


'''simple docstring'''

from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class _lowerCamelCase :



		'''simple docstring'''







		def __init__(      self	:		List[str]						,  _A	:		str    )						->				None:
			__magic_name__ :					Dict  			=  vocab
			__magic_name__ :					Tuple  			=  normalizers.BertNormalizer(
			    clean_text=_A						,  handle_chinese_chars=_A						,  strip_accents=_A						,  lowercase=_A						,  )
			try:
				import rjieba
			except ImportError:
				raise ImportError(
				    'You need to install rjieba to use RoFormerTokenizer. '
				    'See https://pypi.org/project/rjieba/ for installation.'    )
			__magic_name__ :					int  			=  rjieba







		def      __lowerCAmelCase	(      self	:		int						,  _A	:		Any						,  _A	:		Optional[Any]    )						->				List[NormalizedString]:
			__magic_name__ :					List[Any]  			=  []

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



'''simple docstring'''

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


lowerCAmelCase   :List[Any]								=    logging.get_logger(__name__)

lowerCAmelCase   :Dict								=    {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}
class _lowerCamelCase (		lowercase__							):



		'''simple docstring'''







		A_			:    Optional[Any]   					=       """yoso"""







		def __init__(      self	:		Any						,  _A	:		List[Any]=50265						,  _A	:		Any=768						,  _A	:		List[str]=12						,  _A	:		Dict=12						,  _A	:		Union[str, Any]=3072						,  _A	:		List[Any]="gelu"						,  _A	:		Dict=0.1						,  _A	:		Union[str, Any]=0.1						,  _A	:		Union[str, Any]=4096						,  _A	:		Dict=1						,  _A	:		Tuple=0.02						,  _A	:		Any=1E-12						,  _A	:		Optional[int]="absolute"						,  _A	:		Tuple=True						,  _A	:		Any=9						,  _A	:		Tuple=64						,  _A	:		str=None						,  _A	:		Dict=True						,  _A	:		str=True						,  _A	:		int=1						,  _A	:		Optional[int]=0						,  _A	:		Tuple=2						,  **_A	:		Optional[Any]						,  )						->				Any:
			super().__init__(pad_token_id=_A						,  bos_token_id=_A						,  eos_token_id=_A						,  **_A    )

			__magic_name__ :					List[Any]  			=  vocab_size
			__magic_name__ :					int  			=  max_position_embeddings
			__magic_name__ :					int  			=  hidden_size
			__magic_name__ :					Any  			=  num_hidden_layers
			__magic_name__ :					List[str]  			=  num_attention_heads
			__magic_name__ :					str  			=  intermediate_size
			__magic_name__ :					Any  			=  hidden_act
			__magic_name__ :					Union[str, Any]  			=  hidden_dropout_prob
			__magic_name__ :					str  			=  attention_probs_dropout_prob
			__magic_name__ :					int  			=  initializer_range
			__magic_name__ :					List[Any]  			=  type_vocab_size
			__magic_name__ :					Tuple  			=  layer_norm_eps
			__magic_name__ :					Tuple  			=  position_embedding_type
			__magic_name__ :					Optional[Any]  			=  use_expectation
			__magic_name__ :					Union[str, Any]  			=  hash_code_len
			__magic_name__ :					int  			=  num_hash
			__magic_name__ :					List[Any]  			=  conv_window
			__magic_name__ :					Optional[Any]  			=  use_fast_hash
			__magic_name__ :					int  			=  lsh_backward