



"""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 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





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

								if self.tokenized:
											lowerCamelCase__						:						int						      =seq_length * num_of_sequences
											lowerCamelCase__						:						List[Any]						      ='''input_ids'''
								else:
											lowerCamelCase__						:						List[Any]						      =seq_length * chars_per_token * num_of_sequences
											lowerCamelCase__						:						Tuple						      ='''content'''



					def __iter__(			self				:    List[str]	)->   Any:
								lowerCamelCase__						:						Optional[Any]						      =iter(self.dataset	)
								lowerCamelCase__						:						Tuple						      =True
								while more_examples:
											lowerCamelCase__      ,  lowerCamelCase__						:						str						      =[], 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:
																				lowerCamelCase__						:						Optional[Any]						      =iter(self.dataset	)
																				self.epoch += 1
																				logger.info(F'''Dataset epoch: {self.epoch}'''	)
																	else:
																				lowerCamelCase__						:						List[str]						      =False
																				break
											if self.tokenized:
														lowerCamelCase__						:						Union[str, Any]						      =buffer
											else:
														lowerCamelCase__						:						Any						      =self.tokenizer(lowerCamelCase,    truncation=lowerCamelCase	)['''input_ids''']
											lowerCamelCase__						:						Optional[int]						      =[]
											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	):
														lowerCamelCase__						:						Tuple						      =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				:    Tuple,    lowerCamelCase				:    Optional[int]=1000	)->   Optional[int]:
								return ShufflerIterDataPipe(self,    buffer_size=lowerCamelCase	)






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






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






def 	snake_case__  (						__lowerCamelCase							:       Dict				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       int=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]      ):
			"""simple docstring"""
			lowerCamelCase__      ,  lowerCamelCase__						:						Dict						      =[], []
			for n, p in model.named_parameters():
						if any(nd in n for nd in no_decay      ):
									params_without_wd.append(__lowerCamelCase      )
						else:
									params_with_wd.append(__lowerCamelCase      )
			return [
			    {"params": params_with_wd, "weight_decay": args.weight_decay},
			    {"params": params_without_wd, "weight_decay": 0.0},
			]






def 	snake_case__  (						__lowerCamelCase							:       Tuple				,						__lowerCamelCase							:       Optional[int]      ):
			"""simple docstring"""
			logger.info(f'''Step {step}: {metrics}'''      )
			if accelerator.is_main_process:
						accelerator.log(__lowerCamelCase				,						__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Dict      ):
			"""simple docstring"""
			# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
			lowerCamelCase__						:						Tuple						      =accelerator.unwrap_model(__lowerCamelCase      ).config
			lowerCamelCase__						:						Any						      =4 if args.gradient_checkpointing else 3
			lowerCamelCase__						:						Any						      =args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
			lowerCamelCase__						:						str						      =24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
			lowerCamelCase__						:						Dict						      =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__						:						Dict						      =flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
			return tflops






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]      ):
			"""simple docstring"""
			model.eval()
			lowerCamelCase__						:						str						      =[]
			for step, batch in enumerate(__lowerCamelCase      ):
						with torch.no_grad():
									lowerCamelCase__						:						Optional[Any]						      =model(__lowerCamelCase				,						labels=__lowerCamelCase      )
						lowerCamelCase__						:						int						      =outputs.loss.repeat(args.valid_batch_size      )
						losses.append(accelerator.gather(__lowerCamelCase      )      )
						if args.max_eval_steps > 0 and step >= args.max_eval_steps:
									break
			lowerCamelCase__						:						str						      =torch.cat(__lowerCamelCase      )
			lowerCamelCase__						:						str						      =losses[: eval_dataloader.dataset.current_size].mean()
			try:
						lowerCamelCase__						:						Optional[Any]						      =torch.exp(__lowerCamelCase      )
			except OverflowError:
						lowerCamelCase__						:						Dict						      =float('''inf'''      )
			return loss.item(), perplexity.item()


# Settings
_lowercase					:					Dict 		=		HfArgumentParser(TrainingArguments)
_lowercase					:					Optional[Any] 		=		parser.parse_args()

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

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

# Clone model repository
if accelerator.is_main_process:
		_lowercase					:					List[Any] 		=		Repository(args.save_dir, clone_from=args.model_ckpt)

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

# Load dataset and dataloader
_lowercase		,      _lowercase					:					Union[str, Any] 		=		create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_lowercase					:					Tuple 		=		AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_lowercase					:					List[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 	snake_case__  (						):
			"""simple docstring"""
			return optimizer.param_groups[0]["lr"]


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

# Train model
model.train()
_lowercase					:					Optional[int] 		=		0
_lowercase					:					List[Any] 		=		time.time()
_lowercase					:					Dict 		=		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					:					int 		=		model(batch, labels=batch, use_cache=False).loss
		_lowercase					:					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()})
		_lowercase					:					Dict 		=		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					:					List[Any] 		=		get_lr()
				accelerator.backward(loss)
				accelerator.clip_grad_norm_(model.parameters(), 1.0)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
				_lowercase					:					str 		=		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					:					List[Any] 		=		time.time()
				_lowercase					:					Union[str, Any] 		=		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					:					Union[str, 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=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					:					List[str] 		=		accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
_lowercase					:					Optional[int] 		=		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 argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCAmelCase_   				=  logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

UpperCAmelCase_   				=  {
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCAmelCase_   				=  {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCAmelCase_   				=  sorted(arg_to_scheduler.keys())
UpperCAmelCase_   				=  """{""" + """, """.join(arg_to_scheduler_choices) + """}"""
class       UpperCamelCase_		(	pl.LightningModule       ):




					def __init__(		self					,    lowerCAmelCase_					,    lowerCAmelCase_=None					,    lowerCAmelCase_="base"					,    lowerCAmelCase_=None					,    lowerCAmelCase_=None					,    lowerCAmelCase_=None					,    **lowerCAmelCase_					,    )		->				Union[str, Any]:
										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

										self.save_hyperparameters(lowerCAmelCase_	)
										_snake_case						=					0
										_snake_case						=					Path(self.hparams.output_dir	)
										_snake_case						=					self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
															_snake_case						=					AutoConfig.from_pretrained(
															    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path					,    **({'num_labels': num_labels} if num_labels is not None else {})					,    cache_dir=lowerCAmelCase_					,    **lowerCAmelCase_					,    )
										else:
															_snake_case						=					config

										_snake_case						=					('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
										for p in extra_model_params:
															if getattr(self.hparams					,    lowerCAmelCase_					,    lowerCAmelCase_	):
																				assert hasattr(self.config					,    lowerCAmelCase_	), F'''model config doesn\'t have a `{p}` attribute'''
																				setattr(self.config					,    lowerCAmelCase_					,    getattr(self.hparams					,    lowerCAmelCase_	)	)

										if tokenizer is None:
															_snake_case						=					AutoTokenizer.from_pretrained(
															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,    cache_dir=lowerCAmelCase_					,    )
										else:
															_snake_case						=					tokenizer
										_snake_case						=					MODEL_MODES[mode]
										if model is None:
															_snake_case						=					self.model_type.from_pretrained(
															    self.hparams.model_name_or_path					,    from_tf=bool('.ckpt' in self.hparams.model_name_or_path	)					,    config=self.config					,    cache_dir=lowerCAmelCase_					,    )
										else:
															_snake_case						=					model




					def 						lowerCAmelCase      (		self					,    *lowerCAmelCase_					,    **lowerCAmelCase_	)		->				str:
										_snake_case						=					self.model_type.from_pretrained(*lowerCAmelCase_					,    **lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				List[Any]:
										_snake_case						=					arg_to_scheduler[self.hparams.lr_scheduler]
										_snake_case						=					get_schedule_func(
										    self.opt					,    num_warmup_steps=self.hparams.warmup_steps					,    num_training_steps=self.total_steps()	)
										_snake_case						=					{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
										return scheduler




					def 						lowerCAmelCase      (		self	)		->				int:
										_snake_case						=					self.model
										_snake_case						=					['bias', 'LayerNorm.weight']
										_snake_case						=					[
										    {
										        'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
										        'weight_decay': self.hparams.weight_decay,
										    },
										    {
										        'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)],
										        'weight_decay': 0.0,
										    },
										]
										if self.hparams.adafactor:
															_snake_case						=					Adafactor(
															    lowerCAmelCase_					,    lr=self.hparams.learning_rate					,    scale_parameter=lowerCAmelCase_					,    relative_step=lowerCAmelCase_	)

										else:
															_snake_case						=					AdamW(
															    lowerCAmelCase_					,    lr=self.hparams.learning_rate					,    eps=self.hparams.adam_epsilon	)
										_snake_case						=					optimizer

										_snake_case						=					self.get_lr_scheduler()

										return [optimizer], [scheduler]




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				List[str]:
										return self.validation_step(lowerCAmelCase_					,    lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				Optional[int]:
										return self.validation_end(lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				int:
										_snake_case						=					max(1					,    self.hparams.gpus	)  # TODO: consider num_tpu_cores
										_snake_case						=					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				Tuple:
										if mode == "test":
															_snake_case						=					len(self.test_dataloader().dataset	)
										else:
															_snake_case						=					self.get_dataloader('train'					,    self.hparams.train_batch_size					,    shuffle=lowerCAmelCase_	)
															_snake_case						=					len(self.train_dataloader().dataset	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_ = False	)		->				Dict:
										raise NotImplementedError('You must implement this for your task'	)




					def 						lowerCAmelCase      (		self	)		->				Any:
										return self.train_loader




					def 						lowerCAmelCase      (		self	)		->				int:
										return self.get_dataloader('dev'					,    self.hparams.eval_batch_size					,    shuffle=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Tuple:
										return self.get_dataloader('test'					,    self.hparams.eval_batch_size					,    shuffle=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				int:
										return os.path.join(
										    self.hparams.data_dir					,    'cached_{}_{}_{}'.format(
										        lowerCAmelCase_					,    list(filter(lowerCAmelCase_					,    self.hparams.model_name_or_path.split('/'	)	)	).pop()					,    str(self.hparams.max_seq_length	)					,    )					,    )




					@pl.utilities.rank_zero_only
					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				None:
										_snake_case						=					self.output_dir.joinpath('best_tfmr'	)
										_snake_case						=					self.step_count
										self.model.save_pretrained(lowerCAmelCase_	)
										self.tokenizer.save_pretrained(lowerCAmelCase_	)






					@staticmethod
					def 						lowerCAmelCase      (		lowerCAmelCase_					,    lowerCAmelCase_	)		->				Dict:
										parser.add_argument(
										    '--model_name_or_path'					,    default=lowerCAmelCase_					,    type=lowerCAmelCase_					,    required=lowerCAmelCase_					,    help='Path to pretrained model or model identifier from huggingface.co/models'					,    )
										parser.add_argument(
										    '--config_name'					,    default=''					,    type=lowerCAmelCase_					,    help='Pretrained config name or path if not the same as model_name'	)
										parser.add_argument(
										    '--tokenizer_name'					,    default=lowerCAmelCase_					,    type=lowerCAmelCase_					,    help='Pretrained tokenizer name or path if not the same as model_name'					,    )
										parser.add_argument(
										    '--cache_dir'					,    default=''					,    type=lowerCAmelCase_					,    help='Where do you want to store the pre-trained models downloaded from huggingface.co'					,    )
										parser.add_argument(
										    '--encoder_layerdrop'					,    type=lowerCAmelCase_					,    help='Encoder layer dropout probability (Optional). Goes into model.config'					,    )
										parser.add_argument(
										    '--decoder_layerdrop'					,    type=lowerCAmelCase_					,    help='Decoder layer dropout probability (Optional). Goes into model.config'					,    )
										parser.add_argument(
										    '--dropout'					,    type=lowerCAmelCase_					,    help='Dropout probability (Optional). Goes into model.config'					,    )
										parser.add_argument(
										    '--attention_dropout'					,    type=lowerCAmelCase_					,    help='Attention dropout probability (Optional). Goes into model.config'					,    )
										parser.add_argument('--learning_rate'					,    default=5E-5					,    type=lowerCAmelCase_					,    help='The initial learning rate for Adam.'	)
										parser.add_argument(
										    '--lr_scheduler'					,    default='linear'					,    choices=lowerCAmelCase_					,    metavar=lowerCAmelCase_					,    type=lowerCAmelCase_					,    help='Learning rate scheduler'					,    )
										parser.add_argument('--weight_decay'					,    default=0.0					,    type=lowerCAmelCase_					,    help='Weight decay if we apply some.'	)
										parser.add_argument('--adam_epsilon'					,    default=1E-8					,    type=lowerCAmelCase_					,    help='Epsilon for Adam optimizer.'	)
										parser.add_argument('--warmup_steps'					,    default=0					,    type=lowerCAmelCase_					,    help='Linear warmup over warmup_steps.'	)
										parser.add_argument('--num_workers'					,    default=4					,    type=lowerCAmelCase_					,    help='kwarg passed to DataLoader'	)
										parser.add_argument('--num_train_epochs'					,    dest='max_epochs'					,    default=3					,    type=lowerCAmelCase_	)
										parser.add_argument('--train_batch_size'					,    default=32					,    type=lowerCAmelCase_	)
										parser.add_argument('--eval_batch_size'					,    default=32					,    type=lowerCAmelCase_	)
										parser.add_argument('--adafactor'					,    action='store_true'	)





class       UpperCamelCase_		(	pl.Callback       ):




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				int:
										_snake_case						=					trainer.lr_schedulers[0]['scheduler']
										_snake_case						=					{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
										pl_module.logger.log_metrics(lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				str:
										rank_zero_info('***** Validation results *****'	)
										_snake_case						=					trainer.callback_metrics
										# Log results
										for key in sorted(lowerCAmelCase_	):
															if key not in ["log", "progress_bar"]:
																				rank_zero_info('{} = {}\n'.format(lowerCAmelCase_					,    str(metrics[key]	)	)	)






					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				List[Any]:
										rank_zero_info('***** Test results *****'	)
										_snake_case						=					trainer.callback_metrics
										# Log and save results to file
										_snake_case						=					os.path.join(pl_module.hparams.output_dir					,    'test_results.txt'	)
										with open(lowerCAmelCase_					,    'w'	) as writer:
															for key in sorted(lowerCAmelCase_	):
																				if key not in ["log", "progress_bar"]:
																									rank_zero_info('{} = {}\n'.format(lowerCAmelCase_					,    str(metrics[key]	)	)	)
																									writer.write('{} = {}\n'.format(lowerCAmelCase_					,    str(metrics[key]	)	)	)





def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Union[str, Any]    )      ->			None:



					'''simple docstring'''




					parser.add_argument(
					    '--output_dir'							,     default=UpperCamelCase__							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     help='The output directory where the model predictions and checkpoints will be written.'							,     )
					parser.add_argument(
					    '--fp16'							,     action='store_true'							,     help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'							,     )

					parser.add_argument(
					    '--fp16_opt_level'							,     type=UpperCamelCase__							,     default='O2'							,     help=(
					        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
					        'See details at https://nvidia.github.io/apex/amp.html'
					    )							,     )
					parser.add_argument('--n_tpu_cores'							,     dest='tpu_cores'							,     type=UpperCamelCase__    )
					parser.add_argument('--max_grad_norm'							,     dest='gradient_clip_val'							,     default=1.0							,     type=UpperCamelCase__							,     help='Max gradient norm'    )
					parser.add_argument('--do_train'							,     action='store_true'							,     help='Whether to run training.'    )
					parser.add_argument('--do_predict'							,     action='store_true'							,     help='Whether to run predictions on the test set.'    )
					parser.add_argument(
					    '--gradient_accumulation_steps'							,     dest='accumulate_grad_batches'							,     type=UpperCamelCase__							,     default=1							,     help='Number of updates steps to accumulate before performing a backward/update pass.'							,     )
					parser.add_argument('--seed'							,     type=UpperCamelCase__							,     default=42							,     help='random seed for initialization'    )
					parser.add_argument(
					    '--data_dir'							,     default=UpperCamelCase__							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'							,     )





def     lowerCamelCase__  (  UpperCamelCase__      :							BaseTransformer							,     UpperCamelCase__      :							argparse.Namespace							,     UpperCamelCase__      :							Optional[Any]=None							,     UpperCamelCase__      :							Union[str, Any]=True							,     UpperCamelCase__      :							Optional[Any]=[]							,     UpperCamelCase__      :							int=None							,     UpperCamelCase__      :							Tuple=None							,     **UpperCamelCase__      :							List[Any]							,     )      ->			Any:



					'''simple docstring'''




					pl.seed_everything(args.seed    )

					# init model
					_snake_case						=					Path(model.hparams.output_dir    )
					odir.mkdir(exist_ok=UpperCamelCase__    )

					# add custom checkpoints
					if checkpoint_callback is None:
										_snake_case						=					pl.callbacks.ModelCheckpoint(
										    filepath=args.output_dir							,     prefix='checkpoint'							,     monitor='val_loss'							,     mode='min'							,     save_top_k=1    )
					if early_stopping_callback:
										extra_callbacks.append(UpperCamelCase__    )
					if logging_callback is None:
										_snake_case						=					LoggingCallback()

					_snake_case						=					{}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										_snake_case						=					16
										_snake_case						=					args.fpaa_opt_level

					if args.gpus > 1:
										_snake_case						=					'ddp'

					_snake_case						=					args.accumulate_grad_batches
					_snake_case						=					extra_train_kwargs.get('accelerator'							,     UpperCamelCase__    )
					_snake_case						=					extra_train_kwargs.get('profiler'							,     UpperCamelCase__    )

					_snake_case						=					pl.Trainer.from_argparse_args(
					    UpperCamelCase__							,     weights_summary=UpperCamelCase__							,     callbacks=[logging_callback] + extra_callbacks							,     logger=UpperCamelCase__							,     checkpoint_callback=UpperCamelCase__							,     **UpperCamelCase__							,     )

					if args.do_train:
										trainer.fit(UpperCamelCase__    )

					return trainer



