






"""simple docstring"""

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_	):







				def __init__(				self				:    Optional[Any]    ,     __lowerCamelCase				:    List[str]    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    int=False    ,     __lowerCamelCase				:    Tuple=1_0_2_4    ,     __lowerCamelCase				:    int=1_0_2_4    ,     __lowerCamelCase				:    Any=3.6    ,     __lowerCamelCase				:    str=False    ,     )					->       Optional[Any]:
										__magic_name__      =  tokenizer
										__magic_name__      =  tokenizer.bos_token_id
										__magic_name__      =  dataset
										__magic_name__      =  seq_length
										__magic_name__      =  0
										__magic_name__      =  infinite
										__magic_name__      =  0
										__magic_name__      =  tokenized

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







				def __iter__(				self				:    Any						)					->       str:
										__magic_name__      =  iter(self.dataset						)
										__magic_name__      =  True
										while more_examples:
																__magic_name__   ,     __magic_name__      =  [], 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__      =  iter(self.dataset						)
																																		self.epoch += 1
																																		logger.info(f'''Dataset epoch: {self.epoch}'''						)
																												else:
																																		__magic_name__      =  False
																																		break
																if self.tokenized:
																						__magic_name__      =  buffer
																else:
																						__magic_name__      =  self.tokenizer(__lowerCamelCase    ,     truncation=__lowerCamelCase						)["input_ids"]
																__magic_name__      =  []
																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__      =  all_token_ids[i : i + self.seq_length]
																						if len(__lowerCamelCase						) == self.seq_length:
																												self.current_size += 1
																												yield torch.tensor(__lowerCamelCase						)







				def      _snake_case       (				self				:    Union[str, Any]    ,     __lowerCamelCase				:    int=1_0_0_0						)					->       str:
										return ShufflerIterDataPipe(self    ,     buffer_size=__lowerCamelCase						)






def _lowerCAmelCase						(	__lowerCamelCase:Union[str, Any] ):

						'''simple docstring'''






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






def _lowerCAmelCase						(	__lowerCamelCase:Dict ):

						'''simple docstring'''






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






def _lowerCAmelCase						(	__lowerCamelCase:List[str]			,    __lowerCamelCase:List[str]			,    __lowerCamelCase:int=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"] ):

						'''simple docstring'''






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






def _lowerCAmelCase						(	__lowerCamelCase:List[str]			,    __lowerCamelCase:Optional[int] ):

						'''simple docstring'''






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






def _lowerCAmelCase						(	__lowerCamelCase:Dict			,    __lowerCamelCase:List[Any]			,    __lowerCamelCase:Union[str, Any] ):

						'''simple docstring'''






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






def _lowerCAmelCase						(	__lowerCamelCase:List[Any] ):

						'''simple docstring'''






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

						'''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''')











"""simple docstring"""

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import CamembertModel

@require_torch
@require_sentencepiece
@require_tokenizers
class     A_				(   unittest.TestCase	):







				@slow
				def      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     device=__lowerCamelCase    ,     dtype=torch.float    ,     )
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]    ,     __lowerCamelCase    ,     atol=1e-4						)						)



