"""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					(	UpperCamelCase__				):






							"""simple docstring"""





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


__lowerCamelCase											=  logging.getLogger(__name__)






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""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=_lowerCamelCase       ,    filename=_lowerCamelCase       ,    monitor=F'''val_{metric}'''       ,    mode='max'       ,    save_top_k=3       ,    every_n_epochs=1       ,    )
							return checkpoint_callback






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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


class     UpperCamelCase__(			pl.Callback ):





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							{f'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
													pl_module.logger.log_metrics(_A     )





						@rank_zero_only
						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=True     )					->				Dict:
													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=_A     )
																				generations_file.parent.mkdir(exist_ok=_A     )
													with open(_A		,'a+'     ) as writer:
																				for key in sorted(_A     ):
																											if key in ["log", "progress_bar", "preds"]:
																																		continue
																											A__	     =							metrics[key]
																											if isinstance(_A		,torch.Tensor     ):
																																		A__	     =							val.item()
																											A__	     =							f'''{key}: {val:.6f}\n'''
																											writer.write(_A     )

													if not save_generations:
																				return

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





						@rank_zero_only
						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				List[str]:

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

													A__	     =							count_trainable_parameters(_A     )
													# 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 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Union[str, Any]:
													save_json(pl_module.metrics		,pl_module.metrics_save_path     )
													return self._write_logs(_A		,_A		,'test'     )





						@rank_zero_only
						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				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_imagegpt import ImageGPTImageProcessor


__lowerCamelCase											=  logging.get_logger(__name__)
class     UpperCamelCase__(			__A ):





						def __init__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				None:
													warnings.warn(
													    'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
													    ' Please use ImageGPTImageProcessor instead.'		,__UpperCAmelCase		,)
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase     )





