



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


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

a     :    int      						=					{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

a     :    Optional[int]      						=					{
    """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"""
        ),
    },
}

a     :    Union[str, Any]      						=					{
    """squeezebert/squeezebert-uncased""": 5_1_2,
    """squeezebert/squeezebert-mnli""": 5_1_2,
    """squeezebert/squeezebert-mnli-headless""": 5_1_2,
}


a     :    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_				(							__magic_name__    ):
	lowercase    =				VOCAB_FILES_NAMES
	lowercase    =				PRETRAINED_VOCAB_FILES_MAP
	lowercase    =				PRETRAINED_INIT_CONFIGURATION
	lowercase    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase    =				SqueezeBertTokenizer





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

								UpperCAmelCase  :	Dict							  =	json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
								if (
								    normalizer_state.get("""lowercase"""	,	A					) != do_lower_case
								    or normalizer_state.get("""strip_accents"""	,	A					) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars"""	,	A					) != tokenize_chinese_chars
								):
															UpperCAmelCase  :	Tuple							  =	getattr(A	,	normalizer_state.pop("""type"""					)					)
															UpperCAmelCase  :	Optional[int]							  =	do_lower_case
															UpperCAmelCase  :	Any							  =	strip_accents
															UpperCAmelCase  :	List[Any]							  =	tokenize_chinese_chars
															UpperCAmelCase  :	Dict							  =	normalizer_class(**A					)

								UpperCAmelCase  :	int							  =	do_lower_case





	def 				_lowercase(						self	,	A	,	A=None					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Optional[Any]							  =	[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 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:
								UpperCAmelCase  :	Optional[Any]							  =	[self.sep_token_id]
								UpperCAmelCase  :	List[str]							  =	[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 				_lowercase(						self	,	A	,	A = None					)    ->    Tuple[str]:
								UpperCAmelCase  :	Optional[int]							  =	self._tokenizer.model.save(A	,	name=A					)
								return tuple(A					)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a     :    Optional[Any]      						=					{
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Dict      						=					["""BertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    List[Any]      						=					[
							    """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BertForMaskedLM""",
							    """BertForMultipleChoice""",
							    """BertForNextSentencePrediction""",
							    """BertForPreTraining""",
							    """BertForQuestionAnswering""",
							    """BertForSequenceClassification""",
							    """BertForTokenClassification""",
							    """BertLayer""",
							    """BertLMHeadModel""",
							    """BertModel""",
							    """BertPreTrainedModel""",
							    """load_tf_weights_in_bert""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    List[str]      						=					[
							    """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFBertEmbeddings""",
							    """TFBertForMaskedLM""",
							    """TFBertForMultipleChoice""",
							    """TFBertForNextSentencePrediction""",
							    """TFBertForPreTraining""",
							    """TFBertForQuestionAnswering""",
							    """TFBertForSequenceClassification""",
							    """TFBertForTokenClassification""",
							    """TFBertLMHeadModel""",
							    """TFBertMainLayer""",
							    """TFBertModel""",
							    """TFBertPreTrainedModel""",
							]
try:
							if not is_tensorflow_text_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    int      						=					["""TFBertTokenizer"""]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Optional[Any]      						=					[
							    """FlaxBertForCausalLM""",
							    """FlaxBertForMaskedLM""",
							    """FlaxBertForMultipleChoice""",
							    """FlaxBertForNextSentencePrediction""",
							    """FlaxBertForPreTraining""",
							    """FlaxBertForQuestionAnswering""",
							    """FlaxBertForSequenceClassification""",
							    """FlaxBertForTokenClassification""",
							    """FlaxBertModel""",
							    """FlaxBertPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
							from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_fast import BertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bert import (
														    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BertForMaskedLM,
														    BertForMultipleChoice,
														    BertForNextSentencePrediction,
														    BertForPreTraining,
														    BertForQuestionAnswering,
														    BertForSequenceClassification,
														    BertForTokenClassification,
														    BertLayer,
														    BertLMHeadModel,
														    BertModel,
														    BertPreTrainedModel,
														    load_tf_weights_in_bert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_bert import (
														    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFBertEmbeddings,
														    TFBertForMaskedLM,
														    TFBertForMultipleChoice,
														    TFBertForNextSentencePrediction,
														    TFBertForPreTraining,
														    TFBertForQuestionAnswering,
														    TFBertForSequenceClassification,
														    TFBertForTokenClassification,
														    TFBertLMHeadModel,
														    TFBertMainLayer,
														    TFBertModel,
														    TFBertPreTrainedModel,
														)

							try:
														if not is_tensorflow_text_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_tf import TFBertTokenizer

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_bert import (
														    FlaxBertForCausalLM,
														    FlaxBertForMaskedLM,
														    FlaxBertForMultipleChoice,
														    FlaxBertForNextSentencePrediction,
														    FlaxBertForPreTraining,
														    FlaxBertForQuestionAnswering,
														    FlaxBertForSequenceClassification,
														    FlaxBertForTokenClassification,
														    FlaxBertModel,
														    FlaxBertPreTrainedModel,
														)

else:
							import sys

							a     :    Optional[int]      						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

