




"""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				(			A__:    Dict     )  ->     Optional[int]:
			__lowerCamelCase					:	Any	       =				filter(lambda A__     : p.requires_grad							,				model.parameters()     )
			__lowerCamelCase					:	List[str]	       =				sum([np.prod(p.size()     ) for p in model_parameters]     )
			return params


a_				:    Union[str, Any]										=     logging.getLogger(__name__)





def 						UpperCAmelCase				(			A__:    Optional[int]							,				A__:    Any     )  ->     Optional[int]:
			if metric == "rouge2":
						__lowerCamelCase					:	List[Any]	       =				'{val_avg_rouge2:.4f}-{step_count}'
			elif metric == "bleu":
						__lowerCamelCase					:	List[Any]	       =				'{val_avg_bleu:.4f}-{step_count}'
			elif metric == "em":
						__lowerCamelCase					:	Any	       =				'{val_avg_em:.4f}-{step_count}'
			elif metric == "loss":
						__lowerCamelCase					:	Optional[int]	       =				'{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.'     )

			__lowerCamelCase					:	Optional[int]	       =				ModelCheckpoint(
			    dirpath=A__							,				filename=A__							,				monitor=f'''val_{metric}'''							,				mode='max'							,				save_top_k=1							,				every_n_epochs=1							,				)

			return checkpoint_callback





def 						UpperCAmelCase				(			A__:    Dict							,				A__:    int     )  ->     Union[str, Any]:
			return EarlyStopping(
			    monitor=f'''val_{metric}'''							,				mode='min' if 'loss' in metric else 'max'							,				patience=A__							,				verbose=A__							,				)






class __lowercase(      pl.Callback				):




					'''simple docstring'''


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

								if not save_generations:
											return

								if "preds" in metrics:
											__lowerCamelCase					:	Optional[Any]	       =				'\n'.join(metrics['preds']     )
											generations_file.open('w+'     ).write(__a     )
					@rank_zero_only
					def     snake_case_				(    self						,	__a						,	__a     ):
								try:
											__lowerCamelCase					:	Union[str, Any]	       =				pl_module.model.model.num_parameters()
								except AttributeError:
											__lowerCamelCase					:	Optional[int]	       =				pl_module.model.num_parameters()

								__lowerCamelCase					:	Optional[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     snake_case_				(    self						,	__a						,	__a     ):
								save_json(pl_module.metrics						,	pl_module.metrics_save_path     )
								return self._write_logs(__a						,	__a						,	'test'     )





					@rank_zero_only
					def     snake_case_				(    self						,	__a						,	__a     ):
								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 random
from typing import Any





def 						UpperCAmelCase				(			A__:    list     )  ->     list[Any]:
			for _ in range(len(A__     )     ):
						__lowerCamelCase					:	List[Any]	       =				random.randint(0							,				len(A__     ) - 1     )
						__lowerCamelCase					:	Optional[Any]	       =				random.randint(0							,				len(A__     ) - 1     )
						__lowerCamelCase       ,      __lowerCamelCase					:	Any	       =				data[b], data[a]
			return data


if __name__ == "__main__":
					a_				:    Any										=     [0, 1, 2, 3, 4, 5, 6, 7]
					a_				:    int										=     ['''python''', '''says''', '''hello''', '''!''']
					print('''Fisher-Yates Shuffle:''')
					print('''List''', integers, strings)
					print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))



