

"""simple docstring"""






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 	__UpperCamelCase    (    unittest.TestCase						):

		def __init__(		self    ,_A    ,_A=13    ,_A=7    ,_A=True    ,_A=True    ,_A=True    ,_A=True    ,_A=99    ,_A=32    ,_A=5    ,_A=4    ,_A=37    ,_A="gelu"    ,_A=0.1    ,_A=0.1    ,_A=512    ,_A=16    ,_A=2    ,_A=0.0_2    ,_A=4    ,):
						'''simple docstring'''

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

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

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

						_lowerCAmelCase       :   Optional[Any]  =    None
						if self.use_attention_mask:
										_lowerCAmelCase       :   Tuple  =    random_attention_mask([self.batch_size, self.seq_length]      )

						_lowerCAmelCase       :   List[Any]  =    None
						if self.use_token_type_ids:
										_lowerCAmelCase       :   Optional[int]  =    ids_tensor([self.batch_size, self.seq_length]    ,self.type_vocab_size      )

						_lowerCAmelCase       :   Tuple  =    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=UpperCamelCase__    ,initializer_range=self.initializer_range    ,)

						return config, input_ids, token_type_ids, attention_mask

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   Tuple  =    self.prepare_config_and_inputs()
						_lowerCAmelCase       :   Dict  =    config_and_inputs
						_lowerCAmelCase       :   List[Any]  =    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict







		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   Tuple  =    self.prepare_config_and_inputs()
						_lowerCAmelCase       :   Tuple  =    config_and_inputs

						_lowerCAmelCase       :   Dict  =    True
						_lowerCAmelCase       :   Optional[int]  =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
						_lowerCAmelCase       :   Dict  =    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 	__UpperCamelCase    (    __A							,							unittest.TestCase						):
		_UpperCAmelCase          =		True

		_UpperCAmelCase          =		(
		    (
		        FlaxBertModel,
		        FlaxBertForPreTraining,
		        FlaxBertForMaskedLM,
		        FlaxBertForMultipleChoice,
		        FlaxBertForQuestionAnswering,
		        FlaxBertForNextSentencePrediction,
		        FlaxBertForSequenceClassification,
		        FlaxBertForTokenClassification,
		        FlaxBertForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						_lowerCAmelCase       :   List[Any]  =    FlaxBertModelTester(self      )







		@slow
		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

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








"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


_lowerCAmelCase						=		logging.get_logger(__name__)

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

_lowerCAmelCase						=		{
    """vocab_file""": {
        """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt""",
        """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt""",
        """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/vocab.txt""",
        """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/vocab.txt""",
        """bert-base-multilingual-uncased""": (
            """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"""
        ),
        """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt""",
        """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt""",
        """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt""",
        """bert-large-uncased-whole-word-masking""": (
            """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"""
        ),
        """bert-large-cased-whole-word-masking""": (
            """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"""
        ),
        """bert-large-uncased-whole-word-masking-finetuned-squad""": (
            """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"""
        ),
        """bert-large-cased-whole-word-masking-finetuned-squad""": (
            """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"""
        ),
        """bert-base-cased-finetuned-mrpc""": (
            """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"""
        ),
        """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt""",
        """bert-base-german-dbmdz-uncased""": (
            """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"""
        ),
        """TurkuNLP/bert-base-finnish-cased-v1""": (
            """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"""
        ),
        """TurkuNLP/bert-base-finnish-uncased-v1""": (
            """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"""
        ),
        """wietsedv/bert-base-dutch-cased""": (
            """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json""",
        """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json""",
        """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json""",
        """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json""",
        """bert-base-multilingual-uncased""": (
            """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"""
        ),
        """bert-base-multilingual-cased""": (
            """https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"""
        ),
        """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json""",
        """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json""",
        """bert-large-uncased-whole-word-masking""": (
            """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"""
        ),
        """bert-large-cased-whole-word-masking""": (
            """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"""
        ),
        """bert-large-uncased-whole-word-masking-finetuned-squad""": (
            """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"""
        ),
        """bert-large-cased-whole-word-masking-finetuned-squad""": (
            """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"""
        ),
        """bert-base-cased-finetuned-mrpc""": (
            """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"""
        ),
        """bert-base-german-dbmdz-cased""": (
            """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"""
        ),
        """bert-base-german-dbmdz-uncased""": (
            """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"""
        ),
        """TurkuNLP/bert-base-finnish-cased-v1""": (
            """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"""
        ),
        """TurkuNLP/bert-base-finnish-uncased-v1""": (
            """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"""
        ),
        """wietsedv/bert-base-dutch-cased""": (
            """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"""
        ),
    },
}

_lowerCAmelCase						=		{
    """bert-base-uncased""": 5_1_2,
    """bert-large-uncased""": 5_1_2,
    """bert-base-cased""": 5_1_2,
    """bert-large-cased""": 5_1_2,
    """bert-base-multilingual-uncased""": 5_1_2,
    """bert-base-multilingual-cased""": 5_1_2,
    """bert-base-chinese""": 5_1_2,
    """bert-base-german-cased""": 5_1_2,
    """bert-large-uncased-whole-word-masking""": 5_1_2,
    """bert-large-cased-whole-word-masking""": 5_1_2,
    """bert-large-uncased-whole-word-masking-finetuned-squad""": 5_1_2,
    """bert-large-cased-whole-word-masking-finetuned-squad""": 5_1_2,
    """bert-base-cased-finetuned-mrpc""": 5_1_2,
    """bert-base-german-dbmdz-cased""": 5_1_2,
    """bert-base-german-dbmdz-uncased""": 5_1_2,
    """TurkuNLP/bert-base-finnish-cased-v1""": 5_1_2,
    """TurkuNLP/bert-base-finnish-uncased-v1""": 5_1_2,
    """wietsedv/bert-base-dutch-cased""": 5_1_2,
}

_lowerCAmelCase						=		{
    """bert-base-uncased""": {"""do_lower_case""": True},
    """bert-large-uncased""": {"""do_lower_case""": True},
    """bert-base-cased""": {"""do_lower_case""": False},
    """bert-large-cased""": {"""do_lower_case""": False},
    """bert-base-multilingual-uncased""": {"""do_lower_case""": True},
    """bert-base-multilingual-cased""": {"""do_lower_case""": False},
    """bert-base-chinese""": {"""do_lower_case""": False},
    """bert-base-german-cased""": {"""do_lower_case""": False},
    """bert-large-uncased-whole-word-masking""": {"""do_lower_case""": True},
    """bert-large-cased-whole-word-masking""": {"""do_lower_case""": False},
    """bert-large-uncased-whole-word-masking-finetuned-squad""": {"""do_lower_case""": True},
    """bert-large-cased-whole-word-masking-finetuned-squad""": {"""do_lower_case""": False},
    """bert-base-cased-finetuned-mrpc""": {"""do_lower_case""": False},
    """bert-base-german-dbmdz-cased""": {"""do_lower_case""": False},
    """bert-base-german-dbmdz-uncased""": {"""do_lower_case""": True},
    """TurkuNLP/bert-base-finnish-cased-v1""": {"""do_lower_case""": False},
    """TurkuNLP/bert-base-finnish-uncased-v1""": {"""do_lower_case""": True},
    """wietsedv/bert-base-dutch-cased""": {"""do_lower_case""": False},
}







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		PRETRAINED_INIT_CONFIGURATION
  _UpperCAmelCase          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		BertTokenizer

  def __init__(		self    ,_A=None    ,_A=None    ,_A=True    ,_A="[UNK]"    ,_A="[SEP]"    ,_A="[PAD]"    ,_A="[CLS]"    ,_A="[MASK]"    ,_A=True    ,_A=None    ,**_A    ,):
      '''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    ,)

      _lowerCAmelCase       :   Tuple  =    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
      ):
          _lowerCAmelCase       :   Dict  =    getattr(_A    ,normalizer_state.pop('type'      )      )
          _lowerCAmelCase       :   Dict  =    do_lower_case
          _lowerCAmelCase       :   Optional[int]  =    strip_accents
          _lowerCAmelCase       :   Union[str, Any]  =    tokenize_chinese_chars
          _lowerCAmelCase       :   Dict  =    normalizer_class(**_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    do_lower_case

  def __lowerCamelCase		(		self    ,_A    ,_A=None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   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 __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    [self.sep_token_id]
      _lowerCAmelCase       :   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 __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self._tokenizer.model.save(_A    ,name=_A      )
      return tuple(_A      )





