code: 
  
  
  # 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)
  lowercase_								=    1e-10
  lowercase_								=    'This is incredible! I love it, this is the best chicken I have ever had.'
  lowercase_								=    1_00
  
  
  
  
  class 					_UpperCamelCase (      nn.Module	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def __init__(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				int		,				SCREAMING_SNAKE_CASE_  :				str="gpt2-medium"		,				SCREAMING_SNAKE_CASE_  :				Tuple=False		,				SCREAMING_SNAKE_CASE_  :				Tuple="cpu"		):
  													super().__init__()
  													_a     =			GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_		)
  													_a     =			GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE_		)
  													_a     =			self.encoder.transformer.config.hidden_size
  													_a     =			ClassificationHead(class_size=SCREAMING_SNAKE_CASE_		,				embed_size=self.embed_size		)
  													_a     =			cached_mode
  													_a     =			device
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Any		):
  													return self.classifier_head
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
  													for param in self.encoder.parameters():
  																			_a     =			False
  													self.classifier_head.train()
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Dict		,				SCREAMING_SNAKE_CASE_  :				str		):
  													_a     =			x.ne(0		).unsqueeze(2		).repeat(1		,				1		,				self.embed_size		).float().to(self.device		).detach()
  													_a     =			self.encoder.transformer(SCREAMING_SNAKE_CASE_		)['last_hidden_state']
  													_a     =			hidden * mask
  													_a     =			torch.sum(SCREAMING_SNAKE_CASE_		,				dim=1		) / (torch.sum(SCREAMING_SNAKE_CASE_		,				dim=1		).detach() + EPSILON)
  													return avg_hidden
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		):
  													if self.cached_mode:
  																			_a     =			x.to(self.device		)
  													else:
  																			_a     =			self.avg_representation(x.to(self.device		)		)
  
  													_a     =			self.classifier_head(SCREAMING_SNAKE_CASE_		)
  													_a     =			nn.functional.log_softmax(SCREAMING_SNAKE_CASE_		,				dim=-1		)
  
  													return probs
  
  
  
  
  class 					_UpperCamelCase (      data.Dataset	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def __init__(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Any		):
  													_a     =			X
  													_a     =			y
  
  
  
  
  
  
  
  							def __len__(		self  :				List[Any]		):
  													return len(self.X		)
  
  
  
  
  
  
  
  							def __getitem__(		self  :				int		,				SCREAMING_SNAKE_CASE_  :				Tuple		):
  													_a     =			{}
  													_a     =			self.X[index]
  													_a     =			self.y[index]
  													return data
  
  
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Dict:
  
  
  
  
  
  
  
  						def pad_sequences(_UpperCAmelCase				):
  												_a     =			[len(lowercase__				) for seq in sequences]
  
  												_a     =			torch.zeros(len(lowercase__				)							,     max(lowercase__				)				).long()  # padding value = 0
  
  												for i, seq in enumerate(lowercase__				):
  																		_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 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Dict:
  						_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 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase=0							,     _UpperCAmelCase=10							,     _UpperCAmelCase="cpu"				)    ->  int:
  						_a     =			0
  						discriminator.train_custom()
  						for batch_idx, (input_t, target_t) in enumerate(lowercase__				):
  												_a ,	_a     =			input_t.to(lowercase__				), target_t.to(lowercase__				)
  
  												optimizer.zero_grad()
  
  												_a     =			discriminator(lowercase__				)
  												_a     =			nn.functional.nll_loss(lowercase__							,     lowercase__				)
  												loss.backward(retain_graph=lowercase__				)
  												optimizer.step()
  
  												samples_so_far += len(lowercase__				)
  
  												if batch_idx % log_interval == 0:
  																		print(
  																		    'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
  																		        epoch + 1							,     lowercase__							,     len(data_loader.dataset				)							,     100 * samples_so_far / len(data_loader.dataset				)							,     loss.item()							,     )				)
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase="cpu"				)    ->  Dict:
  						discriminator.eval()
  						_a     =			0
  						_a     =			0
  						with torch.no_grad():
  												for input_t, target_t in data_loader:
  																		_a ,	_a     =			input_t.to(lowercase__				), target_t.to(lowercase__				)
  																		_a     =			discriminator(lowercase__				)
  																		# sum up batch loss
  																		test_loss += nn.functional.nll_loss(lowercase__							,     lowercase__							,     reduction='sum'				).item()
  																		# get the index of the max log-probability
  																		_a     =			output_t.argmax(dim=1							,     keepdim=lowercase__				)
  																		correct += pred_t.eq(target_t.view_as(lowercase__				)				).sum().item()
  
  						test_loss /= len(data_loader.dataset				)
  
  						print(
  						    'Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
  						        lowercase__							,     lowercase__							,     len(data_loader.dataset				)							,     100.0 * correct / len(data_loader.dataset				)				)				)
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase=False							,     _UpperCAmelCase="cpu"				)    ->  List[str]:
  						_a     =			model.tokenizer.encode(lowercase__				)
  						_a     =			torch.tensor([input_t]							,     dtype=torch.long							,     device=lowercase__				)
  						if cached:
  												_a     =			model.avg_representation(lowercase__				)
  
  						_a     =			model(lowercase__				).data.cpu().numpy().flatten().tolist()
  						print('Input sentence:'							,     lowercase__				)
  						print(
  						    'Predictions:'							,     ', '.join('{}: {:.4f}'.format(lowercase__							,     math.exp(lowercase__				)				) for c, log_prob in zip(lowercase__							,     lowercase__				)				)							,     )
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase=False							,     _UpperCAmelCase="cpu"				)    ->  Union[str, Any]:
  						_a     =			torch.utils.data.DataLoader(dataset=lowercase__							,     batch_size=lowercase__							,     collate_fn=lowercase__				)
  
  						_a     =			[]
  						_a     =			[]
  						for batch_idx, (x, y) in enumerate(tqdm(lowercase__							,     ascii=lowercase__				)				):
  												with torch.no_grad():
  																		_a     =			x.to(lowercase__				)
  																		_a     =			discriminator.avg_representation(lowercase__				).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(lowercase__							,     lowercase__				)							,     batch_size=lowercase__							,     shuffle=lowercase__							,     collate_fn=lowercase__				)
  
  						return data_loader
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=None							,     _UpperCAmelCase="gpt2-medium"							,     _UpperCAmelCase=10							,     _UpperCAmelCase=64							,     _UpperCAmelCase=10							,     _UpperCAmelCase=False							,     _UpperCAmelCase=False							,     _UpperCAmelCase=False							,     )    ->  Dict:
  						_a     =			'cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'
  
  						print('Preprocessing {} dataset...'.format(lowercase__				)				)
  						_a     =			time.time()
  
  						if dataset == "SST":
  												_a     =			['positive', 'negative', 'very positive', 'very negative', 'neutral']
  												_a     =			{c: i for i, c in enumerate(lowercase__				)}
  
  												_a     =			Discriminator(
  												    class_size=len(lowercase__				)							,     pretrained_model=lowercase__							,     cached_mode=lowercase__							,     device=lowercase__				).to(lowercase__				)
  
  												_a     =			torchtext_data.Field()
  												_a     =			torchtext_data.Field(sequential=lowercase__				)
  												_a ,	_a ,	_a     =			datasets.SST.splits(
  												    lowercase__							,     lowercase__							,     fine_grained=lowercase__							,     train_subtrees=lowercase__							,     )
  
  												_a     =			[]
  												_a     =			[]
  												for i in trange(len(lowercase__				)							,     ascii=lowercase__				):
  																		_a     =			TreebankWordDetokenizer().detokenize(vars(train_data[i]				)['text']				)
  																		_a     =			discriminator.tokenizer.encode(lowercase__				)
  																		_a     =			torch.tensor([50256] + seq							,     device=lowercase__							,     dtype=torch.long				)
  																		x.append(lowercase__				)
  																		y.append(classaidx[vars(train_data[i]				)['label']]				)
  												_a     =			Dataset(lowercase__							,     lowercase__				)
  
  												_a     =			[]
  												_a     =			[]
  												for i in trange(len(lowercase__				)							,     ascii=lowercase__				):
  																		_a     =			TreebankWordDetokenizer().detokenize(vars(test_data[i]				)['text']				)
  																		_a     =			discriminator.tokenizer.encode(lowercase__				)
  																		_a     =			torch.tensor([50256] + seq							,     device=lowercase__							,     dtype=torch.long				)
  																		test_x.append(lowercase__				)
  																		test_y.append(classaidx[vars(test_data[i]				)['label']]				)
  												_a     =			Dataset(lowercase__							,     lowercase__				)
  
  												_a     =			{
  												    'class_size': len(lowercase__				),
  												    '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(lowercase__				)}
  
  												_a     =			Discriminator(
  												    class_size=len(lowercase__				)							,     pretrained_model=lowercase__							,     cached_mode=lowercase__							,     device=lowercase__				).to(lowercase__				)
  
  												with open('datasets/clickbait/clickbait_train_prefix.txt'				) as f:
  																		_a     =			[]
  																		for i, line in enumerate(lowercase__				):
  																								try:
  																														data.append(eval(lowercase__				)				)
  																								except Exception:
  																														print('Error evaluating line {}: {}'.format(lowercase__							,     lowercase__				)				)
  																														continue
  												_a     =			[]
  												_a     =			[]
  												with open('datasets/clickbait/clickbait_train_prefix.txt'				) as f:
  																		for i, line in enumerate(tqdm(lowercase__							,     ascii=lowercase__				)				):
  																								try:
  																														_a     =			eval(lowercase__				)
  																														_a     =			discriminator.tokenizer.encode(d['text']				)
  
  																														if len(lowercase__				) < max_length_seq:
  																																				_a     =			torch.tensor([50256] + seq							,     device=lowercase__							,     dtype=torch.long				)
  																														else:
  																																				print('Line {} is longer than maximum length {}'.format(lowercase__							,     lowercase__				)				)
  																																				continue
  																														x.append(lowercase__				)
  																														y.append(d['label']				)
  																								except Exception:
  																														print('Error evaluating / tokenizing line {}, skipping it'.format(lowercase__				)				)
  																														pass
  
  												_a     =			Dataset(lowercase__							,     lowercase__				)
  												_a     =			int(0.9 * len(lowercase__				)				)
  												_a     =			len(lowercase__				) - train_size
  												_a ,	_a     =			torch.utils.data.random_split(lowercase__							,     [train_size, test_size]				)
  
  												_a     =			{
  												    'class_size': len(lowercase__				),
  												    '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(lowercase__				)}
  
  												_a     =			Discriminator(
  												    class_size=len(lowercase__				)							,     pretrained_model=lowercase__							,     cached_mode=lowercase__							,     device=lowercase__				).to(lowercase__				)
  
  												_a     =			[]
  												_a     =			[]
  												with open('datasets/toxic/toxic_train.txt'				) as f:
  																		for i, line in enumerate(tqdm(lowercase__							,     ascii=lowercase__				)				):
  																								try:
  																														_a     =			eval(lowercase__				)
  																														_a     =			discriminator.tokenizer.encode(d['text']				)
  
  																														if len(lowercase__				) < max_length_seq:
  																																				_a     =			torch.tensor([50256] + seq							,     device=lowercase__							,     dtype=torch.long				)
  																														else:
  																																				print('Line {} is longer than maximum length {}'.format(lowercase__							,     lowercase__				)				)
  																																				continue
  																														x.append(lowercase__				)
  																														y.append(int(np.sum(d['label']				) > 0				)				)
  																								except Exception:
  																														print('Error evaluating / tokenizing line {}, skipping it'.format(lowercase__				)				)
  																														pass
  
  												_a     =			Dataset(lowercase__							,     lowercase__				)
  												_a     =			int(0.9 * len(lowercase__				)				)
  												_a     =			len(lowercase__				) - train_size
  												_a ,	_a     =			torch.utils.data.random_split(lowercase__							,     [train_size, test_size]				)
  
  												_a     =			{
  												    'class_size': len(lowercase__				),
  												    '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(lowercase__				) as f:
  																		_a     =			csv.reader(lowercase__							,     delimiter='\t'				)
  																		for row in tqdm(lowercase__							,     ascii=lowercase__				):
  																								if row:
  																														classes.add(row[0]				)
  
  												_a     =			sorted(lowercase__				)
  												_a     =			{c: i for i, c in enumerate(lowercase__				)}
  
  												_a     =			Discriminator(
  												    class_size=len(lowercase__				)							,     pretrained_model=lowercase__							,     cached_mode=lowercase__							,     device=lowercase__				).to(lowercase__				)
  
  												_a     =			[]
  												_a     =			[]
  												with open(lowercase__				) as f:
  																		_a     =			csv.reader(lowercase__							,     delimiter='\t'				)
  																		for i, row in enumerate(tqdm(lowercase__							,     ascii=lowercase__				)				):
  																								if row:
  																														_a     =			row[0]
  																														_a     =			row[1]
  
  																														try:
  																																				_a     =			discriminator.tokenizer.encode(lowercase__				)
  																																				if len(lowercase__				) < max_length_seq:
  																																										_a     =			torch.tensor([50256] + seq							,     device=lowercase__							,     dtype=torch.long				)
  
  																																				else:
  																																										print('Line {} is longer than maximum length {}'.format(lowercase__							,     lowercase__				)				)
  																																										continue
  
  																																				x.append(lowercase__				)
  																																				y.append(classaidx[label]				)
  
  																														except Exception:
  																																				print('Error tokenizing line {}, skipping it'.format(lowercase__				)				)
  																																				pass
  
  												_a     =			Dataset(lowercase__							,     lowercase__				)
  												_a     =			int(0.9 * len(lowercase__				)				)
  												_a     =			len(lowercase__				) - train_size
  												_a ,	_a     =			torch.utils.data.random_split(lowercase__							,     [train_size, test_size]				)
  
  												_a     =			{
  												    'class_size': len(lowercase__				),
  												    'embed_size': discriminator.embed_size,
  												    'pretrained_model': pretrained_model,
  												    'class_vocab': classaidx,
  												    'default_class': 0,
  												}
  
  						_a     =			time.time()
  						print('Preprocessed {} data points'.format(len(lowercase__				) + len(lowercase__				)				)				)
  						print('Data preprocessing took: {:.3f}s'.format(end - start				)				)
  
  						if cached:
  												print('Building representation cache...'				)
  
  												_a     =			time.time()
  
  												_a     =			get_cached_data_loader(lowercase__							,     lowercase__							,     lowercase__							,     shuffle=lowercase__							,     device=lowercase__				)
  
  												_a     =			get_cached_data_loader(lowercase__							,     lowercase__							,     lowercase__							,     device=lowercase__				)
  
  												_a     =			time.time()
  												print('Building representation cache took: {:.3f}s'.format(end - start				)				)
  
  						else:
  												_a     =			torch.utils.data.DataLoader(
  												    dataset=lowercase__							,     batch_size=lowercase__							,     shuffle=lowercase__							,     collate_fn=lowercase__				)
  												_a     =			torch.utils.data.DataLoader(dataset=lowercase__							,     batch_size=lowercase__							,     collate_fn=lowercase__				)
  
  						if save_model:
  												with open('{}_classifier_head_meta.json'.format(lowercase__				)							,     'w'				) as meta_file:
  																		json.dump(lowercase__							,     lowercase__				)
  
  						_a     =			optim.Adam(discriminator.parameters()							,     lr=0.0001				)
  
  						for epoch in range(lowercase__				):
  												_a     =			time.time()
  												print('\nEpoch'							,     epoch + 1				)
  
  												train_epoch(
  												    discriminator=lowercase__							,     data_loader=lowercase__							,     optimizer=lowercase__							,     epoch=lowercase__							,     log_interval=lowercase__							,     device=lowercase__							,     )
  												evaluate_performance(data_loader=lowercase__							,     discriminator=lowercase__							,     device=lowercase__				)
  
  												_a     =			time.time()
  												print('Epoch took: {:.3f}s'.format(end - start				)				)
  
  												print('\nExample prediction'				)
  												predict(lowercase__							,     lowercase__							,     lowercase__							,     cached=lowercase__							,     device=lowercase__				)
  
  												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(lowercase__							,     epoch + 1				)							,     )
  
  
  if __name__ == "__main__":
  			lowercase_								=    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')
  			lowercase_								=    parser.parse_args()
  
  			train_discriminator(**(vars(args)))
  
  
  
style_context: 
  
  
  import argparse
  import json
  from collections import OrderedDict
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowercase_								=    logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  lowercase_								=    []
  for i in range(6):
  		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  		rename_keys.append(
  		    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
  		)
  		rename_keys.append(
  		    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
  		)
  		rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
  		rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
  		rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
  		rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
  		rename_keys.append(
  		    (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
  		)
  		rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
  		rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
  		rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
  		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  		rename_keys.append(
  		    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
  		)
  		rename_keys.append(
  		    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
  		)
  		rename_keys.append(
  		    (
  		        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
  		        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
  		    )
  		)
  		rename_keys.append(
  		    (
  		        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
  		        f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
  		    )
  		)
  		rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
  		rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
  		rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
  		rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
  		rename_keys.append(
  		    (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
  		)
  		rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
  		rename_keys.append(
  		    (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
  		)
  		rename_keys.append(
  		    (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
  		)
  		rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
  		rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))
  
  # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
  rename_keys.extend(
      [
          ('input_proj.weight', 'input_projection.weight'),
          ('input_proj.bias', 'input_projection.bias'),
          ('query_embed.weight', 'query_position_embeddings.weight'),
          ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
          ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
          ('class_embed.weight', 'class_labels_classifier.weight'),
          ('class_embed.bias', 'class_labels_classifier.bias'),
          ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
          ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
          ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
          ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
          ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
          ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
      ]
  )
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Union[str, Any]:
  						_a     =			state_dict.pop(_UpperCAmelCase				)
  						_a     =			val
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[str]:
  						_a     =			OrderedDict()
  						for key, value in state_dict.items():
  												if "backbone.0.body" in key:
  																		_a     =			key.replace('backbone.0.body'							,     'backbone.conv_encoder.model'				)
  																		_a     =			value
  												else:
  																		_a     =			value
  
  						return new_state_dict
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=False				)    ->  Tuple:
  						_a     =			''
  						if is_panoptic:
  												_a     =			'detr.'
  
  						# first: transformer encoder
  						for i in range(6				):
  												# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
  												_a     =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
  												_a     =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""				)
  												# next, add query, keys and values (in that order) to the state dict
  												_a     =			in_proj_weight[:256, :]
  												_a     =			in_proj_bias[:256]
  												_a     =			in_proj_weight[256:512, :]
  												_a     =			in_proj_bias[256:512]
  												_a     =			in_proj_weight[-256:, :]
  												_a     =			in_proj_bias[-256:]
  						# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
  						for i in range(6				):
  												# read in weights + bias of input projection layer of self-attention
  												_a     =			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
  												_a     =			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""				)
  												# next, add query, keys and values (in that order) to the state dict
  												_a     =			in_proj_weight[:256, :]
  												_a     =			in_proj_bias[:256]
  												_a     =			in_proj_weight[256:512, :]
  												_a     =			in_proj_bias[256:512]
  												_a     =			in_proj_weight[-256:, :]
  												_a     =			in_proj_bias[-256:]
  												# read in weights + bias of input projection layer of cross-attention
  												_a     =			state_dict.pop(
  												    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
  												_a     =			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""				)
  												# next, add query, keys and values (in that order) of cross-attention to the state dict
  												_a     =			in_proj_weight_cross_attn[:256, :]
  												_a     =			in_proj_bias_cross_attn[:256]
  												_a     =			in_proj_weight_cross_attn[256:512, :]
  												_a     =			in_proj_bias_cross_attn[256:512]
  												_a     =			in_proj_weight_cross_attn[-256:, :]
  												_a     =			in_proj_bias_cross_attn[-256:]
  
  
  def 							SCREAMING_SNAKE_CASE					(  )    ->  Union[str, Any]:
  						_a     =			'http://images.cocodataset.org/val2017/000000039769.jpg'
  						_a     =			Image.open(requests.get(_UpperCAmelCase							,     stream=_UpperCAmelCase				).raw				)
  
  						return im
  
  
  @torch.no_grad()
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  List[str]:
  						_a     =			DetrConfig()
  						# set backbone and dilation attributes
  						if "resnet101" in model_name:
  												_a     =			'resnet101'
  						if "dc5" in model_name:
  												_a     =			True
  						_a     =			'panoptic' in model_name
  						if is_panoptic:
  												_a     =			250
  						else:
  												_a     =			91
  												_a     =			'huggingface/label-files'
  												_a     =			'coco-detection-id2label.json'
  												_a     =			json.load(open(hf_hub_download(_UpperCAmelCase							,     _UpperCAmelCase							,     repo_type='dataset'				)							,     'r'				)				)
  												_a     =			{int(_UpperCAmelCase				): v for k, v in idalabel.items()}
  												_a     =			idalabel
  												_a     =			{v: k for k, v in idalabel.items()}
  
  						# load image processor
  						_a     =			'coco_panoptic' if is_panoptic else 'coco_detection'
  						_a     =			DetrImageProcessor(format=_UpperCAmelCase				)
  
  						# prepare image
  						_a     =			prepare_img()
  						_a     =			image_processor(images=_UpperCAmelCase							,     return_tensors='pt'				)
  						_a     =			encoding['pixel_values']
  
  						logger.info(f"""Converting model {model_name}..."""				)
  
  						# load original model from torch hub
  						_a     =			torch.hub.load('facebookresearch/detr'							,     _UpperCAmelCase							,     pretrained=_UpperCAmelCase				).eval()
  						_a     =			detr.state_dict()
  						# rename keys
  						for src, dest in rename_keys:
  												if is_panoptic:
  																		_a     =			'detr.' + src
  												rename_key(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  						_a     =			rename_backbone_keys(_UpperCAmelCase				)
  						# query, key and value matrices need special treatment
  						read_in_q_k_v(_UpperCAmelCase							,     is_panoptic=_UpperCAmelCase				)
  						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  						_a     =			'detr.model.' if is_panoptic else 'model.'
  						for key in state_dict.copy().keys():
  												if is_panoptic:
  																		if (
  																		    key.startswith('detr'				)
  																		    and not key.startswith('class_labels_classifier'				)
  																		    and not key.startswith('bbox_predictor'				)
  																		):
  																								_a     =			state_dict.pop(_UpperCAmelCase				)
  																								_a     =			val
  																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
  																								_a     =			state_dict.pop(_UpperCAmelCase				)
  																								_a     =			val
  																		elif key.startswith('bbox_attention'				) or key.startswith('mask_head'				):
  																								continue
  																		else:
  																								_a     =			state_dict.pop(_UpperCAmelCase				)
  																								_a     =			val
  												else:
  																		if not key.startswith('class_labels_classifier'				) and not key.startswith('bbox_predictor'				):
  																								_a     =			state_dict.pop(_UpperCAmelCase				)
  																								_a     =			val
      # finally, create HuggingFace model and load state dict
  						_a     =			DetrForSegmentation(_UpperCAmelCase				) if is_panoptic else DetrForObjectDetection(_UpperCAmelCase				)
  						model.load_state_dict(_UpperCAmelCase				)
  						model.eval()
  						# verify our conversion
  						_a     =			detr(_UpperCAmelCase				)
  						_a     =			model(_UpperCAmelCase				)
  						assert torch.allclose(outputs.logits							,     original_outputs['pred_logits']							,     atol=1e-4				)
  						assert torch.allclose(outputs.pred_boxes							,     original_outputs['pred_boxes']							,     atol=1e-4				)
  						if is_panoptic:
  												assert torch.allclose(outputs.pred_masks							,     original_outputs['pred_masks']							,     atol=1e-4				)
  
  						# Save model and image processor
  						logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""				)
  						Path(_UpperCAmelCase				).mkdir(exist_ok=_UpperCAmelCase				)
  						model.save_pretrained(_UpperCAmelCase				)
  						image_processor.save_pretrained(_UpperCAmelCase				)
  
  
  if __name__ == "__main__":
  		lowercase_								=    argparse.ArgumentParser()
  
  		parser.add_argument(
  		    '--model_name', default='detr_resnet50', type=str, help='Name of the DETR model you\'d like to convert.'
  		)
  		parser.add_argument(
  		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
  		)
  		lowercase_								=    parser.parse_args()
  		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
  
  
  