





'''simple docstring'''




import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__UpperCAmelCase            =       logging.getLogger(__name__)



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Optional[Any]		=			np.argmax(lowerCamelCase_			,			axis=1	)
		return np.sum(outputs == labels	)



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		with open(lowerCamelCase_			,			encoding="""utf_8"""	) as f:
				SCREAMING_SNAKE_CASE :				List[Any]		=			csv.reader(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				Any		=			[]
				next(lowerCamelCase_	)  # skip the first line
				for line in tqdm(lowerCamelCase_	):
						output.append((""" """.join(line[1:5]	), line[5], line[6], int(line[-1]	) - 1)	)
		return output



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				str		=			[]
		for dataset in encoded_datasets:
				SCREAMING_SNAKE_CASE :				Any		=			len(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				List[str]		=			np.zeros((n_batch, 2, input_len)			,			dtype=np.intaa	)
				SCREAMING_SNAKE_CASE :				List[str]		=			np.zeros((n_batch, 2)			,			dtype=np.intaa	)
				SCREAMING_SNAKE_CASE :				int		=			np.full((n_batch, 2, input_len)			,			fill_value=-1_00			,			dtype=np.intaa	)
				SCREAMING_SNAKE_CASE :				Dict		=			np.zeros((n_batch,)			,			dtype=np.intaa	)
				for (
				    i,
				    (story, conta, conta, mc_label),
				) in enumerate(lowerCamelCase_	):
						SCREAMING_SNAKE_CASE :				Tuple		=			[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
						SCREAMING_SNAKE_CASE :				Any		=			[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
						SCREAMING_SNAKE_CASE :				Tuple		=			with_conta
						SCREAMING_SNAKE_CASE :				str		=			with_conta
						SCREAMING_SNAKE_CASE :				int		=			len(lowerCamelCase_	) - 1
						SCREAMING_SNAKE_CASE :				Optional[Any]		=			len(lowerCamelCase_	) - 1
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			with_conta
						SCREAMING_SNAKE_CASE :				List[str]		=			with_conta
						SCREAMING_SNAKE_CASE :				str		=			mc_label
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			(input_ids, mc_token_ids, lm_labels, mc_labels)
				tensor_datasets.append(tuple(torch.tensor(lowerCamelCase_	) for t in all_inputs	)	)
		return tensor_datasets



def 		__A		(  ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[Any]		=			argparse.ArgumentParser()
		parser.add_argument("""--model_name"""			,			type=lowerCamelCase_			,			default="""openai-gpt"""			,			help="""pretrained model name"""	)
		parser.add_argument("""--do_train"""			,			action="""store_true"""			,			help="""Whether to run training."""	)
		parser.add_argument("""--do_eval"""			,			action="""store_true"""			,			help="""Whether to run eval on the dev set."""	)
		parser.add_argument(
		    """--output_dir"""			,			default=lowerCamelCase_			,			type=lowerCamelCase_			,			required=lowerCamelCase_			,			help="""The output directory where the model predictions and checkpoints will be written."""			,			)
		parser.add_argument("""--train_dataset"""			,			type=lowerCamelCase_			,			default=""""""	)
		parser.add_argument("""--eval_dataset"""			,			type=lowerCamelCase_			,			default=""""""	)
		parser.add_argument("""--seed"""			,			type=lowerCamelCase_			,			default=42	)
		parser.add_argument("""--num_train_epochs"""			,			type=lowerCamelCase_			,			default=3	)
		parser.add_argument("""--train_batch_size"""			,			type=lowerCamelCase_			,			default=8	)
		parser.add_argument("""--eval_batch_size"""			,			type=lowerCamelCase_			,			default=16	)
		parser.add_argument("""--adam_epsilon"""			,			default=1E-8			,			type=lowerCamelCase_			,			help="""Epsilon for Adam optimizer."""	)
		parser.add_argument("""--max_grad_norm"""			,			type=lowerCamelCase_			,			default=1	)
		parser.add_argument(
		    """--max_steps"""			,			default=-1			,			type=lowerCamelCase_			,			help=(
		        """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
		    )			,			)
		parser.add_argument(
		    """--gradient_accumulation_steps"""			,			type=lowerCamelCase_			,			default=1			,			help="""Number of updates steps to accumulate before                        performing a backward/update pass."""			,			)
		parser.add_argument("""--learning_rate"""			,			type=lowerCamelCase_			,			default=6.25E-5	)
		parser.add_argument("""--warmup_steps"""			,			default=0			,			type=lowerCamelCase_			,			help="""Linear warmup over warmup_steps."""	)
		parser.add_argument("""--lr_schedule"""			,			type=lowerCamelCase_			,			default="""warmup_linear"""	)
		parser.add_argument("""--weight_decay"""			,			type=lowerCamelCase_			,			default=0.01	)
		parser.add_argument("""--lm_coef"""			,			type=lowerCamelCase_			,			default=0.9	)
		parser.add_argument("""--n_valid"""			,			type=lowerCamelCase_			,			default=3_74	)

		parser.add_argument("""--server_ip"""			,			type=lowerCamelCase_			,			default=""""""			,			help="""Can be used for distant debugging."""	)
		parser.add_argument("""--server_port"""			,			type=lowerCamelCase_			,			default=""""""			,			help="""Can be used for distant debugging."""	)
		SCREAMING_SNAKE_CASE :				List[Any]		=			parser.parse_args()
		print(lowerCamelCase_	)

		if args.server_ip and args.server_port:
				# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
				import ptvsd

				print("""Waiting for debugger attach"""	)
				ptvsd.enable_attach(address=(args.server_ip, args.server_port)			,			redirect_output=lowerCamelCase_	)
				ptvsd.wait_for_attach()

		random.seed(args.seed	)
		np.random.seed(args.seed	)
		torch.manual_seed(args.seed	)
		torch.cuda.manual_seed_all(args.seed	)

		SCREAMING_SNAKE_CASE :				Dict		=			torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""	)
		SCREAMING_SNAKE_CASE :				List[str]		=			torch.cuda.device_count()
		logger.info("""device: {}, n_gpu {}""".format(lowerCamelCase_			,			lowerCamelCase_	)	)

		if not args.do_train and not args.do_eval:
				raise ValueError("""At least one of `do_train` or `do_eval` must be True."""	)

		if not os.path.exists(args.output_dir	):
				os.makedirs(args.output_dir	)

		# Load tokenizer and model
		# This loading functions also add new tokens and embeddings called `special tokens`
		# These new embeddings will be fine-tuned on the RocStories dataset
		SCREAMING_SNAKE_CASE :				int		=			["""_start_""", """_delimiter_""", """_classify_"""]
		SCREAMING_SNAKE_CASE :				List[str]		=			OpenAIGPTTokenizer.from_pretrained(args.model_name	)
		tokenizer.add_tokens(lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Any		=			tokenizer.convert_tokens_to_ids(lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name	)
		model.resize_token_embeddings(len(lowerCamelCase_	)	)
		model.to(lowerCamelCase_	)

		# Load and encode the datasets
		def tokenize_and_encode(lowerCamelCase_	):
				if isinstance(lowerCamelCase_			,			lowerCamelCase_	):
						return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(lowerCamelCase_	)	)
				elif isinstance(lowerCamelCase_			,			lowerCamelCase_	):
						return obj
				return [tokenize_and_encode(lowerCamelCase_	) for o in obj]

		logger.info("""Encoding dataset..."""	)
		SCREAMING_SNAKE_CASE :				Optional[int]		=			load_rocstories_dataset(args.train_dataset	)
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			load_rocstories_dataset(args.eval_dataset	)
		SCREAMING_SNAKE_CASE :				List[Any]		=			(train_dataset, eval_dataset)
		SCREAMING_SNAKE_CASE :				str		=			tokenize_and_encode(lowerCamelCase_	)

		# Compute the max input length for the Transformer
		SCREAMING_SNAKE_CASE :				int		=			model.config.n_positions // 2 - 2
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			max(
		    len(story[:max_length]	) + max(len(conta[:max_length]	)			,			len(conta[:max_length]	)	) + 3
		    for dataset in encoded_datasets
		    for story, conta, conta, _ in dataset	)
		SCREAMING_SNAKE_CASE :				int		=			min(lowerCamelCase_			,			model.config.n_positions	)  # Max size of input for the pre-trained model

		# Prepare inputs tensors and dataloaders
		SCREAMING_SNAKE_CASE :				Optional[int]		=			pre_process_datasets(lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			*lowerCamelCase_	)
		SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				str		=			tensor_datasets[0], tensor_datasets[1]

		SCREAMING_SNAKE_CASE :				Optional[int]		=			TensorDataset(*lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Dict		=			RandomSampler(lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Optional[int]		=			DataLoader(lowerCamelCase_			,			sampler=lowerCamelCase_			,			batch_size=args.train_batch_size	)

		SCREAMING_SNAKE_CASE :				Optional[int]		=			TensorDataset(*lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			SequentialSampler(lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			DataLoader(lowerCamelCase_			,			sampler=lowerCamelCase_			,			batch_size=args.eval_batch_size	)

		# Prepare optimizer
		if args.do_train:
				if args.max_steps > 0:
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			args.max_steps
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			args.max_steps // (len(lowerCamelCase_	) // args.gradient_accumulation_steps) + 1
				else:
						SCREAMING_SNAKE_CASE :				Any		=			len(lowerCamelCase_	) // args.gradient_accumulation_steps * args.num_train_epochs

				SCREAMING_SNAKE_CASE :				List[str]		=			list(model.named_parameters()	)
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			[
				    {
				        """params""": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay	)],
				        """weight_decay""": args.weight_decay,
				    },
				    {"""params""": [p for n, p in param_optimizer if any(nd in n for nd in no_decay	)], """weight_decay""": 0.0},
				]
				SCREAMING_SNAKE_CASE :				str		=			AdamW(lowerCamelCase_			,			lr=args.learning_rate			,			eps=args.adam_epsilon	)
				SCREAMING_SNAKE_CASE :				int		=			get_linear_schedule_with_warmup(
				    lowerCamelCase_			,			num_warmup_steps=args.warmup_steps			,			num_training_steps=lowerCamelCase_	)

		if args.do_train:
				SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Dict		=			0, 0, None
				model.train()
				for _ in trange(int(args.num_train_epochs	)			,			desc="""Epoch"""	):
						SCREAMING_SNAKE_CASE :				Any		=			0
						SCREAMING_SNAKE_CASE :				Optional[Any]		=			0
						SCREAMING_SNAKE_CASE :				Dict		=			tqdm(lowerCamelCase_			,			desc="""Training"""	)
						for step, batch in enumerate(lowerCamelCase_	):
								SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tuple(t.to(lowerCamelCase_	) for t in batch	)
								SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				int		=			batch
								SCREAMING_SNAKE_CASE :				str		=			model(lowerCamelCase_			,			mc_token_ids=lowerCamelCase_			,			lm_labels=lowerCamelCase_			,			mc_labels=lowerCamelCase_	)
								SCREAMING_SNAKE_CASE :				List[Any]		=			args.lm_coef * losses[0] + losses[1]
								loss.backward()
								optimizer.step()
								scheduler.step()
								optimizer.zero_grad()
								tr_loss += loss.item()
								SCREAMING_SNAKE_CASE :				Union[str, Any]		=			(
								    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
								)
								nb_tr_steps += 1
								SCREAMING_SNAKE_CASE :				str		=			"""Training loss: {:.2e} lr: {:.2e}""".format(lowerCamelCase_			,			scheduler.get_lr()[0]	)

    # Save a trained model
		if args.do_train:
				# Save a trained model, configuration and tokenizer
				SCREAMING_SNAKE_CASE :				Optional[int]		=			model.module if hasattr(lowerCamelCase_			,			"""module"""	) else model  # Only save the model itself

				# If we save using the predefined names, we can load using `from_pretrained`
				SCREAMING_SNAKE_CASE :				Any		=			os.path.join(args.output_dir			,			lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				List[str]		=			os.path.join(args.output_dir			,			lowerCamelCase_	)

				torch.save(model_to_save.state_dict()			,			lowerCamelCase_	)
				model_to_save.config.to_json_file(lowerCamelCase_	)
				tokenizer.save_vocabulary(args.output_dir	)

				# Load a trained model and vocabulary that you have fine-tuned
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir	)
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			OpenAIGPTTokenizer.from_pretrained(args.output_dir	)
				model.to(lowerCamelCase_	)

		if args.do_eval:
				model.eval()
				SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Dict		=			0, 0
				SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				str		=			0, 0
				for batch in tqdm(lowerCamelCase_			,			desc="""Evaluating"""	):
						SCREAMING_SNAKE_CASE :				Any		=			tuple(t.to(lowerCamelCase_	) for t in batch	)
						SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Any		=			batch
						with torch.no_grad():
								SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				List[str]		=			model(
								    lowerCamelCase_			,			mc_token_ids=lowerCamelCase_			,			lm_labels=lowerCamelCase_			,			mc_labels=lowerCamelCase_	)

						SCREAMING_SNAKE_CASE :				List[Any]		=			mc_logits.detach().cpu().numpy()
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			mc_labels.to("""cpu"""	).numpy()
						SCREAMING_SNAKE_CASE :				List[str]		=			accuracy(lowerCamelCase_			,			lowerCamelCase_	)

						eval_loss += mc_loss.mean().item()
						eval_accuracy += tmp_eval_accuracy

						nb_eval_examples += input_ids.size(0	)
						nb_eval_steps += 1

				SCREAMING_SNAKE_CASE :				List[Any]		=			eval_loss / nb_eval_steps
				SCREAMING_SNAKE_CASE :				Tuple		=			eval_accuracy / nb_eval_examples
				SCREAMING_SNAKE_CASE :				Any		=			tr_loss / nb_tr_steps if args.do_train else None
				SCREAMING_SNAKE_CASE :				int		=			{"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}

				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			os.path.join(args.output_dir			,			"""eval_results.txt"""	)
				with open(lowerCamelCase_			,			"""w"""	) as writer:
						logger.info("""***** Eval results *****"""	)
						for key in sorted(result.keys()	):
								logger.info("""  %s = %s"""			,			lowerCamelCase_			,			str(result[key]	)	)
								writer.write("""%s = %s\n""" % (key, str(result[key]	))	)


if __name__ == "__main__":
							main()










'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	(UnCLIPScheduler,)


					def lowerCamelCase_			(						self				:			List[str]      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			{
							    """num_train_timesteps""": 10_00,
							    """variance_type""": """fixed_small_log""",
							    """clip_sample""": True,
							    """clip_sample_range""": 1.0,
							    """prediction_type""": """epsilon""",
							}

							config.update(**lowerCamelCase_						)
							return config


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for timesteps in [1, 5, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							for variance in ["fixed_small_log", "learned_range"]:
									self.check_over_configs(variance_type=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							for clip_sample_range in [1, 5, 10, 20]:
									self.check_over_configs(clip_sample_range=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(prediction_type=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for time_step in [0, 5_00, 9_99]:
									for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
											if prev_timestep is not None and prev_timestep >= time_step:
													continue

											self.check_over_forward(time_step=lowerCamelCase_      ,						prev_timestep=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config(variance_type="""fixed_small_log"""						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler_class(**lowerCamelCase_						)

							assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.0000e-10						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87						) - 0.0_549_625						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.9_994_987						)						) < 1e-5


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.get_scheduler_config(variance_type="""learned_range"""						)
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[str]		=			0.5

							assert scheduler._get_variance(1      ,						predicted_variance=lowerCamelCase_						) - -10.1_712_790 < 1e-5
							assert scheduler._get_variance(4_87      ,						predicted_variance=lowerCamelCase_						) - -5.7_998_052 < 1e-5
							assert scheduler._get_variance(9_99      ,						predicted_variance=lowerCamelCase_						) - -0.0_010_011 < 1e-5


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[Any]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler.timesteps

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_sample_deter
							SCREAMING_SNAKE_CASE :				str		=			torch.manual_seed(0						)

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				List[str]		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE :				Optional[int]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						generator=lowerCamelCase_						).prev_sample

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Optional[int]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				int		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 252.2_682_495						) < 1e-2
							assert abs(result_mean.item() - 0.3_284_743						) < 1e-3


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							scheduler.set_timesteps(25						)

							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler.timesteps

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_sample_deter
							SCREAMING_SNAKE_CASE :				Any		=			torch.manual_seed(0						)

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				int		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									if i + 1 == timesteps.shape[0]:
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			None
									else:
											SCREAMING_SNAKE_CASE :				int		=			timesteps[i + 1]

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE :				int		=			scheduler.step(
									    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						prev_timestep=lowerCamelCase_      ,						generator=lowerCamelCase_						).prev_sample

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 258.2_044_983						) < 1e-2
							assert abs(result_mean.item() - 0.3_362_038						) < 1e-3


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							pass



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							pass



