowercase		,	unk_token="""<unk>"""		,	pad_token="""<pad>"""			)
    tokenizer.save_pretrained(self.tmpdirname			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							Optional[Any]:
    __UpperCamelCase				      =  """this is a test"""
    __UpperCamelCase				      =  """this is a test"""
    return input_text, output_text
  
  
  
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    __UpperCamelCase				      =  """<pad>"""
    __UpperCamelCase				      =  0
  
    self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase			)		,	lowercase			)
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase			)		,	lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    __UpperCamelCase				      =  list(self.get_tokenizer().get_vocab().keys()			)
  
    self.assertEqual(vocab_keys[0]		,	"""<pad>"""			)
    self.assertEqual(vocab_keys[1]		,	"""<unk>"""			)
    self.assertEqual(vocab_keys[-1]		,	"""▁eloquent"""			)
    self.assertEqual(len(lowercase			)		,	3_0_0_0_0			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
    self.assertEqual(self.get_tokenizer().vocab_size		,	3_0_0_0_0			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    if not self.test_rust_tokenizer:
     return
  
    __UpperCamelCase				      =  self.get_tokenizer()
    __UpperCamelCase				      =  self.get_rust_tokenizer()
  
    __UpperCamelCase				      =  """I was born in 92000, and this is falsé."""
  
    __UpperCamelCase				      =  tokenizer.tokenize(lowercase			)
    __UpperCamelCase				      =  rust_tokenizer.tokenize(lowercase			)
    self.assertListEqual(lowercase		,	lowercase			)
  
    __UpperCamelCase				      =  tokenizer.encode(lowercase		,	add_special_tokens=lowercase			)
    __UpperCamelCase				      =  rust_tokenizer.encode(lowercase		,	add_special_tokens=lowercase			)
    self.assertListEqual(lowercase		,	lowercase			)
  
    __UpperCamelCase				      =  self.get_rust_tokenizer()
    __UpperCamelCase				      =  tokenizer.encode(lowercase			)
    __UpperCamelCase				      =  rust_tokenizer.encode(lowercase			)
    self.assertListEqual(lowercase		,	lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Any:
    __UpperCamelCase				      =  ErnieMTokenizer(lowercase		,	do_lower_case=lowercase		,	unk_token="""<unk>"""		,	pad_token="""<pad>"""			)
  
    __UpperCamelCase				      =  tokenizer.tokenize("""This is a test"""			)
    self.assertListEqual(lowercase		,	["""▁this""", """▁is""", """▁a""", """▁test"""]			)
  
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase			)		,	[4_8, 2_5, 2_1, 1_2_8_9]			)
  
    __UpperCamelCase				      =  tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
    # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
    self.assertListEqual(
        lowercase		,	["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]			)
    __UpperCamelCase				      =  tokenizer.convert_tokens_to_ids(lowercase			)
    self.assertListEqual(lowercase		,	[3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]			)
  
    __UpperCamelCase				      =  tokenizer.convert_ids_to_tokens(lowercase			)
    self.assertListEqual(
        lowercase		,	["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]		,	)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
    __UpperCamelCase				      =  ErnieMTokenizer(lowercase		,	unk_token="""<unk>"""		,	pad_token="""<pad>"""			)
  
    __UpperCamelCase				      =  tokenizer.encode("""sequence builders"""			)
    __UpperCamelCase				      =  tokenizer.encode("""multi-sequence build"""			)
  
    __UpperCamelCase				      =  tokenizer.build_inputs_with_special_tokens(lowercase			)
    __UpperCamelCase				      =  tokenizer.build_inputs_with_special_tokens(lowercase		,	lowercase			)
  
    assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
    assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
        tokenizer.sep_token_id
    ] + text_a + [tokenizer.sep_token_id]
  
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
    # fmt: off
    __UpperCamelCase				      =  {"""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, 9, 3_0_4, 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, 5, 5, 5, 1_6, 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, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 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, 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, 1, 1, 1, 1]]}
    # fmt: on
  
    self.tokenizer_integration_test_util(
        expected_encoding=lowercase		,	model_name="""susnato/ernie-m-base_pytorch"""		,	sequences=[
            """Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
            """general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
            """Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained """
            """models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
            """BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
            """conditioning on both left and right context in all layers.""",
            """The quick brown fox jumps over the lazy dog.""",
        ]		,	)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  a__						:			Any										=			{
      '0000': '0',
      '0001': '1',
      '0010': '2',
      '0011': '3',
      '0100': '4',
      '0101': '5',
      '0110': '6',
      '0111': '7',
      '1000': '8',
      '1001': '9',
      '1010': 'a',
      '1011': 'b',
      '1100': 'c',
      '1101': 'd',
      '1110': 'e',
      '1111': 'f',
  }
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  str(__A       ).strip()
  
   # Exceptions
   if not binary_str:
    raise ValueError("""Empty string was passed to the function"""       )
   __UpperCamelCase				      =  binary_str[0] == """-"""
   __UpperCamelCase				      =  binary_str[1:] if is_negative else binary_str
   if not all(char in """01""" for char in binary_str       ):
    raise ValueError("""Non-binary value was passed to the function"""       )
  
   __UpperCamelCase				      =  (
       """0""" * (4 * (divmod(len(__A       )							,4       )[0] + 1) - len(__A       )) + binary_str
   )
  
   __UpperCamelCase				      =  []
   for x in range(0							,len(__A       )							,4       ):
    hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]       )
   __UpperCamelCase				      =  """0x""" + """""".join(__A       )
  
   return "-" + hexadecimal_str if is_negative else hexadecimal_str
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
  
  
  
  
  
  