





"""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							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Tuple				   =			filter(lambda SCREAMING_SNAKE_CASE     : p.requires_grad							,     model.parameters()     )
						UpperCamelCase				:		List[str]				   =			sum([np.prod(p.size()     ) for p in model_parameters]     )
						return params


__magic_name__       :					Tuple						=			logging.getLogger(__name__)

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						if metric == "rouge2":
												UpperCamelCase				:		Tuple				   =			"""{val_avg_rouge2:.4f}-{step_count}"""
						elif metric == "bleu":
												UpperCamelCase				:		int				   =			"""{val_avg_bleu:.4f}-{step_count}"""
						elif metric == "em":
												UpperCamelCase				:		Union[str, Any]				   =			"""{val_avg_em:.4f}-{step_count}"""
						elif metric == "loss":
												UpperCamelCase				:		Tuple				   =			"""{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."""     )

						UpperCamelCase				:		int				   =			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      UpperCamelCase							(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 							lowercase__			(   pl.Callback	):

			"""simple docstring"""







			def   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			{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   _a      (      self						,   _A						,   _A						,   _A						,   _A=True    ):






									'''simple docstring'''







									logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""    )
									UpperCamelCase				:		List[Any]				   =			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
									UpperCamelCase				:		int				   =			Path(pl_module.hparams.output_dir    )
									if type_path == "test":
															UpperCamelCase				:		Tuple				   =			od / """test_results.txt"""
															UpperCamelCase				:		List[str]				   =			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.
															UpperCamelCase				:		int				   =			od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
															UpperCamelCase				:		str				   =			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
																					UpperCamelCase				:		int				   =			metrics[key]
																					if isinstance(_A						,   torch.Tensor    ):
																											UpperCamelCase				:		Optional[int]				   =			val.item()
																					UpperCamelCase				:		Union[str, Any]				   =			f"""{key}: {val:.6f}\n"""
																					writer.write(_A    )

									if not save_generations:
															return

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







			@rank_zero_only
			def   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									try:
															UpperCamelCase				:		Any				   =			pl_module.model.model.num_parameters()
									except AttributeError:
															UpperCamelCase				:		Any				   =			pl_module.model.num_parameters()

									UpperCamelCase				:		Any				   =			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   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									save_json(pl_module.metrics						,   pl_module.metrics_save_path    )
									return self._write_logs(_A						,   _A						,   """test"""    )







			@rank_zero_only
			def   _a      (      self						,   _A						,   _A    ):






									'''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")








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase    	=		logging.get_logger(__name__)



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''timm_backbone'''




     def __init__(					self			,    a__=None			,    a__=3			,    a__=True			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(**a__)
         A__					=       backbone
         A__					=       num_channels
         A__					=       features_only
         A__					=       use_pretrained_backbone
         A__					=       True
         A__					=       out_indices if out_indices is not None else (-1,)
