



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
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json

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







				__a					       =						filter(lambda _SCREAMING_SNAKE_CASE      : p.requires_grad			,       model.parameters()      )
				__a					       =						sum([np.prod(p.size()      ) for p in model_parameters]      )
				return params


lowerCamelCase__     =	logging.getLogger(__name__)






class 				SCREAMING_SNAKE_CASE     (							pl.Callback   ):





							def   UpperCamelCase_   (				self						:						Dict ,			__lowercase						:						Dict ,			__lowercase						:						str		):

											'''simple docstring'''
											__a					       =						{F"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
											pl_module.logger.log_metrics(__lowercase		)





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule ,			__lowercase						:						str ,			__lowercase						:						Tuple=True		):

											'''simple docstring'''
											logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"		)
											__a					       =						trainer.callback_metrics
											trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}		)
											# Log results
											__a					       =						Path(pl_module.hparams.output_dir		)
											if type_path == "test":
															__a					       =						od / """test_results.txt"""
															__a					       =						od / """test_generations.txt"""
											else:
															# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
															# If people want this it will be easy enough to add back.
															__a					       =						od / F"{type_path}_results/{trainer.global_step:05d}.txt"
															__a					       =						od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
															results_file.parent.mkdir(exist_ok=__lowercase		)
															generations_file.parent.mkdir(exist_ok=__lowercase		)
											with open(__lowercase ,			"""a+"""		) as writer:
															for key in sorted(__lowercase		):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__a					       =						metrics[key]
																			if isinstance(__lowercase ,			torch.Tensor		):
																							__a					       =						val.item()
																			__a					       =						F"{key}: {val:.6f}\n"
																			writer.write(__lowercase		)

											if not save_generations:
															return

											if "preds" in metrics:
															__a					       =						"""\n""".join(metrics["""preds"""]		)
															generations_file.open("""w+"""		).write(__lowercase		)





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						List[Any]		):

											'''simple docstring'''
											try:
															__a					       =						pl_module.model.model.num_parameters()
											except AttributeError:
															__a					       =						pl_module.model.num_parameters()

											__a					       =						count_trainable_parameters(__lowercase		)
											# mp stands for million parameters
											trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6}		)





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Union[str, Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule		):

											'''simple docstring'''
											save_json(pl_module.metrics ,			pl_module.metrics_save_path		)
											return self._write_logs(__lowercase ,			__lowercase ,			"""test"""		)







							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[int] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						Dict		):

											'''simple docstring'''
											save_json(pl_module.metrics ,			pl_module.metrics_save_path		)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")



def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[int]			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]			,       _SCREAMING_SNAKE_CASE    :     Tuple=1			,       _SCREAMING_SNAKE_CASE    :     List[str]=False      ):
				"""simple docstring"""







				if metric == "rouge2":
								__a					       =						"""{val_avg_rouge2:.4f}-{step_count}"""
				elif metric == "bleu":
								__a					       =						"""{val_avg_bleu:.4f}-{step_count}"""
				elif metric == "loss":
								__a					       =						"""{val_avg_loss:.4f}-{step_count}"""
				else:
								raise NotImplementedError(
								    f"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
								    """ this function."""      )

				__a					       =						ModelCheckpoint(
				    dirpath=_SCREAMING_SNAKE_CASE			,       filename=_SCREAMING_SNAKE_CASE			,       monitor=f"val_{metric}"			,       mode="""min""" if """loss""" in metric else """max"""			,       save_top_k=_SCREAMING_SNAKE_CASE			,       )
				return checkpoint_callback

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







				return EarlyStopping(
				    monitor=f"val_{metric}"			,       mode="""min""" if """loss""" in metric else """max"""			,       patience=_SCREAMING_SNAKE_CASE			,       verbose=_SCREAMING_SNAKE_CASE			,       )
