code: 
  
  
  
  """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 							_UpperCAmelCase							(				a	,unittest.TestCase   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =MPNetTokenizer
       a__             =MPNetTokenizerFast
       a__             =True
       a__             =True
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      int:
           super().setUp()
  
           _UpperCAmelCase							:						str       							=					[
               '''[UNK]''',
               '''[CLS]''',
               '''[SEP]''',
               '''[PAD]''',
               '''[MASK]''',
               '''want''',
               '''##want''',
               '''##ed''',
               '''wa''',
               '''un''',
               '''runn''',
               '''##ing''',
               ''',''',
               '''low''',
               '''lowest''',
           ]
           _UpperCAmelCase							:						Union[str, Any]       							=					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	)							->      Union[str, Any]:
           _UpperCAmelCase							:						List[Any]       							=					'''UNwant\u00E9d,running'''
           _UpperCAmelCase							:						Tuple       							=					'''unwanted, running'''
           return input_text, output_text
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Any:
           _UpperCAmelCase							:						int       							=					self.tokenizer_class(self.vocab_file	)
  
           _UpperCAmelCase							:						Dict       							=					tokenizer.tokenize('''UNwant\u00E9d,running'''	)
           self.assertListEqual(A ,			['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']	)
           self.assertListEqual(tokenizer.convert_tokens_to_ids(A	) ,			[9, 6, 7, 1_2, 1_0, 1_1]	)
  
  
  
  
  
       @slow
       def 				__lowerCAmelCase			(      self	)							->      List[str]:
           _UpperCAmelCase							:						Optional[int]       							=					self.tokenizer_class.from_pretrained('''microsoft/mpnet-base'''	)
  
           _UpperCAmelCase							:						List[str]       							=					tokenizer.encode('''sequence builders''' ,			add_special_tokens=A	)
           _UpperCAmelCase							:						Dict       							=					tokenizer.encode('''multi-sequence build''' ,			add_special_tokens=A	)
  
           _UpperCAmelCase							:						Dict       							=					tokenizer.build_inputs_with_special_tokens(A	)
           _UpperCAmelCase							:						str       							=					tokenizer.build_inputs_with_special_tokens(A ,			A	)
  
           assert encoded_sentence == [0] + text + [2]
           assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  
  from ... import PreTrainedTokenizer, TensorType, is_torch_available
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast, PatchingSpec
  from ...utils import logging
  
  
  _lowerCAmelCase					:Union[str, Any]  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					:List[str]  =      {
      'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
      'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
      'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
      'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
      'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
  }
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             ='''gpt2'''
       a__             =['''past_key_values''']
       a__             ={
           '''hidden_size''': '''n_embd''',
           '''max_position_embeddings''': '''n_positions''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }
  
  
  
  
  
       def __init__(      self ,			A=5_0_2_5_7 ,			A=1_0_2_4 ,			A=7_6_8 ,			A=1_2 ,			A=1_2 ,			A=None ,			A="gelu_new" ,			A=0.1 ,			A=0.1 ,			A=0.1 ,			A=1E-5 ,			A=0.02 ,			A="cls_index" ,			A=True ,			A=None ,			A=True ,			A=0.1 ,			A=True ,			A=True ,			A=5_0_2_5_6 ,			A=5_0_2_5_6 ,			A=False ,			A=False ,			**A ,			)							->      Union[str, Any]:
           _UpperCAmelCase							:						Optional[int]       							=					vocab_size
           _UpperCAmelCase							:						Union[str, Any]       							=					n_positions
           _UpperCAmelCase							:						Optional[Any]       							=					n_embd
           _UpperCAmelCase							:						Tuple       							=					n_layer
           _UpperCAmelCase							:						Any       							=					n_head
           _UpperCAmelCase							:						Union[str, Any]       							=					n_inner
           _UpperCAmelCase							:						Optional[int]       							=					activation_function
           _UpperCAmelCase							:						Optional[int]       							=					resid_pdrop
           _UpperCAmelCase							:						Dict       							=					embd_pdrop
           _UpperCAmelCase							:						List[Any]       							=					attn_pdrop
           _UpperCAmelCase							:						Tuple       							=					layer_norm_epsilon
           _UpperCAmelCase							:						Optional[int]       							=					initializer_range
           _UpperCAmelCase							:						List[str]       							=					summary_type
           _UpperCAmelCase							:						Tuple       							=					summary_use_proj
           _UpperCAmelCase							:						List[Any]       							=					summary_activation
           _UpperCAmelCase							:						Optional[int]       							=					summary_first_dropout
           _UpperCAmelCase							:						int       							=					summary_proj_to_labels
           _UpperCAmelCase							:						Union[str, Any]       							=					scale_attn_weights
           _UpperCAmelCase							:						Any       							=					use_cache
           _UpperCAmelCase							:						Tuple       							=					scale_attn_by_inverse_layer_idx
           _UpperCAmelCase							:						Optional[Any]       							=					reorder_and_upcast_attn
  
           _UpperCAmelCase							:						Union[str, Any]       							=					bos_token_id
           _UpperCAmelCase							:						Optional[int]       							=					eos_token_id
  
           super().__init__(bos_token_id=A ,			eos_token_id=A ,			**A	)
  
  
  
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A = "default" ,			A = None ,			A = False ,			)							->      str:
           super().__init__(A ,			task=A ,			patching_specs=A ,			use_past=A	)
           if not getattr(self._config ,			'''pad_token_id''' ,			A	):
               # TODO: how to do that better?
               _UpperCAmelCase							:						Optional[int]       							=					0
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      Mapping[str, Mapping[int, str]]:
           _UpperCAmelCase							:						List[str]       							=					OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
           if self.use_past:
               self.fill_with_past_key_values_(A ,			direction='''inputs'''	)
               _UpperCAmelCase							:						Union[str, Any]       							=					{0: '''batch''', 1: '''past_sequence + sequence'''}
           else:
               _UpperCAmelCase							:						Tuple       							=					{0: '''batch''', 1: '''sequence'''}
  
           return common_inputs
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      int:
           return self._config.n_layer
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      int:
           return self._config.n_head
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = -1 ,			A = -1 ,			A = False ,			A = None ,			)							->      Mapping[str, Any]:
           _UpperCAmelCase							:						int       							=					super(A ,			self	).generate_dummy_inputs(
               A ,			batch_size=A ,			seq_length=A ,			is_pair=A ,			framework=A	)
  
           # We need to order the input in the way they appears in the forward()
           _UpperCAmelCase							:						List[Any]       							=					OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)
  
           # Need to add the past_keys
           if self.use_past:
               if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
               else:
                   import torch
  
                   _UpperCAmelCase							,       _UpperCAmelCase							:						Optional[Any]       							=					common_inputs['''input_ids'''].shape
                   # Not using the same length for past_key_values
                   _UpperCAmelCase							:						Optional[Any]       							=					seqlen + 2
                   _UpperCAmelCase							:						Union[str, Any]       							=					(
                       batch,
                       self.num_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // self.num_attention_heads,
                   )
                   _UpperCAmelCase							:						List[Any]       							=					[
                       (torch.zeros(A	), torch.zeros(A	)) for _ in range(self.num_layers	)
                   ]
  
           _UpperCAmelCase							:						int       							=					common_inputs['''attention_mask''']
           if self.use_past:
               _UpperCAmelCase							:						int       							=					ordered_inputs['''attention_mask'''].dtype
               _UpperCAmelCase							:						Dict       							=					torch.cat(
                   [ordered_inputs['''attention_mask'''], torch.ones(A ,			A ,			dtype=A	)] ,			dim=1	)
  
           return ordered_inputs
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      int:
           return 1_3
  
  
  