

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

lowerCAmelCase_     :   Dict 			=			16
lowerCAmelCase_     :   Tuple 			=			32


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = 16 ):
						snake_case_     =			AutoTokenizer.from_pretrained('''bert-base-cased''' )
						snake_case_     =			load_dataset('''glue'''						,	'''mrpc''' )

						def tokenize_function(SCREAMING_SNAKE_CASE__ ):
												# max_length=None => use the model max length (it's actually the default)
												snake_case_     =			tokenizer(examples['''sentence1''']						,	examples['''sentence2''']						,	truncation=UpperCamelCase__						,	max_length=UpperCamelCase__ )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												snake_case_     =			datasets.map(
												    UpperCamelCase__						,	batched=UpperCamelCase__						,	remove_columns=['''idx''', '''sentence1''', '''sentence2''']						,	)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						snake_case_     =			tokenized_datasets.rename_column('''label'''						,	'''labels''' )

						def collate_fn(SCREAMING_SNAKE_CASE__ ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												snake_case_     =			128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		snake_case_     =			16
												elif accelerator.mixed_precision != "no":
																		snake_case_     =			8
												else:
																		snake_case_     =			None

												return tokenizer.pad(
												    UpperCamelCase__						,	padding='''longest'''						,	max_length=UpperCamelCase__						,	pad_to_multiple_of=UpperCamelCase__						,	return_tensors='''pt'''						,	)

						# Instantiate dataloaders.
						snake_case_     =			DataLoader(
						    tokenized_datasets['''train''']						,	shuffle=UpperCamelCase__						,	collate_fn=UpperCamelCase__						,	batch_size=UpperCamelCase__ )
						snake_case_     =			DataLoader(
						    tokenized_datasets['''validation''']						,	shuffle=UpperCamelCase__						,	collate_fn=UpperCamelCase__						,	batch_size=UpperCamelCase__ )

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					lowerCAmelCase_     :   List[Any] 			=			mocked_dataloaders  # noqa: F811


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,	UpperCamelCase__ ) == "1":
												snake_case_     =			2
						# Initialize accelerator
						snake_case_     =			Accelerator(cpu=args.cpu						,	mixed_precision=args.mixed_precision )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case_     =			config["lr"]
						snake_case_     =			int(config['''num_epochs'''] )
						snake_case_     =			int(config['''seed'''] )
						snake_case_     =			int(config['''batch_size'''] )

						# New Code #
						# Parse out whether we are saving every epoch or after a certain number of batches
						if hasattr(args.checkpointing_steps						,	'''isdigit''' ):
												if args.checkpointing_steps == "epoch":
																		snake_case_     =			args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		snake_case_     =			int(args.checkpointing_steps )
												else:
																		raise ValueError(
																		    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''' )
						else:
												snake_case_     =			None

						set_seed(UpperCamelCase__ )

						snake_case_     =			get_dataloaders(UpperCamelCase__						,	UpperCamelCase__ )
						snake_case_     =			evaluate.load('''glue'''						,	'''mrpc''' )

						# If the batch size is too big we use gradient accumulation
						snake_case_     =			1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												snake_case_     =			batch_size // MAX_GPU_BATCH_SIZE
												snake_case_     =			MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case_     =			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''						,	return_dict=UpperCamelCase__ )

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						snake_case_     =			model.to(accelerator.device )

						# Instantiate optimizer
						snake_case_     =			AdamW(params=model.parameters()						,	lr=UpperCamelCase__ )

						# Instantiate scheduler
						snake_case_     =			get_linear_schedule_with_warmup(
						    optimizer=UpperCamelCase__						,	num_warmup_steps=100						,	num_training_steps=(len(UpperCamelCase__ ) * num_epochs) // gradient_accumulation_steps						,	)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						snake_case_     =			accelerator.prepare(
						    UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ )

						# New Code #
						# We need to keep track of how many total steps we have iterated over
						snake_case_     =			0
						# We also need to keep track of the stating epoch so files are named properly
						snake_case_     =			0

						# We need to load the checkpoint back in before training here with `load_state`
						# The total number of epochs is adjusted based on where the state is being loaded from,
						# as we assume continuation of the same training script
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}''' )
																		accelerator.load_state(args.resume_from_checkpoint )
																		snake_case_     =			os.path.basename(args.resume_from_checkpoint )
												else:
																		# Get the most recent checkpoint
																		snake_case_     =			[f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime )
																		snake_case_     =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												snake_case_     =			os.path.splitext(UpperCamelCase__ )[0]

												if "epoch" in training_difference:
																		snake_case_     =			int(training_difference.replace('''epoch_'''						,	'''''' ) ) + 1
																		snake_case_     =			None
												else:
																		snake_case_     =			int(training_difference.replace('''step_'''						,	'''''' ) )
																		snake_case_     =			resume_step // len(UpperCamelCase__ )
																		resume_step -= starting_epoch * len(UpperCamelCase__ )

    # Now we train the model
						for epoch in range(UpperCamelCase__						,	UpperCamelCase__ ):
												model.train()
												# New Code #
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		snake_case_     =			accelerator.skip_first_batches(UpperCamelCase__						,	UpperCamelCase__ )
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		snake_case_     =			train_dataloader
												for step, batch in enumerate(UpperCamelCase__ ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device )
																		snake_case_     =			model(**UpperCamelCase__ )
																		snake_case_     =			outputs.loss
																		snake_case_     =			loss / gradient_accumulation_steps
																		accelerator.backward(UpperCamelCase__ )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()
																		# New Code #
																		overall_step += 1

																		# New Code #
																		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																		# These are saved to folders named `step_{overall_step}`
																		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																		# If mixed precision was used, will also save a "scalar.bin" file
																		if isinstance(UpperCamelCase__						,	UpperCamelCase__ ):
																								snake_case_     =			F'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				snake_case_     =			os.path.join(args.output_dir						,	UpperCamelCase__ )
																														accelerator.save_state(UpperCamelCase__ )

												model.eval()
												for step, batch in enumerate(UpperCamelCase__ ):
																		# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
																		batch.to(accelerator.device )
																		with torch.no_grad():
																								snake_case_     =			model(**UpperCamelCase__ )
																		snake_case_     =			outputs.logits.argmax(dim=-1 )
																		snake_case_     =			accelerator.gather_for_metrics((predictions, batch['''labels''']) )
																		metric.add_batch(
																		    predictions=UpperCamelCase__						,	references=UpperCamelCase__						,	)

												snake_case_     =			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''						,	UpperCamelCase__ )

												# New Code #
												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
												# These are saved to folders named `epoch_{epoch}`
												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
												# If mixed precision was used, will also save a "scalar.bin" file
												if checkpointing_steps == "epoch":
																		snake_case_     =			F'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								snake_case_     =			os.path.join(args.output_dir						,	UpperCamelCase__ )
																		accelerator.save_state(UpperCamelCase__ )


def 						__SCREAMING_SNAKE_CASE      ():
						snake_case_     =			argparse.ArgumentParser(description='''Simple example of training script.''' )
						parser.add_argument(
						    '''--mixed_precision'''						,	type=UpperCamelCase__						,	default=UpperCamelCase__						,	choices=['''no''', '''fp16''', '''bf16''', '''fp8''']						,	help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.'''						,	)
						parser.add_argument('''--cpu'''						,	action='''store_true'''						,	help='''If passed, will train on the CPU.''' )
						parser.add_argument(
						    '''--checkpointing_steps'''						,	type=UpperCamelCase__						,	default=UpperCamelCase__						,	help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''						,	)
						parser.add_argument(
						    '''--output_dir'''						,	type=UpperCamelCase__						,	default='''.'''						,	help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''						,	)
						parser.add_argument(
						    '''--resume_from_checkpoint'''						,	type=UpperCamelCase__						,	default=UpperCamelCase__						,	help='''If the training should continue from a checkpoint folder.'''						,	)
						snake_case_     =			parser.parse_args()
						snake_case_     =			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(UpperCamelCase__						,	UpperCamelCase__ )


if __name__ == "__main__":
					main()


import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput





class 			snake_case_     (	nn.Module      ):







			'''simple docstring'''

			def __init__(  self				:       Optional[Any]							,     _UpperCamelCase				:       Dict							,     _UpperCamelCase				:       List[str]=1.0							)   ->Tuple:
									super().__init__()

									snake_case_     =			cut_size
									snake_case_     =			cut_power







			def        snake_case__(  self				:       str							,     _UpperCamelCase				:       List[str]							,     _UpperCamelCase				:       Dict							)   ->Union[str, Any]:
									snake_case_,					snake_case_     =			pixel_values.shape[2:4]
									snake_case_     =			min(_UpperCamelCase							,     _UpperCamelCase							)
									snake_case_     =			min(_UpperCamelCase							,     _UpperCamelCase							,     self.cut_size							)
									snake_case_     =			[]
									for _ in range(_UpperCamelCase							):
															snake_case_     =			int(torch.rand([]							) ** self.cut_power * (max_size - min_size) + min_size							)
															snake_case_     =			torch.randint(0							,     sideX - size + 1							,     ()							)
															snake_case_     =			torch.randint(0							,     sideY - size + 1							,     ()							)
															snake_case_     =			pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
															cutouts.append(F.adaptive_avg_poolad(_UpperCamelCase							,     self.cut_size							)							)
									return torch.cat(_UpperCamelCase							)






def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			F.normalize(SCREAMING_SNAKE_CASE__						,	dim=-1 )
						snake_case_     =			F.normalize(SCREAMING_SNAKE_CASE__						,	dim=-1 )
						return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						for param in model.parameters():
												snake_case_     =			value





class 			snake_case_     (	__A      ):







			'''simple docstring'''

			def __init__(  self				:       List[Any]							,     _UpperCamelCase				:       AutoencoderKL							,     _UpperCamelCase				:       CLIPTextModel							,     _UpperCamelCase				:       CLIPModel							,     _UpperCamelCase				:       CLIPTokenizer							,     _UpperCamelCase				:       UNetaDConditionModel							,     _UpperCamelCase				:       Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]							,     _UpperCamelCase				:       CLIPImageProcessor							,     )   ->Union[str, Any]:
									super().__init__()
									self.register_modules(
									    vae=_UpperCamelCase							,     text_encoder=_UpperCamelCase							,     clip_model=_UpperCamelCase							,     tokenizer=_UpperCamelCase							,     unet=_UpperCamelCase							,     scheduler=_UpperCamelCase							,     feature_extractor=_UpperCamelCase							,     )

									snake_case_     =			transforms.Normalize(mean=feature_extractor.image_mean							,     std=feature_extractor.image_std							)
									snake_case_     =			(
									    feature_extractor.size
									    if isinstance(feature_extractor.size							,     _UpperCamelCase							)
									    else feature_extractor.size['''shortest_edge''']
									)
									snake_case_     =			MakeCutouts(self.cut_out_size							)

									set_requires_grad(self.text_encoder							,     _UpperCamelCase							)
									set_requires_grad(self.clip_model							,     _UpperCamelCase							)







			def        snake_case__(  self				:       List[Any]							,     _UpperCamelCase				:       Optional[Union[str, int]] = "auto"							)   ->int:
									if slice_size == "auto":
															# half the attention head size is usually a good trade-off between
															# speed and memory
															snake_case_     =			self.unet.config.attention_head_dim // 2
									self.unet.set_attention_slice(_UpperCamelCase							)







			def        snake_case__(  self				:       str							)   ->Optional[Any]:
									self.enable_attention_slicing(_UpperCamelCase							)







			def        snake_case__(  self				:       Any							)   ->Union[str, Any]:
									set_requires_grad(self.vae							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Any							)   ->Dict:
									set_requires_grad(self.vae							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Dict							)   ->Optional[int]:
									set_requires_grad(self.unet							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Optional[int]							)   ->List[Any]:
									set_requires_grad(self.unet							,     _UpperCamelCase							)







			@torch.enable_grad()
			def        snake_case__(  self				:       Optional[Any]							,     _UpperCamelCase				:       Tuple							,     _UpperCamelCase				:       str							,     _UpperCamelCase				:       Any							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       Optional[Any]							,     _UpperCamelCase				:       Optional[int]							,     _UpperCamelCase				:       Tuple=True							,     )   ->Union[str, Any]:
									snake_case_     =			latents.detach().requires_grad_()

									snake_case_     =			self.scheduler.scale_model_input(_UpperCamelCase							,     _UpperCamelCase							)

									# predict the noise residual
									snake_case_     =			self.unet(_UpperCamelCase							,     _UpperCamelCase							,     encoder_hidden_states=_UpperCamelCase							).sample

									if isinstance(self.scheduler							,     (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)							):
															snake_case_     =			self.scheduler.alphas_cumprod[timestep]
															snake_case_     =			1 - alpha_prod_t
															# compute predicted original sample from predicted noise also called
															# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
															snake_case_     =			(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

															snake_case_     =			torch.sqrt(_UpperCamelCase							)
															snake_case_     =			pred_original_sample * (fac) + latents * (1 - fac)
									elif isinstance(self.scheduler							,     _UpperCamelCase							):
															snake_case_     =			self.scheduler.sigmas[index]
															snake_case_     =			latents - sigma * noise_pred
									else:
															raise ValueError(f'''scheduler type {type(self.scheduler							)} not supported'''							)

									snake_case_     =			1 / self.vae.config.scaling_factor * sample
									snake_case_     =			self.vae.decode(_UpperCamelCase							).sample
									snake_case_     =			(image / 2 + 0.5).clamp(0							,     1							)

									if use_cutouts:
															snake_case_     =			self.make_cutouts(_UpperCamelCase							,     _UpperCamelCase							)
									else:
															snake_case_     =			transforms.Resize(self.cut_out_size							)(_UpperCamelCase							)
									snake_case_     =			self.normalize(_UpperCamelCase							).to(latents.dtype							)

									snake_case_     =			self.clip_model.get_image_features(_UpperCamelCase							)
									snake_case_     =			image_embeddings_clip / image_embeddings_clip.norm(p=2							,     dim=-1							,     keepdim=_UpperCamelCase							)

									if use_cutouts:
															snake_case_     =			spherical_dist_loss(_UpperCamelCase							,     _UpperCamelCase							)
															snake_case_     =			dists.view([num_cutouts, sample.shape[0], -1]							)
															snake_case_     =			dists.sum(2							).mean(0							).sum() * clip_guidance_scale
									else:
															snake_case_     =			spherical_dist_loss(_UpperCamelCase							,     _UpperCamelCase							).mean() * clip_guidance_scale

									snake_case_     =			-torch.autograd.grad(_UpperCamelCase							,     _UpperCamelCase							)[0]

									if isinstance(self.scheduler							,     _UpperCamelCase							):
															snake_case_     =			latents.detach() + grads * (sigma**2)
															snake_case_     =			noise_pred_original
									else:
															snake_case_     =			noise_pred_original - torch.sqrt(_UpperCamelCase							) * grads
									return noise_pred, latents







			@torch.no_grad()
			def __call__(  self				:       Optional[int]							,     _UpperCamelCase				:       Union[str, List[str]]							,     _UpperCamelCase				:       Optional[int] = 5_1_2							,     _UpperCamelCase				:       Optional[int] = 5_1_2							,     _UpperCamelCase				:       Optional[int] = 5_0							,     _UpperCamelCase				:       Optional[float] = 7.5							,     _UpperCamelCase				:       Optional[int] = 1							,     _UpperCamelCase				:       float = 0.0							,     _UpperCamelCase				:       Optional[float] = 1_0_0							,     _UpperCamelCase				:       Optional[Union[str, List[str]]] = None							,     _UpperCamelCase				:       Optional[int] = 4							,     _UpperCamelCase				:       Optional[bool] = True							,     _UpperCamelCase				:       Optional[torch.Generator] = None							,     _UpperCamelCase				:       Optional[torch.FloatTensor] = None							,     _UpperCamelCase				:       Optional[str] = "pil"							,     _UpperCamelCase				:       bool = True							,     )   ->str:
									if isinstance(_UpperCamelCase							,     _UpperCamelCase							):
															snake_case_     =			1
									elif isinstance(_UpperCamelCase							,     _UpperCamelCase							):
															snake_case_     =			len(_UpperCamelCase							)
									else:
															raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase							)}'''							)

									if height % 8 != 0 or width % 8 != 0:
															raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''							)

									# get prompt text embeddings
									snake_case_     =			self.tokenizer(
									    _UpperCamelCase							,     padding='''max_length'''							,     max_length=self.tokenizer.model_max_length							,     truncation=_UpperCamelCase							,     return_tensors='''pt'''							,     )
									snake_case_     =			self.text_encoder(text_input.input_ids.to(self.device							)							)[0]
									# duplicate text embeddings for each generation per prompt
									snake_case_     =			text_embeddings.repeat_interleave(_UpperCamelCase							,     dim=0							)

									if clip_guidance_scale > 0:
															if clip_prompt is not None:
																					snake_case_     =			self.tokenizer(
																					    _UpperCamelCase							,     padding='''max_length'''							,     max_length=self.tokenizer.model_max_length							,     truncation=_UpperCamelCase							,     return_tensors='''pt'''							,     ).input_ids.to(self.device							)
															else:
																					snake_case_     =			text_input.input_ids.to(self.device							)
															snake_case_     =			self.clip_model.get_text_features(_UpperCamelCase							)
															snake_case_     =			text_embeddings_clip / text_embeddings_clip.norm(p=2							,     dim=-1							,     keepdim=_UpperCamelCase							)
															# duplicate text embeddings clip for each generation per prompt
															snake_case_     =			text_embeddings_clip.repeat_interleave(_UpperCamelCase							,     dim=0							)

									# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
									# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
									# corresponds to doing no classifier free guidance.
									snake_case_     =			guidance_scale > 1.0
									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance:
															snake_case_     =			text_input.input_ids.shape[-1]
															snake_case_     =			self.tokenizer(['''''']							,     padding='''max_length'''							,     max_length=_UpperCamelCase							,     return_tensors='''pt'''							)
															snake_case_     =			self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
															# duplicate unconditional embeddings for each generation per prompt
															snake_case_     =			uncond_embeddings.repeat_interleave(_UpperCamelCase							,     dim=0							)

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															snake_case_     =			torch.cat([uncond_embeddings, text_embeddings]							)

									# get the initial random noise unless the user supplied it

									# Unlike in other pipelines, latents need to be generated in the target device
									# for 1-to-1 results reproducibility with the CompVis implementation.
									# However this currently doesn't work in `mps`.
									snake_case_     =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
									snake_case_     =			text_embeddings.dtype
									if latents is None:
															if self.device.type == "mps":
																					# randn does not work reproducibly on mps
																					snake_case_     =			torch.randn(_UpperCamelCase							,     generator=_UpperCamelCase							,     device='''cpu'''							,     dtype=_UpperCamelCase							).to(
																					    self.device							)
															else:
																					snake_case_     =			torch.randn(_UpperCamelCase							,     generator=_UpperCamelCase							,     device=self.device							,     dtype=_UpperCamelCase							)
									else:
															if latents.shape != latents_shape:
																					raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''							)
															snake_case_     =			latents.to(self.device							)

									# set timesteps
									snake_case_     =			'''offset''' in set(inspect.signature(self.scheduler.set_timesteps							).parameters.keys()							)
									snake_case_     =			{}
									if accepts_offset:
															snake_case_     =			1

									self.scheduler.set_timesteps(_UpperCamelCase							,     **_UpperCamelCase							)

									# Some schedulers like PNDM have timesteps as arrays
									# It's more optimized to move all timesteps to correct device beforehand
									snake_case_     =			self.scheduler.timesteps.to(self.device							)

									# scale the initial noise by the standard deviation required by the scheduler
									snake_case_     =			latents * self.scheduler.init_noise_sigma

									# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
									# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
									# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
									# and should be between [0, 1]
									snake_case_     =			'''eta''' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
									snake_case_     =			{}
									if accepts_eta:
															snake_case_     =			eta

									# check if the scheduler accepts generator
									snake_case_     =			'''generator''' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
									if accepts_generator:
															snake_case_     =			generator

									for i, t in enumerate(self.progress_bar(_UpperCamelCase							)							):
															# expand the latents if we are doing classifier free guidance
															snake_case_     =			torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
															snake_case_     =			self.scheduler.scale_model_input(_UpperCamelCase							,     _UpperCamelCase							)

															# predict the noise residual
															snake_case_     =			self.unet(_UpperCamelCase							,     _UpperCamelCase							,     encoder_hidden_states=_UpperCamelCase							).sample

															# perform classifier free guidance
															if do_classifier_free_guidance:
																					snake_case_,					snake_case_     =			noise_pred.chunk(2							)
																					snake_case_     =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# perform clip guidance
															if clip_guidance_scale > 0:
																					snake_case_     =			(
																					    text_embeddings.chunk(2							)[1] if do_classifier_free_guidance else text_embeddings
																					)
																					snake_case_,					snake_case_     =			self.cond_fn(
																					    _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     )

															# compute the previous noisy sample x_t -> x_t-1
															snake_case_     =			self.scheduler.step(_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     **_UpperCamelCase							).prev_sample

									# scale and decode the image latents with vae
									snake_case_     =			1 / self.vae.config.scaling_factor * latents
									snake_case_     =			self.vae.decode(_UpperCamelCase							).sample

									snake_case_     =			(image / 2 + 0.5).clamp(0							,     1							)
									snake_case_     =			image.cpu().permute(0							,     2							,     3							,     1							).numpy()

									if output_type == "pil":
															snake_case_     =			self.numpy_to_pil(_UpperCamelCase							)

									if not return_dict:
															return (image, None)

									return StableDiffusionPipelineOutput(images=_UpperCamelCase							,     nsfw_content_detected=_UpperCamelCase							)