


"""simple docstring"""



import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.albert.modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
  )







class _lowerCAmelCase				(      unittest.TestCase	):







   def __init__(	self						,	UpperCamelCase__						,	UpperCamelCase__=13						,	UpperCamelCase__=7						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=99						,	UpperCamelCase__=32						,	UpperCamelCase__=5						,	UpperCamelCase__=4						,	UpperCamelCase__=37						,	UpperCamelCase__="gelu"						,	UpperCamelCase__=0.1						,	UpperCamelCase__=0.1						,	UpperCamelCase__=512						,	UpperCamelCase__=16						,	UpperCamelCase__=2						,	UpperCamelCase__=0.02						,	UpperCamelCase__=4						,	)	->		Optional[Any]:

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




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

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

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

       snake_case    :      List[str]      					=    AlbertConfig(
           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						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      Any      					=    self.prepare_config_and_inputs()
       snake_case					,snake_case					,snake_case					,snake_case    :      Any      					=    config_and_inputs
       snake_case    :      Optional[Any]      					=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
       return config, inputs_dict







@require_flax
class _lowerCAmelCase				(      snake_case_    ,							unittest.TestCase	):
   __UpperCAmelCase :       int							  =  (
       (
           FlaxAlbertModel,
           FlaxAlbertForPreTraining,
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      Tuple      					=    FlaxAlbertModelTester(self						)







   @slow
   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       for model_class_name in self.all_model_classes:
           snake_case    :      str      					=    model_class_name.from_pretrained("albert-base-v2"						)
           snake_case    :      Tuple      					=    model(np.ones((1, 1)						)						)
           self.assertIsNotNone(UpperCamelCase__						)







@require_flax
class _lowerCAmelCase				(      unittest.TestCase	):







   @slow
   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    FlaxAlbertModel.from_pretrained("albert-base-v2"						)
       snake_case    :      List[Any]      					=    np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
       snake_case    :      List[Any]      					=    np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
       snake_case    :      Optional[int]      					=    model(UpperCamelCase__						,	attention_mask=UpperCamelCase__						)[0]
       snake_case    :      Optional[Any]      					=    (1, 11, 768)
       self.assertEqual(output.shape						,	UpperCamelCase__						)
       snake_case    :      List[str]      					=    np.array(
           [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]						)

       self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]						,	UpperCamelCase__						,	atol=1e-4						)						)









"""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:
  __snake_case   			=    None

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

__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""",
    },
}

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

__snake_case   			=    """▁"""







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       List[str]							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase :       Union[str, Any]							  =  PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase :       str							  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase :       str							  =  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__						,	)	->		Tuple:

       '''simple docstring'''




       snake_case    :      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__						,	)

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







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    [self.sep_token_id]
       snake_case    :      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  lowerCamelCase		(	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  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    [self.sep_token_id]
       snake_case    :      Optional[int]      					=    [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						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		Tuple[str]:

       '''simple docstring'''




       if not os.path.isdir(UpperCamelCase__						):
           logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase__						)						)
           return
       snake_case    :      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,)





