



"""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]







"""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


