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 	__lowerCamelCase       (				A__				,							unittest.TestCase    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    a_			:       Dict      =							ErnieMTokenizer
    a_			:       Optional[int]      =							False
    a_			:       Optional[int]      =							True
    a_			:       Optional[int]      =							False
    a_			:       Any      =							False
  
  
  
  
  
    def      lowerCamelCase (     self							:			str			):
      super().setUp()
  
      # We have a SentencePiece fixture for testing
      lowerCAmelCase_     :							Union[str, Any]             =    ErnieMTokenizer(a_		,    unk_token="<unk>"		,    pad_token="<pad>"			)
      tokenizer.save_pretrained(self.tmpdirname			)
  
  
  
  
  
    def      lowerCamelCase (     self							:			Optional[int]		,    a_							:			List[Any]			):
      lowerCAmelCase_     :							str             =    "this is a test"
      lowerCAmelCase_     :							Dict             =    "this is a test"
      return input_text, output_text
  
  
  
  
  
    def      lowerCamelCase (     self							:			Optional[Any]			):
      lowerCAmelCase_     :							int             =    "<pad>"
      lowerCAmelCase_     :							List[str]             =    0
  
      self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_			)		,    a_			)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_			)		,    a_			)
  
  
  
  
  
    def      lowerCamelCase (     self							:			str			):
      lowerCAmelCase_     :							Optional[Any]             =    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_00_00			)
  
  
  
  
  
    def      lowerCamelCase (     self							:			List[Any]			):
      self.assertEqual(self.get_tokenizer().vocab_size		,    3_00_00			)
  
  
  
  
  
    def      lowerCamelCase (     self							:			Union[str, Any]			):
      if not self.test_rust_tokenizer:
        return
  
      lowerCAmelCase_     :							str             =    self.get_tokenizer()
      lowerCAmelCase_     :							List[Any]             =    self.get_rust_tokenizer()
  
      lowerCAmelCase_     :							int             =    "I was born in 92000, and this is falsé."
  
      lowerCAmelCase_     :							int             =    tokenizer.tokenize(a_			)
      lowerCAmelCase_     :							Dict             =    rust_tokenizer.tokenize(a_			)
      self.assertListEqual(a_		,    a_			)
  
      lowerCAmelCase_     :							Any             =    tokenizer.encode(a_		,    add_special_tokens=a_			)
      lowerCAmelCase_     :							Tuple             =    rust_tokenizer.encode(a_		,    add_special_tokens=a_			)
      self.assertListEqual(a_		,    a_			)
  
      lowerCAmelCase_     :							List[str]             =    self.get_rust_tokenizer()
      lowerCAmelCase_     :							Union[str, Any]             =    tokenizer.encode(a_			)
      lowerCAmelCase_     :							Dict             =    rust_tokenizer.encode(a_			)
      self.assertListEqual(a_		,    a_			)
  
  
  
  
  
    def      lowerCamelCase (     self							:			Any			):
      lowerCAmelCase_     :							List[str]             =    ErnieMTokenizer(a_		,    do_lower_case=a_		,    unk_token="<unk>"		,    pad_token="<pad>"			)
  
      lowerCAmelCase_     :							str             =    tokenizer.tokenize("This is a test"			)
      self.assertListEqual(a_		,    ["▁this", "▁is", "▁a", "▁test"]			)
  
      self.assertListEqual(tokenizer.convert_tokens_to_ids(a_			)		,    [48, 25, 21, 12_89]			)
  
      lowerCAmelCase_     :							List[Any]             =    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", "."]			)
      lowerCAmelCase_     :							Optional[int]             =    tokenizer.convert_tokens_to_ids(a_			)
      self.assertListEqual(a_		,    [31, 23, 3_86, 19, 5_18, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]			)
  
      lowerCAmelCase_     :							int             =    tokenizer.convert_ids_to_tokens(a_			)
      self.assertListEqual(
          a_		,    ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]		,    )
  
  
  
  
  
    def      lowerCamelCase (     self							:			int			):
      lowerCAmelCase_     :							Dict             =    ErnieMTokenizer(a_		,    unk_token="<unk>"		,    pad_token="<pad>"			)
  
      lowerCAmelCase_     :							Tuple             =    tokenizer.encode("sequence builders"			)
      lowerCAmelCase_     :							Union[str, Any]             =    tokenizer.encode("multi-sequence build"			)
  
      lowerCAmelCase_     :							Optional[int]             =    tokenizer.build_inputs_with_special_tokens(a_			)
      lowerCAmelCase_     :							Union[str, Any]             =    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      lowerCamelCase (     self							:			int			):
      # fmt: off
      lowerCAmelCase_     :							Dict             =    {"input_ids": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 9, 3_04, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 5, 5, 5, 16, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 64_60, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 35_59, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 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_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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"""
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowercase__      =				{
      """configuration_mobilevitv2""": [
          """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """MobileViTV2Config""",
          """MobileViTV2OnnxConfig""",
      ],
  }
  
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					lowercase__      =				[
  					    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
  					    """MobileViTV2ForImageClassification""",
  					    """MobileViTV2ForSemanticSegmentation""",
  					    """MobileViTV2Model""",
  					    """MobileViTV2PreTrainedModel""",
  					]
  
  if TYPE_CHECKING:
  					from .configuration_mobilevitva import (
  					    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  					    MobileViTVaConfig,
  					    MobileViTVaOnnxConfig,
  					)
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_mobilevitva import (
  										    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    MobileViTVaForImageClassification,
  										    MobileViTVaForSemanticSegmentation,
  										    MobileViTVaModel,
  										    MobileViTVaPreTrainedModel,
  										)
  
  else:
  					import sys
  
  					lowercase__      =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  