






"""simple docstring"""



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 import save_json




def     lowerCamelCase_					(	__A	:		Dict      ):
					lowerCamelCase__					=filter(lambda __A      : p.requires_grad      ,						model.parameters()      )
					lowerCamelCase__					=sum([np.prod(p.size()      ) for p in model_parameters]      )
					return params


_snake_case							:   str	  =       logging.getLogger(__name__)




class 							__lowercase(							pl.Callback	):






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




											"""simple docstring"""





											lowerCamelCase__					={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 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE=True)      ->	int:




											"""simple docstring"""





											logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****")
											lowerCamelCase__					=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__					=Path(pl_module.hparams.output_dir)
											if type_path == "test":
																lowerCamelCase__					=od / """test_results.txt"""
																lowerCamelCase__					=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__					=od / F"{type_path}_results/{trainer.global_step:05d}.txt"
																lowerCamelCase__					=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
																					lowerCamelCase__					=metrics[key]
																					if isinstance(_lowerCAmelCase     ,    torch.Tensor):
																										lowerCamelCase__					=val.item()
																					lowerCamelCase__					=F"{key}: {val:.6f}\n"
																					writer.write(_lowerCAmelCase)

											if not save_generations:
																return

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






						@rank_zero_only
						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	Tuple:




											"""simple docstring"""





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

											lowerCamelCase__					=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 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	List[str]:




											"""simple docstring"""





											save_json(pl_module.metrics     ,    pl_module.metrics_save_path)
											return self._write_logs(_lowerCAmelCase     ,    _lowerCAmelCase     ,    'test')






						@rank_zero_only
						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	List[str]:




											"""simple docstring"""





											save_json(pl_module.metrics     ,    pl_module.metrics_save_path)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")




def     lowerCamelCase_					(	__A	:		str      ,						__A	:		Optional[int]      ,						__A	:		Dict=1      ,						__A	:		Optional[Any]=False      ):
					if metric == "rouge2":
										lowerCamelCase__					="""{val_avg_rouge2:.4f}-{step_count}"""
					elif metric == "bleu":
										lowerCamelCase__					="""{val_avg_bleu:.4f}-{step_count}"""
					elif metric == "loss":
										lowerCamelCase__					="""{val_avg_loss:.4f}-{step_count}"""
					else:
										raise NotImplementedError(
										    f"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
										    ' this function.'      )

					lowerCamelCase__					=ModelCheckpoint(
					    dirpath=__SCREAMING_SNAKE_CASE      ,						filename=__SCREAMING_SNAKE_CASE      ,						monitor=f"val_{metric}"      ,						mode='min' if 'loss' in metric else 'max'      ,						save_top_k=__SCREAMING_SNAKE_CASE      ,						)
					return checkpoint_callback




def     lowerCamelCase_					(	__A	:		Union[str, Any]      ,						__A	:		Tuple      ):
					return EarlyStopping(
					    monitor=f"val_{metric}"      ,						mode='min' if 'loss' in metric else 'max'      ,						patience=__SCREAMING_SNAKE_CASE      ,						verbose=__SCREAMING_SNAKE_CASE      ,						)
import warnings

from .generation import FlaxGenerationMixin







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       # warning at import time
       warnings.warn(
           "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
           "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."   ,							__UpperCamelCase   ,							)



