



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


lowercase_								=     logging.getLogger(__name__)



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):



			if metric == "rouge2":
						lowercase__					  =				"""{val_avg_rouge2:.4f}-{step_count}"""
			elif metric == "bleu":
						lowercase__					  =				"""{val_avg_bleu:.4f}-{step_count}"""
			elif metric == "em":
						lowercase__					  =				"""{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."  )

			lowercase__					  =				ModelCheckpoint(
			    dirpath=lowerCamelCase_						,  filename=lowerCamelCase_						,  monitor=f'''val_{metric}'''						,  mode="max"						,  save_top_k=3						,  every_n_epochs=1						,  )
			return checkpoint_callback



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			return EarlyStopping(
			    monitor=f'''val_{metric}'''						,  mode="min" if "loss" in metric else "max"						,  patience=lowerCamelCase_						,  verbose=lowerCamelCase_						,  )




class 				_snake_case			(					pl.Callback):




				def 							A__      (						self					:						Any,					__lowercase					:						List[Any],					__lowercase					:						str						):
							lowercase__					  =				{F'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
							pl_module.logger.log_metrics(lowerCamelCase_						)




				@rank_zero_only
				def 							A__      (						self					:						List[Any],					__lowercase					:						pl.Trainer,					__lowercase					:						pl.LightningModule,					__lowercase					:						str,					__lowercase					:						List[str]=True						):
							logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''						)
							lowercase__					  =				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
							lowercase__					  =				Path(pl_module.hparams.output_dir						)
							if type_path == "test":
										lowercase__					  =				od / """test_results.txt"""
										lowercase__					  =				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.
										lowercase__					  =				od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
										lowercase__					  =				od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
										results_file.parent.mkdir(exist_ok=lowerCamelCase_						)
										generations_file.parent.mkdir(exist_ok=lowerCamelCase_						)
							with open(lowerCamelCase_,					"a+"						) as writer:
										for key in sorted(lowerCamelCase_						):
													if key in ["log", "progress_bar", "preds"]:
																continue
													lowercase__					  =				metrics[key]
													if isinstance(lowerCamelCase_,					torch.Tensor						):
																lowercase__					  =				val.item()
													lowercase__					  =				F'''{key}: {val:.6f}\n'''
													writer.write(lowerCamelCase_						)

							if not save_generations:
										return

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




				@rank_zero_only
				def 							A__      (						self					:						str,					__lowercase					:						Optional[int],					__lowercase					:						Any						):



							try:
										lowercase__					  =				pl_module.model.model.num_parameters()
							except AttributeError:
										lowercase__					  =				pl_module.model.num_parameters()

							lowercase__					  =				count_trainable_parameters(lowerCamelCase_						)
							# 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 							A__      (						self					:						List[str],					__lowercase					:						pl.Trainer,					__lowercase					:						pl.LightningModule						):
							save_json(pl_module.metrics,					pl_module.metrics_save_path						)
							return self._write_logs(lowerCamelCase_,					lowerCamelCase_,					"test"						)




				@rank_zero_only
				def 							A__      (						self					:						List[str],					__lowercase					:						pl.Trainer,					__lowercase					:						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")









from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def __lowerCAmelCase (     ):
			lowercase__					  =				HfArgumentParser(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			lowercase__					  =				TensorFlowBenchmark(args=SCREAMING_SNAKE_CASE_  )
			try:
						lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			except ValueError as e:
						lowercase__					  =				"Arg --no_{0} is no longer used, please use --no-{0} instead."
						lowercase__					  =				" ".join(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[:-1]  )
						lowercase__					  =				""
						lowercase__					  =				eval(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[-1]  )
						lowercase__					  =				[]
						for arg in depreciated_args:
									# arg[2:] removes '--'
									if arg[2:] in TensorFlowBenchmark.deprecated_args:
												# arg[5:] removes '--no_'
												full_error_msg += arg_error_msg.format(arg[5:]  )
									else:
												wrong_args.append(SCREAMING_SNAKE_CASE_  )
						if len(SCREAMING_SNAKE_CASE_  ) > 0:
									lowercase__					  =				full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE_  )
						raise ValueError(SCREAMING_SNAKE_CASE_  )
			benchmark.run()


if __name__ == "__main__":
							main()




