




"""simple docstring"""

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, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__A							=     logging.getLogger(__name__)



def 					UpperCamelCase__       (						lowercase__					:       Any				):
		snake_case						:  str        =			p * torch.log(_lowerCamelCase				)
		snake_case						:  int        =			0
		return -plogp.sum(dim=-1				)



def 					UpperCamelCase__       (						lowercase__					:       Optional[Any]				):
		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 					UpperCamelCase__       (						lowercase__					:       Tuple    ,       lowercase__					:       List[Any]    ,       lowercase__					:       int    ,       lowercase__					:       Union[str, Any]=True    ,       lowercase__					:       int=True    ,       lowercase__					:       int=None    ,       lowercase__					:       List[Any]=False				):
		snake_case						:  int        =			model.config.num_hidden_layers, model.config.num_attention_heads
		snake_case						:  Optional[int]        =			torch.zeros(_lowerCamelCase    ,       _lowerCamelCase				).to(args.device				)
		snake_case						:  Any        =			torch.zeros(_lowerCamelCase    ,       _lowerCamelCase				).to(args.device				)

		if head_mask is None:
				snake_case						:  Optional[Any]        =			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:
				snake_case						:  str        =			None

		snake_case						:  List[str]        =			None
		snake_case						:  Any        =			None
		snake_case						:  Union[str, Any]        =			0.0

		for step, inputs in enumerate(tqdm(_lowerCamelCase    ,       desc="Iteration"    ,       disable=args.local_rank not in [-1, 0]				)				):
				for k, v in inputs.items():
						snake_case						:  Optional[int]        =			v.to(args.device				)

				# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
				snake_case						:  Tuple        =			model(**_lowerCamelCase    ,       head_mask=_lowerCamelCase				)
				snake_case						:  Optional[int]        =			(
				    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

				if compute_entropy:
						for layer, attn in enumerate(_lowerCamelCase				):
								snake_case						:  str        =			entropy(attn.detach()				) * inputs["""attention_mask"""].float().unsqueeze(1				)
								attn_entropy[layer] += masked_entropy.sum(-1				).sum(0				).detach()

				if compute_importance:
						head_importance += head_mask.grad.abs().detach()

				# Also store our logits/labels if we want to compute metrics afterwards
				if preds is None:
						snake_case						:  Optional[int]        =			logits.detach().cpu().numpy()
						snake_case						:  Tuple        =			inputs["""labels"""].detach().cpu().numpy()
				else:
						snake_case						:  int        =			np.append(_lowerCamelCase    ,       logits.detach().cpu().numpy()    ,       axis=0				)
						snake_case						:  int        =			np.append(_lowerCamelCase    ,       inputs["labels"].detach().cpu().numpy()    ,       axis=0				)

				tot_tokens += inputs["attention_mask"].float().detach().sum().data

		# Normalize
		attn_entropy /= tot_tokens
		head_importance /= tot_tokens
		# Layerwise importance normalization
		if not args.dont_normalize_importance_by_layer:
				snake_case						:  int        =			2
				snake_case						:  Union[str, Any]        =			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:
				snake_case						:  str        =			(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

		# Print/save matrices
		np.save(os.path.join(args.output_dir    ,       "attn_entropy.npy"				)    ,       attn_entropy.detach().cpu().numpy()				)
		np.save(os.path.join(args.output_dir    ,       "head_importance.npy"				)    ,       head_importance.detach().cpu().numpy()				)

		logger.info("Attention entropies"				)
		print_ad_tensor(_lowerCamelCase				)
		logger.info("Head importance scores"				)
		print_ad_tensor(_lowerCamelCase				)
		logger.info("Head ranked by importance scores"				)
		snake_case						:  List[Any]        =			torch.zeros(head_importance.numel()    ,       dtype=torch.long    ,       device=args.device				)
		snake_case						:  Union[str, Any]        =			torch.arange(
		    head_importance.numel()    ,       device=args.device				)
		snake_case						:  Union[str, Any]        =			head_ranks.view_as(_lowerCamelCase				)
		print_ad_tensor(_lowerCamelCase				)

		return attn_entropy, head_importance, preds, labels



def 					UpperCamelCase__       (						lowercase__					:       List[str]    ,       lowercase__					:       Union[str, Any]    ,       lowercase__					:       Union[str, Any]				):
		snake_case						:  Optional[Any]        =			compute_heads_importance(_lowerCamelCase    ,       _lowerCamelCase    ,       _lowerCamelCase    ,       compute_entropy=_lowerCamelCase				)
		snake_case						:  Optional[Any]        =			np.argmax(_lowerCamelCase    ,       axis=1				) if args.output_mode == """classification""" else np.squeeze(_lowerCamelCase				)
		snake_case						:  List[Any]        =			glue_compute_metrics(args.task_name    ,       _lowerCamelCase    ,       _lowerCamelCase				)[args.metric_name]
		logger.info("Pruning: original score: %f, threshold: %f"    ,       _lowerCamelCase    ,       original_score * args.masking_threshold				)

		snake_case						:  Any        =			torch.ones_like(_lowerCamelCase				)
		snake_case						:  int        =			max(1    ,       int(new_head_mask.numel() * args.masking_amount				)				)

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

				if len(_lowerCamelCase				) <= num_to_mask:
						break

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

				# Compute metric and head importance again
				snake_case						:  str        =			compute_heads_importance(
				    _lowerCamelCase    ,       _lowerCamelCase    ,       _lowerCamelCase    ,       compute_entropy=_lowerCamelCase    ,       head_mask=_lowerCamelCase				)
				snake_case						:  Any        =			np.argmax(_lowerCamelCase    ,       axis=1				) if args.output_mode == """classification""" else np.squeeze(_lowerCamelCase				)
				snake_case						:  Optional[int]        =			glue_compute_metrics(args.task_name    ,       _lowerCamelCase    ,       _lowerCamelCase				)[args.metric_name]
				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 					UpperCamelCase__       (						lowercase__					:       Any    ,       lowercase__					:       Tuple    ,       lowercase__					:       Union[str, Any]    ,       lowercase__					:       Tuple				):
		snake_case						:  Optional[int]        =			datetime.now()
		snake_case						:  int        =			compute_heads_importance(
		    _lowerCamelCase    ,       _lowerCamelCase    ,       _lowerCamelCase    ,       compute_entropy=_lowerCamelCase    ,       compute_importance=_lowerCamelCase    ,       head_mask=_lowerCamelCase				)
		snake_case						:  str        =			np.argmax(_lowerCamelCase    ,       axis=1				) if args.output_mode == """classification""" else np.squeeze(_lowerCamelCase				)
		snake_case						:  Any        =			glue_compute_metrics(args.task_name    ,       _lowerCamelCase    ,       _lowerCamelCase				)[args.metric_name]
		snake_case						:  List[str]        =			datetime.now() - before_time

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

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

		snake_case						:  Optional[Any]        =			datetime.now()
		snake_case						:  List[str]        =			compute_heads_importance(
		    _lowerCamelCase    ,       _lowerCamelCase    ,       _lowerCamelCase    ,       compute_entropy=_lowerCamelCase    ,       compute_importance=_lowerCamelCase    ,       head_mask=_lowerCamelCase    ,       actually_pruned=_lowerCamelCase    ,       )
		snake_case						:  int        =			np.argmax(_lowerCamelCase    ,       axis=1				) if args.output_mode == """classification""" else np.squeeze(_lowerCamelCase				)
		snake_case						:  List[Any]        =			glue_compute_metrics(args.task_name    ,       _lowerCamelCase    ,       _lowerCamelCase				)[args.metric_name]
		snake_case						:  List[str]        =			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 (new timing / original timing): %f percents"    ,       original_time / new_time * 100				)



def 					UpperCamelCase__       (						):
		snake_case						:  List[str]        =			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(
		    "--task_name"    ,       default=_lowerCamelCase    ,       type=_lowerCamelCase    ,       required=_lowerCamelCase    ,       help="The name of the task to train selected in the list: " + ", ".join(glue_processors.keys()				)    ,       )
		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 huggingface.co"    ,       )
		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."				)
		snake_case						:  Union[str, Any]        =			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:
				snake_case						:  List[Any]        =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"				)
				snake_case						:  Union[str, Any]        =			0 if args.no_cuda else torch.cuda.device_count()
		else:
				torch.cuda.set_device(args.local_rank				)
				snake_case						:  Optional[int]        =			torch.device("cuda"    ,       args.local_rank				)
				snake_case						:  str        =			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				)				)				)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(args.local_rank				):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

		# Set seeds
		set_seed(args.seed				)

		# Prepare GLUE task
		snake_case						:  List[str]        =			args.task_name.lower()
		if args.task_name not in glue_processors:
				raise ValueError("Task not found: %s" % (args.task_name)				)
		snake_case						:  Union[str, Any]        =			glue_processors[args.task_name]()
		snake_case						:  Optional[int]        =			glue_output_modes[args.task_name]
		snake_case						:  Optional[int]        =			processor.get_labels()
		snake_case						:  Tuple        =			len(_lowerCamelCase				)

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		snake_case						:  List[str]        =			AutoConfig.from_pretrained(
		    args.config_name if args.config_name else args.model_name_or_path    ,       num_labels=_lowerCamelCase    ,       finetuning_task=args.task_name    ,       output_attentions=_lowerCamelCase    ,       cache_dir=args.cache_dir    ,       )
		snake_case						:  List[str]        =			AutoTokenizer.from_pretrained(
		    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path    ,       cache_dir=args.cache_dir    ,       )
		snake_case						:  Any        =			AutoModelForSequenceClassification.from_pretrained(
		    args.model_name_or_path    ,       from_tf=bool(".ckpt" in args.model_name_or_path				)    ,       config=_lowerCamelCase    ,       cache_dir=args.cache_dir    ,       )

		# Distributed and parallel training
		model.to(args.device				)
		if args.local_rank != -1:
				snake_case						:  Optional[Any]        =			nn.parallel.DistributedDataParallel(
				    _lowerCamelCase    ,       device_ids=[args.local_rank]    ,       output_device=args.local_rank    ,       find_unused_parameters=_lowerCamelCase				)
		elif args.n_gpu > 1:
				snake_case						:  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 for the GLUE task
		snake_case						:  List[str]        =			GlueDataset(_lowerCamelCase    ,       tokenizer=_lowerCamelCase    ,       mode="dev"				)
		if args.data_subset > 0:
				snake_case						:  Optional[int]        =			Subset(_lowerCamelCase    ,       list(range(min(args.data_subset    ,       len(_lowerCamelCase				)				)				)				)				)
		snake_case						:  Tuple        =			SequentialSampler(_lowerCamelCase				) if args.local_rank == -1 else DistributedSampler(_lowerCamelCase				)
		snake_case						:  Union[str, Any]        =			DataLoader(
		    _lowerCamelCase    ,       sampler=_lowerCamelCase    ,       batch_size=args.batch_size    ,       collate_fn=_lowerCamelCase				)

		# 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:
				snake_case						:  Union[str, Any]        =			mask_heads(_lowerCamelCase    ,       _lowerCamelCase    ,       _lowerCamelCase				)
				prune_heads(_lowerCamelCase    ,       _lowerCamelCase    ,       _lowerCamelCase    ,       _lowerCamelCase				)


if __name__ == "__main__":
							main()







"""simple docstring"""

from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


__A							=     "\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n"
class      lowerCamelCase__      (					lowerCamelCase_     ):


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					super().__init__()

					self.register_modules(
					    unet=SCREAMING_SNAKE_CASE			,	scheduler=SCREAMING_SNAKE_CASE			,	)

					snake_case						:  List[str]        =			None


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE=0							):



					"""simple docstring"""







					if is_accelerate_available() and is_accelerate_version(">="			,	"0.14.0"							):
							from accelerate import cpu_offload
					else:
							raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"							)

					snake_case						:  Tuple        =			torch.device(F'''cuda:{gpu_id}'''							)

					if self.device.type != "cpu":
							self.to("cpu"			,	silence_dtype_warnings=SCREAMING_SNAKE_CASE							)
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

					for cpu_offloaded_model in [self.unet]:
							cpu_offload(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

					if self.safety_checker is not None:
							cpu_offload(self.safety_checker			,	execution_device=SCREAMING_SNAKE_CASE			,	offload_buffers=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE=0							):



					"""simple docstring"""







					if is_accelerate_available() and is_accelerate_version(">="			,	"0.17.0.dev0"							):
							from accelerate import cpu_offload_with_hook
					else:
							raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."							)

					snake_case						:  Union[str, Any]        =			torch.device(F'''cuda:{gpu_id}'''							)

					if self.device.type != "cpu":
							self.to("cpu"			,	silence_dtype_warnings=SCREAMING_SNAKE_CASE							)
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

					snake_case						:  List[str]        =			None
					for cpu_offloaded_model in [self.unet]:
							snake_case     ,			snake_case						:  Optional[int]        =			cpu_offload_with_hook(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	prev_module_hook=SCREAMING_SNAKE_CASE							)

					if self.safety_checker is not None:
							snake_case     ,			snake_case						:  List[str]        =			cpu_offload_with_hook(self.safety_checker			,	SCREAMING_SNAKE_CASE			,	prev_module_hook=SCREAMING_SNAKE_CASE							)

					# We'll offload the last model manually.
					snake_case						:  Optional[Any]        =			hook


			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					if not hasattr(self.unet			,	"_hf_hook"							):
							return self.device
					for module in self.unet.modules():
							if (
							    hasattr(SCREAMING_SNAKE_CASE			,	"_hf_hook"							)
							    and hasattr(module._hf_hook			,	"execution_device"							)
							    and module._hf_hook.execution_device is not None
							):
									return torch.device(module._hf_hook.execution_device							)
					return self.device


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







					snake_case						:  Any        =			(batch_size, num_channels, height, width)
					if isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							) and len(SCREAMING_SNAKE_CASE							) != batch_size:
							raise ValueError(
							    F'''You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE							)}, but requested an effective batch'''
							    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''							)

					if latents is None:
							snake_case						:  Any        =			randn_tensor(SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE			,	device=SCREAMING_SNAKE_CASE			,	dtype=SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  Tuple        =			latents.to(device=SCREAMING_SNAKE_CASE			,	dtype=SCREAMING_SNAKE_CASE							)

					# scale the initial noise by the standard deviation required by the scheduler
					snake_case						:  Optional[int]        =			latents * self.scheduler.init_noise_sigma
					return latents


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = "pil"							):



					"""simple docstring"""







					if output_type not in ["pt", "np", "pil"]:
							raise ValueError(
							    F'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''							)

					# Equivalent to diffusers.VaeImageProcessor.denormalize
					snake_case						:  List[str]        =			(sample / 2 + 0.5).clamp(0			,	1							)
					if output_type == "pt":
							return sample

					# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
					snake_case						:  Optional[int]        =			sample.cpu().permute(0			,	2			,	3			,	1							).numpy()
					if output_type == "np":
							return sample

					# Output_type must be 'pil'
					snake_case						:  List[str]        =			self.numpy_to_pil(SCREAMING_SNAKE_CASE							)
					return sample


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







					if self.unet.config.num_class_embeds is not None:
							if isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):
									snake_case						:  Optional[Any]        =			torch.tensor(SCREAMING_SNAKE_CASE			,	dtype=torch.int							)
							elif isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):
									assert batch_size == 1, "Batch size must be 1 if classes is an int"
									snake_case						:  str        =			torch.tensor([class_labels]			,	dtype=torch.int							)
							elif class_labels is None:
									# Randomly generate batch_size class labels
									# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
									snake_case						:  Tuple        =			torch.randint(0			,	self.unet.config.num_class_embeds			,	size=(batch_size,)							)
							snake_case						:  int        =			class_labels.to(SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  str        =			None
					return class_labels


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if num_inference_steps is None and timesteps is None:
							raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."							)

					if num_inference_steps is not None and timesteps is not None:
							logger.warning(
							    F'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
							    " `timesteps` will be used over `num_inference_steps`."							)

					if latents is not None:
							snake_case						:  str        =			(batch_size, 3, img_size, img_size)
							if latents.shape != expected_shape:
									raise ValueError(F'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''							)

					if (callback_steps is None) or (
					    callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							) or callback_steps <= 0)
					):
							raise ValueError(
							    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
							    F''' {type(SCREAMING_SNAKE_CASE							)}.'''							)



			@torch.no_grad()
			@replace_example_docstring(SCREAMING_SNAKE_CASE							)
			def __call__(       self			,	SCREAMING_SNAKE_CASE = 1			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = 1			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = "pil"			,	SCREAMING_SNAKE_CASE = True			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = 1			,	):



					"""simple docstring"""







					snake_case						:  List[str]        =			self.unet.config.sample_size
					snake_case						:  Dict        =			self._execution_device

					# 1. Check inputs
					self.check_inputs(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

					# 2. Prepare image latents
					# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
					snake_case						:  Any        =			self.prepare_latents(
					    batch_size=SCREAMING_SNAKE_CASE			,	num_channels=self.unet.config.in_channels			,	height=SCREAMING_SNAKE_CASE			,	width=SCREAMING_SNAKE_CASE			,	dtype=self.unet.dtype			,	device=SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE			,	latents=SCREAMING_SNAKE_CASE			,	)

					# 3. Handle class_labels for class-conditional models
					snake_case						:  Union[str, Any]        =			self.prepare_class_labels(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	class_labels=SCREAMING_SNAKE_CASE							)

					# 4. Prepare timesteps
					if timesteps is not None:
							self.scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE			,	device=SCREAMING_SNAKE_CASE							)
							snake_case						:  Dict        =			self.scheduler.timesteps
							snake_case						:  str        =			len(SCREAMING_SNAKE_CASE							)
					else:
							self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE							)
							snake_case						:  str        =			self.scheduler.timesteps

					# 5. Denoising loop
					# Multistep sampling: implements Algorithm 1 in the paper
					with self.progress_bar(total=SCREAMING_SNAKE_CASE							) as progress_bar:
							for i, t in enumerate(SCREAMING_SNAKE_CASE							):
									snake_case						:  Any        =			self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)
									snake_case						:  Dict        =			self.unet(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	class_labels=SCREAMING_SNAKE_CASE			,	return_dict=SCREAMING_SNAKE_CASE							)[0]

									snake_case						:  List[Any]        =			self.scheduler.step(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE							)[0]

									# call the callback, if provided
									progress_bar.update()
									if callback is not None and i % callback_steps == 0:
											callback(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

        # 6. Post-process image sample
					snake_case						:  Dict        =			self.postprocess_image(SCREAMING_SNAKE_CASE			,	output_type=SCREAMING_SNAKE_CASE							)

					# Offload last model to CPU
					if hasattr(self			,	"final_offload_hook"							) and self.final_offload_hook is not None:
							self.final_offload_hook.offload()

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE							)

