




from typing import Callable, List, Optional, Union

import torch

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


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


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


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def __init__(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						super().__init__()

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

						lowerCamelCase_				:						Union[str, Any]				  =				None

					def       lowercase_				(							self					,			__lowercase=0   ):







						'''simple docstring'''




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

						lowerCamelCase_				:						Tuple				  =				torch.device(f"cuda:{gpu_id}"   )

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

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

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

					def       lowercase_				(							self					,			__lowercase=0   ):







						'''simple docstring'''




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

						lowerCamelCase_				:						Tuple				  =				torch.device(f"cuda:{gpu_id}"   )

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

						lowerCamelCase_				:						List[str]				  =				None
						for cpu_offloaded_model in [self.unet]:
							lowerCamelCase_  ,     lowerCamelCase_				:						List[str]				  =				cpu_offload_with_hook(__lowercase					,			__lowercase					,			prev_module_hook=__lowercase   )

						if self.safety_checker is not None:
							lowerCamelCase_  ,     lowerCamelCase_				:						int				  =				cpu_offload_with_hook(self.safety_checker					,			__lowercase					,			prev_module_hook=__lowercase   )

						# We'll offload the last model manually.
						lowerCamelCase_				:						str				  =				hook

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







						'''simple docstring'''




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

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				(batch_size, num_channels, height, width)
						if isinstance(__lowercase					,			__lowercase   ) and len(__lowercase   ) != batch_size:
							raise ValueError(
							    f"You have passed a list of generators of length {len(__lowercase   )}, but requested an effective batch"
							    f" size of {batch_size}. Make sure the batch size matches the length of the generators."   )

						if latents is None:
							lowerCamelCase_				:						str				  =				randn_tensor(__lowercase					,			generator=__lowercase					,			device=__lowercase					,			dtype=__lowercase   )
						else:
							lowerCamelCase_				:						Optional[int]				  =				latents.to(device=__lowercase					,			dtype=__lowercase   )

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

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = "pil"   ):







						'''simple docstring'''




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

						# Equivalent to diffusers.VaeImageProcessor.denormalize
						lowerCamelCase_				:						Any				  =				(sample / 2 + 0.5).clamp(0					,			1   )
						if output_type == "pt":
							return sample

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

						# Output_type must be 'pil'
						lowerCamelCase_				:						int				  =				self.numpy_to_pil(__lowercase   )
						return sample

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




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

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




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

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

						if latents is not None:
							lowerCamelCase_				:						int				  =				(batch_size, 3, img_size, img_size)
							if latents.shape != expected_shape:
								raise ValueError(f"The shape of latents is {latents.shape} but is expected to be {expected_shape}."   )

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





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







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				self.unet.config.sample_size
						lowerCamelCase_				:						Optional[int]				  =				self._execution_device

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

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

						# 3. Handle class_labels for class-conditional models
						lowerCamelCase_				:						str				  =				self.prepare_class_labels(__lowercase					,			__lowercase					,			class_labels=__lowercase   )

						# 4. Prepare timesteps
						if timesteps is not None:
							self.scheduler.set_timesteps(timesteps=__lowercase					,			device=__lowercase   )
							lowerCamelCase_				:						Optional[int]				  =				self.scheduler.timesteps
							lowerCamelCase_				:						Optional[int]				  =				len(__lowercase   )
						else:
							self.scheduler.set_timesteps(__lowercase   )
							lowerCamelCase_				:						Tuple				  =				self.scheduler.timesteps

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

								lowerCamelCase_				:						Optional[Any]				  =				self.scheduler.step(__lowercase					,			__lowercase					,			__lowercase					,			generator=__lowercase   )[0]

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

        # 6. Post-process image sample
						lowerCamelCase_				:						List[Any]				  =				self.postprocess_image(__lowercase					,			output_type=__lowercase   )

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

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=__lowercase   )







import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  int				,						a					:  List[Any]				,						a					:  Tuple				,						a					:  Dict=False				,						a					:  Union[str, Any]=10_24				,						a					:  List[str]=10_24				,						a					:  Optional[Any]=3.6				,						a					:  List[Any]=False				,						)						->List[str]:
													SCREAMING_SNAKE_CASE__  :					str =       tokenizer
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       tokenizer.bos_token_id
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       dataset
													SCREAMING_SNAKE_CASE__  :					List[Any] =       seq_length
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       0
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       infinite
													SCREAMING_SNAKE_CASE__  :					Tuple =       0
													SCREAMING_SNAKE_CASE__  :					Dict =       tokenized

													if self.tokenized:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       seq_length * num_of_sequences
																			SCREAMING_SNAKE_CASE__  :					Dict =       "input_ids"
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       seq_length * chars_per_token * num_of_sequences
																			SCREAMING_SNAKE_CASE__  :					Any =       "content"

							def __iter__(   self					:  Union[str, Any]     )						->List[str]:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       iter(self.dataset     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       True
													while more_examples:
																			SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       [], 0
																			while True:
																									if buffer_len >= self.max_buffer_size:
																															break
																									try:
																															buffer.append(next(a     )[self.content_field]     )
																															buffer_len += len(buffer[-1]     )
																									except StopIteration:
																															if self.infinite:
																																					SCREAMING_SNAKE_CASE__  :					Dict =       iter(self.dataset     )
																																					self.epoch += 1
																																					logger.info(f"""Dataset epoch: {self.epoch}"""     )
																															else:
																																					SCREAMING_SNAKE_CASE__  :					int =       False
																																					break
																			if self.tokenized:
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       buffer
																			else:
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.tokenizer(a				,						truncation=a     )["input_ids"]
																			SCREAMING_SNAKE_CASE__  :					Tuple =       []
																			for tokenized_input in tokenized_inputs:
																									all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
																			for i in range(0				,						len(a     )				,						self.seq_length     ):
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       all_token_ids[i : i + self.seq_length]
																									if len(a     ) == self.seq_length:
																															self.current_size += 1
																															yield torch.tensor(a     )






							def   A_	(   self					:  Optional[Any]				,						a					:  Optional[Any]=10_00     )						->Union[str, Any]:
													return ShufflerIterDataPipe(self				,						buffer_size=a     )







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[Any] =       args.model_ckpt.split("/"  )[-1]
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       logging.getLogger(__name__  )
						SCREAMING_SNAKE_CASE__  :					Tuple =       Path(args.save_dir  ) / "log/"
						log_dir.mkdir(exist_ok=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					str =       f"""debug_{accelerator.process_index}.log"""
						logging.basicConfig(
						    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"      , datefmt="%m/%d/%Y %H:%M:%S"      , level=logging.INFO      , handlers=[logging.FileHandler(log_dir / filename  ), logging.StreamHandler()]      , )
						if accelerator.is_main_process:  # we only want to setup logging once
												accelerator.init_trackers(_lowerCamelCase      , vars(_lowerCamelCase  )  )
												SCREAMING_SNAKE_CASE__  :					Optional[int] =       accelerator.trackers[0].run.name
												logger.setLevel(logging.INFO  )
												datasets.utils.logging.set_verbosity_info()
												transformers.utils.logging.set_verbosity_info()
						else:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       ""
												logger.setLevel(logging.ERROR  )
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						return logger, run_name







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[str] =       {"streaming": True}
						SCREAMING_SNAKE_CASE__  :					str =       load_dataset(args.dataset_name_train      , split="train"      , **_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					str =       train_data.shuffle(buffer_size=args.shuffle_buffer      , seed=args.seed  )
						SCREAMING_SNAKE_CASE__  :					Any =       load_dataset(args.dataset_name_valid      , split="train"      , **_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Any =       ConstantLengthDataset(
						    _lowerCamelCase      , _lowerCamelCase      , infinite=_lowerCamelCase      , seq_length=args.seq_length      , tokenized=args.tokenized  )
						SCREAMING_SNAKE_CASE__  :					str =       ConstantLengthDataset(
						    _lowerCamelCase      , _lowerCamelCase      , infinite=_lowerCamelCase      , seq_length=args.seq_length      , tokenized=args.tokenized  )
						SCREAMING_SNAKE_CASE__  :					Any =       train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
						SCREAMING_SNAKE_CASE__  :					Dict =       DataLoader(_lowerCamelCase      , batch_size=args.train_batch_size      , shuffle=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       DataLoader(_lowerCamelCase      , batch_size=args.valid_batch_size  )
						return train_dataloader, eval_dataloader







def    UpperCAmelCase				(  _lowerCamelCase   :	Any      , _lowerCamelCase   :	Dict      , _lowerCamelCase   :	List[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					int =       [], []
						for n, p in model.named_parameters():
												if any(nd in n for nd in no_decay  ):
																		params_without_wd.append(_lowerCamelCase  )
												else:
																		params_with_wd.append(_lowerCamelCase  )
						return [
						    {"params": params_with_wd, "weight_decay": args.weight_decay},
						    {"params": params_without_wd, "weight_decay": 0.0},
						]







def    UpperCAmelCase				(  _lowerCamelCase   :	Union[str, Any]      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						logger.info(f"""Step {step}: {metrics}"""  )
						if accelerator.is_main_process:
												accelerator.log(_lowerCamelCase      , _lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Tuple      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[int] =       accelerator.unwrap_model(_lowerCamelCase  ).config
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       4 if args.gradient_checkpointing else 3
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       factor * (
						    1.0
						    + (args.seq_length / (6.0 * config_model.n_embd))
						    + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
						)
						SCREAMING_SNAKE_CASE__  :					Tuple =       flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						model.eval()
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
						for step, batch in enumerate(_lowerCamelCase  ):
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE__  :					List[str] =       model(_lowerCamelCase      , labels=_lowerCamelCase  )
												SCREAMING_SNAKE_CASE__  :					Any =       outputs.loss.repeat(args.valid_batch_size  )
												losses.append(accelerator.gather(_lowerCamelCase  )  )
												if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																		break
						SCREAMING_SNAKE_CASE__  :					str =       torch.cat(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Dict =       losses[: eval_dataloader.dataset.current_size].mean()
						try:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       torch.exp(_lowerCamelCase  )
						except OverflowError:
												SCREAMING_SNAKE_CASE__  :					Optional[Any] =       float("inf"  )
						return loss.item(), perplexity.item()


# Settings
__lowercase							:List[Any]							=	HfArgumentParser(TrainingArguments)
__lowercase							:Any							=	parser.parse_args()

# Accelerator
__lowercase							:List[Any]							=	ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
__lowercase							:Optional[int]							=	Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
__lowercase							:str							=	{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

__lowercase							:List[Any]							=	Namespace(**vars(args), **acc_state)
__lowercase							:Optional[Any]							=	accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
				__lowercase							:Dict							=	Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
__lowercase							,  __lowercase							:Union[str, Any]							=	setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
				hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
__lowercase							:Tuple							=	AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
				model.gradient_checkpointing_enable()
__lowercase							:Union[str, Any]							=	AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__lowercase							,  __lowercase							:Any							=	create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__lowercase							:int							=	AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowercase							:str							=	get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)







def    UpperCAmelCase				(  ):





						'''simple docstring'''


						return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
__lowercase							,  __lowercase							,  __lowercase							,  __lowercase							:Dict							=	accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# 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)
								__lowercase							:List[str]							=	os.path.basename(args.resume_from_checkpoint)
				else:
								# Get the most recent checkpoint
								__lowercase							:Union[str, Any]							=	[f.name for f in os.scandir(args.save_dir) if f.is_dir() and "step" in str(f)]
								dirs.sort(key=os.path.getctime)
								__lowercase							:Dict							=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract the step of the checkpoint to continue from there
				__lowercase							:Any							=	os.path.splitext(path)[0]
				__lowercase							:str							=	int(training_difference.replace("step_", ""))

# Train model
model.train()
__lowercase							:int							=	0
__lowercase							:List[Any]							=	time.time()
__lowercase							:Any							=	0
for step, batch in enumerate(train_dataloader, start=1):
				if args.resume_from_checkpoint and step < resume_step:
								continue  # we need to skip steps until we reach the resumed step
				__lowercase							:List[str]							=	model(batch, labels=batch, use_cache=False).loss
				__lowercase							:Union[str, Any]							=	accelerator.gather(loss.repeat(args.train_batch_size)).mean()
				loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
				log_metrics(step, {"samples": step * samples_per_step, "loss_per_step/train": loss.item()})
				__lowercase							:List[Any]							=	loss / args.gradient_accumulation_steps
				if step % args.gradient_accumulation_steps != 0:
								# Prevent backward from doing gradient all_reduce in every step
								if accelerator.distributed_type == DistributedType.MULTI_GPU:
												with model.no_sync():
																accelerator.backward(loss)
								else:
												accelerator.backward(loss)
				else:
								__lowercase							:List[Any]							=	get_lr()
								accelerator.backward(loss)
								accelerator.clip_grad_norm_(model.parameters(), 1.0)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()
								__lowercase							:str							=	time.time() - t_start
								__lowercase							:List[str]							=	compute_tflops(elapsed_time, accelerator, args)
								log_metrics(
								    step,
								    {
								        "steps": completed_steps,
								        "loss/train": loss_tracking,
								        "lr": lr,
								        "tflops": tflops,
								        "time_per_iteration": elapsed_time,
								    },
								)
								__lowercase							:str							=	time.time()
								__lowercase							:Optional[int]							=	0
								completed_steps += 1
				if step % args.save_checkpoint_steps == 0:
								logger.info("Evaluating and saving model checkpoint")
								__lowercase							,  __lowercase							:List[str]							=	evaluate(args)
								log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
								accelerator.wait_for_everyone()
								__lowercase							:int							=	os.path.join(args.save_dir, f"step_{step}")
								accelerator.save_state(save_dir)
								if accelerator.is_main_process:
												hf_repo.push_to_hub(commit_message=f"step {step}")
								model.train()
				if completed_steps >= args.max_train_steps:
								break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
__lowercase							,  __lowercase							:List[str]							=	evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
__lowercase							:List[str]							=	accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__lowercase							:List[Any]							=	os.path.join(args.save_dir, f"step_{step}")
accelerator.save_state(save_dir)
if accelerator.is_main_process:
				hf_repo.push_to_hub(commit_message="final model")