






'''simple docstring'''





from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


lowerCamelCase_		  =						getLogger(__name__)


lowerCamelCase_		  =						4
lowerCamelCase_		  =						7

lowerCamelCase_		  =						{
    'float16': torch.floataa,
    'float32': torch.floataa,
}
def   lowerCamelCase      (		a_			)			->    Union[str, Any]:



							if isinstance(__A				,		torch.Tensor			):
														return image
							elif isinstance(__A				,		PIL.Image.Image			):
														lowerCAmelCase_       		=  [image]

							if isinstance(image[0]				,		PIL.Image.Image			):
														lowerCAmelCase_		,     lowerCAmelCase_       		=  image[0].size
														lowerCAmelCase_		,     lowerCAmelCase_       		=  (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

														lowerCAmelCase_       		=  [np.array(i.resize((w, h)			)			)[None, :] for i in image]
														lowerCAmelCase_       		=  np.concatenate(__A				,		axis=0			)
														lowerCAmelCase_       		=  np.array(__A			).astype(np.floataa			) / 255.0
														lowerCAmelCase_       		=  image.transpose(0				,		3				,		1				,		2			)
														lowerCAmelCase_       		=  2.0 * image - 1.0
														lowerCAmelCase_       		=  torch.from_numpy(__A			)
							elif isinstance(image[0]				,		torch.Tensor			):
														lowerCAmelCase_       		=  torch.cat(__A				,		dim=0			)

							return image




class 							a_				(		UpperCAmelCase_	):


							'''simple docstring'''






							def __init__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_ = 3_5_0    ,    )      ->      Any:
														'''simple docstring'''

														super().__init__(
														    vae=lowercase_    ,    text_encoder=lowercase_    ,    tokenizer=lowercase_    ,    unet=lowercase_    ,    low_res_scheduler=lowercase_    ,    scheduler=lowercase_    ,    safety_checker=lowercase_    ,    feature_extractor=lowercase_    ,    watermarker=lowercase_    ,    max_noise_level=lowercase_    ,    )

							def __call__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_ = 7_5    ,    lowercase_ = 9.0    ,    lowercase_ = 2_0    ,    lowercase_ = None    ,    lowercase_ = 1    ,    lowercase_ = 0.0    ,    lowercase_ = None    ,    lowercase_ = None    ,    lowercase_ = None    ,    lowercase_ = None    ,    lowercase_ = "pil"    ,    lowercase_ = True    ,    lowercase_ = None    ,    lowercase_ = 1    ,    )      ->      Dict:
														'''simple docstring'''

														self.check_inputs(lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_     )

														# 2. Define call parameters
														if prompt is not None and isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  1
														elif prompt is not None and isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  len(lowercase_     )
														else:
																					lowerCAmelCase_       		=  prompt_embeds.shape[0]

														lowerCAmelCase_       		=  self._execution_device
														# 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.
														lowerCAmelCase_       		=  guidance_scale > 1.0

														# 3. Encode input prompt
														lowerCAmelCase_       		=  self._encode_prompt(
														    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    prompt_embeds=lowercase_    ,    negative_prompt_embeds=lowercase_    ,    )

														lowerCAmelCase_       		=  ORT_TO_PT_TYPE[str(text_embeddings.dtype     )]

														# 4. Preprocess image
														lowerCAmelCase_       		=  preprocess(lowercase_     )
														lowerCAmelCase_       		=  image.cpu()

														# 5. set timesteps
														self.scheduler.set_timesteps(lowercase_    ,    device=lowercase_     )
														lowerCAmelCase_       		=  self.scheduler.timesteps

														# 5. Add noise to image
														lowerCAmelCase_       		=  torch.tensor([noise_level]    ,    dtype=torch.long    ,    device=lowercase_     )
														lowerCAmelCase_       		=  torch.randn(image.shape    ,    generator=lowercase_    ,    device=lowercase_    ,    dtype=lowercase_     )
														lowerCAmelCase_       		=  self.low_res_scheduler.add_noise(lowercase_    ,    lowercase_    ,    lowercase_     )

														lowerCAmelCase_       		=  2 if do_classifier_free_guidance else 1
														lowerCAmelCase_       		=  np.concatenate([image] * batch_multiplier * num_images_per_prompt     )
														lowerCAmelCase_       		=  np.concatenate([noise_level] * image.shape[0]     )

														# 6. Prepare latent variables
														lowerCAmelCase_		,     lowerCAmelCase_       		=  image.shape[2:]
														lowerCAmelCase_       		=  self.prepare_latents(
														    batch_size * num_images_per_prompt    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    )

														# 7. Check that sizes of image and latents match
														lowerCAmelCase_       		=  image.shape[1]
														if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
																					raise ValueError(
																					    'Incorrect configuration settings! The config of `pipeline.unet` expects'
																					    f''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
																					    f''' `num_channels_image`: {num_channels_image} '''
																					    f''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
																					    ' `pipeline.unet` or your `image` input.'     )

														# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
														lowerCAmelCase_       		=  self.prepare_extra_step_kwargs(lowercase_    ,    lowercase_     )

														lowerCAmelCase_       		=  next(
														    (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')    ,    'tensor(float)'     )
														lowerCAmelCase_       		=  ORT_TO_NP_TYPE[timestep_dtype]

														# 9. Denoising loop
														lowerCAmelCase_       		=  len(lowercase_     ) - num_inference_steps * self.scheduler.order
														with self.progress_bar(total=lowercase_     ) as progress_bar:
																					for i, t in enumerate(lowercase_     ):
																												# expand the latents if we are doing classifier free guidance
																												lowerCAmelCase_       		=  np.concatenate([latents] * 2     ) if do_classifier_free_guidance else latents

																												# concat latents, mask, masked_image_latents in the channel dimension
																												lowerCAmelCase_       		=  self.scheduler.scale_model_input(lowercase_    ,    lowercase_     )
																												lowerCAmelCase_       		=  np.concatenate([latent_model_input, image]    ,    axis=1     )

																												# timestep to tensor
																												lowerCAmelCase_       		=  np.array([t]    ,    dtype=lowercase_     )

																												# predict the noise residual
																												lowerCAmelCase_       		=  self.unet(
																												    sample=lowercase_    ,    timestep=lowercase_    ,    encoder_hidden_states=lowercase_    ,    class_labels=noise_level.astype(np.intaa     )    ,    )[0]

																												# perform guidance
																												if do_classifier_free_guidance:
																																			lowerCAmelCase_		,     lowerCAmelCase_       		=  np.split(lowercase_    ,    2     )
																												lowerCAmelCase_       		=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																												# compute the previous noisy sample x_t -> x_t-1
																												lowerCAmelCase_       		=  self.scheduler.step(
																												    torch.from_numpy(lowercase_     )    ,    lowercase_    ,    lowercase_    ,    **lowercase_     ).prev_sample

																												# call the callback, if provided
																												if i == len(lowercase_     ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
																																			progress_bar.update()
																																			if callback is not None and i % callback_steps == 0:
																																										callback(lowercase_    ,    lowercase_    ,    lowercase_     )

																																										  # 10. Post-processing
														lowerCAmelCase_       		=  self.decode_latents(latents.float()     )

														# 11. Convert to PIL
														if output_type == "pil":
																					lowerCAmelCase_       		=  self.numpy_to_pil(lowercase_     )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=lowercase_     )

							def   _lowercase (	self    ,    lowercase_     )      ->      Any:
														'''simple docstring'''

														lowerCAmelCase_       		=  1 / 0.0_83_33 * latents
														lowerCAmelCase_       		=  self.vae(latent_sample=lowercase_     )[0]
														lowerCAmelCase_       		=  np.clip(image / 2 + 0.5    ,    0    ,    1     )
														lowerCAmelCase_       		=  image.transpose((0, 2, 3, 1)     )
														return image






							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_ = None    ,    lowercase_ = None    ,    )      ->      Dict:
														'''simple docstring'''

														if prompt is not None and isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  1
														elif prompt is not None and isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  len(lowercase_     )
														else:
																					lowerCAmelCase_       		=  prompt_embeds.shape[0]

														if prompt_embeds is None:
																					lowerCAmelCase_       		=  self.tokenizer(
																					    lowercase_    ,    padding='max_length'    ,    max_length=self.tokenizer.model_max_length    ,    truncation=lowercase_    ,    return_tensors='pt'    ,    )
																					lowerCAmelCase_       		=  text_inputs.input_ids
																					lowerCAmelCase_       		=  self.tokenizer(lowercase_    ,    padding='longest'    ,    return_tensors='pt'     ).input_ids

																					if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
																					    lowercase_    ,    lowercase_     ):
																												lowerCAmelCase_       		=  self.tokenizer.batch_decode(
																												    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]     )
																												logger.warning(
																												    'The following part of your input was truncated because CLIP can only handle sequences up to'
																												    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''     )

																					# no positional arguments to text_encoder
																					lowerCAmelCase_       		=  self.text_encoder(
																					    input_ids=text_input_ids.int().to(lowercase_     )    ,    )
																					lowerCAmelCase_       		=  prompt_embeds[0]

														lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_       		=  prompt_embeds.shape
														# duplicate text embeddings for each generation per prompt, using mps friendly method
														lowerCAmelCase_       		=  prompt_embeds.repeat(1    ,    lowercase_     )
														lowerCAmelCase_       		=  prompt_embeds.reshape(bs_embed * num_images_per_prompt    ,    lowercase_    ,    -1     )

														# get unconditional embeddings for classifier free guidance
														if do_classifier_free_guidance and negative_prompt_embeds is None:
																					lowerCAmelCase_       		=  4_2
																					if negative_prompt is None:
																												lowerCAmelCase_       		=  [''] * batch_size
																					elif type(lowercase_     ) is not type(lowercase_     ):
																												raise TypeError(
																												    f'''`negative_prompt` should be the same type to `prompt`, but got {type(lowercase_     )} !='''
																												    f''' {type(lowercase_     )}.'''     )
																					elif isinstance(lowercase_    ,    lowercase_     ):
																												lowerCAmelCase_       		=  [negative_prompt]
																					elif batch_size != len(lowercase_     ):
																												raise ValueError(
																												    f'''`negative_prompt`: {negative_prompt} has batch size {len(lowercase_     )}, but `prompt`:'''
																												    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																												    ' the batch size of `prompt`.'     )
																					else:
																												lowerCAmelCase_       		=  negative_prompt

																					lowerCAmelCase_       		=  text_input_ids.shape[-1]
																					lowerCAmelCase_       		=  self.tokenizer(
																					    lowercase_    ,    padding='max_length'    ,    max_length=lowercase_    ,    truncation=lowercase_    ,    return_tensors='pt'    ,    )

																					# if hasattr(uncond_input, "attention_mask"):
																					#     attention_mask = uncond_input.attention_mask.to(device)
																					# else:
																					#     attention_mask = None

																					lowerCAmelCase_       		=  self.text_encoder(
																					    input_ids=uncond_input.input_ids.int().to(lowercase_     )    ,    )
																					lowerCAmelCase_       		=  uncond_embeddings[0]

														if do_classifier_free_guidance:
																					lowerCAmelCase_       		=  uncond_embeddings.shape[1]
																					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																					lowerCAmelCase_       		=  uncond_embeddings.repeat(1    ,    lowercase_     )
																					lowerCAmelCase_       		=  uncond_embeddings.reshape(batch_size * num_images_per_prompt    ,    lowercase_    ,    -1     )

																					# 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
																					lowerCAmelCase_       		=  np.concatenate([uncond_embeddings, prompt_embeds]     )

														return prompt_embeds











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_				(		a_	):


							'''simple docstring'''






							def __init__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_=False    ,    lowercase_=1_0_2_4    ,    lowercase_=1_0_2_4    ,    lowercase_=3.6    ,    lowercase_=False    ,    )      ->      Any:
														'''simple docstring'''

														lowerCAmelCase_       		=  tokenizer
														lowerCAmelCase_       		=  tokenizer.bos_token_id
														lowerCAmelCase_       		=  dataset
														lowerCAmelCase_       		=  seq_length
														lowerCAmelCase_       		=  0
														lowerCAmelCase_       		=  infinite
														lowerCAmelCase_       		=  0
														lowerCAmelCase_       		=  tokenized

														if self.tokenized:
																					lowerCAmelCase_       		=  seq_length * num_of_sequences
																					lowerCAmelCase_       		=  'input_ids'
														else:
																					lowerCAmelCase_       		=  seq_length * chars_per_token * num_of_sequences
																					lowerCAmelCase_       		=  'content'

							def __iter__(	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  iter(self.dataset     )
														lowerCAmelCase_       		=  True
														while more_examples:
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  [], 0
																					while True:
																												if buffer_len >= self.max_buffer_size:
																																			break
																												try:
																																			buffer.append(next(lowercase_     )[self.content_field]     )
																																			buffer_len += len(buffer[-1]     )
																												except StopIteration:
																																			if self.infinite:
																																										lowerCAmelCase_       		=  iter(self.dataset     )
																																										self.epoch += 1
																																										logger.info(f'''Dataset epoch: {self.epoch}'''     )
																																			else:
																																										lowerCAmelCase_       		=  False
																																										break
																					if self.tokenized:
																												lowerCAmelCase_       		=  buffer
																					else:
																												lowerCAmelCase_       		=  self.tokenizer(lowercase_    ,    truncation=lowercase_     )['input_ids']
																					lowerCAmelCase_       		=  []
																					for tokenized_input in tokenized_inputs:
																												all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
																					for i in range(0    ,    len(lowercase_     )    ,    self.seq_length     ):
																												lowerCAmelCase_       		=  all_token_ids[i : i + self.seq_length]
																												if len(lowercase_     ) == self.seq_length:
																																			self.current_size += 1
																																			yield torch.tensor(lowercase_     )






							def   _lowercase (	self    ,    lowercase_=1_0_0_0     )      ->      Any:
														'''simple docstring'''

														return ShufflerIterDataPipe(self    ,    buffer_size=lowercase_     )


def   lowerCamelCase      (		a_			)			->    Any:
							lowerCAmelCase_       		=  args.model_ckpt.split('/'			)[-1]
							lowerCAmelCase_       		=  logging.getLogger(__name__			)
							lowerCAmelCase_       		=  Path(args.save_dir			) / 'log/'
							log_dir.mkdir(exist_ok=a_			)
							lowerCAmelCase_       		=  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(a_				,		vars(a_			)			)
														lowerCAmelCase_       		=  accelerator.trackers[0].run.name
														logger.setLevel(logging.INFO			)
														datasets.utils.logging.set_verbosity_info()
														transformers.utils.logging.set_verbosity_info()
							else:
														lowerCAmelCase_       		=  ''
														logger.setLevel(logging.ERROR			)
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()
							return logger, run_name
def   lowerCamelCase      (		a_			)			->    Union[str, Any]:
							lowerCAmelCase_       		=  {'streaming': True}
							lowerCAmelCase_       		=  load_dataset(args.dataset_name_train				,		split='train'				,		**a_			)
							lowerCAmelCase_       		=  train_data.shuffle(buffer_size=args.shuffle_buffer				,		seed=args.seed			)
							lowerCAmelCase_       		=  load_dataset(args.dataset_name_valid				,		split='train'				,		**a_			)
							lowerCAmelCase_       		=  ConstantLengthDataset(
							    a_				,		a_				,		infinite=a_				,		seq_length=args.seq_length				,		tokenized=args.tokenized			)
							lowerCAmelCase_       		=  ConstantLengthDataset(
							    a_				,		a_				,		infinite=a_				,		seq_length=args.seq_length				,		tokenized=args.tokenized			)
							lowerCAmelCase_       		=  train_dataset.shuffle(buffer_size=args.shuffle_buffer			)
							lowerCAmelCase_       		=  DataLoader(a_				,		batch_size=args.train_batch_size				,		shuffle=a_			)
							lowerCAmelCase_       		=  DataLoader(a_				,		batch_size=args.valid_batch_size			)
							return train_dataloader, eval_dataloader
def   lowerCamelCase      (		a_				,		a_				,		a_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]			)			->    List[str]:
							lowerCAmelCase_		,     lowerCAmelCase_       		=  [], []
							for n, p in model.named_parameters():
														if any(nd in n for nd in no_decay			):
																					params_without_wd.append(a_			)
														else:
																					params_with_wd.append(a_			)
							return [
							    {"params": params_with_wd, "weight_decay": args.weight_decay},
							    {"params": params_without_wd, "weight_decay": 0.0},
							]
def   lowerCamelCase      (		a_				,		a_			)			->    List[str]:
							logger.info(F'''Step {step}: {metrics}'''			)
							if accelerator.is_main_process:
														accelerator.log(a_				,		a_			)
def   lowerCamelCase      (		a_				,		a_				,		a_			)			->    Tuple:
							# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
							lowerCAmelCase_       		=  accelerator.unwrap_model(a_			).config
							lowerCAmelCase_       		=  4 if args.gradient_checkpointing else 3
							lowerCAmelCase_       		=  args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
							lowerCAmelCase_       		=  24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
							lowerCAmelCase_       		=  factor * (
							    1.0
							    + (args.seq_length / (6.0 * config_model.n_embd))
							    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
							)
							lowerCAmelCase_       		=  flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
							return tflops
def   lowerCamelCase      (		a_			)			->    List[Any]:
							model.eval()
							lowerCAmelCase_       		=  []
							for step, batch in enumerate(a_			):
														with torch.no_grad():
																					lowerCAmelCase_       		=  model(a_				,		labels=a_			)
														lowerCAmelCase_       		=  outputs.loss.repeat(args.valid_batch_size			)
														losses.append(accelerator.gather(a_			)			)
														if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																					break
							lowerCAmelCase_       		=  torch.cat(a_			)
							lowerCAmelCase_       		=  losses[: eval_dataloader.dataset.current_size].mean()
							try:
														lowerCAmelCase_       		=  torch.exp(a_			)
							except OverflowError:
														lowerCAmelCase_       		=  float('inf'			)
							return loss.item(), perplexity.item()


# Settings
lowerCamelCase_		  =						HfArgumentParser(TrainingArguments)
lowerCamelCase_		  =						parser.parse_args()

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

lowerCamelCase_		  =						Namespace(**vars(args), **acc_state)
lowerCamelCase_		  =						accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
lowerCamelCase_  ,  lowerCamelCase_		  =						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
lowerCamelCase_		  =						AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
lowerCamelCase_		  =						AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
lowerCamelCase_  ,  lowerCamelCase_		  =						create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowerCamelCase_		  =						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowerCamelCase_		  =						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   lowerCamelCase      (		)			->    str:
							return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
lowerCamelCase_  ,  lowerCamelCase_  ,  lowerCamelCase_  ,  lowerCamelCase_		  =						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)
				lowerCamelCase_		  =						os.path.basename(args.resume_from_checkpoint)
		else:
				# Get the most recent checkpoint
				lowerCamelCase_		  =						[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)
				lowerCamelCase_		  =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract the step of the checkpoint to continue from there
		lowerCamelCase_		  =						os.path.splitext(path)[0]
		lowerCamelCase_		  =						int(training_difference.replace("""step_""", """"""))

# Train model
model.train()
lowerCamelCase_		  =						0
lowerCamelCase_		  =						time.time()
lowerCamelCase_		  =						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
		lowerCamelCase_		  =						model(batch, labels=batch, use_cache=False).loss
		lowerCamelCase_		  =						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()})
		lowerCamelCase_		  =						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:
				lowerCamelCase_		  =						get_lr()
				accelerator.backward(loss)
				accelerator.clip_grad_norm_(model.parameters(), 1.0)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
				lowerCamelCase_		  =						time.time() - t_start
				lowerCamelCase_		  =						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,
				    },
				)
				lowerCamelCase_		  =						time.time()
				lowerCamelCase_		  =						0
				completed_steps += 1
		if step % args.save_checkpoint_steps == 0:
				logger.info("""Evaluating and saving model checkpoint""")
				lowerCamelCase_  ,  lowerCamelCase_		  =						evaluate(args)
				log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
				accelerator.wait_for_everyone()
				lowerCamelCase_		  =						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""")
lowerCamelCase_  ,  lowerCamelCase_		  =						evaluate(args)
log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
accelerator.wait_for_everyone()
lowerCamelCase_		  =						accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
lowerCamelCase_		  =						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""")



