from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






'''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_convbert import ConvBertTokenizer


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {'''vocab_file''': '''vocab.txt'''}

lowercase_     		= {
    '''vocab_file''': {
        '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
        '''YituTech/conv-bert-medium-small''': (
            '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
        ),
        '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
    }
}

lowercase_     		= {
    '''YituTech/conv-bert-base''': 512,
    '''YituTech/conv-bert-medium-small''': 512,
    '''YituTech/conv-bert-small''': 512,
}


lowercase_     		= {
    '''YituTech/conv-bert-base''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Union[str, Any]       =							VOCAB_FILES_NAMES
		lowercase   :      Tuple       =							PRETRAINED_VOCAB_FILES_MAP
		lowercase   :      Optional[int]       =							PRETRAINED_INIT_CONFIGURATION
		lowercase   :      int       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase   :      List[str]       =							ConvBertTokenizer






		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		,						)

								_lowerCAmelCase										=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
								):
														_lowerCAmelCase										=getattr(__A		,						normalizer_state.pop('type'				)				)
														_lowerCAmelCase										=do_lower_case
														_lowerCAmelCase										=strip_accents
														_lowerCAmelCase										=tokenize_chinese_chars
														_lowerCAmelCase										=normalizer_class(**__A				)

								_lowerCAmelCase										=do_lower_case






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     int:
								_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		,						__A		,						__A = 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		,						__A		,						__A = None				) ->     Tuple[str]:
								_lowerCAmelCase										=self._tokenizer.model.save(__A		,						name=__A				)
								return tuple(__A				)





