





"""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


lowerCamelCase__      =      get_tests_dir("fixtures/spiece.model")



@require_sentencepiece
@require_tokenizers
class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase						,     unittest.TestCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Optional[Any]		   =    ErnieMTokenizer
  SCREAMING_SNAKE_CASE__							:str		   =    False
  SCREAMING_SNAKE_CASE__							:str		   =    True
  SCREAMING_SNAKE_CASE__							:Any		   =    False
  SCREAMING_SNAKE_CASE__							:Dict		   =    False

  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]		)						-> Optional[Any]:
        super().setUp()

        # We have a SentencePiece fixture for testing
        _UpperCamelCase					:		List[Any]  =				ErnieMTokenizer(__a    ,      unk_token="<unk>"    ,      pad_token="<pad>"		)
        tokenizer.save_pretrained(self.tmpdirname		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]    ,      __a      :				Union[str, Any]		)						-> Union[str, Any]:
        _UpperCamelCase					:		str  =				"this is a test"
        _UpperCamelCase					:		Dict  =				"this is a test"
        return input_text, output_text

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> List[Any]:
        _UpperCamelCase					:		List[str]  =				"<pad>"
        _UpperCamelCase					:		List[Any]  =				0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a		)    ,      __a		)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a		)    ,      __a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Dict		)						-> Union[str, Any]:
        _UpperCamelCase					:		int  =				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       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]		)						-> List[str]:
        self.assertEqual(self.get_tokenizer().vocab_size    ,      3_0000		)

  def       __SCREAMING_SNAKE_CASE (						self      :				str		)						-> List[str]:
        if not self.test_rust_tokenizer:
              return

        _UpperCamelCase					:		Any  =				self.get_tokenizer()
        _UpperCamelCase					:		Any  =				self.get_rust_tokenizer()

        _UpperCamelCase					:		Optional[Any]  =				"I was born in 92000, and this is falsé."

        _UpperCamelCase					:		Tuple  =				tokenizer.tokenize(__a		)
        _UpperCamelCase					:		Any  =				rust_tokenizer.tokenize(__a		)
        self.assertListEqual(__a    ,      __a		)

        _UpperCamelCase					:		Tuple  =				tokenizer.encode(__a    ,      add_special_tokens=__a		)
        _UpperCamelCase					:		int  =				rust_tokenizer.encode(__a    ,      add_special_tokens=__a		)
        self.assertListEqual(__a    ,      __a		)

        _UpperCamelCase					:		Dict  =				self.get_rust_tokenizer()
        _UpperCamelCase					:		List[Any]  =				tokenizer.encode(__a		)
        _UpperCamelCase					:		Any  =				rust_tokenizer.encode(__a		)
        self.assertListEqual(__a    ,      __a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]		)						-> List[Any]:
        _UpperCamelCase					:		Union[str, Any]  =				ErnieMTokenizer(__a    ,      do_lower_case=__a    ,      unk_token="<unk>"    ,      pad_token="<pad>"		)

        _UpperCamelCase					:		Optional[int]  =				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]		)

        _UpperCamelCase					:		Optional[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", "."]		)
        _UpperCamelCase					:		Any  =				tokenizer.convert_tokens_to_ids(__a		)
        self.assertListEqual(__a    ,      [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]		)

        _UpperCamelCase					:		str  =				tokenizer.convert_ids_to_tokens(__a		)
        self.assertListEqual(
            __a    ,      ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]    ,      )

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> Union[str, Any]:
        _UpperCamelCase					:		List[Any]  =				ErnieMTokenizer(__a    ,      unk_token="<unk>"    ,      pad_token="<pad>"		)

        _UpperCamelCase					:		Any  =				tokenizer.encode("sequence builders"		)
        _UpperCamelCase					:		Dict  =				tokenizer.encode("multi-sequence build"		)

        _UpperCamelCase					:		List[Any]  =				tokenizer.build_inputs_with_special_tokens(__a		)
        _UpperCamelCase					:		int  =				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       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> Dict:
        # fmt: off
        _UpperCamelCase					:		str  =				{"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.",
            ]    ,      )







"""simple docstring"""


def     lowercase__			(    lowercase_	)    ->						str:




      """simple docstring"""

      return " ".join(sorted(set(sentence.split()	)	)	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
