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     snake_case_			(						__A			):






   def __init__( self							:	Optional[Any]      ,		lowercase_							:	int      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	Union[str, Any]=False      ,		lowercase_							:	List[str]=10_24      ,		lowercase_							:	Any=10_24      ,		lowercase_							:	Union[str, Any]=3.6      ,		lowercase_							:	List[str]=False      ,		)   ->		Optional[Any]:
       lowercase__					:  int          =							tokenizer
       lowercase__					:  Any          =							tokenizer.bos_token_id
       lowercase__					:  Tuple          =							dataset
       lowercase__					:  List[str]          =							seq_length
       lowercase__					:  int          =							0
       lowercase__					:  Optional[int]          =							infinite
       lowercase__					:  Tuple          =							0
       lowercase__					:  Union[str, Any]          =							tokenized

       if self.tokenized:
           lowercase__					:  List[Any]          =							seq_length * num_of_sequences
           lowercase__					:  Union[str, Any]          =							'input_ids'
       else:
           lowercase__					:  Dict          =							seq_length * chars_per_token * num_of_sequences
           lowercase__					:  str          =							'content'






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







   def  __UpperCamelCase	( self							:	Tuple      ,		lowercase_							:	Union[str, Any]=10_00    )   ->		Optional[Any]:
       return ShufflerIterDataPipe(self      ,		buffer_size=snake_case__    )



def      lowercase_							(     _lowerCamelCase				:			List[str]):
    lowercase__					:  Optional[Any]          =							args.model_ckpt.split("/")[-1]
    lowercase__					:  List[Any]          =							logging.getLogger(__name__)
    lowercase__					:  str          =							Path(args.save_dir) / 'log/'
    log_dir.mkdir(exist_ok=_A)
    lowercase__					:  Optional[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(_A						,      vars(_A))
        lowercase__					:  Optional[Any]          =							accelerator.trackers[0].run.name
        logger.setLevel(logging.INFO)
        datasets.utils.logging.set_verbosity_info()
        transformers.utils.logging.set_verbosity_info()
    else:
        lowercase__					:  Optional[int]          =							''
        logger.setLevel(logging.ERROR)
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    return logger, run_name

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

def      lowercase_							(     _lowerCamelCase				:			Optional[int]						,      _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]):
    lowercase__					:  Dict          =							[], []
    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      lowercase_							(     _lowerCamelCase				:			List[str]						,      _lowerCamelCase				:			Optional[Any]):
    logger.info(f'''Step {step}: {metrics}''')
    if accelerator.is_main_process:
        accelerator.log(_A						,      _A)

def      lowercase_							(     _lowerCamelCase				:			Dict						,      _lowerCamelCase				:			int						,      _lowerCamelCase				:			Any):
    lowercase__					:  Union[str, Any]          =							accelerator.unwrap_model(_A).config
    lowercase__					:  List[str]          =							4 if args.gradient_checkpointing else 3
    lowercase__					:  int          =							args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
    lowercase__					:  int          =							24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
    lowercase__					:  Optional[int]          =							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__					:  List[str]          =							flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
    return tflops

def      lowercase_							(     _lowerCamelCase				:			Optional[Any]):
    model.eval()
    lowercase__					:  Dict          =							[]
    for step, batch in enumerate(_A):
        with torch.no_grad():
            lowercase__					:  Optional[int]          =							model(_A						,      labels=_A)
        lowercase__					:  List[Any]          =							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
    lowercase__					:  List[Any]          =							torch.cat(_A)
    lowercase__					:  str          =							losses[: eval_dataloader.dataset.current_size].mean()
    try:
        lowercase__					:  Optional[int]          =							torch.exp(_A)
    except OverflowError:
        lowercase__					:  List[str]          =							float("inf")
    return loss.item(), perplexity.item()


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

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

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

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

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

# Load dataset and dataloader
UpperCamelCase    				=		create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
UpperCamelCase    				=		AdamW(get_grouped_params(model, args), lr=args.learning_rate)
UpperCamelCase    				=		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      lowercase_							(     ):
    return optimizer.param_groups[0]["lr"]


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

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_lowerCAmelCase	:		Dict		  =						{}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase	:		Union[str, Any]		  =						['BarthezTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase	:		Union[str, Any]		  =						['BarthezTokenizerFast']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_barthez import BarthezTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_barthez_fast import BarthezTokenizerFast

else:
				import sys

				_lowerCAmelCase	:		List[str]		  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
