
"""simple docstring"""


# 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								=						1_00

class    A_     (					nn.Module       ):



							"""simple docstring"""






							def __init__(  self  :Optional[Any]					,							lowerCamelCase_  :Any					,							lowerCamelCase_  :List[Any]="gpt2-medium"					,							lowerCamelCase_  :List[Any]=False					,							lowerCamelCase_  :Optional[Any]="cpu"	):





										"""simple docstring"""

										super().__init__()
										lowerCamelCase__     :					Dict		=GPTaTokenizer.from_pretrained(lowerCamelCase_	)
										lowerCamelCase__     :					int		=GPTaLMHeadModel.from_pretrained(lowerCamelCase_	)
										lowerCamelCase__     :					List[str]		=self.encoder.transformer.config.hidden_size
										lowerCamelCase__     :					List[Any]		=ClassificationHead(class_size=lowerCamelCase_					,							embed_size=self.embed_size	)
										lowerCamelCase__     :					Any		=cached_mode
										lowerCamelCase__     :					Optional[Any]		=device





							def 				UpperCAmelCase__							(  self  :str	):





										"""simple docstring"""

										return self.classifier_head





							def 				UpperCAmelCase__							(  self  :List[str]	):





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :Optional[int]					,							lowerCamelCase_  :Optional[Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					Dict		=x.ne(0	).unsqueeze(2	).repeat(1					,							1					,							self.embed_size	).float().to(self.device	).detach()
										lowerCamelCase__     :					Tuple		=self.encoder.transformer(lowerCamelCase_	)['last_hidden_state']
										lowerCamelCase__     :					List[Any]		=hidden * mask
										lowerCamelCase__     :					List[str]		=torch.sum(lowerCamelCase_					,							dim=1	) / (torch.sum(lowerCamelCase_					,							dim=1	).detach() + EPSILON)
										return avg_hidden





							def 				UpperCAmelCase__							(  self  :str					,							lowerCamelCase_  :List[str]	):





										"""simple docstring"""

										if self.cached_mode:
													lowerCamelCase__     :					int		=x.to(self.device	)
										else:
													lowerCamelCase__     :					List[str]		=self.avg_representation(x.to(self.device	)	)

										lowerCamelCase__     :					List[Any]		=self.classifier_head(lowerCamelCase_	)
										lowerCamelCase__     :					Dict		=nn.functional.log_softmax(lowerCamelCase_					,							dim=-1	)

										return probs






class    A_     (					data.Dataset       ):



							"""simple docstring"""






							def __init__(  self  :Optional[Any]					,							lowerCamelCase_  :int					,							lowerCamelCase_  :List[str]	):





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=X
										lowerCamelCase__     :					str		=y





							def __len__(  self  :List[str]	):





										"""simple docstring"""

										return len(self.X	)





							def __getitem__(  self  :Any					,							lowerCamelCase_  :Any	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[int]		={}
										lowerCamelCase__     :					str		=self.X[index]
										lowerCamelCase__     :					str		=self.y[index]
										return data






def        lowerCAmelCase_       (		snake_case_		:      int      )       ->List[str]:
			def pad_sequences(snake_case_		:      Union[str, Any]      ):
						lowerCamelCase__     :					Optional[int]		=[len(snake_case_      ) for seq in sequences]

						lowerCamelCase__     :					int		=torch.zeros(len(snake_case_      )							,   max(snake_case_      )      ).long()  # padding value = 0

						for i, seq in enumerate(snake_case_      ):
									lowerCamelCase__     :					int		=lengths[i]
									lowerCamelCase__     :					Union[str, Any]		=seq[:end]

						return padded_sequences, lengths

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

			lowerCamelCase__	,    lowerCamelCase__     :					Union[str, Any]		=pad_sequences(item_info['X']      )
			lowerCamelCase__     :					Tuple		=torch.tensor(item_info['y']							,   dtype=torch.long      )

			return x_batch, y_batch






def        lowerCAmelCase_       (		snake_case_		:      Optional[int]      )       ->Optional[Any]:
			lowerCamelCase__     :					int		={}
			for key in data[0].keys():
						lowerCamelCase__     :					Dict		=[d[key] for d in data]

			lowerCamelCase__     :					str		=torch.cat(item_info['X']							,   0      )
			lowerCamelCase__     :					int		=torch.tensor(item_info['y']							,   dtype=torch.long      )

			return x_batch, y_batch






def        lowerCAmelCase_       (		snake_case_		:      List[Any]							,   snake_case_		:      Tuple							,   snake_case_		:      List[str]							,   snake_case_		:      Dict=0							,   snake_case_		:      str=1_0							,   snake_case_		:      int="cpu"      )       ->Optional[int]:
			lowerCamelCase__     :					Any		=0
			discriminator.train_custom()
			for batch_idx, (input_t, target_t) in enumerate(snake_case_      ):
						lowerCamelCase__	,    lowerCamelCase__     :					Optional[Any]		=input_t.to(snake_case_      ), target_t.to(snake_case_      )

						optimizer.zero_grad()

						lowerCamelCase__     :					Any		=discriminator(snake_case_      )
						lowerCamelCase__     :					Optional[Any]		=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      )							,   1_0_0 * samples_so_far / len(data_loader.dataset      )							,   loss.item()							,   )      )






def        lowerCAmelCase_       (		snake_case_		:      int							,   snake_case_		:      Union[str, Any]							,   snake_case_		:      Optional[Any]="cpu"      )       ->int:
			discriminator.eval()
			lowerCamelCase__     :					Dict		=0
			lowerCamelCase__     :					Optional[Any]		=0
			with torch.no_grad():
						for input_t, target_t in data_loader:
									lowerCamelCase__	,    lowerCamelCase__     :					int		=input_t.to(snake_case_      ), target_t.to(snake_case_      )
									lowerCamelCase__     :					Dict		=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
									lowerCamelCase__     :					Tuple		=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      )							,   100.0 * correct / len(data_loader.dataset      )      )      )






def        lowerCAmelCase_       (		snake_case_		:      int							,   snake_case_		:      Union[str, Any]							,   snake_case_		:      Dict							,   snake_case_		:      Tuple=False							,   snake_case_		:      int="cpu"      )       ->Union[str, Any]:
			lowerCamelCase__     :					Any		=model.tokenizer.encode(snake_case_      )
			lowerCamelCase__     :					List[Any]		=torch.tensor([input_t]							,   dtype=torch.long							,   device=snake_case_      )
			if cached:
						lowerCamelCase__     :					List[str]		=model.avg_representation(snake_case_      )

			lowerCamelCase__     :					Optional[Any]		=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        lowerCAmelCase_       (		snake_case_		:      List[str]							,   snake_case_		:      List[Any]							,   snake_case_		:      Dict							,   snake_case_		:      Optional[Any]=False							,   snake_case_		:      List[Any]="cpu"      )       ->str:
			lowerCamelCase__     :					List[Any]		=torch.utils.data.DataLoader(dataset=snake_case_							,   batch_size=snake_case_							,   collate_fn=snake_case_      )

			lowerCamelCase__     :					Optional[Any]		=[]
			lowerCamelCase__     :					Union[str, Any]		=[]
			for batch_idx, (x, y) in enumerate(tqdm(snake_case_							,   ascii=snake_case_      )      ):
						with torch.no_grad():
									lowerCamelCase__     :					Optional[Any]		=x.to(snake_case_      )
									lowerCamelCase__     :					Optional[int]		=discriminator.avg_representation(snake_case_      ).cpu().detach()
									lowerCamelCase__     :					Union[str, Any]		=torch.unbind(avg_rep.unsqueeze(1      )      )
									xs += avg_rep_list
									ys += y.cpu().numpy().tolist()

			lowerCamelCase__     :					Dict		=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        lowerCAmelCase_       (		snake_case_		:      int							,   snake_case_		:      str=None							,   snake_case_		:      Dict="gpt2-medium"							,   snake_case_		:      List[Any]=1_0							,   snake_case_		:      Optional[Any]=6_4							,   snake_case_		:      Any=1_0							,   snake_case_		:      Optional[int]=False							,   snake_case_		:      Any=False							,   snake_case_		:      Optional[Any]=False							,   )       ->int:
			lowerCamelCase__     :					List[Any]		='cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'

			print('Preprocessing {} dataset...'.format(snake_case_      )      )
			lowerCamelCase__     :					int		=time.time()

			if dataset == "SST":
						lowerCamelCase__     :					Any		=['positive', 'negative', 'very positive', 'very negative', 'neutral']
						lowerCamelCase__     :					Union[str, Any]		={c: i for i, c in enumerate(snake_case_      )}

						lowerCamelCase__     :					List[str]		=Discriminator(
						    class_size=len(snake_case_      )							,   pretrained_model=snake_case_							,   cached_mode=snake_case_							,   device=snake_case_      ).to(snake_case_      )

						lowerCamelCase__     :					List[Any]		=torchtext_data.Field()
						lowerCamelCase__     :					List[str]		=torchtext_data.Field(sequential=snake_case_      )
						lowerCamelCase__	,    lowerCamelCase__	,    lowerCamelCase__     :					Optional[Any]		=datasets.SST.splits(
						    snake_case_							,   snake_case_							,   fine_grained=snake_case_							,   train_subtrees=snake_case_							,   )

						lowerCamelCase__     :					str		=[]
						lowerCamelCase__     :					Optional[Any]		=[]
						for i in trange(len(snake_case_      )							,   ascii=snake_case_      ):
									lowerCamelCase__     :					Union[str, Any]		=TreebankWordDetokenizer().detokenize(vars(train_data[i]      )['text']      )
									lowerCamelCase__     :					Dict		=discriminator.tokenizer.encode(snake_case_      )
									lowerCamelCase__     :					Any		=torch.tensor([5_0_2_5_6] + seq							,   device=snake_case_							,   dtype=torch.long      )
									x.append(snake_case_      )
									y.append(classaidx[vars(train_data[i]      )['label']]      )
						lowerCamelCase__     :					Any		=Dataset(snake_case_							,   snake_case_      )

						lowerCamelCase__     :					Dict		=[]
						lowerCamelCase__     :					Dict		=[]
						for i in trange(len(snake_case_      )							,   ascii=snake_case_      ):
									lowerCamelCase__     :					str		=TreebankWordDetokenizer().detokenize(vars(test_data[i]      )['text']      )
									lowerCamelCase__     :					List[Any]		=discriminator.tokenizer.encode(snake_case_      )
									lowerCamelCase__     :					int		=torch.tensor([5_0_2_5_6] + seq							,   device=snake_case_							,   dtype=torch.long      )
									test_x.append(snake_case_      )
									test_y.append(classaidx[vars(test_data[i]      )['label']]      )
						lowerCamelCase__     :					Union[str, Any]		=Dataset(snake_case_							,   snake_case_      )

						lowerCamelCase__     :					int		={
						    'class_size': len(snake_case_      ),
						    'embed_size': discriminator.embed_size,
						    'pretrained_model': pretrained_model,
						    'class_vocab': classaidx,
						    'default_class': 2,
						}

			elif dataset == "clickbait":
						lowerCamelCase__     :					Union[str, Any]		=['non_clickbait', 'clickbait']
						lowerCamelCase__     :					List[Any]		={c: i for i, c in enumerate(snake_case_      )}

						lowerCamelCase__     :					Tuple		=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:
									lowerCamelCase__     :					Any		=[]
									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
						lowerCamelCase__     :					Any		=[]
						lowerCamelCase__     :					str		=[]
						with open('datasets/clickbait/clickbait_train_prefix.txt'      ) as f:
									for i, line in enumerate(tqdm(snake_case_							,   ascii=snake_case_      )      ):
												try:
															lowerCamelCase__     :					Union[str, Any]		=eval(snake_case_      )
															lowerCamelCase__     :					Union[str, Any]		=discriminator.tokenizer.encode(d['text']      )

															if len(snake_case_      ) < max_length_seq:
																		lowerCamelCase__     :					Tuple		=torch.tensor([5_0_2_5_6] + 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

						lowerCamelCase__     :					List[str]		=Dataset(snake_case_							,   snake_case_      )
						lowerCamelCase__     :					List[Any]		=int(0.9 * len(snake_case_      )      )
						lowerCamelCase__     :					Union[str, Any]		=len(snake_case_      ) - train_size
						lowerCamelCase__	,    lowerCamelCase__     :					Optional[int]		=torch.utils.data.random_split(snake_case_							,   [train_size, test_size]      )

						lowerCamelCase__     :					Tuple		={
						    'class_size': len(snake_case_      ),
						    'embed_size': discriminator.embed_size,
						    'pretrained_model': pretrained_model,
						    'class_vocab': classaidx,
						    'default_class': 1,
						}

			elif dataset == "toxic":
						lowerCamelCase__     :					Optional[int]		=['non_toxic', 'toxic']
						lowerCamelCase__     :					Tuple		={c: i for i, c in enumerate(snake_case_      )}

						lowerCamelCase__     :					Tuple		=Discriminator(
						    class_size=len(snake_case_      )							,   pretrained_model=snake_case_							,   cached_mode=snake_case_							,   device=snake_case_      ).to(snake_case_      )

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

															if len(snake_case_      ) < max_length_seq:
																		lowerCamelCase__     :					Optional[Any]		=torch.tensor([5_0_2_5_6] + 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

						lowerCamelCase__     :					Dict		=Dataset(snake_case_							,   snake_case_      )
						lowerCamelCase__     :					Tuple		=int(0.9 * len(snake_case_      )      )
						lowerCamelCase__     :					List[Any]		=len(snake_case_      ) - train_size
						lowerCamelCase__	,    lowerCamelCase__     :					List[Any]		=torch.utils.data.random_split(snake_case_							,   [train_size, test_size]      )

						lowerCamelCase__     :					str		={
						    '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.'      )

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

						lowerCamelCase__     :					List[Any]		=sorted(snake_case_      )
						lowerCamelCase__     :					Dict		={c: i for i, c in enumerate(snake_case_      )}

						lowerCamelCase__     :					Any		=Discriminator(
						    class_size=len(snake_case_      )							,   pretrained_model=snake_case_							,   cached_mode=snake_case_							,   device=snake_case_      ).to(snake_case_      )

						lowerCamelCase__     :					int		=[]
						lowerCamelCase__     :					List[Any]		=[]
						with open(snake_case_      ) as f:
									lowerCamelCase__     :					int		=csv.reader(snake_case_							,   delimiter='\t'      )
									for i, row in enumerate(tqdm(snake_case_							,   ascii=snake_case_      )      ):
												if row:
															lowerCamelCase__     :					List[Any]		=row[0]
															lowerCamelCase__     :					Union[str, Any]		=row[1]

															try:
																		lowerCamelCase__     :					Dict		=discriminator.tokenizer.encode(snake_case_      )
																		if len(snake_case_      ) < max_length_seq:
																					lowerCamelCase__     :					Optional[Any]		=torch.tensor([5_0_2_5_6] + 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

						lowerCamelCase__     :					int		=Dataset(snake_case_							,   snake_case_      )
						lowerCamelCase__     :					List[Any]		=int(0.9 * len(snake_case_      )      )
						lowerCamelCase__     :					Any		=len(snake_case_      ) - train_size
						lowerCamelCase__	,    lowerCamelCase__     :					int		=torch.utils.data.random_split(snake_case_							,   [train_size, test_size]      )

						lowerCamelCase__     :					List[Any]		={
						    'class_size': len(snake_case_      ),
						    'embed_size': discriminator.embed_size,
						    'pretrained_model': pretrained_model,
						    'class_vocab': classaidx,
						    'default_class': 0,
						}

			lowerCamelCase__     :					Dict		=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...'      )

						lowerCamelCase__     :					int		=time.time()

						lowerCamelCase__     :					Tuple		=get_cached_data_loader(snake_case_							,   snake_case_							,   snake_case_							,   shuffle=snake_case_							,   device=snake_case_      )

						lowerCamelCase__     :					Any		=get_cached_data_loader(snake_case_							,   snake_case_							,   snake_case_							,   device=snake_case_      )

						lowerCamelCase__     :					Optional[Any]		=time.time()
						print('Building representation cache took: {:.3f}s'.format(end - start      )      )

			else:
						lowerCamelCase__     :					Tuple		=torch.utils.data.DataLoader(
						    dataset=snake_case_							,   batch_size=snake_case_							,   shuffle=snake_case_							,   collate_fn=snake_case_      )
						lowerCamelCase__     :					int		=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_      )

			lowerCamelCase__     :					str		=optim.Adam(discriminator.parameters()							,   lr=0.0001      )

			for epoch in range(snake_case_      ):
						lowerCamelCase__     :					Dict		=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_      )

						lowerCamelCase__     :					Dict		=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__":
							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)))

"""simple docstring"""


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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


lowerCAmelCase								=						logging.get_logger(__name__)

lowerCAmelCase								=						{
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}

class    A_     (					A__       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       """blenderbot"""
							SCREAMING_SNAKE_CASE_     				=       ["""past_key_values"""]
							SCREAMING_SNAKE_CASE_     				=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





							def __init__(  self  :Optional[int]					,							lowerCamelCase_  :Optional[Any]=8_008					,							lowerCamelCase_  :Optional[Any]=128					,							lowerCamelCase_  :Optional[int]=2					,							lowerCamelCase_  :Tuple=10_240					,							lowerCamelCase_  :Optional[int]=32					,							lowerCamelCase_  :Optional[int]=24					,							lowerCamelCase_  :Optional[Any]=10_240					,							lowerCamelCase_  :Union[str, Any]=32					,							lowerCamelCase_  :Any=0.0					,							lowerCamelCase_  :int=0.0					,							lowerCamelCase_  :str=True					,							lowerCamelCase_  :List[Any]=True					,							lowerCamelCase_  :Tuple="gelu"					,							lowerCamelCase_  :str=2_560					,							lowerCamelCase_  :Union[str, Any]=0.1					,							lowerCamelCase_  :Optional[int]=0.0					,							lowerCamelCase_  :Union[str, Any]=0.0					,							lowerCamelCase_  :List[str]=0.02					,							lowerCamelCase_  :Any=1					,							lowerCamelCase_  :List[Any]=False					,							lowerCamelCase_  :str=0					,							lowerCamelCase_  :Dict=1					,							lowerCamelCase_  :List[Any]=2					,							lowerCamelCase_  :int=3					,							lowerCamelCase_  :int=2					,							**lowerCamelCase_  :List[str]					,							):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[int]		=vocab_size
										lowerCamelCase__     :					Optional[int]		=max_position_embeddings
										lowerCamelCase__     :					Dict		=d_model
										lowerCamelCase__     :					str		=encoder_ffn_dim
										lowerCamelCase__     :					Optional[Any]		=encoder_layers
										lowerCamelCase__     :					List[Any]		=encoder_attention_heads
										lowerCamelCase__     :					Any		=decoder_ffn_dim
										lowerCamelCase__     :					Any		=decoder_layers
										lowerCamelCase__     :					Any		=decoder_attention_heads
										lowerCamelCase__     :					Dict		=dropout
										lowerCamelCase__     :					Any		=attention_dropout
										lowerCamelCase__     :					Dict		=activation_dropout
										lowerCamelCase__     :					Tuple		=activation_function
										lowerCamelCase__     :					List[Any]		=init_std
										lowerCamelCase__     :					str		=encoder_layerdrop
										lowerCamelCase__     :					Any		=decoder_layerdrop
										lowerCamelCase__     :					Union[str, Any]		=use_cache
										lowerCamelCase__     :					List[str]		=encoder_layers
										lowerCamelCase__     :					Optional[int]		=scale_embedding  # scale factor will be sqrt(d_model) if True

										super().__init__(
										    pad_token_id=lowerCamelCase_					,							bos_token_id=lowerCamelCase_					,							eos_token_id=lowerCamelCase_					,							is_encoder_decoder=lowerCamelCase_					,							decoder_start_token_id=lowerCamelCase_					,							encoder_no_repeat_ngram_size=lowerCamelCase_					,							forced_eos_token_id=lowerCamelCase_					,							**lowerCamelCase_					,							)






class    A_     (					A__       ):



							"""simple docstring"""






							@property
							def 				UpperCAmelCase__							(  self  :Tuple	):





										"""simple docstring"""

										if self.task in ["default", "seq2seq-lm"]:
													lowerCamelCase__     :					Dict		=OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]	)
													if self.use_past:
																lowerCamelCase__     :					Dict		={0: 'batch'}
																lowerCamelCase__     :					Tuple		={0: 'batch', 1: 'past_decoder_sequence + sequence'}
													else:
																lowerCamelCase__     :					Optional[int]		={0: 'batch', 1: 'decoder_sequence'}
																lowerCamelCase__     :					Tuple		={0: 'batch', 1: 'decoder_sequence'}
													if self.use_past:
																self.fill_with_past_key_values_(lowerCamelCase_					,							direction='inputs'	)
										elif self.task == "causal-lm":
													lowerCamelCase__     :					Any		=OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]	)
													if self.use_past:
																lowerCamelCase__	,    lowerCamelCase__     :					List[Any]		=self.num_layers
																for i in range(lowerCamelCase_	):
																			lowerCamelCase__     :					Tuple		={0: 'batch', 2: 'past_sequence + sequence'}
																			lowerCamelCase__     :					Optional[int]		={0: 'batch', 2: 'past_sequence + sequence'}
										else:
													lowerCamelCase__     :					List[str]		=OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
													        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
													    ]	)

										return common_inputs





							@property
							# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
							def 				UpperCAmelCase__							(  self  :List[str]	):





										"""simple docstring"""

										if self.task in ["default", "seq2seq-lm"]:
													lowerCamelCase__     :					Tuple		=super().outputs
										else:
													lowerCamelCase__     :					List[Any]		=super(lowerCamelCase_					,							self	).outputs
													if self.use_past:
																lowerCamelCase__	,    lowerCamelCase__     :					List[Any]		=self.num_layers
																for i in range(lowerCamelCase_	):
																			lowerCamelCase__     :					Tuple		={0: 'batch', 2: 'past_sequence + sequence'}
																			lowerCamelCase__     :					str		={0: 'batch', 2: 'past_sequence + sequence'}
										return common_outputs





							def 				UpperCAmelCase__							(  self  :List[Any]					,							lowerCamelCase_  :PreTrainedTokenizer					,							lowerCamelCase_  :int = -1					,							lowerCamelCase_  :int = -1					,							lowerCamelCase_  :bool = False					,							lowerCamelCase_  :Optional[TensorType] = None					,							):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[int]		=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_	)
										# Generate decoder inputs
										lowerCamelCase__     :					List[str]		=seq_length if not self.use_past else 1
										lowerCamelCase__     :					Optional[Any]		=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_	)
										lowerCamelCase__     :					int		={f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
										lowerCamelCase__     :					int		=dict(**lowerCamelCase_					,							**lowerCamelCase_	)

										if self.use_past:
													if not is_torch_available():
																raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
													else:
																import torch
													lowerCamelCase__	,    lowerCamelCase__     :					List[str]		=common_inputs['input_ids'].shape
													lowerCamelCase__     :					Tuple		=common_inputs['decoder_input_ids'].shape[1]
													lowerCamelCase__	,    lowerCamelCase__     :					int		=self.num_attention_heads
													lowerCamelCase__     :					Optional[Any]		=(
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													lowerCamelCase__     :					Any		=decoder_seq_length
													lowerCamelCase__     :					Optional[Any]		=(
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)
													lowerCamelCase__     :					Optional[int]		=torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(lowerCamelCase_					,							lowerCamelCase_	)]					,							dim=1	)
													lowerCamelCase__     :					List[str]		=[]
													lowerCamelCase__	,    lowerCamelCase__     :					Dict		=self.num_layers

													for _ in range(lowerCamelCase_	):
																common_inputs["past_key_values"].append(
																    (
																        torch.zeros(lowerCamelCase_	),
																        torch.zeros(lowerCamelCase_	),
																        torch.zeros(lowerCamelCase_	),
																        torch.zeros(lowerCamelCase_	),
																    )	)
										return common_inputs





							def 				UpperCAmelCase__							(  self  :int					,							lowerCamelCase_  :PreTrainedTokenizer					,							lowerCamelCase_  :int = -1					,							lowerCamelCase_  :int = -1					,							lowerCamelCase_  :bool = False					,							lowerCamelCase_  :Optional[TensorType] = None					,							):





										"""simple docstring"""

										lowerCamelCase__     :					Union[str, Any]		=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_	)

										if self.use_past:
													if not is_torch_available():
																raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
													else:
																import torch
													lowerCamelCase__	,    lowerCamelCase__     :					Optional[int]		=common_inputs['input_ids'].shape
													lowerCamelCase__     :					List[Any]		=seqlen
													lowerCamelCase__	,    lowerCamelCase__     :					Tuple		=self.num_layers
													lowerCamelCase__	,    lowerCamelCase__     :					List[str]		=self.num_attention_heads
													lowerCamelCase__     :					Optional[int]		=(
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													lowerCamelCase__     :					List[str]		=common_inputs['attention_mask'].dtype
													lowerCamelCase__     :					Tuple		=torch.cat(
													    [common_inputs['attention_mask'], torch.ones(lowerCamelCase_					,							lowerCamelCase_					,							dtype=lowerCamelCase_	)]					,							dim=1	)
													lowerCamelCase__     :					Optional[int]		=[
													    (torch.zeros(lowerCamelCase_	), torch.zeros(lowerCamelCase_	)) for _ in range(lowerCamelCase_	)
													]
										return common_inputs





							def 				UpperCAmelCase__							(  self  :Union[str, Any]					,							lowerCamelCase_  :PreTrainedTokenizer					,							lowerCamelCase_  :int = -1					,							lowerCamelCase_  :int = -1					,							lowerCamelCase_  :bool = False					,							lowerCamelCase_  :Optional[TensorType] = None					,							):





										"""simple docstring"""

										lowerCamelCase__     :					List[Any]		=compute_effective_axis_dimension(
										    lowerCamelCase_					,							fixed_dimension=OnnxConfig.default_fixed_batch					,							num_token_to_add=0	)

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										lowerCamelCase__     :					Optional[int]		=tokenizer.num_special_tokens_to_add(lowerCamelCase_	)
										lowerCamelCase__     :					int		=compute_effective_axis_dimension(
										    lowerCamelCase_					,							fixed_dimension=OnnxConfig.default_fixed_sequence					,							num_token_to_add=lowerCamelCase_	)

										# Generate dummy inputs according to compute batch and sequence
										lowerCamelCase__     :					List[Any]		=[' '.join([tokenizer.unk_token]	) * seq_length] * batch_size
										lowerCamelCase__     :					Optional[Any]		=dict(tokenizer(lowerCamelCase_					,							return_tensors=lowerCamelCase_	)	)
										return common_inputs





							def 				UpperCAmelCase__							(  self  :Tuple					,							lowerCamelCase_  :PreTrainedTokenizer					,							lowerCamelCase_  :int = -1					,							lowerCamelCase_  :int = -1					,							lowerCamelCase_  :bool = False					,							lowerCamelCase_  :Optional[TensorType] = None					,							):





										"""simple docstring"""

										if self.task in ["default", "seq2seq-lm"]:
													lowerCamelCase__     :					str		=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    lowerCamelCase_					,							batch_size=lowerCamelCase_					,							seq_length=lowerCamelCase_					,							is_pair=lowerCamelCase_					,							framework=lowerCamelCase_	)

										elif self.task == "causal-lm":
													lowerCamelCase__     :					List[Any]		=self._generate_dummy_inputs_for_causal_lm(
													    lowerCamelCase_					,							batch_size=lowerCamelCase_					,							seq_length=lowerCamelCase_					,							is_pair=lowerCamelCase_					,							framework=lowerCamelCase_	)
										else:
													lowerCamelCase__     :					Dict		=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    lowerCamelCase_					,							batch_size=lowerCamelCase_					,							seq_length=lowerCamelCase_					,							is_pair=lowerCamelCase_					,							framework=lowerCamelCase_	)

										return common_inputs





							def 				UpperCAmelCase__							(  self  :Optional[int]					,							lowerCamelCase_  :Tuple					,							lowerCamelCase_  :Optional[Any]					,							lowerCamelCase_  :List[Any]					,							lowerCamelCase_  :Dict	):





										"""simple docstring"""

										if self.task in ["default", "seq2seq-lm"]:
													lowerCamelCase__     :					List[Any]		=super()._flatten_past_key_values_(lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_	)
										else:
													lowerCamelCase__     :					List[Any]		=super(lowerCamelCase_					,							self	)._flatten_past_key_values_(
													    lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Any					,							lowerCamelCase_  :Mapping[str, Mapping[int, str]]					,							lowerCamelCase_  :str	):





										"""simple docstring"""

										if direction not in ["inputs", "outputs"]:
													raise ValueError(f"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""	)

										lowerCamelCase__     :					Optional[Any]		='past_key_values' if direction == 'inputs' else 'present'
										lowerCamelCase__	,    lowerCamelCase__     :					Union[str, Any]		=self.num_layers

										lowerCamelCase__     :					Optional[Any]		='past_encoder_sequence'
										lowerCamelCase__     :					Union[str, Any]		='past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

										for i in range(lowerCamelCase_	):
													lowerCamelCase__     :					Union[str, Any]		={0: 'batch', 2: decoder_sequence}
													lowerCamelCase__     :					Any		={0: 'batch', 2: decoder_sequence}
													lowerCamelCase__     :					List[Any]		={0: 'batch', 2: encoder_sequence}
													lowerCamelCase__     :					Tuple		={0: 'batch', 2: encoder_sequence}