


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)
_A							     =       1e-10
_A							     =       """This is incredible! I love it, this is the best chicken I have ever had."""
_A							     =       100

class 			lowerCamelCase_					(    nn.Module    ):





							def __init__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE="gpt2-medium"			,       _SCREAMING_SNAKE_CASE=False			,       _SCREAMING_SNAKE_CASE="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   __magic_name__      (							self    ):
								return self.classifier_head





							def   __magic_name__      (							self    ):
								for param in self.encoder.parameters():
									a_     			=   False
								self.classifier_head.train()





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								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   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								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 			lowerCamelCase_					(    data.Dataset    ):





							def __init__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   X
								a_     			=   y





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





							def __getitem__(							self			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   {}
								a_     			=   self.X[index]
								a_     			=   self.y[index]
								return data







def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Dict	)     ->	List[str]:



	"""simple docstring"""






	def pad_sequences(UpperCamelCase		:      Tuple	):
		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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Any	)     ->	List[Any]:



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



	"""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	)     ,					100 * samples_so_far / len(data_loader.dataset	)     ,					loss.item()     ,					)	)

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



	"""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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Dict     ,					UpperCamelCase		:      Dict     ,					UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Any=False     ,					UpperCamelCase		:      int="cpu"	)     ->	List[str]:



	"""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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[Any]     ,					UpperCamelCase		:      Dict     ,					UpperCamelCase		:      Optional[Any]     ,					UpperCamelCase		:      List[Any]=False     ,					UpperCamelCase		:      Dict="cpu"	)     ->	List[Any]:



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



	"""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_0256] + 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_0256] + 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_0256] + 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_0256] + 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_0256] + 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=0.0_001	)

	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__":
			_A							     =       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')
			_A							     =       parser.parse_args()

			train_discriminator(**(vars(args)))


"""simple docstring"""

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a__			:						Optional[int]							=			logging.get_logger(__name__)





def 					__UpperCamelCase (     a__:			str      )					->      List[str]:




					"""simple docstring"""



					if "backbone.0.body" in orig_key:
										__lowercase        =      orig_key.replace('''backbone.0.body'''			,						'''backbone.conv_encoder.model'''      )
					if "transformer" in orig_key:
										__lowercase        =      orig_key.replace('''transformer.'''			,						''''''      )
					if "norm1" in orig_key:
										if "encoder" in orig_key:
															__lowercase        =      orig_key.replace('''norm1'''			,						'''self_attn_layer_norm'''      )
										else:
															__lowercase        =      orig_key.replace('''norm1'''			,						'''encoder_attn_layer_norm'''      )
					if "norm2" in orig_key:
										if "encoder" in orig_key:
															__lowercase        =      orig_key.replace('''norm2'''			,						'''final_layer_norm'''      )
										else:
															__lowercase        =      orig_key.replace('''norm2'''			,						'''self_attn_layer_norm'''      )
					if "norm3" in orig_key:
										__lowercase        =      orig_key.replace('''norm3'''			,						'''final_layer_norm'''      )
					if "linear1" in orig_key:
										__lowercase        =      orig_key.replace('''linear1'''			,						'''fc1'''      )
					if "linear2" in orig_key:
										__lowercase        =      orig_key.replace('''linear2'''			,						'''fc2'''      )
					if "query_embed" in orig_key:
										__lowercase        =      orig_key.replace('''query_embed'''			,						'''query_position_embeddings'''      )
					if "cross_attn" in orig_key:
										__lowercase        =      orig_key.replace('''cross_attn'''			,						'''encoder_attn'''      )

					return orig_key





def 					__UpperCamelCase (     a__:			Union[str, Any]      )					->      Tuple:




					"""simple docstring"""



					for i in range(6      ):
										# read in weights + bias of input projection layer of self-attention
										__lowercase        =      state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''      )
										__lowercase        =      state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_bias'''      )
										# next, add query, keys and values (in that order) to the state dict
										__lowercase        =      in_proj_weight[:2_56, :]
										__lowercase        =      in_proj_bias[:2_56]
										__lowercase        =      in_proj_weight[2_56:5_12, :]
										__lowercase        =      in_proj_bias[2_56:5_12]
										__lowercase        =      in_proj_weight[-2_56:, :]
										__lowercase        =      in_proj_bias[-2_56:]





def 					__UpperCamelCase (     )					->      str:




					"""simple docstring"""



					__lowercase        =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					__lowercase        =      Image.open(requests.get(a__			,						stream=a__      ).raw      )

					return im





@torch.no_grad()
def 					__UpperCamelCase (     a__:			List[Any]			,						a__:			Union[str, Any]			,						a__:			Optional[int]			,						a__:			Optional[int]			,						a__:			List[Any]			,						a__:			Optional[Any]			,						a__:			str			,						)					->      List[str]:




					"""simple docstring"""



					__lowercase        =      DeformableDetrConfig()
					# set config attributes
					if single_scale:
										__lowercase        =      1
					__lowercase        =      dilation
					__lowercase        =      with_box_refine
					__lowercase        =      two_stage
					# set labels
					__lowercase        =      91
					__lowercase        =      '''huggingface/label-files'''
					__lowercase        =      '''coco-detection-id2label.json'''
					__lowercase        =      json.load(open(cached_download(hf_hub_url(a__			,						a__			,						repo_type='''dataset'''      )      )			,						'''r'''      )      )
					__lowercase        =      {int(a__      ): v for k, v in idalabel.items()}
					__lowercase        =      idalabel
					__lowercase        =      {v: k for k, v in idalabel.items()}

					# load image processor
					__lowercase        =      DeformableDetrImageProcessor(format='''coco_detection'''      )

					# prepare image
					__lowercase        =      prepare_img()
					__lowercase        =      image_processor(images=a__			,						return_tensors='''pt'''      )
					__lowercase        =      encoding['''pixel_values''']

					logger.info('''Converting model...'''      )

					# load original state dict
					__lowercase        =      torch.load(a__			,						map_location='''cpu'''      )['''model''']
					# rename keys
					for key in state_dict.copy().keys():
										__lowercase        =      state_dict.pop(a__      )
										__lowercase        =      val
					# query, key and value matrices need special treatment
					read_in_q_k_v(a__      )
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					__lowercase        =      '''model.'''
					for key in state_dict.copy().keys():
										if not key.startswith('''class_embed'''      ) and not key.startswith('''bbox_embed'''      ):
															__lowercase        =      state_dict.pop(a__      )
															__lowercase        =      val
    # finally, create HuggingFace model and load state dict
					__lowercase        =      DeformableDetrForObjectDetection(a__      )
					model.load_state_dict(a__      )
					model.eval()

					__lowercase        =      '''cuda''' if torch.cuda.is_available() else '''cpu'''
					model.to(a__      )
					# verify our conversion
					__lowercase        =      model(pixel_values.to(a__      )      )

					__lowercase        =      torch.tensor(
					    [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]      )
					__lowercase        =      torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]      )

					if single_scale:
										__lowercase        =      torch.tensor(
										    [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]      )
										__lowercase        =      torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]      )

					if single_scale and dilation:
										__lowercase        =      torch.tensor(
										    [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]      )
										__lowercase        =      torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]      )

					if with_box_refine:
										__lowercase        =      torch.tensor(
										    [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]      )
										__lowercase        =      torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]      )

					if with_box_refine and two_stage:
										__lowercase        =      torch.tensor(
										    [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]      )
										__lowercase        =      torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]      )

					print('''Logits:'''			,						outputs.logits[0, :3, :3]      )

					assert torch.allclose(outputs.logits[0, :3, :3]			,						expected_logits.to(a__      )			,						atol=1e-4      )
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,						expected_boxes.to(a__      )			,						atol=1e-4      )

					print('''Everything ok!'''      )

					# Save model and image processor
					logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''      )
					Path(a__      ).mkdir(exist_ok=a__      )
					model.save_pretrained(a__      )
					image_processor.save_pretrained(a__      )

					# Push to hub
					if push_to_hub:
										__lowercase        =      '''deformable-detr'''
										model_name += "-single-scale" if single_scale else ""
										model_name += "-dc5" if dilation else ""
										model_name += "-with-box-refine" if with_box_refine else ""
										model_name += "-two-stage" if two_stage else ""
										print('''Pushing model to hub...'''      )
										model.push_to_hub(repo_path_or_name=a__			,						organization='''nielsr'''			,						commit_message='''Add model'''      )


if __name__ == "__main__":
							a__			:						str							=			argparse.ArgumentParser()

							parser.add_argument(
							    """--checkpoint_path""",
							    type=str,
							    default="""/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth""",
							    help="""Path to Pytorch checkpoint (.pth file) you'd like to convert.""",
							)
							parser.add_argument("""--single_scale""", action="""store_true""", help="""Whether to set config.num_features_levels = 1.""")
							parser.add_argument("""--dilation""", action="""store_true""", help="""Whether to set config.dilation=True.""")
							parser.add_argument("""--with_box_refine""", action="""store_true""", help="""Whether to set config.with_box_refine=True.""")
							parser.add_argument("""--two_stage""", action="""store_true""", help="""Whether to set config.two_stage=True.""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path to the folder to output PyTorch model.""",
							)
							parser.add_argument(
							    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
							)
							a__			:						Union[str, Any]							=			parser.parse_args()
							convert_deformable_detr_checkpoint(
							    args.checkpoint_path,
							    args.single_scale,
							    args.dilation,
							    args.with_box_refine,
							    args.two_stage,
							    args.pytorch_dump_folder_path,
							    args.push_to_hub,
							)


