



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase	:		Union[str, Any]				=   {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		List[str]				=   [
	    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SEWForCTC''',
	    '''SEWForSequenceClassification''',
	    '''SEWModel''',
	    '''SEWPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sew import (
		    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SEWForCTC,
		    SEWForSequenceClassification,
		    SEWModel,
		    SEWPreTrainedModel,
		)


else:
	import sys

	lowerCAmelCase	:		Union[str, Any]				=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


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

lowerCAmelCase	:		Optional[Any]				=   {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase	:		str				=   {
    '''vocab_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
        ),
        '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase	:		Optional[Any]				=   {
    '''squeezebert/squeezebert-uncased''': 5_1_2,
    '''squeezebert/squeezebert-mnli''': 5_1_2,
    '''squeezebert/squeezebert-mnli-headless''': 5_1_2,
}


lowerCAmelCase	:		Tuple				=   {
    '''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Dict        =							VOCAB_FILES_NAMES
					a      :     Any        =							PRETRAINED_VOCAB_FILES_MAP
					a      :     Dict        =							PRETRAINED_INIT_CONFIGURATION
					a      :     str        =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a      :     Optional[Any]        =							SqueezeBertTokenizer



					def __init__(    self			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=True			,				UpperCamelCase="[UNK]"			,				UpperCamelCase="[SEP]"			,				UpperCamelCase="[PAD]"			,				UpperCamelCase="[CLS]"			,				UpperCamelCase="[MASK]"			,				UpperCamelCase=True			,				UpperCamelCase=None			,				**UpperCamelCase			,				)     ->			List[Any]:
						super().__init__(
						    UpperCamelCase			,				tokenizer_file=UpperCamelCase			,				do_lower_case=UpperCamelCase			,				unk_token=UpperCamelCase			,				sep_token=UpperCamelCase			,				pad_token=UpperCamelCase			,				cls_token=UpperCamelCase			,				mask_token=UpperCamelCase			,				tokenize_chinese_chars=UpperCamelCase			,				strip_accents=UpperCamelCase			,				**UpperCamelCase			,				)

						__lowerCAmelCase												=  json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
						if (
						    normalizer_state.get("lowercase"			,				UpperCamelCase     ) != do_lower_case
						    or normalizer_state.get("strip_accents"			,				UpperCamelCase     ) != strip_accents
						    or normalizer_state.get("handle_chinese_chars"			,				UpperCamelCase     ) != tokenize_chinese_chars
						):
							__lowerCAmelCase												=  getattr(UpperCamelCase			,				normalizer_state.pop("type"     )     )
							__lowerCAmelCase												=  do_lower_case
							__lowerCAmelCase												=  strip_accents
							__lowerCAmelCase												=  tokenize_chinese_chars
							__lowerCAmelCase												=  normalizer_class(**UpperCamelCase     )

						__lowerCAmelCase												=  do_lower_case



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=None     )     ->			str:
						__lowerCAmelCase												=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			List[int]:
						__lowerCAmelCase												=  [self.sep_token_id]
						__lowerCAmelCase												=  [self.cls_token_id]
						if token_ids_a is None:
							return len(cls + token_ids_a + sep     ) * [0]
						return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			Tuple[str]:
						__lowerCAmelCase												=  self._tokenizer.model.save(UpperCamelCase			,				name=UpperCamelCase     )
						return tuple(UpperCamelCase     )