

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_         =      logging.get_logger(__name__)

a_         =      {
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}






class _UpperCamelCase							(							snake_case_				):
			'''simple docstring'''







			lowerCamelCase__          =SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
			lowerCamelCase__          ="""squeezebert"""
			def __init__(     self							:	Any , a							:	List[str]=3_0522 , a							:	Dict=768 , a							:	str=12 , a							:	Union[str, Any]=12 , a							:	Union[str, Any]=3072 , a							:	Optional[int]="gelu" , a							:	Tuple=0.1 , a							:	Tuple=0.1 , a							:	Optional[Any]=512 , a							:	str=2 , a							:	Optional[int]=0.02 , a							:	Any=1e-12 , a							:	Any=0 , a							:	int=768 , a							:	Union[str, Any]=4 , a							:	Optional[int]=4 , a							:	List[Any]=4 , a							:	List[Any]=1 , a							:	Union[str, Any]=4 , a							:	Any=4 , **a							:	Tuple , )							->    str:

				"""simple docstring"""

				super().__init__(pad_token_id=a , **a		)

				SCREAMING_SNAKE_CASE				: Any			       =    vocab_size
				SCREAMING_SNAKE_CASE				: Tuple			       =    hidden_size
				SCREAMING_SNAKE_CASE				: str			       =    num_hidden_layers
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    num_attention_heads
				SCREAMING_SNAKE_CASE				: List[Any]			       =    hidden_act
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    intermediate_size
				SCREAMING_SNAKE_CASE				: int			       =    hidden_dropout_prob
				SCREAMING_SNAKE_CASE				: List[Any]			       =    attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE				: Any			       =    max_position_embeddings
				SCREAMING_SNAKE_CASE				: List[Any]			       =    type_vocab_size
				SCREAMING_SNAKE_CASE				: Any			       =    initializer_range
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    layer_norm_eps
				SCREAMING_SNAKE_CASE				: Any			       =    embedding_size
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    q_groups
				SCREAMING_SNAKE_CASE				: List[str]			       =    k_groups
				SCREAMING_SNAKE_CASE				: List[Any]			       =    v_groups
				SCREAMING_SNAKE_CASE				: str			       =    post_attention_groups
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    intermediate_groups
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    output_groups






class _UpperCamelCase							(							snake_case_				):
			'''simple docstring'''







			@property
			def 							__UpperCamelCase	(     self							:	Dict		)							->    Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task == "multiple-choice":
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    {0: "batch", 1: "choice", 2: "sequence"}
				else:
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    {0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]		)


from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Optional[int] , a							:	Any		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    vocab
				SCREAMING_SNAKE_CASE				: str			       =    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."		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    rjieba
			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	int , a							:	NormalizedString		)							->    List[NormalizedString]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    []

				# 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:
						SCREAMING_SNAKE_CASE				: Any			       =    self.normalizers.normalize_str(a		).split()
						for token in token_list:
							if token:
								SCREAMING_SNAKE_CASE				: Optional[int]			       =    start + len(a		)
								splits.append(normalized_string[start:end]		)
								SCREAMING_SNAKE_CASE				: List[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 							__UpperCamelCase	(     self							:	Optional[int] , a							:	PreTokenizedString		)							->    Tuple:

				"""simple docstring"""

				pretok.split(self.jieba_split		)