



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__       (       _snake_case       ):


					"""simple docstring"""



					def __init__(		self     :Optional[int]      ,  lowercase__     :Any      ,  lowercase__     :str      ,  lowercase__     :Optional[int]=False      ,  lowercase__     :List[Any]=1024      ,  lowercase__     :Dict=1024      ,  lowercase__     :str=3.6      ,  lowercase__     :Optional[int]=False      ,  ):
										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     :Tuple    ):
										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(lowercase__    )[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(lowercase__      ,  truncation=lowercase__    )['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(lowercase__    )      ,  self.seq_length    ):
																				lowercase						   =       all_token_ids[i : i + self.seq_length]
																				if len(lowercase__    ) == self.seq_length:
																									self.current_size += 1
																									yield torch.tensor(lowercase__    )



					def 				__UpperCAmelCase		(		self     :int      ,  lowercase__     :Any=1000    ):
										return ShufflerIterDataPipe(self      ,  buffer_size=lowercase__    )





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       args.model_ckpt.split('/'				)[-1]
					lowercase						   =       logging.getLogger(__name__				)
					lowercase						   =       Path(args.save_dir				) / 'log/'
					log_dir.mkdir(exist_ok=_UpperCAmelCase				)
					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(_UpperCAmelCase       ,			vars(_UpperCAmelCase				)				)
										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 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






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





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]				):
					"""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(_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 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






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





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       accelerator.unwrap_model(_UpperCAmelCase				).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 / (16.0 * config_model.n_layer * config_model.n_embd))
					)
					lowercase						   =       flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
					return tflops





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






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


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

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

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

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

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

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

# Prepare the optimizer and learning rate scheduler
__magic_name__       =     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__magic_name__       =     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 		__snake_case						(    ):
					"""simple docstring"""






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


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

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


class 	a__       (       unittest.TestCase       ):


					"""simple docstring"""



					@cached_property
					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'    )

					@cached_property
					def 				__UpperCAmelCase		(		self     :Any    ):
										return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'    )

					def 				__UpperCAmelCase		(		self     :str    ):
										lowercase						   =       self.tokenizer_ab
										lowercase						   =       ' I am a small frog.'
										lowercase						   =       tok([src_text]      ,  padding=lowercase__      ,  truncation=lowercase__    )['input_ids']
										lowercase						   =       tok.batch_decode(lowercase__      ,  skip_special_tokens=lowercase__      ,  clean_up_tokenization_spaces=lowercase__    )[0]
										assert src_text == decoded

					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										lowercase						   =       self.rust_tokenizer_ab
										lowercase						   =       ' I am a small frog.'
										lowercase						   =       tok([src_text]      ,  padding=lowercase__      ,  truncation=lowercase__    )['input_ids']
										lowercase						   =       tok.batch_decode(lowercase__      ,  skip_special_tokens=lowercase__      ,  clean_up_tokenization_spaces=lowercase__    )[0]
										assert src_text == decoded

					def 				__UpperCAmelCase		(		self     :Any    ):
										assert self.tokenizer_ab.add_prefix_space
										assert self.tokenizer_ab([' Sam', 'Sam']    ).input_ids == [[5502, 2], [5502, 2]]



					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										assert self.rust_tokenizer_ab.add_prefix_space
										assert self.rust_tokenizer_ab([' Sam', 'Sam']    ).input_ids == [[5502, 2], [5502, 2]]






