


"""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:
       lowerCamelCase_       :       Optional[int]      						=			None

lowerCamelCase_       :       Union[str, Any]      						=			logging.get_logger(__name__)
lowerCamelCase_       :       str      						=			{"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_       :       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""",
    },
}

lowerCamelCase_       :       Optional[Any]      						=			{
    """google/rembert""": 2_5_6,
}

lowerCamelCase_       :       Optional[Any]      						=			"""▁"""






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  VOCAB_FILES_NAMES
 __lowerCAmelCase			 =  PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase			 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase			 =  RemBertTokenizer


 def __init__(		self			,   __A=None			,   __A=None			,   __A=True			,   __A=True			,   __A=False			,   __A="[CLS]"			,   __A="[SEP]"			,   __A="<unk>"			,   __A="[SEP]"			,   __A="<pad>"			,   __A="[CLS]"			,   __A="[MASK]"			,   **__A			,   ) ->					Dict:
     # Mask token behave like a normal word, i.e. include the space before it
     a					=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			,   )

     a					=do_lower_case
     a					=remove_space
     a					=keep_accents
     a					=vocab_file
     a					=False if not self.vocab_file else True


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					List[int]:
     a					=[self.sep_token_id]
     a					=[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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None			,   __A = False						) ->					List[int]:

     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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					List[int]:
     a					=[self.sep_token_id]
     a					=[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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					Tuple[str]:
     if not os.path.isdir(__A						):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(__A						)						)
         return
     a					=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"""







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
       import tensorflow as tf

if is_tensorflow_text_available():
       from transformers.models.bert import TFBertTokenizer


lowerCamelCase_       :       str      						=  ["""bert-base-uncased""", """bert-base-cased"""]
lowerCamelCase_       :       List[str]      						=  """hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():






       class 				__A    (	tf.keras.Model      ):






        """simple docstring"""




        def __init__(		self			,   __A						) ->					Dict:
            super().__init__()
            a					=tokenizer
            a					=AutoConfig.from_pretrained(__A						)
            a					=TFAutoModel.from_config(__A						)





        def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					int:
            a					=self.tokenizer(__A						)
            a					=self.bert(**__A						)
            return out["pooler_output"]






@require_tf
@require_tensorflow_text
class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     super().setUp()

     a					=[
         BertTokenizer.from_pretrained(__A						) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     a					=[TFBertTokenizer.from_pretrained(__A						) for checkpoint in TOKENIZER_CHECKPOINTS] + [
         TFBertTokenizer.from_pretrained(__A			,   use_fast_bert_tokenizer=__A						)
         for checkpoint in TOKENIZER_CHECKPOINTS
     ]
     assert len(self.tokenizers						) == len(self.tf_tokenizers						)

     a					=[
         '''This is a straightforward English test sentence.''',
         '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
         '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
         '''And some much more rare Chinese: 齉 堃 齉堃''',
         '''Je vais aussi écrire en français pour tester les accents''',
         '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
     ]
     a					=list(zip(self.test_sentences			,   self.test_sentences[::-1]						)						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     for tokenizer, tf_tokenizer in zip(self.tokenizers			,   self.tf_tokenizers						):
         for test_inputs in (self.test_sentences, self.paired_sentences):
             a					=tokenizer(__A			,   return_tensors='''tf'''			,   padding='''longest'''						)
             a					=tf_tokenizer(__A						)

             for key in python_outputs.keys():
                 self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape						)						)
                 self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]			,   tf.intaa						) == tf_outputs[key]						)						)


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     for tf_tokenizer in self.tf_tokenizers:
         a					=tf_tokenizer(self.paired_sentences						)
         a					=tf_tokenizer(
             text=[sentence[0] for sentence in self.paired_sentences]			,   text_pair=[sentence[1] for sentence in self.paired_sentences]			,   )
         for key in merged_outputs.keys():
             self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]			,   tf.intaa						) == separated_outputs[key]						)						)


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     for tf_tokenizer in self.tf_tokenizers:
         a					=tf.function(__A						)
         for test_inputs in (self.test_sentences, self.paired_sentences):
             a					=tf.constant(__A						)
             a					=compiled_tokenizer(__A						)
             a					=tf_tokenizer(__A						)

             for key in eager_outputs.keys():
                 self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     for tf_tokenizer in self.tf_tokenizers:
         a					=ModelToSave(tokenizer=__A						)
         a					=tf.convert_to_tensor(self.test_sentences						)
         a					=model(__A						)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             a					=Path(__A						) / '''saved.model'''
             model.save(__A						)
             a					=tf.keras.models.load_model(__A						)
         a					=loaded_model(__A						)
         # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
         self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output						)						)			,   1E-5						)