




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 			a__       (					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Dict							=	MPNetTokenizer
     a		:			Dict							=	MPNetTokenizerFast
     a		:			Optional[int]							=	True
     a		:			Any							=	True







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''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 			lowerCAmelCase_				(	self  ,		A )  ->    Any:



          '''simple docstring'''



          a      =      "UNwant\u00E9d,running"
          a      =      "unwanted, running"
          return input_text, output_text







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a      =      self.tokenizer_class(self.vocab_file )

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







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          a      =      self.tokenizer_class.from_pretrained("microsoft/mpnet-base" )

          a      =      tokenizer.encode("sequence builders"  ,		add_special_tokens=A )
          a      =      tokenizer.encode("multi-sequence build"  ,		add_special_tokens=A )

          a      =      tokenizer.build_inputs_with_special_tokens(A )
          a      =      tokenizer.build_inputs_with_special_tokens(A  ,		A )

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







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


lowercase__      :			Optional[Any]           =					logging.get_logger(__name__)

lowercase__      :			Optional[int]           =					{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class 			a__       (					UpperCamelCase__      ):
     a		:			Dict							=	"""yoso"""







     def __init__(	self  ,		A=50265  ,		A=768  ,		A=12  ,		A=12  ,		A=3072  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=4096  ,		A=1  ,		A=0.0_2  ,		A=1e-12  ,		A="absolute"  ,		A=True  ,		A=9  ,		A=64  ,		A=None  ,		A=True  ,		A=True  ,		A=1  ,		A=0  ,		A=2  ,		**A  ,		)  ->    Dict:



          '''simple docstring'''



          super().__init__(pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A  ,		**A )

          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      =      layer_norm_eps
          a      =      position_embedding_type
          a      =      use_expectation
          a      =      hash_code_len
          a      =      num_hash
          a      =      conv_window
          a      =      use_fast_hash
          a      =      lsh_backward

