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


__lowerCAmelCase												=	logging.getLogger(__name__)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							if metric == "rouge2":
														_snake_case       =  """{val_avg_rouge2:.4f}-{step_count}"""
							elif metric == "bleu":
														_snake_case       =  """{val_avg_bleu:.4f}-{step_count}"""
							elif metric == "em":
														_snake_case       =  """{val_avg_em:.4f}-{step_count}"""
							elif metric == "loss":
														_snake_case       =  """{val_avg_loss:.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."""				)

							_snake_case       =  ModelCheckpoint(
							    dirpath=_SCREAMING_SNAKE_CASE							, filename=_SCREAMING_SNAKE_CASE							, monitor=f"""val_{metric}"""							, mode="""max"""							, save_top_k=1							, every_n_epochs=1							, )

							return checkpoint_callback






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


class       _lowerCAmelCase   (   pl.Callback				):







							'''simple docstring'''

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

														if not save_generations:
																					return

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

							@rank_zero_only
							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[int]:
														try:
																					_snake_case       =  pl_module.model.model.num_parameters()
														except AttributeError:
																					_snake_case       =  pl_module.model.num_parameters()

														_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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Dict:
														save_json(pl_module.metrics  ,  pl_module.metrics_save_path  )
														return self._write_logs(UpperCAmelCase  ,  UpperCAmelCase  ,  """test"""  )





							@rank_zero_only
							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       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 diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)