


"""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)
__SCREAMING_SNAKE_CASE					=						1E-1_0
__SCREAMING_SNAKE_CASE					=						"""This is incredible! I love it, this is the best chicken I have ever had."""
__SCREAMING_SNAKE_CASE					=						1_00





class __snake_case		(					nn.Module      ):







   """simple docstring"""




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




   def SCREAMING_SNAKE_CASE_      (		self							:str       ):
          return self.classifier_head




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
          for param in self.encoder.parameters():
                 _a          =				False
          self.classifier_head.train()




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:List[str]       ):
          _a          =				x.ne(0       ).unsqueeze(2       ).repeat(1				,  1				,  self.embed_size       ).float().to(self.device       ).detach()
          _a          =				self.encoder.transformer(UpperCamelCase__       )["last_hidden_state"]
          _a          =				hidden * mask
          _a          =				torch.sum(UpperCamelCase__				,  dim=1       ) / (torch.sum(UpperCamelCase__				,  dim=1       ).detach() + EPSILON)
          return avg_hidden




   def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  UpperCamelCase__							:str       ):
          if self.cached_mode:
                 _a          =				x.to(self.device       )
          else:
                 _a          =				self.avg_representation(x.to(self.device       )       )

          _a          =				self.classifier_head(UpperCamelCase__       )
          _a          =				nn.functional.log_softmax(UpperCamelCase__				,  dim=-1       )

          return probs






class __snake_case		(					data.Dataset      ):







   """simple docstring"""




   def __init__(		self							:str				,  UpperCamelCase__							:Union[str, Any]				,  UpperCamelCase__							:Union[str, Any]       ):
          _a          =				X
          _a          =				y




   def __len__(		self							:Tuple       ):
          return len(self.X       )




   def __getitem__(		self							:List[str]				,  UpperCamelCase__							:List[str]       ):
          _a          =				{}
          _a          =				self.X[index]
          _a          =				self.y[index]
          return data






def 						__a      (  a					):





       """simple docstring"""







       def pad_sequences(a					):
              _a          =				[len(a					) for seq in sequences]

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

              for i, seq in enumerate(a					):
                     _a          =				lengths[i]
                     _a          =				seq[:end]

              return padded_sequences, lengths

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

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

       return x_batch, y_batch



def 						__a      (  a					):





       """simple docstring"""







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

       _a          =				torch.cat(item_info["X"],		0					)
       _a          =				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"					):





       """simple docstring"""







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

              optimizer.zero_grad()

              _a          =				discriminator(a					)
              _a          =				nn.functional.nll_loss(a,		a					)
              loss.backward(retain_graph=a					)
              optimizer.step()

              samples_so_far += len(a					)

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



def 						__a      (  a,		a,		a="cpu"					):





       """simple docstring"""







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

       test_loss /= len(data_loader.dataset					)

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



def 						__a      (  a,		a,		a,		a=False,		a="cpu"					):





       """simple docstring"""







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

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



def 						__a      (  a,		a,		a,		a=False,		a="cpu"					):





       """simple docstring"""







       _a          =				torch.utils.data.DataLoader(dataset=a,		batch_size=a,		collate_fn=a					)

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

       _a          =				torch.utils.data.DataLoader(
           dataset=Dataset(a,		a					),		batch_size=a,		shuffle=a,		collate_fn=a					)

       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,		):





       """simple docstring"""







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

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

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

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

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

              _a          =				[]
              _a          =				[]
              for i in trange(len(a					),		ascii=a					):
                     _a          =				TreebankWordDetokenizer().detokenize(vars(train_data[i]					)["text"]					)
                     _a          =				discriminator.tokenizer.encode(a					)
                     _a          =				torch.tensor([5_0_2_5_6] + seq,		device=a,		dtype=torch.long					)
                     x.append(a					)
                     y.append(classaidx[vars(train_data[i]					)["label"]]					)
              _a          =				Dataset(a,		a					)

              _a          =				[]
              _a          =				[]
              for i in trange(len(a					),		ascii=a					):
                     _a          =				TreebankWordDetokenizer().detokenize(vars(test_data[i]					)["text"]					)
                     _a          =				discriminator.tokenizer.encode(a					)
                     _a          =				torch.tensor([5_0_2_5_6] + seq,		device=a,		dtype=torch.long					)
                     test_x.append(a					)
                     test_y.append(classaidx[vars(test_data[i]					)["label"]]					)
              _a          =				Dataset(a,		a					)

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

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

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

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

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

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

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

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

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

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

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

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

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

       else:  # if dataset == "generic":
              # This assumes the input dataset is a TSV with the following structure:
              # class \t text

              if dataset_fp is None:
                     raise ValueError("When generic dataset is selected, dataset_fp needs to be specified aswell."					)

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

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

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

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

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

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

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

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

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

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

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

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

              _a          =				time.time()

              _a          =				get_cached_data_loader(a,		a,		a,		shuffle=a,		device=a					)

              _a          =				get_cached_data_loader(a,		a,		a,		device=a					)

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

       else:
              _a          =				torch.utils.data.DataLoader(
                  dataset=a,		batch_size=a,		shuffle=a,		collate_fn=a					)
              _a          =				torch.utils.data.DataLoader(dataset=a,		batch_size=a,		collate_fn=a					)

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

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

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

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

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

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

              if save_model:
                     # torch.save(discriminator.state_dict(),
                     #           "{}_discriminator_{}.pt".format(
                     #               args.dataset, epoch + 1
                     #               ))
                     torch.save(
                         discriminator.get_classifier().state_dict(),		"{}_classifier_head_epoch_{}.pt".format(a,		epoch + 1					),		)


if __name__ == "__main__":
   __SCREAMING_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=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""")
   __SCREAMING_SNAKE_CASE					=						parser.parse_args()

   train_discriminator(**(vars(args)))




"""simple docstring"""




from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)





class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   def __init__(		self							:List[Any]				,  UpperCamelCase__							:AutoencoderKL				,  UpperCamelCase__							:CLIPTextModel				,  UpperCamelCase__							:CLIPTokenizer				,  UpperCamelCase__							:UNetaDConditionModel				,  UpperCamelCase__							:Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]				,  ):
          super().__init__()

          self.register_modules(vae=UpperCamelCase__				,  text_encoder=UpperCamelCase__				,  tokenizer=UpperCamelCase__				,  unet=UpperCamelCase__				,  scheduler=UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:str       ):
          with torch.no_grad():
                 _a          =				self.vae.encode(tfms.ToTensor()(UpperCamelCase__       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
                 _a          =				0.18215 * latent.latent_dist.sample()
          return latent




   def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:Any       ):
          _a          =				(1 / 0.18215) * latent
          with torch.no_grad():
                 _a          =				self.vae.decode(UpperCamelCase__       ).sample
          _a          =				(img / 2 + 0.5).clamp(0				,  1       )
          _a          =				img.detach().cpu().permute(0				,  2				,  3				,  1       ).numpy()
          _a          =				(img * 255).round().astype("uint8"       )
          return Image.fromarray(img[0]       )




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:List[Any]       ):
          _a          =				self.tokenizer(
              UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )

          _a          =				self.text_encoder(text_input.input_ids.to(self.device       )       )[0]

          _a          =				self.tokenizer(
              ""				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )

          _a          =				self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

          return torch.cat([uncond_embedding, text_embedding]       )




   def __call__(		self							:Tuple				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:str				,  UpperCamelCase__							:float = 0.3				,  UpperCamelCase__							:float = 0.6				,  UpperCamelCase__							:float = 0.5				,  UpperCamelCase__							:int = 42				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:float = 7.5				,  ):
          _a          =				steps - int(kmin * steps       )
          _a          =				steps - int(kmax * steps       )

          _a          =				self.prep_text(UpperCamelCase__       )

          self.scheduler.set_timesteps(UpperCamelCase__       )

          _a   , _a          =				img.size
          _a          =				self.encode(UpperCamelCase__       )

          torch.manual_seed(UpperCamelCase__       )
          _a          =				torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8)				,  ).to(self.device       )

          _a          =				self.scheduler.add_noise(
              UpperCamelCase__				,  UpperCamelCase__				,  timesteps=self.scheduler.timesteps[tmax]				,  )

          _a          =				torch.cat([latents] * 2       )

          _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  self.scheduler.timesteps[tmax]       )

          with torch.no_grad():
                 _a          =				self.unet(
                     UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  encoder_hidden_states=UpperCamelCase__				,  ).sample

          _a   , _a          =				pred.chunk(2       )
          _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)

          _a          =				self.scheduler.step(UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  UpperCamelCase__       ).prev_sample

          for i, t in enumerate(tqdm(self.scheduler.timesteps       )       ):
                 if i > tmax:
                        if i < tmin:  # layout generation phase
                               _a          =				self.scheduler.add_noise(
                                   UpperCamelCase__				,  UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )

                               _a          =				(mix_factor * latents) + (
                                   1 - mix_factor
                               ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                               _a          =				torch.cat([input] * 2       )

                        else:  # content generation phase
                               _a          =				torch.cat([latents] * 2       )

                        _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  UpperCamelCase__       )

                        with torch.no_grad():
                               _a          =				self.unet(
                                   UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__				,  ).sample

                        _a   , _a          =				pred.chunk(2       )
                        _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)

                        _a          =				self.scheduler.step(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       ).prev_sample

          return self.decode(UpperCamelCase__       )
