




"""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      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Tuple    =    filter(lambda lowercase__		: p.requires_grad							,    model.parameters()		)
				_lowerCamelCase     :			int    =    sum([np.prod(p.size()		) for p in model_parameters]		)
				return params


lowercase__     						=						logging.getLogger(__name__)







def      _snake_case			(				lowercase__							,    lowercase__		):
				if metric == "rouge2":
								_lowerCamelCase     :			str    =    '{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								_lowerCamelCase     :			List[str]    =    '{val_avg_bleu:.4f}-{step_count}'
				elif metric == "em":
								_lowerCamelCase     :			Union[str, Any]    =    '{val_avg_em:.4f}-{step_count}'
				elif metric == "loss":
								_lowerCamelCase     :			Dict    =    '{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     :			List[Any]    =    ModelCheckpoint(
				    dirpath=lowercase__							,    filename=lowercase__							,    monitor=f'''val_{metric}'''							,    mode='max'							,    save_top_k=1							,    every_n_epochs=1							,    )

				return checkpoint_callback







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






class lowerCAmelCase__	(     pl.Callback						):






			'''simple docstring'''



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

							if not save_generations:
											return

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

							_lowerCamelCase     :			List[Any]    =    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 					A_    (  self  ,      lowercase  ,      lowercase  ):
							save_json(pl_module.metrics  ,      pl_module.metrics_save_path  )
							return self._write_logs(lowercase  ,      lowercase  ,      'test'  )






			@rank_zero_only
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							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 math
import random




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = False   ):




					'''simple docstring'''


					if deriv:
										return value * (1 - value)
					return 1 / (1 + math.exp(-value   ))


# Initial Value
a__				:							Tuple						=     0.02




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				float(2 * (random.randint(1  ,						100   )) - 1   )

					for _ in range(lowerCAmelCase_   ):
										# Forward propagation
										__SCREAMING_SNAKE_CASE						   =				sigmoid_function(INITIAL_VALUE * weight   )
										# How much did we miss?
										__SCREAMING_SNAKE_CASE						   =				(expected / 100) - layer_a
										# Error delta
										__SCREAMING_SNAKE_CASE						   =				layer_1_error * sigmoid_function(lowerCAmelCase_  ,						lowerCAmelCase_   )
										# Update weight
										weight += INITIAL_VALUE * layer_1_delta

					return layer_a * 100


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a__				:							List[str]						=     int(input('''Expected value: '''))
		a__				:							str						=     int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
