"""simple docstring"""


from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers






class  a__     :


   def __init__(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				vocab
        lowercase__								=				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."	)
        lowercase__								=				rjieba


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				[]

        # 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:
                  lowercase__								=				self.normalizers.normalize_str(_UpperCAmelCase	).split()
                  for token in token_list:
                       if token:
                            lowercase__								=				start + len(_UpperCAmelCase	)
                            splits.append(normalized_string[start:end]	)
                            lowercase__								=				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,					_UpperCAmelCase	):
        '''simple docstring'''



        pretok.split(self.jieba_split	)





"""simple docstring"""


import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


lowerCAmelCase_:       Union[str, Any]			     =       logging.get_logger(__name__)

lowerCAmelCase_:       Dict			     =       {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}






class  a__     ( _a						):
   snake_case_				=   "vit-hybrid"


   def __init__(  self,					_UpperCAmelCase=None,					_UpperCAmelCase=768,					_UpperCAmelCase=12,					_UpperCAmelCase=12,					_UpperCAmelCase=3072,					_UpperCAmelCase="gelu",					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.02,					_UpperCAmelCase=1E-12,					_UpperCAmelCase=224,					_UpperCAmelCase=1,					_UpperCAmelCase=3,					_UpperCAmelCase=[1, 1024, 24, 24],					_UpperCAmelCase=True,					**_UpperCAmelCase,					):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase	)

        if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."	)
             lowercase__								=				{
                 "global_padding": "same",
                 "layer_type": "bottleneck",
                 "depths": [3, 4, 9],
                 "out_features": ["stage3"],
                 "embedding_dynamic_padding": True,
             }

        if isinstance(_UpperCAmelCase,					_UpperCAmelCase	):
             if "model_type" in backbone_config:
                  lowercase__								=				CONFIG_MAPPING[backbone_config["model_type"]]
             else:
                  logger.info(
                      "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."	)
                  lowercase__								=				BitConfig
             lowercase__								=				backbone_config_class(**_UpperCAmelCase	)

        lowercase__								=				backbone_featmap_shape
        lowercase__								=				backbone_config
        lowercase__								=				hidden_size
        lowercase__								=				num_hidden_layers
        lowercase__								=				num_attention_heads
        lowercase__								=				intermediate_size
        lowercase__								=				hidden_act
        lowercase__								=				hidden_dropout_prob
        lowercase__								=				attention_probs_dropout_prob
        lowercase__								=				initializer_range
        lowercase__								=				layer_norm_eps
        lowercase__								=				image_size
        lowercase__								=				patch_size
        lowercase__								=				num_channels
        lowercase__								=				qkv_bias


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				copy.deepcopy(self.__dict__	)
        lowercase__								=				self.backbone_config.to_dict()
        lowercase__								=				self.__class__.model_type
        return output




