

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


lowercase_			=  logging.get_logger(__name__)

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

lowercase_			=  {
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

lowercase_			=  {
    "yjernite/retribert-base-uncased": 512,
}


lowercase_			=  {
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							VOCAB_FILES_NAMES
    lowerCamelCase		     =							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase		     =							PRETRAINED_INIT_CONFIGURATION
    lowerCamelCase		     =							RetriBertTokenizer
    lowerCamelCase		     =							['input_ids', 'attention_mask']


    def __init__(				self			:	Dict,lowercase_			:	List[str]=None,lowercase_			:	str=None,lowercase_			:	List[Any]=True,lowercase_			:	int="[UNK]",lowercase_			:	int="[SEP]",lowercase_			:	Union[str, Any]="[PAD]",lowercase_			:	Tuple="[CLS]",lowercase_			:	Tuple="[MASK]",lowercase_			:	str=True,lowercase_			:	Optional[Any]=None,**lowercase_			:	str,)->   List[str]:





        '''simple docstring'''







        super().__init__(
            lowercase_,tokenizer_file=lowercase_,do_lower_case=lowercase_,unk_token=lowercase_,sep_token=lowercase_,pad_token=lowercase_,cls_token=lowercase_,mask_token=lowercase_,tokenize_chinese_chars=lowercase_,strip_accents=lowercase_,**lowercase_,)

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

        A__   				=       do_lower_case


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	Any,lowercase_			:	Optional[int]=None		)->   Any:





        '''simple docstring'''







        A__   				=       [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 						snake_case__					(				self			:	str,lowercase_			:	List[int],lowercase_			:	Optional[List[int]] = None		)->   List[int]:





        '''simple docstring'''







        A__   				=       [self.sep_token_id]
        A__   				=       [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 						snake_case__					(				self			:	List[str],lowercase_			:	str,lowercase_			:	Optional[str] = None		)->   Tuple[str]:





        '''simple docstring'''







        A__   				=       self._tokenizer.model.save(lowercase_,name=lowercase_		)
        return tuple(lowercase_		)




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import (
         ImageTextPipelineOutput,
         UniDiffuserPipeline,
     )
else:
     from .modeling_text_decoder import UniDiffuserTextDecoder
     from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
     from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline

