


'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE       :   Any        = {
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   List[Any]        = ["DebertaV2TokenizerFast"]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   int        = [
		    "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFDebertaV2ForMaskedLM",
		    "TFDebertaV2ForQuestionAnswering",
		    "TFDebertaV2ForSequenceClassification",
		    "TFDebertaV2ForTokenClassification",
		    "TFDebertaV2Model",
		    "TFDebertaV2PreTrainedModel",
		]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   Union[str, Any]        = [
		    "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DebertaV2ForMaskedLM",
		    "DebertaV2ForMultipleChoice",
		    "DebertaV2ForQuestionAnswering",
		    "DebertaV2ForSequenceClassification",
		    "DebertaV2ForTokenClassification",
		    "DebertaV2Model",
		    "DebertaV2PreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_deberta_va import (
		    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DebertaVaConfig,
		    DebertaVaOnnxConfig,
		)
		from .tokenization_deberta_va import DebertaVaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deberta_va import (
				    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDebertaVaForMaskedLM,
				    TFDebertaVaForQuestionAnswering,
				    TFDebertaVaForSequenceClassification,
				    TFDebertaVaForTokenClassification,
				    TFDebertaVaModel,
				    TFDebertaVaPreTrainedModel,
				)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deberta_va import (
				    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DebertaVaForMaskedLM,
				    DebertaVaForMultipleChoice,
				    DebertaVaForQuestionAnswering,
				    DebertaVaForSequenceClassification,
				    DebertaVaForTokenClassification,
				    DebertaVaModel,
				    DebertaVaPreTrainedModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE       :   Dict        = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''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 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						def __init__(  self:	List[Any]      ,							__UpperCamelCase:	str      ,							__UpperCamelCase:	Optional[Any]      ,							__UpperCamelCase:	Optional[int]=False      ,							__UpperCamelCase:	Optional[int]=1024      ,							__UpperCamelCase:	Union[str, Any]=1024      ,							__UpperCamelCase:	Optional[int]=3.6      ,							__UpperCamelCase:	str=False      ,							)							->	Optional[int]:
										__magic_name__				:					Union[str, Any]						       =       tokenizer
										__magic_name__				:					Any						       =       tokenizer.bos_token_id
										__magic_name__				:					Optional[int]						       =       dataset
										__magic_name__				:					List[Any]						       =       seq_length
										__magic_name__				:					Union[str, Any]						       =       0
										__magic_name__				:					Dict						       =       infinite
										__magic_name__				:					Any						       =       0
										__magic_name__				:					int						       =       tokenized

										if self.tokenized:
														__magic_name__				:					Dict						       =       seq_length * num_of_sequences
														__magic_name__				:					Optional[Any]						       =       "input_ids"
										else:
														__magic_name__				:					str						       =       seq_length * chars_per_token * num_of_sequences
														__magic_name__				:					Tuple						       =       "content"







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







						def lowerCAmelCase__ (  self:	Tuple      ,							__UpperCamelCase:	str=1000     )							->	List[str]:
										return ShufflerIterDataPipe(self      ,							buffer_size=__UpperCamelCase     )






def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]		):






				"""simple docstring"""



				__magic_name__       ,     __magic_name__				:					List[str]						       =       [], []
				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 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



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


# Settings
_SCREAMING_SNAKE_CASE       :   Optional[int]        = HfArgumentParser(TrainingArguments)
_SCREAMING_SNAKE_CASE       :   Any        = parser.parse_args()

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

_SCREAMING_SNAKE_CASE       :   Optional[Any]        = Namespace(**vars(args), **acc_state)
_SCREAMING_SNAKE_CASE       :   Optional[Any]        = accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   Dict        = 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
_SCREAMING_SNAKE_CASE       :   Any        = AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
_SCREAMING_SNAKE_CASE       :   Optional[Any]        = AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   Dict        = create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_SCREAMING_SNAKE_CASE       :   Tuple        = AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_SCREAMING_SNAKE_CASE       :   str        = 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 						_UpperCamelCase							( ):






				"""simple docstring"""



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


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

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