



"""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


_UpperCAmelCase       =     get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class    a		(					lowercase_		, unittest.TestCase    ):
  UpperCamelCase		:			List[str]         =   ErnieMTokenizer
  UpperCamelCase		:			str         =   False
  UpperCamelCase		:			int         =   True
  UpperCamelCase		:			Union[str, Any]         =   False
  UpperCamelCase		:			str         =   False



  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Optional[Any]:


     '''simple docstring'''




     super().setUp()

     # We have a SentencePiece fixture for testing
     SCREAMING_SNAKE_CASE_:	int            =ErnieMTokenizer(UpperCamelCase_    ,						unk_token="""<unk>"""    ,						pad_token="""<pad>"""						)
     tokenizer.save_pretrained(self.tmpdirname						)



  def 		lowerCamelCase__    (  self	:   Optional[Any]    ,						lowerCAmelCase	:   Tuple						)       ->   str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            ="""this is a test"""
     SCREAMING_SNAKE_CASE_:	Tuple            ="""this is a test"""
     return input_text, output_text



  def 		lowerCamelCase__    (  self	:   Optional[int]						)       ->   str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            ="""<pad>"""
     SCREAMING_SNAKE_CASE_:	int            =0

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



  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	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(UpperCamelCase_						)    ,						3_0000						)



  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   List[Any]:


     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size    ,						3_0000						)



  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   Union[str, Any]:


     '''simple docstring'''




     if not self.test_rust_tokenizer:
        return

     SCREAMING_SNAKE_CASE_:	List[Any]            =self.get_tokenizer()
     SCREAMING_SNAKE_CASE_:	List[str]            =self.get_rust_tokenizer()

     SCREAMING_SNAKE_CASE_:	Any            ="""I was born in 92000, and this is falsé."""

     SCREAMING_SNAKE_CASE_:	Dict            =tokenizer.tokenize(UpperCamelCase_						)
     SCREAMING_SNAKE_CASE_:	int            =rust_tokenizer.tokenize(UpperCamelCase_						)
     self.assertListEqual(UpperCamelCase_    ,						UpperCamelCase_						)

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =tokenizer.encode(UpperCamelCase_    ,						add_special_tokens=UpperCamelCase_						)
     SCREAMING_SNAKE_CASE_:	Any            =rust_tokenizer.encode(UpperCamelCase_    ,						add_special_tokens=UpperCamelCase_						)
     self.assertListEqual(UpperCamelCase_    ,						UpperCamelCase_						)

     SCREAMING_SNAKE_CASE_:	Dict            =self.get_rust_tokenizer()
     SCREAMING_SNAKE_CASE_:	List[Any]            =tokenizer.encode(UpperCamelCase_						)
     SCREAMING_SNAKE_CASE_:	List[str]            =rust_tokenizer.encode(UpperCamelCase_						)
     self.assertListEqual(UpperCamelCase_    ,						UpperCamelCase_						)



  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            =ErnieMTokenizer(UpperCamelCase_    ,						do_lower_case=UpperCamelCase_    ,						unk_token="""<unk>"""    ,						pad_token="""<pad>"""						)

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =tokenizer.tokenize("""This is a test"""						)
     self.assertListEqual(UpperCamelCase_    ,						["""▁this""", """▁is""", """▁a""", """▁test"""]						)

     self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_						)    ,						[48, 25, 21, 1289]						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =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(
         UpperCamelCase_    ,						["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =tokenizer.convert_tokens_to_ids(UpperCamelCase_						)
     self.assertListEqual(UpperCamelCase_    ,						[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]						)

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



  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =ErnieMTokenizer(UpperCamelCase_    ,						unk_token="""<unk>"""    ,						pad_token="""<pad>"""						)

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

     SCREAMING_SNAKE_CASE_:	List[Any]            =tokenizer.build_inputs_with_special_tokens(UpperCamelCase_						)
     SCREAMING_SNAKE_CASE_:	List[str]            =tokenizer.build_inputs_with_special_tokens(UpperCamelCase_    ,						UpperCamelCase_						)

     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	:   List[str]						)       ->   Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            ={"""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=UpperCamelCase_    ,						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"""
from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   List[str]						)       ->   None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =vocab
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =normalizers.BertNormalizer(
         clean_text=lowerCAmelCase    ,						handle_chinese_chars=lowerCAmelCase    ,						strip_accents=lowerCAmelCase    ,						lowercase=lowerCAmelCase    ,						)
     try:
        import rjieba
     except ImportError:
        raise ImportError(
            """You need to install rjieba to use RoFormerTokenizer. """
            """See https://pypi.org/project/rjieba/ for installation."""						)
     SCREAMING_SNAKE_CASE_:	Dict            =rjieba



  def 		lowerCamelCase__    (  self	:   Tuple    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   NormalizedString						)       ->   List[NormalizedString]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =[]

     # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
     for token, start, end in self.jieba.tokenize(str(lowerCAmelCase						)    ,						hmm=lowerCAmelCase						):
        if token in self.vocab:
           splits.append(normalized_string[start:end]						)
        else:
           SCREAMING_SNAKE_CASE_:	List[Any]            =self.normalizers.normalize_str(lowerCAmelCase						).split()
           for token in token_list:
              if token:
                 SCREAMING_SNAKE_CASE_:	Dict            =start + len(lowerCAmelCase						)
                 splits.append(normalized_string[start:end]						)
                 SCREAMING_SNAKE_CASE_:	Optional[Any]            =end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

     return splits





  def 		lowerCamelCase__    (  self	:   Union[str, Any]    ,						lowerCAmelCase	:   PreTokenizedString						)       ->   Tuple:


     '''simple docstring'''




     pretok.split(self.jieba_split						)



