



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 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):





       def __init__(				self						:						Tuple ,			__lowercase						:						List[str] ,			__lowercase						:						Tuple ,			__lowercase						:						Union[str, Any]=False ,			__lowercase						:						Dict=1024 ,			__lowercase						:						Tuple=1024 ,			__lowercase						:						Optional[Any]=3.6 ,			__lowercase						:						Any=False ,			):

           '''simple docstring'''
           __a					       =						tokenizer
           __a					       =						tokenizer.bos_token_id
           __a					       =						dataset
           __a					       =						seq_length
           __a					       =						0
           __a					       =						infinite
           __a					       =						0
           __a					       =						tokenized

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





       def __iter__(				self						:						Optional[Any]		):

           '''simple docstring'''
           __a					       =						iter(self.dataset		)
           __a					       =						True
           while more_examples:
               __a       ,   __a					       =						[], 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:
                           __a					       =						iter(self.dataset		)
                           self.epoch += 1
                           logger.info(F"Dataset epoch: {self.epoch}"		)
                       else:
                           __a					       =						False
                           break
               if self.tokenized:
                   __a					       =						buffer
               else:
                   __a					       =						self.tokenizer(__lowercase ,			truncation=__lowercase		)["""input_ids"""]
               __a					       =						[]
               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		):
                   __a					       =						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						:						Tuple ,			__lowercase						:						List[Any]=1000		):

           '''simple docstring'''
           return ShufflerIterDataPipe(self ,			buffer_size=__lowercase		)



def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     List[str]      ):
    """simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[int]      ):
    """simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple			,       _SCREAMING_SNAKE_CASE    :     Dict			,       _SCREAMING_SNAKE_CASE    :     Union[str, Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]      ):
    """simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     str			,       _SCREAMING_SNAKE_CASE    :     Optional[int]      ):
    """simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     Optional[int]			,       _SCREAMING_SNAKE_CASE    :     Tuple      ):
    """simple docstring"""







    __a					       =						accelerator.unwrap_model(_SCREAMING_SNAKE_CASE      ).config
    __a					       =						4 if args.gradient_checkpointing else 3
    __a					       =						args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
    __a					       =						24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
    __a					       =						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))
    )
    __a					       =						flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
    return tflops

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     int      ):
    """simple docstring"""







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


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

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

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

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

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

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

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

# Train model
model.train()
lowerCamelCase__     =	0
lowerCamelCase__     =	time.time()
lowerCamelCase__     =	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__     =	model(batch, labels=batch, use_cache=False).loss
      lowerCamelCase__     =	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__     =	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__     =	get_lr()
            accelerator.backward(loss)
            accelerator.clip_grad_norm_(model.parameters(), 1.0)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            lowerCamelCase__     =	time.time() - t_start
            lowerCamelCase__     =	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__     =	time.time()
            lowerCamelCase__     =	0
            completed_steps += 1
      if step % args.save_checkpoint_steps == 0:
            logger.info("""Evaluating and saving model checkpoint""")
            lowerCamelCase__     ,							lowerCamelCase__     =	evaluate(args)
            log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
            accelerator.wait_for_everyone()
            lowerCamelCase__     =	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__     =	evaluate(args)
log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
accelerator.wait_for_everyone()
lowerCamelCase__     =	accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
lowerCamelCase__     =	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 logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


lowerCamelCase__     =	logging.getLogger(__name__)






@dataclass
class 				SCREAMING_SNAKE_CASE     :
							__lowerCamelCase		:		str      =field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}   )
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}   )
							__lowerCamelCase		:		bool      =field(default=lowerCamelCase__     , metadata={'help': 'Set this flag to use fast tokenization.'}   )
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}     , )



def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

				if len(sys.argv      ) == 2 and sys.argv[1].endswith(""".json"""      ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								__a       ,   __a       ,   __a					       =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
				else:
								__a       ,   __a       ,   __a					       =						parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir      )
				    and os.listdir(training_args.output_dir      )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
								    """ --overwrite_output_dir to overcome."""      )

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""			,       datefmt="""%m/%d/%Y %H:%M:%S"""			,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN			,       )
				logger.warning(
				    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""			,       training_args.local_rank			,       training_args.device			,       training_args.n_gpu			,       bool(training_args.local_rank != -1      )			,       training_args.fpaa			,       )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank      ):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info("""Training/evaluation parameters %s"""			,       _SCREAMING_SNAKE_CASE      )

				# Prepare Question-Answering task
				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				__a					       =						AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,       cache_dir=model_args.cache_dir			,       )
				__a					       =						AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,       cache_dir=model_args.cache_dir			,       use_fast=_SCREAMING_SNAKE_CASE			,       )
				__a					       =						AutoModelForQuestionAnswering.from_pretrained(
				    model_args.model_name_or_path			,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path      )			,       config=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir			,       )

				# Get datasets
				__a					       =						hasattr(model.config			,       """lang2id"""      )
				__a					       =						(
				    SquadDataset(
				        _SCREAMING_SNAKE_CASE			,       tokenizer=_SCREAMING_SNAKE_CASE			,       is_language_sensitive=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir      )
				    if training_args.do_train
				    else None
				)
				__a					       =						(
				    SquadDataset(
				        _SCREAMING_SNAKE_CASE			,       tokenizer=_SCREAMING_SNAKE_CASE			,       mode="""dev"""			,       is_language_sensitive=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir			,       )
				    if training_args.do_eval
				    else None
				)

				# Data collator
				__a					       =						DataCollatorWithPadding(_SCREAMING_SNAKE_CASE			,       pad_to_multiple_of=8      ) if training_args.fpaa else None

				# Initialize our Trainer
				__a					       =						Trainer(
				    model=_SCREAMING_SNAKE_CASE			,       args=_SCREAMING_SNAKE_CASE			,       train_dataset=_SCREAMING_SNAKE_CASE			,       eval_dataset=_SCREAMING_SNAKE_CASE			,       data_collator=_SCREAMING_SNAKE_CASE			,       )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir      )

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				main()


if __name__ == "__main__":
						main()
