


'''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 					snake_case   (   lowercase      ,						unittest.TestCase      ):





  """simple docstring"""




  _lowerCamelCase		   =  MPNetTokenizer
  _lowerCamelCase		   =  MPNetTokenizerFast
  _lowerCamelCase		   =  True
  _lowerCamelCase		   =  True






  def   snake_case		(   self					):







   """simple docstring"""
   super().setUp()

   lowerCamelCase_								=  [
       "[UNK]",
       "[CLS]",
       "[SEP]",
       "[PAD]",
       "[MASK]",
       "want",
       "##want",
       "##ed",
       "wa",
       "un",
       "runn",
       "##ing",
       ",",
       "low",
       "lowest",
   ]
   lowerCamelCase_								=  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   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  "UNwant\u00E9d,running"
   lowerCamelCase_								=  "unwanted, running"
   return input_text, output_text






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.tokenizer_class(self.vocab_file					)

   lowerCamelCase_								=  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   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.tokenizer_class.from_pretrained("microsoft/mpnet-base"					)

   lowerCamelCase_								=  tokenizer.encode("sequence builders"      ,  add_special_tokens=UpperCamelCase					)
   lowerCamelCase_								=  tokenizer.encode("multi-sequence build"      ,  add_special_tokens=UpperCamelCase					)

   lowerCamelCase_								=  tokenizer.build_inputs_with_special_tokens(UpperCamelCase					)
   lowerCamelCase_								=  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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

a_  :    Tuple      =			{
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  "electra"






  def __init__(   self      ,  UpperCamelCase=3_0522      ,  UpperCamelCase=128      ,  UpperCamelCase=256      ,  UpperCamelCase=12      ,  UpperCamelCase=4      ,  UpperCamelCase=1024      ,  UpperCamelCase="gelu"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0.1      ,  UpperCamelCase=512      ,  UpperCamelCase=2      ,  UpperCamelCase=0.02      ,  UpperCamelCase=1e-12      ,  UpperCamelCase="first"      ,  UpperCamelCase=True      ,  UpperCamelCase="gelu"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0      ,  UpperCamelCase="absolute"      ,  UpperCamelCase=True      ,  UpperCamelCase=None      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   super().__init__(pad_token_id=UpperCamelCase      ,  **UpperCamelCase					)

   lowerCamelCase_								=  vocab_size
   lowerCamelCase_								=  embedding_size
   lowerCamelCase_								=  hidden_size
   lowerCamelCase_								=  num_hidden_layers
   lowerCamelCase_								=  num_attention_heads
   lowerCamelCase_								=  intermediate_size
   lowerCamelCase_								=  hidden_act
   lowerCamelCase_								=  hidden_dropout_prob
   lowerCamelCase_								=  attention_probs_dropout_prob
   lowerCamelCase_								=  max_position_embeddings
   lowerCamelCase_								=  type_vocab_size
   lowerCamelCase_								=  initializer_range
   lowerCamelCase_								=  layer_norm_eps

   lowerCamelCase_								=  summary_type
   lowerCamelCase_								=  summary_use_proj
   lowerCamelCase_								=  summary_activation
   lowerCamelCase_								=  summary_last_dropout
   lowerCamelCase_								=  position_embedding_type
   lowerCamelCase_								=  use_cache
   lowerCamelCase_								=  classifier_dropout





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  @property
  def   snake_case		(   self					):







   """simple docstring"""
   if self.task == "multiple-choice":
    lowerCamelCase_								=  {0: "batch", 1: "choice", 2: "sequence"}
   else:
    lowerCamelCase_								=  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
           ("token_type_ids", dynamic_axis),
       ]					)



