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						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    Tuple         =				filter(lambda __lowercase     : p.requires_grad						,model.parameters()     )
		A_		:    Optional[Any]         =				sum([np.prod(p.size()     ) for p in model_parameters]     )
		return params


_UpperCAmelCase            =		logging.getLogger(__name__)




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			Dict     ):
		'''simple docstring'''




		if metric == "rouge2":
				A_		:    List[str]         =				'{val_avg_rouge2:.4f}-{step_count}'
		elif metric == "bleu":
				A_		:    List[Any]         =				'{val_avg_bleu:.4f}-{step_count}'
		elif metric == "em":
				A_		:    Tuple         =				'{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_		:    Union[str, Any]         =				ModelCheckpoint(
		    dirpath=__lowercase						,filename=__lowercase						,monitor=f'''val_{metric}'''						,mode='max'						,save_top_k=3						,every_n_epochs=1						,)
		return checkpoint_callback




def     UpperCamelCase						(	__lowercase						:			List[Any]						,__lowercase						:			Dict     ):
		'''simple docstring'''




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





class UpperCAmelCase						( pl.Callback  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    int         =				{F'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
						pl_module.logger.log_metrics(lowercase )
				@rank_zero_only
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase=True ):



						"""simple docstring"""





						logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
						A_		:    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
						A_		:    Any         =				Path(pl_module.hparams.output_dir )
						if type_path == "test":
								A_		:    List[str]         =				od / 'test_results.txt'
								A_		:    Union[str, 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.
								A_		:    Union[str, Any]         =				od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
								A_		:    List[Any]         =				od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
								results_file.parent.mkdir(exist_ok=lowercase )
								generations_file.parent.mkdir(exist_ok=lowercase )
						with open(lowercase		,     'a+' ) as writer:
								for key in sorted(lowercase ):
										if key in ["log", "progress_bar", "preds"]:
												continue
										A_		:    List[Any]         =				metrics[key]
										if isinstance(lowercase		,     torch.Tensor ):
												A_		:    str         =				val.item()
										A_		:    int         =				F'''{key}: {val:.6f}\n'''
										writer.write(lowercase )

						if not save_generations:
								return

						if "preds" in metrics:
								A_		:    Optional[int]         =				'\n'.join(metrics['preds'] )
								generations_file.open('w+' ).write(lowercase )
				@rank_zero_only
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase ):



						"""simple docstring"""





						try:
								A_		:    Any         =				pl_module.model.model.num_parameters()
						except AttributeError:
								A_		:    Optional[int]         =				pl_module.model.num_parameters()

						A_		:    Optional[int]         =				count_trainable_parameters(lowercase )
						# 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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase ):



						"""simple docstring"""





						save_json(pl_module.metrics		,     pl_module.metrics_save_path )
						return self._write_logs(lowercase		,     lowercase		,     'test' )







				@rank_zero_only
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase ):



						"""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")







_UpperCAmelCase            =		"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Tuple         =				input('Enter message: '     )
		A_		:    int         =				input('Enter key [alphanumeric]: '     )
		A_		:    Optional[Any]         =				input('Encrypt/Decrypt [e/d]: '     )

		if mode.lower().startswith('e'     ):
				A_		:    List[Any]         =				'encrypt'
				A_		:    int         =				encrypt_message(__lowercase						,__lowercase     )
		elif mode.lower().startswith('d'     ):
				A_		:    Optional[Any]         =				'decrypt'
				A_		:    Dict         =				decrypt_message(__lowercase						,__lowercase     )

		print(f'''\n{mode.title()}ed message:'''     )
		print(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		return translate_message(__lowercase						,__lowercase						,'encrypt'     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		return translate_message(__lowercase						,__lowercase						,'decrypt'     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    Tuple         =				[]
		A_		:    str         =				0
		A_		:    Optional[int]         =				key.upper()

		for symbol in message:
				A_		:    Optional[Any]         =				LETTERS.find(symbol.upper()     )
				if num != -1:
						if mode == "encrypt":
								num += LETTERS.find(key[key_index]     )
						elif mode == "decrypt":
								num -= LETTERS.find(key[key_index]     )

						num %= len(__lowercase     )

						if symbol.isupper():
								translated.append(LETTERS[num]     )
						elif symbol.islower():
								translated.append(LETTERS[num].lower()     )

						key_index += 1
						if key_index == len(__lowercase     ):
								A_		:    str         =				0
				else:
						translated.append(__lowercase     )
		return "".join(__lowercase     )


if __name__ == "__main__":
	main()






