






'''simple docstring'''







import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class      __SCREAMING_SNAKE_CASE					(__A       ,					unittest.TestCase					):


  """simple docstring"""


  _a     :   int			 =    MPNetTokenizer
  _a     :   Optional[Any]			 =    MPNetTokenizerFast
  _a     :   Optional[Any]			 =    True
  _a     :   List[str]			 =    True







  def       _a					( self  ):






         """simple docstring"""


         super().setUp()

         a_	    = [
             '''[UNK]''',
             '''[CLS]''',
             '''[SEP]''',
             '''[PAD]''',
             '''[MASK]''',
             '''want''',
             '''##want''',
             '''##ed''',
             '''wa''',
             '''un''',
             '''runn''',
             '''##ing''',
             ''',''',
             '''low''',
             '''lowest''',
         ]
         a_	    = os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['vocab_file']  )
         with open(self.vocab_file      ,  'w'      ,  encoding='utf-8'  ) as vocab_writer:
                vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]  )  )







  def       _a					( self      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_	    = '''UNwant\u00E9d,running'''
         a_	    = '''unwanted, running'''
         return input_text, output_text







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.tokenizer_class(self.vocab_file  )

         a_	    = tokenizer.tokenize('UNwant\u00E9d,running'  )
         self.assertListEqual(UpperCamelCase__      ,  ['un', '##want', '##ed', ',', 'runn', '##ing']  )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__  )      ,  [9, 6, 7, 12, 10, 11]  )







  @slow
  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.tokenizer_class.from_pretrained('microsoft/mpnet-base'  )

         a_	    = tokenizer.encode('sequence builders'      ,  add_special_tokens=UpperCamelCase__  )
         a_	    = tokenizer.encode('multi-sequence build'      ,  add_special_tokens=UpperCamelCase__  )

         a_	    = tokenizer.build_inputs_with_special_tokens(UpperCamelCase__  )
         a_	    = tokenizer.build_inputs_with_special_tokens(UpperCamelCase__      ,  UpperCamelCase__  )

         assert encoded_sentence == [0] + text + [2]
         assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]












'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    "uw-madison/nystromformer-512": "https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}




class      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  _a     :   Union[str, Any]			 =    '''nystromformer'''







  def __init__( self      ,  UpperCamelCase__=30_000      ,  UpperCamelCase__=768      ,  UpperCamelCase__=12      ,  UpperCamelCase__=12      ,  UpperCamelCase__=3_072      ,  UpperCamelCase__="gelu_new"      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=510      ,  UpperCamelCase__=2      ,  UpperCamelCase__=64      ,  UpperCamelCase__=64      ,  UpperCamelCase__=65      ,  UpperCamelCase__=False      ,  UpperCamelCase__=0.02      ,  UpperCamelCase__=1e-5      ,  UpperCamelCase__=1      ,  UpperCamelCase__=0      ,  UpperCamelCase__=2      ,  **UpperCamelCase__      ,  ):






         """simple docstring"""


         a_	    = vocab_size
         a_	    = max_position_embeddings
         a_	    = hidden_size
         a_	    = num_hidden_layers
         a_	    = num_attention_heads
         a_	    = intermediate_size
         a_	    = hidden_act
         a_	    = hidden_dropout_prob
         a_	    = attention_probs_dropout_prob
         a_	    = initializer_range
         a_	    = type_vocab_size
         a_	    = segment_means_seq_len
         a_	    = num_landmarks
         a_	    = conv_kernel_size
         a_	    = inv_coeff_init_option
         a_	    = layer_norm_eps
         super().__init__(pad_token_id=UpperCamelCase__      ,  bos_token_id=UpperCamelCase__      ,  eos_token_id=UpperCamelCase__      ,  **UpperCamelCase__  )




