


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)
_snake_case				=			1e-10
_snake_case				=			"This is incredible! I love it, this is the best chicken I have ever had."
_snake_case				=			1_0_0




class  A_ (			nn.Module  ):







 """simple docstring"""





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


      for param in self.encoder.parameters():
           UpperCAmelCase    :					List[Any]			=      False
      self.classifier_head.train()
 def 	_UpperCamelCase     (      self       :			Tuple							,     _a       :			Optional[Any]					)     ->      int:
      UpperCAmelCase    :					List[Any]			=      x.ne(0					).unsqueeze(2					).repeat(1							,     1							,     self.embed_size					).float().to(self.device					).detach()
      UpperCAmelCase    :					Dict			=      self.encoder.transformer(a_					)["last_hidden_state"]
      UpperCAmelCase    :					Any			=      hidden * mask
      UpperCAmelCase    :					Optional[int]			=      torch.sum(a_							,     dim=1					) / (torch.sum(a_							,     dim=1					).detach() + EPSILON)
      return avg_hidden

 def 	_UpperCamelCase     (      self       :			Optional[int]							,     _a       :			List[str]					)     ->      Any:


      if self.cached_mode:
           UpperCAmelCase    :					List[Any]			=      x.to(self.device					)
      else:
           UpperCAmelCase    :					Optional[int]			=      self.avg_representation(x.to(self.device					)					)

      UpperCAmelCase    :					Optional[int]			=      self.classifier_head(a_					)
      UpperCAmelCase    :					Any			=      nn.functional.log_softmax(a_							,     dim=-1					)

      return probs




class  A_ (			data.Dataset  ):







 """simple docstring"""





 def __init__(      self       :			Union[str, Any]							,     _a       :			str							,     _a       :			str					)     ->      Tuple:
      UpperCAmelCase    :					Union[str, Any]			=      X
      UpperCAmelCase    :					Optional[int]			=      y
 def __len__(      self       :			Optional[int]					)     ->      Tuple:
      return len(self.X					)

 def __getitem__(      self       :			Optional[int]							,     _a       :			Dict					)     ->      int:
      UpperCAmelCase    :					Optional[int]			=      {}
      UpperCAmelCase    :					Optional[Any]			=      self.X[index]
      UpperCAmelCase    :					Any			=      self.y[index]
      return data







def 	A (		A__			):







     def pad_sequences(A__			):
          UpperCAmelCase    :					List[Any]			=      [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    :					Optional[int]			=      seq[:end]

          return padded_sequences, lengths

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

     UpperCAmelCase	,			UpperCAmelCase    :					str			=      pad_sequences(item_info["X"]			)
     UpperCAmelCase    :					Any			=      torch.tensor(item_info["y"]		,		dtype=torch.long			)

     return x_batch, y_batch







def 	A (		A__			):
     UpperCAmelCase    :					Tuple			=      {}
     for key in data[0].keys():
          UpperCAmelCase    :					List[Any]			=      [d[key] for d in data]

     UpperCAmelCase    :					int			=      torch.cat(item_info["X"]		,		0			)
     UpperCAmelCase    :					Dict			=      torch.tensor(item_info["y"]		,		dtype=torch.long			)

     return x_batch, y_batch







def 	A (		A__		,		A__		,		A__		,		A__=0		,		A__=1_0		,		A__="cpu"			):
     UpperCAmelCase    :					str			=      0
     discriminator.train_custom()
     for batch_idx, (input_t, target_t) in enumerate(UpperCamelCase_			):
          UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      input_t.to(UpperCamelCase_			), target_t.to(UpperCamelCase_			)

          optimizer.zero_grad()

          UpperCAmelCase    :					Any			=      discriminator(UpperCamelCase_			)
          UpperCAmelCase    :					List[str]			=      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 	A (		A__		,		A__		,		A__="cpu"			):
     discriminator.eval()
     UpperCAmelCase    :					Tuple			=      0
     UpperCAmelCase    :					Any			=      0
     with torch.no_grad():
          for input_t, target_t in data_loader:
               UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      input_t.to(UpperCamelCase_			), target_t.to(UpperCamelCase_			)
               UpperCAmelCase    :					Optional[int]			=      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    :					Dict			=      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 	A (		A__		,		A__		,		A__		,		A__=False		,		A__="cpu"			):
     UpperCAmelCase    :					Tuple			=      model.tokenizer.encode(UpperCamelCase_			)
     UpperCAmelCase    :					Tuple			=      torch.tensor([input_t]		,		dtype=torch.long		,		device=UpperCamelCase_			)
     if cached:
          UpperCAmelCase    :					Union[str, Any]			=      model.avg_representation(UpperCamelCase_			)

     UpperCAmelCase    :					List[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 	A (		A__		,		A__		,		A__		,		A__=False		,		A__="cpu"			):
     UpperCAmelCase    :					Any			=      torch.utils.data.DataLoader(dataset=UpperCamelCase_		,		batch_size=UpperCamelCase_		,		collate_fn=UpperCamelCase_			)

     UpperCAmelCase    :					Tuple			=      []
     UpperCAmelCase    :					List[Any]			=      []
     for batch_idx, (x, y) in enumerate(tqdm(UpperCamelCase_		,		ascii=UpperCamelCase_			)			):
          with torch.no_grad():
               UpperCAmelCase    :					Optional[Any]			=      x.to(UpperCamelCase_			)
               UpperCAmelCase    :					List[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    :					List[str]			=      torch.utils.data.DataLoader(
         dataset=Dataset(UpperCamelCase_		,		UpperCamelCase_			)		,		batch_size=UpperCamelCase_		,		shuffle=UpperCamelCase_		,		collate_fn=UpperCamelCase_			)

     return data_loader







def 	A (		A__		,		A__=None		,		A__="gpt2-medium"		,		A__=1_0		,		A__=6_4		,		A__=1_0		,		A__=False		,		A__=False		,		A__=False		,		):
     UpperCAmelCase    :					Any			=      "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

     print("Preprocessing {} dataset...".format(UpperCamelCase_			)			)
     UpperCAmelCase    :					Union[str, Any]			=      time.time()

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

          UpperCAmelCase    :					Optional[int]			=      Discriminator(
              class_size=len(UpperCamelCase_			)		,		pretrained_model=UpperCamelCase_		,		cached_mode=UpperCamelCase_		,		device=UpperCamelCase_			).to(UpperCamelCase_			)

          UpperCAmelCase    :					Dict			=      torchtext_data.Field()
          UpperCAmelCase    :					List[Any]			=      torchtext_data.Field(sequential=UpperCamelCase_			)
          UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					Union[str, Any]			=      datasets.SST.splits(
              UpperCamelCase_		,		UpperCamelCase_		,		fine_grained=UpperCamelCase_		,		train_subtrees=UpperCamelCase_		,		)

          UpperCAmelCase    :					List[Any]			=      []
          UpperCAmelCase    :					Dict			=      []
          for i in trange(len(UpperCamelCase_			)		,		ascii=UpperCamelCase_			):
               UpperCAmelCase    :					str			=      TreebankWordDetokenizer().detokenize(vars(train_data[i]			)["text"]			)
               UpperCAmelCase    :					str			=      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    :					str			=      Dataset(UpperCamelCase_		,		UpperCamelCase_			)

          UpperCAmelCase    :					Optional[int]			=      []
          UpperCAmelCase    :					Optional[Any]			=      []
          for i in trange(len(UpperCamelCase_			)		,		ascii=UpperCamelCase_			):
               UpperCAmelCase    :					Optional[int]			=      TreebankWordDetokenizer().detokenize(vars(test_data[i]			)["text"]			)
               UpperCAmelCase    :					Optional[int]			=      discriminator.tokenizer.encode(UpperCamelCase_			)
               UpperCAmelCase    :					Dict			=      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    :					Dict			=      Dataset(UpperCamelCase_		,		UpperCamelCase_			)

          UpperCAmelCase    :					Optional[Any]			=      {
              "class_size": len(UpperCamelCase_			),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 2,
          }

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

          UpperCAmelCase    :					str			=      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    :					Any			=      []
               for i, line in enumerate(UpperCamelCase_			):
                    try:
                         data.append(eval(UpperCamelCase_			)			)
                    except Exception:
                         print("Error evaluating line {}: {}".format(UpperCamelCase_		,		UpperCamelCase_			)			)
                         continue
          UpperCAmelCase    :					str			=      []
          UpperCAmelCase    :					Optional[int]			=      []
          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    :					int			=      discriminator.tokenizer.encode(d["text"]			)

                         if len(UpperCamelCase_			) < max_length_seq:
                              UpperCAmelCase    :					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    :					Dict			=      Dataset(UpperCamelCase_		,		UpperCamelCase_			)
          UpperCAmelCase    :					Optional[Any]			=      int(0.9 * len(UpperCamelCase_			)			)
          UpperCAmelCase    :					Union[str, Any]			=      len(UpperCamelCase_			) - train_size
          UpperCAmelCase	,			UpperCAmelCase    :					Union[str, Any]			=      torch.utils.data.random_split(UpperCamelCase_		,		[train_size, test_size]			)

          UpperCAmelCase    :					Union[str, Any]			=      {
              "class_size": len(UpperCamelCase_			),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 1,
          }

     elif dataset == "toxic":
          UpperCAmelCase    :					Tuple			=      ["non_toxic", "toxic"]
          UpperCAmelCase    :					Any			=      {c: i for i, c in enumerate(UpperCamelCase_			)}

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

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

                         if len(UpperCamelCase_			) < max_length_seq:
                              UpperCAmelCase    :					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    :					Optional[Any]			=      Dataset(UpperCamelCase_		,		UpperCamelCase_			)
          UpperCAmelCase    :					Union[str, Any]			=      int(0.9 * len(UpperCamelCase_			)			)
          UpperCAmelCase    :					Any			=      len(UpperCamelCase_			) - train_size
          UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      torch.utils.data.random_split(UpperCamelCase_		,		[train_size, test_size]			)

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

          UpperCAmelCase    :					Tuple			=      sorted(UpperCamelCase_			)
          UpperCAmelCase    :					List[str]			=      {c: i for i, c in enumerate(UpperCamelCase_			)}

          UpperCAmelCase    :					Optional[int]			=      Discriminator(
              class_size=len(UpperCamelCase_			)		,		pretrained_model=UpperCamelCase_		,		cached_mode=UpperCamelCase_		,		device=UpperCamelCase_			).to(UpperCamelCase_			)

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

                         try:
                              UpperCAmelCase    :					Optional[Any]			=      discriminator.tokenizer.encode(UpperCamelCase_			)
                              if len(UpperCamelCase_			) < max_length_seq:
                                   UpperCAmelCase    :					Any			=      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    :					Optional[int]			=      Dataset(UpperCamelCase_		,		UpperCamelCase_			)
          UpperCAmelCase    :					Optional[int]			=      int(0.9 * len(UpperCamelCase_			)			)
          UpperCAmelCase    :					Tuple			=      len(UpperCamelCase_			) - train_size
          UpperCAmelCase	,			UpperCAmelCase    :					List[Any]			=      torch.utils.data.random_split(UpperCamelCase_		,		[train_size, test_size]			)

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

     UpperCAmelCase    :					Dict			=      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    :					List[Any]			=      time.time()

          UpperCAmelCase    :					Dict			=      get_cached_data_loader(UpperCamelCase_		,		UpperCamelCase_		,		UpperCamelCase_		,		shuffle=UpperCamelCase_		,		device=UpperCamelCase_			)

          UpperCAmelCase    :					List[Any]			=      get_cached_data_loader(UpperCamelCase_		,		UpperCamelCase_		,		UpperCamelCase_		,		device=UpperCamelCase_			)

          UpperCAmelCase    :					Optional[int]			=      time.time()
          print("Building representation cache took: {:.3f}s".format(end - start			)			)

     else:
          UpperCAmelCase    :					Dict			=      torch.utils.data.DataLoader(
              dataset=UpperCamelCase_		,		batch_size=UpperCamelCase_		,		shuffle=UpperCamelCase_		,		collate_fn=UpperCamelCase_			)
          UpperCAmelCase    :					Union[str, 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    :					Tuple			=      optim.Adam(discriminator.parameters()		,		lr=0.0001			)

     for epoch in range(UpperCamelCase_			):
          UpperCAmelCase    :					Union[str, 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    :					Any			=      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__":
      _snake_case				=			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")
      _snake_case				=			parser.parse_args()

      train_discriminator(**(vars(args)))











"""simple docstring"""




import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
     from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import SwinaSRImageProcessor




class  lowercase_     :






      def __init__(     self  ,  a_  ,  a_=1_3  ,  a_=3_2  ,  a_=1  ,  a_=3  ,  a_=1_6  ,  a_=[1, 2, 1]  ,  a_=[2, 2, 4]  ,  a_=2  ,  a_=2.0  ,  a_=True  ,  a_=0.0  ,  a_=0.0  ,  a_=0.1  ,  a_="gelu"  ,  a_=False  ,  a_=True  ,  a_=0.02  ,  a_=1E-5  ,  a_=True  ,  a_=None  ,  a_=False  ,  a_=2  ,  )						->str:







        '''simple docstring'''


        _a					      =						parent
        _a					      =						batch_size
        _a					      =						image_size
        _a					      =						patch_size
        _a					      =						num_channels
        _a					      =						embed_dim
        _a					      =						depths
        _a					      =						num_heads
        _a					      =						window_size
        _a					      =						mlp_ratio
        _a					      =						qkv_bias
        _a					      =						hidden_dropout_prob
        _a					      =						attention_probs_dropout_prob
        _a					      =						drop_path_rate
        _a					      =						hidden_act
        _a					      =						use_absolute_embeddings
        _a					      =						patch_norm
        _a					      =						layer_norm_eps
        _a					      =						initializer_range
        _a					      =						is_training
        _a					      =						scope
        _a					      =						use_labels
        _a					      =						upscale

        # here we set some attributes to make tests pass
        _a					      =						len(a_						)
        _a					      =						embed_dim
        _a					      =						(image_size // patch_size) ** 2






      def       lowerCamelCase__     (     self						)						->Any:







        '''simple docstring'''


        _a					      =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

        _a					      =						None
        if self.use_labels:
          _a					      =						ids_tensor([self.batch_size]  ,  self.type_sequence_label_size						)

        _a					      =						self.get_config()

        return config, pixel_values, labels






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







        '''simple docstring'''


        return SwinaSRConfig(
            image_size=self.image_size  ,  patch_size=self.patch_size  ,  num_channels=self.num_channels  ,  embed_dim=self.embed_dim  ,  depths=self.depths  ,  num_heads=self.num_heads  ,  window_size=self.window_size  ,  mlp_ratio=self.mlp_ratio  ,  qkv_bias=self.qkv_bias  ,  hidden_dropout_prob=self.hidden_dropout_prob  ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,  drop_path_rate=self.drop_path_rate  ,  hidden_act=self.hidden_act  ,  use_absolute_embeddings=self.use_absolute_embeddings  ,  path_norm=self.patch_norm  ,  layer_norm_eps=self.layer_norm_eps  ,  initializer_range=self.initializer_range  ,  upscale=self.upscale  ,  )






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







        '''simple docstring'''


        _a					      =						SwinaSRModel(config=a_						)
        model.to(a_						)
        model.eval()
        _a					      =						model(a_						)

        self.parent.assertEqual(
            result.last_hidden_state.shape  ,  (self.batch_size, self.embed_dim, self.image_size, self.image_size)						)






      def       lowerCamelCase__     (     self  ,  a_  ,  a_  ,  a_						)						->Optional[int]:







        '''simple docstring'''


        _a					      =						SwinaSRForImageSuperResolution(a_						)
        model.to(a_						)
        model.eval()
        _a					      =						model(a_						)

        _a					      =						self.image_size * self.upscale
        self.parent.assertEqual(
            result.reconstruction.shape  ,  (self.batch_size, self.num_channels, expected_image_size, expected_image_size)						)






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a					      =						self.prepare_config_and_inputs()
        _a      ,						_a      ,						_a					      =						config_and_inputs
        _a					      =						{"pixel_values": pixel_values}
        return config, inputs_dict







@require_torch
class  lowercase_     (_UpperCAmelCase,     _UpperCAmelCase,     unittest.TestCase  ):
      A__						:		str							      =					(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
      A__						:		Optional[Any]							      =					{'''feature-extraction''': SwinaSRModel} if is_torch_available() else {}

      A__						:		List[str]							      =					False
      A__						:		Optional[Any]							      =					False
      A__						:		str							      =					False
      A__						:		Optional[int]							      =					False
      A__						:		List[Any]							      =					False






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







        '''simple docstring'''


        _a					      =						SwinaSRModelTester(self						)
        _a					      =						ConfigTester(self  ,  config_class=a_  ,  embed_dim=3_7						)






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







        '''simple docstring'''


        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()






      def       lowerCamelCase__     (     self						)						->int:







        '''simple docstring'''


        _a					      =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a_						)






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







        '''simple docstring'''


        _a					      =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_super_resolution(*a_						)






      @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."						)
      def       lowerCamelCase__     (     self						)						->int:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Swin2SR does not use inputs_embeds"						)
      def       lowerCamelCase__     (     self						)						->Union[str, Any]:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Swin2SR does not support training yet"						)
      def       lowerCamelCase__     (     self						)						->Union[str, Any]:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Swin2SR does not support training yet"						)
      def       lowerCamelCase__     (     self						)						->Any:







        '''simple docstring'''


        pass






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          _a					      =						model_class(a_						)
          self.assertIsInstance(model.get_input_embeddings()  ,  (nn.Module)						)
          _a					      =						model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(a_  ,  nn.Linear						)						)






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







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          _a					      =						model_class(a_						)
          _a					      =						inspect.signature(model.forward						)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          _a					      =						[*signature.parameters.keys()]

          _a					      =						["pixel_values"]
          self.assertListEqual(arg_names[:1]  ,  a_						)






      @slow
      def       lowerCamelCase__     (     self						)						->Dict:







        '''simple docstring'''


        for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _a					      =						SwinaSRModel.from_pretrained(a_						)
          self.assertIsNotNone(a_						)






      def       lowerCamelCase__     (     self						)						->Dict:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()

        _a					      =						_config_zero_init(a_						)
        for model_class in self.all_model_classes:
          _a					      =						model_class(config=a_						)
          for name, param in model.named_parameters():
            if "logit_scale" in name:
              continue
            if param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1E9).round() / 1E9).item()  ,  [0.0, 1.0]  ,  msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''  ,  )






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()
        _a					      =						True

        for model_class in self.all_model_classes:
          _a					      =						True
          _a					      =						False
          _a					      =						True
          _a					      =						model_class(a_						)
          model.to(a_						)
          model.eval()
          with torch.no_grad():
            _a					      =						model(**self._prepare_for_class(a_  ,  a_						)						)
          _a					      =						outputs.attentions
          _a					      =						len(self.model_tester.depths						)
          self.assertEqual(len(a_						)  ,  a_						)

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          _a					      =						True
          _a					      =						config.window_size**2
          _a					      =						model_class(a_						)
          model.to(a_						)
          model.eval()
          with torch.no_grad():
            _a					      =						model(**self._prepare_for_class(a_  ,  a_						)						)
          _a					      =						outputs.attentions
          self.assertEqual(len(a_						)  ,  a_						)

          self.assertListEqual(
              list(attentions[0].shape[-3:]						)  ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,  )
          _a					      =						len(a_						)

          # Check attention is always last and order is fine
          _a					      =						True
          _a					      =						True
          _a					      =						model_class(a_						)
          model.to(a_						)
          model.eval()
          with torch.no_grad():
            _a					      =						model(**self._prepare_for_class(a_  ,  a_						)						)

          self.assertEqual(out_len + 1  ,  len(a_						)						)

          _a					      =						outputs.attentions

          self.assertEqual(len(a_						)  ,  a_						)

          self.assertListEqual(
              list(self_attentions[0].shape[-3:]						)  ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,  )







@require_vision
@require_torch
@slow
class  lowercase_     (unittest.TestCase  ):






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







        '''simple docstring'''


        _a					      =						SwinaSRImageProcessor()
        _a					      =						SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"						).to(a_						)

        _a					      =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
        _a					      =						processor(images=a_  ,  return_tensors="pt"						).to(a_						)

        # forward pass
        with torch.no_grad():
          _a					      =						model(**a_						)

        # verify the logits
        _a					      =						torch.Size([1, 3, 9_7_6, 1_2_9_6]						)
        self.assertEqual(outputs.reconstruction.shape  ,  a_						)
        _a					      =						torch.tensor(
            [[0.5_458, 0.5_546, 0.5_638], [0.5_526, 0.5_565, 0.5_651], [0.5_396, 0.5_426, 0.5_621]]						).to(a_						)
        self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]  ,  a_  ,  atol=1E-4						)						)


