

from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowerCamelCase						=		logging.get_logger(__name__)


if is_torch_available():
		import torch
		from torch import nn
		from torch.utils.data import Dataset

		from transformers import Trainer





		class  _a							(    _lowercase):




			def __init__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		int = 101		)->	Tuple:
					lowerCAmelCase__       :      Any   					= length




			def __len__( self						:		List[str]		)->	Dict:
					return self.length




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





		class  _a							:




			def __call__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		str		)->	Tuple:
					return {"input_ids": torch.tensor(_SCREAMING_SNAKE_CASE		), "labels": torch.tensor(_SCREAMING_SNAKE_CASE		)}





		class  _a							(    nn.Module):




			def __init__( self						:		str		)->	str:
					super().__init__()
					# Add some (unused) params otherwise DDP will complain.
					lowerCAmelCase__       :      str   					= nn.Linear(120		, 80		)




			def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Tuple=None		)->	Union[str, Any]:
					if labels is not None:
							return torch.tensor(0.0		, device=input_ids.device		), input_ids
					else:
							return input_ids





class  _a							(    _lowercase):




	@require_torch_neuroncore
	def 							UpperCAmelCase__( self						:		str		)->	Any:
			lowerCAmelCase__       :      Tuple   					= F'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
			lowerCAmelCase__       :      Any   					= self.get_auto_remove_tmp_dir()
			lowerCAmelCase__       :      Optional[Any]   					= F'--output_dir {output_dir}'.split()
			lowerCAmelCase__       :      int   					= ['''torchrun'''] + distributed_args + args
			execute_subprocess_async(_SCREAMING_SNAKE_CASE		, env=self.get_env()		)
			# successful return here == success - any errors would have caused an error in the sub-call





class  _a							(    _lowercase):




	@require_torch_multi_gpu
	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	Tuple:
			lowerCAmelCase__       :      Union[str, Any]   					= F'--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
			lowerCAmelCase__       :      List[Any]   					= self.get_auto_remove_tmp_dir()
			lowerCAmelCase__       :      List[str]   					= F'--output_dir {output_dir}'.split()
			lowerCAmelCase__       :      str   					= ['''torchrun'''] + distributed_args + args
			execute_subprocess_async(_SCREAMING_SNAKE_CASE		, env=self.get_env()		)
			# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
		# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
		#
		# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

		lowerCamelCase						=		HfArgumentParser((TrainingArguments,))
		lowerCamelCase						=		parser.parse_args_into_dataclasses()[0]

		logger.warning(
		    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
		    f'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
		)

		# Essentially, what we want to verify in the distributed case is that we get all samples back,
		# in the right order. (this is crucial for prediction for instance)
		for dataset_length in [101, 40, 7]:
				lowerCamelCase						=		DummyDataset(dataset_length)






				def 					lowerCamelCase_   (	_a	):




						"""simple docstring"""






						lowerCAmelCase__       :      Dict   					= list(range(len(_a	)	)	)
						lowerCAmelCase__       :      Union[str, Any]   					= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
						if not success and training_args.local_rank == 0:
								logger.warning(
								    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
								    f'{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'	)
						return {"success": success}

				lowerCamelCase						=		Trainer(
				    model=DummyModel(),
				    args=training_args,
				    data_collator=DummyDataCollator(),
				    eval_dataset=dataset,
				    compute_metrics=compute_metrics,
				)
				lowerCamelCase						=		trainer.evaluate()
				logger.info(metrics)
				if metrics["eval_success"] is not True:
						logger.error(metrics)
						exit(1)

				lowerCamelCase						=		trainer.predict(dataset)
				logger.info(p.metrics)
				if p.metrics["test_success"] is not True:
						logger.error(p.metrics)
						exit(1)

				lowerCamelCase						=		2

				lowerCamelCase						=		trainer.evaluate()
				logger.info(metrics)
				if metrics["eval_success"] is not True:
						logger.error(metrics)
						exit(1)

				lowerCamelCase						=		trainer.predict(dataset)
				logger.info(p.metrics)
				if p.metrics["test_success"] is not True:
						logger.error(p.metrics)
						exit(1)




				lowerCamelCase						=		None








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_rag import save_json






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= filter(lambda _a	: p.requires_grad     ,       model.parameters()	)
		lowerCAmelCase__       :      str   					= sum([np.prod(p.size()	) for p in model_parameters]	)
		return params


lowerCamelCase						=		logging.getLogger(__name__)






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		if metric == "rouge2":
				lowerCAmelCase__       :      Optional[int]   					= '''{val_avg_rouge2:.4f}-{step_count}'''
		elif metric == "bleu":
				lowerCAmelCase__       :      Optional[int]   					= '''{val_avg_bleu:.4f}-{step_count}'''
		elif metric == "em":
				lowerCAmelCase__       :      List[Any]   					= '''{val_avg_em:.4f}-{step_count}'''
		else:
				raise NotImplementedError(
				    f'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
				    ''' function.'''	)

		lowerCAmelCase__       :      Dict   					= ModelCheckpoint(
		    dirpath=_a     ,       filename=_a     ,       monitor=f'val_{metric}'     ,       mode='''max'''     ,       save_top_k=3     ,       every_n_epochs=1     ,       )
		return checkpoint_callback






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






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





class  _a							(    pl.Callback):




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Any		)->	Optional[int]:
			lowerCAmelCase__       :      Dict   					= {F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
			pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE		)




	@rank_zero_only
	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		pl.Trainer		, _SCREAMING_SNAKE_CASE						:		pl.LightningModule		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		List[str]=True		)->	None:
			logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'		)
			lowerCAmelCase__       :      List[Any]   					= 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
			lowerCAmelCase__       :      List[Any]   					= Path(pl_module.hparams.output_dir		)
			if type_path == "test":
					lowerCAmelCase__       :      Optional[int]   					= od / '''test_results.txt'''
					lowerCAmelCase__       :      Tuple   					= 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.
					lowerCAmelCase__       :      int   					= od / F'{type_path}_results/{trainer.global_step:05d}.txt'
					lowerCAmelCase__       :      int   					= od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
					results_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
					generations_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
			with open(_SCREAMING_SNAKE_CASE		, '''a+'''		) as writer:
					for key in sorted(_SCREAMING_SNAKE_CASE		):
							if key in ["log", "progress_bar", "preds"]:
									continue
							lowerCAmelCase__       :      Optional[int]   					= metrics[key]
							if isinstance(_SCREAMING_SNAKE_CASE		, torch.Tensor		):
									lowerCAmelCase__       :      List[str]   					= val.item()
							lowerCAmelCase__       :      List[str]   					= F'{key}: {val:.6f}\n'
							writer.write(_SCREAMING_SNAKE_CASE		)

			if not save_generations:
					return

			if "preds" in metrics:
					lowerCAmelCase__       :      Dict   					= '''\n'''.join(metrics['''preds''']		)
					generations_file.open('''w+'''		).write(_SCREAMING_SNAKE_CASE		)




	@rank_zero_only
	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Optional[int]:
			try:
					lowerCAmelCase__       :      Tuple   					= pl_module.model.model.num_parameters()
			except AttributeError:
					lowerCAmelCase__       :      Optional[Any]   					= pl_module.model.num_parameters()

			lowerCAmelCase__       :      Dict   					= count_trainable_parameters(_SCREAMING_SNAKE_CASE		)
			# 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						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		pl.Trainer		, _SCREAMING_SNAKE_CASE						:		pl.LightningModule		)->	Optional[Any]:
			save_json(pl_module.metrics		, pl_module.metrics_save_path		)
			return self._write_logs(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, '''test'''		)




	@rank_zero_only
	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		pl.Trainer		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	List[Any]:
			save_json(pl_module.metrics		, pl_module.metrics_save_path		)
			# Uncommenting this will save val generations
			# return self._write_logs(trainer, pl_module, "valid")




