
import argparse
import logging
import os
from datetime import datetime

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

from transformers import GPTaLMHeadModel


lowercase						:      Union[str, Any]									= logging.getLogger(__name__)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    int)      ->					Union[str, Any]:






							'''simple docstring'''





							if os.path.exists(_lowerCamelCase):
														if os.path.exists(os.path.join(_lowerCamelCase		,						"config.json")) and os.path.isfile(
														    os.path.join(_lowerCamelCase		,						"config.json")):
																					os.remove(os.path.join(_lowerCamelCase		,						"config.json"))
														if os.path.exists(os.path.join(_lowerCamelCase		,						"pytorch_model.bin")) and os.path.isfile(
														    os.path.join(_lowerCamelCase		,						"pytorch_model.bin")):
																					os.remove(os.path.join(_lowerCamelCase		,						"pytorch_model.bin"))
							else:
														os.makedirs(_lowerCamelCase)
							model.save_pretrained(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int		,						_lowerCamelCase				:    Optional[int]=False)      ->					Any:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      2
							if unlogit:
														__UpperCamelCase			:     Tuple         =      torch.pow(_lowerCamelCase		,						_lowerCamelCase)
							__UpperCamelCase			:     Optional[int]         =      p * torch.log(_lowerCamelCase)
							__UpperCamelCase			:     Optional[int]         =      0
							return -plogp.sum(dim=-1)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					Union[str, Any]:






							'''simple docstring'''





							logger.info("lv, h >\t" + "\t".join(F'{x + 1}' for x in range(len(_lowerCamelCase))))
							for row in range(len(_lowerCamelCase)):
														if tensor.dtype != torch.long:
																					logger.info(F'layer {row + 1}:\t' + "\t".join(F'{x:.5f}' for x in tensor[row].cpu().data))
														else:
																					logger.info(F'layer {row + 1}:\t' + "\t".join(F'{x:d}' for x in tensor[row].cpu().data))




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Any=True		,						_lowerCamelCase				:    Dict=True		,						_lowerCamelCase				:    str=None		,						_lowerCamelCase				:    List[Any]=False)      ->					int:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      model.config.num_hidden_layers, model.config.num_attention_heads
							__UpperCamelCase			:     Optional[Any]         =      torch.zeros(_lowerCamelCase		,						_lowerCamelCase).to(args.device)
							__UpperCamelCase			:     Union[str, Any]         =      torch.zeros(_lowerCamelCase		,						_lowerCamelCase).to(args.device)

							if head_mask is None:
														__UpperCamelCase			:     str         =      torch.ones(_lowerCamelCase		,						_lowerCamelCase).to(args.device)

							head_mask.requires_grad_(requires_grad=_lowerCamelCase)
							# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
							if actually_pruned:
														__UpperCamelCase			:     Optional[Any]         =      None

							__UpperCamelCase			:     Dict         =      0.0
							__UpperCamelCase			:     Optional[Any]         =      0.0
							for step, inputs in enumerate(tqdm(_lowerCamelCase		,						desc="Iteration"		,						disable=args.local_rank not in [-1, 0])):
														__UpperCamelCase			:     int         =      tuple(t.to(args.device) for t in inputs)
														((__UpperCamelCase)     ,		)			:     Union[str, Any]         =      inputs

														# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
														__UpperCamelCase			:     Tuple         =      model(_lowerCamelCase		,						labels=_lowerCamelCase		,						head_mask=_lowerCamelCase)
														#  (loss), lm_logits, presents, (all hidden_states), (attentions)
														__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Optional[Any]         =      (
														    outputs[0],
														    outputs[1],
														    outputs[-1],
														)  # Loss and logits are the first, attention the last
														loss.backward()  # Backpropagate to populate the gradients in the head mask
														total_loss += loss.detach().cpu().numpy()
														if compute_entropy:
																					for layer, attn in enumerate(_lowerCamelCase):
																												__UpperCamelCase			:     Tuple         =      entropy(attn.detach()		,						_lowerCamelCase)
																												attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()

														if compute_importance:
																					head_importance += head_mask.grad.abs().detach()
														tot_tokens += torch.ones_like(_lowerCamelCase).float().detach().sum().data

							# Normalize
							attn_entropy /= tot_tokens
							head_importance /= tot_tokens
							# Layerwise importance normalization
							if not args.dont_normalize_importance_by_layer:
														__UpperCamelCase			:     Optional[int]         =      2
														__UpperCamelCase			:     Dict         =      torch.pow(torch.pow(_lowerCamelCase		,						_lowerCamelCase).sum(-1)		,						1 / exponent)
														head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20

							if not args.dont_normalize_global_importance:
														__UpperCamelCase			:     Optional[Any]         =      (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

							# Print matrices
							if compute_entropy:
														logger.info("Attention entropies")
														print_ad_tensor(_lowerCamelCase)
							if compute_importance:
														logger.info("Head importance scores")
														print_ad_tensor(_lowerCamelCase)
							logger.info("Head ranked by importance scores")
							__UpperCamelCase			:     Any         =      torch.zeros(head_importance.numel()		,						dtype=torch.long		,						device=args.device)
							__UpperCamelCase			:     int         =      torch.arange(
							    head_importance.numel()		,						device=args.device)
							__UpperCamelCase			:     int         =      head_ranks.view_as(_lowerCamelCase)
							print_ad_tensor(_lowerCamelCase)
							return attn_entropy, head_importance, total_loss




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int		,						_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    str)      ->					str:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Tuple         =      compute_heads_importance(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						compute_entropy=_lowerCamelCase)
							__UpperCamelCase			:     int         =      1 / loss  # instead of downsteam score use the LM loss
							logger.info("Pruning: original score: %f, threshold: %f"		,						_lowerCamelCase		,						original_score * args.masking_threshold)

							__UpperCamelCase			:     int         =      torch.ones_like(_lowerCamelCase)
							__UpperCamelCase			:     List[str]         =      max(1		,						int(new_head_mask.numel() * args.masking_amount))

							__UpperCamelCase			:     Dict         =      original_score
							while current_score >= original_score * args.masking_threshold:
														__UpperCamelCase			:     List[str]         =      new_head_mask.clone().detach()  # save current head mask
														# heads from least important to most - keep only not-masked heads
														__UpperCamelCase			:     Optional[int]         =      float("Inf")
														__UpperCamelCase			:     Optional[Any]         =      head_importance.view(-1).sort()[1]

														if len(_lowerCamelCase) <= num_to_mask:
																					print("BREAK BY num_to_mask")
																					break

														# mask heads
														__UpperCamelCase			:     Tuple         =      current_heads_to_mask[:num_to_mask]
														logger.info("Heads to mask: %s"		,						str(current_heads_to_mask.tolist()))
														__UpperCamelCase			:     int         =      new_head_mask.view(-1)
														__UpperCamelCase			:     Optional[Any]         =      0.0
														__UpperCamelCase			:     Optional[Any]         =      new_head_mask.view_as(_lowerCamelCase)
														__UpperCamelCase			:     Any         =      new_head_mask.clone().detach()
														print_ad_tensor(_lowerCamelCase)

														# Compute metric and head importance again
														__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      compute_heads_importance(
														    _lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						compute_entropy=_lowerCamelCase		,						head_mask=_lowerCamelCase)
														__UpperCamelCase			:     Optional[Any]         =      1 / loss
														logger.info(
														    "Masking: current score: %f, remaining heads %d (%.1f percents)"		,						_lowerCamelCase		,						new_head_mask.sum()		,						new_head_mask.sum() / new_head_mask.numel() * 100		,						)

							logger.info("Final head mask")
							print_ad_tensor(_lowerCamelCase)
							np.save(os.path.join(args.output_dir		,						"head_mask.npy")		,						head_mask.detach().cpu().numpy())

							return head_mask




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    Any		,						_lowerCamelCase				:    Tuple)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     int         =      datetime.now()
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     List[str]         =      compute_heads_importance(
							    _lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						compute_entropy=_lowerCamelCase		,						compute_importance=_lowerCamelCase		,						head_mask=_lowerCamelCase)
							__UpperCamelCase			:     int         =      1 / loss
							__UpperCamelCase			:     Optional[Any]         =      datetime.now() - before_time

							__UpperCamelCase			:     Tuple         =      sum(p.numel() for p in model.parameters())
							__UpperCamelCase			:     Any         =      {
							    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_lowerCamelCase))
							}

							for k, v in heads_to_prune.items():
														if isinstance(_lowerCamelCase		,						_lowerCamelCase):
																					__UpperCamelCase			:     Optional[Any]         =      [
																					    v,
																					]

							assert sum(len(_lowerCamelCase) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
							model.prune_heads(_lowerCamelCase)
							__UpperCamelCase			:     Union[str, Any]         =      sum(p.numel() for p in model.parameters())

							__UpperCamelCase			:     Dict         =      datetime.now()
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      compute_heads_importance(
							    _lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						compute_entropy=_lowerCamelCase		,						compute_importance=_lowerCamelCase		,						head_mask=_lowerCamelCase		,						actually_pruned=_lowerCamelCase		,						)

							__UpperCamelCase			:     Optional[int]         =      1 / loss
							__UpperCamelCase			:     Optional[int]         =      datetime.now() - before_time

							logger.info(
							    "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"		,						_lowerCamelCase		,						_lowerCamelCase		,						pruned_num_params / original_num_params * 100		,						)
							logger.info("Pruning: score with masking: %f score with pruning: %f"		,						_lowerCamelCase		,						_lowerCamelCase)
							logger.info("Pruning: speed ratio (original timing / new timing): %f percents"		,						original_time / new_time * 100)
							save_model(_lowerCamelCase		,						args.output_dir)




def 			_SCREAMING_SNAKE_CASE      (	)      ->					Dict:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--data_dir"		,						default=_lowerCamelCase		,						type=_lowerCamelCase		,						required=_lowerCamelCase		,						help="The input data dir. Should contain the .tsv files (or other data files) for the task."		,						)
							parser.add_argument(
							    "--model_name_or_path"		,						default=_lowerCamelCase		,						type=_lowerCamelCase		,						required=_lowerCamelCase		,						help="Path to pretrained model or model identifier from huggingface.co/models"		,						)
							parser.add_argument(
							    "--output_dir"		,						default=_lowerCamelCase		,						type=_lowerCamelCase		,						required=_lowerCamelCase		,						help="The output directory where the model predictions and checkpoints will be written."		,						)

							# Other parameters
							parser.add_argument(
							    "--config_name"		,						default=""		,						type=_lowerCamelCase		,						help="Pretrained config name or path if not the same as model_name_or_path"		,						)
							parser.add_argument(
							    "--tokenizer_name"		,						default=""		,						type=_lowerCamelCase		,						help="Pretrained tokenizer name or path if not the same as model_name_or_path"		,						)
							parser.add_argument(
							    "--cache_dir"		,						default=_lowerCamelCase		,						type=_lowerCamelCase		,						help="Where do you want to store the pre-trained models downloaded from s3"		,						)
							parser.add_argument(
							    "--data_subset"		,						type=_lowerCamelCase		,						default=-1		,						help="If > 0: limit the data to a subset of data_subset instances.")
							parser.add_argument(
							    "--overwrite_output_dir"		,						action="store_true"		,						help="Whether to overwrite data in output directory")
							parser.add_argument(
							    "--overwrite_cache"		,						action="store_true"		,						help="Overwrite the cached training and evaluation sets")

							parser.add_argument(
							    "--dont_normalize_importance_by_layer"		,						action="store_true"		,						help="Don't normalize importance score by layers")
							parser.add_argument(
							    "--dont_normalize_global_importance"		,						action="store_true"		,						help="Don't normalize all importance scores between 0 and 1"		,						)

							parser.add_argument(
							    "--try_masking"		,						action="store_true"		,						help="Whether to try to mask head until a threshold of accuracy.")
							parser.add_argument(
							    "--masking_threshold"		,						default=0.9		,						type=_lowerCamelCase		,						help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."		,						)
							parser.add_argument(
							    "--masking_amount"		,						default=0.1		,						type=_lowerCamelCase		,						help="Amount to heads to masking at each masking step.")
							parser.add_argument("--metric_name"		,						default="acc"		,						type=_lowerCamelCase		,						help="Metric to use for head masking.")

							parser.add_argument(
							    "--max_seq_length"		,						default=128		,						type=_lowerCamelCase		,						help=(
							        "The maximum total input sequence length after WordPiece tokenization. \n"
							        "Sequences longer than this will be truncated, sequences shorter padded."
							    )		,						)
							parser.add_argument("--batch_size"		,						default=1		,						type=_lowerCamelCase		,						help="Batch size.")

							parser.add_argument("--seed"		,						type=_lowerCamelCase		,						default=42)
							parser.add_argument("--local_rank"		,						type=_lowerCamelCase		,						default=-1		,						help="local_rank for distributed training on gpus")
							parser.add_argument("--no_cuda"		,						action="store_true"		,						help="Whether not to use CUDA when available")
							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.")
							__UpperCamelCase			:     Dict         =      parser.parse_args()

							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()

							# Setup devices and distributed training
							if args.local_rank == -1 or args.no_cuda:
														__UpperCamelCase			:     List[str]         =      torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
														__UpperCamelCase			:     List[Any]         =      0 if args.no_cuda else torch.cuda.device_count()
							else:
														torch.cuda.set_device(args.local_rank)
														__UpperCamelCase			:     Optional[Any]         =      torch.device("cuda"		,						args.local_rank)
														__UpperCamelCase			:     Optional[int]         =      1
														torch.distributed.init_process_group(backend="nccl")  # Initializes the distributed backend

							# Setup logging
							logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
							logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device		,						args.n_gpu		,						bool(args.local_rank != -1)))

							__UpperCamelCase			:     List[str]         =      GPTaLMHeadModel.from_pretrained(args.model_name_or_path)

							# Distributed and parallel training
							model.to(args.device)
							if args.local_rank != -1:
														__UpperCamelCase			:     List[Any]         =      nn.parallel.DistributedDataParallel(
														    _lowerCamelCase		,						device_ids=[args.local_rank]		,						output_device=args.local_rank		,						find_unused_parameters=_lowerCamelCase)
							elif args.n_gpu > 1:
														__UpperCamelCase			:     Union[str, Any]         =      nn.DataParallel(_lowerCamelCase)

							# Print/save training arguments
							os.makedirs(args.output_dir		,						exist_ok=_lowerCamelCase)
							torch.save(_lowerCamelCase		,						os.path.join(args.output_dir		,						"run_args.bin"))
							logger.info("Training/evaluation parameters %s"		,						_lowerCamelCase)

							# Prepare dataset
							__UpperCamelCase			:     Union[str, Any]         =      np.concatenate(
							    [
							        np.loadtxt(args.data_dir		,						dtype=np.intaa),
							    ])
							__UpperCamelCase			:     str         =      (torch.from_numpy(_lowerCamelCase),)
							__UpperCamelCase			:     int         =      TensorDataset(*_lowerCamelCase)
							__UpperCamelCase			:     Union[str, Any]         =      RandomSampler(_lowerCamelCase)
							__UpperCamelCase			:     List[str]         =      DataLoader(_lowerCamelCase		,						sampler=_lowerCamelCase		,						batch_size=args.batch_size)

							# Compute head entropy and importance score
							compute_heads_importance(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)

							# Try head masking (set heads to zero until the score goes under a threshole)
							# and head pruning (remove masked heads and see the effect on the network)
							if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
														__UpperCamelCase			:     int         =      mask_heads(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
														prune_heads(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)


if __name__ == "__main__":
		main()

import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowercase						:      Any									= logging.get_logger(__name__)  # pylint: disable=invalid-name




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Tuple:






							'''simple docstring'''





							warnings.warn(
							    "The preprocess method is deprecated and will be removed in a future version. Please"
							    " use VaeImageProcessor.preprocess instead"		,						_lowerCamelCase		,						)
							if isinstance(_lowerCamelCase		,						torch.Tensor):
														return image
							elif isinstance(_lowerCamelCase		,						PIL.Image.Image):
														__UpperCamelCase			:     int         =      [image]

							if isinstance(image[0]		,						PIL.Image.Image):
														__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      image[0].size
														__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														__UpperCamelCase			:     Any         =      [np.array(i.resize((w, h)		,						resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
														__UpperCamelCase			:     Dict         =      np.concatenate(_lowerCamelCase		,						axis=0)
														__UpperCamelCase			:     Any         =      np.array(_lowerCamelCase).astype(np.floataa) / 2_5_5.0
														__UpperCamelCase			:     Union[str, Any]         =      image.transpose(0		,						3		,						1		,						2)
														__UpperCamelCase			:     Optional[int]         =      2.0 * image - 1.0
														__UpperCamelCase			:     List[str]         =      torch.from_numpy(_lowerCamelCase)
							elif isinstance(image[0]		,						torch.Tensor):
														__UpperCamelCase			:     Dict         =      torch.cat(_lowerCamelCase		,						dim=0)
							return image




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Optional[int]:






							'''simple docstring'''





							if isinstance(_lowerCamelCase		,						torch.Tensor):
														return mask
							elif isinstance(_lowerCamelCase		,						PIL.Image.Image):
														__UpperCamelCase			:     Any         =      [mask]

							if isinstance(mask[0]		,						PIL.Image.Image):
														__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      mask[0].size
														__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														__UpperCamelCase			:     List[str]         =      [np.array(m.convert("L").resize((w, h)		,						resample=PIL_INTERPOLATION["nearest"]))[None, :] for m in mask]
														__UpperCamelCase			:     Dict         =      np.concatenate(_lowerCamelCase		,						axis=0)
														__UpperCamelCase			:     Any         =      mask.astype(np.floataa) / 2_5_5.0
														__UpperCamelCase			:     List[str]         =      0
														__UpperCamelCase			:     Dict         =      1
														__UpperCamelCase			:     List[Any]         =      torch.from_numpy(_lowerCamelCase)
							elif isinstance(mask[0]		,						torch.Tensor):
														__UpperCamelCase			:     Optional[Any]         =      torch.cat(_lowerCamelCase		,						dim=0)
							return mask




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  42
	_A			=  42







	def __init__(      self	:Optional[Any]  ,     a	:str  ,     a	:List[str]		)	->				Union[str, Any]:
								super().__init__()
								self.register_modules(unet=a  ,     scheduler=a		)







	@torch.no_grad()
	def __call__(      self	:Union[str, Any]  ,     a	:Union[torch.Tensor, PIL.Image.Image]  ,     a	:Union[torch.Tensor, PIL.Image.Image]  ,     a	:int = 2_5_0  ,     a	:float = 0.0  ,     a	:int = 1_0  ,     a	:int = 1_0  ,     a	:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     a	:Optional[str] = "pil"  ,     a	:bool = True  ,     )	->				Union[ImagePipelineOutput, Tuple]:
								__UpperCamelCase			:     Tuple         =      image

								__UpperCamelCase			:     Optional[Any]         =      _preprocess_image(a		)
								__UpperCamelCase			:     Optional[int]         =      original_image.to(device=self.device  ,     dtype=self.unet.dtype		)
								__UpperCamelCase			:     Dict         =      _preprocess_mask(a		)
								__UpperCamelCase			:     Any         =      mask_image.to(device=self.device  ,     dtype=self.unet.dtype		)

								__UpperCamelCase			:     str         =      original_image.shape[0]

								# sample gaussian noise to begin the loop
								if isinstance(a  ,     a		) and len(a		) != batch_size:
															raise ValueError(
															    f'You have passed a list of generators of length {len(a		)}, but requested an effective batch'
															    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'		)

								__UpperCamelCase			:     List[str]         =      original_image.shape
								__UpperCamelCase			:     List[Any]         =      randn_tensor(a  ,     generator=a  ,     device=self.device  ,     dtype=self.unet.dtype		)

								# set step values
								self.scheduler.set_timesteps(a  ,     a  ,     a  ,     self.device		)
								__UpperCamelCase			:     Tuple         =      eta

								__UpperCamelCase			:     List[str]         =      self.scheduler.timesteps[0] + 1
								__UpperCamelCase			:     str         =      generator[0] if isinstance(a  ,     a		) else generator
								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															if t < t_last:
																						# predict the noise residual
																						__UpperCamelCase			:     Union[str, Any]         =      self.unet(a  ,     a		).sample
																						# compute previous image: x_t -> x_t-1
																						__UpperCamelCase			:     Tuple         =      self.scheduler.step(a  ,     a  ,     a  ,     a  ,     a  ,     a		).prev_sample

															else:
																						# compute the reverse: x_t-1 -> x_t
																						__UpperCamelCase			:     List[str]         =      self.scheduler.undo_step(a  ,     a  ,     a		)
															__UpperCamelCase			:     Dict         =      t

								__UpperCamelCase			:     Union[str, Any]         =      (image / 2 + 0.5).clamp(0  ,     1		)
								__UpperCamelCase			:     Tuple         =      image.cpu().permute(0  ,     2  ,     3  ,     1		).numpy()
								if output_type == "pil":
															__UpperCamelCase			:     str         =      self.numpy_to_pil(a		)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a		)