

"""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__    ):

			"""simple docstring"""

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

								if self.tokenized:
													UpperCamelCase__		:    int         =seq_length * num_of_sequences
													UpperCamelCase__		:    Tuple         ='''input_ids'''
								else:
													UpperCamelCase__		:    Tuple         =seq_length * chars_per_token * num_of_sequences
													UpperCamelCase__		:    Optional[int]         ='''content'''






			def __iter__(							self     :					Union[str, Any]						):
								UpperCamelCase__		:    int         =iter(self.dataset						)
								UpperCamelCase__		:    Dict         =True
								while more_examples:
													UpperCamelCase__  ,			UpperCamelCase__		:    int         =[], 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:
																												UpperCamelCase__		:    str         =iter(self.dataset						)
																												self.epoch += 1
																												logger.info(f'''Dataset epoch: {self.epoch}'''						)
																							else:
																												UpperCamelCase__		:    Dict         =False
																												break
													if self.tokenized:
																		UpperCamelCase__		:    str         =buffer
													else:
																		UpperCamelCase__		:    Optional[int]         =self.tokenizer(lowercase_						,  truncation=lowercase_						)['''input_ids''']
													UpperCamelCase__		:    int         =[]
													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						):
																		UpperCamelCase__		:    Tuple         =all_token_ids[i : i + self.seq_length]
																		if len(lowercase_						) == self.seq_length:
																							self.current_size += 1
																							yield torch.tensor(lowercase_						)






			def    _lowerCAmelCase			(							self     :					List[str]						,  lowercase_     :					Optional[int]=1000						):
								return ShufflerIterDataPipe(self						,  buffer_size=lowercase_						)



def        _lowerCAmelCase      (						UpperCAmelCase     :							str				):


					'''simple docstring'''






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



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


					'''simple docstring'''






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



def        _lowerCAmelCase      (						UpperCAmelCase     :							Optional[int]      ,							UpperCAmelCase     :							Optional[int]      ,							UpperCAmelCase     :							Dict=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]				):


					'''simple docstring'''






					UpperCamelCase__  ,			UpperCamelCase__		:    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        _lowerCAmelCase      (						UpperCAmelCase     :							str      ,							UpperCAmelCase     :							int				):


					'''simple docstring'''






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



def        _lowerCAmelCase      (						UpperCAmelCase     :							Dict      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							Tuple				):


					'''simple docstring'''






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



def        _lowerCAmelCase      (						UpperCAmelCase     :							int				):


					'''simple docstring'''






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


# Settings
_SCREAMING_SNAKE_CASE       :  Dict        = HfArgumentParser(TrainingArguments)
_SCREAMING_SNAKE_CASE       :  List[Any]        = parser.parse_args()

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

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

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

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

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       :  List[Any]        = create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_SCREAMING_SNAKE_CASE       :  Optional[int]        = AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_SCREAMING_SNAKE_CASE       :  Dict        = 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`.
_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       :  str        = 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       :  Any        = os.path.basename(args.resume_from_checkpoint)
				else:
								# Get the most recent checkpoint
								_SCREAMING_SNAKE_CASE       :  int        = [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       :  List[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[Any]        = os.path.splitext(path)[0]
				_SCREAMING_SNAKE_CASE       :  Dict        = int(training_difference.replace("""step_""", """"""))

# Train model
model.train()
_SCREAMING_SNAKE_CASE       :  Tuple        = 0
_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = time.time()
_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = 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       :  Union[str, Any]        = model(batch, labels=batch, use_cache=False).loss
				_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = 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       :  Any        = 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       :  List[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       :  str        = time.time() - t_start
								_SCREAMING_SNAKE_CASE       :  Optional[int]        = 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       :  Union[str, Any]        = time.time()
								_SCREAMING_SNAKE_CASE       :  List[Any]        = 0
								completed_steps += 1
				if step % args.save_checkpoint_steps == 0:
								logger.info("""Evaluating and saving model checkpoint""")
								_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       :  Optional[Any]        = 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       :  int        = evaluate(args)
log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
accelerator.wait_for_everyone()
_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
_SCREAMING_SNAKE_CASE       :  List[str]        = 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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE       :  List[Any]        = {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = ["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  str        = ["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = [
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

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


