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
  
  
  lowercase_			       =				get_tests_dir("""fixtures/spiece.model""")
  
  
  @require_sentencepiece
  @require_tokenizers
  class       a_  (       snake_case_   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				ErnieMTokenizer
      UpperCamelCase									=				False
      UpperCamelCase									=				True
      UpperCamelCase									=				False
      UpperCamelCase									=				False
  
  
      def     snake_case_(	self )	->     Dict:
          super().setUp()
  
          # We have a SentencePiece fixture for testing
          _SCREAMING_SNAKE_CASE             =    ErnieMTokenizer(A				,	unk_token="""<unk>"""				,	pad_token="""<pad>""" )
          tokenizer.save_pretrained(self.tmpdirname )
  
  
      def     snake_case_(	self				,	A )	->     str:
          _SCREAMING_SNAKE_CASE             =    """this is a test"""
          _SCREAMING_SNAKE_CASE             =    """this is a test"""
          return input_text, output_text
  
  
      def     snake_case_(	self )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    """<pad>"""
          _SCREAMING_SNAKE_CASE             =    0
  
          self.assertEqual(self.get_tokenizer()._convert_token_to_id(A )				,	A )
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(A )				,	A )
  
  
      def     snake_case_(	self )	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE             =    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(A )				,	3_0000 )
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          self.assertEqual(self.get_tokenizer().vocab_size				,	3_0000 )
  
  
      def     snake_case_(	self )	->     str:
          if not self.test_rust_tokenizer:
              return
  
          _SCREAMING_SNAKE_CASE             =    self.get_tokenizer()
          _SCREAMING_SNAKE_CASE             =    self.get_rust_tokenizer()
  
          _SCREAMING_SNAKE_CASE             =    """I was born in 92000, and this is falsé."""
  
          _SCREAMING_SNAKE_CASE             =    tokenizer.tokenize(A )
          _SCREAMING_SNAKE_CASE             =    rust_tokenizer.tokenize(A )
          self.assertListEqual(A				,	A )
  
          _SCREAMING_SNAKE_CASE             =    tokenizer.encode(A				,	add_special_tokens=A )
          _SCREAMING_SNAKE_CASE             =    rust_tokenizer.encode(A				,	add_special_tokens=A )
          self.assertListEqual(A				,	A )
  
          _SCREAMING_SNAKE_CASE             =    self.get_rust_tokenizer()
          _SCREAMING_SNAKE_CASE             =    tokenizer.encode(A )
          _SCREAMING_SNAKE_CASE             =    rust_tokenizer.encode(A )
          self.assertListEqual(A				,	A )
  
  
      def     snake_case_(	self )	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE             =    ErnieMTokenizer(A				,	do_lower_case=A				,	unk_token="""<unk>"""				,	pad_token="""<pad>""" )
  
          _SCREAMING_SNAKE_CASE             =    tokenizer.tokenize("""This is a test""" )
          self.assertListEqual(A				,	["""▁this""", """▁is""", """▁a""", """▁test"""] )
  
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A )				,	[48, 25, 21, 1289] )
  
          _SCREAMING_SNAKE_CASE             =    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(
              A				,	["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""] )
          _SCREAMING_SNAKE_CASE             =    tokenizer.convert_tokens_to_ids(A )
          self.assertListEqual(A				,	[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9] )
  
          _SCREAMING_SNAKE_CASE             =    tokenizer.convert_ids_to_tokens(A )
          self.assertListEqual(
              A				,	["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]				,	)
  
  
      def     snake_case_(	self )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    ErnieMTokenizer(A				,	unk_token="""<unk>"""				,	pad_token="""<pad>""" )
  
          _SCREAMING_SNAKE_CASE             =    tokenizer.encode("""sequence builders""" )
          _SCREAMING_SNAKE_CASE             =    tokenizer.encode("""multi-sequence build""" )
  
          _SCREAMING_SNAKE_CASE             =    tokenizer.build_inputs_with_special_tokens(A )
          _SCREAMING_SNAKE_CASE             =    tokenizer.build_inputs_with_special_tokens(A				,	A )
  
          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     snake_case_(	self )	->     Optional[Any]:
          # fmt: off
          _SCREAMING_SNAKE_CASE             =    {"""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, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 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, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 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, 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, 1, 1, 1, 1]]}
          # fmt: on
  
          self.tokenizer_integration_test_util(
              expected_encoding=A				,	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'''
  
  import math
  from timeit import timeit
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      _SCREAMING_SNAKE_CASE             =    0
      _SCREAMING_SNAKE_CASE             =    abs(__lowerCamelCase					)
      while True:
          _SCREAMING_SNAKE_CASE             =    n // 10
          digits += 1
          if n == 0:
              break
      return digits
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      return 1 if n == 0 else math.floor(math.log(abs(__lowerCamelCase					)      , 10					) + 1					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      return len(str(abs(__lowerCamelCase					)					)					)
  
  
  
  
  
  def lowerCamelCase		(  )		->None:
      from collections.abc import Callable
  
      def benchmark_a_function(__lowerCamelCase				:					Callable      , __lowerCamelCase				:					int					) -> None:
          _SCREAMING_SNAKE_CASE             =    F'{func.__name__}({value})'
          _SCREAMING_SNAKE_CASE             =    timeit(F'__main__.{call}'      , setup="""import __main__"""					)
          print(F'{call}: {func(__lowerCamelCase					)} -- {timing} seconds'					)
  
      for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
          for func in (num_digits, num_digits_fast, num_digits_faster):
              benchmark_a_function(__lowerCamelCase      , __lowerCamelCase					)
          print()
  
  
  if __name__ == "__main__":
        import doctest
  
        doctest.testmod()
        benchmark()
  
  
  
  