
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


__lowerCAmelCase			:     Tuple           =   logging.get_logger(__name__)

__lowerCAmelCase			:     Tuple           =   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase			:     Tuple           =   {
    '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'
        ),
    },
}

__lowerCAmelCase			:     List[Any]           =   {
    'yjernite/retribert-base-uncased': 512,
}


__lowerCAmelCase			:     str           =   {
    'yjernite/retribert-base-uncased': {'do_lower_case': True},
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		VOCAB_FILES_NAMES
  a__           =		PRETRAINED_VOCAB_FILES_MAP
  a__           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__           =		PRETRAINED_INIT_CONFIGURATION
  a__           =		RetriBertTokenizer
  a__           =		["""input_ids""", """attention_mask"""]
  def __init__( self			: str			,							UpperCamelCase__			: int=None			,							UpperCamelCase__			: Dict=None			,							UpperCamelCase__			: List[Any]=True			,							UpperCamelCase__			: Union[str, Any]="[UNK]"			,							UpperCamelCase__			: int="[SEP]"			,							UpperCamelCase__			: List[Any]="[PAD]"			,							UpperCamelCase__			: int="[CLS]"			,							UpperCamelCase__			: Any="[MASK]"			,							UpperCamelCase__			: int=True			,							UpperCamelCase__			: Tuple=None			,							**UpperCamelCase__			: List[str]			,							)			->	str:







    """simple docstring"""






    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__			,							)

    __magic_name__				     =       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
    ):
      __magic_name__				     =       getattr(UpperCamelCase__			,							normalizer_state.pop("""type"""    )    )
      __magic_name__				     =       do_lower_case
      __magic_name__				     =       strip_accents
      __magic_name__				     =       tokenize_chinese_chars
      __magic_name__				     =       normalizer_class(**UpperCamelCase__    )

    __magic_name__				     =       do_lower_case
  def 							_lowercase							( self			: Any			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: Any=None    )			->	int:







    """simple docstring"""






    __magic_name__				     =       [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			: Union[str, Any]			,							UpperCamelCase__			: List[int]			,							UpperCamelCase__			: Optional[List[int]] = None    )			->	List[int]:







    """simple docstring"""






    __magic_name__				     =       [self.sep_token_id]
    __magic_name__				     =       [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			: List[str]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: Optional[str] = None    )			->	Tuple[str]:







    """simple docstring"""






    __magic_name__				     =       self._tokenizer.model.save(UpperCamelCase__			,							name=UpperCamelCase__    )
    return tuple(UpperCamelCase__    )




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase					:			Tuple  				=			{
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase					:			Optional[int]  				=			["LlamaTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase					:			Optional[int]  				=			["LlamaTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase					:			List[Any]  				=			[
						    "LlamaForCausalLM",
						    "LlamaModel",
						    "LlamaPreTrainedModel",
						    "LlamaForSequenceClassification",
						]


if TYPE_CHECKING:
						from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama import LlamaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama_fast import LlamaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
						import sys

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






