





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

import argparse
import csv
import json
import math
import time

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

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
_UpperCAmelCase				       = 1e-10
_UpperCAmelCase				       = 'This is incredible! I love it, this is the best chicken I have ever had.'
_UpperCAmelCase				       = 1_0_0



class 							_UpperCamelCase    (		nn.Module   ):

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




              """simple docstring"""


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

       def lowercase       (						self:       Union[str, Any]						)				->       str:




              """simple docstring"""


              return self.classifier_head

       def lowercase       (						self:       Dict						)				->       List[str]:




              """simple docstring"""


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

       def lowercase       (						self:       Optional[Any]    ,      _SCREAMING_SNAKE_CASE:       List[Any]						)				->       Tuple:




              """simple docstring"""


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



       def lowercase       (						self:       List[Any]    ,      _SCREAMING_SNAKE_CASE:       List[Any]						)				->       str:




              """simple docstring"""


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

              UpperCamelCase_      				=     self.classifier_head(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     nn.functional.log_softmax(_SCREAMING_SNAKE_CASE    ,      dim=-1						)

              return probs



class 							_UpperCamelCase    (		data.Dataset   ):

       def __init__(						self:       Dict    ,      _SCREAMING_SNAKE_CASE:       Dict    ,      _SCREAMING_SNAKE_CASE:       int						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     X
              UpperCamelCase_      				=     y

       def __len__(						self:       Tuple						)				->       List[Any]:




              """simple docstring"""


              return len(self.X						)



       def __getitem__(						self:       Tuple    ,      _SCREAMING_SNAKE_CASE:       Optional[Any]						)				->       Tuple:




              """simple docstring"""


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




def        lowerCAmelCase_       (							UpperCamelCase_     )   ->						str:
       def pad_sequences(UpperCamelCase_     ):
              UpperCamelCase_      				=     [len(UpperCamelCase_     ) for seq in sequences]

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

              for i, seq in enumerate(UpperCamelCase_     ):
                     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_       (							UpperCamelCase_     )   ->						int:
       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_       (							UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_=0    ,						UpperCamelCase_=10    ,						UpperCamelCase_="cpu"     )   ->						Optional[int]:
       UpperCamelCase_      				=     0
       discriminator.train_custom()
       for batch_idx, (input_t, target_t) in enumerate(UpperCamelCase_     ):
              UpperCamelCase_				,       UpperCamelCase_      				=     input_t.to(UpperCamelCase_     ), target_t.to(UpperCamelCase_     )

              optimizer.zero_grad()

              UpperCamelCase_      				=     discriminator(UpperCamelCase_     )
              UpperCamelCase_      				=     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     )    ,						100 * samples_so_far / len(data_loader.dataset     )    ,						loss.item()    ,						)     )




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_="cpu"     )   ->						Tuple:
       discriminator.eval()
       UpperCamelCase_      				=     0
       UpperCamelCase_      				=     0
       with torch.no_grad():
              for input_t, target_t in data_loader:
                     UpperCamelCase_				,       UpperCamelCase_      				=     input_t.to(UpperCamelCase_     ), target_t.to(UpperCamelCase_     )
                     UpperCamelCase_      				=     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_      				=     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     )    ,						1_00.0 * correct / len(data_loader.dataset     )     )     )




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_=False    ,						UpperCamelCase_="cpu"     )   ->						Optional[Any]:
       UpperCamelCase_      				=     model.tokenizer.encode(UpperCamelCase_     )
       UpperCamelCase_      				=     torch.tensor([input_t]    ,						dtype=torch.long    ,						device=UpperCamelCase_     )
       if cached:
              UpperCamelCase_      				=     model.avg_representation(UpperCamelCase_     )

       UpperCamelCase_      				=     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        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_=False    ,						UpperCamelCase_="cpu"     )   ->						str:
       UpperCamelCase_      				=     torch.utils.data.DataLoader(dataset=UpperCamelCase_    ,						batch_size=UpperCamelCase_    ,						collate_fn=UpperCamelCase_     )

       UpperCamelCase_      				=     []
       UpperCamelCase_      				=     []
       for batch_idx, (x, y) in enumerate(tqdm(UpperCamelCase_    ,						ascii=UpperCamelCase_     )     ):
              with torch.no_grad():
                     UpperCamelCase_      				=     x.to(UpperCamelCase_     )
                     UpperCamelCase_      				=     discriminator.avg_representation(UpperCamelCase_     ).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(UpperCamelCase_    ,						UpperCamelCase_     )    ,						batch_size=UpperCamelCase_    ,						shuffle=UpperCamelCase_    ,						collate_fn=UpperCamelCase_     )

       return data_loader




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_=None    ,						UpperCamelCase_="gpt2-medium"    ,						UpperCamelCase_=10    ,						UpperCamelCase_=64    ,						UpperCamelCase_=10    ,						UpperCamelCase_=False    ,						UpperCamelCase_=False    ,						UpperCamelCase_=False    ,						)   ->						Any:
       UpperCamelCase_      				=     "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

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

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

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

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

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

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

              UpperCamelCase_      				=     {
                  "class_size": len(UpperCamelCase_     ),
                  "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(UpperCamelCase_     )}

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

                                   if len(UpperCamelCase_     ) < max_length_seq:
                                          UpperCamelCase_      				=     torch.tensor([50256] + 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_      				=     Dataset(UpperCamelCase_    ,						UpperCamelCase_     )
              UpperCamelCase_      				=     int(0.9 * len(UpperCamelCase_     )     )
              UpperCamelCase_      				=     len(UpperCamelCase_     ) - train_size
              UpperCamelCase_				,       UpperCamelCase_      				=     torch.utils.data.random_split(UpperCamelCase_    ,						[train_size, test_size]     )

              UpperCamelCase_      				=     {
                  "class_size": len(UpperCamelCase_     ),
                  "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(UpperCamelCase_     )}

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

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

                                   if len(UpperCamelCase_     ) < max_length_seq:
                                          UpperCamelCase_      				=     torch.tensor([50256] + 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_      				=     Dataset(UpperCamelCase_    ,						UpperCamelCase_     )
              UpperCamelCase_      				=     int(0.9 * len(UpperCamelCase_     )     )
              UpperCamelCase_      				=     len(UpperCamelCase_     ) - train_size
              UpperCamelCase_				,       UpperCamelCase_      				=     torch.utils.data.random_split(UpperCamelCase_    ,						[train_size, test_size]     )

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

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

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

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

                                   try:
                                          UpperCamelCase_      				=     discriminator.tokenizer.encode(UpperCamelCase_     )
                                          if len(UpperCamelCase_     ) < max_length_seq:
                                                 UpperCamelCase_      				=     torch.tensor([50256] + 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_      				=     Dataset(UpperCamelCase_    ,						UpperCamelCase_     )
              UpperCamelCase_      				=     int(0.9 * len(UpperCamelCase_     )     )
              UpperCamelCase_      				=     len(UpperCamelCase_     ) - train_size
              UpperCamelCase_				,       UpperCamelCase_      				=     torch.utils.data.random_split(UpperCamelCase_    ,						[train_size, test_size]     )

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

       UpperCamelCase_      				=     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_      				=     time.time()

              UpperCamelCase_      				=     get_cached_data_loader(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						shuffle=UpperCamelCase_    ,						device=UpperCamelCase_     )

              UpperCamelCase_      				=     get_cached_data_loader(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						device=UpperCamelCase_     )

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

       else:
              UpperCamelCase_      				=     torch.utils.data.DataLoader(
                  dataset=UpperCamelCase_    ,						batch_size=UpperCamelCase_    ,						shuffle=UpperCamelCase_    ,						collate_fn=UpperCamelCase_     )
              UpperCamelCase_      				=     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_      				=     optim.Adam(discriminator.parameters()    ,						lr=0.00_01     )

       for epoch in range(UpperCamelCase_     ):
              UpperCamelCase_      				=     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_      				=     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__":
     _UpperCAmelCase				       = argparse.ArgumentParser(description='Train a discriminator on top of GPT-2 representations')
     parser.add_argument(
         '--dataset',
         type=str,
         default='SST',
         choices=('SST', 'clickbait', 'toxic', 'generic'),
         help=(
             'dataset to train the discriminator on.'
             'In case of generic, the dataset is expected'
             'to be a TSBV file with structure: class \\t text'
         ),
     )
     parser.add_argument(
         '--dataset_fp',
         type=str,
         default='',
         help='File path of the dataset to use. Needed only in case of generic datadset',
     )
     parser.add_argument(
         '--pretrained_model', type=str, default='gpt2-medium', help='Pretrained model to use as encoder'
     )
     parser.add_argument('--epochs', type=int, default=1_0, metavar='N', help='Number of training epochs')
     parser.add_argument(
         '--batch_size', type=int, default=6_4, metavar='N', help='input batch size for training (default: 64)'
     )
     parser.add_argument(
         '--log_interval',
         type=int,
         default=1_0,
         metavar='N',
         help='how many batches to wait before logging training status',
     )
     parser.add_argument('--save_model', action='store_true', help='whether to save the model')
     parser.add_argument('--cached', action='store_true', help='whether to cache the input representations')
     parser.add_argument('--no_cuda', action='store_true', help='use to turn off cuda')
     _UpperCAmelCase				       = parser.parse_args()

     train_discriminator(**(vars(args)))








import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

_UpperCAmelCase				       = logging.get_logger(__name__)




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_ = True     )   ->						Any:
       UpperCamelCase_      				=     {}
       for name in model.attn_processors.keys():
              UpperCamelCase_      				=     name.endswith("attn2.processor"     ) and not (
                  name.startswith("transformer_in"     ) or "temp_attentions" in name.split("."     )
              )
              UpperCamelCase_      				=     model.config.cross_attention_dim if has_cross_attention else None
              if name.startswith("mid_block"     ):
                     UpperCamelCase_      				=     model.config.block_out_channels[-1]
              elif name.startswith("up_blocks"     ):
                     UpperCamelCase_      				=     int(name[len("up_blocks."     )]     )
                     UpperCamelCase_      				=     list(reversed(model.config.block_out_channels     )     )[block_id]
              elif name.startswith("down_blocks"     ):
                     UpperCamelCase_      				=     int(name[len("down_blocks."     )]     )
                     UpperCamelCase_      				=     model.config.block_out_channels[block_id]
              elif name.startswith("transformer_in"     ):
                     # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
                     UpperCamelCase_      				=     8 * model.config.attention_head_dim

              UpperCamelCase_      				=     LoRAAttnProcessor(hidden_size=UpperCamelCase_    ,						cross_attention_dim=UpperCamelCase_     )
              UpperCamelCase_      				=     lora_attn_procs[name].to(model.device     )

              if mock_weights:
                     # add 1 to weights to mock trained weights
                     with torch.no_grad():
                            lora_attn_procs[name].to_q_lora.up.weight += 1
                            lora_attn_procs[name].to_k_lora.up.weight += 1
                            lora_attn_procs[name].to_v_lora.up.weight += 1
                            lora_attn_procs[name].to_out_lora.up.weight += 1

       return lora_attn_procs







@skip_mps
class 							_UpperCamelCase    (		lowerCAmelCase_     ,  lowerCAmelCase_     ,  unittest.TestCase   ):
       _UpperCamelCase :    Optional[Any]   			=      UNetaDConditionModel
       _UpperCamelCase :    List[Any]   			=      '''sample'''

       @property
       def lowercase       (						self:       Dict						)				->       Tuple:




              """simple docstring"""


              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     (32, 32)

              UpperCamelCase_      				=     floats_tensor((batch_size, num_channels, num_frames) + sizes						).to(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     torch.tensor([10]						).to(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     floats_tensor((batch_size, 4, 32)						).to(_SCREAMING_SNAKE_CASE						)

              return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}

       @property
       def lowercase       (						self:       Tuple						)				->       str:




              """simple docstring"""


              return (4, 4, 32, 32)

       @property
       def lowercase       (						self:       List[str]						)				->       List[str]:




              """simple docstring"""


              return (4, 4, 32, 32)

       def lowercase       (						self:       Optional[Any]						)				->       Optional[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     {
                  "block_out_channels": (32, 64),
                  "down_block_types": (
                      "CrossAttnDownBlock3D",
                      "DownBlock3D",
                  ),
                  "up_block_types": ("UpBlock3D", "CrossAttnUpBlock3D"),
                  "cross_attention_dim": 32,
                  "attention_head_dim": 8,
                  "out_channels": 4,
                  "in_channels": 4,
                  "layers_per_block": 1,
                  "sample_size": 32,
              }
              UpperCamelCase_      				=     self.dummy_input
              return init_dict, inputs_dict

       @unittest.skipIf(
           torch_device != "cuda" or not is_xformers_available()    ,      reason="XFormers attention is only available with CUDA and `xformers` installed"    ,      )
       def lowercase       (						self:       str						)				->       Optional[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)

              model.enable_xformers_memory_efficient_attention()

              assert (
                  model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
                  == "XFormersAttnProcessor"
              ), "xformers is not enabled"

       def lowercase       (						self:       List[str]						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     32

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)

                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     output.sample

              self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     inputs_dict["sample"].shape
              self.assertEqual(output.shape    ,      _SCREAMING_SNAKE_CASE    ,      "Input and output shapes do not match"						)

       def lowercase       (						self:       Optional[int]						)				->       int:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              with torch.no_grad():
                     # Warmup pass when using mps (see #372)
                     if torch_device == "mps" and isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            model(**self.dummy_input						)

                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     first.sample

                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     second.sample

              UpperCamelCase_      				=     first.cpu().numpy()
              UpperCamelCase_      				=     second.cpu().numpy()
              UpperCamelCase_      				=     out_a[~np.isnan(_SCREAMING_SNAKE_CASE						)]
              UpperCamelCase_      				=     out_a[~np.isnan(_SCREAMING_SNAKE_CASE						)]
              UpperCamelCase_      				=     np.amax(np.abs(out_a - out_a						)						)
              self.assertLessEqual(_SCREAMING_SNAKE_CASE    ,      1e-5						)

       def lowercase       (						self:       int						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              model.set_attention_slice("auto"						)
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
              assert output is not None

              model.set_attention_slice("max"						)
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
              assert output is not None

              model.set_attention_slice(2						)
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
              assert output is not None

       def lowercase       (						self:       Optional[Any]						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)

              # make sure we can set a list of attention processors
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              # test that attn processors can be set to itself
              model.set_attn_processor(model.attn_processors						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.0}						).sample
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

              assert (samplea - samplea).abs().max() < 3e-3
              assert (samplea - samplea).abs().max() < 3e-3

              # sample 2 and sample 3 should be different
              assert (samplea - samplea).abs().max() > 3e-3

       def lowercase       (						self:       Any						)				->       int:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

              with tempfile.TemporaryDirectory() as tmpdirname:
                     model.save_attn_procs(_SCREAMING_SNAKE_CASE						)
                     self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE    ,      "pytorch_lora_weights.bin"						)						)						)
                     torch.manual_seed(0						)
                     UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
                     new_model.to(_SCREAMING_SNAKE_CASE						)
                     new_model.load_attn_procs(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     new_model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

              assert (sample - new_sample).abs().max() < 1e-3

              # LoRA and no LoRA should NOT be the same
              assert (sample - old_sample).abs().max() > 1e-4

       def lowercase       (						self:       List[str]						)				->       Optional[int]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

              with tempfile.TemporaryDirectory() as tmpdirname:
                     model.save_attn_procs(_SCREAMING_SNAKE_CASE    ,      safe_serialization=_SCREAMING_SNAKE_CASE						)
                     self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE    ,      "pytorch_lora_weights.safetensors"						)						)						)
                     torch.manual_seed(0						)
                     UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
                     new_model.to(_SCREAMING_SNAKE_CASE						)
                     new_model.load_attn_procs(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     new_model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

              assert (sample - new_sample).abs().max() < 3e-3

              # LoRA and no LoRA should NOT be the same
              assert (sample - old_sample).abs().max() > 1e-4

       def lowercase       (						self:       int						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE    ,      mock_weights=_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)
              # Saving as torch, properly reloads with directly filename
              with tempfile.TemporaryDirectory() as tmpdirname:
                     model.save_attn_procs(_SCREAMING_SNAKE_CASE						)
                     self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE    ,      "pytorch_lora_weights.bin"						)						)						)
                     torch.manual_seed(0						)
                     UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
                     new_model.to(_SCREAMING_SNAKE_CASE						)
                     new_model.load_attn_procs(_SCREAMING_SNAKE_CASE    ,      weight_name="pytorch_lora_weights.bin"						)

       def lowercase       (						self:       Any						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE    ,      mock_weights=_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)
              # Saving as torch, properly reloads with directly filename
              with tempfile.TemporaryDirectory() as tmpdirname:
                     model.save_attn_procs(_SCREAMING_SNAKE_CASE						)
                     self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE    ,      "pytorch_lora_weights.bin"						)						)						)
                     torch.manual_seed(0						)
                     UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
                     new_model.to(_SCREAMING_SNAKE_CASE						)
                     with self.assertRaises(_SCREAMING_SNAKE_CASE						) as e:
                            new_model.load_attn_procs(_SCREAMING_SNAKE_CASE    ,      use_safetensors=_SCREAMING_SNAKE_CASE						)
                     self.assertIn("Error no file named pytorch_lora_weights.safetensors"    ,      str(e.exception						)						)

       def lowercase       (						self:       Optional[Any]						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.0}						).sample

              model.set_attn_processor(AttnProcessor()						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              assert (sample - new_sample).abs().max() < 1e-4
              assert (sample - old_sample).abs().max() < 3e-3

       @unittest.skipIf(
           torch_device != "cuda" or not is_xformers_available()    ,      reason="XFormers attention is only available with CUDA and `xformers` installed"    ,      )
       def lowercase       (						self:       Union[str, Any]						)				->       Dict:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     4

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              # default
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

                     model.enable_xformers_memory_efficient_attention()
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

                     model.disable_xformers_memory_efficient_attention()
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              assert (sample - on_sample).abs().max() < 1e-4
              assert (sample - off_sample).abs().max() < 1e-4



       def lowercase       (						self:       Optional[int]						)				->       Optional[int]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()
              UpperCamelCase_      				=     32

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)[0]

              model.enable_forward_chunking()
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)[0]

              self.assertEqual(output.shape    ,      output_a.shape    ,      "Shape doesn't match"						)
              assert np.abs(output.cpu() - output_a.cpu()						).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.

