import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


_SCREAMING_SNAKE_CASE:						Optional[Any]    	=       16
_SCREAMING_SNAKE_CASE:						str    	=       32






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			return int(x / 2**20  )

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __enter__(			self    :				int   ):






					"""simple docstring"""
					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					_snake_case              =      torch.cuda.memory_allocated()
					_snake_case              =      psutil.Process()

					_snake_case              =      self.cpu_mem_used()
					_snake_case              =      True
					_snake_case              =      threading.Thread(target=self.peak_monitor_func   )
					_snake_case              =      True
					peak_monitor_thread.start()
					return self



		def 				_a				(			self    :				Any   ):






					"""simple docstring"""
					return self.process.memory_info().rss



		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					_snake_case              =      -1

					while True:
								_snake_case              =      max(self.cpu_mem_used()  ,					self.cpu_peak   )

								# can't sleep or will not catch the peak right (this comment is here on purpose)
								# time.sleep(0.001) # 1msec

								if not self.peak_monitoring:
											break







		def __exit__(			self    :				Optional[Any]  ,					*__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					_snake_case              =      False

					gc.collect()
					torch.cuda.empty_cache()
					_snake_case              =      torch.cuda.memory_allocated()
					_snake_case              =      torch.cuda.max_memory_allocated()
					_snake_case              =      bamb(self.end - self.begin   )
					_snake_case              =      bamb(self.peak - self.begin   )

					_snake_case              =      self.cpu_mem_used()
					_snake_case              =      bamb(self.cpu_end - self.cpu_begin   )
					_snake_case              =      bamb(self.cpu_peak - self.cpu_begin   )
					# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


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

			_SCREAMING_SNAKE_CASE:						str    	=       mocked_dataloaders  # noqa: F811






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			if os.environ.get("TESTING_MOCKED_DATALOADERS"							,	lowerCamelCase_  ) == "1":
						_snake_case              =      2

			# New Code #
			# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
			_snake_case              =      FullyShardedDataParallelPlugin(
			    state_dict_config=FullStateDictConfig(offload_to_cpu=lowerCamelCase_							,	ranka_only=lowerCamelCase_  )							,	optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowerCamelCase_							,	ranka_only=lowerCamelCase_  )							,	)

			# Initialize accelerator
			if args.with_tracking:
						_snake_case              =      Accelerator(
						    cpu=args.cpu							,	mixed_precision=args.mixed_precision							,	log_with="wandb"							,	project_dir=args.logging_dir							,	fsdp_plugin=lowerCamelCase_							,	)
			else:
						_snake_case              =      Accelerator(fsdp_plugin=lowerCamelCase_  )
			accelerator.print(accelerator.distributed_type  )

			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
			# 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"]  )

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						_snake_case              =      vars(lowerCamelCase_  )
						accelerator.init_trackers("fsdp_glue_no_trainer"							,	lowerCamelCase_  )

			_snake_case              =      AutoTokenizer.from_pretrained(args.model_name_or_path  )
			_snake_case              =      load_dataset("glue"							,	"mrpc"  )
			_snake_case              =      evaluate.load("glue"							,	"mrpc"  )

			def tokenize_function(lowerCamelCase_  ):
						# max_length=None => use the model max length (it's actually the default)
						_snake_case              =      tokenizer(examples["sentence1"]							,	examples["sentence2"]							,	truncation=lowerCamelCase_							,	max_length=lowerCamelCase_  )
						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(
						    lowerCamelCase_							,	batched=lowerCamelCase_							,	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"  )

			# 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

			def collate_fn(lowerCamelCase_  ):
						# 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(
						    lowerCamelCase_							,	padding="longest"							,	max_length=lowerCamelCase_							,	pad_to_multiple_of=lowerCamelCase_							,	return_tensors="pt"							,	)

			# Instantiate dataloaders.
			_snake_case              =      DataLoader(
			    tokenized_datasets["train"]							,	shuffle=lowerCamelCase_							,	collate_fn=lowerCamelCase_							,	batch_size=lowerCamelCase_  )
			_snake_case              =      DataLoader(
			    tokenized_datasets["validation"]							,	shuffle=lowerCamelCase_							,	collate_fn=lowerCamelCase_							,	batch_size=lowerCamelCase_  )

			set_seed(lowerCamelCase_  )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			_snake_case              =      AutoModelForSequenceClassification.from_pretrained(
			    args.model_name_or_path							,	return_dict=lowerCamelCase_							,	low_cpu_mem_usage=lowerCamelCase_  )

			# New Code #
			# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
			_snake_case              =      accelerator.prepare(lowerCamelCase_  )
			accelerator.print(lowerCamelCase_  )

			# Instantiate optimizer
			# New Code #
			# For FSDP feature, at present it doesn't support multiple parameter groups,
			# so we need to create a single parameter group for the whole model
			_snake_case              =      torch.optim.AdamW(params=model.parameters()							,	lr=lowerCamelCase_							,	weight_decay=2E-4  )

			# Instantiate scheduler
			_snake_case              =      get_linear_schedule_with_warmup(
			    optimizer=lowerCamelCase_							,	num_warmup_steps=10							,	num_training_steps=(len(lowerCamelCase_  ) * num_epochs) // gradient_accumulation_steps							,	)

			# New Code #
			# For FSDP feature, prepare everything except the model as we have already prepared the model
			# before creating the optimizer
			# 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              =      accelerator.prepare(
			    lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )

			_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(lowerCamelCase_  )[0]

						if "epoch" in training_difference:
									num_epochs -= int(training_difference.replace("epoch_"							,	""  )  )
									_snake_case              =      None
						else:
									_snake_case              =      int(training_difference.replace("step_"							,	""  )  )
									num_epochs -= resume_step // len(lowerCamelCase_  )
									# If resuming by step, we also need to know exactly how far into the DataLoader we went
									_snake_case              =      (num_epochs * len(lowerCamelCase_  )) - resume_step

    # Now we train the model
			for epoch in range(lowerCamelCase_  ):
						# New Code #
						# context manager to track the peak memory usage during the training epoch
						with TorchTracemalloc() as tracemalloc:
									model.train()
									if args.with_tracking:
												_snake_case              =      0
									for step, batch in enumerate(lowerCamelCase_  ):
												# We need to skip steps until we reach the resumed step
												if args.resume_from_checkpoint and epoch == 0:
															if resume_step is not None and step < resume_step:
																		pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device  )
												_snake_case              =      model(**lowerCamelCase_  )
												_snake_case              =      outputs.loss
												_snake_case              =      loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
															total_loss += loss.detach().float()
												accelerator.backward(lowerCamelCase_  )
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															# accelerator.print(lr_scheduler.get_lr())

												overall_step += 1

												if isinstance(lowerCamelCase_							,	lowerCamelCase_  ):
															_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							,	lowerCamelCase_  )
																		accelerator.save_state(lowerCamelCase_  )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin  )  )  )
						accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used  )  )
						accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked  )  )
						accelerator.print(
						    "Total Peak Memory consumed during the train (max): {}".format(
						        tracemalloc.peaked + bamb(tracemalloc.begin  )  )  )
						# Logging the peak memory usage of the GPU to the tracker
						if args.with_tracking:
									accelerator.log(
									    {
									        "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin  ),
									    }							,	step=lowerCamelCase_							,	)

						# New Code #
						# context manager to track the peak memory usage during the evaluation
						with TorchTracemalloc() as tracemalloc:
									model.eval()
									for step, batch in enumerate(lowerCamelCase_  ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device  )
												with torch.no_grad():
															_snake_case              =      model(**lowerCamelCase_  )
												_snake_case              =      outputs.logits.argmax(dim=-1  )
												_snake_case       , _snake_case              =      accelerator.gather_for_metrics((predictions, batch["labels"])  )
												metric.add_batch(
												    predictions=lowerCamelCase_							,	references=lowerCamelCase_							,	)

									_snake_case              =      metric.compute()
									# Use accelerator.print to print only on the main process.
									accelerator.print(f'epoch {epoch}:'							,	lowerCamelCase_  )
									if args.with_tracking:
												accelerator.log(
												    {
												        "accuracy": eval_metric["accuracy"],
												        "f1": eval_metric["f1"],
												        "train_loss": total_loss.item() / len(lowerCamelCase_  ),
												    }							,	step=lowerCamelCase_							,	)

									if checkpointing_steps == "epoch":
												_snake_case              =      f'epoch_{epoch}'
												if args.output_dir is not None:
															_snake_case              =      os.path.join(args.output_dir							,	lowerCamelCase_  )
												accelerator.save_state(lowerCamelCase_  )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin  )  )  )
						accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used  )  )
						accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked  )  )
						accelerator.print(
						    "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin  )  )  )
						# Logging the peak memory usage of the GPU to the tracker
						if args.with_tracking:
									accelerator.log(
									    {
									        "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin  ),
									    }							,	step=lowerCamelCase_							,	)

			if args.with_tracking:
						accelerator.end_training()






def    UpperCAmelCase__(						):



			"""simple docstring"""





			_snake_case              =      argparse.ArgumentParser(description="Simple example of training script."  )
			parser.add_argument(
			    "--mixed_precision"							,	type=lowerCamelCase_							,	default=lowerCamelCase_							,	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=lowerCamelCase_							,	default=lowerCamelCase_							,	help="Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."							,	)
			parser.add_argument(
			    "--resume_from_checkpoint"							,	type=lowerCamelCase_							,	default=lowerCamelCase_							,	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(
			    "--output_dir"							,	type=lowerCamelCase_							,	default="."							,	help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."							,	)
			parser.add_argument(
			    "--logging_dir"							,	type=lowerCamelCase_							,	default="logs"							,	help="Location on where to store experiment tracking logs`"							,	)
			parser.add_argument(
			    "--model_name_or_path"							,	type=lowerCamelCase_							,	help="Path to pretrained model or model identifier from huggingface.co/models."							,	required=lowerCamelCase_							,	)
			_snake_case              =      parser.parse_args()
			_snake_case              =      {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(lowerCamelCase_							,	lowerCamelCase_  )


if __name__ == "__main__":
			main()







from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				AutoencoderKL  ,					__SCREAMING_SNAKE_CASE    :				CLIPTextModel  ,					__SCREAMING_SNAKE_CASE    :				CLIPTokenizer  ,					__SCREAMING_SNAKE_CASE    :				UNetaDConditionModel  ,					__SCREAMING_SNAKE_CASE    :				DDIMScheduler  ,					__SCREAMING_SNAKE_CASE    :				float = 0.9_3  ,					__SCREAMING_SNAKE_CASE    :				bool = True  ,					):






					"""simple docstring"""
					_snake_case              =      mixing_coeff
					_snake_case              =      leapfrog_steps

					super().__init__()
					self.register_modules(
					    vae=__SCREAMING_SNAKE_CASE  ,					text_encoder=__SCREAMING_SNAKE_CASE  ,					tokenizer=__SCREAMING_SNAKE_CASE  ,					unet=__SCREAMING_SNAKE_CASE  ,					scheduler=__SCREAMING_SNAKE_CASE  ,					)

					_snake_case              =      2 ** (len(self.vae.config.block_out_channels   ) - 1)
					_snake_case              =      VaeImageProcessor(vae_scale_factor=self.vae_scale_factor   )



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None  ,					__SCREAMING_SNAKE_CASE    :				bool = False   ):






					"""simple docstring"""
					_snake_case              =      self.tokenizer(
					    __SCREAMING_SNAKE_CASE  ,					padding="max_length"  ,					max_length=self.tokenizer.model_max_length  ,					truncation=__SCREAMING_SNAKE_CASE  ,					return_tensors="pt"  ,					)

					_snake_case              =      self.text_encoder(text_inputs.input_ids.to(self.device   )   ).last_hidden_state

					_snake_case              =      prompt_embeds.to(dtype=self.text_encoder.dtype  ,					device=self.device   )

					if do_classifier_free_guidance:
								_snake_case              =      "" if negative_prompt is None else negative_prompt

								_snake_case              =      self.tokenizer(
								    __SCREAMING_SNAKE_CASE  ,					padding="max_length"  ,					max_length=self.tokenizer.model_max_length  ,					truncation=__SCREAMING_SNAKE_CASE  ,					return_tensors="pt"  ,					)

								_snake_case              =      self.text_encoder(uncond_input.input_ids.to(self.device   )   ).last_hidden_state

								_snake_case              =      torch.cat([negative_prompt_embeds, prompt_embeds]   )

					return prompt_embeds



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      self.mixing_coeff * x + (1 - self.mixing_coeff) * y
					_snake_case              =      self.mixing_coeff * y + (1 - self.mixing_coeff) * x

					return [x, y]



		def 				_a				(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
					_snake_case              =      (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

					return [x, y]



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					# as self.alphas_cumprod is always in cpu
					_snake_case              =      int(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

					return alpha_prod, 1 - alpha_prod



		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					):






					"""simple docstring"""
					_snake_case              =      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )
					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
					_snake_case              =      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

					_snake_case              =      (base - b_t * model_output) / a_t

					return model_input, next_model_input.to(base.dtype   )



		def 				_a				(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					):






					"""simple docstring"""
					_snake_case              =      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )
					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
					_snake_case              =      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
					_snake_case              =      a_t * base + b_t * model_output

					return model_input, next_model_input.to(base.dtype   )



		@torch.no_grad()
		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      1 / self.vae.config.scaling_factor * latents
					_snake_case              =      self.vae.decode(__SCREAMING_SNAKE_CASE   ).sample
					_snake_case              =      (image / 2 + 0.5).clamp(0  ,					1   )
					return image



		@torch.no_grad()
		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Image.Image  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				float  ,					__SCREAMING_SNAKE_CASE    :				Optional[torch.Generator] = None  ,					):






					"""simple docstring"""
					_snake_case              =      guidance_scale > 1.0

					_snake_case              =      image.to(device=self.device  ,					dtype=text_embeds.dtype   )
					_snake_case              =      self.vae.encode(__SCREAMING_SNAKE_CASE   ).latent_dist.sample(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.vae.config.scaling_factor * latent

					_snake_case              =      [latent.clone(), latent.clone()]

					for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE   )  ,					total=len(__SCREAMING_SNAKE_CASE   )   ):
								_snake_case              =      self.noise_mixing_layer(x=coupled_latents[0]  ,					y=coupled_latents[1]   )

								# j - model_input index, k - base index
								for j in range(2   ):
											_snake_case              =      j ^ 1

											if self.leapfrog_steps:
														if i % 2 == 0:
																	_snake_case       , _snake_case              =      j, k

											_snake_case              =      coupled_latents[j]
											_snake_case              =      coupled_latents[k]

											_snake_case              =      torch.cat([model_input] * 2   ) if do_classifier_free_guidance else model_input

											_snake_case              =      self.unet(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE   ).sample

											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)

											_snake_case       , _snake_case              =      self.noise_step(
											    base=__SCREAMING_SNAKE_CASE  ,					model_input=__SCREAMING_SNAKE_CASE  ,					model_output=__SCREAMING_SNAKE_CASE  ,					timestep=__SCREAMING_SNAKE_CASE  ,					)

											_snake_case              =      model_input

					return coupled_latents







		@torch.no_grad()
		def __call__(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Image.Image  ,					__SCREAMING_SNAKE_CASE    :				float = 3.0  ,					__SCREAMING_SNAKE_CASE    :				int = 50  ,					__SCREAMING_SNAKE_CASE    :				float = 0.8  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[torch.Generator] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = "pil"  ,					):






					"""simple docstring"""
					_snake_case              =      guidance_scale > 1.0

					_snake_case              =      self.image_processor.preprocess(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self._encode_prompt(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self._encode_prompt(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  ,					self.device   )

					_snake_case              =      num_inference_steps - int(num_inference_steps * strength   )
					_snake_case              =      self.scheduler.timesteps[t_limit:]
					_snake_case              =      fwd_timesteps.flip(0   )

					_snake_case              =      self.prepare_latents(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE   )  ,					total=len(__SCREAMING_SNAKE_CASE   )   ):
								# j - model_input index, k - base index
								for k in range(2   ):
											_snake_case              =      k ^ 1

											if self.leapfrog_steps:
														if i % 2 == 1:
																	_snake_case       , _snake_case              =      j, k

											_snake_case              =      coupled_latents[j]
											_snake_case              =      coupled_latents[k]

											_snake_case              =      torch.cat([model_input] * 2   ) if do_classifier_free_guidance else model_input

											_snake_case              =      self.unet(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE   ).sample

											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)

											_snake_case       , _snake_case              =      self.denoise_step(
											    base=__SCREAMING_SNAKE_CASE  ,					model_input=__SCREAMING_SNAKE_CASE  ,					model_output=__SCREAMING_SNAKE_CASE  ,					timestep=__SCREAMING_SNAKE_CASE  ,					)

											_snake_case              =      model_input

								_snake_case              =      self.denoise_mixing_layer(x=coupled_latents[0]  ,					y=coupled_latents[1]   )

					# either one is fine
					_snake_case              =      coupled_latents[0]

					if output_type not in ["latent", "pt", "np", "pil"]:
								_snake_case              =      (
								    F'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
								    "`pil`, `np`, `pt`, `latent`"
								)
								deprecate("Unsupported output_type"  ,					"1.0.0"  ,					__SCREAMING_SNAKE_CASE  ,					standard_warn=__SCREAMING_SNAKE_CASE   )
								_snake_case              =      "np"

					if output_type == "latent":
								_snake_case              =      final_latent
					else:
								_snake_case              =      self.decode_latents(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      self.image_processor.postprocess(__SCREAMING_SNAKE_CASE  ,					output_type=__SCREAMING_SNAKE_CASE   )

					return image

