




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__    (       __SCREAMING_SNAKE_CASE			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=False    ,					lowerCamelCase=1024    ,					lowerCamelCase=1024    ,					lowerCamelCase=3.6    ,					lowerCamelCase=False    ,					)				->       Union[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							tokenizer
						__magic_name__ :							Any		       =							tokenizer.bos_token_id
						__magic_name__ :							Optional[int]		       =							dataset
						__magic_name__ :							List[Any]		       =							seq_length
						__magic_name__ :							Union[str, Any]		       =							0
						__magic_name__ :							Optional[Any]		       =							infinite
						__magic_name__ :							Any		       =							0
						__magic_name__ :							List[Any]		       =							tokenized

						if self.tokenized:
											__magic_name__ :							int		       =							seq_length * num_of_sequences
											__magic_name__ :							Optional[int]		       =							'''input_ids'''
						else:
											__magic_name__ :							str		       =							seq_length * chars_per_token * num_of_sequences
											__magic_name__ :							List[str]		       =							'''content'''






	def __iter__(     self						)				->       Optional[int]:




						"""simple docstring"""






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






	def 			lowercase				(     self    ,					lowerCamelCase=1000						)				->       Any:




						"""simple docstring"""






						return ShufflerIterDataPipe(self    ,					buffer_size=lowerCamelCase						)







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->List[Any]:






					"""simple docstring"""






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







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->Any:






					"""simple docstring"""






					__magic_name__ :							str		       =							{'''streaming''': True}
					__magic_name__ :							Union[str, Any]		       =							load_dataset(args.dataset_name_train,     split='''train''',     **UpperCAmelCase	)
					__magic_name__ :							Optional[int]		       =							train_data.shuffle(buffer_size=args.shuffle_buffer,     seed=args.seed	)
					__magic_name__ :							Union[str, Any]		       =							load_dataset(args.dataset_name_valid,     split='''train''',     **UpperCAmelCase	)
					__magic_name__ :							Optional[Any]		       =							ConstantLengthDataset(
					    UpperCAmelCase,     UpperCAmelCase,     infinite=UpperCAmelCase,     seq_length=args.seq_length,     tokenized=args.tokenized	)
					__magic_name__ :							Union[str, Any]		       =							ConstantLengthDataset(
					    UpperCAmelCase,     UpperCAmelCase,     infinite=UpperCAmelCase,     seq_length=args.seq_length,     tokenized=args.tokenized	)
					__magic_name__ :							Union[str, Any]		       =							train_dataset.shuffle(buffer_size=args.shuffle_buffer	)
					__magic_name__ :							Optional[int]		       =							DataLoader(UpperCAmelCase,     batch_size=args.train_batch_size,     shuffle=UpperCAmelCase	)
					__magic_name__ :							List[Any]		       =							DataLoader(UpperCAmelCase,     batch_size=args.valid_batch_size	)
					return train_dataloader, eval_dataloader







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	)  ->Tuple:






					"""simple docstring"""






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







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->Optional[Any]:






					"""simple docstring"""






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







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)  ->Dict:






					"""simple docstring"""






					__magic_name__ :							Optional[int]		       =							accelerator.unwrap_model(UpperCAmelCase	).config
					__magic_name__ :							Any		       =							4 if args.gradient_checkpointing else 3
					__magic_name__ :							List[str]		       =							args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
					__magic_name__ :							List[Any]		       =							24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
					__magic_name__ :							Optional[Any]		       =							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))
					)
					__magic_name__ :							Optional[int]		       =							flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
					return tflops







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->int:






					"""simple docstring"""






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


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

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

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

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

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

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

# Prepare the optimizer and learning rate scheduler
lowercase_		=							AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowercase_		=							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`.
lowercase_,   lowercase_,   lowercase_,   lowercase_		=							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_		=							os.path.basename(args.resume_from_checkpoint)
				else:
								# Get the most recent checkpoint
								lowercase_		=							[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_		=							dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract the step of the checkpoint to continue from there
				lowercase_		=							os.path.splitext(path)[0]
				lowercase_		=							int(training_difference.replace('''step_''', ''''''))

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

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
				from PIL import Image





class 		A__    (       unittest.TestCase		,					__SCREAMING_SNAKE_CASE			):






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							load_tool('''image-question-answering'''						)
						self.tool.setup()
						__magic_name__ :							Any		       =							load_tool('''image-question-answering'''    ,					remote=lowerCamelCase						)






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							int		       =							self.tool(lowerCamelCase    ,					'''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							int		       =							self.remote_tool(lowerCamelCase    ,					'''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							int		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							Tuple		       =							self.tool(image=lowerCamelCase    ,					question='''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							Tuple		       =							self.remote_tool(image=lowerCamelCase    ,					question='''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)



