


'''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 lowerCamelCase     (     lowerCAmelCase							:      int						):





	"""simple docstring"""







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


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





def lowerCamelCase     (     lowerCAmelCase							:      Dict						,						lowerCAmelCase							:      Optional[Any]						):





	"""simple docstring"""







	if metric == "rouge2":
		__magic_name__			:       Dict	      =	'{val_avg_rouge2:.4f}-{step_count}'
	elif metric == "bleu":
		__magic_name__			:       Union[str, Any]	      =	'{val_avg_bleu:.4f}-{step_count}'
	elif metric == "em":
		__magic_name__			:       Optional[Any]	      =	'{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.'						)

	__magic_name__			:       Union[str, Any]	      =	ModelCheckpoint(
	    dirpath=__lowerCAmelCase						,						filename=__lowerCAmelCase						,						monitor=f'val_{metric}'						,						mode='max'						,						save_top_k=3						,						every_n_epochs=1						,						)
	return checkpoint_callback





def lowerCamelCase     (     lowerCAmelCase							:      Any						,						lowerCAmelCase							:      Union[str, Any]						):





	"""simple docstring"""







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





class _lowerCamelCase			(		pl.Callback				):



		'''simple docstring'''







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

			if not save_generations:
				return

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







		@rank_zero_only
		def   __lowerCAmelCase						(   self						:		str    ,	_A						:		Any    ,	_A						:		int     )    ->					str:
			try:
				__magic_name__			:       Tuple	      =	pl_module.model.model.num_parameters()
			except AttributeError:
				__magic_name__			:       str	      =	pl_module.model.num_parameters()

			__magic_name__			:       List[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   __lowerCAmelCase						(   self						:		str    ,	_A						:		Union[str, Any]    ,	_A						:		Optional[Any]     )    ->					Dict:
			save_json(pl_module.metrics    ,	pl_module.metrics_save_path     )
			return self._write_logs(_A    ,	_A    ,	'test'     )







		@rank_zero_only
		def   __lowerCAmelCase						(   self						:		Union[str, Any]    ,	_A						:		Tuple    ,	_A						:		List[Any]     )    ->					Any:
			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 __future__ import annotations

import math





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5     ,		int(math.sqrt(__lowerCAmelCase				) + 1				)     ,		6				):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					str(__lowerCAmelCase				)
    snake_case__    =					[n]
    for i in range(1     ,		len(__lowerCAmelCase				)				):
        list_nums.append(int(str_num[i:]				)				)
        list_nums.append(int(str_num[:-i]				)				)
    return list_nums





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if len(str(__lowerCAmelCase				)				) > 3:
        if not is_prime(int(str(__lowerCAmelCase				)[-3:]				)				) or not is_prime(int(str(__lowerCAmelCase				)[:3]				)				):
            return False
    return True





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase = 11				):
    snake_case__    =					[]
    snake_case__    =					13
    while len(__lowerCAmelCase				) != count:
        if validate(__lowerCAmelCase				):
            snake_case__    =					list_truncated_nums(__lowerCAmelCase				)
            if all(is_prime(__lowerCAmelCase				) for i in list_nums				):
                list_truncated_primes.append(__lowerCAmelCase				)
        num += 2
    return list_truncated_primes





def 						SCREAMING_SNAKE_CASE__     (   ):
    return sum(compute_truncated_primes(11				)				)


if __name__ == "__main__":
     print(F'''{sum(compute_truncated_primes(11)) = }''')



