


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    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE=1024    ,					_SCREAMING_SNAKE_CASE=1024    ,					_SCREAMING_SNAKE_CASE=3.6    ,					_SCREAMING_SNAKE_CASE=False    ,					)				-> int:




									"""simple docstring"""





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

									if self.tokenized:
																_UpperCAmelCase					=    seq_length * num_of_sequences
																_UpperCAmelCase					=    'input_ids'
									else:
																_UpperCAmelCase					=    seq_length * chars_per_token * num_of_sequences
																_UpperCAmelCase					=    'content'
		def __iter__(    self		)				-> str:




									"""simple docstring"""





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






		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=1000		)				-> Optional[int]:




									"""simple docstring"""





									return ShufflerIterDataPipe(self    ,					buffer_size=_SCREAMING_SNAKE_CASE		)



def  lowerCAmelCase__				(    a__:	List[Any] )						->	Union[str, Any]:


							'''simple docstring'''





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

def  lowerCAmelCase__				(    a__:	Any )						->	Optional[Any]:


							'''simple docstring'''





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

def  lowerCAmelCase__				(    a__:	Tuple    ,						a__:	Dict    ,						a__:	int=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"] )						->	Tuple:


							'''simple docstring'''





							_UpperCAmelCase	, _UpperCAmelCase					=    [], []
							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__:	List[str]    ,						a__:	Optional[int] )						->	Tuple:


							'''simple docstring'''





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

def  lowerCAmelCase__				(    a__:	List[str]    ,						a__:	Any    ,						a__:	Optional[Any] )						->	List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=    accelerator.unwrap_model(a__ ).config
							_UpperCAmelCase					=    4 if args.gradient_checkpointing else 3
							_UpperCAmelCase					=    args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
							_UpperCAmelCase					=    2_4 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
							_UpperCAmelCase					=    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))
							)
							_UpperCAmelCase					=    flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (1_0**1_2))
							return tflops

def  lowerCAmelCase__				(    a__:	List[str] )						->	List[str]:


							'''simple docstring'''





							model.eval()
							_UpperCAmelCase					=    []
							for step, batch in enumerate(a__ ):
														with torch.no_grad():
																					_UpperCAmelCase					=    model(a__    ,						labels=a__ )
														_UpperCAmelCase					=    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
							_UpperCAmelCase					=    torch.cat(a__ )
							_UpperCAmelCase					=    losses[: eval_dataloader.dataset.current_size].mean()
							try:
														_UpperCAmelCase					=    torch.exp(a__ )
							except OverflowError:
														_UpperCAmelCase					=    float('inf' )
							return loss.item(), perplexity.item()


# Settings
lowerCAmelCase__						:Any         =							HfArgumentParser(TrainingArguments)
lowerCAmelCase__						:Tuple         =							parser.parse_args()

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

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

# Clone model repository
if accelerator.is_main_process:
		lowerCAmelCase__						:Union[str, Any]         =							Repository(args.save_dir, clone_from=args.model_ckpt)

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

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

# Prepare the optimizer and learning rate scheduler
lowerCAmelCase__						:Optional[Any]         =							AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowerCAmelCase__						:Any         =							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__				(    )						->	Tuple:


							'''simple docstring'''





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


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

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




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__						:int         =							logging.get_logger(__name__)

lowerCAmelCase__						:Optional[int]         =							{
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}

class 	__a   (       UpperCAmelCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=256    ,					_SCREAMING_SNAKE_CASE=1024    ,					_SCREAMING_SNAKE_CASE=16    ,					_SCREAMING_SNAKE_CASE=16    ,					_SCREAMING_SNAKE_CASE=4    ,					_SCREAMING_SNAKE_CASE="gelu"    ,					_SCREAMING_SNAKE_CASE=1e-6    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> Tuple:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    hidden_size
									_UpperCAmelCase					=    image_size
									_UpperCAmelCase					=    patch_size
									_UpperCAmelCase					=    image_size // patch_size
									_UpperCAmelCase					=    mask_input_channels
									_UpperCAmelCase					=    num_point_embeddings
									_UpperCAmelCase					=    hidden_act
									_UpperCAmelCase					=    layer_norm_eps



class 	__a   (       UpperCAmelCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=256    ,					_SCREAMING_SNAKE_CASE="relu"    ,					_SCREAMING_SNAKE_CASE=2048    ,					_SCREAMING_SNAKE_CASE=2    ,					_SCREAMING_SNAKE_CASE=8    ,					_SCREAMING_SNAKE_CASE=2    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=256    ,					_SCREAMING_SNAKE_CASE=1e-6    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> List[Any]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    hidden_size
									_UpperCAmelCase					=    hidden_act
									_UpperCAmelCase					=    mlp_dim
									_UpperCAmelCase					=    num_hidden_layers
									_UpperCAmelCase					=    num_attention_heads
									_UpperCAmelCase					=    attention_downsample_rate
									_UpperCAmelCase					=    num_multimask_outputs
									_UpperCAmelCase					=    iou_head_depth
									_UpperCAmelCase					=    iou_head_hidden_dim
									_UpperCAmelCase					=    layer_norm_eps



class 	__a   (       UpperCAmelCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=768    ,					_SCREAMING_SNAKE_CASE=256    ,					_SCREAMING_SNAKE_CASE=12    ,					_SCREAMING_SNAKE_CASE=12    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=1024    ,					_SCREAMING_SNAKE_CASE=16    ,					_SCREAMING_SNAKE_CASE="gelu"    ,					_SCREAMING_SNAKE_CASE=1e-0_6    ,					_SCREAMING_SNAKE_CASE=0.0    ,					_SCREAMING_SNAKE_CASE=1e-1_0    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=4.0    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=14    ,					_SCREAMING_SNAKE_CASE=[2, 5, 8, 11]    ,					_SCREAMING_SNAKE_CASE=128    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> List[str]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    hidden_size
									_UpperCAmelCase					=    output_channels
									_UpperCAmelCase					=    num_hidden_layers
									_UpperCAmelCase					=    num_attention_heads
									_UpperCAmelCase					=    num_channels
									_UpperCAmelCase					=    image_size
									_UpperCAmelCase					=    patch_size
									_UpperCAmelCase					=    hidden_act
									_UpperCAmelCase					=    layer_norm_eps
									_UpperCAmelCase					=    attention_dropout
									_UpperCAmelCase					=    initializer_range
									_UpperCAmelCase					=    qkv_bias
									_UpperCAmelCase					=    mlp_ratio
									_UpperCAmelCase					=    use_abs_pos
									_UpperCAmelCase					=    use_rel_pos
									_UpperCAmelCase					=    window_size
									_UpperCAmelCase					=    global_attn_indexes
									_UpperCAmelCase					=    num_pos_feats
									_UpperCAmelCase					=    int(hidden_size * mlp_ratio		) if mlp_dim is None else mlp_dim



class 	__a   (       UpperCAmelCase    ):
		_a			:						Any    			=			'sam'
		_a			:						Any    			=			True
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=0.02    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> Tuple:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    vision_config if vision_config is not None else {}
									_UpperCAmelCase					=    prompt_encoder_config if prompt_encoder_config is not None else {}
									_UpperCAmelCase					=    mask_decoder_config if mask_decoder_config is not None else {}

									if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    vision_config.to_dict()
									if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    prompt_encoder_config.to_dict()
									if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    mask_decoder_config.to_dict()

									_UpperCAmelCase					=    SamVisionConfig(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    SamPromptEncoderConfig(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    SamMaskDecoderConfig(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    initializer_range






		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									_UpperCAmelCase					=    copy.deepcopy(self.__dict__		)
									_UpperCAmelCase					=    self.vision_config.to_dict()
									_UpperCAmelCase					=    self.prompt_encoder_config.to_dict()
									_UpperCAmelCase					=    self.mask_decoder_config.to_dict()
									_UpperCAmelCase					=    self.__class__.model_type
									return output
