





"""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						(      _lowerCAmelCase				):






				def __init__( self		:    str, lowerCAmelCase__		:    Optional[Any], lowerCAmelCase__		:    Optional[Any], lowerCAmelCase__		:    str=False, lowerCAmelCase__		:    Tuple=1_0_2_4, lowerCAmelCase__		:    Optional[int]=1_0_2_4, lowerCAmelCase__		:    Union[str, Any]=3.6, lowerCAmelCase__		:    str=False, )	->      List[str]:




										'''simple docstring'''



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

										if self.tokenized:
																_UpperCamelCase			:					Union[str, Any]     =    seq_length * num_of_sequences
																_UpperCamelCase			:					int     =    '''input_ids'''
										else:
																_UpperCamelCase			:					str     =    seq_length * chars_per_token * num_of_sequences
																_UpperCamelCase			:					Tuple     =    '''content'''






				def __iter__( self		:    Any							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    iter(self.dataset							)
										_UpperCamelCase			:					List[str]     =    True
										while more_examples:
																_UpperCamelCase   ,							_UpperCamelCase			:					Any     =    [], 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:
																																		_UpperCamelCase			:					List[str]     =    iter(self.dataset							)
																																		self.epoch += 1
																																		logger.info(f"""Dataset epoch: {self.epoch}"""							)
																												else:
																																		_UpperCamelCase			:					List[Any]     =    False
																																		break
																if self.tokenized:
																						_UpperCamelCase			:					Any     =    buffer
																else:
																						_UpperCamelCase			:					Optional[Any]     =    self.tokenizer(lowerCAmelCase__, truncation=lowerCAmelCase__							)['''input_ids''']
																_UpperCamelCase			:					Optional[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							):
																						_UpperCamelCase			:					Optional[Any]     =    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__		:    Any=1_0_0_0							)	->      Optional[int]:




										'''simple docstring'''



										return ShufflerIterDataPipe(self, buffer_size=lowerCAmelCase__							)





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





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





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]   ):
						_UpperCamelCase   ,							_UpperCamelCase			:					Optional[Any]     =    [], []
						for n, p in model.named_parameters():
												if any(nd in n for nd in no_decay   ):
																		params_without_wd.append(_lowercase   )
												else:
																		params_with_wd.append(_lowercase   )
						return [
						    {"params": params_with_wd, "weight_decay": args.weight_decay},
						    {"params": params_without_wd, "weight_decay": 0.0},
						]





def        a_				(							_lowercase       ,						_lowercase   ):
						logger.info(F"""Step {step}: {metrics}"""   )
						if accelerator.is_main_process:
												accelerator.log(_lowercase       ,						_lowercase   )





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
						_UpperCamelCase			:					Union[str, Any]     =    accelerator.unwrap_model(_lowercase   ).config
						_UpperCamelCase			:					str     =    4 if args.gradient_checkpointing else 3
						_UpperCamelCase			:					Optional[int]     =    args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						_UpperCamelCase			:					Tuple     =    24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						_UpperCamelCase			:					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))
						)
						_UpperCamelCase			:					Tuple     =    flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops





def        a_				(							_lowercase   ):
						model.eval()
						_UpperCamelCase			:					Union[str, Any]     =    []
						for step, batch in enumerate(_lowercase   ):
												with torch.no_grad():
																		_UpperCamelCase			:					List[Any]     =    model(_lowercase       ,						labels=_lowercase   )
												_UpperCamelCase			:					Dict     =    outputs.loss.repeat(args.valid_batch_size   )
												losses.append(accelerator.gather(_lowercase   )   )
												if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																		break
						_UpperCamelCase			:					int     =    torch.cat(_lowercase   )
						_UpperCamelCase			:					Optional[int]     =    losses[: eval_dataloader.dataset.current_size].mean()
						try:
												_UpperCamelCase			:					Optional[Any]     =    torch.exp(_lowercase   )
						except OverflowError:
												_UpperCamelCase			:					Dict     =    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_      ,	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_      ,	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        a_				(							):
						return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
UpperCamelCase_      ,	UpperCamelCase_      ,	UpperCamelCase_      ,	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_      ,	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_      ,	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_torch_available


A_			: Dict				=			{"configuration_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			: Tuple				=			[
          "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "QDQBertForMaskedLM",
          "QDQBertForMultipleChoice",
          "QDQBertForNextSentencePrediction",
          "QDQBertForQuestionAnswering",
          "QDQBertForSequenceClassification",
          "QDQBertForTokenClassification",
          "QDQBertLayer",
          "QDQBertLMHeadModel",
          "QDQBertModel",
          "QDQBertPreTrainedModel",
          "load_tf_weights_in_qdqbert",
      ]


if TYPE_CHECKING:
      from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_qdqbert import (
                QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                QDQBertForMaskedLM,
                QDQBertForMultipleChoice,
                QDQBertForNextSentencePrediction,
                QDQBertForQuestionAnswering,
                QDQBertForSequenceClassification,
                QDQBertForTokenClassification,
                QDQBertLayer,
                QDQBertLMHeadModel,
                QDQBertModel,
                QDQBertPreTrainedModel,
                load_tf_weights_in_qdqbert,
            )


else:
      import sys

      A_			: Tuple				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

