'''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:
    _a   :	Any 							=   None

_a   :	List[Any] 							=   logging.get_logger(__name__)
_a   :	int 							=   {'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

_a   :	int 							=   {
    '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',
    },
}

_a   :	Optional[Any] 							=   {
    'google/rembert': 256,
}

_a   :	Tuple 							=   '▁'






class 		__A       (__lowercase   ):
       snake_case       :Optional[Any]				    =					VOCAB_FILES_NAMES
       snake_case       :Optional[int]				    =					PRETRAINED_VOCAB_FILES_MAP
       snake_case       :Union[str, Any]				    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case       :Optional[int]				    =					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_				,     ):
              # Mask token behave like a normal word, i.e. include the space before it
              __UpperCAmelCase  :				Dict       				=		AddedToken(__a				,     lstrip=__a				,     rstrip=__a      ) if isinstance(__a				,     __a      ) else mask_token

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

              __UpperCAmelCase  :				Optional[Any]       				=		do_lower_case
              __UpperCAmelCase  :				str       				=		remove_space
              __UpperCAmelCase  :				Any       				=		keep_accents
              __UpperCAmelCase  :				Optional[Any]       				=		vocab_file
              __UpperCAmelCase  :				Optional[int]       				=		False if not self.vocab_file else True






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              __UpperCAmelCase  :				Optional[Any]       				=		[self.sep_token_id]
              __UpperCAmelCase  :				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 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = False      ):

              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(__a      )) + [1] + ([0] * len(__a      )) + [1]
              return [1] + ([0] * len(__a      )) + [1]






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		[self.sep_token_id]
              __UpperCAmelCase  :				List[str]       				=		[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				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              if not os.path.isdir(__a      ):
                     logger.error("Vocabulary path ({}) should be a directory".format(__a      )      )
                     return
              __UpperCAmelCase  :				Optional[Any]       				=		os.path.join(
                  __a				,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

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

              return (out_vocab_file,)







'''simple docstring'''




# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
   import torch






class 		__A       (TensorFormatter[Mapping, "torch.Tensor", Mapping]   ):






       def __init__(      self				,     UpperCamelCase_=None				,     **UpperCamelCase_      ):
              super().__init__(features=UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		torch_tensor_kwargs
              import torch  # noqa import torch at initialization






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              import torch

              if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) and column:
                     if all(
                         isinstance(UpperCamelCase_				,     torch.Tensor      ) and x.shape == column[0].shape and x.dtype == column[0].dtype
                         for x in column      ):
                            return torch.stack(UpperCamelCase_      )
              return column






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              import torch

              if isinstance(UpperCamelCase_				,     (str, bytes, type(UpperCamelCase_      ))      ):
                     return value
              elif isinstance(UpperCamelCase_				,     (np.character, np.ndarray)      ) and np.issubdtype(value.dtype				,     np.character      ):
                     return value.tolist()

              __UpperCAmelCase  :				int       				=		{}

              if isinstance(UpperCamelCase_				,     (np.number, np.ndarray)      ) and np.issubdtype(value.dtype				,     np.integer      ):
                     __UpperCAmelCase  :				Optional[int]       				=		{"dtype": torch.intaa}
              elif isinstance(UpperCamelCase_				,     (np.number, np.ndarray)      ) and np.issubdtype(value.dtype				,     np.floating      ):
                     __UpperCAmelCase  :				str       				=		{"dtype": torch.floataa}
              elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                     import PIL.Image

                     if isinstance(UpperCamelCase_				,     PIL.Image.Image      ):
                            __UpperCAmelCase  :				str       				=		np.asarray(UpperCamelCase_      )
              return torch.tensor(UpperCamelCase_				,     **{**default_dtype, **self.torch_tensor_kwargs}      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              import torch

              # support for torch, tf, jax etc.
              if hasattr(UpperCamelCase_				,     "__array__"      ) and not isinstance(UpperCamelCase_				,     torch.Tensor      ):
                     __UpperCAmelCase  :				Dict       				=		data_struct.__array__()
              # support for nested types like struct of list of struct
              if isinstance(UpperCamelCase_				,     np.ndarray      ):
                     if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                            return self._consolidate([self.recursive_tensorize(UpperCamelCase_      ) for substruct in data_struct]      )
              elif isinstance(UpperCamelCase_				,     (list, tuple)      ):
                     return self._consolidate([self.recursive_tensorize(UpperCamelCase_      ) for substruct in data_struct]      )
              return self._tensorize(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return map_nested(self._recursive_tensorize				,     UpperCamelCase_				,     map_list=UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		self.numpy_arrow_extractor().extract_row(UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		self.python_features_decoder.decode_row(UpperCamelCase_      )
              return self.recursive_tensorize(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		self.numpy_arrow_extractor().extract_column(UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[Any]       				=		self.python_features_decoder.decode_column(UpperCamelCase_				,     pa_table.column_names[0]      )
              __UpperCAmelCase  :				List[Any]       				=		self.recursive_tensorize(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		self._consolidate(UpperCamelCase_      )
              return column







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		self.numpy_arrow_extractor().extract_batch(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		self.python_features_decoder.decode_batch(UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		self.recursive_tensorize(UpperCamelCase_      )
              for column_name in batch:
                     __UpperCAmelCase  :				Tuple       				=		self._consolidate(batch[column_name]      )
              return batch






