





from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[Any]							=    'lilt'


      def __init__(self       :      Union[str, Any]				,			a__       :      Any=3_0522				,			a__       :      List[Any]=768				,			a__       :      List[Any]=12				,			a__       :      Tuple=12				,			a__       :      str=3072				,			a__       :      Union[str, Any]="gelu"				,			a__       :      str=0.1				,			a__       :      Optional[Any]=0.1				,			a__       :      Optional[Any]=512				,			a__       :      List[Any]=2				,			a__       :      str=0.0_2				,			a__       :      Optional[int]=1E-12				,			a__       :      Union[str, Any]=0				,			a__       :      Dict="absolute"				,			a__       :      Optional[Any]=None				,			a__       :      Dict=4				,			a__       :      Tuple=1024				,			**a__       :      Dict				,			):





         """simple docstring"""



         super().__init__(pad_token_id=a__				,			**a__      )

         __snake_case							       =   vocab_size
         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   hidden_act
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   max_position_embeddings
         __snake_case							       =   type_vocab_size
         __snake_case							       =   initializer_range
         __snake_case							       =   layer_norm_eps
         __snake_case							       =   position_embedding_type
         __snake_case							       =   classifier_dropout
         __snake_case							       =   channel_shrink_ratio
         __snake_case							       =   max_ad_position_embeddings








import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin

@require_jieba
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[str]							=    CpmAntTokenizer
      A_						:				Optional[int]							=    False


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         super().setUp()

         __snake_case							       =   [
             '''<d>''',
             '''</d>''',
             '''<s>''',
             '''</s>''',
             '''</_>''',
             '''<unk>''',
             '''<pad>''',
             '''</n>''',
             '''我''',
             '''是''',
             '''C''',
             '''P''',
             '''M''',
             '''A''',
             '''n''',
             '''t''',
         ]
         __snake_case							       =   os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''vocab_file''']      )
         with open(self.vocab_file				,			'''w'''				,			encoding='''utf-8'''      ) as vocab_writer:
            vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]      )      )


      @tooslow
      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b'''      )
         __snake_case							       =   '''今天天气真好！'''
         __snake_case							       =   ['''今天''', '''天气''', '''真''', '''好''', '''！''']
         __snake_case							       =   tokenizer.tokenize(a__      )
         self.assertListEqual(a__				,			a__      )
         __snake_case							       =   '''今天天气真好！'''
         __snake_case							       =   [tokenizer.bos_token] + tokens

         __snake_case							       =   [6, 9802, 1_4962, 2082, 831, 244]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__      )				,			a__      )

         __snake_case							       =   tokenizer.decode(a__      )
         self.assertEqual(a__				,			a__      )

