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

import argparse
import csv
import json
import math
import time

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

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
__lowerCamelCase     :					Tuple				  =						1e-10
__lowerCamelCase     :					Optional[Any]				  =						'''This is incredible! I love it, this is the best chicken I have ever had.'''
__lowerCamelCase     :					Optional[Any]				  =						100







class __snake_case							(		nn.Module						):




   def __init__(       self     :							List[str]			,      _lowercase     :							Tuple			,      _lowercase     :							List[Any]="gpt2-medium"			,      _lowercase     :							List[Any]=False			,      _lowercase     :							List[str]="cpu" ):



     """simple docstring"""






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




   def       __a		(       self     :							int ):



     """simple docstring"""






     return self.classifier_head




   def       __a		(       self     :							Dict ):



     """simple docstring"""






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




   def       __a		(       self     :							List[Any]			,      _lowercase     :							List[str] ):



     """simple docstring"""






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




   def       __a		(       self     :							Dict			,      _lowercase     :							int ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__     				= self.classifier_head(_lowercase )
     SCREAMING_SNAKE_CASE__     				= nn.functional.log_softmax(_lowercase			,      dim=-1 )

     return probs







class __snake_case							(		data.Dataset						):




   def __init__(       self     :							Optional[int]			,      _lowercase     :							int			,      _lowercase     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= X
     SCREAMING_SNAKE_CASE__     				= y




   def __len__(       self     :							Dict ):



     """simple docstring"""






     return len(self.X )




   def __getitem__(       self     :							str			,      _lowercase     :							Optional[int] ):



     """simple docstring"""






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





def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Optional[int]      )				->   Dict:







  """simple docstring"""





  def pad_sequences(__UpperCamelCase       :			List[Any]      ):
    SCREAMING_SNAKE_CASE__     				= [len(__UpperCamelCase      ) for seq in sequences]

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

    for i, seq in enumerate(__UpperCamelCase      ):
      SCREAMING_SNAKE_CASE__     				= lengths[i]
      SCREAMING_SNAKE_CASE__     				= seq[:end]

    return padded_sequences, lengths

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

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

  return x_batch, y_batch



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Optional[Any]      )				->   List[str]:







  """simple docstring"""





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

  SCREAMING_SNAKE_CASE__     				= torch.cat(item_info["""X"""]					,	0      )
  SCREAMING_SNAKE_CASE__     				= torch.tensor(item_info["""y"""]					,	dtype=torch.long      )

  return x_batch, y_batch



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			List[str]					,	__UpperCamelCase       :			List[Any]					,	__UpperCamelCase       :			Optional[Any]					,	__UpperCamelCase       :			List[str]=0					,	__UpperCamelCase       :			Union[str, Any]=10					,	__UpperCamelCase       :			List[Any]="cpu"      )				->   str:







  """simple docstring"""





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

    optimizer.zero_grad()

    SCREAMING_SNAKE_CASE__     				= discriminator(__UpperCamelCase      )
    SCREAMING_SNAKE_CASE__     				= 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_00 * samples_so_far / len(data_loader.dataset      )					,	loss.item()					,	)      )



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Dict					,	__UpperCamelCase       :			str					,	__UpperCamelCase       :			Union[str, Any]="cpu"      )				->   Optional[Any]:







  """simple docstring"""





  discriminator.eval()
  SCREAMING_SNAKE_CASE__     				= 0
  SCREAMING_SNAKE_CASE__     				= 0
  with torch.no_grad():
    for input_t, target_t in data_loader:
      SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= input_t.to(__UpperCamelCase      ), target_t.to(__UpperCamelCase      )
      SCREAMING_SNAKE_CASE__     				= 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
      SCREAMING_SNAKE_CASE__     				= output_t.argmax(dim=1					,	keepdim=__UpperCamelCase      )
      correct += pred_t.eq(target_t.view_as(__UpperCamelCase      )      ).sum().item()

  test_loss /= len(data_loader.dataset      )

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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Dict					,	__UpperCamelCase       :			str					,	__UpperCamelCase       :			int					,	__UpperCamelCase       :			Dict=False					,	__UpperCamelCase       :			Union[str, Any]="cpu"      )				->   Any:







  """simple docstring"""





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

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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]					,	__UpperCamelCase       :			str					,	__UpperCamelCase       :			Dict					,	__UpperCamelCase       :			Optional[Any]=False					,	__UpperCamelCase       :			Any="cpu"      )				->   Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= torch.utils.data.DataLoader(dataset=__UpperCamelCase					,	batch_size=__UpperCamelCase					,	collate_fn=__UpperCamelCase      )

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

  SCREAMING_SNAKE_CASE__     				= torch.utils.data.DataLoader(
      dataset=Dataset(__UpperCamelCase					,	__UpperCamelCase      )					,	batch_size=__UpperCamelCase					,	shuffle=__UpperCamelCase					,	collate_fn=__UpperCamelCase      )

  return data_loader



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			str					,	__UpperCamelCase       :			Any=None					,	__UpperCamelCase       :			str="gpt2-medium"					,	__UpperCamelCase       :			List[Any]=10					,	__UpperCamelCase       :			int=64					,	__UpperCamelCase       :			Optional[Any]=10					,	__UpperCamelCase       :			str=False					,	__UpperCamelCase       :			Tuple=False					,	__UpperCamelCase       :			Optional[int]=False					,	)				->   int:







  """simple docstring"""





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

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

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

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

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

    SCREAMING_SNAKE_CASE__     				= []
    SCREAMING_SNAKE_CASE__     				= []
    for i in trange(len(__UpperCamelCase      )					,	ascii=__UpperCamelCase      ):
      SCREAMING_SNAKE_CASE__     				= TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["""text"""]      )
      SCREAMING_SNAKE_CASE__     				= discriminator.tokenizer.encode(__UpperCamelCase      )
      SCREAMING_SNAKE_CASE__     				= torch.tensor([5_02_56] + seq					,	device=__UpperCamelCase					,	dtype=torch.long      )
      x.append(__UpperCamelCase      )
      y.append(classaidx[vars(train_data[i]      )["""label"""]]      )
    SCREAMING_SNAKE_CASE__     				= Dataset(__UpperCamelCase					,	__UpperCamelCase      )

    SCREAMING_SNAKE_CASE__     				= []
    SCREAMING_SNAKE_CASE__     				= []
    for i in trange(len(__UpperCamelCase      )					,	ascii=__UpperCamelCase      ):
      SCREAMING_SNAKE_CASE__     				= TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["""text"""]      )
      SCREAMING_SNAKE_CASE__     				= discriminator.tokenizer.encode(__UpperCamelCase      )
      SCREAMING_SNAKE_CASE__     				= torch.tensor([5_02_56] + seq					,	device=__UpperCamelCase					,	dtype=torch.long      )
      test_x.append(__UpperCamelCase      )
      test_y.append(classaidx[vars(test_data[i]      )["""label"""]]      )
    SCREAMING_SNAKE_CASE__     				= Dataset(__UpperCamelCase					,	__UpperCamelCase      )

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

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

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

          if len(__UpperCamelCase      ) < max_length_seq:
            SCREAMING_SNAKE_CASE__     				= torch.tensor([5_02_56] + 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

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

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

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

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

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

          if len(__UpperCamelCase      ) < max_length_seq:
            SCREAMING_SNAKE_CASE__     				= torch.tensor([5_02_56] + 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

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

    SCREAMING_SNAKE_CASE__     				= {
        """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."""      )

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

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

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

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

          try:
            SCREAMING_SNAKE_CASE__     				= discriminator.tokenizer.encode(__UpperCamelCase      )
            if len(__UpperCamelCase      ) < max_length_seq:
              SCREAMING_SNAKE_CASE__     				= torch.tensor([5_02_56] + 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

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

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

  SCREAMING_SNAKE_CASE__     				= 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..."""      )

    SCREAMING_SNAKE_CASE__     				= time.time()

    SCREAMING_SNAKE_CASE__     				= get_cached_data_loader(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase					,	shuffle=__UpperCamelCase					,	device=__UpperCamelCase      )

    SCREAMING_SNAKE_CASE__     				= get_cached_data_loader(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase					,	device=__UpperCamelCase      )

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

  else:
    SCREAMING_SNAKE_CASE__     				= torch.utils.data.DataLoader(
        dataset=__UpperCamelCase					,	batch_size=__UpperCamelCase					,	shuffle=__UpperCamelCase					,	collate_fn=__UpperCamelCase      )
    SCREAMING_SNAKE_CASE__     				= 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      )

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

  for epoch in range(__UpperCamelCase      ):
    SCREAMING_SNAKE_CASE__     				= 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      )

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

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

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


if __name__ == "__main__":
   __lowerCamelCase     :					int				  =						argparse.ArgumentParser(description='''Train a discriminator on top of GPT-2 representations''')
   parser.add_argument(
       '''--dataset''',
       type=str,
       default='''SST''',
       choices=('''SST''', '''clickbait''', '''toxic''', '''generic'''),
       help=(
           '''dataset to train the discriminator on.'''
           '''In case of generic, the dataset is expected'''
           '''to be a TSBV file with structure: class \\t text'''
       ),
   )
   parser.add_argument(
       '''--dataset_fp''',
       type=str,
       default='''''',
       help='''File path of the dataset to use. Needed only in case of generic datadset''',
   )
   parser.add_argument(
       '''--pretrained_model''', type=str, default='''gpt2-medium''', help='''Pretrained model to use as encoder'''
   )
   parser.add_argument('''--epochs''', type=int, default=10, metavar='''N''', help='''Number of training epochs''')
   parser.add_argument(
       '''--batch_size''', type=int, default=64, metavar='''N''', help='''input batch size for training (default: 64)'''
   )
   parser.add_argument(
       '''--log_interval''',
       type=int,
       default=10,
       metavar='''N''',
       help='''how many batches to wait before logging training status''',
   )
   parser.add_argument('''--save_model''', action='''store_true''', help='''whether to save the model''')
   parser.add_argument('''--cached''', action='''store_true''', help='''whether to cache the input representations''')
   parser.add_argument('''--no_cuda''', action='''store_true''', help='''use to turn off cuda''')
   __lowerCamelCase     :					str				  =						parser.parse_args()

   train_discriminator(**(vars(args)))

import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference







@slow
@require_torch_gpu
class __snake_case							(		unittest.TestCase						):




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """runwayml/stable-diffusion-v1-5"""
     SCREAMING_SNAKE_CASE__     				= TextToVideoZeroPipeline.from_pretrained(_lowercase			,      torch_dtype=torch.floataa ).to("""cuda""" )
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler.from_config(pipe.scheduler.config )
     SCREAMING_SNAKE_CASE__     				= torch.Generator(device="""cuda""" ).manual_seed(0 )

     SCREAMING_SNAKE_CASE__     				= """A bear is playing a guitar on Times Square"""
     SCREAMING_SNAKE_CASE__     				= pipe(prompt=_lowercase			,      generator=_lowercase ).images

     SCREAMING_SNAKE_CASE__     				= load_pt(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt""" )

     assert_mean_pixel_difference(_lowercase			,      _lowercase )
