




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 			__magic_name__				( A		:					List[str]   ):






			'''simple docstring'''







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


__lowerCAmelCase		:				int       			=		logging.getLogger(__name__)




def 			__magic_name__				( A		:					List[Any],  A		:					Dict   ):






			'''simple docstring'''







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

			a    =			ModelCheckpoint(
			    dirpath=A,  filename=A,  monitor=F"""val_{metric}""",  mode="max",  save_top_k=3,  every_n_epochs=1,  )
			return checkpoint_callback




def 			__magic_name__				( A		:					str,  A		:					int   ):






			'''simple docstring'''







			return EarlyStopping(
			    monitor=F"""val_{metric}""",  mode="min" if "loss" in metric else "max",  patience=A,  verbose=A,  )






class     snake_case__							(pl.Callback   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Any							)					->     Union[str, Any]:
							a    =			{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 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     pl.Trainer					,      __lowerCamelCase				:     pl.LightningModule					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     List[Any]=True							)					->     None:
							logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
							a    =			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
							a    =			Path(pl_module.hparams.output_dir							)
							if type_path == "test":
										a    =			od / "test_results.txt"
										a    =			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.
										a    =			od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
										a    =			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
													a    =			metrics[key]
													if isinstance(__lowerCamelCase					,      torch.Tensor							):
																a    =			val.item()
													a    =			f"""{key}: {val:.6f}\n"""
													writer.write(__lowerCamelCase							)

							if not save_generations:
										return

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





				@rank_zero_only
				def 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     Tuple							)					->     Any:
							try:
										a    =			pl_module.model.model.num_parameters()
							except AttributeError:
										a    =			pl_module.model.num_parameters()

							a    =			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 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     pl.Trainer					,      __lowerCamelCase				:     pl.LightningModule							)					->     Optional[Any]:
							save_json(pl_module.metrics					,      pl_module.metrics_save_path							)
							return self._write_logs(__lowerCamelCase					,      __lowerCamelCase					,      "test"							)





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










'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__snake_case    						=logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	bool		=     field(default=__lowercase       ,	metadata={'''help''': '''Whether to use SortishSampler or not.'''}		)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}		)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[Union[str, Path, GenerationConfig]]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }       ,	)





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        lowerCAmelCase     =			super().to_dict()
        for k, v in d.items():
           if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			v.to_dict()
        return d





