



"""simple docstring"""






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def     _lowercase       (						)		->     str:
			__lowerCAmelCase   :						Dict				= ArgumentParser(
			    description=(
			        "PyTorch TPU distributed training launch "
			        "helper utility that will spawn up "
			        "multiple distributed processes"
			    )			)

			# Optional arguments for the launch helper
			parser.add_argument("--num_cores"      ,type=__snake_case      ,default=1      ,help="Number of TPU cores to use (1 or 8)."			)

			# positional
			parser.add_argument(
			    "training_script"      ,type=__snake_case      ,help=(
			        "The full path to the single TPU training "
			        "program/script to be launched in parallel, "
			        "followed by all the arguments for the "
			        "training script"
			    )      ,)

			# rest from the training program
			parser.add_argument("training_script_args"      ,nargs=__snake_case			)

			return parser.parse_args()


def     _lowercase       (						)		->     Optional[Any]:
			__lowerCAmelCase   :						Tuple				= parse_args()

			# Import training_script as a module.
			__lowerCAmelCase   :						Dict				= Path(args.training_script			)
			sys.path.append(str(script_fpath.parent.resolve()			)			)
			__lowerCAmelCase   :						str				= script_fpath.stem
			__lowerCAmelCase   :						Optional[int]				= importlib.import_module(__snake_case			)

			# Patch sys.argv
			__lowerCAmelCase   :						List[str]				= [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores			)]
			xmp.spawn(mod._mp_fn      ,args=()      ,nprocs=args.num_cores			)


if __name__ == "__main__":
	main()




"""simple docstring"""






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


__snake_case							:     Optional[Any]   =					logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

__snake_case							:     List[Any]   =					{
    '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
__snake_case							:     Dict   =					{
    '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
}
__snake_case							:     int   =					sorted(arg_to_scheduler.keys())
__snake_case							:     Optional[Any]   =					'{' + ', '.join(arg_to_scheduler_choices) + '}'



class     A__     (						pl.LightningModule							):




	'''simple docstring'''





	def __init__(  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	argparse.Namespace ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=None ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]="base" ,    _SCREAMING_SNAKE_CASE:	Optional[int]=None ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=None ,    _SCREAMING_SNAKE_CASE:	Any=None ,    **_SCREAMING_SNAKE_CASE:	int ,    )       -> int:

				"""simple docstring"""

				super().__init__()
				# TODO: move to self.save_hyperparameters()
				# self.save_hyperparameters()
				# can also expand arguments into trainer signature for easier reading

				self.save_hyperparameters(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Optional[Any]				= 0
				__lowerCAmelCase   :						int				= Path(self.hparams.output_dir)
				__lowerCAmelCase   :						List[str]				= self.hparams.cache_dir if self.hparams.cache_dir else None
				if config is None:
							__lowerCAmelCase   :						Optional[int]				= 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=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE ,    )
				else:
							__lowerCAmelCase   :						PretrainedConfig				= config

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

				if tokenizer is None:
							__lowerCAmelCase   :						Optional[int]				= AutoTokenizer.from_pretrained(
							    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path ,    cache_dir=_SCREAMING_SNAKE_CASE ,    )
				else:
							__lowerCAmelCase   :						PreTrainedTokenizer				= tokenizer
				__lowerCAmelCase   :						int				= MODEL_MODES[mode]
				if model is None:
							__lowerCAmelCase   :						Any				= 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=_SCREAMING_SNAKE_CASE ,    )
				else:
							__lowerCAmelCase   :						Union[str, Any]				= model

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    *_SCREAMING_SNAKE_CASE:	Union[str, Any] ,    **_SCREAMING_SNAKE_CASE:	Dict)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= self.model_type.from_pretrained(*_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= arg_to_scheduler[self.hparams.lr_scheduler]
				__lowerCAmelCase   :						Tuple				= get_schedule_func(
				    self.opt ,    num_warmup_steps=self.hparams.warmup_steps ,    num_training_steps=self.total_steps())
				__lowerCAmelCase   :						Any				= {"scheduler": scheduler, "interval": "step", "frequency": 1}
				return scheduler

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= self.model
				__lowerCAmelCase   :						Tuple				= ["bias", "LayerNorm.weight"]
				__lowerCAmelCase   :						Optional[Any]				= [
				    {
				        "params": [
				            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)
				        ],  # check this named paramters
				        "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:
							__lowerCAmelCase   :						Dict				= Adafactor(
							    _SCREAMING_SNAKE_CASE ,    lr=self.hparams.learning_rate ,    scale_parameter=_SCREAMING_SNAKE_CASE ,    relative_step=_SCREAMING_SNAKE_CASE)

				else:
							__lowerCAmelCase   :						Dict				= AdamW(
							    _SCREAMING_SNAKE_CASE ,    lr=self.hparams.learning_rate ,    eps=self.hparams.adam_epsilon)
				__lowerCAmelCase   :						int				= optimizer

				__lowerCAmelCase   :						Optional[int]				= self.get_lr_scheduler()

				return [optimizer], [scheduler]

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	Any)       -> Union[str, Any]:

				"""simple docstring"""

				return self.validation_step(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Any)       -> List[Any]:

				"""simple docstring"""

				return self.validation_end(_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= max(1 ,    self.hparams.gpus)  # TODO: consider num_tpu_cores
				__lowerCAmelCase   :						Tuple				= self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
				return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	Union[str, Any])       -> Union[str, Any]:

				"""simple docstring"""

				if stage == "test":
							__lowerCAmelCase   :						List[str]				= len(self.test_dataloader().dataset)
				else:
							__lowerCAmelCase   :						Tuple				= self.get_dataloader("train" ,    self.hparams.train_batch_size ,    shuffle=_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						Tuple				= len(self.train_dataloader().dataset)

	def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	bool = False)       -> int:

				"""simple docstring"""

				raise NotImplementedError("You must implement this for your task")

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> List[str]:

				"""simple docstring"""

				return self.train_loader

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Tuple:

				"""simple docstring"""

				return self.get_dataloader("dev" ,    self.hparams.eval_batch_size ,    shuffle=_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Optional[int]:

				"""simple docstring"""

				return self.get_dataloader("test" ,    self.hparams.eval_batch_size ,    shuffle=_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	Any)       -> int:

				"""simple docstring"""

				return os.path.join(
				    self.hparams.data_dir ,    "cached_{}_{}_{}".format(
				        _SCREAMING_SNAKE_CASE ,    list(filter(_SCREAMING_SNAKE_CASE ,    self.hparams.model_name_or_path.split("/"))).pop() ,    str(self.hparams.max_seq_length) ,    ) ,    )

	@pl.utilities.rank_zero_only
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Dict[str, Any])       -> None:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= self.output_dir.joinpath("best_tfmr")
				__lowerCAmelCase   :						str				= self.step_count
				self.model.save_pretrained(_SCREAMING_SNAKE_CASE)
				self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE)







	@staticmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	Dict)       -> Optional[Any]:

				"""simple docstring"""

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






class     A__     (						pl.Callback							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	Tuple)       -> Any:

				"""simple docstring"""

				if (
				    trainer.is_global_zero and trainer.global_rank == 0
				):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
							pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class     A__     (						pl.Callback							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Tuple)       -> Optional[int]:

				"""simple docstring"""

				for name, param in pl_module.model.rag.named_parameters():
							if param.grad is None:
										print(_SCREAMING_SNAKE_CASE)






class     A__     (						pl.Callback							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	int)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= trainer.lr_schedulers[0]["scheduler"]
				__lowerCAmelCase   :						str				= {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr())}
				pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	pl.Trainer ,    _SCREAMING_SNAKE_CASE:	pl.LightningModule)       -> str:

				"""simple docstring"""

				rank_zero_info("***** Validation results *****")
				__lowerCAmelCase   :						Tuple				= trainer.callback_metrics
				# Log results
				for key in sorted(_SCREAMING_SNAKE_CASE):
							if key not in ["log", "progress_bar"]:
										rank_zero_info("{} = {}\n".format(_SCREAMING_SNAKE_CASE ,    str(metrics[key])))







	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	pl.Trainer ,    _SCREAMING_SNAKE_CASE:	pl.LightningModule)       -> List[Any]:

				"""simple docstring"""

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






def     _lowercase       (						__snake_case      ,__snake_case			)		->     None:
			#  To allow all pl args uncomment the following line
			#  parser = pl.Trainer.add_argparse_args(parser)
			parser.add_argument(
			    "--output_dir"      ,default=str(Path(__snake_case			).parent / "test_run" / "model_checkpoints"			)      ,type=__snake_case      ,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=__snake_case      ,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=__snake_case			)
			parser.add_argument("--max_grad_norm"      ,dest="gradient_clip_val"      ,default=1.0      ,type=__snake_case      ,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=__snake_case      ,default=1      ,help="Number of updates steps to accumulate before performing a backward/update pass."      ,)
			parser.add_argument("--seed"      ,type=__snake_case      ,default=42      ,help="random seed for initialization"			)
			parser.add_argument(
			    "--data_dir"      ,default=str(Path(__snake_case			).parent / "test_run" / "dummy-train-data"			)      ,type=__snake_case      ,help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."      ,)





def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case=None      ,__snake_case=True      ,__snake_case=[]      ,__snake_case=None      ,__snake_case=None      ,**__snake_case      ,)		->     Tuple:
			pl.seed_everything(args.seed			)

			# init model
			__lowerCAmelCase   :						List[Any]				= Path(model.hparams.output_dir			)
			odir.mkdir(exist_ok=__snake_case			)

			# add custom checkpoints
			if checkpoint_callback is None:
						__lowerCAmelCase   :						Optional[Any]				= 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(__snake_case			)
			if logging_callback is None:
						__lowerCAmelCase   :						Optional[Any]				= LoggingCallback()

			__lowerCAmelCase   :						int				= {}

			if args.fpaa:
						__lowerCAmelCase   :						Optional[int]				= 16

			if args.gpus > 1:
						__lowerCAmelCase   :						int				= "auto"
						__lowerCAmelCase   :						List[Any]				= "ddp"

			__lowerCAmelCase   :						Optional[int]				= args.accumulate_grad_batches
			__lowerCAmelCase   :						int				= None
			__lowerCAmelCase   :						Any				= "auto"

			__lowerCAmelCase   :						Optional[Any]				= pl.Trainer.from_argparse_args(
			    __snake_case      ,weights_summary=__snake_case      ,callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]      ,logger=__snake_case      ,val_check_interval=1      ,num_sanity_val_steps=2      ,**__snake_case      ,)

			if args.do_train:
						trainer.fit(__snake_case			)

			else:
						print("RAG modeling tests with new set functions successfuly executed!"			)
			return trainer