


"""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_rag import save_json




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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


a__       :							Tuple        =  logging.getLogger(__name__)




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


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

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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


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




class 				UpperCamelCase_			(				pl.Callback):

	"""simple docstring"""





	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       List[Any]    ,    UpperCAmelCase__    :       Union[str, Any]     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
						pl_module.logger.log_metrics(UpperCAmelCase__     )




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

						if not save_generations:
											return

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




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

						__SCREAMING_SNAKE_CASE		     =       count_trainable_parameters(UpperCAmelCase__     )
						# 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    :       int    ,    UpperCAmelCase__    :       pl.Trainer    ,    UpperCAmelCase__    :       pl.LightningModule     ) ->			Any:
						save_json(pl_module.metrics    ,    pl_module.metrics_save_path     )
						return self._write_logs(UpperCAmelCase__    ,    UpperCAmelCase__    ,    "test"     )




	@rank_zero_only
	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       pl.Trainer    ,    UpperCAmelCase__    :       Optional[int]     ) ->			Optional[int]:
						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 warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


a__       :							Dict        =  logging.get_logger(__name__)




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	def __init__(				self    :       str    ,    *UpperCAmelCase__    :       List[Any]    ,    **UpperCAmelCase__    :       Tuple     ) ->			None:
						warnings.warn(
						    "The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use VideoMAEImageProcessor instead."    ,    UpperCAmelCase__    ,    )
						super().__init__(*UpperCAmelCase__    ,    **UpperCAmelCase__     )
