


import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)






def 				UpperCamelCase      (			__magic_name__  :							int  )     ->						Tuple:
		"""simple docstring"""




		lowercase__      =	CLIPSegTextConfig()
		lowercase__      =	CLIPSegVisionConfig(patch_size=16  )

		lowercase__      =	True if """refined""" in model_name else False
		lowercase__      =	16 if """rd16""" in model_name else 64

		lowercase__      =	CLIPSegConfig.from_text_vision_configs(
		    __magic_name__					,					__magic_name__					,					use_complex_transposed_convolution=__magic_name__					,					reduce_dim=__magic_name__					,					)
		return config






def 				UpperCamelCase      (			__magic_name__  :							Any  )     ->						Optional[Any]:
		"""simple docstring"""




		if "clip_model" in name:
				lowercase__      =	name.replace("""clip_model"""					,					"""clip"""  )
		if "transformer" in name:
				if "visual" in name:
						lowercase__      =	name.replace("""visual.transformer"""					,					"""vision_model"""  )
				else:
						lowercase__      =	name.replace("""transformer"""					,					"""text_model"""  )
		if "resblocks" in name:
				lowercase__      =	name.replace("""resblocks"""					,					"""encoder.layers"""  )
		if "ln_1" in name:
				lowercase__      =	name.replace("""ln_1"""					,					"""layer_norm1"""  )
		if "ln_2" in name:
				lowercase__      =	name.replace("""ln_2"""					,					"""layer_norm2"""  )
		if "c_fc" in name:
				lowercase__      =	name.replace("""c_fc"""					,					"""fc1"""  )
		if "c_proj" in name:
				lowercase__      =	name.replace("""c_proj"""					,					"""fc2"""  )
		if "attn" in name and "self" not in name:
				lowercase__      =	name.replace("""attn"""					,					"""self_attn"""  )
		# text encoder
		if "token_embedding" in name:
				lowercase__      =	name.replace("""token_embedding"""					,					"""text_model.embeddings.token_embedding"""  )
		if "positional_embedding" in name and "visual" not in name:
				lowercase__      =	name.replace("""positional_embedding"""					,					"""text_model.embeddings.position_embedding.weight"""  )
		if "ln_final" in name:
				lowercase__      =	name.replace("""ln_final"""					,					"""text_model.final_layer_norm"""  )
		# vision encoder
		if "visual.class_embedding" in name:
				lowercase__      =	name.replace("""visual.class_embedding"""					,					"""vision_model.embeddings.class_embedding"""  )
		if "visual.conv1" in name:
				lowercase__      =	name.replace("""visual.conv1"""					,					"""vision_model.embeddings.patch_embedding"""  )
		if "visual.positional_embedding" in name:
				lowercase__      =	name.replace("""visual.positional_embedding"""					,					"""vision_model.embeddings.position_embedding.weight"""  )
		if "visual.ln_pre" in name:
				lowercase__      =	name.replace("""visual.ln_pre"""					,					"""vision_model.pre_layrnorm"""  )
		if "visual.ln_post" in name:
				lowercase__      =	name.replace("""visual.ln_post"""					,					"""vision_model.post_layernorm"""  )
		# projection layers
		if "visual.proj" in name:
				lowercase__      =	name.replace("""visual.proj"""					,					"""visual_projection.weight"""  )
		if "text_projection" in name:
				lowercase__      =	name.replace("""text_projection"""					,					"""text_projection.weight"""  )
		# decoder
		if "trans_conv" in name:
				lowercase__      =	name.replace("""trans_conv"""					,					"""transposed_convolution"""  )
		if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
				lowercase__      =	"""decoder.""" + name
		if "blocks" in name:
				lowercase__      =	name.replace("""blocks"""					,					"""decoder.layers"""  )
		if "linear1" in name:
				lowercase__      =	name.replace("""linear1"""					,					"""mlp.fc1"""  )
		if "linear2" in name:
				lowercase__      =	name.replace("""linear2"""					,					"""mlp.fc2"""  )
		if "norm1" in name and "layer_" not in name:
				lowercase__      =	name.replace("""norm1"""					,					"""layer_norm1"""  )
		if "norm2" in name and "layer_" not in name:
				lowercase__      =	name.replace("""norm2"""					,					"""layer_norm2"""  )

		return name






def 				UpperCamelCase      (			__magic_name__  :							Dict					,					__magic_name__  :							Dict  )     ->						str:
		"""simple docstring"""




		for key in orig_state_dict.copy().keys():
				lowercase__      =	orig_state_dict.pop(__magic_name__  )

				if key.startswith("""clip_model"""  ) and "attn.in_proj" in key:
						lowercase__      =	key.split("""."""  )
						if "visual" in key:
								lowercase__      =	int(key_split[4]  )
								lowercase__      =	config.vision_config.hidden_size
								lowercase__      =	"""vision_model"""
						else:
								lowercase__      =	int(key_split[3]  )
								lowercase__      =	config.text_config.hidden_size
								lowercase__      =	"""text_model"""

						if "weight" in key:
								lowercase__      =	val[:dim, :]
								lowercase__      =	val[
								    dim : dim * 2, :
								]
								lowercase__      =	val[-dim:, :]
						else:
								lowercase__      =	val[:dim]
								lowercase__      =	val[dim : dim * 2]
								lowercase__      =	val[-dim:]
				elif "self_attn" in key and "out_proj" not in key:
						lowercase__      =	key.split("""."""  )
						lowercase__      =	int(key_split[1]  )
						lowercase__      =	config.reduce_dim
						if "weight" in key:
								lowercase__      =	val[:dim, :]
								lowercase__      =	val[dim : dim * 2, :]
								lowercase__      =	val[-dim:, :]
						else:
								lowercase__      =	val[:dim]
								lowercase__      =	val[dim : dim * 2]
								lowercase__      =	val[-dim:]
				else:
						lowercase__      =	rename_key(__magic_name__  )
						if "visual_projection" in new_name or "text_projection" in new_name:
								lowercase__      =	val.T
						lowercase__      =	val

		return orig_state_dict






def 				UpperCamelCase      (			)     ->						Tuple:
		"""simple docstring"""




		lowercase__      =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		lowercase__      =	Image.open(requests.get(__magic_name__					,					stream=__magic_name__  ).raw  )
		return image






def 				UpperCamelCase      (			__magic_name__  :							Dict					,					__magic_name__  :							str					,					__magic_name__  :							List[str]					,					__magic_name__  :							List[str]  )     ->						Union[str, Any]:
		"""simple docstring"""




		lowercase__      =	get_clipseg_config(__magic_name__  )
		lowercase__      =	CLIPSegForImageSegmentation(__magic_name__  )
		model.eval()

		lowercase__      =	torch.load(__magic_name__					,					map_location="""cpu"""  )

		# remove some keys
		for key in state_dict.copy().keys():
				if key.startswith("""model"""  ):
						state_dict.pop(__magic_name__					,					__magic_name__  )

    # rename some keys
		lowercase__      =	convert_state_dict(__magic_name__					,					__magic_name__  )
		lowercase__    ,			lowercase__      =	model.load_state_dict(__magic_name__					,					strict=__magic_name__  )

		if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
				raise ValueError("""Missing keys that are not expected: {}""".format(__magic_name__  )  )
		if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
				raise ValueError(f'''Unexpected keys: {unexpected_keys}'''  )

		lowercase__      =	ViTImageProcessor(size=352  )
		lowercase__      =	CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""  )
		lowercase__      =	CLIPSegProcessor(image_processor=__magic_name__					,					tokenizer=__magic_name__  )

		lowercase__      =	prepare_img()
		lowercase__      =	["""a glass""", """something to fill""", """wood""", """a jar"""]

		lowercase__      =	processor(text=__magic_name__					,					images=[image] * len(__magic_name__  )					,					padding="""max_length"""					,					return_tensors="""pt"""  )

		with torch.no_grad():
				lowercase__      =	model(**__magic_name__  )

		# verify values
		lowercase__      =	torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]  )
		lowercase__      =	torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]  )
		if model_name == "clipseg-rd64-refined":
				lowercase__      =	torch.tensor(
				    [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]  )
		elif model_name == "clipseg-rd64":
				lowercase__      =	torch.tensor(
				    [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]  )
		elif model_name == "clipseg-rd16":
				lowercase__      =	torch.tensor(
				    [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]  )
		else:
				raise ValueError(f'''Model name {model_name} not supported.'''  )

		assert torch.allclose(outputs.logits[0, :3, :3]					,					__magic_name__					,					atol=1E-3  )
		assert torch.allclose(outputs.conditional_embeddings[0, :3]					,					__magic_name__					,					atol=1E-3  )
		assert torch.allclose(outputs.pooled_output[0, :3]					,					__magic_name__					,					atol=1E-3  )
		print("""Looks ok!"""  )

		if pytorch_dump_folder_path is not None:
				print(f'''Saving model and processor to {pytorch_dump_folder_path}'''  )
				model.save_pretrained(__magic_name__  )
				processor.save_pretrained(__magic_name__  )

		if push_to_hub:
				print(f'''Pushing model and processor for {model_name} to the hub'''  )
				model.push_to_hub(f'''CIDAS/{model_name}'''  )
				processor.push_to_hub(f'''CIDAS/{model_name}'''  )


if __name__ == "__main__":
	A :	Optional[int]												=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='clipseg-rd64',
	    type=str,
	    choices=['clipseg-rd16', 'clipseg-rd64', 'clipseg-rd64-refined'],
	    help=(
	        'Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning'
	        ' reduce dimension)'
	    ),
	)
	parser.add_argument(
	    '--checkpoint_path',
	    default='/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth',
	    type=str,
	    help=(
	        'Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and'
	        ' the decoder weights.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)

	A :	List[Any]												=     parser.parse_args()
	convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





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					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Optional[int]   ,      _UpperCAmelCase					:		Optional[int]   ,      _UpperCAmelCase					:		int   ,      _UpperCAmelCase					:		int=False   ,      _UpperCAmelCase					:		List[str]=1024   ,      _UpperCAmelCase					:		Dict=1024   ,      _UpperCAmelCase					:		List[str]=3.6   ,      _UpperCAmelCase					:		Optional[int]=False   ,      )		->     List[str]:




							"""simple docstring"""


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

							if self.tokenized:
									lowercase__      =	seq_length * num_of_sequences
									lowercase__      =	"""input_ids"""
							else:
									lowercase__      =	seq_length * chars_per_token * num_of_sequences
									lowercase__      =	"""content"""

					def __iter__(self					:		Dict	)		->     Any:




							"""simple docstring"""


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


					def 						lowerCamelCase__  (self					:		Optional[Any]   ,      _UpperCAmelCase					:		Optional[Any]=1000	)		->     Optional[int]:




							"""simple docstring"""


							return ShufflerIterDataPipe(self   ,      buffer_size=_UpperCAmelCase	)






def 				UpperCamelCase      (			__magic_name__  :							Optional[int]  )     ->						Tuple:
		"""simple docstring"""




		lowercase__      =	args.model_ckpt.split("""/"""  )[-1]
		lowercase__      =	logging.getLogger(__name__  )
		lowercase__      =	Path(args.save_dir  ) / """log/"""
		log_dir.mkdir(exist_ok=__magic_name__  )
		lowercase__      =	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(__magic_name__					,					vars(__magic_name__  )  )
				lowercase__      =	accelerator.trackers[0].run.name
				logger.setLevel(logging.INFO  )
				datasets.utils.logging.set_verbosity_info()
				transformers.utils.logging.set_verbosity_info()
		else:
				lowercase__      =	""""""
				logger.setLevel(logging.ERROR  )
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()
		return logger, run_name






def 				UpperCamelCase      (			__magic_name__  :							Optional[Any]  )     ->						List[str]:
		"""simple docstring"""




		lowercase__      =	{"""streaming""": True}
		lowercase__      =	load_dataset(args.dataset_name_train					,					split="""train"""					,					**__magic_name__  )
		lowercase__      =	train_data.shuffle(buffer_size=args.shuffle_buffer					,					seed=args.seed  )
		lowercase__      =	load_dataset(args.dataset_name_valid					,					split="""train"""					,					**__magic_name__  )
		lowercase__      =	ConstantLengthDataset(
		    __magic_name__					,					__magic_name__					,					infinite=__magic_name__					,					seq_length=args.seq_length					,					tokenized=args.tokenized  )
		lowercase__      =	ConstantLengthDataset(
		    __magic_name__					,					__magic_name__					,					infinite=__magic_name__					,					seq_length=args.seq_length					,					tokenized=args.tokenized  )
		lowercase__      =	train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
		lowercase__      =	DataLoader(__magic_name__					,					batch_size=args.train_batch_size					,					shuffle=__magic_name__  )
		lowercase__      =	DataLoader(__magic_name__					,					batch_size=args.valid_batch_size  )
		return train_dataloader, eval_dataloader






def 				UpperCamelCase      (			__magic_name__  :							int					,					__magic_name__  :							List[Any]					,					__magic_name__  :							int=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  )     ->						List[str]:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							Tuple					,					__magic_name__  :							Dict  )     ->						Optional[int]:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							Union[str, Any]					,					__magic_name__  :							List[str]  )     ->						str:
		"""simple docstring"""




		lowercase__      =	accelerator.unwrap_model(__magic_name__  ).config
		lowercase__      =	4 if args.gradient_checkpointing else 3
		lowercase__      =	args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
		lowercase__      =	24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
		lowercase__      =	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))
		)
		lowercase__      =	flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
		return tflops






def 				UpperCamelCase      (			__magic_name__  :							str  )     ->						int:
		"""simple docstring"""




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


# Settings
A :	Any												=     HfArgumentParser(TrainingArguments)
A :	int												=     parser.parse_args()

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

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

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

# Logging
A						,   A :	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
A :	Tuple												=     AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
	model.gradient_checkpointing_enable()
A :	List[Any]												=     AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
A						,   A :	List[Any]												=     create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
A :	int												=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
A :	int												=     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      (			)     ->						Any:
		"""simple docstring"""




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


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

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

