



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_		(					snake_case__					)	->     Dict:



				"""simple docstring"""





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


_lowerCAmelCase	:   Tuple    =   logging.getLogger(__name__)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Any:



				"""simple docstring"""





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

				lowerCAmelCase__		  =  ModelCheckpoint(
				    dirpath=_UpperCAmelCase    ,			filename=_UpperCAmelCase    ,			monitor=f'val_{metric}'    ,			mode='max'    ,			save_top_k=3    ,			every_n_epochs=1    ,			)
				return checkpoint_callback







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





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


class 	__snake_case   ( pl.Callback ):
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_						):







						"""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(a_						)
		@rank_zero_only
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_=True						):







						"""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=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
														lowerCAmelCase__		  =  metrics[key]
														if isinstance(a_    ,torch.Tensor						):
																		lowerCAmelCase__		  =  val.item()
														lowerCAmelCase__		  =  f'{key}: {val:.6f}\n'
														writer.write(a_						)

						if not save_generations:
										return

						if "preds" in metrics:
										lowerCAmelCase__		  =  "\n".join(metrics['preds']						)
										generations_file.open('w+'						).write(a_						)
		@rank_zero_only
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_						):







						"""simple docstring"""
						try:
										lowerCAmelCase__		  =  pl_module.model.model.num_parameters()
						except AttributeError:
										lowerCAmelCase__		  =  pl_module.model.num_parameters()

						lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							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 						SCREAMING_SNAKE_CASE_					(							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")


import functools
from typing import Any



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str]  )   ->			bool:






						'''simple docstring'''

						if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) or len(_UpperCAmelCase  ) == 0:
												raise ValueError("the string should be not empty string"  )

						if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) or not all(
						    isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) and len(_UpperCAmelCase  ) > 0 for item in words  ):
												raise ValueError("the words should be a list of non-empty strings"  )

						# Build trie
						_lowercase       :	dict[str, Any]           =   {}
						_lowercase       :	List[Any]           =   "WORD_KEEPER"

						for word in words:
												_lowercase       :	Optional[Any]           =   trie
												for c in word:
																		if c not in trie_node:
																								_lowercase       :	List[Any]           =   {}

																		_lowercase       :	Optional[int]           =   trie_node[c]

												_lowercase       :	str           =   True

						_lowercase       :	int           =   len(_UpperCAmelCase  )

						# Dynamic programming method
						@functools.cache
						def is_breakable(_UpperCAmelCase  :   int  ) -> bool:
												if index == len_string:
																		return True

												_lowercase       :	Union[str, Any]           =   trie
												for i in range(_UpperCAmelCase ,			_UpperCAmelCase  ):
																		_lowercase       :	List[str]           =   trie_node.get(string[i] ,			_UpperCAmelCase  )

																		if trie_node is None:
																								return False

																		if trie_node.get(_UpperCAmelCase ,			_UpperCAmelCase  ) and is_breakable(i + 1  ):
																								return True

												return False

						return is_breakable(0  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




