





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:
    lowercase				:	Any      						=  None

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

lowercase				:	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""",
    },
}

lowercase				:	Optional[int]      						=  {
    """google/rembert""": 2_5_6,
}

lowercase				:	List[str]      						=  """▁"""





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    str	  =  VOCAB_FILES_NAMES
   __A       :    Tuple	  =  PRETRAINED_VOCAB_FILES_MAP
   __A       :    Any	  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A       :    Dict	  =  RemBertTokenizer

   def __init__( self ,  lowercase=None ,  lowercase=None ,  lowercase=True ,  lowercase=True ,  lowercase=False ,  lowercase="[CLS]" ,  lowercase="[SEP]" ,  lowercase="<unk>" ,  lowercase="[SEP]" ,  lowercase="<pad>" ,  lowercase="[CLS]" ,  lowercase="[MASK]" ,  **lowercase ,  )      ->						List[str]:







          '''simple docstring'''

          a__						:     List[Any]			=    AddedToken(lowercase ,  lstrip=lowercase ,  rstrip=lowercase) if isinstance(lowercase ,  lowercase) else mask_token

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

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

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						List[int]:







          '''simple docstring'''

          a__						:     Any			=    [self.sep_token_id]
          a__						:     Optional[int]			=    [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 							__lowercase		( self ,  lowercase ,  lowercase = None ,  lowercase = 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(lowercase)) + [1] + ([0] * len(lowercase)) + [1]
          return [1] + ([0] * len(lowercase)) + [1]

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						List[int]:







          '''simple docstring'''

          a__						:     Any			=    [self.sep_token_id]
          a__						:     List[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 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						Tuple[str]:







          '''simple docstring'''

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

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

          return (out_vocab_file,)









import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    from transformers.models.bert.modeling_flax_bert import (
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
    )





class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase=13 ,  lowercase=7 ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=99 ,  lowercase=32 ,  lowercase=5 ,  lowercase=4 ,  lowercase=37 ,  lowercase="gelu" ,  lowercase=0.1 ,  lowercase=0.1 ,  lowercase=512 ,  lowercase=16 ,  lowercase=2 ,  lowercase=0.02 ,  lowercase=4 ,  )      ->						Tuple:







          '''simple docstring'''

          a__						:     str			=    parent
          a__						:     Optional[Any]			=    batch_size
          a__						:     str			=    seq_length
          a__						:     int			=    is_training
          a__						:     str			=    use_attention_mask
          a__						:     List[str]			=    use_token_type_ids
          a__						:     Optional[Any]			=    use_labels
          a__						:     List[Any]			=    vocab_size
          a__						:     Tuple			=    hidden_size
          a__						:     Dict			=    num_hidden_layers
          a__						:     List[str]			=    num_attention_heads
          a__						:     int			=    intermediate_size
          a__						:     Any			=    hidden_act
          a__						:     Optional[int]			=    hidden_dropout_prob
          a__						:     Tuple			=    attention_probs_dropout_prob
          a__						:     Tuple			=    max_position_embeddings
          a__						:     Optional[int]			=    type_vocab_size
          a__						:     List[Any]			=    type_sequence_label_size
          a__						:     Union[str, Any]			=    initializer_range
          a__						:     str			=    num_choices

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     str			=    ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

          a__						:     Dict			=    None
          if self.use_attention_mask:
                 a__						:     Optional[int]			=    random_attention_mask([self.batch_size, self.seq_length])

          a__						:     Dict			=    None
          if self.use_token_type_ids:
                 a__						:     int			=    ids_tensor([self.batch_size, self.seq_length] ,  self.type_vocab_size)

          a__						:     Dict			=    BertConfig(
              vocab_size=self.vocab_size ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_size=self.intermediate_size ,  hidden_act=self.hidden_act ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  type_vocab_size=self.type_vocab_size ,  is_decoder=lowercase ,  initializer_range=self.initializer_range ,  )

          return config, input_ids, token_type_ids, attention_mask

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Tuple			=    self.prepare_config_and_inputs()
          a__			,							a__			,							a__			,							a__						:     Optional[int]			=    config_and_inputs
          a__						:     str			=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict

   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.prepare_config_and_inputs()
          a__			,							a__			,							a__			,							a__						:     int			=    config_and_inputs

          a__						:     str			=    True
          a__						:     Any			=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          a__						:     List[str]			=    ids_tensor([self.batch_size, self.seq_length] ,  vocab_size=2)

          return (
              config,
              input_ids,
              attention_mask,
              encoder_hidden_states,
              encoder_attention_mask,
          )





@require_flax
class 			A__   (							__UpperCAmelCase			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Optional[Any]	  =  True

   __A       :    Tuple	  =  (
       (
           FlaxBertModel,
           FlaxBertForPreTraining,
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForQuestionAnswering,
           FlaxBertForNextSentencePrediction,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Optional[Any]			=    FlaxBertModelTester(self)

   @slow
   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    FlaxBertModel.from_pretrained('bert-base-cased')
          a__						:     Optional[Any]			=    model(np.ones((1, 1)))
          self.assertIsNotNone(lowercase)


