
import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator






def 	a_		(	__lowercase       :     Optional[Any]			)			->  Any:
			_snake_case          =      fname.split(os.path.sep			)[-1]
			return re.search(r'^(.*)_\d+\.jpg$'	, __lowercase			).groups()[0]




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def __init__(  self     :   Optional[int]      ,  lowercase     :   List[Any]      ,  lowercase     :   int=None      ,  lowercase     :   int=None     ):


				'''simple docstring'''


				_snake_case          =      file_names
				_snake_case          =      image_transform
				_snake_case          =      label_to_id



	def __len__(  self     :   List[str]     ):


				'''simple docstring'''


				return len(self.file_names     )





	def __getitem__(  self     :   Dict      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.file_names[idx]
				_snake_case          =      PIL.Image.open(lowercase     )
				_snake_case          =      raw_image.convert('RGB'     )
				if self.image_transform is not None:
							_snake_case          =      self.image_transform(lowercase     )
				_snake_case          =      extract_label(lowercase     )
				if self.label_to_id is not None:
							_snake_case          =      self.label_to_id[label]
				return {"image": image, "label": label}






def 	a_		(	__lowercase       :     Union[str, Any]	, __lowercase       :     str			)			->  List[Any]:
			# Initialize accelerator
			if args.with_tracking:
						_snake_case          =      Accelerator(
						    cpu=args.cpu	, mixed_precision=args.mixed_precision	, log_with='all'	, project_dir=args.project_dir			)
			else:
						_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']			)
			_snake_case          =      config['image_size']
			if not isinstance(__lowercase	, (list, tuple)			):
						_snake_case          =      (image_size, image_size)

			# 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

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						_snake_case          =      os.path.split(__lowercase			)[-1].split('.'			)[0]
						accelerator.init_trackers(__lowercase	, __lowercase			)

			# Grab all the image filenames
			_snake_case          =      [os.path.join(args.data_dir	, __lowercase			) for fname in os.listdir(args.data_dir			) if fname.endswith('.jpg'			)]

			# Build the label correspondences
			_snake_case          =      [extract_label(__lowercase			) for fname in file_names]
			_snake_case          =      list(set(__lowercase			)			)
			id_to_label.sort()
			_snake_case          =      {lbl: i for i, lbl in enumerate(__lowercase			)}

			# Set the seed before splitting the data.
			np.random.seed(__lowercase			)
			torch.manual_seed(__lowercase			)
			torch.cuda.manual_seed_all(__lowercase			)

			# Split our filenames between train and validation
			_snake_case          =      np.random.permutation(len(__lowercase			)			)
			_snake_case          =      int(0.8 * len(__lowercase			)			)
			_snake_case          =      random_perm[:cut]
			_snake_case          =      random_perm[cut:]

			# For training we use a simple RandomResizedCrop
			_snake_case          =      Compose([RandomResizedCrop(__lowercase	, scale=(0.5, 1.0)			), ToTensor()]			)
			_snake_case          =      PetsDataset(
			    [file_names[i] for i in train_split]	, image_transform=__lowercase	, label_to_id=__lowercase			)

			# For evaluation, we use a deterministic Resize
			_snake_case          =      Compose([Resize(__lowercase			), ToTensor()]			)
			_snake_case          =      PetsDataset([file_names[i] for i in eval_split]	, image_transform=__lowercase	, label_to_id=__lowercase			)

			# Instantiate dataloaders.
			_snake_case          =      DataLoader(__lowercase	, shuffle=__lowercase	, batch_size=__lowercase	, num_workers=4			)
			_snake_case          =      DataLoader(__lowercase	, shuffle=__lowercase	, batch_size=__lowercase	, num_workers=4			)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			_snake_case          =      create_model('resnet50d'	, pretrained=__lowercase	, num_classes=len(__lowercase			)			)

			# 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			)

			# Freezing the base model
			for param in model.parameters():
						_snake_case          =      False
			for param in model.get_classifier().parameters():
						_snake_case          =      True

			# We normalize the batches of images to be a bit faster.
			_snake_case          =      torch.tensor(model.default_cfg['mean']			)[None, :, None, None].to(accelerator.device			)
			_snake_case          =      torch.tensor(model.default_cfg['std']			)[None, :, None, None].to(accelerator.device			)

			# Instantiate optimizer
			_snake_case          =      torch.optim.Adam(params=model.parameters()	, lr=lr / 25			)

			# Instantiate learning rate scheduler
			_snake_case          =      OneCycleLR(optimizer=__lowercase	, max_lr=__lowercase	, epochs=__lowercase	, steps_per_epoch=len(__lowercase			)			)

			# 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     , _snake_case     , _snake_case     , _snake_case     , _snake_case          =      accelerator.prepare(
			    __lowercase	, __lowercase	, __lowercase	, __lowercase	, __lowercase			)
			# 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 starting epoch so files are named properly
			_snake_case          =      0

			# Potentially load in the weights and states from a previous save
			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(__lowercase			)[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(__lowercase			)
									resume_step -= starting_epoch * len(__lowercase			)

    # Now we train the model
			for epoch in range(__lowercase	, __lowercase			):
						model.train()
						if args.with_tracking:
									_snake_case          =      0
						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(__lowercase	, __lowercase			)
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									_snake_case          =      train_dataloader
						for batch in active_dataloader:
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									_snake_case          =      {k: v.to(accelerator.device			) for k, v in batch.items()}
									_snake_case          =      (batch['image'] - mean) / std
									_snake_case          =      model(__lowercase			)
									_snake_case          =      torch.nn.functional.cross_entropy(__lowercase	, batch['label']			)
									# We keep track of the loss at each epoch
									if args.with_tracking:
												total_loss += loss.detach().float()
									accelerator.backward(__lowercase			)
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()
									overall_step += 1
									if isinstance(__lowercase	, __lowercase			):
												_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	, __lowercase			)
															accelerator.save_state(__lowercase			)
						model.eval()
						_snake_case          =      0
						_snake_case          =      0
						for step, batch in enumerate(__lowercase			):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									_snake_case          =      {k: v.to(accelerator.device			) for k, v in batch.items()}
									_snake_case          =      (batch['image'] - mean) / std
									with torch.no_grad():
												_snake_case          =      model(__lowercase			)
									_snake_case          =      outputs.argmax(dim=-1			)
									_snake_case     , _snake_case          =      accelerator.gather_for_metrics((predictions, batch['label'])			)
									_snake_case          =      predictions == references
									num_elems += accurate_preds.shape[0]
									accurate += accurate_preds.long().sum()

						_snake_case          =      accurate.item() / num_elems
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''			)
						if args.with_tracking:
									accelerator.log(
									    {
									        'accuracy': 100 * eval_metric,
									        'train_loss': total_loss.item() / len(__lowercase			),
									        'epoch': epoch,
									    }	, step=__lowercase	, )
						if checkpointing_steps == "epoch":
									_snake_case          =      f'''epoch_{epoch}'''
									if args.output_dir is not None:
												_snake_case          =      os.path.join(args.output_dir	, __lowercase			)
									accelerator.save_state(__lowercase			)

			if args.with_tracking:
						accelerator.end_training()






def 	a_		(	)			->  Tuple:
			_snake_case          =      argparse.ArgumentParser(description='Simple example of training script.'			)
			parser.add_argument('--data_dir'	, required=__lowercase	, help='The data folder on disk.'			)
			parser.add_argument('--fp16'	, action='store_true'	, help='If passed, will use FP16 training.'			)
			parser.add_argument(
			    '--mixed_precision'	, type=__lowercase	, default=__lowercase	, 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=__lowercase	, default=__lowercase	, 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=__lowercase	, 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=__lowercase	, default=__lowercase	, help='If the training should continue from a checkpoint folder.'	, )
			parser.add_argument(
			    '--with_tracking'	, action='store_true'	, help='Whether to load in all available experiment trackers from the environment and use them for logging.'	, )
			parser.add_argument(
			    '--project_dir'	, type=__lowercase	, default='logs'	, help='Location on where to store experiment tracking logs` and relevent project information'	, )
			_snake_case          =      parser.parse_args()
			_snake_case          =      {'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
			training_function(__lowercase	, __lowercase			)


if __name__ == "__main__":
			main()

import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

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






def 	a_		(	__lowercase       :     str	, __lowercase       :     List[str]	, __lowercase       :     Dict			)			->  List[str]:
			if isinstance(__lowercase	, torch.Tensor			):
						return image
			elif isinstance(__lowercase	, PIL.Image.Image			):
						_snake_case          =      [image]

			if isinstance(image[0]	, PIL.Image.Image			):
						_snake_case          =      [np.array(i.resize((w, h)	, resample=PIL_INTERPOLATION['lanczos']			)			)[None, :] for i in image]
						_snake_case          =      np.concatenate(__lowercase	, axis=0			)
						_snake_case          =      np.array(__lowercase			).astype(np.floataa			) / 2_5_5.0
						_snake_case          =      image.transpose(0	, 3	, 1	, 2			)
						_snake_case          =      2.0 * image - 1.0
						_snake_case          =      torch.from_numpy(__lowercase			)
			elif isinstance(image[0]	, torch.Tensor			):
						_snake_case          =      torch.cat(__lowercase	, dim=0			)
			return image






def 	a_		(	__lowercase       :     int	, __lowercase       :     Any	, __lowercase       :     List[Any]	, __lowercase       :     Tuple=0.9_9_9_5			)			->  List[str]:
			if not isinstance(__lowercase	, np.ndarray			):
						_snake_case          =      True
						_snake_case          =      va.device
						_snake_case          =      va.cpu().numpy()
						_snake_case          =      va.cpu().numpy()

			_snake_case          =      np.sum(va * va / (np.linalg.norm(__lowercase			) * np.linalg.norm(__lowercase			))			)
			if np.abs(__lowercase			) > DOT_THRESHOLD:
						_snake_case          =      (1 - t) * va + t * va
			else:
						_snake_case          =      np.arccos(__lowercase			)
						_snake_case          =      np.sin(__lowercase			)
						_snake_case          =      theta_a * t
						_snake_case          =      np.sin(__lowercase			)
						_snake_case          =      np.sin(theta_a - theta_t			) / sin_theta_a
						_snake_case          =      sin_theta_t / sin_theta_a
						_snake_case          =      sa * va + sa * va

			if inputs_are_torch:
						_snake_case          =      torch.from_numpy(__lowercase			).to(__lowercase			)

			return va






def 	a_		(	__lowercase       :     int	, __lowercase       :     Optional[int]			)			->  List[Any]:
			_snake_case          =      F.normalize(__lowercase	, dim=-1			)
			_snake_case          =      F.normalize(__lowercase	, dim=-1			)
			return (x - y).norm(dim=-1			).div(2			).arcsin().pow(2			).mul(2			)






def 	a_		(	__lowercase       :     int	, __lowercase       :     Any			)			->  Optional[Any]:
			for param in model.parameters():
						_snake_case          =      value




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def __init__(  self     :   List[Any]      ,  lowercase     :   AutoencoderKL      ,  lowercase     :   CLIPTextModel      ,  lowercase     :   CLIPModel      ,  lowercase     :   CLIPTokenizer      ,  lowercase     :   UNetaDConditionModel      ,  lowercase     :   Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]      ,  lowercase     :   CLIPFeatureExtractor      ,  lowercase     :   Any=None      ,  lowercase     :   List[str]=None      ,  lowercase     :   List[str]=None      ,  ):


				'''simple docstring'''


				super().__init__()
				self.register_modules(
				    vae=lowercase      ,  text_encoder=lowercase      ,  clip_model=lowercase      ,  tokenizer=lowercase      ,  unet=lowercase      ,  scheduler=lowercase      ,  feature_extractor=lowercase      ,  coca_model=lowercase      ,  coca_tokenizer=lowercase      ,  coca_transform=lowercase      ,  )
				_snake_case          =      (
				    feature_extractor.size
				    if isinstance(feature_extractor.size      ,  lowercase     )
				    else feature_extractor.size['shortest_edge']
				)
				_snake_case          =      transforms.Normalize(mean=feature_extractor.image_mean      ,  std=feature_extractor.image_std     )
				set_requires_grad(self.text_encoder      ,  lowercase     )
				set_requires_grad(self.clip_model      ,  lowercase     )



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   Optional[Union[str, int]] = "auto"     ):


				'''simple docstring'''


				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(lowercase     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				self.enable_attention_slicing(lowercase     )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				set_requires_grad(self.vae      ,  lowercase     )



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


				'''simple docstring'''


				set_requires_grad(self.vae      ,  lowercase     )



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				set_requires_grad(self.unet      ,  lowercase     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				set_requires_grad(self.unet      ,  lowercase     )



	def 			A							(  self     :   str      ,  lowercase     :   Optional[Any]      ,  lowercase     :   Optional[int]      ,  lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      min(int(num_inference_steps * strength     )      ,  lowercase     )

				_snake_case          =      max(num_inference_steps - init_timestep      ,  0     )
				_snake_case          =      self.scheduler.timesteps[t_start:]

				return timesteps, num_inference_steps - t_start



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   List[str]      ,  lowercase     :   Optional[Any]      ,  lowercase     :   str      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   List[str]      ,  lowercase     :   List[Any]=None     ):


				'''simple docstring'''


				if not isinstance(lowercase      ,  torch.Tensor     ):
							raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(lowercase     )}'''     )

				_snake_case          =      image.to(device=lowercase      ,  dtype=lowercase     )

				if isinstance(lowercase      ,  lowercase     ):
							_snake_case          =      [
							    self.vae.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(lowercase     )
							]
							_snake_case          =      torch.cat(lowercase      ,  dim=0     )
				else:
							_snake_case          =      self.vae.encode(lowercase     ).latent_dist.sample(lowercase     )

				# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				_snake_case          =      0.18215 * init_latents
				_snake_case          =      init_latents.repeat_interleave(lowercase      ,  dim=0     )

				_snake_case          =      randn_tensor(init_latents.shape      ,  generator=lowercase      ,  device=lowercase      ,  dtype=lowercase     )

				# get latents
				_snake_case          =      self.scheduler.add_noise(lowercase      ,  lowercase      ,  lowercase     )
				_snake_case          =      init_latents

				return latents



	def 			A							(  self     :   int      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      self.coca_transform(lowercase     ).unsqueeze(0     )
				with torch.no_grad(), torch.cuda.amp.autocast():
							_snake_case          =      self.coca_model.generate(transformed_image.to(device=self.device      ,  dtype=self.coca_model.dtype     )     )
				_snake_case          =      self.coca_tokenizer.decode(generated[0].cpu().numpy()     )
				return generated.split('<end_of_text>'     )[0].replace('<start_of_text>'      ,  ''     ).rstrip(' .,'     )



	def 			A							(  self     :   List[Any]      ,  lowercase     :   Dict      ,  lowercase     :   Any     ):


				'''simple docstring'''


				_snake_case          =      self.feature_extractor.preprocess(lowercase     )
				_snake_case          =      torch.from_numpy(clip_image_input['pixel_values'][0]     ).unsqueeze(0     ).to(self.device     ).half()
				_snake_case          =      self.clip_model.get_image_features(lowercase     )
				_snake_case          =      image_embeddings_clip / image_embeddings_clip.norm(p=2      ,  dim=-1      ,  keepdim=lowercase     )
				_snake_case          =      image_embeddings_clip.repeat_interleave(lowercase      ,  dim=0     )
				return image_embeddings_clip



	@torch.enable_grad()
	def 			A							(  self     :   int      ,  lowercase     :   Optional[int]      ,  lowercase     :   Optional[int]      ,  lowercase     :   Optional[int]      ,  lowercase     :   int      ,  lowercase     :   Any      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Optional[int]      ,  ):


				'''simple docstring'''


				_snake_case          =      latents.detach().requires_grad_()

				_snake_case          =      self.scheduler.scale_model_input(lowercase      ,  lowercase     )

				# predict the noise residual
				_snake_case          =      self.unet(lowercase      ,  lowercase      ,  encoder_hidden_states=lowercase     ).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(lowercase     )
							_snake_case          =      pred_original_sample * (fac) + latents * (1 - fac)
				elif isinstance(self.scheduler      ,  lowercase     ):
							_snake_case          =      self.scheduler.sigmas[index]
							_snake_case          =      latents - sigma * noise_pred
				else:
							raise ValueError(f'''scheduler type {type(self.scheduler     )} not supported'''     )

				# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				_snake_case          =      1 / 0.18215 * sample
				_snake_case          =      self.vae.decode(lowercase     ).sample
				_snake_case          =      (image / 2 + 0.5).clamp(0      ,  1     )

				_snake_case          =      transforms.Resize(self.feature_extractor_size     )(lowercase     )
				_snake_case          =      self.normalize(lowercase     ).to(latents.dtype     )

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

				_snake_case          =      spherical_dist_loss(lowercase      ,  lowercase     ).mean() * clip_guidance_scale

				_snake_case          =      -torch.autograd.grad(lowercase      ,  lowercase     )[0]

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





	@torch.no_grad()
	def __call__(  self     :   int      ,  lowercase     :   Union[torch.FloatTensor, PIL.Image.Image]      ,  lowercase     :   Union[torch.FloatTensor, PIL.Image.Image]      ,  lowercase     :   Optional[str] = None      ,  lowercase     :   Optional[str] = None      ,  lowercase     :   Optional[int] = 512      ,  lowercase     :   Optional[int] = 512      ,  lowercase     :   float = 0.6      ,  lowercase     :   Optional[int] = 50      ,  lowercase     :   Optional[float] = 7.5      ,  lowercase     :   Optional[int] = 1      ,  lowercase     :   float = 0.0      ,  lowercase     :   Optional[float] = 100      ,  lowercase     :   Optional[torch.Generator] = None      ,  lowercase     :   Optional[str] = "pil"      ,  lowercase     :   bool = True      ,  lowercase     :   float = 0.8      ,  lowercase     :   float = 0.1      ,  lowercase     :   float = 0.1      ,  ):


				'''simple docstring'''


				if isinstance(lowercase      ,  lowercase     ) and len(lowercase     ) != batch_size:
							raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(lowercase     )} generators.'''     )

				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}.'''     )

				if isinstance(lowercase      ,  torch.Generator     ) and batch_size > 1:
							_snake_case          =      [generator] + [None] * (batch_size - 1)

				_snake_case          =      [
				    ('model', self.coca_model is None),
				    ('tokenizer', self.coca_tokenizer is None),
				    ('transform', self.coca_transform is None),
				]
				_snake_case          =      [x[0] for x in coca_is_none if x[1]]
				_snake_case          =      ', '.join(lowercase     )
				# generate prompts with coca model if prompt is None
				if content_prompt is None:
							if len(lowercase     ):
										raise ValueError(
										    f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
										    f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''     )
							_snake_case          =      self.get_image_description(lowercase     )
				if style_prompt is None:
							if len(lowercase     ):
										raise ValueError(
										    f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
										    f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''     )
							_snake_case          =      self.get_image_description(lowercase     )

				# get prompt text embeddings for content and style
				_snake_case          =      self.tokenizer(
				    lowercase      ,  padding='max_length'      ,  max_length=self.tokenizer.model_max_length      ,  truncation=lowercase      ,  return_tensors='pt'      ,  )
				_snake_case          =      self.text_encoder(content_text_input.input_ids.to(self.device     )     )[0]

				_snake_case          =      self.tokenizer(
				    lowercase      ,  padding='max_length'      ,  max_length=self.tokenizer.model_max_length      ,  truncation=lowercase      ,  return_tensors='pt'      ,  )
				_snake_case          =      self.text_encoder(style_text_input.input_ids.to(self.device     )     )[0]

				_snake_case          =      slerp(lowercase      ,  lowercase      ,  lowercase     )

				# duplicate text embeddings for each generation per prompt
				_snake_case          =      text_embeddings.repeat_interleave(lowercase      ,  dim=0     )

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

				_snake_case     , _snake_case          =      self.get_timesteps(lowercase      ,  lowercase      ,  self.device     )
				_snake_case          =      timesteps[:1].repeat(lowercase     )

				# Preprocess image
				_snake_case          =      preprocess(lowercase      ,  lowercase      ,  lowercase     )
				_snake_case          =      self.prepare_latents(
				    lowercase      ,  lowercase      ,  lowercase      ,  text_embeddings.dtype      ,  self.device      ,  lowercase     )

				_snake_case          =      preprocess(lowercase      ,  lowercase      ,  lowercase     )
				_snake_case          =      self.prepare_latents(
				    lowercase      ,  lowercase      ,  lowercase      ,  text_embeddings.dtype      ,  self.device      ,  lowercase     )

				_snake_case          =      slerp(lowercase      ,  lowercase      ,  lowercase     )

				if clip_guidance_scale > 0:
							_snake_case          =      self.get_clip_image_embeddings(lowercase      ,  lowercase     )
							_snake_case          =      self.get_clip_image_embeddings(lowercase      ,  lowercase     )
							_snake_case          =      slerp(
							    lowercase      ,  lowercase      ,  lowercase     )

				# 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          =      content_text_input.input_ids.shape[-1]
							_snake_case          =      self.tokenizer(['']      ,  padding='max_length'      ,  max_length=lowercase      ,  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(lowercase      ,  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, 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(lowercase      ,  generator=lowercase      ,  device='cpu'      ,  dtype=lowercase     ).to(
										    self.device     )
							else:
										_snake_case          =      torch.randn(lowercase      ,  generator=lowercase      ,  device=self.device      ,  dtype=lowercase     )
				else:
							if latents.shape != latents_shape:
										raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''     )
							_snake_case          =      latents.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

				with self.progress_bar(total=lowercase     ):
							for i, t in enumerate(lowercase     ):
										# 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(lowercase      ,  lowercase     )

										# predict the noise residual
										_snake_case          =      self.unet(lowercase      ,  lowercase      ,  encoder_hidden_states=lowercase     ).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(
													    lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  )

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				_snake_case          =      1 / 0.18215 * latents
				_snake_case          =      self.vae.decode(lowercase     ).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(lowercase     )

				if not return_dict:
							return (image, None)

				return StableDiffusionPipelineOutput(images=lowercase      ,  nsfw_content_detected=lowercase     )