



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers

class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				str  ,	UpperCamelCase			:				Union[str, Any]					)->None:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						vocab
      __SCREAMING_SNAKE_CASE							:     Dict							=						normalizers.BertNormalizer(
          clean_text=UpperCamelCase  ,	handle_chinese_chars=UpperCamelCase  ,	strip_accents=UpperCamelCase  ,	lowercase=UpperCamelCase  ,	)
      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							:     Optional[Any]							=						rjieba





     def 						__snake_case     (							self			:				Dict  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				NormalizedString					)->List[NormalizedString]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						[]

      # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
      for token, start, end in self.jieba.tokenize(str(UpperCamelCase					)  ,	hmm=UpperCamelCase					):
       if token in self.vocab:
        splits.append(normalized_string[start:end]					)
       else:
        __SCREAMING_SNAKE_CASE							:     Dict							=						self.normalizers.normalize_str(UpperCamelCase					).split()
        for token in token_list:
         if token:
          __SCREAMING_SNAKE_CASE							:     str							=						start + len(UpperCamelCase					)
          splits.append(normalized_string[start:end]					)
          __SCREAMING_SNAKE_CASE							:     Union[str, 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 						__snake_case     (							self			:				str  ,	UpperCamelCase			:				PreTokenizedString					)->Dict:
      pretok.split(self.jieba_split					)












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


__SCREAMING_SNAKE_CASE     : str      =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     : Any      ={
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:						str  ="tvlt"




     def __init__(     self	: List[Any]						,	A__	: List[str]=2_2_4						,	A__	: Optional[int]=2_0_4_8						,	A__	: str=1_2_8						,	A__	: Union[str, Any]=[1_6, 1_6]						,	A__	: List[Any]=[1_6, 1_6]						,	A__	: int=3						,	A__	: Optional[int]=1						,	A__	: Tuple=8						,	A__	: Tuple=7_6_8						,	A__	: List[str]=1_2						,	A__	: Tuple=1_2						,	A__	: int=3_0_7_2						,	A__	: Tuple="gelu"						,	A__	: str=0.0						,	A__	: Dict=0.0						,	A__	: Any=0.02						,	A__	: int=1E-6						,	A__	: List[str]=True						,	A__	: List[str]=False						,	A__	: Optional[Any]=1_6						,	A__	: Optional[Any]=5_1_2						,	A__	: List[Any]=8						,	A__	: Any=2_0_4_8						,	A__	: List[Any]=0.75						,	A__	: List[Any]=0.15						,	A__	: str="frame-level"						,	A__	: Union[str, Any]=True						,	A__	: Optional[int]=True						,	A__	: Optional[Any]="classification"						,	**A__	: int						,	)					->							Tuple:
           super().__init__(**A__      )

           if audio_mask_type not in ("frame-level", "patch_level"):
                 raise ValueError(
                     'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
                     F"""got {audio_mask_type}"""      )

           lowerCamelCase__   	=					image_size
           lowerCamelCase__   	=					spectrogram_length
           lowerCamelCase__   	=					frequency_length
           lowerCamelCase__   	=					image_patch_size
           lowerCamelCase__   	=					audio_patch_size
           lowerCamelCase__   	=					num_image_channels
           lowerCamelCase__   	=					num_audio_channels
           lowerCamelCase__   	=					num_frames

           lowerCamelCase__   	=					hidden_size
           lowerCamelCase__   	=					num_hidden_layers
           lowerCamelCase__   	=					num_attention_heads
           lowerCamelCase__   	=					intermediate_size
           lowerCamelCase__   	=					hidden_act
           lowerCamelCase__   	=					hidden_dropout_prob
           lowerCamelCase__   	=					attention_probs_dropout_prob
           lowerCamelCase__   	=					initializer_range
           lowerCamelCase__   	=					layer_norm_eps
           lowerCamelCase__   	=					qkv_bias
           lowerCamelCase__   	=					use_mean_pooling

           lowerCamelCase__   	=					decoder_num_attention_heads
           lowerCamelCase__   	=					decoder_hidden_size
           lowerCamelCase__   	=					decoder_num_hidden_layers
           lowerCamelCase__   	=					decoder_intermediate_size
           lowerCamelCase__   	=					pixel_mask_ratio
           lowerCamelCase__   	=					audio_mask_ratio
           lowerCamelCase__   	=					audio_mask_type

           lowerCamelCase__   	=					task_matching
           lowerCamelCase__   	=					task_mae
           lowerCamelCase__   	=					loss_type