
"""simple docstring"""


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

import argparse
import csv
import json
import math
import time

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

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
__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       ):





			'''simple docstring'''



			def __init__(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase="gpt2-medium"  ,  __UpperCAmelCase=False  ,  __UpperCAmelCase="cpu"						)				->  Any:
				super().__init__()
				_lowerCAmelCase			=GPTaTokenizer.from_pretrained(__UpperCAmelCase						)
				_lowerCAmelCase			=GPTaLMHeadModel.from_pretrained(__UpperCAmelCase						)
				_lowerCAmelCase			=self.encoder.transformer.config.hidden_size
				_lowerCAmelCase			=ClassificationHead(class_size=__UpperCAmelCase  ,  embed_size=self.embed_size						)
				_lowerCAmelCase			=cached_mode
				_lowerCAmelCase			=device



			def       _lowerCAmelCase	(							self						)				->  Tuple:
				return self.classifier_head



			def       _lowerCAmelCase	(							self						)				->  str:
				for param in self.encoder.parameters():
					_lowerCAmelCase			=False
				self.classifier_head.train()



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



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase						)				->  Optional[Any]:
				if self.cached_mode:
					_lowerCAmelCase			=x.to(self.device						)
				else:
					_lowerCAmelCase			=self.avg_representation(x.to(self.device						)						)

				_lowerCAmelCase			=self.classifier_head(__UpperCAmelCase						)
				_lowerCAmelCase			=nn.functional.log_softmax(__UpperCAmelCase  ,  dim=-1						)

				return probs







class 	lowerCamelCase__      (      data.Dataset       ):





			'''simple docstring'''



			def __init__(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  Tuple:
				_lowerCAmelCase			=X
				_lowerCAmelCase			=y



			def __len__(							self						)				->  Optional[Any]:
				return len(self.X						)



			def __getitem__(							self  ,  __UpperCAmelCase						)				->  Optional[Any]:
				_lowerCAmelCase			={}
				_lowerCAmelCase			=self.X[index]
				_lowerCAmelCase			=self.y[index]
				return data







def 					_lowerCamelCase(__UpperCamelCase )       ->				List[Any]:
	def pad_sequences(__UpperCamelCase ):
		_lowerCAmelCase			=[len(__UpperCamelCase ) for seq in sequences]

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

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

		return padded_sequences, lengths

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

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

	return x_batch, y_batch



def 					_lowerCamelCase(__UpperCamelCase )       ->				Any:
	_lowerCAmelCase			={}
	for key in data[0].keys():
		_lowerCAmelCase			=[d[key] for d in data]

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

	return x_batch, y_batch



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase=0					,				__UpperCamelCase=10					,				__UpperCamelCase="cpu" )       ->				Any:
	_lowerCAmelCase			=0
	discriminator.train_custom()
	for batch_idx, (input_t, target_t) in enumerate(__UpperCamelCase ):
		_lowerCAmelCase		,							_lowerCAmelCase			=input_t.to(__UpperCamelCase ), target_t.to(__UpperCamelCase )

		optimizer.zero_grad()

		_lowerCAmelCase			=discriminator(__UpperCamelCase )
		_lowerCAmelCase			=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 )					,				100 * samples_so_far / len(data_loader.dataset )					,				loss.item()					,				) )



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase="cpu" )       ->				List[Any]:
	discriminator.eval()
	_lowerCAmelCase			=0
	_lowerCAmelCase			=0
	with torch.no_grad():
		for input_t, target_t in data_loader:
			_lowerCAmelCase		,							_lowerCAmelCase			=input_t.to(__UpperCamelCase ), target_t.to(__UpperCamelCase )
			_lowerCAmelCase			=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
			_lowerCAmelCase			=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 )					,				1_00.0 * correct / len(data_loader.dataset ) ) )



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase=False					,				__UpperCamelCase="cpu" )       ->				int:
	_lowerCAmelCase			=model.tokenizer.encode(__UpperCamelCase )
	_lowerCAmelCase			=torch.tensor([input_t]					,				dtype=torch.long					,				device=__UpperCamelCase )
	if cached:
		_lowerCAmelCase			=model.avg_representation(__UpperCamelCase )

	_lowerCAmelCase			=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 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase=False					,				__UpperCamelCase="cpu" )       ->				str:
	_lowerCAmelCase			=torch.utils.data.DataLoader(dataset=__UpperCamelCase					,				batch_size=__UpperCamelCase					,				collate_fn=__UpperCamelCase )

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

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

	return data_loader



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase=None					,				__UpperCamelCase="gpt2-medium"					,				__UpperCamelCase=10					,				__UpperCamelCase=64					,				__UpperCamelCase=10					,				__UpperCamelCase=False					,				__UpperCamelCase=False					,				__UpperCamelCase=False					,				)       ->				Optional[Any]:
	_lowerCAmelCase			="""cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

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

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

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

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

		_lowerCAmelCase			=[]
		_lowerCAmelCase			=[]
		for i in trange(len(__UpperCamelCase )					,				ascii=__UpperCamelCase ):
			_lowerCAmelCase			=TreebankWordDetokenizer().detokenize(vars(train_data[i] )["""text"""] )
			_lowerCAmelCase			=discriminator.tokenizer.encode(__UpperCamelCase )
			_lowerCAmelCase			=torch.tensor([50256] + seq					,				device=__UpperCamelCase					,				dtype=torch.long )
			x.append(__UpperCamelCase )
			y.append(classaidx[vars(train_data[i] )["""label"""]] )
		_lowerCAmelCase			=Dataset(__UpperCamelCase					,				__UpperCamelCase )

		_lowerCAmelCase			=[]
		_lowerCAmelCase			=[]
		for i in trange(len(__UpperCamelCase )					,				ascii=__UpperCamelCase ):
			_lowerCAmelCase			=TreebankWordDetokenizer().detokenize(vars(test_data[i] )["""text"""] )
			_lowerCAmelCase			=discriminator.tokenizer.encode(__UpperCamelCase )
			_lowerCAmelCase			=torch.tensor([50256] + seq					,				device=__UpperCamelCase					,				dtype=torch.long )
			test_x.append(__UpperCamelCase )
			test_y.append(classaidx[vars(test_data[i] )["""label"""]] )
		_lowerCAmelCase			=Dataset(__UpperCamelCase					,				__UpperCamelCase )

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

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

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

					if len(__UpperCamelCase ) < max_length_seq:
						_lowerCAmelCase			=torch.tensor([50256] + 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

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

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

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

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

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

					if len(__UpperCamelCase ) < max_length_seq:
						_lowerCAmelCase			=torch.tensor([50256] + 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

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

		_lowerCAmelCase			={
		    """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.""" )

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

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

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

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

					try:
						_lowerCAmelCase			=discriminator.tokenizer.encode(__UpperCamelCase )
						if len(__UpperCamelCase ) < max_length_seq:
							_lowerCAmelCase			=torch.tensor([50256] + 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

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

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

	_lowerCAmelCase			=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...""" )

		_lowerCAmelCase			=time.time()

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

		_lowerCAmelCase			=get_cached_data_loader(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				device=__UpperCamelCase )

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

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

	_lowerCAmelCase			=optim.Adam(discriminator.parameters()					,				lr=0.00_01 )

	for epoch in range(__UpperCamelCase ):
		_lowerCAmelCase			=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 )

		_lowerCAmelCase			=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__":
			__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

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				Dict:
	_lowerCAmelCase			=UNetaDModel(**__UpperCamelCase )
	_lowerCAmelCase			=checkpoint["""all_modules.0.W"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.0.W"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.1.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.1.bias"""].data

	_lowerCAmelCase			=checkpoint["""all_modules.2.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.2.bias"""].data

	_lowerCAmelCase			=checkpoint["""all_modules.3.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.3.bias"""].data

	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-4]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-3]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-2]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-1]].data

	_lowerCAmelCase			=4

	def set_attention_weights(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase ):
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

	def set_resnet_weights(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase ):
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

		if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
			_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
			_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

	for i, block in enumerate(new_model_architecture.downsample_blocks ):
		_lowerCAmelCase			=hasattr(__UpperCamelCase					,				"""attentions""" )
		for j in range(len(block.resnets ) ):
			set_resnet_weights(block.resnets[j]					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
			if has_attentions:
				set_attention_weights(block.attentions[j]					,				__UpperCamelCase					,				__UpperCamelCase )
				module_index += 1

		if hasattr(__UpperCamelCase					,				"""downsamplers""" ) and block.downsamplers is not None:
			set_resnet_weights(block.resnet_down					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
			module_index += 1

	set_resnet_weights(new_model_architecture.mid_block.resnets[0]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1
	set_attention_weights(new_model_architecture.mid_block.attentions[0]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1
	set_resnet_weights(new_model_architecture.mid_block.resnets[1]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1

	for i, block in enumerate(new_model_architecture.up_blocks ):
		_lowerCAmelCase			=hasattr(__UpperCamelCase					,				"""attentions""" )
		for j in range(len(block.resnets ) ):
			set_resnet_weights(block.resnets[j]					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
		if has_attentions:
			set_attention_weights(
			    block.attentions[0]					,				__UpperCamelCase					,				__UpperCamelCase )  # why can there only be a single attention layer for up?
			module_index += 1

		if hasattr(__UpperCamelCase					,				"""resnet_up""" ) and block.resnet_up is not None:
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
			module_index += 1
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
			module_index += 1
			set_resnet_weights(block.resnet_up					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1

	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
	module_index += 1
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data

	return new_model_architecture.state_dict()


if __name__ == "__main__":
			__A 		= argparse.ArgumentParser()

			parser.add_argument(
			    '--checkpoint_path',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
			    type=str,
			    required=False,
			    help='Path to the checkpoint to convert.',
			)

			parser.add_argument(
			    '--config_file',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
			    type=str,
			    required=False,
			    help='The config json file corresponding to the architecture.',
			)

			parser.add_argument(
			    '--dump_path',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
			    type=str,
			    required=False,
			    help='Path to the output model.',
			)

			__A 		= parser.parse_args()

			__A 		= torch.load(args.checkpoint_path, map_location='cpu')

			with open(args.config_file) as f:
						__A 		= json.loads(f.read())

			__A 		= convert_ncsnpp_checkpoint(
			    checkpoint,
			    config,
			)

			if "sde" in config:
						del config["sde"]

			__A 		= UNetaDModel(**config)
			model.load_state_dict(converted_checkpoint)

			try:
						__A 		= ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

						__A 		= ScoreSdeVePipeline(unet=model, scheduler=scheduler)
						pipe.save_pretrained(args.dump_path)
			except:  # noqa: E722
						model.save_pretrained(args.dump_path)



