





"""simple docstring"""


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


lowerCamelCase__						= logging.get_logger(__name__)

lowerCamelCase__						= {
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}



class   __SCREAMING_SNAKE_CASE     (    lowerCamelCase__ ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Any						  =       "transfo-xl"
  SCREAMING_SNAKE_CASE__				:Tuple						  =       ["mems"]
  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       {
      "n_token": "vocab_size",
      "hidden_size": "d_model",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }

  def __init__(		self :							List[Any]       , __a :							Tuple=26_7735       , __a :							Dict=[2_0000, 4_0000, 20_0000]       , __a :							Optional[int]=1024       , __a :							List[Any]=1024       , __a :							Optional[Any]=16       , __a :							int=64       , __a :							Optional[int]=4096       , __a :							Union[str, Any]=4       , __a :							Union[str, Any]=False       , __a :							Union[str, Any]=18       , __a :							Union[str, Any]=1600       , __a :							Any=1000       , __a :							List[str]=True       , __a :							List[Any]=True       , __a :							str=0       , __a :							str=-1       , __a :							Optional[int]=True       , __a :							List[Any]=0.1       , __a :							Dict=0.0       , __a :							Optional[Any]=True       , __a :							Any="normal"       , __a :							Optional[int]=0.01       , __a :							Optional[Any]=0.01       , __a :							List[str]=0.02       , __a :							Any=1e-5       , __a :							Dict=0       , **__a :							Any       , )		->					Union[str, Any]:
        _UpperCamelCase   :						str    =							vocab_size
        _UpperCamelCase   :						Optional[int]    =							[]
        self.cutoffs.extend(SCREAMING_SNAKE_CASE__						)
        if proj_share_all_but_first:
              _UpperCamelCase   :						Tuple    =							[False] + [True] * len(self.cutoffs						)
        else:
              _UpperCamelCase   :						Optional[Any]    =							[False] + [False] * len(self.cutoffs						)
        _UpperCamelCase   :						Tuple    =							d_model
        _UpperCamelCase   :						int    =							d_embed
        _UpperCamelCase   :						List[str]    =							d_head
        _UpperCamelCase   :						Dict    =							d_inner
        _UpperCamelCase   :						List[str]    =							div_val
        _UpperCamelCase   :						Dict    =							pre_lnorm
        _UpperCamelCase   :						Tuple    =							n_layer
        _UpperCamelCase   :						List[str]    =							n_head
        _UpperCamelCase   :						Any    =							mem_len
        _UpperCamelCase   :						Optional[int]    =							same_length
        _UpperCamelCase   :						Optional[Any]    =							attn_type
        _UpperCamelCase   :						List[Any]    =							clamp_len
        _UpperCamelCase   :						Dict    =							sample_softmax
        _UpperCamelCase   :						Any    =							adaptive
        _UpperCamelCase   :						Tuple    =							dropout
        _UpperCamelCase   :						Optional[int]    =							dropatt
        _UpperCamelCase   :						Union[str, Any]    =							untie_r
        _UpperCamelCase   :						int    =							init
        _UpperCamelCase   :						Dict    =							init_range
        _UpperCamelCase   :						Dict    =							proj_init_std
        _UpperCamelCase   :						List[str]    =							init_std
        _UpperCamelCase   :						Dict    =							layer_norm_epsilon
        super().__init__(eos_token_id=SCREAMING_SNAKE_CASE__       , **SCREAMING_SNAKE_CASE__						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					int:
        logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''						)
        return -1


  @max_position_embeddings.setter
  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							Optional[int]						)		->					List[Any]:
        raise NotImplementedError(
            F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''						)




"""simple docstring"""






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      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							CpmAntTokenizer
		A    =							False
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					super().setUp()

					_UpperCamelCase					:str					     =	[
					    '''<d>''',
					    '''</d>''',
					    '''<s>''',
					    '''</s>''',
					    '''</_>''',
					    '''<unk>''',
					    '''<pad>''',
					    '''</n>''',
					    '''我''',
					    '''是''',
					    '''C''',
					    '''P''',
					    '''M''',
					    '''A''',
					    '''n''',
					    '''t''',
					]
					_UpperCamelCase					:List[str]					     =	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   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b'''   )
					_UpperCamelCase					:List[str]					     =	'''今天天气真好！'''
					_UpperCamelCase					:Tuple					     =	['''今天''', '''天气''', '''真''', '''好''', '''！''']
					_UpperCamelCase					:Tuple					     =	tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Dict					     =	'''今天天气真好！'''
					_UpperCamelCase					:Optional[int]					     =	[tokenizer.bos_token] + tokens

					_UpperCamelCase					:List[str]					     =	[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	tokenizer.decode(SCREAMING_SNAKE_CASE__   )
					self.assertEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

