

'''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__      				=			100



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case     ,      _snake_case="gpt2-medium"     ,      _snake_case=False     ,      _snake_case="cpu")				->					Dict:
								super().__init__()
								UpperCAmelCase_		:   Optional[int]													=       GPTaTokenizer.from_pretrained(_snake_case)
								UpperCAmelCase_		:   Any													=       GPTaLMHeadModel.from_pretrained(_snake_case)
								UpperCAmelCase_		:   str													=       self.encoder.transformer.config.hidden_size
								UpperCAmelCase_		:   Union[str, Any]													=       ClassificationHead(class_size=_snake_case     ,      embed_size=self.embed_size)
								UpperCAmelCase_		:   str													=       cached_mode
								UpperCAmelCase_		:   List[Any]													=       device


							def     _snake_case				(      self)				->					Optional[Any]:
								return self.classifier_head


							def     _snake_case				(      self)				->					Any:
								for param in self.encoder.parameters():
									UpperCAmelCase_		:   str													=       False
								self.classifier_head.train()


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





							def     _snake_case				(      self     ,      _snake_case)				->					Tuple:
								if self.cached_mode:
									UpperCAmelCase_		:   str													=       x.to(self.device)
								else:
									UpperCAmelCase_		:   Optional[Any]													=       self.avg_representation(x.to(self.device))

								UpperCAmelCase_		:   Union[str, Any]													=       self.classifier_head(_snake_case)
								UpperCAmelCase_		:   Dict													=       nn.functional.log_softmax(_snake_case     ,      dim=-1)

								return probs



class        lowercase    (	data.Dataset			):


							def __init__(      self     ,      _snake_case     ,      _snake_case)				->					List[Any]:
								UpperCAmelCase_		:   Any													=       X
								UpperCAmelCase_		:   Any													=       y


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





							def __getitem__(      self     ,      _snake_case)				->					Optional[int]:
								UpperCAmelCase_		:   Tuple													=       {}
								UpperCAmelCase_		:   str													=       self.X[index]
								UpperCAmelCase_		:   str													=       self.y[index]
								return data







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     List[Any]:
	def pad_sequences(UpperCamelCase	):
		UpperCAmelCase_		:   str													=       [len(UpperCamelCase	) for seq in sequences]

		UpperCAmelCase_		:   Any													=       torch.zeros(len(UpperCamelCase	) ,max(UpperCamelCase	)	).long()  # padding value = 0

		for i, seq in enumerate(UpperCamelCase	):
			UpperCAmelCase_		:   str													=       lengths[i]
			UpperCAmelCase_		:   Union[str, Any]													=       seq[:end]

		return padded_sequences, lengths

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

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

	return x_batch, y_batch







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     List[str]:
	UpperCAmelCase_		:   Dict													=       {}
	for key in data[0].keys():
		UpperCAmelCase_		:   Union[str, Any]													=       [d[key] for d in data]

	UpperCAmelCase_		:   Optional[int]													=       torch.cat(item_info['X'] ,0	)
	UpperCAmelCase_		:   Dict													=       torch.tensor(item_info['y'] ,dtype=torch.long	)

	return x_batch, y_batch







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=0 ,UpperCamelCase=1_0 ,UpperCamelCase="cpu"	)     ->     Union[str, Any]:
	UpperCAmelCase_		:   Any													=       0
	discriminator.train_custom()
	for batch_idx, (input_t, target_t) in enumerate(UpperCamelCase	):
		UpperCAmelCase_						,     UpperCAmelCase_		:   Dict													=       input_t.to(UpperCamelCase	), target_t.to(UpperCamelCase	)

		optimizer.zero_grad()

		UpperCAmelCase_		:   List[Any]													=       discriminator(UpperCamelCase	)
		UpperCAmelCase_		:   Union[str, Any]													=       nn.functional.nll_loss(UpperCamelCase ,UpperCamelCase	)
		loss.backward(retain_graph=UpperCamelCase	)
		optimizer.step()

		samples_so_far += len(UpperCamelCase	)

		if batch_idx % log_interval == 0:
			print(
			    'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
			        epoch + 1 ,UpperCamelCase ,len(data_loader.dataset	) ,1_0_0 * samples_so_far / len(data_loader.dataset	) ,loss.item() ,)	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase="cpu"	)     ->     List[str]:
	discriminator.eval()
	UpperCAmelCase_		:   Any													=       0
	UpperCAmelCase_		:   str													=       0
	with torch.no_grad():
		for input_t, target_t in data_loader:
			UpperCAmelCase_						,     UpperCAmelCase_		:   List[Any]													=       input_t.to(UpperCamelCase	), target_t.to(UpperCamelCase	)
			UpperCAmelCase_		:   Any													=       discriminator(UpperCamelCase	)
			# sum up batch loss
			test_loss += nn.functional.nll_loss(UpperCamelCase ,UpperCamelCase ,reduction='sum'	).item()
			# get the index of the max log-probability
			UpperCAmelCase_		:   Optional[Any]													=       output_t.argmax(dim=1 ,keepdim=UpperCamelCase	)
			correct += pred_t.eq(target_t.view_as(UpperCamelCase	)	).sum().item()

	test_loss /= len(data_loader.dataset	)

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







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=False ,UpperCamelCase="cpu"	)     ->     Optional[Any]:
	UpperCAmelCase_		:   List[str]													=       model.tokenizer.encode(UpperCamelCase	)
	UpperCAmelCase_		:   Optional[Any]													=       torch.tensor([input_t] ,dtype=torch.long ,device=UpperCamelCase	)
	if cached:
		UpperCAmelCase_		:   Tuple													=       model.avg_representation(UpperCamelCase	)

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







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=False ,UpperCamelCase="cpu"	)     ->     Optional[int]:
	UpperCAmelCase_		:   Optional[int]													=       torch.utils.data.DataLoader(dataset=UpperCamelCase ,batch_size=UpperCamelCase ,collate_fn=UpperCamelCase	)

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

	UpperCAmelCase_		:   int													=       torch.utils.data.DataLoader(
	    dataset=Dataset(UpperCamelCase ,UpperCamelCase	) ,batch_size=UpperCamelCase ,shuffle=UpperCamelCase ,collate_fn=UpperCamelCase	)

	return data_loader







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase=None ,UpperCamelCase="gpt2-medium" ,UpperCamelCase=1_0 ,UpperCamelCase=6_4 ,UpperCamelCase=1_0 ,UpperCamelCase=False ,UpperCamelCase=False ,UpperCamelCase=False ,)     ->     str:
	UpperCAmelCase_		:   Dict													=       'cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'

	print('Preprocessing {} dataset...'.format(UpperCamelCase	)	)
	UpperCAmelCase_		:   str													=       time.time()

	if dataset == "SST":
		UpperCAmelCase_		:   Optional[int]													=       ['positive', 'negative', 'very positive', 'very negative', 'neutral']
		UpperCAmelCase_		:   Any													=       {c: i for i, c in enumerate(UpperCamelCase	)}

		UpperCAmelCase_		:   int													=       Discriminator(
		    class_size=len(UpperCamelCase	) ,pretrained_model=UpperCamelCase ,cached_mode=UpperCamelCase ,device=UpperCamelCase	).to(UpperCamelCase	)

		UpperCAmelCase_		:   List[str]													=       torchtext_data.Field()
		UpperCAmelCase_		:   int													=       torchtext_data.Field(sequential=UpperCamelCase	)
		UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_		:   str													=       datasets.SST.splits(
		    UpperCamelCase ,UpperCamelCase ,fine_grained=UpperCamelCase ,train_subtrees=UpperCamelCase ,)

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

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

		UpperCAmelCase_		:   Union[str, Any]													=       {
		    'class_size': len(UpperCamelCase	),
		    'embed_size': discriminator.embed_size,
		    'pretrained_model': pretrained_model,
		    'class_vocab': classaidx,
		    'default_class': 2,
		}

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

		UpperCAmelCase_		:   Tuple													=       Discriminator(
		    class_size=len(UpperCamelCase	) ,pretrained_model=UpperCamelCase ,cached_mode=UpperCamelCase ,device=UpperCamelCase	).to(UpperCamelCase	)

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

					if len(UpperCamelCase	) < max_length_seq:
						UpperCAmelCase_		:   List[str]													=       torch.tensor([5_0_2_5_6] + seq ,device=UpperCamelCase ,dtype=torch.long	)
					else:
						print('Line {} is longer than maximum length {}'.format(UpperCamelCase ,UpperCamelCase	)	)
						continue
					x.append(UpperCamelCase	)
					y.append(d['label']	)
				except Exception:
					print('Error evaluating / tokenizing line {}, skipping it'.format(UpperCamelCase	)	)
					pass

		UpperCAmelCase_		:   Optional[int]													=       Dataset(UpperCamelCase ,UpperCamelCase	)
		UpperCAmelCase_		:   int													=       int(0.9 * len(UpperCamelCase	)	)
		UpperCAmelCase_		:   Tuple													=       len(UpperCamelCase	) - train_size
		UpperCAmelCase_						,     UpperCAmelCase_		:   Tuple													=       torch.utils.data.random_split(UpperCamelCase ,[train_size, test_size]	)

		UpperCAmelCase_		:   List[str]													=       {
		    'class_size': len(UpperCamelCase	),
		    'embed_size': discriminator.embed_size,
		    'pretrained_model': pretrained_model,
		    'class_vocab': classaidx,
		    'default_class': 1,
		}

	elif dataset == "toxic":
		UpperCAmelCase_		:   Any													=       ['non_toxic', 'toxic']
		UpperCAmelCase_		:   Optional[Any]													=       {c: i for i, c in enumerate(UpperCamelCase	)}

		UpperCAmelCase_		:   Union[str, Any]													=       Discriminator(
		    class_size=len(UpperCamelCase	) ,pretrained_model=UpperCamelCase ,cached_mode=UpperCamelCase ,device=UpperCamelCase	).to(UpperCamelCase	)

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

					if len(UpperCamelCase	) < max_length_seq:
						UpperCAmelCase_		:   List[str]													=       torch.tensor([5_0_2_5_6] + seq ,device=UpperCamelCase ,dtype=torch.long	)
					else:
						print('Line {} is longer than maximum length {}'.format(UpperCamelCase ,UpperCamelCase	)	)
						continue
					x.append(UpperCamelCase	)
					y.append(int(np.sum(d['label']	) > 0	)	)
				except Exception:
					print('Error evaluating / tokenizing line {}, skipping it'.format(UpperCamelCase	)	)
					pass

		UpperCAmelCase_		:   Tuple													=       Dataset(UpperCamelCase ,UpperCamelCase	)
		UpperCAmelCase_		:   int													=       int(0.9 * len(UpperCamelCase	)	)
		UpperCAmelCase_		:   int													=       len(UpperCamelCase	) - train_size
		UpperCAmelCase_						,     UpperCAmelCase_		:   Union[str, Any]													=       torch.utils.data.random_split(UpperCamelCase ,[train_size, test_size]	)

		UpperCAmelCase_		:   str													=       {
		    'class_size': len(UpperCamelCase	),
		    '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_		:   str													=       set()
		with open(UpperCamelCase	) as f:
			UpperCAmelCase_		:   Any													=       csv.reader(UpperCamelCase ,delimiter='\t'	)
			for row in tqdm(UpperCamelCase ,ascii=UpperCamelCase	):
				if row:
					classes.add(row[0]	)

		UpperCAmelCase_		:   Union[str, Any]													=       sorted(UpperCamelCase	)
		UpperCAmelCase_		:   Any													=       {c: i for i, c in enumerate(UpperCamelCase	)}

		UpperCAmelCase_		:   Union[str, Any]													=       Discriminator(
		    class_size=len(UpperCamelCase	) ,pretrained_model=UpperCamelCase ,cached_mode=UpperCamelCase ,device=UpperCamelCase	).to(UpperCamelCase	)

		UpperCAmelCase_		:   Optional[Any]													=       []
		UpperCAmelCase_		:   Tuple													=       []
		with open(UpperCamelCase	) as f:
			UpperCAmelCase_		:   List[str]													=       csv.reader(UpperCamelCase ,delimiter='\t'	)
			for i, row in enumerate(tqdm(UpperCamelCase ,ascii=UpperCamelCase	)	):
				if row:
					UpperCAmelCase_		:   str													=       row[0]
					UpperCAmelCase_		:   Optional[Any]													=       row[1]

					try:
						UpperCAmelCase_		:   Optional[int]													=       discriminator.tokenizer.encode(UpperCamelCase	)
						if len(UpperCamelCase	) < max_length_seq:
							UpperCAmelCase_		:   int													=       torch.tensor([5_0_2_5_6] + seq ,device=UpperCamelCase ,dtype=torch.long	)

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

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

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

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

		UpperCAmelCase_		:   Dict													=       {
		    'class_size': len(UpperCamelCase	),
		    'embed_size': discriminator.embed_size,
		    'pretrained_model': pretrained_model,
		    'class_vocab': classaidx,
		    'default_class': 0,
		}

	UpperCAmelCase_		:   List[str]													=       time.time()
	print('Preprocessed {} data points'.format(len(UpperCamelCase	) + len(UpperCamelCase	)	)	)
	print('Data preprocessing took: {:.3f}s'.format(end - start	)	)

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

		UpperCAmelCase_		:   str													=       time.time()

		UpperCAmelCase_		:   Optional[Any]													=       get_cached_data_loader(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,shuffle=UpperCamelCase ,device=UpperCamelCase	)

		UpperCAmelCase_		:   Tuple													=       get_cached_data_loader(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,device=UpperCamelCase	)

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

	else:
		UpperCAmelCase_		:   List[str]													=       torch.utils.data.DataLoader(
		    dataset=UpperCamelCase ,batch_size=UpperCamelCase ,shuffle=UpperCamelCase ,collate_fn=UpperCamelCase	)
		UpperCAmelCase_		:   List[Any]													=       torch.utils.data.DataLoader(dataset=UpperCamelCase ,batch_size=UpperCamelCase ,collate_fn=UpperCamelCase	)

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

	UpperCAmelCase_		:   Optional[Any]													=       optim.Adam(discriminator.parameters() ,lr=0.0001	)

	for epoch in range(UpperCamelCase	):
		UpperCAmelCase_		:   Any													=       time.time()
		print('\nEpoch' ,epoch + 1	)

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

		UpperCAmelCase_		:   Optional[int]													=       time.time()
		print('Epoch took: {:.3f}s'.format(end - start	)	)

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

		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(UpperCamelCase ,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 typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


lowerCAmelCase__      				=			logging.get_logger(__name__)

# General docstring
lowerCAmelCase__      				=			"ResNetConfig"

# Base docstring
lowerCAmelCase__      				=			"microsoft/resnet-50"
lowerCAmelCase__      				=			[1, 2048, 7, 7]

# Image classification docstring
lowerCAmelCase__      				=			"microsoft/resnet-50"
lowerCAmelCase__      				=			"tiger cat"

lowerCAmelCase__      				=			[
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]



class        lowercase    (	tf.keras.layers.Layer			):


							def __init__(      self     ,      _snake_case     ,      _snake_case = 3     ,      _snake_case = 1     ,      _snake_case = "relu"     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   Union[str, Any]													=       kernel_size // 2
								UpperCAmelCase_		:   Dict													=       tf.keras.layers.ConvaD(
								    _snake_case     ,      kernel_size=_snake_case     ,      strides=_snake_case     ,      padding='valid'     ,      use_bias=_snake_case     ,      name='convolution')
								# Use same default momentum and epsilon as PyTorch equivalent
								UpperCAmelCase_		:   Dict													=       tf.keras.layers.BatchNormalization(epsilon=1e-5     ,      momentum=0.9     ,      name='normalization')
								UpperCAmelCase_		:   Optional[int]													=       ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('linear')


							def     _snake_case				(      self     ,      _snake_case)				->					tf.Tensor:
								# Pad to match that done in the PyTorch Conv2D model
								UpperCAmelCase_		:   List[str]													=       (self.pad_value, self.pad_value)
								UpperCAmelCase_		:   Dict													=       tf.pad(_snake_case     ,      [(0, 0), height_pad, width_pad, (0, 0)])
								UpperCAmelCase_		:   str													=       self.conv(_snake_case)
								return hidden_state





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_		:   int													=       self.convolution(_snake_case)
								UpperCAmelCase_		:   Union[str, Any]													=       self.normalization(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   Any													=       self.activation(_snake_case)
								return hidden_state



class        lowercase    (	tf.keras.layers.Layer			):


							def __init__(      self     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   List[Any]													=       TFResNetConvLayer(
								    config.embedding_size     ,      kernel_size=7     ,      stride=2     ,      activation=config.hidden_act     ,      name='embedder'     ,      )
								UpperCAmelCase_		:   str													=       tf.keras.layers.MaxPoolaD(pool_size=3     ,      strides=2     ,      padding='valid'     ,      name='pooler')
								UpperCAmelCase_		:   Any													=       config.num_channels





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_		:   str													=       shape_list(_snake_case)
								if tf.executing_eagerly() and num_channels != self.num_channels:
									raise ValueError(
									    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.')
								UpperCAmelCase_		:   Any													=       pixel_values
								UpperCAmelCase_		:   int													=       self.embedder(_snake_case)
								UpperCAmelCase_		:   Tuple													=       tf.pad(_snake_case     ,      [[0, 0], [1, 1], [1, 1], [0, 0]])
								UpperCAmelCase_		:   Any													=       self.pooler(_snake_case)
								return hidden_state



class        lowercase    (	tf.keras.layers.Layer			):


							def __init__(      self     ,      _snake_case     ,      _snake_case = 2     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   Any													=       tf.keras.layers.ConvaD(
								    _snake_case     ,      kernel_size=1     ,      strides=_snake_case     ,      use_bias=_snake_case     ,      name='convolution')
								# Use same default momentum and epsilon as PyTorch equivalent
								UpperCAmelCase_		:   List[str]													=       tf.keras.layers.BatchNormalization(epsilon=1e-5     ,      momentum=0.9     ,      name='normalization')





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_		:   Dict													=       x
								UpperCAmelCase_		:   List[str]													=       self.convolution(_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       self.normalization(_snake_case     ,      training=_snake_case)
								return hidden_state



class        lowercase    (	tf.keras.layers.Layer			):


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case = 1     ,      _snake_case = "relu"     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   List[Any]													=       in_channels != out_channels or stride != 1
								UpperCAmelCase_		:   List[str]													=       TFResNetConvLayer(_snake_case     ,      stride=_snake_case     ,      name='layer.0')
								UpperCAmelCase_		:   List[str]													=       TFResNetConvLayer(_snake_case     ,      activation=_snake_case     ,      name='layer.1')
								UpperCAmelCase_		:   Union[str, Any]													=       (
								    TFResNetShortCut(_snake_case     ,      stride=_snake_case     ,      name='shortcut')
								    if should_apply_shortcut
								    else tf.keras.layers.Activation('linear'     ,      name='shortcut')
								)
								UpperCAmelCase_		:   int													=       ACTaFN[activation]





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_		:   int													=       hidden_state
								UpperCAmelCase_		:   Tuple													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   List[Any]													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   int													=       self.shortcut(_snake_case     ,      training=_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:   str													=       self.activation(_snake_case)
								return hidden_state



class        lowercase    (	tf.keras.layers.Layer			):


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case = 1     ,      _snake_case = "relu"     ,      _snake_case = 4     ,      **_snake_case     ,      )				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   int													=       in_channels != out_channels or stride != 1
								UpperCAmelCase_		:   Tuple													=       out_channels // reduction
								UpperCAmelCase_		:   int													=       TFResNetConvLayer(_snake_case     ,      kernel_size=1     ,      name='layer.0')
								UpperCAmelCase_		:   List[str]													=       TFResNetConvLayer(_snake_case     ,      stride=_snake_case     ,      name='layer.1')
								UpperCAmelCase_		:   List[Any]													=       TFResNetConvLayer(_snake_case     ,      kernel_size=1     ,      activation=_snake_case     ,      name='layer.2')
								UpperCAmelCase_		:   Optional[int]													=       (
								    TFResNetShortCut(_snake_case     ,      stride=_snake_case     ,      name='shortcut')
								    if should_apply_shortcut
								    else tf.keras.layers.Activation('linear'     ,      name='shortcut')
								)
								UpperCAmelCase_		:   Tuple													=       ACTaFN[activation]





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_		:   Dict													=       hidden_state
								UpperCAmelCase_		:   Tuple													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   Union[str, Any]													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       self.shortcut(_snake_case     ,      training=_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:   str													=       self.activation(_snake_case)
								return hidden_state



class        lowercase    (	tf.keras.layers.Layer			):


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case = 2     ,      _snake_case = 2     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)

								UpperCAmelCase_		:   Dict													=       TFResNetBottleNeckLayer if config.layer_type == 'bottleneck' else TFResNetBasicLayer

								UpperCAmelCase_		:   List[Any]													=       [layer(_snake_case     ,      _snake_case     ,      stride=_snake_case     ,      activation=config.hidden_act     ,      name='layers.0')]
								layers += [
								    layer(_snake_case     ,      _snake_case     ,      activation=config.hidden_act     ,      name=F"""layers.{i + 1}""")
								    for i in range(depth - 1)
								]
								UpperCAmelCase_		:   Optional[Any]													=       layers





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								for layer in self.stage_layers:
									UpperCAmelCase_		:   List[Any]													=       layer(_snake_case     ,      training=_snake_case)
								return hidden_state



class        lowercase    (	tf.keras.layers.Layer			):


							def __init__(      self     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
								UpperCAmelCase_		:   Optional[int]													=       [
								    TFResNetStage(
								        _snake_case     ,      config.embedding_size     ,      config.hidden_sizes[0]     ,      stride=2 if config.downsample_in_first_stage else 1     ,      depth=config.depths[0]     ,      name='stages.0'     ,      )
								]
								for i, (in_channels, out_channels, depth) in enumerate(
								    zip(config.hidden_sizes     ,      config.hidden_sizes[1:]     ,      config.depths[1:])):
									self.stages.append(TFResNetStage(_snake_case     ,      _snake_case     ,      _snake_case     ,      depth=_snake_case     ,      name=F"""stages.{i + 1}"""))





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False     ,      _snake_case = True     ,      _snake_case = False     ,      )				->					TFBaseModelOutputWithNoAttention:
								UpperCAmelCase_		:   Optional[Any]													=       () if output_hidden_states else None

								for stage_module in self.stages:
									if output_hidden_states:
										UpperCAmelCase_		:   Optional[Any]													=       hidden_states + (hidden_state,)

									UpperCAmelCase_		:   List[Any]													=       stage_module(_snake_case     ,      training=_snake_case)

								if output_hidden_states:
									UpperCAmelCase_		:   int													=       hidden_states + (hidden_state,)

								if not return_dict:
									return tuple(v for v in [hidden_state, hidden_states] if v is not None)

								return TFBaseModelOutputWithNoAttention(last_hidden_state=_snake_case     ,      hidden_states=_snake_case)



class        lowercase    (	a_			):
							_lowerCamelCase		:   Optional[int]= ResNetConfig
							_lowerCamelCase		:   str= "resnet"
							_lowerCamelCase		:   Optional[int]= "pixel_values"


							@property
							def     _snake_case				(      self)				->					int:
								return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)     ,      dtype=tf.floataa)}


lowerCAmelCase__      				=			r"\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"


lowerCAmelCase__      				=			r"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"



@keras_serializable
class        lowercase    (	tf.keras.layers.Layer			):
							_lowerCamelCase		:   Any= ResNetConfig


							def __init__(      self     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   List[Any]													=       config
								UpperCAmelCase_		:   Dict													=       TFResNetEmbeddings(_snake_case     ,      name='embedder')
								UpperCAmelCase_		:   Optional[Any]													=       TFResNetEncoder(_snake_case     ,      name='encoder')
								UpperCAmelCase_		:   Optional[Any]													=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=_snake_case)





							@unpack_inputs
							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = False     ,      )				->					Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
								UpperCAmelCase_		:   Any													=       (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase_		:   Tuple													=       return_dict if return_dict is not None else self.config.use_return_dict

								# TF 2.0 image layers can't use NCHW format when running on CPU.
								# We transpose to NHWC format and then transpose back after the full forward pass.
								# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
								UpperCAmelCase_		:   Any													=       tf.transpose(_snake_case     ,      perm=[0, 2, 3, 1])
								UpperCAmelCase_		:   Any													=       self.embedder(_snake_case     ,      training=_snake_case)

								UpperCAmelCase_		:   str													=       self.encoder(
								    _snake_case     ,      output_hidden_states=_snake_case     ,      return_dict=_snake_case     ,      training=_snake_case)

								UpperCAmelCase_		:   List[Any]													=       encoder_outputs[0]

								UpperCAmelCase_		:   Union[str, Any]													=       self.pooler(_snake_case)

								# Transpose all the outputs to the NCHW format
								# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
								UpperCAmelCase_		:   Any													=       tf.transpose(_snake_case     ,      (0, 3, 1, 2))
								UpperCAmelCase_		:   Dict													=       tf.transpose(_snake_case     ,      (0, 3, 1, 2))
								UpperCAmelCase_		:   Any													=       ()
								for hidden_state in encoder_outputs[1:]:
									UpperCAmelCase_		:   str													=       hidden_states + tuple(tf.transpose(_snake_case     ,      (0, 3, 1, 2)) for h in hidden_state)

								if not return_dict:
									return (last_hidden_state, pooled_output) + hidden_states

								UpperCAmelCase_		:   Any													=       hidden_states if output_hidden_states else None

								return TFBaseModelOutputWithPoolingAndNoAttention(
								    last_hidden_state=_snake_case     ,      pooler_output=_snake_case     ,      hidden_states=_snake_case     ,      )



@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top.",       a_,       )
class        lowercase    (	a_			):


							def __init__(      self     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(_snake_case     ,      **_snake_case)
								UpperCAmelCase_		:   str													=       TFResNetMainLayer(config=_snake_case     ,      name='resnet')





							@add_start_docstrings_to_model_forward(_snake_case)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC     ,      output_type=_snake_case     ,      config_class=_CONFIG_FOR_DOC     ,      modality='vision'     ,      expected_output=_EXPECTED_OUTPUT_SHAPE     ,      )
							@unpack_inputs
							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = False     ,      )				->					Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
								UpperCAmelCase_		:   Optional[Any]													=       (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase_		:   Tuple													=       return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_		:   str													=       self.resnet(
								    pixel_values=_snake_case     ,      output_hidden_states=_snake_case     ,      return_dict=_snake_case     ,      training=_snake_case     ,      )
								return resnet_outputs



@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    ",       a_,       )
class        lowercase    (	a_,       a_			):


							def __init__(      self     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(_snake_case     ,      **_snake_case)
								UpperCAmelCase_		:   Tuple													=       config.num_labels
								UpperCAmelCase_		:   Optional[Any]													=       TFResNetMainLayer(_snake_case     ,      name='resnet')
								# classification head
								UpperCAmelCase_		:   str													=       (
								    tf.keras.layers.Dense(config.num_labels     ,      name='classifier.1')
								    if config.num_labels > 0
								    else tf.keras.layers.Activation('linear'     ,      name='classifier.1')
								)


							def     _snake_case				(      self     ,      _snake_case)				->					tf.Tensor:
								UpperCAmelCase_		:   List[str]													=       tf.keras.layers.Flatten()(_snake_case)
								UpperCAmelCase_		:   List[Any]													=       self.classifier_layer(_snake_case)
								return logits





							@add_start_docstrings_to_model_forward(_snake_case)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT     ,      output_type=_snake_case     ,      config_class=_CONFIG_FOR_DOC     ,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT     ,      )
							@unpack_inputs
							def     _snake_case				(      self     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = False     ,      )				->					Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
								UpperCAmelCase_		:   Dict													=       return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_		:   List[Any]													=       self.resnet(
								    _snake_case     ,      output_hidden_states=_snake_case     ,      return_dict=_snake_case     ,      training=_snake_case)

								UpperCAmelCase_		:   Any													=       outputs.pooler_output if return_dict else outputs[1]

								UpperCAmelCase_		:   Union[str, Any]													=       self.classifier(_snake_case)

								UpperCAmelCase_		:   Optional[int]													=       None if labels is None else self.hf_compute_loss(_snake_case     ,      _snake_case)

								if not return_dict:
									UpperCAmelCase_		:   int													=       (logits,) + outputs[2:]
									return (loss,) + output if loss is not None else output

								return TFImageClassifierOutputWithNoAttention(loss=_snake_case     ,      logits=_snake_case     ,      hidden_states=outputs.hidden_states)






