





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


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

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

_SCREAMING_SNAKE_CASE 		=						{
    """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"""
        ),
    },
}

_SCREAMING_SNAKE_CASE 		=						{
    """yjernite/retribert-base-uncased""": 5_12,
}


_SCREAMING_SNAKE_CASE 		=						{
    """yjernite/retribert-base-uncased""": {"""do_lower_case""": True},
}
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     str	 =    VOCAB_FILES_NAMES
						__magic_name__:     Optional[int]	 =    PRETRAINED_VOCAB_FILES_MAP
						__magic_name__:     str	 =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__magic_name__:     str	 =    PRETRAINED_INIT_CONFIGURATION
						__magic_name__:     List[Any]	 =    RetriBertTokenizer
						__magic_name__:     Optional[Any]	 =    ["input_ids", "attention_mask"]






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







							"""simple docstring"""


							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						,				)

							snake_case_						:     List[str]     =		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
							):
								snake_case_						:     Any     =		getattr(_A						,				normalizer_state.pop('type'		)		)
								snake_case_						:     List[str]     =		do_lower_case
								snake_case_						:     Optional[Any]     =		strip_accents
								snake_case_						:     Optional[Any]     =		tokenize_chinese_chars
								snake_case_						:     List[Any]     =		normalizer_class(**_A		)

							snake_case_						:     Tuple     =		do_lower_case






						def UpperCAmelCase_			( self : Optional[int]						,				_A : Any						,				_A : List[Any]=None		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		[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 : int						,				_A : List[int]						,				_A : Optional[List[int]] = None		)							->      List[int]:







							"""simple docstring"""


							snake_case_						:     Dict     =		[self.sep_token_id]
							snake_case_						:     int     =		[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 : List[Any]						,				_A : str						,				_A : Optional[str] = None		)							->      Tuple[str]:







							"""simple docstring"""


							snake_case_						:     Any     =		self._tokenizer.model.save(_A						,				name=_A		)
							return tuple(_A		)








_SCREAMING_SNAKE_CASE 		=						{
    """Pillow""": """Pillow""",
    """accelerate""": """accelerate>=0.11.0""",
    """compel""": """compel==0.1.8""",
    """black""": """black~=23.1""",
    """datasets""": """datasets""",
    """filelock""": """filelock""",
    """flax""": """flax>=0.4.1""",
    """hf-doc-builder""": """hf-doc-builder>=0.3.0""",
    """huggingface-hub""": """huggingface-hub>=0.13.2""",
    """requests-mock""": """requests-mock==1.10.0""",
    """importlib_metadata""": """importlib_metadata""",
    """invisible-watermark""": """invisible-watermark""",
    """isort""": """isort>=5.5.4""",
    """jax""": """jax>=0.2.8,!=0.3.2""",
    """jaxlib""": """jaxlib>=0.1.65""",
    """Jinja2""": """Jinja2""",
    """k-diffusion""": """k-diffusion>=0.0.12""",
    """torchsde""": """torchsde""",
    """note_seq""": """note_seq""",
    """librosa""": """librosa""",
    """numpy""": """numpy""",
    """omegaconf""": """omegaconf""",
    """parameterized""": """parameterized""",
    """protobuf""": """protobuf>=3.20.3,<4""",
    """pytest""": """pytest""",
    """pytest-timeout""": """pytest-timeout""",
    """pytest-xdist""": """pytest-xdist""",
    """ruff""": """ruff>=0.0.241""",
    """safetensors""": """safetensors""",
    """sentencepiece""": """sentencepiece>=0.1.91,!=0.1.92""",
    """scipy""": """scipy""",
    """onnx""": """onnx""",
    """regex""": """regex!=2019.12.17""",
    """requests""": """requests""",
    """tensorboard""": """tensorboard""",
    """torch""": """torch>=1.4""",
    """torchvision""": """torchvision""",
    """transformers""": """transformers>=4.25.1""",
    """urllib3""": """urllib3<=2.0.0""",
}

