
'''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 		__lowercase			(					_lowercase      ):

			def __init__(self     ,				A     ,				A     ,				A=False     ,				A=1_0_2_4     ,				A=1_0_2_4     ,				A=3.6     ,				A=False     ,				):
								lowerCamelCase_   : Optional[int]       =						tokenizer
								lowerCamelCase_   : List[Any]       =						tokenizer.bos_token_id
								lowerCamelCase_   : Optional[int]       =						dataset
								lowerCamelCase_   : int       =						seq_length
								lowerCamelCase_   : int       =						0
								lowerCamelCase_   : Optional[int]       =						infinite
								lowerCamelCase_   : int       =						0
								lowerCamelCase_   : str       =						tokenized

								if self.tokenized:
													lowerCamelCase_   : List[str]       =						seq_length * num_of_sequences
													lowerCamelCase_   : Dict       =						'''input_ids'''
								else:
													lowerCamelCase_   : Optional[int]       =						seq_length * chars_per_token * num_of_sequences
													lowerCamelCase_   : Dict       =						'''content'''

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






			def     UpperCAmelCase__	(self     ,				A=1_0_0_0 ):
								return ShufflerIterDataPipe(self     ,				buffer_size=A )




def lowercase_		(				_lowercase			)       ->							List[Any]:
					'''simple docstring'''

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




def lowercase_		(				_lowercase			)       ->							str:
					'''simple docstring'''

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




def lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]			)       ->							Any:
					'''simple docstring'''

					lowerCamelCase_, lowerCamelCase_   : 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 lowercase_		(				_lowercase     ,    _lowercase			)       ->							Dict:
					'''simple docstring'''

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




def lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase			)       ->							Tuple:
					'''simple docstring'''

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




def lowercase_		(				_lowercase			)       ->							Optional[int]:
					'''simple docstring'''

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


# Settings
__lowercase							:				Tuple					=       HfArgumentParser(TrainingArguments)
__lowercase							:				Any					=       parser.parse_args()

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

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

# Clone model repository
if accelerator.is_main_process:
					__lowercase							:				Optional[int]					=       Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
__lowercase			,						__lowercase							:				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
__lowercase							:				Union[str, Any]					=       AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
					model.gradient_checkpointing_enable()
__lowercase							:				Optional[int]					=       AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__lowercase			,						__lowercase							:				Tuple					=       create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__lowercase							:				Dict					=       AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowercase							:				Tuple					=       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 lowercase_		(				)       ->							str:
					'''simple docstring'''

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


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

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




'''simple docstring'''


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__lowercase							:				Optional[Any]					=       logging.getLogger()




def lowercase_		(				)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_   : Any       =						argparse.ArgumentParser()
					parser.add_argument('''-f'''			)
					lowerCamelCase_   : Optional[int]       =						parser.parse_args()
					return args.f






class 		__lowercase			(					_lowercase      ):

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Dict       =						logging.StreamHandler(sys.stdout )
								logger.addHandler(A )

								lowerCamelCase_   : int       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Dict       =						F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

								with patch.object(A     ,				'''argv'''     ,				A ):
													lowerCamelCase_   : Optional[Any]       =						run_glue_with_pabee.main()
													for value in result.values():
																		self.assertGreaterEqual(A     ,				0.75 )


