# 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)
lowerCamelCase__   = 1e-10
lowerCamelCase__   = '''This is incredible! I love it, this is the best chicken I have ever had.'''
lowerCamelCase__   = 100




class  _UpperCAmelCase       (			nn.Module   ):



 '''simple docstring'''




 def __init__(						self							:					List[str]				,      lowercase_							:					Dict				,      lowercase_							:					Optional[Any]="gpt2-medium"				,      lowercase_							:					Any=False				,      lowercase_							:					int="cpu")		->     Dict:


   """simple docstring"""







   super().__init__()
   _UpperCamelCase				=  GPTaTokenizer.from_pretrained(lowercase_)
   _UpperCamelCase				=  GPTaLMHeadModel.from_pretrained(lowercase_)
   _UpperCamelCase				=  self.encoder.transformer.config.hidden_size
   _UpperCamelCase				=  ClassificationHead(class_size=lowercase_				,      embed_size=self.embed_size)
   _UpperCamelCase				=  cached_mode
   _UpperCamelCase				=  device







 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Dict:


   """simple docstring"""







   return self.classifier_head







 def 						__UpperCAmelCase							(						self							:					Tuple)		->     str:


   """simple docstring"""







   for param in self.encoder.parameters():
     _UpperCamelCase				=  False
   self.classifier_head.train()







 def 						__UpperCAmelCase							(						self							:					int				,      lowercase_							:					Optional[Any])		->     str:


   """simple docstring"""







   _UpperCamelCase				=  x.ne(0).unsqueeze(2).repeat(1				,      1				,      self.embed_size).float().to(self.device).detach()
   _UpperCamelCase				=  self.encoder.transformer(lowercase_)["last_hidden_state"]
   _UpperCamelCase				=  hidden * mask
   _UpperCamelCase				=  torch.sum(lowercase_				,      dim=1) / (torch.sum(lowercase_				,      dim=1).detach() + EPSILON)
   return avg_hidden







 def 						__UpperCAmelCase							(						self							:					Optional[int]				,      lowercase_							:					Optional[Any])		->     List[Any]:


   """simple docstring"""







   if self.cached_mode:
     _UpperCamelCase				=  x.to(self.device)
   else:
     _UpperCamelCase				=  self.avg_representation(x.to(self.device))

   _UpperCamelCase				=  self.classifier_head(lowercase_)
   _UpperCamelCase				=  nn.functional.log_softmax(lowercase_				,      dim=-1)

   return probs




class  _UpperCAmelCase       (			data.Dataset   ):



 '''simple docstring'''




 def __init__(						self							:					str				,      lowercase_							:					Dict				,      lowercase_							:					Optional[Any])		->     Any:


   """simple docstring"""







   _UpperCamelCase				=  X
   _UpperCamelCase				=  y







 def __len__(						self							:					Optional[Any])		->     str:


   """simple docstring"""







   return len(self.X)







 def __getitem__(						self							:					str				,      lowercase_							:					str)		->     int:


   """simple docstring"""







   _UpperCamelCase				=  {}
   _UpperCamelCase				=  self.X[index]
   _UpperCamelCase				=  self.y[index]
   return data


def 							lowerCAmelCase__    (       a__      )	->Tuple:




  '''simple docstring'''

  def pad_sequences(a__      ):
    _UpperCamelCase				=  [len(a__      ) for seq in sequences]

    _UpperCamelCase				=  torch.zeros(len(a__      )  ,			max(a__      )      ).long()  # padding value = 0

    for i, seq in enumerate(a__      ):
      _UpperCamelCase				=  lengths[i]
      _UpperCamelCase				=  seq[:end]

    return padded_sequences, lengths

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

  _UpperCamelCase	,		_UpperCamelCase				=  pad_sequences(item_info["X"]      )
  _UpperCamelCase				=  torch.tensor(item_info["y"]  ,			dtype=torch.long      )

  return x_batch, y_batch


def 							lowerCAmelCase__    (       a__      )	->Optional[int]:




  '''simple docstring'''

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

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

  return x_batch, y_batch


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__=0  ,			a__=10  ,			a__="cpu"      )	->Optional[int]:




  '''simple docstring'''

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

    optimizer.zero_grad()

    _UpperCamelCase				=  discriminator(a__      )
    _UpperCamelCase				=  nn.functional.nll_loss(a__  ,			a__      )
    loss.backward(retain_graph=a__      )
    optimizer.step()

    samples_so_far += len(a__      )

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


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__="cpu"      )	->List[Any]:




  '''simple docstring'''

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

  test_loss /= len(data_loader.dataset      )

  print(
      "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
          a__  ,			a__  ,			len(data_loader.dataset      )  ,			100.0 * correct / len(data_loader.dataset      )      )      )


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__=False  ,			a__="cpu"      )	->List[str]:




  '''simple docstring'''

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

  _UpperCamelCase				=  model(a__      ).data.cpu().numpy().flatten().tolist()
  print("Input sentence:"  ,			a__      )
  print(
      "Predictions:"  ,			", ".join("{}: {:.4f}".format(a__  ,			math.exp(a__      )      ) for c, log_prob in zip(a__  ,			a__      )      )  ,			)


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__=False  ,			a__="cpu"      )	->List[Any]:




  '''simple docstring'''

  _UpperCamelCase				=  torch.utils.data.DataLoader(dataset=a__  ,			batch_size=a__  ,			collate_fn=a__      )

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

  _UpperCamelCase				=  torch.utils.data.DataLoader(
      dataset=Dataset(a__  ,			a__      )  ,			batch_size=a__  ,			shuffle=a__  ,			collate_fn=a__      )

  return data_loader


def 							lowerCAmelCase__    (       a__  ,			a__=None  ,			a__="gpt2-medium"  ,			a__=10  ,			a__=64  ,			a__=10  ,			a__=False  ,			a__=False  ,			a__=False  ,			)	->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase				=  "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

  print("Preprocessing {} dataset...".format(a__      )      )
  _UpperCamelCase				=  time.time()

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

    _UpperCamelCase				=  Discriminator(
        class_size=len(a__      )  ,			pretrained_model=a__  ,			cached_mode=a__  ,			device=a__      ).to(a__      )

    _UpperCamelCase				=  torchtext_data.Field()
    _UpperCamelCase				=  torchtext_data.Field(sequential=a__      )
    _UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase				=  datasets.SST.splits(
        a__  ,			a__  ,			fine_grained=a__  ,			train_subtrees=a__  ,			)

    _UpperCamelCase				=  []
    _UpperCamelCase				=  []
    for i in trange(len(a__      )  ,			ascii=a__      ):
      _UpperCamelCase				=  TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["text"]      )
      _UpperCamelCase				=  discriminator.tokenizer.encode(a__      )
      _UpperCamelCase				=  torch.tensor([50_256] + seq  ,			device=a__  ,			dtype=torch.long      )
      x.append(a__      )
      y.append(classaidx[vars(train_data[i]      )["label"]]      )
    _UpperCamelCase				=  Dataset(a__  ,			a__      )

    _UpperCamelCase				=  []
    _UpperCamelCase				=  []
    for i in trange(len(a__      )  ,			ascii=a__      ):
      _UpperCamelCase				=  TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["text"]      )
      _UpperCamelCase				=  discriminator.tokenizer.encode(a__      )
      _UpperCamelCase				=  torch.tensor([50_256] + seq  ,			device=a__  ,			dtype=torch.long      )
      test_x.append(a__      )
      test_y.append(classaidx[vars(test_data[i]      )["label"]]      )
    _UpperCamelCase				=  Dataset(a__  ,			a__      )

    _UpperCamelCase				=  {
        "class_size": len(a__      ),
        "embed_size": discriminator.embed_size,
        "pretrained_model": pretrained_model,
        "class_vocab": classaidx,
        "default_class": 2,
    }

  elif dataset == "clickbait":
    _UpperCamelCase				=  ["non_clickbait", "clickbait"]
    _UpperCamelCase				=  {c: i for i, c in enumerate(a__      )}

    _UpperCamelCase				=  Discriminator(
        class_size=len(a__      )  ,			pretrained_model=a__  ,			cached_mode=a__  ,			device=a__      ).to(a__      )

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

          if len(a__      ) < max_length_seq:
            _UpperCamelCase				=  torch.tensor([50_256] + seq  ,			device=a__  ,			dtype=torch.long      )
          else:
            print("Line {} is longer than maximum length {}".format(a__  ,			a__      )      )
            continue
          x.append(a__      )
          y.append(d["label"]      )
        except Exception:
          print("Error evaluating / tokenizing line {}, skipping it".format(a__      )      )
          pass

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

    _UpperCamelCase				=  {
        "class_size": len(a__      ),
        "embed_size": discriminator.embed_size,
        "pretrained_model": pretrained_model,
        "class_vocab": classaidx,
        "default_class": 1,
    }

  elif dataset == "toxic":
    _UpperCamelCase				=  ["non_toxic", "toxic"]
    _UpperCamelCase				=  {c: i for i, c in enumerate(a__      )}

    _UpperCamelCase				=  Discriminator(
        class_size=len(a__      )  ,			pretrained_model=a__  ,			cached_mode=a__  ,			device=a__      ).to(a__      )

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

          if len(a__      ) < max_length_seq:
            _UpperCamelCase				=  torch.tensor([50_256] + seq  ,			device=a__  ,			dtype=torch.long      )
          else:
            print("Line {} is longer than maximum length {}".format(a__  ,			a__      )      )
            continue
          x.append(a__      )
          y.append(int(np.sum(d["label"]      ) > 0      )      )
        except Exception:
          print("Error evaluating / tokenizing line {}, skipping it".format(a__      )      )
          pass

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

    _UpperCamelCase				=  {
        "class_size": len(a__      ),
        "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."      )

    _UpperCamelCase				=  set()
    with open(a__      ) as f:
      _UpperCamelCase				=  csv.reader(a__  ,			delimiter="\t"      )
      for row in tqdm(a__  ,			ascii=a__      ):
        if row:
          classes.add(row[0]      )

    _UpperCamelCase				=  sorted(a__      )
    _UpperCamelCase				=  {c: i for i, c in enumerate(a__      )}

    _UpperCamelCase				=  Discriminator(
        class_size=len(a__      )  ,			pretrained_model=a__  ,			cached_mode=a__  ,			device=a__      ).to(a__      )

    _UpperCamelCase				=  []
    _UpperCamelCase				=  []
    with open(a__      ) as f:
      _UpperCamelCase				=  csv.reader(a__  ,			delimiter="\t"      )
      for i, row in enumerate(tqdm(a__  ,			ascii=a__      )      ):
        if row:
          _UpperCamelCase				=  row[0]
          _UpperCamelCase				=  row[1]

          try:
            _UpperCamelCase				=  discriminator.tokenizer.encode(a__      )
            if len(a__      ) < max_length_seq:
              _UpperCamelCase				=  torch.tensor([50_256] + seq  ,			device=a__  ,			dtype=torch.long      )

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

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

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

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

    _UpperCamelCase				=  {
        "class_size": len(a__      ),
        "embed_size": discriminator.embed_size,
        "pretrained_model": pretrained_model,
        "class_vocab": classaidx,
        "default_class": 0,
    }

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

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

    _UpperCamelCase				=  time.time()

    _UpperCamelCase				=  get_cached_data_loader(a__  ,			a__  ,			a__  ,			shuffle=a__  ,			device=a__      )

    _UpperCamelCase				=  get_cached_data_loader(a__  ,			a__  ,			a__  ,			device=a__      )

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

  else:
    _UpperCamelCase				=  torch.utils.data.DataLoader(
        dataset=a__  ,			batch_size=a__  ,			shuffle=a__  ,			collate_fn=a__      )
    _UpperCamelCase				=  torch.utils.data.DataLoader(dataset=a__  ,			batch_size=a__  ,			collate_fn=a__      )

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

  _UpperCamelCase				=  optim.Adam(discriminator.parameters()  ,			lr=0.0001      )

  for epoch in range(a__      ):
    _UpperCamelCase				=  time.time()
    print("\nEpoch"  ,			epoch + 1      )

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

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

    print("\nExample prediction"      )
    predict(a__  ,			a__  ,			a__  ,			cached=a__  ,			device=a__      )

    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(a__  ,			epoch + 1      )  ,			)


if __name__ == "__main__":
   lowerCamelCase__   = 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''')
   lowerCamelCase__   = parser.parse_args()

   train_discriminator(**(vars(args)))





from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase__   = logging.get_logger(__name__)

lowerCamelCase__   = {
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 __A           = '''ernie'''







 def __init__(						self							:					Dict				,      lowercase_							:					List[Any]=30522				,      lowercase_							:					Optional[int]=768				,      lowercase_							:					Tuple=12				,      lowercase_							:					Optional[int]=12				,      lowercase_							:					Any=3072				,      lowercase_							:					int="gelu"				,      lowercase_							:					List[Any]=0.1				,      lowercase_							:					Any=0.1				,      lowercase_							:					str=512				,      lowercase_							:					Any=2				,      lowercase_							:					Any=3				,      lowercase_							:					List[Any]=False				,      lowercase_							:					List[Any]=0.02				,      lowercase_							:					str=1e-1_2				,      lowercase_							:					int=0				,      lowercase_							:					str="absolute"				,      lowercase_							:					Dict=True				,      lowercase_							:					List[str]=None				,      **lowercase_							:					Dict				,      )		->     Union[str, Any]:


   """simple docstring"""







   super().__init__(pad_token_id=lowercase_				,      **lowercase_)

   _UpperCamelCase				=  vocab_size
   _UpperCamelCase				=  hidden_size
   _UpperCamelCase				=  num_hidden_layers
   _UpperCamelCase				=  num_attention_heads
   _UpperCamelCase				=  hidden_act
   _UpperCamelCase				=  intermediate_size
   _UpperCamelCase				=  hidden_dropout_prob
   _UpperCamelCase				=  attention_probs_dropout_prob
   _UpperCamelCase				=  max_position_embeddings
   _UpperCamelCase				=  type_vocab_size
   _UpperCamelCase				=  task_type_vocab_size
   _UpperCamelCase				=  use_task_id
   _UpperCamelCase				=  initializer_range
   _UpperCamelCase				=  layer_norm_eps
   _UpperCamelCase				=  position_embedding_type
   _UpperCamelCase				=  use_cache
   _UpperCamelCase				=  classifier_dropout




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 @property
 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     Mapping[str, Mapping[int, str]]:


   """simple docstring"""







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




