


# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
UpperCAmelCase__            =							1e-10
UpperCAmelCase__            =							'''This is incredible! I love it, this is the best chicken I have ever had.'''
UpperCAmelCase__            =							100







class 				lowerCamelCase__       (	nn.Module):





     def __init__(self		,					UpperCAmelCase		,					UpperCAmelCase="gpt2-medium"		,					UpperCAmelCase=False		,					UpperCAmelCase="cpu"	)					->       Tuple:
      super().__init__()
      _lowercase											=GPTaTokenizer.from_pretrained(UpperCAmelCase	)
      _lowercase											=GPTaLMHeadModel.from_pretrained(UpperCAmelCase	)
      _lowercase											=self.encoder.transformer.config.hidden_size
      _lowercase											=ClassificationHead(class_size=UpperCAmelCase		,					embed_size=self.embed_size	)
      _lowercase											=cached_mode
      _lowercase											=device





     def 					__A (self	)					->       Union[str, Any]:
      return self.classifier_head





     def 					__A (self	)					->       Dict:
      for param in self.encoder.parameters():
       _lowercase											=False
      self.classifier_head.train()





     def 					__A (self		,					UpperCAmelCase	)					->       Optional[Any]:
      _lowercase											=x.ne(0	).unsqueeze(2	).repeat(1		,					1		,					self.embed_size	).float().to(self.device	).detach()
      _lowercase											=self.encoder.transformer(UpperCAmelCase	)['''last_hidden_state''']
      _lowercase											=hidden * mask
      _lowercase											=torch.sum(UpperCAmelCase		,					dim=1	) / (torch.sum(UpperCAmelCase		,					dim=1	).detach() + EPSILON)
      return avg_hidden





     def 					__A (self		,					UpperCAmelCase	)					->       Tuple:
      if self.cached_mode:
       _lowercase											=x.to(self.device	)
      else:
       _lowercase											=self.avg_representation(x.to(self.device	)	)

      _lowercase											=self.classifier_head(UpperCAmelCase	)
      _lowercase											=nn.functional.log_softmax(UpperCAmelCase		,					dim=-1	)

      return probs







class 				lowerCamelCase__       (	data.Dataset):





     def __init__(self		,					UpperCAmelCase		,					UpperCAmelCase	)					->       Optional[int]:
      _lowercase											=X
      _lowercase											=y





     def __len__(self	)					->       List[Any]:
      return len(self.X	)





     def __getitem__(self		,					UpperCAmelCase	)					->       Union[str, Any]:
      _lowercase											={}
      _lowercase											=self.X[index]
      _lowercase											=self.y[index]
      return data



def     UpperCAmelCase_ (							__snake_case						)					->      Union[str, Any]:







 """simple docstring"""


 def pad_sequences(__snake_case						):
  _lowercase											=[len(__snake_case						) for seq in sequences]

  _lowercase											=torch.zeros(len(__snake_case						)					,						max(__snake_case						)						).long()  # padding value = 0

  for i, seq in enumerate(__snake_case						):
   _lowercase											=lengths[i]
   _lowercase											=seq[:end]

  return padded_sequences, lengths

 _lowercase											={}
 for key in data[0].keys():
  _lowercase											=[d[key] for d in data]

 _lowercase				,   _lowercase											=pad_sequences(item_info['''X''']						)
 _lowercase											=torch.tensor(item_info['''y''']					,						dtype=torch.long						)

 return x_batch, y_batch
def     UpperCAmelCase_ (							__snake_case						)					->      Any:







 """simple docstring"""


 _lowercase											={}
 for key in data[0].keys():
  _lowercase											=[d[key] for d in data]

 _lowercase											=torch.cat(item_info['''X''']					,						0						)
 _lowercase											=torch.tensor(item_info['''y''']					,						dtype=torch.long						)

 return x_batch, y_batch
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case=0					,						__snake_case=10					,						__snake_case="cpu"						)					->      int:







 """simple docstring"""


 _lowercase											=0
 discriminator.train_custom()
 for batch_idx, (input_t, target_t) in enumerate(__snake_case						):
  _lowercase				,   _lowercase											=input_t.to(__snake_case						), target_t.to(__snake_case						)

  optimizer.zero_grad()

  _lowercase											=discriminator(__snake_case						)
  _lowercase											=nn.functional.nll_loss(__snake_case					,						__snake_case						)
  loss.backward(retain_graph=__snake_case						)
  optimizer.step()

  samples_so_far += len(__snake_case						)

  if batch_idx % log_interval == 0:
   print(
       '''Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'''.format(
           epoch + 1					,						__snake_case					,						len(data_loader.dataset						)					,						100 * samples_so_far / len(data_loader.dataset						)					,						loss.item()					,						)						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case="cpu"						)					->      Dict:







 """simple docstring"""


 discriminator.eval()
 _lowercase											=0
 _lowercase											=0
 with torch.no_grad():
  for input_t, target_t in data_loader:
   _lowercase				,   _lowercase											=input_t.to(__snake_case						), target_t.to(__snake_case						)
   _lowercase											=discriminator(__snake_case						)
   # sum up batch loss
   test_loss += nn.functional.nll_loss(__snake_case					,						__snake_case					,						reduction='''sum'''						).item()
   # get the index of the max log-probability
   _lowercase											=output_t.argmax(dim=1					,						keepdim=__snake_case						)
   correct += pred_t.eq(target_t.view_as(__snake_case						)						).sum().item()

 test_loss /= len(data_loader.dataset						)

 print(
     '''Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'''.format(
         __snake_case					,						__snake_case					,						len(data_loader.dataset						)					,						1_00.0 * correct / len(data_loader.dataset						)						)						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case=False					,						__snake_case="cpu"						)					->      str:







 """simple docstring"""


 _lowercase											=model.tokenizer.encode(__snake_case						)
 _lowercase											=torch.tensor([input_t]					,						dtype=torch.long					,						device=__snake_case						)
 if cached:
  _lowercase											=model.avg_representation(__snake_case						)

 _lowercase											=model(__snake_case						).data.cpu().numpy().flatten().tolist()
 print('''Input sentence:'''					,						__snake_case						)
 print(
     '''Predictions:'''					,						''', '''.join('''{}: {:.4f}'''.format(__snake_case					,						math.exp(__snake_case						)						) for c, log_prob in zip(__snake_case					,						__snake_case						)						)					,						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case=False					,						__snake_case="cpu"						)					->      List[Any]:







 """simple docstring"""


 _lowercase											=torch.utils.data.DataLoader(dataset=__snake_case					,						batch_size=__snake_case					,						collate_fn=__snake_case						)

 _lowercase											=[]
 _lowercase											=[]
 for batch_idx, (x, y) in enumerate(tqdm(__snake_case					,						ascii=__snake_case						)						):
  with torch.no_grad():
   _lowercase											=x.to(__snake_case						)
   _lowercase											=discriminator.avg_representation(__snake_case						).cpu().detach()
   _lowercase											=torch.unbind(avg_rep.unsqueeze(1						)						)
   xs += avg_rep_list
   ys += y.cpu().numpy().tolist()

 _lowercase											=torch.utils.data.DataLoader(
     dataset=Dataset(__snake_case					,						__snake_case						)					,						batch_size=__snake_case					,						shuffle=__snake_case					,						collate_fn=__snake_case						)

 return data_loader
def     UpperCAmelCase_ (							__snake_case					,						__snake_case=None					,						__snake_case="gpt2-medium"					,						__snake_case=10					,						__snake_case=64					,						__snake_case=10					,						__snake_case=False					,						__snake_case=False					,						__snake_case=False					,						)					->      int:







 """simple docstring"""


 _lowercase											='''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

 print('''Preprocessing {} dataset...'''.format(__snake_case						)						)
 _lowercase											=time.time()

 if dataset == "SST":
  _lowercase											=['''positive''', '''negative''', '''very positive''', '''very negative''', '''neutral''']
  _lowercase											={c: i for i, c in enumerate(__snake_case						)}

  _lowercase											=Discriminator(
      class_size=len(__snake_case						)					,						pretrained_model=__snake_case					,						cached_mode=__snake_case					,						device=__snake_case						).to(__snake_case						)

  _lowercase											=torchtext_data.Field()
  _lowercase											=torchtext_data.Field(sequential=__snake_case						)
  _lowercase				,   _lowercase				,   _lowercase											=datasets.SST.splits(
      __snake_case					,						__snake_case					,						fine_grained=__snake_case					,						train_subtrees=__snake_case					,						)

  _lowercase											=[]
  _lowercase											=[]
  for i in trange(len(__snake_case						)					,						ascii=__snake_case						):
   _lowercase											=TreebankWordDetokenizer().detokenize(vars(train_data[i]						)['''text''']						)
   _lowercase											=discriminator.tokenizer.encode(__snake_case						)
   _lowercase											=torch.tensor([50256] + seq					,						device=__snake_case					,						dtype=torch.long						)
   x.append(__snake_case						)
   y.append(classaidx[vars(train_data[i]						)['''label''']]						)
  _lowercase											=Dataset(__snake_case					,						__snake_case						)

  _lowercase											=[]
  _lowercase											=[]
  for i in trange(len(__snake_case						)					,						ascii=__snake_case						):
   _lowercase											=TreebankWordDetokenizer().detokenize(vars(test_data[i]						)['''text''']						)
   _lowercase											=discriminator.tokenizer.encode(__snake_case						)
   _lowercase											=torch.tensor([50256] + seq					,						device=__snake_case					,						dtype=torch.long						)
   test_x.append(__snake_case						)
   test_y.append(classaidx[vars(test_data[i]						)['''label''']]						)
  _lowercase											=Dataset(__snake_case					,						__snake_case						)

  _lowercase											={
      '''class_size''': len(__snake_case						),
      '''embed_size''': discriminator.embed_size,
      '''pretrained_model''': pretrained_model,
      '''class_vocab''': classaidx,
      '''default_class''': 2,
  }

 elif dataset == "clickbait":
  _lowercase											=['''non_clickbait''', '''clickbait''']
  _lowercase											={c: i for i, c in enumerate(__snake_case						)}

  _lowercase											=Discriminator(
      class_size=len(__snake_case						)					,						pretrained_model=__snake_case					,						cached_mode=__snake_case					,						device=__snake_case						).to(__snake_case						)

  with open('''datasets/clickbait/clickbait_train_prefix.txt'''						) as f:
   _lowercase											=[]
   for i, line in enumerate(__snake_case						):
    try:
     data.append(eval(__snake_case						)						)
    except Exception:
     print('''Error evaluating line {}: {}'''.format(__snake_case					,						__snake_case						)						)
     continue
  _lowercase											=[]
  _lowercase											=[]
  with open('''datasets/clickbait/clickbait_train_prefix.txt'''						) as f:
   for i, line in enumerate(tqdm(__snake_case					,						ascii=__snake_case						)						):
    try:
     _lowercase											=eval(__snake_case						)
     _lowercase											=discriminator.tokenizer.encode(d['''text''']						)

     if len(__snake_case						) < max_length_seq:
      _lowercase											=torch.tensor([50256] + seq					,						device=__snake_case					,						dtype=torch.long						)
     else:
      print('''Line {} is longer than maximum length {}'''.format(__snake_case					,						__snake_case						)						)
      continue
     x.append(__snake_case						)
     y.append(d['''label''']						)
    except Exception:
     print('''Error evaluating / tokenizing line {}, skipping it'''.format(__snake_case						)						)
     pass

  _lowercase											=Dataset(__snake_case					,						__snake_case						)
  _lowercase											=int(0.9 * len(__snake_case						)						)
  _lowercase											=len(__snake_case						) - train_size
  _lowercase				,   _lowercase											=torch.utils.data.random_split(__snake_case					,						[train_size, test_size]						)

  _lowercase											={
      '''class_size''': len(__snake_case						),
      '''embed_size''': discriminator.embed_size,
      '''pretrained_model''': pretrained_model,
      '''class_vocab''': classaidx,
      '''default_class''': 1,
  }

 elif dataset == "toxic":
  _lowercase											=['''non_toxic''', '''toxic''']
  _lowercase											={c: i for i, c in enumerate(__snake_case						)}

  _lowercase											=Discriminator(
      class_size=len(__snake_case						)					,						pretrained_model=__snake_case					,						cached_mode=__snake_case					,						device=__snake_case						).to(__snake_case						)

  _lowercase											=[]
  _lowercase											=[]
  with open('''datasets/toxic/toxic_train.txt'''						) as f:
   for i, line in enumerate(tqdm(__snake_case					,						ascii=__snake_case						)						):
    try:
     _lowercase											=eval(__snake_case						)
     _lowercase											=discriminator.tokenizer.encode(d['''text''']						)

     if len(__snake_case						) < max_length_seq:
      _lowercase											=torch.tensor([50256] + seq					,						device=__snake_case					,						dtype=torch.long						)
     else:
      print('''Line {} is longer than maximum length {}'''.format(__snake_case					,						__snake_case						)						)
      continue
     x.append(__snake_case						)
     y.append(int(np.sum(d['''label''']						) > 0						)						)
    except Exception:
     print('''Error evaluating / tokenizing line {}, skipping it'''.format(__snake_case						)						)
     pass

  _lowercase											=Dataset(__snake_case					,						__snake_case						)
  _lowercase											=int(0.9 * len(__snake_case						)						)
  _lowercase											=len(__snake_case						) - train_size
  _lowercase				,   _lowercase											=torch.utils.data.random_split(__snake_case					,						[train_size, test_size]						)

  _lowercase											={
      '''class_size''': len(__snake_case						),
      '''embed_size''': discriminator.embed_size,
      '''pretrained_model''': pretrained_model,
      '''class_vocab''': classaidx,
      '''default_class''': 0,
  }

 else:  # if dataset == "generic":
  # This assumes the input dataset is a TSV with the following structure:
  # class \t text

  if dataset_fp is None:
   raise ValueError('''When generic dataset is selected, dataset_fp needs to be specified aswell.'''						)

  _lowercase											=set()
  with open(__snake_case						) as f:
   _lowercase											=csv.reader(__snake_case					,						delimiter='''\t'''						)
   for row in tqdm(__snake_case					,						ascii=__snake_case						):
    if row:
     classes.add(row[0]						)

  _lowercase											=sorted(__snake_case						)
  _lowercase											={c: i for i, c in enumerate(__snake_case						)}

  _lowercase											=Discriminator(
      class_size=len(__snake_case						)					,						pretrained_model=__snake_case					,						cached_mode=__snake_case					,						device=__snake_case						).to(__snake_case						)

  _lowercase											=[]
  _lowercase											=[]
  with open(__snake_case						) as f:
   _lowercase											=csv.reader(__snake_case					,						delimiter='''\t'''						)
   for i, row in enumerate(tqdm(__snake_case					,						ascii=__snake_case						)						):
    if row:
     _lowercase											=row[0]
     _lowercase											=row[1]

     try:
      _lowercase											=discriminator.tokenizer.encode(__snake_case						)
      if len(__snake_case						) < max_length_seq:
       _lowercase											=torch.tensor([50256] + seq					,						device=__snake_case					,						dtype=torch.long						)

      else:
       print('''Line {} is longer than maximum length {}'''.format(__snake_case					,						__snake_case						)						)
       continue

      x.append(__snake_case						)
      y.append(classaidx[label]						)

     except Exception:
      print('''Error tokenizing line {}, skipping it'''.format(__snake_case						)						)
      pass

  _lowercase											=Dataset(__snake_case					,						__snake_case						)
  _lowercase											=int(0.9 * len(__snake_case						)						)
  _lowercase											=len(__snake_case						) - train_size
  _lowercase				,   _lowercase											=torch.utils.data.random_split(__snake_case					,						[train_size, test_size]						)

  _lowercase											={
      '''class_size''': len(__snake_case						),
      '''embed_size''': discriminator.embed_size,
      '''pretrained_model''': pretrained_model,
      '''class_vocab''': classaidx,
      '''default_class''': 0,
  }

 _lowercase											=time.time()
 print('''Preprocessed {} data points'''.format(len(__snake_case						) + len(__snake_case						)						)						)
 print('''Data preprocessing took: {:.3f}s'''.format(end - start						)						)

 if cached:
  print('''Building representation cache...'''						)

  _lowercase											=time.time()

  _lowercase											=get_cached_data_loader(__snake_case					,						__snake_case					,						__snake_case					,						shuffle=__snake_case					,						device=__snake_case						)

  _lowercase											=get_cached_data_loader(__snake_case					,						__snake_case					,						__snake_case					,						device=__snake_case						)

  _lowercase											=time.time()
  print('''Building representation cache took: {:.3f}s'''.format(end - start						)						)

 else:
  _lowercase											=torch.utils.data.DataLoader(
      dataset=__snake_case					,						batch_size=__snake_case					,						shuffle=__snake_case					,						collate_fn=__snake_case						)
  _lowercase											=torch.utils.data.DataLoader(dataset=__snake_case					,						batch_size=__snake_case					,						collate_fn=__snake_case						)

 if save_model:
  with open('''{}_classifier_head_meta.json'''.format(__snake_case						)					,						'''w'''						) as meta_file:
   json.dump(__snake_case					,						__snake_case						)

 _lowercase											=optim.Adam(discriminator.parameters()					,						lr=0.00_01						)

 for epoch in range(__snake_case						):
  _lowercase											=time.time()
  print('''\nEpoch'''					,						epoch + 1						)

  train_epoch(
      discriminator=__snake_case					,						data_loader=__snake_case					,						optimizer=__snake_case					,						epoch=__snake_case					,						log_interval=__snake_case					,						device=__snake_case					,						)
  evaluate_performance(data_loader=__snake_case					,						discriminator=__snake_case					,						device=__snake_case						)

  _lowercase											=time.time()
  print('''Epoch took: {:.3f}s'''.format(end - start						)						)

  print('''\nExample prediction'''						)
  predict(__snake_case					,						__snake_case					,						__snake_case					,						cached=__snake_case					,						device=__snake_case						)

  if save_model:
   # torch.save(discriminator.state_dict(),
   #           "{}_discriminator_{}.pt".format(
   #               args.dataset, epoch + 1
   #               ))
   torch.save(
       discriminator.get_classifier().state_dict()					,						'''{}_classifier_head_epoch_{}.pt'''.format(__snake_case					,						epoch + 1						)					,						)


if __name__ == "__main__":
  UpperCAmelCase__            =							argparse.ArgumentParser(description='''Train a discriminator on top of GPT-2 representations''')
  parser.add_argument(
      '''--dataset''',
      type=str,
      default='''SST''',
      choices=('''SST''', '''clickbait''', '''toxic''', '''generic'''),
      help=(
          '''dataset to train the discriminator on.'''
          '''In case of generic, the dataset is expected'''
          '''to be a TSBV file with structure: class \\t text'''
      ),
  )
  parser.add_argument(
      '''--dataset_fp''',
      type=str,
      default='''''',
      help='''File path of the dataset to use. Needed only in case of generic datadset''',
  )
  parser.add_argument(
      '''--pretrained_model''', type=str, default='''gpt2-medium''', help='''Pretrained model to use as encoder'''
  )
  parser.add_argument('''--epochs''', type=int, default=10, metavar='''N''', help='''Number of training epochs''')
  parser.add_argument(
      '''--batch_size''', type=int, default=64, metavar='''N''', help='''input batch size for training (default: 64)'''
  )
  parser.add_argument(
      '''--log_interval''',
      type=int,
      default=10,
      metavar='''N''',
      help='''how many batches to wait before logging training status''',
  )
  parser.add_argument('''--save_model''', action='''store_true''', help='''whether to save the model''')
  parser.add_argument('''--cached''', action='''store_true''', help='''whether to cache the input representations''')
  parser.add_argument('''--no_cuda''', action='''store_true''', help='''use to turn off cuda''')
  UpperCAmelCase__            =							parser.parse_args()

  train_discriminator(**(vars(args)))





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


UpperCAmelCase__            =							logging.get_logger(__name__)

UpperCAmelCase__            =							{
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}







class 				lowerCamelCase__       (	lowerCAmelCase):
     SCREAMING_SNAKE_CASE__				      =      '''deberta'''





     def __init__(self		,					UpperCAmelCase=5_0_2_6_5		,					UpperCAmelCase=7_6_8		,					UpperCAmelCase=1_2		,					UpperCAmelCase=1_2		,					UpperCAmelCase=3_0_7_2		,					UpperCAmelCase="gelu"		,					UpperCAmelCase=0.1		,					UpperCAmelCase=0.1		,					UpperCAmelCase=5_1_2		,					UpperCAmelCase=0		,					UpperCAmelCase=0.02		,					UpperCAmelCase=1e-7		,					UpperCAmelCase=False		,					UpperCAmelCase=-1		,					UpperCAmelCase=0		,					UpperCAmelCase=True		,					UpperCAmelCase=None		,					UpperCAmelCase=0		,					UpperCAmelCase="gelu"		,					**UpperCAmelCase		,					)					->       Dict:
      super().__init__(**UpperCAmelCase	)

      _lowercase											=hidden_size
      _lowercase											=num_hidden_layers
      _lowercase											=num_attention_heads
      _lowercase											=intermediate_size
      _lowercase											=hidden_act
      _lowercase											=hidden_dropout_prob
      _lowercase											=attention_probs_dropout_prob
      _lowercase											=max_position_embeddings
      _lowercase											=type_vocab_size
      _lowercase											=initializer_range
      _lowercase											=relative_attention
      _lowercase											=max_relative_positions
      _lowercase											=pad_token_id
      _lowercase											=position_biased_input

      # Backwards compatibility
      if type(UpperCAmelCase	) == str:
       _lowercase											=[x.strip() for x in pos_att_type.lower().split('''|'''	)]

      _lowercase											=pos_att_type
      _lowercase											=vocab_size
      _lowercase											=layer_norm_eps

      _lowercase											=kwargs.get('''pooler_hidden_size'''		,					UpperCAmelCase	)
      _lowercase											=pooler_dropout
      _lowercase											=pooler_hidden_act







class 				lowerCamelCase__       (	lowerCAmelCase):





     @property
     def 					__A (self	)					->       Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       _lowercase											={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
       _lowercase											={0: '''batch''', 1: '''sequence'''}
      if self._config.type_vocab_size > 0:
       return OrderedDict(
           [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]	)
      else:
       return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]	)





     @property
     def 					__A (self	)					->       int:
      return 1_2





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase = -1		,					UpperCAmelCase = -1		,					UpperCAmelCase = -1		,					UpperCAmelCase = False		,					UpperCAmelCase = None		,					UpperCAmelCase = 3		,					UpperCAmelCase = 4_0		,					UpperCAmelCase = 4_0		,					UpperCAmelCase = None		,					)					->       Mapping[str, Any]:
      _lowercase											=super().generate_dummy_inputs(preprocessor=UpperCAmelCase		,					framework=UpperCAmelCase	)
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
       del dummy_inputs["token_type_ids"]
      return dummy_inputs

