"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_rembert import RemBertTokenizer
else:
       __SCREAMING_SNAKE_CASE								=None

__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE								={"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

__SCREAMING_SNAKE_CASE								={
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

__SCREAMING_SNAKE_CASE								={
    "google/rembert": 256,
}

__SCREAMING_SNAKE_CASE								="▁"







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						VOCAB_FILES_NAMES
   lowercase							=						PRETRAINED_VOCAB_FILES_MAP
   lowercase							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=						RemBertTokenizer
   def __init__(    self  ,__UpperCamelCase=None  ,__UpperCamelCase=None  ,__UpperCamelCase=True  ,__UpperCamelCase=True  ,__UpperCamelCase=False  ,__UpperCamelCase="[CLS]"  ,__UpperCamelCase="[SEP]"  ,__UpperCamelCase="<unk>"  ,__UpperCamelCase="[SEP]"  ,__UpperCamelCase="<pad>"  ,__UpperCamelCase="[CLS]"  ,__UpperCamelCase="[MASK]"  ,**__UpperCamelCase  ,)			->   int:


        '''simple docstring'''


        lowercase_			:       Dict									=			AddedToken(__UpperCamelCase  ,lstrip=__UpperCamelCase  ,rstrip=__UpperCamelCase    ) if isinstance(__UpperCamelCase  ,__UpperCamelCase    ) else mask_token

        super().__init__(
            __UpperCamelCase  ,tokenizer_file=__UpperCamelCase  ,do_lower_case=__UpperCamelCase  ,remove_space=__UpperCamelCase  ,keep_accents=__UpperCamelCase  ,bos_token=__UpperCamelCase  ,eos_token=__UpperCamelCase  ,unk_token=__UpperCamelCase  ,sep_token=__UpperCamelCase  ,pad_token=__UpperCamelCase  ,cls_token=__UpperCamelCase  ,mask_token=__UpperCamelCase  ,**__UpperCamelCase  ,)

        lowercase_			:       Tuple									=			do_lower_case
        lowercase_			:       Optional[Any]									=			remove_space
        lowercase_			:       Union[str, Any]									=			keep_accents
        lowercase_			:       List[str]									=			vocab_file
        lowercase_			:       int									=			False if not self.vocab_file else True
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   List[int]:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			[self.sep_token_id]
        lowercase_			:       Dict									=			[self.cls_token_id]
        if token_ids_a is None:
             return cls + token_ids_a + sep
        return cls + token_ids_a + sep + token_ids_a + sep
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = False    )			->   List[int]:


        '''simple docstring'''


        if already_has_special_tokens:
             if token_ids_a is not None:
                  raise ValueError(
                      'You should not supply a second sequence if the provided sequence of '
                      'ids is already formatted with special tokens for the model.'    )
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

        if token_ids_a is not None:
             return [1] + ([0] * len(__UpperCamelCase    )) + [1] + ([0] * len(__UpperCamelCase    )) + [1]
        return [1] + ([0] * len(__UpperCamelCase    )) + [1]
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   List[int]:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			[self.sep_token_id]
        lowercase_			:       Union[str, Any]									=			[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  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   Tuple[str]:


        '''simple docstring'''


        if not os.path.isdir(__UpperCamelCase    ):
             logger.error('Vocabulary path ({}) should be a directory'.format(__UpperCamelCase    )    )
             return
        lowercase_			:       List[str]									=			os.path.join(
            __UpperCamelCase  ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

        if os.path.abspath(self.vocab_file    ) != os.path.abspath(__UpperCamelCase    ):
             copyfile(self.vocab_file  ,__UpperCamelCase    )

        return (out_vocab_file,)


"""simple docstring"""







__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",
}

