




"""simple docstring"""

from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class      lowerCamelCase__      :


			def __init__(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]        =			vocab
					snake_case						:  Dict        =			normalizers.BertNormalizer(
					    clean_text=SCREAMING_SNAKE_CASE			,	handle_chinese_chars=SCREAMING_SNAKE_CASE			,	strip_accents=SCREAMING_SNAKE_CASE			,	lowercase=SCREAMING_SNAKE_CASE			,	)
					try:
							import rjieba
					except ImportError:
							raise ImportError(
							    "You need to install rjieba to use RoFormerTokenizer. "
							    "See https://pypi.org/project/rjieba/ for installation."							)
					snake_case						:  int        =			rjieba


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  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(SCREAMING_SNAKE_CASE							)			,	hmm=SCREAMING_SNAKE_CASE							):
							if token in self.vocab:
									splits.append(normalized_string[start:end]							)
							else:
									snake_case						:  int        =			self.normalizers.normalize_str(SCREAMING_SNAKE_CASE							).split()
									for token in token_list:
											if token:
													snake_case						:  List[str]        =			start + len(SCREAMING_SNAKE_CASE							)
													splits.append(normalized_string[start:end]							)
													snake_case						:  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			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					pretok.split(self.jieba_split							)







"""simple docstring"""

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


__A							=     logging.get_logger(__name__)

__A							=     {
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      lowerCamelCase__      (					lowerCamelCase_     ):
			a__	: Optional[int]        =       """vilt"""


			def __init__(       self			,	SCREAMING_SNAKE_CASE=30_522			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=40			,	SCREAMING_SNAKE_CASE=768			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=3_072			,	SCREAMING_SNAKE_CASE="gelu"			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.02			,	SCREAMING_SNAKE_CASE=1E-12			,	SCREAMING_SNAKE_CASE=384			,	SCREAMING_SNAKE_CASE=32			,	SCREAMING_SNAKE_CASE=3			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=-1			,	SCREAMING_SNAKE_CASE=False			,	SCREAMING_SNAKE_CASE=-1			,	**SCREAMING_SNAKE_CASE			,	):



					"""simple docstring"""







					super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE			,	**SCREAMING_SNAKE_CASE							)

					snake_case						:  str        =			vocab_size
					snake_case						:  Optional[Any]        =			type_vocab_size
					snake_case						:  List[str]        =			modality_type_vocab_size
					snake_case						:  Optional[Any]        =			max_position_embeddings

					snake_case						:  Tuple        =			hidden_size
					snake_case						:  List[str]        =			num_hidden_layers
					snake_case						:  int        =			num_attention_heads
					snake_case						:  Optional[int]        =			intermediate_size
					snake_case						:  List[Any]        =			hidden_act
					snake_case						:  List[str]        =			hidden_dropout_prob
					snake_case						:  Union[str, Any]        =			attention_probs_dropout_prob
					snake_case						:  Any        =			initializer_range
					snake_case						:  List[Any]        =			layer_norm_eps

					snake_case						:  Any        =			image_size
					snake_case						:  str        =			patch_size
					snake_case						:  Optional[int]        =			num_channels
					snake_case						:  List[str]        =			qkv_bias
					snake_case						:  Optional[int]        =			max_image_length
					snake_case						:  Any        =			num_images

